Abt
Abt Associates Inc. a 4800 Montgomery Lane ¦
Bethesda, MD 20814 a www.abtassociates.com
Abt Associates Inc.
Environmental
U.S. Version
Benefits Mapping and Analysis Program
User's Manual
November 2003
Prepared for
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
Bryan Hubbell, Project Manager
Prepared by
Abt Associates Inc.
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Welcome to BenMAP, the Environmental Benefits Mapping and
Analysis Program
BenMAP is a new tool that allows you to perform customized health benefits analyses for
changes in air quality, in a powerful, yet easy- to-use program. BenMAP allows you to select
and customize the data inputs, select from a range of modeling options, and report the results in a
variety of ways. In addition, BenMAP provides a range of mapping options. The model
estimates the reduction in the incidence of adverse health effects, as well as the estimated
economic value of such a reduction, and it also reports air quality and population exposure results.
BenMAP can be used for a variety of purposes:
^ Generation of population/community level ambient pollution exposure maps;
^ Comparing benefits associated with regulatory programs;
^ Estimating health impacts and costs of existing air pollution concentrations;
^ Estimating health benefits of alternative ambient air quality standards;
^ Performing sensitivity analyses of health or valuation functions, or of other inputs; and
^ Screening analyses.
To calculate people's exposure to air pollution, BenMAP combines different sources of data,
including air pollution monitoring data, modeling data, census data, and county-level population
projections. And to quickly generate additional exposure scenarios, BenMAP also provides
several options to directly reduce or "roll back" monitored pollution levels. Using these data and
rollback capabilities, BenMAP can estimate population exposure for any particular year, and for
the specified set of air pollution assumptions. Typically, you will specify two different scenarios,
and BenMAP will estimate the change in population exposure between them. Using this change
in population exposure, BenMAP then calculates the associated change in the incidence of
adverse health effects, and its estimated economic value. For these calculations, BenMAP
allows you the option of choosing from a large set of EPA Standard functions to estimate adverse
health effects and to value these health effects. In addition, BenMAP gives you the flexibility to
add your own health effect and valuation functions.
While BenMAP is not an air quality modeling program, it does contain a powerful set of functions
to explore the impact of reducing monitored concentrations of air pollution. These functions,
available under the monitor rollback option under the "Create Air Quality Grids" button, allow the
user to specify specific percent or incremental reductions, or to examine a wide variety of forms
of ambient air quality standards, including annual or daily standards, and incorporating such
factors as ordinality, anthropogenic background, and metric form, e.g. daily average or maximum
daily 8-hour average. These functions can be used without any additional input data from the
user, by accessing the air quality monitoring data provided with the BenMAP program.
BenMAP also provides powerful tools for quality assurance and results presentation. These tools
include a set of mapping and data query tools, and an "audit trail" feature which clearly
documents all inputs, assumptions, and modeling choices used to generate a specific set of
population exposure, incidence, and valuation results. This tool allows for easy QA checks, as
well as comparison with other BenMAP results for purposes of replication and validation.
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Introduction
Exhibit 0-1 summarizes the flow of calculations in BenMAP, and the types of choices that you
make regarding the modeling of population exposure, the types of health effects to model, and
how to place an economic value on these health effects. This exhibit also highlights that
BenMAP does not have air modeling capabilities, and instead relies on modeling and monitoring
inputs. Note that the current version of BenMAP requires the use of the population data and
population projections that come with the model. See Appendix B for details on the population
data included in the model.
Exhibit 0-1. BenMAP Flow Diagram
/ us- \
Census Data
/Population,
Projections
Population
Estimates
Population
Exposure
/Baseline\
Incidence
Rates
Adverse
Health
Effects
BenMAP Input
User Input Choice
Economic
Costs
Result from Inputs
Air Quality
Monitoring
Air Quality
Modeling
Health
Functions
Valuation
Functions
Who Can Use BenMAP?
BenMAP can be used by a wide range of persons, including scientists, policy analysts, and
decisionmakers. Advanced users can explore a wide range of advanced options, such as filtering
of monitor data, using the map querying features, and exploring the impacts of different health and
valuation functions. Less experienced users can simply apply the EPA Standard functions and
reproduce the types of analyses performed in regulatory impact analyses.
Power users will also find a number of convenient features in BenMAP, including the availability
of a command line version of the model that allows the user to run BenMAP in "batch mode,"
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Introduction
which can save a great deal of time when a large number of scenarios need to be analyzed. The
command line version of BenMAP is available by contacting Bryan Hubbell at the U.S. EPA
(hubbell. brv an@,epa. gov).
How to Use this Manual
This manual has two main parts. Chapters 1 through 10 provide step-by-step instructions on how
to use BenMAP. Appendices A through I present details on the databases and methods built into
the model.
New users should start with Chapters 1 and 2, which are both very short, but provide a good
overview of the model and how it works, and explain some potentially confusing terminology.
You can then use Chapter 3 to get started using the model. The tutorial in Chapter 3 will show
you how to define two simple scenarios, calculate the change in health effects between them,
assign economic valuations, and create reports and maps. Once you have gone through this
simple tutorial, you can go on to try more advanced functions. Use the rest of the manual to
answer any specific questions you may have, or to walk you step-by-step through the various
model functions. Chapters 4 through 7 cover each of the four main buttons, Chapter 8 covers
mapping, and Chapters 9 and 10 explain the Data and Tools menus.
Each of the first ten chapters is introduced by a short section which describes what you can find
within the chapter, and provides an outline of the chapter's contents. This is a good place to go if
the Table of Contents does not provide enough detail for you to find the section you need. The
end of most chapters has a series of "Frequently Asked Questions," which may also be helpful in
answering specific questions. In sections that provide instructions on navigating the model, the
following conventions are observed: menu items, buttons, and tab and selection box labels are in
bold type; prompts and messages are enclosed in quotation marks; and drop-down menu items,
options to click or check, and items that need to be filled in or selected by the user are italicized.
Throughout the first ten chapters you will also see boxes that say TIP. These boxes present
common mistakes and important things to remember when working with BenMAP. Common
terms are defined in Chapter 1, but you can also find definitions in boxes along the right margins
inside other chapters.
Computer Requirements
BenMAP requires a computer with:
^Windows 2000 or greater.
^1 GB RAM or greater (or 512 MB RAM if no 12 xl2 air quality grids, e.g. CAMx or
REMSAD need to be created). In general, more RAM is preferred, although BenMAP can be
run with the 512 minimum if certain choices within the model are constrained.
5s" 1 GHZ processor or greater recommended.
BenMAP does not model air quality, so to estimate population exposure to air pollution you need
to supply modeling data, monitoring data, or both. BenMAP comes supplied with a number of
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Introduction
years of monitor data, as well as some air quality modeling files that enable you to get started
right away. Chapter 3 provides an example for you to understand how these might be used.
Installing BenMAP
^Install Interbase by running ibinstall.exe.
• At the "Select Installation Type" prompt, select Interbase Client/Server.
• When prompted, restart the machine.
^Run Setup.exe. This will install BenMAP 2003 Beta 2.0.
Uninstalling BenMAP
^Go to Control Panel / Add/Remove Programs.
^Select BenMAP 2003 Beta 2.0, and click Change/Remove.
^When prompted, select Yes to completely remove BenMAP 2003 Beta 2.0 and all its
components.
^Select Interbase 6.0, and click Change/Remove.
^Select Automatic at the "Select Uninstall Method" prompt and click Next.
^Click Finish.
Contacts for Comments and Questions
For comments and questions, please contact Bryan Hubbell at the U
Agency.
Address: C339-01, USEPA Mailroom, Research Triangle Park, NC
Email: hubbell.brvan@,epa.gov
Telephone: 919-541-0621.
Sources for More Information
For files that you can use in BenMAP:
^U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards
(OAQPS), BenMAP website. Available at:
http://www.epa.gov/ttn/ecas/analguid.html
For more information on conducting benefit analysis, see the following documents:
^U.S. Environmental Protection Agency, National Center for Environmental Economics
(NCEE), Guidelines for Preparing Economic Analyses. Available at:
http: //vos emite. epa. go v/ee/epa/eed. nsf/pages/guidelines
.S. Environmental Protection
27711
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Introduction
^U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards
(OAQPS), Economic Analysis Resource Document. Available at:
http ://www. epa. gov/ttn/ecas/analguid. pdf
^U.S. Environmental Protection Agency, Office of Air and Radiation, Costs and Benefits of the
Clean Air Act. Available at: http://www, epa. gov/oar/sect812/
^U.S. Environmental Protection Agency, Office of Air and Radiation, Draft Regulatory Impact
Analysis: Control of Emissions fromNonroad Diesel Engines, Chapter 9. EPA420-R-03-008,
April 2003. Available at: http://www.epa.gov/nonroad/r03008.pdf
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Table of Contents
Welcome to BenMAP, the Environmental Benefits Mapping and Analysis Program -i-
Who Can Use BenMAP? -ii-
How to Use this Manual -iii-
Computer Requirements -iii-
Contacts for Comments and Questions -iv-
Sources for More Information -iv-
1. Terminology and File Types 1-1
1.1 Common Terms 1-1
1.2 File Types 1-4
2. Overview of BenMAP Components 2-1
2.1 Main Buttons 2-1
2.1.1 Create Air Quality Grids 2-2
2.1.2 Create and Run Configuration 2-3
2.1.3 Specify Aggregation, Pooling, and Valuation 2-4
2.1.4 Generate Reports 2-4
2.2 Menus 2-5
2.2.1 Data 2-5
2.2.2 Tools 2-5
3. BenMAP Quick Start Tutorial 3-1
Step 1. Start BenMAP 3-1
Step 2. Create an Air Quality Grid for the Baseline Scenario 3-2
Step 3. Create an Air Quality Grid for the Control Scenario 3-3
Step 4. Specify Configuration Settings 3-4
Step 5. Select Concentration-Response Functions 3-6
Step 6. Specify Aggregation, Pooling and Valuation 3-8
Step 7. Generate Reports 3-13
Step 8. View Your Reports 3-16
Step 9. Map Your Results 3-18
4. Creating Air Quality Grids 4-1
4.1 Model Direct 4-2
4.2 Monitor Direct 4-5
4.2.1 Closest Monitor for Monitor Direct 4-6
4.2.2 Voronoi Neighbor Averaging (VNA) for Monitor Direct 4-6
4.2.3 Kriging for Monitor Direct 4-8
4.2.4 Other Monitor Direct options 4-9
4.3 Monitor and Model Relative 4-11
4.3.1 Spatial Scaling 4-12
4.3.2 Temporal Scaling 4-13
4.3.3 Spatial and Temporal Scaling 4-13
4.3.4 Examples 4-13
4.4 Monitor Rollback 4-15
4.4.1 Example: Combining Three Rollback Approaches in Different Regions .... 4-18
4.5 Advanced Monitor options 4-22
4.6 Questions Regarding Creating Air Quality Grids 4-26
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5. Create and Run Configurations 5-1
5.1 Create New Configuration 5-2
5.1.1 The Configuration Settings Form 5-2
5.1.2 Selecting C-R Functions for Configuration 5-4
5.1.3 Running the Health Effects Incidence Configuration 5-10
5.2 Open Existing Configuration 5-10
5.3 Questions Regarding Configurations 5-10
6. Aggregation, Pooling, and Valuation 6-1
6.1 Creating a New Configuration for Aggregation, Pooling, and Valuation 6-1
6.1.1 Pooling Incidence Results 6-2
6.1.2 Valuing Pooled Incidence Results 6-13
6.1.3 APV Configuration Advanced Settings 6-17
6.2 Running the APV Configuration 6-17
6.3 Open Existing Aggregation, Pooling, and Valuation (APV) Configuration File 6-18
7. Create Reports 7-1
7.1 Incidence and Valuation Results: Raw, Aggregated, and Pooled 7-1
7.1.1 Incidence and Valuation Results: Incidence Results 7-2
7.1.2 Incidence and Valuation Results: Aggregated Incidence Results 7-5
7.1.3 Incidence and Valuation Results: Pooled Incidence Results 7-6
7.1.4 Incidence and Valuation Results: Valuation Results 7-6
7.1.5 Incidence and Valuation Results: Aggregated Valuation Results 7-8
7.1.6 Incidence and Valuation Results: Pooled Valuation Results 7-9
7.2 Raw Incidence Results 7-10
7.3 Audit Trail Reports 7-10
7.4 Questions Regarding Creating Reports 7-12
8. Mapping 8-1
8.1 Overview of Mapping Features 8-1
8.1.1 Display Options 8-3
8.1.2 Taskbar Buttons 8-4
8.1.3 Mapping Different File Types with the Tools Menu 8-6
8.2 Viewing Maps in a BenMAP Analysis 8-11
8.3 Questions Regarding Mapping 8-17
9. Viewing and Editing C-R and Valuation Functions 9-1
9.1 C-R Functions 9-1
9.1.1 Viewing C-R Functions 9-2
9.1.2 Adding C-R Functions 9-4
9.1.3 Editing C-R Functions 9-5
9.2 Valuation Functions 9-9
9.3 Frequently Asked Questions Regarding Viewing and Editing C-R Functions and
Valuations 9-13
10. Tools 10-1
10.1 Mapping / GIS 10-1
10.2 Adjustment Factor Creator 10-1
10.3 Neighbor File Creator 10-2
10.4 CAMx / UAM-V Model File Creator 10-3
10.5 Questions Regarding Tool Menu 10-4
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Appendix A: Monitoring Data A-l
A.l Monitoring Data Format and Variable Values A-l
A.2 How BenMAP Filters Monitor Data A-6
A.2.1 Filtering Particulate Matter Monitor Data A-6
A.2.2 Filtering Ozone Monitor Data A-7
A.3 Monitor Rollbacks A-7
A.3.1 Percentage Rollback A-7
A.3.2 Incremental Rollback A-8
A.3.3 Rollback to a Standard A-9
A.4 SAS Code Used to Prepare Monitor Data for BenMAP A-19
A.4.1 SAS Code for Processing Raw Particulate Matter Monitor Data for Input to
BenMAP A-20
A.4.2 SAS Code for Processing Raw Ozone Monitor Data for Input to BenMAP
A-3 2
Appendix B: Population Data B-l
B .1 Census Data 1990 B-2
B .1.1 Other Group Estimation with 1990 Census B-3
B .1.2 Hispanic Group Estimation with 1990 Census B-4
B .2 Census Data 2000 B-4
B .2.1 Matching Racial Categories in the 1990 and 2000 Censuses B-5
B .3 Estimating Population Levels in Alternative Age Groups B-8
B .4 Estimating Population Levels in Non-Census Years B-8
B .4.1 Estimating Population Levels in 1991-1999 B-8
B .4.2 Forecasting Population Levels after 2000 B-9
B .5 County Population Forecasts 2000-2025 B-13
B .5.1 Aligning Woods & Poole FIPS Codes with BenMAP FIPS Codes B-13
B .5.2 Age, Gender, Race, and Ethnicity B-l5
B .5.3 Creating Growth Ratios from Absolute Population Values B-15
Appendix C: Air Pollution Exposure Estimation Algorithms C-l
C . 1 Direct Modeling C-2
C .2 Closest Monitor C-2
C.2.1 Closest Monitor - Temporal Scaling C-3
C .2.2 Closest Monitor - Spatial Scaling C-4
C .2.3 Closest Monitor - Temporal and Spatial Scaling C-5
C .3 Voronoi Neighbor Averaging (VNA) C-6
C .3.1 Voronoi Neighbor Averaging (VNA) - Temporal Scaling C-8
C .3.2 Voronoi Neighbor Averaging (VNA) - Spatial Scaling C-9
C .3.3 Voronoi Neighbor Averaging (VNA) - Temporal & Spatial Scaling C-10
C .4 Kriging C-l 1
C.4.1 Ordinary Kriging C-ll
C .4.2 Block Kriging C-13
C .4.3 Kriging in BenMAP ( -14
C .5 Temporal and Spatial Scaling Adjustment Factors C-l5
C.5.1 Calculation of Scaling Factors C-15
C .5.2 How BenMAP Scales PM and Ozone Monitor Data C-16
C .6 Binned Metrics C-l8
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Appendix D: Types of Concentration-Response Functions & Issues in the Estimation of Adverse
Health Effects D-l
D.l Overview D-l
D.l.l Review Relative Risk and Odds Ratio D-l
D .2 The estimation of health effect incidence change D-3
D .2.1 Linear Model D-3
D .2.2 Log-linear Model D-4
D .2.3 Logistic Model D-6
D .2.4 Cox proportional Hazards Model D-ll
D .3 General Issues in Estimating Health & Welfare Benefits D-12
D .3 .1 Choosing Epidemiological Studies and Developing Concentration-Response
Functions D-12
D .4 Issues in Using Concentration-Response Functions D-19
D .4.1 S-Plus Issue D-19
D .4.2 Thresholds D-20
D .4.3 Degree of Prematurity of Mortality D-21
D .4.4 Estimating Effects for Multiple Age Groups D-21
Appendix E: Sources of Prevalence and Incidence Data E-l
E.l Mortality E-l
E .2 Hospitalizations E-l
E .3 Emergency Room Visits for Asthma E-3
E .4 Nonfatal Heart Attacks E-4
E .5 School Loss Days E-5
E.5.1 All-Cause School Loss Rates E-5
E .5.2 Illness-Related School Loss Rates E-5
E .6 Other Acute and Chronic Effects E-6
E.6.1 Acute Bronchitis E-7
E .6.2 Chronic Bronchitis Incidence Rate E-7
E .6.3 Chronic Bronchitis Prevalence Rate E-8
E .6.4 Lower Respiratory Symptoms E-8
E .6.5 Minor Restricted Activity Days (MRAD) E-8
E .6.6 Work Loss Days E-8
E .7 Asthma-Related Health Effects E-8
E.7.1 Asthma Attacks E-9
E .7.2 Asthma Exacerbation E-9
E .7.3 Shortness of Breath E-9
E .7.4 Wheeze E-10
E .7.5 Cough E-10
E .7.6 One or More Symptoms E-10
E .7.7 Chronic Asthma E-10
E .7.8 Upper Respiratory Symptoms E-10
E .7.9 Asthma Population Estimates E-10
Appendix F: Particulate Matter Concentration-Response Functions F-l
F .1 Long-term Mortality F-3
F . 1.1 Mortality - Mean, All Cause (Krewski et al., 2000) - Reanalysis of Pope et al.
(1995) F-3
F . 1.2 Mortality - Median, All Cause (Krewski et al., 2000) - Reanalysis of Pope et al.
(1995) 1-4
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F . 1.3 Mortality - Median, Random Effects with Regional Adjustment (Krewski et al.,
2000) - Reanalysis of Pope et al. (1995) F-4
F . 1.4 Mortality - Median, Random Effects with Independent Cities (Krewski et al.,
2000) - Reanalysis of Pope et al. (1995) F-5
F .1.5 Mortality (Krewski et al., 2000) - Reanalysis of Dockery et al. (1993) F-5
F .1.6 Mortality, All Cause (Pope et al., 1995) F-6
F .1.7 Mortality, All Cause (Dockery et al., 1993) F-7
F .1.8 Mortality, All Cause (Pope et al., 2002) - Based on ACS Cohort F-7
F .1.9 Mortality, Cardiopulmonary (Pope et al., 2002) - Based on ACS Cohort .... F-9
F .1.10 Mortality, Lung Cancer (Pope et al., 2002) - Based on ACS Cohort F-10
F .1.11 Infant Mortality (Woodruff et al., 1997) F-12
F .2 Short-term Mortality F-14
F .2.1 Short-Term Mortality, Non-Accidental (Fairley, 2003) F-14
F .2.2 Short-Term Mortality, Non-Accidental (Ito, 2003) F-14
F .2.3 Short-Term Mortality, Non-Accidental (Klemm and Mason, 2003) F-15
F .2.4 Short-Term Mortality, Non-Accidental (Moolgavkar, 2003) F-15
F .2.5 Short-Term Mortality, Non-Accidental (Schwartz et al., 1996) F-16
F .2.6 Short-Term Mortality, Non-Accidental (Schwartz, 2003) F-17
F .2.7 Short-Term Mortality, Chronic Lung Disease - Lag Adjusted (Schwartz et al.,
1996) F-18
F .3 Chronic Illness F-20
F .3.1 Chronic Bronchitis (Abbey et al., 1995c, California) F-20
F .3.2 Chronic Bronchitis (Schwartz, 1993) F-21
F .4 Hospitalizations F-29
F .4.1 Hospital Admissions for All Respiratory (Burnett et al., 1997, Toronto) .... F-29
F .4.2 Hospital Admissions for All Respiratory (Burnett et al., 2001, Toronto) .... F-31
F .4.3 Hospital Admissions for All Respiratory (Schwartz, 1995, New Haven) .... F-33
F .4.4 Hospital Admissions for All Respiratory (Schwartz, 1995, Tacoma) F-34
F .4.5 Hospital Admissions for All Respiratory (Thurston et al., 1994, Toronto) . . F-34
F .4.6 Hospital Admissions for Asthma (Burnett et al., 1999, Toronto) F-36
F .4.7 Hospital Admissions for Asthma (Lin et al., 2002, Toronto) F-38
F .4.8 Hospital Admissions for Asthma (Sheppard et al., 1999; Sheppard, 2003) . . F-42
F .4.9 Hospital Admissions for Asthma (Thurston et al., 1994, Toronto) F-43
F .4.10 Hospital Admissions for Chronic Lung Disease (Lippmann et al., 2000; Ito, 2003)
F-45
F .4.11 Hospital Admissions for Chronic Lung Disease (Moolgavkar, 2000c;
Moolgavkar, 2003) F-46
F .4.12 Hospital Admissions for Chronic Lung Disease (Moolgavkar et al., 1997,
Minneapolis) F-49
F .4.13 Hospital Admissions for Chronic Lung Disease (Schwartz, 1994a, Minneapolis)
F-49
F .4.14 Hospital Admissions for Chronic Lung Disease (less Asthma) (Burnett et al.,
1999, Toronto) F-50
F .4.15 Hospital Admissions for Chronic Lung Disease (less Asthma) (Moolgavkar,
2000c) F-51
F .4.16 Hospital Admissions for Chronic Lung Disease (less Asthma) (Samet et al.,
2000, 14 Cities) 1-52
F .4.17 Hospital Admissions for Chronic Lung Disease (less Asthma) (Schwartz, 1994b,
Detroit) F-5 3
F .4.18 Hospital Admissions for Pneumonia (Burnett et al., 1999, Toronto) F-54
F .4.19 Hospital Admissions for Pneumonia (Lippmann et al., 2000; Ito, 2003) .... F-55
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F .4.20 Hospital Admissions for Pneumonia (Moolgavkar et al., 1997, Minneapolis)
F-56
F .4.21 Hospital Admissions for Pneumonia (Samet et al., 2000, 14 Cities) F-57
F .4.22 Hospital Admissions for Pneumonia (Schwartz, 1994a, Minneapolis) F-58
F .4.23 Hospital Admissions for Pneumonia (Schwartz, 1994b, Detroit) F-59
F .4.24 Hospital Admissions for All Cardiovascular (Burnett et al., 1997, Toronto) . F-59
F .4.25 Hospital Admissions for All Cardiovascular (Moolgavkar, 2000b; Moolgavkar,
2003) F-62
F .4.26 Hospital Admissions for All Cardiovascular (Samet et al., 2000, 14 Cities)
F-64
F .4.27 Hospital Admissions for Dysrhythmias (Burnett et al., 1999, Toronto) F-65
F .4.28 Hospital Admissions for Dysrhythmia (Lippmann et al., 2000; Ito, 2003) . . . F-67
F .4.29 Hospital Admissions for Congestive Heart Failure (Lippmann et al., 2000; Ito,
2003) F-68
F .4.30 Hospital Admissions for Ischemic Heart Disease (Lippmann et al., 2000; Ito,
2003) 1-69
F .5 Emergency Room Visits F-72
F .5.1 Emergency Room Visits for Asthma (Norris et al., 1999) F-72
F .5.2 Emergency Room Visits for Asthma (Schwartz et al., 1993, Seattle) F-73
F .6 Acute Effects F-75
F .6.1 Acute Bronchitis (Dockery et al., 1996) F-75
F .6.2 Acute Myocardial Infarction (Heart Attacks), Nonfatal (Peters et al., 2001)
F-75
F .6.3 Any of 19 Respiratory Symptoms (Krupnick et al., 1990) F-77
F .6.4 Lower Respiratory Symptoms (Schwartz and Neas, 2000) F-80
F .6.5 Lower Respiratory Symptoms (Schwartz et al., 1994) F-81
F .6.6 Minor Restricted Activity Days: Ostro and Rothschild (1989) F-82
F .6.7 School Loss Days, All Cause (Chen et al., 2000) F-83
F .6.8 School Loss Days, All Cause (Gilliland et al., 2001) F-84
F .6.9 School Loss Days, All Cause (Ransom and Pope, 1992, Provo) F-85
F .6.10 School Loss Days, All Cause (Ransom and Pope, 1992, Orem) F-86
F .6.11 School Loss Days, Illness-Related (Gilliland et al., 2001) F-88
F .6.12 School Loss Days, Respiratory Illness-Related (Gilliland et al., 2001) F-89
F .6.13 Work Loss Days (Ostro, 1987) F-90
F .7 Asthma-Related Effects F-95
F .7.1 Acute Bronchitis (McConnell et al., 1999) F-95
F .7.2 Asthma Attacks (Whittemore and Korn, 1980) F-96
F .7.3 Asthma Exacerbation, Cough (Ostro et al., 2001) F-97
F .7.4 Asthma Exacerbation, Cough (Vedal et al., 1998) F-99
F .7.5 Asthma Exacerbation, Moderate or Worse (Ostro et al., 1991) F-99
F .7.6 Asthma Exacerbation, One or More Symptoms (Yu et al., 2000) F-100
F .7.7 Asthma Exacerbation, Shortness of Breath (Ostro et al., 1995) F-101
F .7.8 Asthma Exacerbation, Shortness of Breath (Ostro et al., 2001) F-102
F .7.9 Asthma Exacerbation, Wheeze (Ostro et al., 2001) F-104
F .7.10 Chronic Phlegm (McConnell et al., 1999) F-106
F .7.11 Upper Respiratory Symptoms (Pope et al., 1991) F-107
F .8 Welfare Effects F-110
F .8.1 Household Soiling Damage (ESEERCO, 1994) F-110
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Appendix G: Ozone Concentration-Response Functions G-l
G . 1 Short-term Mortality G-3
G.l.l Short-Term Mortality, Non-Accidental (Fairley, 2003) G-3
G .1.2 Short-Term Mortality, Non-Accidental (Ito and Thurston, 1996, Chicago) . . G-4
G.1.3 Short-Term Mortality, Non-Accidental (Kinney et al., 1995, Los Angeles) . G-4
G . 1.4 Short-Term Mortality, Non-Accidental (Moolgavkar et al., 1995, Philadelphia)
G-5
G.1.5 Short-Term Mortality, Non-Accidental (Samet et al., 1997, Philadelphia) .. G-5
G . 1.6 Short-Term Mortality, Non-Accidental (World Health Organization (WHO)
Working Group, 2003, Europe) G-6
G .2 Chronic Illness G-9
G.2.1 Chronic Asthma (McDonnell et al., 1999) G-9
G .3 Hospital Admissions G-12
G .3.1 Hospital Admissions for All Respiratory (Burnett et al., 1997, Toronto) . . . G-12
G .3.2 Hospital Admissions for All Respiratory (Burnett et al., 2001, Toronto) . . . G-13
G .3.3 Hospital Admissions for All Respiratory (Schwartz, 1995, New Haven) . . . G-14
G .3.4 Hospital Admissions for All Respiratory (Schwartz, 1995, Tacoma) G-15
G.3.5 Hospital Admissions for All Respiratory (Thurston et al., 1994, Toronto) . G-16
G.3.6 Hospital Admissions for Asthma (Burnett et al., 1999, Toronto) G-17
G .3.7 Hospital Admissions for Asthma (Sheppard et al., 1999, Seattle) G-18
G .3.8 Hospital Admissions for Asthma (Thurston et al., 1994, Toronto) G-19
G .3.9 Hospital Admissions for Chronic Lung Disease (Moolgavkar et al., 1997,
Minneapolis) G-19
G .3.10 Hospital Admissions for Chronic Lung Disease (less Asthma) (Burnett et al.,
1999, Toronto) G-20
G .3.11 Hospital Admissions for Chronic Lung Disease (less Asthma) (Schwartz, 1994b,
Detroit) G-21
G .3.12 Hospital Admissions for Pneumonia (Burnett et al., 1999, Toronto) G-21
G .3.13 Hospital Admissions for Pneumonia (Moolgavkar et al., 1997, Minneapolis)
G-22
G .3.14 Hospital Admissions for Pneumonia (Schwartz, 1994b, Detroit) G-23
G .3.15 Hospital Admissions for Pneumonia (Schwartz, 1994a, Minneapolis) G-23
G .3.16 Hospital Admissions for All Cardiovascular (Burnett et al., 1997, Toronto)
G-24
G .3.17 Hospital Admissions for Dysrhythmia (Burnett et al., 1999, Toronto) G-25
G .4 Emergency Room Visits G-28
G .4.1 Emergency Room Visits for Asthma (Cody et al., 1992, Northern NJ) .... G-28
G .4.2 Emergency Room Visits for Asthma (Jaffe et al., 2003) G-28
G .4.3 Emergency Room Visits for Asthma (Norris et al., 1999) G-29
G .4.4 Emergency Room Visits for Asthma (Schwartz et al., 1993, Seattle) G-29
G .4.5 Emergency Room Visits for Asthma (Stieb et al., 1996, New Brunswick) . G-29
G .4.6 Emergency Room Visits for Asthma (Weisel et al., 1995, Northern NJ) . . . G-31
G .5 Acute Morbidity G-3 3
G.5.1 Any of 19 Respiratory Symptoms: Krupnick (1990) G-33
G .5.2 Minor Restricted Activity Days: Ostro and Rothschild (1989) G-35
G .5.3 School Loss Days, All Cause (Chen et al., 2000) G-37
G .5.4 School Loss Days, All Cause (Gilliland et al., 2001) G-38
G.5.5 School Loss Days, Illness-Related (Gilliland et al., 2001) G-39
G .5.6 School Loss Days, Respiratory Illness-Related (Gilliland et al., 2001) .... G-41
G.5.7 Worker Productivity: Crocker and Horst (1981) G-42
G .6 Asthma-Related Effects G-45
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Table of Contents
G.6.1 Asthma Attacks (Whittemore and Korn, 1980) G-45
G .6.2 Asthma Exacerbation, Cough (Ostro et al., 2001) G-45
G .6.3 Asthma Exacerbation, Shortness of Breath (Ostro et al., 1995) G-47
G .6.4 Asthma Exacerbation, Shortness of Breath (Ostro et al., 2001) G-47
G .6.5 Asthma Exacerbation, Wheeze (Ostro et al., 2001) G-49
Appendix H: Economic Value of Health Effects H-l
H.l Overview of Valuation H-l
H.2 Mortality H-3
H.2.1 Value of a Statistical Life Based on 26 Studies H-3
H.2.2 Value of a Statistical Life Based on Selected Studies H-3
H.3 Chronic Illness H-4
H.3.1 Chronic Bronchitis H-4
H.3.2 Chronic Bronchitis Reversals H-7
H.3.3 Chronic Asthma H-7
H.3.4 Non-Fatal Myocardial Infarctions (Heart Attacks) H-7
H.4 Hospital Admissions & Emergency Room Visits H-10
H.4.1 Hospital Admissions H-10
H.4.2 Emergency Room Visits for Asthma H-ll
H.5 Acute Symptoms and Illness Not Requiring Hospitalization H-12
H.5.1 Acute Bronchitis in Children H-l3
H.5.2 Upper Respiratory Symptoms (URS) in Children H-14
H.5.3 Lower Respiratory Symptoms (LRS) in Children H-15
H.5.4 "Any of 19 Respiratory Symptoms" H-15
11.5.5 Work Loss Days (WLDs) 11-16
H.5.6 Minor Restricted Activity Days (MRADs) H-16
H.5.7 Asthma Exacerbation H-17
H.5.8 School Loss Days H-17
Appendix I: Uncertainty & Pooling 1-1
I.1 Uncertainty 1-1
I.1.1 Characterization of Uncertainty Surrounding Incidence Changes 1-1
1.1.2 Characterization of Uncertainty Surrounding Dollar Benefits 1-2
1.2 Pooling 1-3
1.2.1 Weights Used for Pooling 1-3
1.2.2 The Mechanics of Pooling in BenMAP 1-8
1.2.3 Summing Distributions 1-9
1.2.4 Subtracting Distributions 1-9
References J-l
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Exhibit List
Exhibit 0-1. BenMAP Flow Diagram -ii-
Exhibit 1-1. File Types Generated by BenMAP 1-5
Exhibit 4-1. Air Quality Model Data Structure 4-3
Exhibit 4-2. Eastern and Western Modeling Domains for CAMx and UAM-V 4-5
Exhibit 4-3. Monitor Data File Format 4-11
Exhibit 4-5. Default Options Used by BenMAP to Filter Air Quality Monitoring Data 4-12
Exhibit 5-1. Classification of C-R Functions Using Endpoint Groups and Endpoints 5-5
Exhibit 6-1. Pooling Approaches for Incidence and Valuation Results 6-6
Exhibit 7-1. Summary of the Reports Generated from APVR File 7-2
Exhibit 7-2. Selected Variables in the Reports Based on the APVR file 7-4
Exhibit 9-1. Selected Variables in the C-R Function Database 9-3
Exhibit 9-4. Selected Variables in the Valuation Database 9-11
Exhibit 10-1. Variables in Data File Generated by the Neighbor File Creator 10-2
Exhibit A-1. Format for Air Quality Monitoring Data A-2
Exhibit A-2. Format for Air Quality Monitoring Data A-3
Exhibit A-4. Common Measurement Units Used for Monitoring Data in BenMAP A-4
Exhibit A-5. Description of Monitor Variables A-4
Exhibit B-l Demographic Groups and Variables Available in BenMAP B-l
Exhibit B-2 Race, Ethnicity and Age Variables in 1990 Census Block Data B-2
Exhibit B-3 Race, Ethnicity and Age Variables in 1990 Census Tract Data B-2
Exhibit B-4 Race, Ethnicity and Age Variables in 2000 Census Block Data B-5
Exhibit B-5. Distribution of Racial Groups B-6
Exhibit B-6. State-Level Population Estimates by Age Group B-l 1
Exhibit B-7. Linkage Between Woods & Poole County Definitions and BenMAP County Definitions
B-l3
Exhibit C-l. Metrics Typically Used in Concentration-Response Functions for Criteria Air Pollutants
C-2
Exhibit C-2. Types of Analyses Using Scaling Factors C-15
Exhibit D-1. Relative Risk and Odds Ratio Notation D-2
Exhibit D-2. Summary of Considerations Used in Selecting C-R Functions D-14
Exhibit D-3. Description of Selection Criteria D-16
Exhibit E-l. National Mortality Rates for Selected Conditions, by Age Group E-l
Exhibit E-2. Hospitalization Rates, by Region and Age Group E-3
Exhibit E-3. Emergency Room Visit Rates for Asthma, by Region and Age Group E-4
Exhibit E-4. Nonfatal Heart Attack Rates, by Region and Age Group E-5
Exhibit E-5. School Loss Day Rates E-6
Exhibit E-6. Selected Acute and Chronic Effects Rates E-7
Exhibit E-7. Asthma-Related Health Effects Rates E-9
Exhibit E-8. Asthma Prevalence Rates Used to Estimate Asthmatic Populations E-l 1
Exhibit F-l. Concentration-Response (C-R) Functions for Particulate Matter and Long-Term Mortality
F-2
Exhibit F-2. Concentration-Response (C-R) Functions for Particulate Matter and Short-Term Mortality
F-l 3
Exhibit F-3. Concentration-Response (C-R) Functions for Particulate Matter and Chronic Illness . . . F-19
Exhibit F-4. Concentration-Response (C-R) Functions for Particulate Matter and Hospital Admissions
1-23
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Exhibit List
Exhibit F-5. Concentration-Response (C-R) Functions for Particulate Matter and Emergency Room
Visits F-71
Exhibit F-6. Concentration-Response (C-R) Functions for Particulate Matter and Acute Effects .... F-74
Exhibit F-7. Concentration-Response (C-R) Functions for Particulate Matter and Asthma-Related
Effects F-93
Exhibit F-8. Concentration-Response (C-R) Functions for Particulate Matter and Welfare Effects
F-109
Exhibit G-l. Concentration-Response (C-R) Functions for Ozone and Short-Term Mortality G-2
Exhibit G-2. Concentration-Response (C-R) Functions for Ozone and Chronic Illness G-8
Exhibit G-3. Concentration-Response (C-R) Functions for Ozone and Hospital Admissions G-10
Exhibit G-4. Concentration-Response (C-R) Functions for Ozone and Emergency Room Visits . . . G-27
Exhibit G-5. Concentration-Response (C-R) Functions for Ozone and Acute Effects G-32
Exhibit G-6. Concentration-Response (C-R) Functions for Ozone and Asthma-Related Effects .... G-44
Exhibit H-l. Unit Values Available for Mortality H-4
Exhibit H-2. Unit Values Available for Chronic Bronchitis H-6
Exhibit H-3. Unit Values Available for Chronic Asthma H-7
Exhibit H-4 Unit Values Available for Myocardial Infarction H-9
Exhibit H-5. Unit Values Available for Hospital Admissions H-ll
Exhibit H-6. Unit Values Available for Asthma-Related ER Visits H-12
Exhibit H-7. Median WTP Estimates and Derived Midrange Estimates (in 1999 $) H-l3
Exhibit H-8. Women with Children: Number and Percent in the Labor Force, 2000, and Weighted
Average Participation Rate H-l8
Exhibit H-9. Unit Values Available for Acute Symptoms and Illnesses H-19
Exhibit H-10. Unit Values Available for Asthma-related Acute Symptoms and Illnesses H-20
Exhibit H-ll. Unit Value Uncertainty Distributions and Their Parameters H-21
Exhibit 1-2. Example of Fixed Effects Model Calculations 1-5
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1.
Terminology and File Types
The first section of this chapter explains common terms used in this user's manual and in the
model, and references, where possible, other sections in this manual to find more detailed
information. Section 1.2 describes in detail the necessary format for externally-generated model
and monitor data files that can be read into BenMAP and used to generate the air quality grid
files.
1.1 Common Terms
^Aggregation. The summing of grid cell level results to the county, state and national levels.
^APV Configuration (Aggregation, Pooling, and Valuation). APV Configurations store the
aggregation levels, pooling options, and valuation methods used in the analysis. In particular, you may
specify the aggregation level for incidence estimates, how to pool incidence estimates, the valuation
estimates to use, the aggregation level for the valuation estimates, and how to pool the valuation estimates.
APV Configurations are stored in files with an "apv" file extension. The results derived from an APV
Configuration have an "apvr" file extension. APV files are typically stored in the Configurations folder,
and APV Results files are typically stored in the Configuration Results folder.
>^Air Quality Grid. An air quality grid contains air pollution data. BenMAP uses one air quality grid
for the baseline scenario and a second for the control scenario, in order to estimate the change in the
number of adverse health effects between the two scenarios. Air Quality Grids are stored in files with an
"aqg" file extension. AQG files are typically stored in the Air Quality Grids folder.
>*Air Quality Metric. One of the measures typically used for air pollution. Particulate matter is
typically measured on a daily basis by air quality monitors, and has three metrics: daily average, annual
average, and the median daily average over the course of a year. Since ozone is measured hourly by
monitors, the metrics include: highest hour over the course of a day, as well as a series of averages for
specified parts of the day: five-hour (10am - 3pm), eight-hour (9am - 5pm), 12-hour (8am - 8pm), and 24-
hour.
>"Binning. The process of summarizing data by sorting the data values from low to high, dividing the
sorted data into a pre-determined number of groups, and then choosing a representative value for each
group, by either averaging the values in each group, or picking the mid-point of each group.
>CAMx (Comprehensive Air Quality Model with Extensions). An air quality model used to
measure ozone levels. It has grid cells with dimensions of approximately 12 kilometers by 12 kilometers.
>^CMAQ (Community Multi-Scale Air Quality). A state-of-the-art air quality model able to model
ambient particulate levels, as well as other pollutants, including ozone. The grid-size of CMAQ is
approximately 36 kilometers by 36 kilometers.
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^Configuration. The Configuration stores the C-R functions and model options used to estimate
adverse health effects. Configurations are stored in files with a "cfg" file extension. CFG files are
typically stored in the Configurations folder. The results derived from a Configuration have a "cfgr" file
extension. CFGR files are typically stored in the Configuration Results folder.
^C-R (Concentration-Response) Function. A C-R function calculates the change in adverse health
effects associated with a change in exposure to air population. A typical C-R function has inputs
specifying the air quality metric and pollutant, the age, race and ethnicity of the population affected, and
the incidence rate of the adverse health effect.
>*Cost of Illness (COI). The cost of illness includes the direct medical costs and lost earnings
associated with illness. These estimates generally understate the true value of reductions in risk of a
health effect, as they include just the direct expenditures related to treatment and lost earnings, but not the
value of avoided pain and suffering from the health effect.
End point. An endpoint is a subset of an endpoint group, and represents a more specific class of
adverse health effects. For example, within the endpoint group Mortality, there are the endpoints
Mortality, Long Term, All Cause and Mortality, Long Term, Cardiopulmonary. In cases where an
endpoint group has only a single endpoint, they share the same name.
^Endpoint Group. A endpoint group represents a broad class of adverse health effects, such as
premature mortality, chronic bronchitis, and hospital admissions. BenMAP only allows pooling of adverse
health effects to occur within a given endpoint group, as it generally does not make sense to sum together
the number of cases of disparate health effects, such as premature mortality and chronic bronchitis.
Fixed Effects Pooling. Fixed effects pooling is used to combine two or more distributions
(represented by Latin Hypercube points) into a single new distribution. Fixed effects pooling assumes that
there is a single true underlying relationship between these component distributions, and that differences
among estimated parameters are the result of sampling error. Weights for the pooling are generated via
inverse variance weighting, thus giving more weight to the input distributions with lower variance and less
weight to the input distributions with higher variance.
^Grid cell. One of the many geographic, or spatial components within an air quality model, or grid. For
example, the REMSAD model is composed of grid cells that are approximately 36 kilometers by 36
kilometers.
^ Incidence Rate. The incidence rate is the average number of adverse health effects per person per
unit of time, typically a day or a year. Appendix E discusses the data sources for the incidence rates in
BenMAP. Note, to avoid potentially small numbers and to ease comparison of different rates, Appendix
E reports all of the incidence rates as the number of cases per 100 individuals per year.
^ Interpolation. The process of estimating the air quality level in an unmonitored area by using one or
more nearby air quality monitors. BenMAP uses three types of interpolation procedures: one is to simply
choose the closest monitor, another is to use a technique called Voronoi Neighbor Averaging, and the
third uses a statistical technique called Kriging. These interpolation methods are discussed in more detail
in Appendix C.
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^ Kriging. A particular interpolation method for spatial data. With kriging, BenMAP calculates a
weighted average of the data of neighboring monitors within a user defined distance based on the
covariance structure derived from these neighboring monitors.
>*Latin Hypercube. A series of points generated by using specified percentiles in a given distribution,
such as that of a C-R coefficient. It is a short-cut method designed to represent a distribution, while at
the same time saving on computation time. For example, when using 20 Latin Hypercube points,
BenMAP would use the 2.5th, 7.5th, 12.5th,..., and 97.5th points from the distribution. The Latin
Hypercube points are used when combining the results of different C-R functions (discussed in Chapter
6), and in presenting confidence intervals for the incidence estimates (discussed in Chapter 7).
^Modeling. Estimating air pollution levels through the use of air quality models. The EPA website
discusses a wide range of air quality models: http://www.epa. gov/ebtpages/airairquaairqualitvmodels.html
and at http: //www, epa. gov/ttn/s cram/.
^Monitoring. Actual measurements of air pollution levels. Appendix A discusses the air quality
monitoring data used in BenMAP. The U.S. Environmental Protection Agency has monitoring data, as
well as other information related to monitoring, available through its Air Quality System (AQS):
http://www.epa.gov/air/data/aasdb.html.
^Particulate Matter. Includes PM2 5 (particles less than 2.5 microns in aerodynamic diameter), PM10
(particles less than 10 microns in aerodynamic diameter), and PMC (particles between 2.5 and 10 microns
in aerodynamic diameter).
^Pooling. The combining of different sets of data. BenMAP has several pooling methods, including
fixed effects, fixed/random effects, and subjective weighting. Appendix I discusses the pooling
approaches available in BenMAP.
Point Mode. When defining the configuration, you may choose to either estimate adverse health
effects in point mode or using a Latin Hypercube. The point mode simply means that BenMAP will use
the mean value of the coefficient in the C-R function.
^Population Exposure versus Personal Exposure. Population (or ambient) exposure refers to the
average air pollution level measured in a grid cell. In contrast, personal exposure keeps track over the
course of a day the exposure individuals encounter in different micro-environments, such as the freeway,
outdoors and indoors. BenMAP only keeps track of population exposure.
^Prevalence. The prevalence specifies the percentage of individuals with a given adverse health
effect.
^Random Effects Pooling. Random effects pooling is an alternative to the fixed effects model (see
Fixed Effects Pooling, above), and allows the possibility that the estimated parameter from different
studies may in fact be estimates of different parameters, rather than just different estimates of a single
underlying parameter.
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^Relative Risk. Relative risk typically is used as a measure of the change in risk of an adverse health
effect associated with an increase in air pollution levels. More specifically, it is the ratio of the risk of
illness with higher pollution to the risk of illness with a lower pollution level, where the "risk" is defined as
the probability that an individual will become ill.
>-REMSAD (Regulatory Model System for Aerosols and Deposition). An air quality model able
to calculate particulate matter levels, as well as other pollutants, including ozone. It has two types of grid
cells, one with dimensions of approximately 12 kilometers by 12 kilometers (REMSAD12), and the second
with dimensions of approximately 36 kilometers by 36 kilometers (REMSAD36).
^Subjective Weighting. Subjective weights let you specify the weights that you want to use when
combining two or more distributions of results. The weights should sum to one. If not, BenMAP
normalizes the weights so that they do.
>^UAIVI-V (Urban Airshed Monitoring - Variable grid). An air quality model typically used to
measure ozone levels. It has grid cells with dimensions of approximately 12 kilometers by 12 kilometers.
^VNA (Voronoi Neighbor Averaging). An algorithm used by BenMAP to interpolate air quality
monitoring data to an unmonitored location. BenMAP first identifies the set of monitors that best
"surround" the center of the population grid cell, and then takes an inverse-distance weighted average of
the monitoring values. This is discussed in detail in Appendix C.
>"WTP (Willingness to Pay). The willingness of individuals to pay for a good, such as a reduction in
the risk of illness. In general, economists tend to view an individual's WTP for a improvement in
environmental quality as the appropriate measure of the value of a risk reduction. An individual's
willingness-to-accept (WTA) compensation for not receiving an improvement is also a valid measure.
However, WTP is generally considered to be a more readily available and conservative measure of
benefits.
1.2 File Types
To calculate population exposure to air pollution, BenMAP depends on externally-generated
model and monitor data files. The model files can come from four models: REMSAD, UAM-V,
CAMx, and CMAQ. Chapter 4 provides additional details on how these model files are used.
You may use PM2 5, PM10, and ozone monitor data from a library in BenMAP, or use your own
monitor data. This is discussed in greater detail in Chapter 4, and Appendix A provides additional
details on the data in the monitor library installed with BenMAP.
To provide additional flexibility, BenMAP has a number of file types that you can use to store the
settings used in a BenMAP run, the results of a run, as well as maps. Exhibit 1-1 presents the
names of the different file types, their functions, and their default folder locations.
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Chapter 1. Terminology and File Types
Exhibit 1-1. File Types Generated by BenMAP
File Extension Description
Default Folder Location
aqg
*.adj
*.cfg
*.cfgr
*
apv
'.apvr
.shp
Air quality grid.
Adjustment factor file generated from air quality modeling data.
The adjustment factor file can be used in the creation of air quality
grids by adjusting air quality monitoring data.
Configuration specifying the C-R functions used to generate
incidence estimates.
Configuration results, containing incidence results at the grid cell
level.
Air Quality Grids
Model Data
Configurations
Configuration Results
Aggregation, Pooling, and Valuation configuration specifying the Configurations
aggregation levels, pooling options, and valuation methods used to
generate aggregated incidence estimates, pooled incidence estimates,
valuation estimates, aggregated valuation estimates, and pooled
valuation estimates.
Aggregation, Pooling, and Valuation configuration results,
containing incidence results at the grid cell level, aggregated
incidence results, valuation results, aggregated valuation results, and
pooled valuation results.
Shape files generated by BenMAP's mapping capabilities. These
files can be viewed within BenMAP or within shape file viewers,
such as ArcView.
Reports are exported as *.csv files, which may be viewed in a text
editor, or easily viewed in programs such as Excel.
Configuration Results
Maps
Reports
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CHAPTER 2
In this chapter...
Get an overview of the
functions available with each
of BenMAP's four main buttons
and two menu items.
Learn about the different
options for each function.
Overview of
BenMAP
Components
Chapter Overview
2.1 Main Buttons 2-1
2.1.1 Create Air Quality Grids 2-2
2.1.2 Create and Run Configuration 2-3
2.1.3 Specify Pooling and Aggregation 2-4
2.1.4 Generate Reports 2-4
2.2 Menus 2-5
2.2.1 Data 2-5
2.2.2 Tools 2-5
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2.
Overview of Ben MAP Components
You can access most of BenMAP's functions by using the four large buttons on the opening
screen. These buttons allow you to perform all of the actions needed to estimate the health
benefits of a change in air quality. The two drop down menus at the top of the screen, Data and
Tools, are for less frequently used functions, such as editing concentration-response (C-R)
functions and mapping. Section 2.1 describes the main buttons and their functions, and Section
2.2 describes the Data and Tools menus. All of these topics are covered in greater detail in
subsequent chapters of this manual.
2.1 Main Buttons
The four buttons take you through the steps of an analysis. The first button allows you to create
air quality grids which contain estimates of population-level exposure to air pollution. The second
button lets you choose the air quality grids for a particular analysis, and then to choose the C-R
functions to estimate the incidence of adverse health effects. The third button gives you different
options for combining the health effects estimates and placing an economic value on them. Using
the fourth button, you can generate several different kinds of reports.
4^ BenMAP 2003 Beta 2.0
Data lools Help
Environmental Benefits Mapping and Analysis Program
(&J f
Create Air Quality Grids
Create and Run
Configuration
Aggregation, Pooling,
and Valuation
Create Reports
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2.1.1 Create Air Quality Grids
BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it
relies on the air quality inputs given to it. To estimate population exposure to air pollution,
BenMAP combines population data with an Air Quality Grid, which it generates using some
combination of air quality modeling and/or monitoring data. The Create Air Quality Grids
button allows you to put your air quality data into the format that is used by BenMAP. You must
complete this step before you can perform an analysis in BenMAP.
Grid types
Air quality grids contain air pollution exposure estimates for a patchwork or "grid" that typically
covers the continental United States. The grids used by BenMAP are not arbitrarily chosen, but
instead either exactly match the grid or cell pattern used in common air quality models
(REMSAD, CMAQ, CAMx, and UAM-V), or match political units, such as Counties.
Population data
BenMAP uses specially designed population files matched to each air quality grid specification,
and then it estimates the air pollution exposure for this population using the air quality data
(modeled or monitored) which you provide. Population files are provided for a limited number of
air quality grid types. In the current version of the model, you must use the population data
provided by BenMAP.
Modeling and Monitoring Data
To generate air quality grids, you can use air quality modeling data and air quality monitoring data
in three different ways, as discussed below. However, once generated, the air quality grids all
have the same structure, and have the same "aqg" extension that BenMAP uses to designate
these file types. The grids contain estimates of air pollution levels for a specific period of time,
depending on the pollutant. For ozone, BenMAP uses a five-month summer ozone season (May
1 through September 30), and for particulate matter, BenMAP uses a year-long period.
Model Direct. The Model Direct grid creation option simply takes raw model data and
converts it into a file that BenMAP recognizes as an air quality grid. With this approach, you
specify a model filename, a pollutant, and a grid type (model type). Currently BenMAP can
generate REMSAD (36km or 12km) and CMAQ grids for PM25, PM10, and PMC, and CAMx
and UAM-V grids for ozone.
^Monitor Direct. Monitor Direct grid creation uses ozone, PM25, PMC, or PM10 air pollution
monitoring data to estimate air pollution levels in the grid type you specify - either REMSAD,
UAM-V / CAMx, CMAQ , or County. This may be done using one of the interpolation
procedures - closest monitor, Voronoi Neighbor Averaging (VNA), or kriging. With closest
monitor, BenMAP simply uses the data of the monitor closest to the grid cell's centroid. With
VNA, BenMAP first identifies the set of monitors that "surround" each grid cell, and then
calculates an inverse-distance weighted average of these neighboring monitors. With Kriging,
BenMAP identifies all monitors within a user defined distance around the grid cell. The data
from these monitors is then used to calculate a weighted average for the grid cell. The weights
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are dependent on the covariance structure of the interpolated monitors. A functional form of the
covariance structure must be supplied by the user.
^Monitor and Model Relative. This approach allows you to combine information from both
monitor and modeling data files. The basic idea is that BenMAP uses modeling data to scale
monitoring data, to compensate for incomplete monitoring data coverage. Like with the Monitor
Direct approach, you choose an interpolation method (closest monitor, VNA, or kriging), but in
addition, you then choose a scaling approach, either temporal, spatial, or both. This approach is
described in more detail in the next chapter, as well as in Appendix C.
^Monitor Rollback. This approach allows you to reduce, or rollback, monitor data using three
methods: percentage rollback, incremental rollback, and rollback to a standard. Percentage
rollback reduces all monitor observations by the same percentage. Incremental rollback reduces
all observations by the same increment. Rollback to a standard reduces monitor observations so
that they just meet a specified standard. This approach is described in more detail in Chapter 4, as
well as in Appendix A.
2.1.2 Create and Run Configuration
Using the two air quality grids as inputs, you can generate the change in adverse health effects
associated with the change in air quality between them. There are several steps in this process.
^ Step 1. Specify the baseline and control air quality grids that you created using the Create
Air Quality Grids button.
^ Step 2. Specify whether BenMAP should make a "point" estimate, or a set of "Latin
Hypercube" points. The point estimate is the change in incidence, generated using the average
coefficient value in the C-R function. The Latin Hypercube points are a series of points
generated by using specified percentiles in the distribution of the C-R coefficient - these points
represent the distribution of incidence values. (The Latin Hypercube points are later used when
combining the results of different C-R functions, and in presenting confidence intervals for the
incidence estimates.)
5s" Step 3. Specify the threshold, or a lowest value for air quality data. Any observations which
fall below this threshold will be replaced with the threshold value in all calculations.
^ Step 4. Choose the C-R functions that will be used in the estimation, and hit the Go! button
to start estimating the change in incidence.
BenMAP can store configuration choices in a user-named file with a "cfg" extension, and can
store incidence estimates in a user-named file with a "cfgr" extension. As needed, you can
access both files for later use.
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2.1.3 Specify Aggregation, Pooling, and Valuation
With this button, you can specify an aggregation level for previously calculated incidence
estimates, pool these aggregated incidence estimates, place an economic value on these pooled
and aggregated incidence estimates, aggregate these economic values, and finally pool these
aggregated economic values. There are several steps in this process.
^ Step 1. Choose a set of incidence estimates with which to work. This is a configuration
results file made with the Create and Run Configuration button, which is stored by BenMAP
with a "cfgr" extension.
^ Step 2. Choose the desired pooling and aggregation options for the incidence results.
^ Step 3. Choose the economic valuation options to apply to the pooled and aggregated
incidence results.
^ Step 4. Choose the desired pooling and aggregation options for the economic valuations, and
hit the Go! button to start generating results.
BenMAP can store APV Configuration choices in a user-named file with an "apv" extension,
and can store APV Configuration results in a user-named file with an "apvr" extension. As
needed, you can access both files for later use.
2.1.4 Generate Reports
BenMAP generates several types of reports - you can access these by clicking on the Create
Reports button.
^ Incidence and Valuation Results use an Aggregation, Pooling, and Valuation Results
file (with the ".apvr" extension) to create reports for incidence, aggregated incidence, pooled
incidence, valuation, aggregated valuation, or pooled valuation results. These reports are comma
separated values (CSV) files (*.csv) which can be read into various spreadsheet and database
programs, such as Microsoft Excel.
^ Raw Incidence Results use a Configuration Results file (with the ".cfgr" extension) to
create reports for incidence results. These reports are CSV files.
^ Audit Trail Reports provides a summary of the assumptions underlying each of five types of
files generated by BenMAP: Air Quality Grid ("aqg"), Configuration ("cfg"), Configuration
Results ( " cfgr"), Aggregation, Pooling, and Valuation (" apv"), and Aggregation,
Pooling, and Valuation Results (".apvr"). These reports can be viewed within BenMAP in an
expandable tree structure, or can be exported to tab-delimited text files.
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2.2 Menus
There are three menu options, found at the top of the initial screen: Data, Tools, and Help. The
Data and Tools menus provide access to tasks that are occasionally needed to perform a
standard analysis, or to better understand the results of an analysis. The Help menu provides
access to information about BenMAP - the version, contact information, and a suggested citation.
2.2.1 Data
The Data menu allows you to view the EPA Standard C-R and valuation functions available for
use in BenMAP, as well as to add your own C-R and valuation functions. BenMAP does not
allow you to edit or add to the set of EPA Standard functions. However, you can copy these
functions, modify them, save them, and use them in your analyses. Additionally, you can create
new functions from scratch.
2.2.2 Tools
The Tools menu allows you to choose: Mapping / GIS, Adjustment Factor Creator, Neighbor
File Generator, CAMx / IJAM-VModel File Generator, and Shapeflle Creator. These are
specialized tools that are not always needed for an analysis.
^The Mapping / GIS tool allows the user to generate a wide variety of maps, including maps of
monitor data, adjustment factors, air quality grids, population data, county incidence rates, and
both incidence and valuation results. In addition, you can export the maps you have generated
and view them in a shapeflle viewer, such as Arcview. Note that BenMAP also has context
specific mapping capabilities in various parts of the application. Chapter 8 provides additional
details on BenMAP's mapping capabilities.
Adjustment Factor Creator. With this option you can generate the adjustment factor files
needed for the Monitor and Model Relative air quality grid creation. To use the Adjustment
Factor Creator, you simply specify a model file, along with the grid type and pollutant. Note that
you can access the Adjustment Factor Creator from the Monitor and Model Relative Settings
screen directly (see Section 4.3 for details). BenMAP also displays the Adjustment Factor
Creator as a separate tool in case you only want to generate adjustment factor files, without
generating air quality grids, or conducting other parts of a typical analysis.
5s*Neighbor File Creator. You can load an air quality grid (aMonitor Direct grid or a Monitor
and Model Relative grid), and generate a tab-delimited file containing information about the
"neighbors" (or contributing monitors) of each grid cell and their associated weights. See the
discussion on interpolation in Section 4.2 for more details on how BenMAP generates these air
quality grids and determines the neighbors of each grid cell.
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^ CAMx / UAM-VModel File Creator. CAMx and UAM-V data comes in a series of
individual files (one per day) for the Eastern United States and a separate series for the Western
states. The CAMx / UAM-V Model File Creator allows you to select all of the Eastern domain
and Western domain files, and produce a single model file that can be used in other parts of
BenMAP requiring model file input, such as the Adjustment Factor Creator.
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CHAPTER 3
In this chapter...
>- Work through the steps of a
simple health benefits analysis,
from creating air quality grids to
generating reports and maps.
>- See each step clearly
explained and illustrated.
Become familiarized with each
of BenMAP's four buttons.
BenMAP
Quick Start
Tutorial
Chapter Overview
Step 1. Start BenMAP 3-1
Step 2. Create an Air Quality Grid for the Baseline Scenario
3-2
Step 3. Create an Air Quality Grid for the Control Scenario . 3-3
Step 4. Specify Configuration Settings 3-4
Step 5. Select Concentration-Response Functions 3-6
Step 6. Specify Aggregation, Pooling and Valuation 3-8
Step 7. Generate Reports 3-13
Step 8. View Your Reports 3-16
Step 8. Map Your Results 3-18
-------
3. Ben MAP Quick Start T utorial
The best way to imderstand what BenMAP does is to start Ben MAP and work through the following
simple tutorial. The tutorial is based on a hypothetical scenario where ambient PYl „ concentrations are
reduced by 10 percent in 2020. The steps in this analysis are as follows:
Step 1. Start BenMAP
Step 2. Create an Air Quality Grid for the Baseline Scenario
Step 3. Create an Air Quality Grid for the Control Scenario
Step 4. Specify Configuration Settings
Step 5. Select Concentration-Response Functions
Step 6. Specify Aggregation, Pooling and Valuation
Step 7. Generate Reports
Step 8. View Your Reports
Step 9. Map Your Results
Each step is explained in detail below.
Step 1. Start BenMAP.
Double-click on the BenMAP icon on your desktop, and the following screen will appear:
# BenMAP 2003 Beta 2.0 Q 5 0
Data Tools Help
Environmental Benefits Mapping and Analyas Program
A a
w
Create Air Quality Grids
Create and Run
Configuration
Aggregation, Pooling,
and Valuation
Create Reports
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Chapter 3. BenMAP Quick Start Tutorial
Step 2. Create an Air Quality Grid for the Baseline Scenario
Click on the Create Air Quality Grids button to
begin inputting the air quality data needed by
BenMAP. This will open up the window where you
will input the air quality data. In general, you need
two air quality grids to conduct a benefits analysis,
one for a baseline scenario and one for the policy
you are evaluating (the control scenario). We will
start by entering the information about the baseline
scenario. For the baseline scenario, we will use
some of the modeling data that is provided with
BenMAP.
Select Model Direct from the list and click on Go'
This will take you to the Model Direct Settings screen where you will enter the information
about the baseline air quality modeling results you want to use.
To enter a Model File location, you can either type
the path name or click on Browse. For this
example, click on Browse. Browse to C; \Program
Files\Abt Associates Inc\BenMAP\Model Data and
click on the file pm25-nonroad-Base2020.dat, then
click Open.
In the Pollutant field, use the pull dow n menu to
select PM2.5.
In the Grid Type field, use the pull down menu to select REMSAD 36km.
f "¦
¦01 Ail Quality Grid C reation ... __ ^
Choose Grid Creation Method
O Model Direct
Monitor Direct
(.} Monitor and Model Relative
Monitor Rollback
Cancel
Go!
Model Direct Settings
Model File:
Browse
Pollutant: |
M
GridType: j
M
[ Map |
Cancel
I Go! |
Model Direct Settings
UeM
M odel File: C: \Program Files\Abt Associates 1 nc\B enMAP
Browse
Pollutant: pM2.5 v|
GridType: REMSAD 36km v
I
Map
Cancel ] |
Go! ||
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Chapter 3. BenMAP Quick Start Tutorial
When your window looks like screen above, click Go!
BenMAP will now prompt you to save the air quality grid. Make sure you are in theAir Quality
Grids subfolder in the BenMAP directory and then save the file as: PM25 2020 REMSAD
Direct example base.aqg (you do not have to enter the "".aqg" extension). BenMAP will now
create an air quality grid that you can use in your benefits analysis. When the progress bar is
complete, BenMAP will return to the main screen.
Step 3. Create an Air Quality Grid for the Control Scenario
Click on the Air Quality Grids button on the main
screen. Select Model Direct from the Air Quality Grid
creation list and click Go!. This will take you back to the
Model Direct Settings screen where you will enter the
information about the air quality modeling data to be used
for the control scenario.
To enter the Model File location, click Browse and
browse to C: Program Files\Abt Associates lnc llgnMAP Model Data and click 011 the file
pm25-nonroad-2020TenPercentReduction.dat, then click Open.
In the Pollutant field, use the pull down menu to select PM2.5.
In the Grid Type field, use the pull down menu to select REMSAD 36km.
¦
Model Diiect Settings
Model File: C:\Prograrri FilesVtot Associates lnc\BenMAP |
Browse
Pollutant: PM2 5 v
GridType: REMSAD 36km v
1
Map
Cancel ] |
Go! J
When your window looks like the screen above, click Go!
BenMAP will now prompt you to save the air quality grid. Make sure you are in the Air Quality
Grids subfolder in the BenMAP directory and then save the file as: PM25 2020 REMSAD
Direct example control.aqg (you do not have to enter the ".aqg" extension). BenMAP will now
create an air quality grid that you can use in your benefits analysis. When the progress bar is
complete, BenMAP will return to the main BenMAP screen.
~IBM
Model File:
| Browse )
Pollutant:
y
GridType:
ki
[ Map |
| Cancel
[ G°! 1
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Step 4. Specify Configuration Settings
On the main BenMAP screen, click on the Create and Run Configuration button. In the
following box, select Create New Configuration and click Go!
This will bring up the Configuration Settings form, where you will enter the basic information
about your analysis before selecting the health effects you wish to estimate.
In the Baseline File field, you can either enter the path for your baseline air quality grid, or click
Open. For this example, click Open and browse to the Air Quality Grids folder. Select PM25
2020 REMSAD Direct example base and click Open.
Next, click on Open next to the Control File field and select PM25 2020 REMSAD Direct
example control and click Open.
Configuration Settings
Select Air Quality Grids
Baseline File:
Control File:
-Settings—
Pollutant:
Population Year:
Latin Hypercube Points:
Run In Point Mode: Q
Threshold: 0.0
~00
~ pen
Create
~ pen
Create
Map Grids
Cancel
Previous
Next
This specifies that you want to conduct a benefits analysis of the difference between the baseline
and control scenarios for which we created air quality grids in steps 3 and 4.
In the Settings section of this window, there are several fields which set the overall scope of the
analysis.
In the Pollutant field, use the drop down menu to select PM2.5.
This tells BenMAP that the pollutant you are analyzing is PM 2.5. Presently BenMAP
only analyzes one pollutant at a time.
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In the Population Year field, enter 2020 or select 2020 from the drop down menu.
This tells BenMAP that you want your analysis to use 2020 projected populations when
calculating health impacts.
In the Latin Hypercube Points field, enter 10 or select 10 from the drop down menu.
This tells BenMAP that you want to estimate the percentiles of the distribution of health
endpoint incidence using Latin Hypercube Sampling with 10 percentiles of the distribution,
representing the 5th, 15th, 25th, and so on up to the 95th percentile.
Leave the Run in Point Mode box unchecked.
You can only choose Latin Hypercube Points or Run in Point Mode. Since we are
using the Latin Hypercube approach in this example, you must leave this box unchecked.
Leave the Threshold field at 0.0.
This tells BenMAP that you want to estimate benefits associated with all changes in
PM2.5, regardless of where those changes occur along the range of PM2.5
concentrations. Selecting a non-zero threshold means that you would only want to
calculate benefits for changes occurring above the threshold.
^Configuration Settings
~US'
Select Air Quality Grids
Baseline File:
Settings—
Pollutant:
Population Year:
Latin Hypercube Points:
CAProgram Files^Abt Associates lnc\BenMAPV\ir Quality Grids\P
~pen
Create
CAProgram FilesSAbt Associates lnc\BenMAPW Quality Grids\P
Open
Create
Map Grids
Run In Point Mode: | |
PM2.5
V
2020
V
10
V
~
0.0
Cancel
Previous
Next
When your window looks like the above, click Next.
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Chapter 3. BenMAP Quick Start Tutorial
This will bring up the next page of the Configuration Settings form: the Select C-R
Functions screen.
Configuration Settings
UloM
Select C-R Functions
EPA Standard C-R Functions
Endpoint Group Endpoint t | LowAge * HighAge > Race i Gender
S) Acute Bronchitis
E) Acute Myocardial Infarction
Si Acute Respiratory Symptoms
E) Asthma Exacerbation
E) Chronic Bronchitis
E) Chronic Phlegm
El Emergency Room Visits, Respira
E) Hospital Admissions, Cardiovasc
E) Hospital Admissions, Respiratory
S Lower Respiratory Symptoms
E) Mortality
E] Work Loss Days
< | mi | > |
User C-R Functions
< in, | >
[ Cancel ] [ Previous ] [j Run j]
(Note: This screen can be resized if you are having trouble seeing all of the information. Individual
columns can also be resized. Just click on the border of a column and drag to increase or
decrease its width.)
Step 5. Select Concentration-Response Functions
In this screen, you can select C-R functions to use in your analysis. For this example, we are
going to estimate the change in incidence of three health endpoints associated with PM2.5: acute
bronchitis, acute myocardial infarctions (heart attacks), and emergency room visits for asthma.
To select a C-R function, you must drag it from the left-hand side of the screen to the list on the
right. You can drag groups of concentration-response functions over, or drill down and drag over
individual C-R functions.
For acute bronchitis, drill down until you see a group titled Dockery et al. Open this heading, and
drag the C-R function titled Dockery et al, 1996 \ 8-12 into the right hand panel of the window.
You should see a new row starting with the Endpoint Group Acute Bronchitis.
For acute myocardial infarctions (AMI), drag the entire group titled Acute Myocardial
Infarction to the right hand panel (do not drill down). This will include the full set of age-specific
C-R functions for AMI. There will be an "extra" C-R function in the list of AMI C-R functions
in the right hand panel, covering an all ages version of the C-R function. Find this function by
looking for the row with 18 in the Low Age column and Max in the High Age column. Click on
this row and press the delete key to remove the function.
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For asthma emergency room visits, drill down the in the group title Emergency Room Visits,
Respiratory until you see individual C-R functions. Drag the C-R function titled Norris, et cil.
1999 | NO2, SO 2 into the right hand panel of the window.
You should now have ten C-R functions listed in the right hand panel: one acute bronchitis
function, eight AMI functions, and one ER visit function.
Select Lr H runctions
EPA Standard C-R Functions
Endpoint Group
Endpoint / LowAge / HighAge t Race / | Gend
(±1 Acute Bronchitis
B- Acute Myocardial Infarction
El Acute Respiratory Symptoms
+)•• Asthma Exacerbation
B Chronic Bronchitis
E Chronic Phlegm
BE mergency R oom Visits, R espira
(i Hospital Admissions, Cardiovasc
B Hospital Admissions, Respiratory
El Lower Respiratory Symptoms
B Mortality
B Work Loss Days
< > i (>]
User C-R Functions
~
Acute Bronchitis
Acute Bronchitis
8
12
All
All
Acute Myocardial Inl
Acute Myocardial Inl
18
24
All
All
Acute Myocardial Inl
Acute Myocardial Inl
25
34
All
All
Acute Myocardial Inl
Acute Myocardial Inl
35
44
All
All
Acute Myocardial Inl
Acute Myocardial Inl
45
54
All
All
Acute Myocardial Inl
Acute Myocardial Inl
55
64
All
All
Acute Myocardial Inl
Acute Myocardial Inl
65
74
All
All
Acute Myocardial Inl
Acute Myocardial Inl
75
84
All
All
Acute Myocardial Inl
Acute Myocardial Inl
85
Max
All
All
Emergency Room Vi
Emergency Room Vi
0
17
All
All
< llll | >
| Cancel ] [ Previous ] |i Run j]
When your window looks like the above, click on Run.
BenMAP will then prompt you to save your file. Click Save. Browse to the Configurations
subfolder within the BenMAP directory and save the file as:
PM25 example config.cfg (you do not need to include the "".clg" extension).
When you have saved the configuration file, click OK to run the configuration.
BenMAP will prompt you to "Save Configuration Results to
File". Browse to the Configuration Results subfolder within
the BenMAP directory and save the file as: PM25 2020
REMSAD Direct example, cfgr (you do not need to include
the ".cfgr" extension)
Once you have entered the filename, BenMAP will begin
calculating the change in incidence for the set of C-R
functions you have selected. The run may take a few minutes to finish; a progress bar will let
The first time you are prompted
to save your configuration is for
the options and C-R functions
you have chosen. The second
time you are prompted to save
your results- the grid-cell level
changes in incidence.
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you know how it is proceeding. When BenMAP is finished running your configuration, it will
return to the main BenMAP screen.
Step 6. Specify Aggregation, Pooling and Valuation
This step allows you to take the incidence results that BenMAP just produced and place an
economic valuation on them, Although not covered in this tutorial, this is also where you can
select the geographic level of aggregation and combine individual incidence results into pooling
groups.
From the main screen, click on the Aggregation, Pooling and Valuation button. This will bring
up a menu screen with two choices: Create New Configuration for Aggregation, Pooling and
Valuation, or Open Existing Configuration for Aggregation, Pooling and Valuation (*.apv
file).
Select Create New Con figuration for Aggregation, Pooling and Valuation and click on Go!
BenMAP will prompt you to open a Configuration Results File. Browse to the Configuration
Results subfolder and select PM25 2020 REMSAD Direct example, cfgr. Then click on Open.
BenMAP will then open the Incidence Pooling and Aggregation window with the results from
running your configuration. You should see a window that looks like the following:
¦0* Incidence Pooling and Aggregation 1- llnlfej
Available Incidence Results
Select Pooling Methods
® Acute Bronchitis
© Acute Myocardial Infarction
G) Emergency Room Visits, Re
Pooling Window 1
Endpoint Group | Endpoint | Author Year L(J Pooling Method
--Window to Delete-- |v [ Delete ] [ Add
3
V
Configuration Results Filename: C:\ProgramFiles\AbtAssociateslnc\BenMAP\ConfigurationResults\ [ Browse ]
| Advanced ] [ Cancel ] f Next
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Click on each of the results groups (acute bronchitis, acute myocardial infarction, and emergency
room visits) and drag them to the right panel.
For this example, we are not pooling any of the incidence results (although we will pool valuations
in the next window), so just click on Next at the bottom of the window.
This will take you to the Select Valuation Methods, Pooling, and Aggregation window.
A) Select a value for acute bronchitis
To select a valuation method for acute bronchitis, drill down the Acute Bronchitis valuation group
until you see individual valuation methods. Click on the WTP: 6 day illness, CVstudies \ 0-17
method and drag it onto the Acute Bronchitis endpoint group in the right hand panel. You should
see the method appear under acute bronchitis in the Valuation Method column in the right hand
panel.
B) Select values for acute myocardial infarctions (heart attacks)
To select valuation methods for acute myocardial infarctions, drill down the AMI valuation group
until you see a (long) list of individual valuation methods. You might find it easier to expand the
column width of the Valuation Methods column (drag the right hand edge of the column to the
right to make it wider). We will be working with the valuation estimates from two studies, Wittels
and Russell. For each of the studies, there are a number of age specific valuations. There are
also two different discount rates (the discount rate is the rate at which future medical costs are
discounted to the present). Drag the age-specific valuation estimates from Wittels. for the 3
percent discount rate (COI, 5 yrs med, 5 yrs wages, 3% DR, Wittels (1990) | age) to each
matching age-specific line in the right hand panel (Pooling Window 1). You may have to scroll
over in the right hand panel to see the Low Age column.
Note that you will need to drag some age-specific valuation estimates to multiple lines in the
pooling window, since there is not a perfect match between the available age-specific valuation
estimates and the age groups for which the incidence of heart attacks was estimated. For
example, you will have to drag the valuation estimate for the 25 to 44 age group to both the 25 to
35 age group and the 35 to 45 age group in the pooling window.
Now repeat this process using the Russell 3 percent discount rate valuation estimates. When you
are finished, you should have two valuation estimates for each AMI age group, and your pooling
window should look like the one below.
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Select Valuation Methods, Pooling, and Aggregation [- ][n1^^
Valuatio
Methods
Pooling Window 1
D Acute Myocardial Infarction
B Acute Myocardial Infarction, Nonfatal
COI: 10 yrs med, 5 yrs wages, 3X DR, Eisenstein (2001) 10-24
COI: 10yrs rned, 5yrs wages, 3X DR, Eisenstein (2001) I 25-44
I-- COI: 10yrs rned, 5yrs wages, 3X DR, Eisenstein (2001) I 45-54
COI: 10 yrs rned, 5 yrs wages, 3X DR, Eisenstein (2001) I 55-65
COI: 10 yrs med, 5 yrs wages, 3X DR, Eisenstein (2001) I 66-Max
!•••• COI: 10 yrs med, 5 yrs wages, 7X DR, Eisenstein (2001) 10-24
COI: 10 yrs med, 5 yrs wages, 7X DR, Eisenstein (2001) I 25-44
;•••• COI: 10 yrs med, 5 yrs wages, IX DR, Eisenstein (2001) I 45-54
COI: 10 yrs med, 5 yrs wages, IX DR, Eisenstein (2001) I 55-65
!•••• COI: 10 yrs med, 5 yrs wages, IX DR, Eisenstein (2001) I 66-Max
!-• COI: 5 yrs med, 5 yrs wages, 3X DR, Russell (1998) 10-24
I COI: 5 yrs med, 5 yrs wages, 3X DR, Russell (1998) I 25-44
j- COI: 5 yrs med, 5 yrs wages, 3X DR, Russell (1998) I 45-54
i COI: 5 yrs med, 5 yrs wages, 3X DR, Russell (1998) I 55-65
COI: 5 yrs med, 5 yrs wages, 3X DR, Russell (1998) I 66-Max
| - COI: 5 yrs med, 5 yrs wages, 3X DR, Wittels (1990) I 0-24
COI: 5 yrs med, 5 yrs wages, 3X DR, Wittels (1990) I 25-44
;¦••• COI: 5 yrs med, 5 yrs wages, 3X DR, Wittels (1990) I 45-54
COI: 5 yrs med, 5 yrs wages, 3X DR, Wittels (1990) I 55-65
COI: 5 yrs med, 5yrs wages, 3X DR, Wittels (1990) I 66-Max
i COI: 5 yrs med, 5 yrs wages, IX DR, Russell (1998) I 0-24
!•••• COI: 5 yrs med, 5 yrs wages, IX DR, Russell (1998) 125-44
COI: 5 yrs med. 5 yrs waoes. 7X DR. Russell f199811 45-54
v|
Endpoint Group
:ation Low Age
Valuation Method Pooling Method
Acute Bronchitis
0
WTP: 6 day illness.
Acute Myocardial Infarction
;ton, MA
None
18
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
25
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
35
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
45
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
55
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
65
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
75
None
COI: 5 yrs med, 5 yrs
User Valuations
COI: 5 yrs med, 5 yrs
85
None
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
Emergency Room Visits, Respiratory
0
-Select"
< i i i» >
[ Advanced | [ Cancel ] [ Previous ] [ Run
Now you can pool the valuation results for heart attacks in each age group using the unit values
from both Wittels and Rusell. In order to do so, you must select a pooling method.
BenMAP lets you select from several different pooling methods. For this example, you will be
using subjective weights. In other applications, you may wish to use fixed or random effects
weights (see Chapter 6 for more information on pooling methods).
To set the pooling method for each age group result, click on the Pooling Method field in the
row ABOVE each pair of valuation methods (where it says None) and use the drop down menu
to select Subjective Weights. You must repeat this for EACH age group in order for pooling to
take place over all age groups.
In addition to pooling the results over the two valuation methods, we also need to aggregate the
results into a total estimate across age groups. In order to do so, in the row with Endpoint
Group (Endpoint Group = Acute Myocardial Infarction) click in the Pooling Method field
and select Sum (Dependent) from the drop down menu.
Your screen should look like the following:
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^Select Valuation Methods. Pooling, and Aggregation
Valuation Methods
EPA Standard Valuations
Pooling Window 1
i-i Acute Myocardial Infc
*
Endpoint Group
Endpoint
Author
Year
Location
Low Age
Valuation Method
Pooling Method
Acute Bronchitis
0
COI
10 yrs m
WTP: G day illness.
COI
10 yrs m
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
Peters et al.
2001
Boston, MA
Sum (Dependent)
COI
10 yrs m
18
Subjective Weights
LUI
10 yrs m
COI: 5 yrs med, 5 yrs
LUI
10 yrs m
COI: 5 yrs med, 5 yrs
COI
10 yrs m
25
Subjective Weights
COI
COI
COI
COI
COI
COI
10 yrs m
10 yrs m
10 yrs m
10 yrs m
5 yrs me
5 yrs me
5 yrs me
5 yrs me
5 yrs me
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
35
Subjective Weights
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
45
Subjective Weights
mi
COI: 5 yrs med, 5 yrs
COI
COI: 5 yrs med, 5 yrs
COI
5 yrs me
55
Subjective Weights
COI
5 yrs me
COI: 5 yrs med, 5 yrs
COI
5 yrs me
COI: 5 yrs med, 5 yrs
COI
5 yrs me
65
Subjective Weights
COI
5 yrs me
COI: 5 yrs med, 5 yrs
CUI
5 yrs me
0
COI: 5 yrs med, 5 yrs
[
75
Subjective Weights
COI: 5 yrs med, 5 yrs
User Valuations
COI: 5 yrs med, 5 yrs
85
Subjective Weights
COI: 5 yrs med, 5 yrs
COI: 5 yrs med, 5 yrs
Emergency Room Visits, Respiratory
0
--Select--
< I
I L>
[ Advanced
Cancel Previous
I r™ I
This pooling configuration for acute myocardial infarctions will assign a starting set of equal
weights to each valuation method for the set of eight age groups, and then create an overall
estimate of acute myocardial infarctions by summing the age-specific pooled estimates, treating
the distributions for each age group as dependent (i.e. a draw from the 5th percentile of the 45 to
54 age group will be added to the draw from the 5th percentile of the 55 to 64 age group and so
on).
C) Select values for asthma emergency room visits
To select values for asthma ER visits, drill down the Emergency Room Visits, Respiratory
heading to the Emergency Room Visits, Asthma, and then to the individual valuation approaches.
Drag both methods (COI: Smith et al, 1997 | O-Max and COI: Standford et al, 1999 | 0 - Max ) to
the Emergency Room Visits entry in the right hand panel.
Again, this will now allow you to assign a pooling method to pool the valuation results using the
two valuation methods. For this exercise, click on the pooling method in the emergency room
visits row (where it says None), and select Subjective Weights from the drop down menu. Your
completed screen should look like the one below.
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^Select Valuation Methods. Pooling, and Aggregation [- l[n]|£^j
Valuation Methods
EPA Standard Valuations
Pooling Window 1
Pooling Method
S Acute Bronchitis
E Acute Myocardial Infarcts
B Emergency Room Visits, 1
Q Emergency Room Vis
; COI: Smith et al.
COI: Standford e
Endpoint Group
Endpoint Author Year Location Low Age
Valuation Method
Acute Bronchitis
0
WTP: 6 day illness.
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
Peters et al.
2001
Boston, MA
Sum (Dependent)
18
Subjective Weights
COI: 5yrs med, 5yr;
COI: 5yrs med, 5yr;
25
Subjective Weights
COI: 5yrs med, 5 yr;
COI: 5yrs med, 5yrs
35
Subjective Weights
COI: 5yrs med, 5yrs
COI: 5yrs med, 5yr;
45
Subjective Weights
COI: 5yrs med, 5yr;
COI: 5yrs med, 5yr;
55
Subjective Weights
COI: 5yrs med, 5yr;
COI: 5yrs med, 5 yr;
65
Subjective Weights
COI: 5yrs med, 5yr;
COI: 5yrs med, 5 yr;
75
Subjective Weights
COI: 5yrs med, 5 yr;
< >
COI: 5yrs med, 5 yr;
User Valuations
85
Subjective Weights
COI: 5yrs med, 5yr;
COI: 5yrs med, 5yr;
Emergency Room Visits, Respiratory
0
Subjective Weights
COI: Smith et al. (1£
COI: Standford et a
[ Advanced |
[ Cancel ] [ Previous ] [ Run
D) Entering subjective weights
Once you have completed this step, click on Run. BenMAP will now bring up a window to allow
you to enter subjective weights.
BenMAP assigns a default equal weight to each selected valuation method. You can change
these weights by clicking in the weight cells. However, for this exercise, you should leave them
at 0.5 for each study. Click on OK at the bottom of the screen. You should see a save dialog
box. Click on Save to save your APV configuration. Save the file as PM25 Direct example
APV.
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^Select Subjective Weights
Pooling Window 1
Endpoint Group
Endpoint
Author
Year
Location
Valuation Method
Pooling Method
Weights
iAcute Bronchitis
WTP: G day illness,
Acute Myocardial Infarction
Acute Myocardial Infarction, Nonfatal
Peters et al.
2001
Boston, M/
Sum (Dependent)
Subjective Weights
CO I: 5yrs med, 5yr?
0.50
CO I: 5yrs med, 5 yrs
0.50
Subjective Weights
CO I: 5yrs med, 5yr?
0.50
CO I: 5yrs med, 5 yr?
0.50
Subjective Weights
CO I: 5yrs med, 5yr?
0.50
CO I: 5yrs med, 5yr*
0.50
Subjective Weights
CO I: 5yrs med, 5 yr?
0.50
CO I: 5yrs med, 5yrs
0.50
Subjective Weights
CO I: 5yrs med, 5yr?
0.50
CO I: 5yrs med, 5yrs
0.50
Subjective Weights
CO I: 5yrs med, 5yr*
0.50
CO I: 5yrs med, 5yr?
0.50
Subjective Weights
CO I: 5yrs med, 5yr*
0.50
CO I: 5yrs med, 5yr?
0.50
Subjective Weights
CO I: 5yrs med, 5yr*
0.50
CO I: 5yrs med, 5 yr?
0.50
Emergency Room Visits, Respiratory
Subjective Weights
COI: Smith et al. (IS
0.50
COI: Standford et a
0.50
<
—
Cancel
II OK 1
Click on OK to start the pooling and aggregation. You will be prompted to enter a filename for
the pooling and aggregation results file. Enter PM25 Direct example APVResults and click on
Save. BenMAP will display a progress bar for the pooling and aggregation. When the progress
bar disappears, you will be returned to the main BenMAP screen.
Step 7. Generate Reports
You may view your results within BenMAP, either in the preview window in the Create
Reports button or through the mapping functions. Alternatively, you may export the results to a
comma separated values file (*.csv), or a shape file (*.shp) which can be viewed in a GIS
program such as ESRI's ArcView product.
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Chapter 3. BenMAP Quick Start Tutorial
A) Generate a Pooled Incidence Results report.
A Pooled Incidence Results report contains the incidence results you previously generated,
using the aggregation level and pooling that you specified in the Incidence Pooling and
Aggregation window. Previously, in Step 6, we did not specify any pooling of incidence results
(although valuations were pooled), so in this case the Pooled Incidence Results report will look
just like the Aggregated Incidence Results report. If some incidence results had been pooled,
the two reports would be different.
Click on the Create Reports button from the main BenMAP screen. This will bring up the
Select Report Type window. Select Incidence and Valuation Results: Raw, Aggregated
and Pooled. Click OK. This will bring up the Select an APV Configuration Results File
screen. Select the PM25 Direct example APV Results.apvr file, and click Open.
In the Choose a Result Type window, choose Pooled
Incidence Results. Then click OK. This will bring up the
Results Grid Report Form, where you can customize your
report display and select the fields you want to see in the
report. In the Pooled C-R Function Fields box, check off
Endpoint Group and Low Age. Then click OK. In the
Save Results to File window type in the file name and
browse to the location where you want to store the exported
file. The Reports subfolder is a good location to keep
exported reports. Type in the name, PM25 Direct example
APV Incidence results in the box and click Save. You can now open the report in another
application, such as a spreadsheet or database program.
~Choose a Result Type
Result Type
O Incidence Results
O Aggregated Incidence Results
® Pooled Incidence Results
O Valuation Results
O Aggregated Valuation Results
O Pooled Valuation Results
Cancel OK
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Chapter 3. BenMAP Quick Start Tutorial
APV Configuration Results Report
!- 100
Column Selection
Grid Fields:
Pooled C-R Function Fields:
Result Fields:
0 Column
0 Row
0 Endpoint Group
Function
Endpoint
Version
~ Author
Year
Location
0 tow Age
High Age
~ Qualifier
[ ] Race
Gender
~ Other Pollutants
~ Metric
V Point Estimate
0 Mean
0 Standard Deviation
0 Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
Display Options
Preview
© Group by Gridcell, then by Pooled C-R Function.
Digits After Decimal Point:
4
IXI
[•«¦]
O Group by Pooled C-R Function, then by Gridcell.
Elements in Preview:
25
171
M
Low Age | Point E stimate ] M ean
Column
Endpoint Group
Stai A
Acute Bronchitis
25046.2090
24906.8340
Acute Myocardial Infarction
12.6133
12.5781
Acute Myocardial Infarction
34.4341
34.0543
Acute Myocardial Infarction
869.8937
367.5135
Acute Myocardial Infarction
2898.4565
2890.6848
102
Acute Myocardial Infarction
6560.5854
3542.9678
Acute Myocardial Infarction
8044.8359
3023.4683
Lance
B) Generate a Pooled Valuation Results report.
This report is similar to the Pooled Incidence Results report, and uses the valuation pooling you
previously specified in the Valuation Pooling and Aggregation window. Click on the Create
Reports button from the main BenMAP screen. This will
bring up the Select Report Type window. Select Incidence
and Valuation Results: Raw, Aggregated and Pooled. Click
OK. This will bring up the Select an APV Configuration
Results File screen. Select the PM25 Direct example APV
Results.apvr file, and click Open.
In the Choose a Result Type window, choose Pooled
Valuation Results. Then click OK. This will bring up the
Results Grid Report Form, where you can customize your
report display and select the fields you want to see in the
^Choose a Result Type lass
Result Type—
O Incidence Results
O Aggregated Incidence Results
O Pooled Incidence Results
O Valuation Results
O Aggregated Valuation Results
® Pooled Valuation Results
Cancel
OK
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Chapter 3. BenMAP Quick Start Tutorial
report. In the Pooled C-R Function Fields box, check off Endpoint Group. Then click OK.
In the Save Results to File window type in the file name and browse to the location where you
want to store the exported file. Type in the name, PM25 Direct example APVvaluation results
in the box and click Save. You can now open the report in another application, such as a
spreadsheet or database program.
APV Configuration Results Report
Column Selection
Grid Fields:
Pooled Valuation Method Fields:
Result Fields:
0 Column
0 Row
0 Endpoint Group
D Endpoint
~ Author
~ Year
~ Location
D Low Age
~ ValuationMethod
0 Point Estimate
0 Mean
0 Standard Deviation
0 Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
© Group by Gridcell, then by Pooled Valuation Method.
O Group by Pooled Valuation Method, then by Gridcell.
Display Options
Digits After Decimal Point:
Elements in Preview: 25
4
IXI
[~]
25
IXI
be]
Preview
I Row
Column
Endpoint Group
Point Estimate
Mean
Standard Deviation
Acute Bronchitis
3912675.0000
8862304.0000
6640653.0000
Acute Myocardial Infarction
2406505728.0000 2400078872.0000 1563884416.0000
E nnergency R oom Visits, R espiratory 4181466.0000
4184483.7500
<
Cancel
1160612.0000
OK
a
Step 8. View Your Reports
BenMAP generates comma separated values files (*.csv) that can be read by various
spreadsheet and database applications, such as Microsoft Excel.
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Chapter 3. BenMAP Quick Start Tutorial
IM] Microsoft Excel - PM2.5 Direct Example APV Incidence Excel Results.csv
l£| File Edit
View
Insert Format Tools Data Window Help
-|slx|
~ £? a
ligl
Itt? (S ^ 10 -
& ^
f* zl li [B & 100%
* 0.
Arial
- 10 » b 7 u s= m =s H $ % j !io " t!
=¦ t- m -
10 - B I U = m = S $ % , too +°o ^ .
A1
= Column
A
B
C
D
E
F
G
1
Column
Row
1
Endpoint Group
Acute Bronchitis
Point Estimate
Mean
8,862,304
Standard Deviation
6,640,653
Variance
44,098
2
1
8,912,675
3
1
1
Acute Myocardial Infarction
2,406,505,728
2,400,079,872
1,563,884,416
2,445,734,423
4
1
1
Emergency Room Visits, Respiratory
4,181,466
4,184,490
1,160,612
1,347
5
-
6
7
8
9
10
11
12
13
14
15
16
17
18
19
H <
~ ~[ \PM2.5 Direct Example APV Valuat/
Ready I I | I iNUM ISCRLI
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Chapter 3. BenMAP Quick Start Tutorial
Step 9. Map Your Results
You can also map any of the results that you have generated so far. This includes the air quality
grids, population data, incidence results, and valuation results. In this example, we will look at the
air quality grid for the base scenario, and view our incidence results. For more information on
these and other mapping functions, see Chapter 8.
To use the BenMAP mapping
functionality, go to the Tools menu and
choose Mapping / GIS. The BenMAP
GIS window will appear, with buttons at
the top for managing files and navigating
the map. To see the name of each
button, simply hold the cursor over it.
Click on the Open a file button, and
select Air Quality Grid from the drop-
down menu. Browse to the file PM25
2020 REMSAD Direct example
control, aqg file and click Open.
BenMAP GIS
L-JjnJB
& y ®
Q. 0 f) O £ Albers Equal Area Conic (v| -- Reference Layer - v|
1 Air Quality Grid
Monitors
Population
County Data
Adjustment Grid
APV Results ~
CFG Results
[ Close |
The name of the file will appear in the left-hand panel under Layers. Double-click on the name
and a small box will appear with Display Options for viewing this layer. Here you select the
variable contained in the layer (file) that you want to view. In the air quality grid, the variables
that are available are the annual average, the annual median, and
24-hr daily average. Select AnnualAvg for the annual average
in the Variable. In this box, you can also change the colors in
the map display, and the maximum and minimum values
displayed. Leave the defaults as they are, but uncheck the box
marked Grid Outline. You should see the map below.
^ Display Options
UaM
Variable: AnnualAvg
Min Value: 0.00
Max Value: 37.00
Start Color: ~
End Color:
Default Color:
Grid Outline: O Decimal Digits: 2
Cancel
OK
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Chapter 3. BenMAP Quick Start Tutorial
BenMAP GIS
& y
Layers
3 PM25 2020 RE MSA
0.00-3.70
3.70-7.40
7.40-11.10
11.10-14.80
14.80-18.50
! 18.50-22.20
22.20-25.90
25.90-29.60
29.60-33.30
33.30-37.00
9 0 ^ s Albers Equal Area Conic | v| [--Reference Layer --
Close
To see state outlines, select States from the Reference Layer drop-dow n menu at the top of the
screen.
& y Albers Equal Area Conic v j States v
BenMAP GIS
Layers
3 PM25 2020 REMSA
0.00-3.70
3.70-7.40
7.40-11.10
11.10-14.80
14.80-18.50
18.50-22.20
¦ 22.20-25.90
¦ 25.90-29.60
I 29.60-33.30
¦ 33.30-37.00
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Chapter 3. BenMAP Quick Start Tutorial
Now we can look at a geographical display of the incidence results we created for cases of
bronchitis, acute myocardial infarctions, and emergency room visits. Click on the Open a file
button at the top of the screen and select APVResults, then Incidence. In the next window,
select PM25 direct example APV results.cipvr, then click Open. BenMAP will load your
incidence results and display them in a table. Because GIS programs can typically only
accommodate field names that are 10 characters or less, there is a new column at the end of the
table labeled Gis Field Name. Here you can name your variables, as shown in the table below.
Edit GIS Field N
imes
Endpoint Group
Endpoint
Pollutant
Author
Year
Qualifier
Gis Field Name
Acute Bronchitis
Acute Bronchitis
PM2.5
Dockery et al.
1996
8-12
Brch812
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
18-24
AM 11824
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
25-34
AM 12534
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
35-44
AM 13544
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
45-54
AM 14554
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
55-64
AM 15564
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
65-74
AM 16574
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
75-84
AM 17584
Acute Myocardial In
Acute Myocardial In
PM2.5
Peters et al.
2001
85+
AMI85up
Emergency Room V
Emergency Room V
PM2.5
N orris et al.
1999
N02,S02
ERvisI
<
1
w
1 1
When you are satisfied with the variable names, click OK.
The new layer will show in the BenMAP GIS window on top
of the first layer. If the previous layer is still checked, then it
will appear, but underneath the new layer. Uncheck the box
next to the bottom (previous) layer to hide it. Your screen
should look like the one below.
You do not have to rename your
variables, but it will make it
easier to identify them when
selecting a variable to map. The
default names (ResultO, Resultl,
etc,) make it difficult to identify
individual results.
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*** BenMAP GIS Q(nj@|
Layers
3 PM25 Direct exampl
~ PM25 2020 RE MSA
0.27-3.48
3.48-6.70
S. 70-3.31
3.31-13.13
13.13-16.34
16.34-13.55
13.55-22.77
¦
22.77-25,38
25.38-23.20
1
28.20-32.41
States
Like the previous layer, double click on the name to bring up the Display Options box. Under
Variable you will see a list of the variable names you defined in the previous step. Select
AMI7584, uncheck the Grid Outline box, and click OK. The viewer will now display the
annual increase in the number of acute myocardial infarctions for people 75 to 84, as calculated
between the base and control scenarios. You can use the Display Options to select other
variables to view or change how the values are displayed.
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Chapter 3. BenMAP Quick Start Tutorial
Close
• BenMAP GIS
~ PM25 2020 REN*
0.27-3.48
3.48-6.70
6.70-9.31
3.31-13.13
13.13-16.34
16.34-19.55
1 q RR.99 77
3 PM25 Direct exa
0.00-32.75
32.75-65.50
65.50-38.26
38.26-131.01
131.01-163.76
B 163.76-136.51
196.51-229.26
229.26-262.02
262.02-234.77
294.77-327.52
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In this chapter...
>- Create air quality grids using
different methods.
Find details on the file
structures for data inputs.
>- Interpolate with Closest Monitor
orVoronoi Neighbor Averaging.
Scale monitor data with
modeling data.
>* Learn about advanced options
like monitor filtering.
Chapter Overview
4.1 Model Direct 4-2
4.2 Monitor Direct 4-5
4.2.1 Closest Monitor for Monitor Direct 4-6
4.2.2 Voronoi Neighbor Averaging (VNA) for
Monitor Direct 4-6
4.2.3 Kriging for Monitor Direct 4-8
4.2.3 Other Monitor Direct Options 4-9
4.3 Monitor and Model Relative 4-11
4.3.1 Spatial Scaling 4-12
4.3.2 Temporal Scaling 4-13
4.3.3 Spatial and Temporal Scaling 4-13
4.3.4 Examples 4-13
4.4. Monitor Rollback 4-15
4.4.1 Example 4-18
4.5 Advanced Monitor options 4-22
4.6 Questions Regarding Creating Air Quality Grids .... 4-26
CHAPTER 4
Creating Air
Quality Grids
-------
4. Creating Air Quality Grids
BenMAP is not an air quality model, nor can it generate air quality data independently. Instead it
relies on the air quality inputs given to it. To estimate population exposure to air pollution,
BenMAP uses air quality grids that it generates from input air quality data (modeling and or
monitoring data).
BenMAP creates air quality grids to estimate the average
exposure to ambient air pollution of people living in some
specified area, or domain, such as that delineated by REMSAD,
CAMx, and CMAQ models, as well as more irregular shapes,
such as counties. However, BenMAP does not estimate
personal exposure. Instead, the air quality grids provide the
average population exposure for each grid cell that BenMAP can
then use in C-R functions.
To create air quality grids, BenMAP uses a number of inputs,
including modeling data, monitoring data, or both. You may enter
your own modeling and monitoring data, provided that the data are in a format recognized by
BenMAP. In addition, BenMAP comes supplied with some sample REMSAD modeling files, as
well as a growing archive that currently has ozone, I'M and PM monitoring data from the
years 1996-2002 . The current version of BenM AP creates files for four pollutants: ozone, PM2 5,
PM10, and PMC (coarse particles).
Note that during the creation of air
quality grids, you have access to
some advanced options that allow
you to use values other than the
defaults typically used. For
example, in using monitor data, you
can specify a subset of the
available data, such as a particular
region of the United States or
monitors that meet certain
completeness criteria. We discuss
the advanced options, as well as the
more standard approaches, below
in the appropriate sections.
Air Quality Grids contain air
pollution data. BenMAP uses one
baseline and one control air quality
grid and estimates the change in
the number of adverse health effects
between the two. CAMx, CMAQ,
REMSAD, and UAM-V are all air
quality models that generate data
used by BenMAP to create air
quality grids.
BenMAP 200) Beta 2 0
Environmen
Ail Quality Grid C reation BUB
Choose Grid Creation Method
O Model Direct
O Monitor Direct
O Monitor and Model Relative
O Monitor Rollback
Cancel
Go!
is Program
Create Air Quality Grids
Create and Run
Configuration
Aggregation, Pooling,
and Valuation
Create Reports
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Chapter 4. Creating Air Quality Grids
To start the grid creation process, click on the Create Air Quality Grids button. BenMAP will
then ask which of the following types of air quality data you wish to use:
Model Direct. Choose this option if you have air quality modeling data from REMSAD,
CAMx, UAM-V or CMAQ that you wish to use directly. Exhibit 4-1 below describes the input
format for each grid type.
Monitor Direct. Choose this option if you wish to import your own monitoring data (see
Exhibit 4-3 for format), or you wish to use monitoring data from the BenMAP library, without
additional modeling data.
Monitor and Model Relative. Choose this option if you wish to use a combination of
monitor and model data.
Monitor Rollback. Choose this option if you want to reduce all monitor levels by a
specified amount.
Select your option and then click Go!. BenMAP will direct you through the necessary steps for
each option.
4.1 Model Direct
After choosing the Model Direct option, you need to specify the location of your data file
(Model File), the pollutant that the data is modeling (Pollutant), and the air quality model from
which the data came (Grid Type).
Model Direct Settings
Model File:
Browse
Pollutant: |
ZB
GridType:
M
Map
Cancel
1 M 1
The Model File specifies the location of the air quality model results that you want to import.
Exhibit 4-1 presents the structure that these files must have, and the pollutants that each grid type
is currently designed to accept. (For more information on these models, the EPA website has
detailed descriptions of a variety of air quality models: http://www.epa.gov/ttn/scram/.)
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Chapter 4. Creating Air Quality Grids
Exhibit 4-1. Air Quality Model Data Structure
Model Pollutant" Modeling Domain and Data File Description b
REMSAD PM25, Two REMSAD Modeling Domains
PM10, and REMSAD12: has grid cells that are 1/6 of a degree longitude wide and 1/9 of a degree latitude high, or
PMC about 12 kilometers by 12 kilometers. The modeling domain extends from longitude -126° to -66°
and latitude 24° to 52°, with a total of 90,720 grid cells that completely cover the continental United
States.
REMSAD36: has grid cells that are 1/2 of a degree longitude wide and 1/3 of a degree latitude high, or
about 36 kilometers by 36 kilometers. The modeling domain extends from longitude -126° to -66°
and latitude 24° to 52°, with a total of 10,080 grid cells that completely cover the continental U.S.
Data File
There is a single file for a year of data. The first line has a description of the data, and the second
line has a list of the variable names: column, row, and 364 variable names for the days January 1
through December 29. (December 30-31 are missing, and a value is present for February 29th.) Each
subsequent line has the actual data in the same order as the variable names. For each column-row
combination there is one line of data. Variable names (on the second line) and values (on each
subsequent line) are separated by whitespace.
Modeling Domain
CAMx and UAM-V have grid cells that are 1/6 of a degree longitude wide and 1/9 of a degree latitude
high, or about 12 kilometers by 12 kilometers. BenMAP assumes a boundary extending from
longitude -127° to -67° and latitude 26° to 52°, with a total number of 84,280 grid cells that cover
most of the continental United States, with the exception of the southern tips of Florida and Texas.
Flowever, modelers often divide the United States into an Eastern and Western modeling domain,
with the Eastern modeling domain bounded by longitude -99° to -67° and latitude 26° to 47°, and the
Western modeling domain bounded by longitude -127° to -99° and latitude 26° to 52°. At the edge of
each of the modeling domains, there is a border of three grid cells with missing data, so some
populated areas of the U.S. are not modeled. The actual area typically modeled in the Eastern
domain extends from long. -98.5° to -67.5° and lat. 26.33° to 46.67°, and the Western domain
extends from long. -126.5° to -99.5° and lat. 26.33° to 51.67° (see Exhibit 4-2).
Data File
The CAMx and UAM-V modeling data typically comes in multiple files, with each file representing
a day of observations. A variable number of modeling days may be used for each domain. For a
number of EPA analyses [e.g., \Abt Associates Inc., 2000 #2140], 30 days of modeling were used for
the Eastern domain and 19 days for the Western domain. The data files must be combined with the
"CAMx / UAM-V Model File Creator", accessible via the Tools Menu.
The inputs to that tool are one or more "East" files and one or more "West" files. BenMAP expects
that the Eastern domain files have data for columns numbered 1-192 and rows numbered 1-189, and
the Eastern domain files have data for columns 1-168 and rows 1-234. Each file should have one line
of data for each column-row combination, with each line having the column, row, and twenty four
observations. Missing observations are denoted with the value -999.0000 (it is important that
missing values have exactly this format). Values are separated by whitespace.
CMAQc PM2 5, Modeling Domain
PM10, and The modeling domain for CMAQ covers the entire continental United States. The size of each grid
PMC cell is roughly comparable to that of REMSAD36.
Data File
CMAQ has the same data file structure as REMSAD.
* Note that the different Grid types are limited to specific pollutants. Currently, BenMAP can only input REMSAD and
CMAQ model data for particulate matter, and CAMx and UAM-V are limited to ozone.
CAMx and Ozone
UAM-V
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Once your file is identified, choose the Pollutant: either 03 (ozone) ,PM25, PM10, or PMC.
You then need to specify the Grid Type - either REMSAD 36km, REMSAD 12km, UAM-V J
CAMx, or CMAO. The current version of Ben MAP is limited to certain pollutant and model
combinations that you are allowed to choose. However, there are no theoretical limitations to
these combinations, and over time BenMAP will accept additional combinations. Currently,
BenMAP allows you to use REMSAD (36km or 12km) and CMAQ with particulate matter, and
CAMx and UAM-V with ozone. Note that the CAMx and UAM-V domains are identical, so
BenMAP has grouped them together.
T IP: Carefully name your air quality grids so you can easily recognize them. Include the won
base or control and a scenario identifier. For example, if you were analyzing a mobile source
emission reduction scenario for 2020 using REMSAD for PM2.5, you might name your grids "Base
2020 mobile REMSAD PM25" and "Control 2020 mobile REMSAD PM25" or something similar.
4.2 Monitor Direct
Using the Monitor Direct grid creation option, you create an air qualify grid directly from air
pollution monitoring data, either ozone, PM2 5, PM1Q, or PMC. At the top of the Monitor Direct
Settings screen, you are asked to select an interpolation method. The interpolation method is
used to move from point-based monitor data to grid cell based air qualify data. That is, some grid
cells will have many monitors in them, some will have just one, and some will have none.
BenMAP uses the interpolation methods to generate representative air qualify metric values for
each grid cell from monitor data for all of these cases.
BenMAP includes three interpolation
methods. The Closest Monitor
method simply uses the monitor closest
to a grid cell's center as its
representative value. The Voronoi
Neighbor Averaging method takes an
inverse-distance weighted average of a
set of the monitors surrounding a grid
cell's center as its representative value.
The Kriging method takes the
weighted average of a set of the
monitors surrounding a grid cell's
center as its representative value. The
kriging method's weights are based on
the covariance structure of the
surrounding monitors. Each method is
described in detail in sections 4.2.1,
4.2.2, and 4.2.3 below. For more detail,
see Appendix C.
¦©¦ Monitoi Direct Settings
[JnM
Select Interpolation Method
O Closest Monitor
O Kriging
O Voronoi Neighbor Averaging (VNA)
0 Use Library Monitor Data
Grid Type:
Pollutant:
Library Monitor Year:
Monitor File:
Browse
Advanced
Map
Cancel
Go!
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Below the interpolation options on the Monitor Direct Settings screen, you can choose the rest
of the options to create your air quality grid. The Use Library Monitor Data option, and the
other options at the bottom of the screen are described in section 4.2.4. The advanced monitor
filtering options that you can access by clicking on the Advanced button at the bottom of the
screen are described in section 4.5.
4.2.1 Closest Monitor for Monitor Direct
If you choose the Closest Monitor option, BenMAP identifies the monitor closest to each grid
cell's center, and then assigns that monitor's data to the grid cell.
Closest Monitor interpolation has one advanced interpolation option, which can be modified by
clicking on the Advanced button at the bottom of the screen and selecting the Interpolation
Options tab:
^ Maximum Neighbor Distance specifies the maximum distance that a monitor may be from
a grid cell (distances are calculated using the grid cell centroid). Cells without any monitors
within this distance will not be included in the resultant air quality grid. The default setting is
infinite (i.e. no limit to the distance between a monitor and a grid cell).
4.2.2 Voronoi Neighbor Averaging (VNA) for Monitor Direct
If you choose the VNA option, BenMAP first identifies the set of monitors that "surround" each
grid cell's center (these monitors are referred to as the grid cell's neighbors), and then BenMAP
calculates an inverse-distance weighted average of these neighboring monitors. In this section,
we provide some examples of the different ways that BenMAP calculates the average of the
neighbor monitors. See Appendix C for an expanded discussion of VNA, including how the VNA
algorithm actually chooses the neighbor monitors, as well as the different ways that it may be
used.
VNA interpolation has three advanced interpolation options, which can be modified by clicking on
the Advanced button at the bottom of the screen and selecting the Interpolation Options tab:
^ Maximum Neighbor Distance specifies the maximum distance that a monitor may be from
a grid cell, and still be included in the set of neighbor monitors used to calculate air pollution
exposure at a particular grid cell. The default setting is infinite (i.e., no limit to the distance
between a monitor and the grid cell).
^ Maximum Relative Distance specifies the maximum ratio for the distance of each monitor
to the distance of the closest monitor. The default setting is infinite.
^ Neighbor Scaling Type specifies whether BenMAP should use inverse-distance weighting
for the monitors, or inverse-distance-squared weighting of the monitors. The default setting is
inverse-distance weighting.
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The following examples illustrate how varying these options affects the final average
concentration estimate.
Example 1: Monitor Direct VNA method
Default options
Consider the following example at an hypothetical rural grid cell, where there are relatively few
monitors, and where the distance from a monitor to the grid cell can be fairly large. With VNA,
BenMAP first identifies the set of "neighbor" monitors for each grid cell. The number of
neighbors is usually in the range of about three to eight. In this case, assume that there are five
monitors at distances of 25, 50, 100, 200, and 400 miles from the grid cell, with annual PM25
levels of 8, 13, 12, 18, and 15 |ig/m\ respectively. BenMAP would calculate an inverse-distance
weighted average of the monitor values as follows:
Example 2: Monitor Direct VNA method
Maximum Neighbor Distance = 75
Using the same example that we used above, let us say you have specified a Maximum
Neighbor Distance of 75 miles, and left unchanged the default options (infinite value) for
Maximum Relative Distance. BenMAP would only consider the first two monitors, and would
calculate an inverse-distance weighted average of the monitor values as follows:
Example 3: Monitor Direct VNA method
Maximum Relative Distance = 10
Alternatively, if you have specified that the Maximum Neighbor Distance is infinite, but the
Maximum Relative Distance should have a value of, say, 10, then BenMAP would calculate
the ratio of the distance for each monitor to distance of the closest monitor. In this case, the
ratios would be 1 (=25/25), 2 (=50/25), 4 (=100/25), 8 (=200/25), and 16 (=400/25), and BenMAP
would drop the monitor with a ratio of 16. BenMAP would then calculate an inverse-distance
weighted average of the monitor values as follows:
PM25 average
= 10.68
11111
25 + 50 + 100 + 200 + 400
PM2 5 average = = 9.67
25 T 50
PM25 average
25 + 50 + 100 + 200
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Example 4: Monitor Direct VNA method
Inverse-distance squared neighbor scaling
In addition, you can specify the an inverse-distance-squared weighting of the monitors. Let us
say that you have left unchanged the defaults (infinite values) for Maximum Neighbor
Distance and Maximum Relative Distance, and specified that the Neighbor Scaling Type is
inverse-distance-squared. BenMAP would then calculate an inverse-distance-squared weighted
average of the monitor values as follows:
l l l l l
625 8+ 2,500 13+ 10,000 '12 + 40,000 18 + 160,OOO15 _
PM2 5 average = jjjjj = 9.26
:+ + + +
625 2,500 10,000 40,000 160,000
Example 5: Monitor Direct VNA method
Maximum Neighbor Distance = 75
Maximum Relative Distance = 10
Inverse-distance squared neighbor scaling
Finally, you could specify changes to all three options: a Maximum Neighbor Distance of 75
miles, a Maximum Relative Distance of 10, and a Neighbor Scaling Type of inverse-distance-
squared weighting. BenMAP would then calculate the following average:
1 1
PM25 average = j L-j = 9.00
625 + 2,500
4.2.3 Kriging for Monitor Direct
If you choose the Kriging option, BenMAP will present a set of options for you to customize.
Please note that some of these options are essential for correct interpolation. See Appendix C for
an expanded discussion of kriging.
You can use the default kriging configuration, or customize it by clicking on the Kriging Settings
button. This lets you set several options.
^ Kriging Type specifies whether BenMAP uses Ordinary kriging or Block kriging. Ordinary
kriging interpolates data using the center point of each grid cell as a reference. Block kriging
allows you to overlay the grid cell with a number of support points to which the monitor data will
be interpolated. The number of support points for Block kriging can be customized using the
Block Kriging Grid options. Block kriging allows for more accurate interpolation of a
representative value for the entire grid cell area. The default setting is Ordinary.
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^ Maximum Cell Distance specifies the maximum distance (in kilometers) for the distance of
each monitor to the center of each grid-cell. Monitors exceeding this distance will not be used in
the interpolation. The default setting is infinite.
>*¦ Co variance Options allows you to choose the Nugget Effect and the Covariance
Function. The default option is a Nugget Effect equal to zero, and no Covariance Function.
Please note: The covariance function must be user specified and must match the empirical
covariance structure of the monitor data as determined by the user. Currently BenMAP relies on
the user to determine this function externally and supply it when setting paramaters. The nugget
effect is the value the covariance function is to assume at a monitor to grid cell distance of 0.
This value is also to be determined empirically and is used to model the typically occurring
discontinuity or the covariance structure at zero distances.
4.2.4 Other Monitor Direct options
You can also input your own monitor data file, so long as it is in the format that BenMAP
recognizes (defined below in Exhibit 4-3). Uncheck the Use Library Monitor Data box and
then type a path and filename into the Monitor File textbox (or you can click the Browse
button, and choose a file). Note that the file format should be a comma-delimited text file, with all
of the variables, and in the same order as they are listed in Exhibit 4-3. If Exhibit 4-3 lists a
variable as not ""Necessary for BenMAP", it still must have a placeholder comma on each line of
the file. Exhibit 4-4 provides a sample of how the data should appear.
Monitor Direct Settings
QsM
Select Interpolation Method
© Closest Monitor
O Kriging
O Voronoi Neighbor Averaging (VNA)
I I Use Library Monitor Data
Grid Type:
Pollutant:
Library Monitor Year:
Monitor File:
REMSAD 36km
PM2.5
2002
Browse
Advanced
Map
Cancel
Go!
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Exhibit 4-3. Monitor Data File Format
Variable "
Necessary for
BenMAP b
Descriptionc
year
yes
The year is a four-digit variable giving the year of the monitoring data.
monitor ID
yes
The monitor ID is a 15 character description of the monitor. It includes a state FIPS
code (2 characters), county FIPS code (3 characters), site ID (4 characters), pollutant
parameter (5 characters), and POC code (1 character).
latitude
yes
The latitude should be in decimal degrees.
longitude
yes
The longitude should be in decimal degrees. (Note that the longitude for the United
States has a negative sign.)
land use
no
Categorization of the prevalent land use within 1/4 mile of the Monitoring Site.
method
no
The method identifies the approach used to collect the monitor data. For example, the
Federal Reference Method for PM2 5 includes method codes 116-120 and 123.
location setting
no
A description of the environmental setting within which the Site is located.
probe location
no
Identification of the location of the sampling Probe.
monitor objective
no
Identification of the reason for measuring air quality by the Monitor.
sample frequency
no
Indicates the scheduled elapsed time period between observations.
sample values
yes
Either 365 daily PM values or 8,760 hourly ozone values. Missing values are
indicated with a dot (.).
1 Monitor data available from the EPA AQS (contact: Virginia Ambrose (ambrose.virginia@epa.gov). Each monitor and
method represents a unique set of sample values, and occupies one line of data. BenMAP allows you to choose the desired
methods, and then averages the data so that a monitor ID has only a single set of sample values.
b The year, monitor ID, latitude, longitude, and sample values are necessary for BenMAP to function. On the other hand, other
variables are not strictly necessary, and may have empty values.
c Appendix A provides further details on the standard values for the variables, such as land use, location setting, and probe
location.
Exhibit 4-4. Sample PM2 5 File Format for User-Generated Monitor Text Files
Description
Sample Data"
List of variables
year, monitor ID, latitude, longitude, land use, method, location setting, probe
location, monitor objective, sample frequency, sample values
Sample daily data with some missing sample
2002 , 010270001881011 , 33.281111, -85.802222, agricultural, 116, rural, side
values.
of building, highest concentration, 3, 15.2, 18.7, . ,. , 12.3, . , . , 22.8, . ,
10, etc.
Sample daily data with some missing sample
2002,010270001881011,33.281111,-85.802222,, 116, ,,,,3, 15.2,. , . ,. ,
values, as well as with missing land use,
18.7, . , . , 12.3,. , . ,22.8, 10, etc.
location setting, probe location, monitor
objective, and sample frequency variables.
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After choosing the source of the Monitor Data, you need to specify the Pollutant and the Grid
Type, and sometimes the Library Monitor Year. The choices for Pollutant include: 03
(ozone), PM2 5, PM10, and PMC. The choices for Grid Type include: REMSAD 36km,
REMSAD 12km, UAM-V/ CAMx, CMAQ, and County. A Library Monitor Year must be
selected when using library monitors. Choices are generated automatically, and include all years
for which the monitor library contains data for the selected Pollutant. For air quality grids
created with the Monitor Direct option, you may use any pollutant / grid type combination.
Not all of the monitor data for a given Pollutant and Library Monitor Year is necessarily used
by BenMAP in a given run. BenMAP has certain defaults that filter the monitor data, and
remove any monitoring data that fails the filtering. For example, BenMAP typically drops any
monitoring data with a POC code greater than four. Exhibit 4-5 presents the default filtering
options used by BenMAP. See Section 4.5 below for details on how to modify the default
settings using the Advanced button.
Exhibit 4-5. Default Options Used by BenMAP to Filter Air Quality Monitoring Data
Pollutant Default Filtering Options
Ozone Use all method codes and objectives, including missing values. Use a maximum POC code equal to four,
and prefer POC code one, followed by two, three, and then four. Use all monitors in the continental
U.S. A valid day has at least 9 observations between 8:00 am and 7:59 pm [Start Hour = 8 and End
Hour = 19]; 50 percent of the days must be valid between May 1 and September 30.
PM2 5 Use Federal Reference Monitors, which have method codes 116-120 and 123. Use all objectives,
including missing values. Use a maximum POC code equal to four, and prefer POC code one, followed
by two, three, and then four. Use all monitors in the continental U.S. Each monitor must have a
minimum of 11 observations each quarter; use only local PM2 5 data.
PM10 Use all method codes and objectives, including missing values. Use a maximum POC code equal to four,
and prefer POC code one, followed by two, three, and then four. Use all monitors in the continental
U.S. Each monitor must have a minimum of 11 observations each quarter; use both standard and local
PM10 data, with a preference for local; output data to local.
4.3 Monitor and Model Relative
The Monitor and Model Relative option lets you scale interpolated monitor values with model
data. As with the Monitor Direct option, you can choose Closest Monitor, Voronoi Neighbor
Averaging (VNA), or Kriging interpolation; see Section 4.2 above for a discussion of those
options. In addition, you can scale the monitor values with three different approaches: Spatial
Only, Temporal Only, and Spatial and Temporal. As discussed below, these approaches let
you combine the advantage of the actual monitor observations with the information provided by
the models.
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Monitor and Model Relative air
quality grid creation is exactly the
same as Monitor Direct air quality
grid creation (see section 4.2) with
the exception of scaling. The
concept of scaling is to use
modeling data to improve
interpolation and/or forecast future
air quality trends.
Rather than using raw modeling
data, BenMAP uses Adjustment
Factors created from modeling
data. These are loaded as the
Base Year Adjustment File and, in
some cases, the Future Year
Adjustment File. These files can
be created using the Adjustment
Factor Creator, accessible via the
Tools menu on BenMAP's main
screen or by clicking the Create buttons next to the Adjustment File input text boxes. The base
year file should be created with modeling data which closely reflects the historical conditions of
the monitor data to be used. Typically multiple future year files will be used to create multiple air
quality grids for use in an analysis. That is, one future year file might represent a future
projection of current trends, while another might represent the results of implementing a
regulatory program.
Adjustment Factors are discussed in detail in Appendix C. Basically, however, they are
representative concentrations sorted low to high per grid cell. Each scaling method uses these
adjustment factors to create ratios which are used to scale the concentrations of each neighboring
monitor used in the interpolation method selected.
S? Monitor and Model Relative Settings
BBS
Select Interpolation Method
O Closest Monitor
O Voronoi Neighbor Averaging f\/NA)
O Kriging
Pollutant:
Grid Type:
Library Monitor Year:
Monitor File:
Base Year Adjustment File:
Select Scaling Method
O Spatial Only
O Temporal Only
O Spatial and Temporal
0 Use Library Monitor Data
Browse
Browse
Create
[ Advanced j [ Map
Cancel
Go!
4.3.1 Spatial Scaling
Spatial scaling involves only a Base Year Adjustment File, and
scales the concentrations of each neighboring monitor by the ratio
of the modeled concentration at the grid cell to the modeled
concentration at the grid cell containing the monitor. This
approach takes into account what the air quality modeling reveals
about spatial heterogeneity in pollution levels. For example, if the
monitors are in relatively polluted urban areas, and the grid cell is
in a relatively unpolluted rural area, then the scaling will result in multiplying the monitor values
with ratios less than one, and thus produce lower values at the rural grid cell than would be
estimated with interpolation of the unsealed monitor data.
Spatial scaling, then, is useful because, while monitors provide invaluable information about
historical conditions, there are only a limited number of monitors. Many areas, particularly rural
You can create Base Year and
Future Year Adjustment Files
using the Adjustment Creator
under the Tools menu.
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areas, do not have close monitors. Model data can provide additional information that improves
the interpolated concentration estimates, and provides a more accurate picture of air quality.
4.3.2 Temporal Scaling
Temporal scaling involves both a Base Year Adjustment File and a Future Year Adjustment
File, and scales the concentrations of each neighboring monitor by the ratio of the modeled
concentration at the grid cell containing the monitor in the future year to the modeled
concentration at the grid cell containing the monitor in the base year. This approach takes into
account what the air quality modeling reveals about the changes in pollution levels over time at the
monitor sites. For example, if the modeling forecasts that in the future, pollution levels will
decrease, then the scaling will result in multiplying the monitor values with a ratio less than one,
and thus produce lower forecasts at the grid cell than would be gotten with the unsealed monitor
data.
Temporal scaling, then, is useful because monitors cannot provide any information about future
conditions. Model data can provide this information, which can then be used to project future
monitor concentrations.
4.3.3 Spatial and Temporal Scaling
Using both spatial and temporal scaling involves both a Base Year Adjustment File and a Future
Year Adjustment File, and is simply a combination of spatial scaling and temporal scaling, except
that the future year data is used for the spatial scaling. That is, it scales the concentrations of
each neighboring monitor first by the ratio of the modeled concentration at the grid cell in the
future year to the modeled concentration at the grid cell containing the monitor in the future year
(spatial scaling), and then by the ratio of the modeled concentration at the grid cell containing the
monitor in the future year to the modeled concentration at the grid cell containing the monitor in
the base year (temporal scaling). Notice, however, that the two future year concentrations at the
grid cell containing the monitor cancel out, allowing the ratio used to be simply the modeled
concentration at the grid cell in the future year to the modeled concentration at the grid cell
containing the monitor in the base year.
Using both spatial and temporal scaling gives the benefits of both approaches - it both improves
the interpolated estimates of air quality, and provides a future forecast of air quality.
4.3.4 Examples
The first example presented is a Monitor Direct example, which will provide a foundation for the
following Monitor and Model Relative examples. Recall that Monitor and Model Relative air
quality grid creation is exactly the same as Monitor Direct grid creation with the exception of
scaling. For additional examples with more detail, see Appendix C.
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Example 1: Monitor Direct VNA Method
Default options
Consider the example at an hypothetical rural grid cell, from Section 4.2.2, where there are five
monitors at a distance of 25, 50, 100, 200, and 400 miles from the grid cell. Further, let us say that
the monitors have annual PM25 levels of 8, 13, 12, 18, and 15 |ig/m\ Without any model-based
scaling, BenMAP would calculate an inverse-distance weighted average of the monitor values as
follows:
11111
8+ 13+ 12+ 18+ 15
25 50 100 200 400
IM25 average = jjjjj = 10.68
25 + 50 + TOO + 200 + 400
Example 2: Monitor and Model Relative VNA Method
Default options
Spatial Only scaling
Assume the same monitors and monitor concentrations as above. Additionally, assume that the
grid cell model value in the base-year is 6 ng/m3, and the model values at the monitors in the
base-year are: 10, 14, 11, 17, and 15 ^g/m3. (This modeling suggests that the grid cell is a less-
polluted area than the area around the monitors.) Incorporating Spatial Only scaling, BenMAP
would calculate an inverse-distance weighted average of the monitor values using the same
approach as before, with the difference being that the monitor values are scaled with the
modeling values:
PM25 average = jjjjj = 5.36
25 + 50 + Too + 200 + 400
Example 3: Monitor and Model Relative Closest Monitor Method
Default options
Spatial Only scaling
Assume the same monitors and monitor concentrations as above. Additionally, assume the same
model values as above. The Closest Monitor interpolation of these same values using Spatial
Only scaling would be calculated as follows:
PMj 5 average = 8 • — =4.8
Example 4: Monitor and Model Relative VNA Method
Default options
Temporal Only scaling
Again, assume the same monitors and monitor concentrations as above. Additionally, assume that
the model values at the monitors in the future-year are: 8, 11, 9, 14, and 11 |ig/m\ and that base-
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year model values at the monitors are: 10, 14, 11, 17, and 15 |ig/m\ (This modeling suggests that
the future-year model values are generally lower.) Incorporating the Temporal Only scaling,
BenMAP would calculate an inverse-distance weighted average of the monitor values as follows:
J_ (
o 8 ) 1
L 11
1
.. 9
1
io 14
1
(ic n)
8-— + —•
13- —
+
12- —
+ •
18- —
+ •
15- —
25 A
o
Lh
O
I 141
100
I 11/
200
I 17/
400
I 15/
PM^ 5 average = j j j j j = 8.52
25 + 50 + 100 + 200 + 400
Example 5: Monitor and Model Relative VNA Method
Default options
Spatial and Temporal scaling
Again, assume the same monitors and monitor concentrations as above. Additionally, assume that
the future-year model value at the grid cell is 4 ng/m3, and that base-year model values at the
monitors are: 10, 14, 11, 17, and 15 |ig/m\ (This modeling suggests that the future-year model
value at the grid cell is significantly lower than the base-year model values at the monitor.)
Incorporating the Spatial and Temporal scaling, BenMAP would calculate an inverse-distance
weighted average of the monitor values as follows:
X (
o 4)
1
L, 4
1
4
1
,o 4
1
ic 4
8 •~~~~
+ —— •
13- —
-1- .
12- —
-1- .
18- —
-I- .
15- —
25 A
10/
50
I 14/
100
I 11/
200
I 17/
400
I 15/
25 + 50 + 100 + 200 + 400
4.4 Monitor Rollback
The Monitor Rollback option allows you to reduce, or "roll back," monitor data using three
methods: percentage rollback, incremental rollback, or rollback to a standard. These approaches
let you quickly test what the benefits would be from reducing historical monitor levels.
Percentage rollback reduces all monitor observations by the same percentage. Incremental
rollback reduces all observations by the same increment. Rollback to a standard lets you choose
a standard, and then reduces monitor observations so that they just meet the standard. Note that
with each of these methods you can use the same three
interpolation algorithms (closest monitor, VNA, and kriging) as
can use with Monitor Direct and Monitor and Model Relative.
To apply a monitor rollback, first click the Create Air Quality
Grids button. On the Air Quality Grid Creation Method
screen, choose Monitor Rollback.
5 you
Air Quality Grid Creation
. Q0S
Choose Grid Creation Method
O Model Direct
O Monitor Direct
O Monitor and Model Relative
® Monitor Rollback
Cancel
1 Gol I
There are three steps to the Monitor Rollback method.
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X. Monitor Rollback Settings:
(1) Select Monitors. Choose the
Pollutant and the Year for the
monitor data. If you want to use
your own data, then uncheck Use
Library Monitor Data, and
browse for the file that you want to
use. The file should have the
format specified in Exhibit 4-3.
After clicking the Next button,
BenMAP filters the monitor data
using the default parameters for
that pollutant, available under the
Advanced button. (See Section 4.5 below for details on how to modify the default settings using
the Advanced button.)
2. Monitor Rollback Settings: Select Rollback Regions and Settings. In this section, you
can specify the type of the rollback method(s) that you would like to use, and you can specify the
location of the monitors that you want to rollback.
Monitor Rollback Settings: (1) Select Monitors
Pollutant: 03
0 Use Library Monitor Data
Library Monitor Year: 2000
| Advanced
Monitor File:
Cancel
Browse
Next
Monitor Rollback Settings: (2) Select Rollback Regions and Settings
TM
Rollback Regions
Select All Deselect All
¦
Add Region Delete Region | - Region to Delete - v I I Export After Rollback
Cancel
Next
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Select Region Rollback
Choosing the Add Region button brings up
the three rollback methods: Percentage
Rollback, Incremental Rollback, and
Rollback to a Standard.
After choosing the rollback type, you then
need to specify the amount of the rollback
and the region to which you want to apply it.
In this example, we specified a 10 percent
reduction, a background of 0 ppb, and
applied it to all monitors in the United States
by clicking the Select All. So, BenMAP will reduce all observations for all monitors in the
United States by 10 percent.
Rollback Type
0 Percentage Rollback
Q Incremental Rollback
O Rollback to a Standard
Cancel
OK
1 Monitor Rollback Settings: (2) Select Rollback Regions and Settings
~ 00
Rollback Regions
I Region 1
Rollback Parameters
Percent: 10
Background: 0.00
# ©s ev q,
Add Region | Delete Region - Region lo Delete ¦¦ v I I Export After Rollback
Select All Deselect All
Cancel
Next
Note that just above the map of the United States there are four buttons, typically seen in
mapping programs, that allow you to zoom in and zoom out, and to focus on the particular groups
of states that interest you.
At any time you can change the states that you have selected. This particular example is quite
simple, so we will use a more complicated example below. After choosing the states where you
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Chapter 4. Creating Air Quality Grids
want to rollback monitors, click on the Next button. BenMAP will then perform the rollback you
specified on the states that you have chosen.
3. Monitor Rollback Settings: Additional Grid Settings.
The third stage is similar to the Monitor Direct grid creation method. As in Monitor Direct, you
need to specify the Interpolation Method (Closest Monitor, VNA, or Kriging) and the Grid
Type. You may select a Scaling Method (None or Spatial Only). If you choose spatial scaling,
it works exactly as described in Section 4.3.1, where the modeling data is used to provide
information in those areas that are unmonitored.
By checking the Make Baseline Grid (in addition to Control Grid) you may create a baseline
grid at the same time as the control grid. The baseline grid uses the same parameters as the
control grid, with the exception of the rollback. That is, the baseline uses the same monitor year
and filtering, interpolation method, scaling (if any), and the same grid type.
^ Monitor Rollback Settings: (3) Additional Giid Settings | - 11 ~ j
Select Interpolation Method
O Closest Monitor
(*) Voronoi Neighbor Averaging [VNA)
O Kriging
Select Scaling Method
© None
O Spatial Only
Grid Type:
Adjustment File:
County
| Browse
0 Make Baseline Grid (in addition to Control Grid).
Create
Advanced
Map
Cancel
Go!
Note that there is an Advanced button that lets you select the Maximum Neighbor Distance,
Maximum Relative Neighbor Distance, and Neighbor Scaling Type. (These are described
further in Section 4.5.) The specific availability of advanced features depends on the interpolation
method that you choose. You may also click the Map button to view the inputs to the rollback
grids that you are creating, as well as to view the grids themselves. Chapter 8 discusses the
mapping of grids in more detail.
4.4.1 Example: Combining Three Rollback Approaches in Different Regions
BenMAP allows you to have different rollback approaches in different regions. In this example,
we combine the three rollback types: Percentage Rollback. Incremental Rollback, and
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Rollback to a Standard. As in previous example, start by clicking on the Create Air Quality
Grids button, and choosing Monitor Rollback. On the Monitor Rollback Settings: (1) Select
Monitors screen, select your pollutant (03 - ozone) and year (2000), and click Next. On the
next screen, click the Add Region button and enter 10 for the Percent. In the previous
example, we used the Select All button to include all states in the rollback region. In this
example we want to create three regions instead, so just click on the 11 Western states to add
them to the region. The states you have added to the region will turn red, as in the picture below.
Select All
Deselect All
Cancel
Add Region Delete Region
• ©x ©s Q
Rollback Regions
I Region 1
Rollback Parameters
Percent: 10
Background: 0.00
.
^ Monitor Rollback Settings (2) Select Rollback Regions and Settings
v HE xport After R ollback
-- Region to Delete --
To add states with a second type of rollback, click on the
Add Region button, choose the rollback type, and then
click on the states to include in this second region, which
BenMAP denotes as "Region 2/*" In this example, we have
chosen an Incremental Rollback of 5 and a background of
20, and applied it to the rest of the states West of the
Mississippi River.
The map now depicts two rollback regions. We can toggle
back and forth between each region by clicking on the
legend on the left side of the map. Any states that have not
yet been included in a region may be added to an existing
region, or we may create one or more regions for these
Add a state to a region by
clicking on it so that it is
highlighted.
Remove a state from a
region by clicking on it again.
Add multiple regions using
the Add Region button.
Change the displayed
region by clicking on the
region name in the legend in
the left-hand panel.
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remaining states. Note that once states have been included in a rollback region, they cannot be
included in a different rollback region. In our example, the 11 westernmost states are highlighted
in dark gray.
^ Monitor Rollback Settings: {2) Select Rollback Regions and Settings
CHS
Rollback Regions
I Region 2
Rollback Parameters
Increment: 5
Background: 20
I Region 1
Rollback Parameters
Percent: 10
Background: 0.00
Select All
f
Add Region! Delete Region •• Region to Delete-- v I I Export After Rollback
Deselect All
Cancel
Next
To add a third rollback type covering the states East of the
Mississippi River, click again on the Add Region button, and then
choose the rollback type. However, instead of choosing individually
the Eastern states, simply click the Select All button. This will select
all of the states that are not yet included in a region, and these
remaining states will now become Region 3.
In this third region, we have chosen a Rollback to a Standard,
which involves two groups of parameters - those associated with the
Attainment Test, which determines whether a monitor is in
attainment (meets the standard), and those associated with the Rollback Methods, which are
used to bring out-of-attainment monitors into attainment.
The Attainment Test parameters are Metric, Ordinality, and Standard. A monitor is
considered in attainment if the /?***' highest value of the metric specified by Metric is at or below
Once you have addled
states to a region, the
Select All button will only
add the remaining states to
the current region. If you
want to add all U.S. states,
you must first delete the
previous region.
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Chapter 4. Creating Air Quality Grids
the value specified by Standard, where n is the value specified by Ordinality. For example, if
Metric is lughlHourDailyMciximum. Ordinality is four, and Standard is eighty-five, a monitor
will be considered in attainment if the fourth highest value of the eight-hour daily maximum is at
or below eighty-five ppb. In this step BenMAP calculates the metric to be used to determine
whether a monitor's values must be rolled back and, if so, how much (e.g., if Metric is
EightHourDailyMaximum, BenMAP calculates the 8-hour daily maximum for each day at each
monitor).
Monitor Rollback Settings: (2) Select Rollback Regions and Settings
U00
Rollback Regions
~ Region 3
Attainment T est
Metric:
EightHourDailyMax
lv
Ordinality:
Standard:
13
185
4th Highest value of EightHourDailyMax >=85
Rollback Methods
nterday Rollback Method, Background:
Percentage
v |0.00
ntraday Rollback Method, Background:
Percentage
v |0.00
[H Region 2
Rollback Parameters
Increment: 5
Background: 20
Region 1
Rollback Parameters
Percent: 10
Background: 0.00
Select All
Deselect All
*
Add Region | | Delete Region | -• Region to Delete
QE xport After R ollbaek
Cancel Next
The Rollback Method parameters are Interday Rollback Method, Interday Background
Level, Intraday Rollback Method, and Intraday Background Level. These four
parameters determine the rollback procedures used to simulate out-of-attainment monitors corning
into attainment. The Interday Rollback Method and Background Level are used to generate
target values for the metric specified by the Attainment Test. The Intraday Rollback
Method and Background Level are used to adjust hourly observations to meet the target
metric values generated in the previous step.
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BenMAP provides several types of Interday Rollback Methods (Percentage, Incremental,
Quadratic, and Peak Shaving) and several types of Intraday Rollback Methods
(Percentage, Incremental, and Quadratic). The methods involved for each can be somewhat
complicated, so we have included a section in Appendix A that goes through several examples.
4.5 Advanced Monitor options
If the default filtering methods are not desired for a particular analysis, you can use the
Advanced option, to choose different filtering methods. This option differs by pollutant, so we
discuss the Advanced option for each pollutant individually below. Access these functions by
clicking on the Advanced button at the bottom of the Monitor Direct Settings screen, the
Monitor and Model Relative Settings screen, or the Monitor Rollback Settings: Select
Monitors screen. The Advanced option works the same in each case.
There are six filtering options common to all pollutants, and several which are specific to
individual pollutants. Once all the options are specified, click the Go! button to have BenMAP
start the monitor filtering. After filtering is completed, you can export the data (click the Export
button), map the data (click the Map button), or simply click OK to confirm the monitor filtering
and proceed with the analysis. If you wish to modify your filtering options, simply do so and click
the Go! button again.
Note that the exported monitor data is in the proper format to be read into BenMAP again in the
future (see Section 4.2.4).
Common Filtering Options
The six filtering options common to all pollutants include:
1. Inclusion of individual monitors by ID. If no monitor IDs are entered, all monitors which meet
the rest of the selection criteria will be included.
2. Exclusion of individual monitors by ID. No monitors are excluded by default.
3. Geographic filtering. Enter comma-separated state abbreviations to include monitors by state -
for example "MD,VA" would only include monitors in Maryland and Virginia. Additionally, enter
minimum and maximum latitude and longitude values for monitor inclusion. The default values
include all monitors in the continental United States.
4. POC code filtering and preferences. No monitors will be included which have POC codes
greater than the specified Maximum POC. Additionally, for those monitor IDs which are
associated with multiple POC codes, the POC Preference Order will be used to determine
which set of sample values to use. The default is to use POC codes less than or equal to four,
and to prefer lower POC codes - one, followed by two, three, and then four.
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5. Inclusion of method codes. Note that in some cases the method code is missing, and you can
exclude these if you want. The default is to use all method codes (including missing method
codes) for 03 and PM10, and to use the Federal Reference Monitors for PM2.5 (method codes
116-120 and 123).
6. Inclusion of monitor objectives. Note that in some cases the objective is missing, and you can
exclude these if you want. The default is to use all objectives, including missing values.
Advanced Monitor Option for Ozone
There is only one ozone specific filtering option:
1. Identify the completeness criteria for valid monitors. This involves specifying what constitutes
a valid day of monitor observations, and the minimum number of valid days over some specified
period of time. To specify a valid day, you specify the Start Hour and the End Hour and the
minimum Number of obs. that must be available between the Start Hour and the End Hour.
You then specify the beginning of the period of interest with the Start Month and Start Day, and
the end of the period of interest with the End Month and End Day. Finally, you specify the
Percent of Days that need to valid in the period of interest. (The default option: A valid day has
9 observations between 8:00 am and 7:59 pm [Start Hour = 8 and End Hour = 19]; 50 percent
of the days must be valid between May 1 and September 30.)
Advanced Options
~IP©
Filter Monitors
If you wish to include individual monitors by ID, enter the IDs
here, separated by commas. If you do not enter any
monitors here, all monitors which meet the rest of the
selection criteria will be included:
If you wish to exclude individual monitors by ID, enter the IDs
here, separated by commas:
If you wish to restrict monitors to certain states or areas, enter
comma separated state abbreviations, and minimum and/or
maximum latitudes and longitudes:
States:
Minimum Longitude:
Maximum Longitude:
-130
-65
Minimum Latitude:
Maximum Latitude:
20
55
Select maximum P0C code to include and the P0C
preference:
Maximum P0C:
M
P0C Preference Order: 1,2,3,4
Select the Methods you wish to include:
0 Method missing.
0 47
0 91
0 3
0 53
0 103
0 11
v" 56
0 112
0 14
3 78
0 134
0 19
0 B7
Select the Monitor Objectives you wish to include:
Select the parameters specific to the pollutant:
-Ozone—
Start Hour:
End Hour:
Number of Obs:
M
13
IXI
[~]
IXI
[~]
Start Month
Start Day
End Month
End Day
Percent of Days
Export
Map
0 EXTREME DOWNWIND
£ GENERAL/BACKGROUND
0 HIGHEST CONCENTRATION
< > |
E
[~]
[~]
[~]
30
[~]
50
[~]
Go!
Cancel
OK
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Chapter 4. Creating Air Quality Grids
Advanced Monitor Option for PM2j
There are two PM2.5 specific filtering options:
1. Identify the completeness criteria for valid monitors. This involves specifying the minimum
number of valid observations per quarter, where the quarters are defined as January-March,
April-June, July-September, and October-December. If a monitor does not have the minimum
number of observations each quarter, it will be filtered out. The default number of valid
observations is eleven.
2. Data type inclusion and preference. PM2.5 monitor data comes in two types - local and
standard. Monitors can be included with one type or the other, or with both types. If both types
are included, a preference may be given (only one type will be used if both are present) to one or
the other. The default is to only use local data.
Advanced Options
¦QSH
Filter Monitors
If you wish to include individual monitors by ID, enter the IDs
here, separated by commas. If you do not enter any
monitors here, all monitors which meet the rest of the
selection criteria will be included:
If you wish to exclude individual monitors by ID, enter the IDs
here, separated by commas:
If you wish to restrict monitors to certain states or areas, enter
comma separated state abbreviations, and minimum and/or
maximum latitudes and longitudes:
States:
Minimum Longitude:
¦130
Minimum Latitude:
20
Maximum Longitude:
¦G5
Maximum Latitude:
55
Select maximum PDC code to include and the POC
preference:
Maximum POC: 4 [jj POC Preference Order: 1,2,3,4
Select the Methods you wish to include:
~ Method missing.
0 IIS
~ 142
0 ne
0 119
~ 143
0 117
0 120
~ 145
<
t>l
Select the Monitor Objectives you wish to include:
0 GENERAL/BACKGROUND
0 HIGHEST CONCENTRATION
0 MAX OZONE CONCENTRATION
!>]
Select the parameters specific to the pollutant:
rPM-
Enter the number of valid
observations required per Quarter:
Select the types of data to use :
Use
© local O standard 0 both
Select the preferred type:
® local O standard
Export
Map
Go!
Cancel
OK
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Chapter 4. Creating Air Quality Grids
Advanced Monitor Option for PM„,
There are three PM10 specific filtering options:
1. Identify the completeness criteria for valid monitors. This involves specifying the minimum
number of valid observations per quarter, where the quarters are defined as January-March.
April-June, July-September, and October-December. If a monitor does not have the minimum
number of observations each quarter, it will be filtered out. The default number of valid
observations is eleven.
2. Data type inclusion and preference. PM2.5 monitor data comes in two types - local and
standard. Monitors can be included with one type or the other, or with both types. If both types
are included, a preference may be given (only one type will be used if both are present) to one or
the other. The default is to use both local and standard data, with a preference to local.
3. Output data type. PM2.5 data can be output as either local or standard. The default is to
output local data.
^ Advanced Options
Filter Monitors
If you wish to include individual monitors by ID, enter the IDs
here, separated by commas. If you do not enter any
monitors here, all monitors which meet the rest of the
selection criteria will be included:
If you wish to exclude individual monitors by ID, enter the IDs
here, separated by commas:
If you wish to restrict monitors to certain states or areas, enter
comma separated state abbreviations, and minimum and/or
maximum latitudes and longitudes:
States:
Minimum Longitude:
Maximum Longitude:
•130
¦65
Minimum Latitude:
Maximum Latitude:
20
55
Select maximum POC code to include and the POC
preference:
Maximum POC: 4 POC Preference Order: 1,2,3,4
Select the Methods you wish to include:
0 Method missing.
0 63
0 71
0 52
0 64
0 73
0 62
0 65
0 76
I
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Chapter 4. Creating Air Quality Grids
4.6 Questions Regarding Creating Air Quality Grids
This section answers some common questions that may arise in the creation of air quality grids.
>^When creating an air quality grid, can I use any combination of Grid Type and Pollutant?
For Monitor Direct grids, yes. However, BenMAP currently has limitations regarding the
possible combinations of Grid Type and Pollutant for modeling data, which means that these
limitations apply to both Model Direct and Monitor and Model Relative grids. REMSAD and
CMAQ may be used with PM25, I'Mj0. and PMC, and UAM-Vand CAMx may be used only with
ozone.
^Can I export the monitor data used to make my air quality grid using the monitor direct
function?
Yes. After choosing the filter options, BenMAP allows you to export a comma-delimited text file
of the resulting monitor data. Simply click on the button Export filtered data to disk, name the
file, and click Save.
^Can I see the weights assigned to each monitor for each grid?
Yes. See the section on the Neighbor File Creator in Chapter 9.
^Can I export my air quality results as a shapefile for use in a GIS program?
Yes. See the section on the Shapefile Creator in Chapter 9.
^Can I use the Advanced option to filter my own monitor data?
No. Currently, this option works only for monitor data sets in the BenMAP monitor library.
^How can I generate a map and export it?
This is explained in Chapter 8.
^For the Rollback to a Standard option, why are there Interday and Intraday rollback options?
This is explained in Appendix A.
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CHAPTER 5
In this chapter...
>- Use the Create and Run
Configuration button to specify
various options for calculating
incidence results.
Create and
Run
Configurations
Learn about baseline and
control scenarios.
>- Learn the difference between
Point Mode and the Latin
Hypercube option.
>* Select Concentration-
Response (C-R) functions.
Run and save a configuration.
Chapter Overview
5.1 Create New Configuration 5-2
5.1.1 The Configuration Settings Form 5-2
5.1.2 Selecting C-R Functions for Configuration . 5-4
5.1.3 Running the Health Effects Incidence
Configuration 5-10
5.2 Open Existing Configuration 5-10
5.3 Questions Regarding Configurations 5-10
-------
5. Create and Run Configurations
A configuration is a record of the choices you make in estimating the change in adverse health
effects between a baseline and control scenario. The choices include the following:
The air quality grids for the baseline and control scenarios;
The year for the analysis;
The threshold for the analysis;
Whether the analysis will focus on a single "point" estimate (Point Mode), or a range of
results that mirror the variability in the inputs to the C-R functions (Latin Hypercube Points);
and,
The concentration-response (C-R) functions to be used in
estimating adverse health effects.
Once these choices are made, they can be saved in a
configuration file for future reuse. BenMAP gives you flexibility
in the creation, editing, and saving of configuration files. You
can open an already existing configuration, and proceed directly
to the estimation of incidence. Or, you can create a new one,
and proceed with the incidence estimation. In addition, you may
save any edits made to existing or new configuration files.
BenMAP saves configuration files with a "\cfg" extension.
After calculating the change in adverse health effects, BenMAP
saves the results in a "configuration results" file with a ".cfgr"
extension.
After clicking the Create and Run Configuration button, you will be asked if you want to
create a new configuration, or open an existing one, if you have already created one that you
would like to use again. Select the desired option and click Go!. Below, we discuss the
subsequent steps for each option.
A C-R (Concentration-Response)
Function calculates the change
in adverse health effects
associated with a change in
exposure to air pollution. A
typical C-R function has inputs
specifying the air quality metric
and pollutant, population
characteristics, and the
incidence rate of the health
effect. The Incidence Rate gives
the average number of adverse
health effects per person per
year.
Configuration Creation Method [-"][~]
O Create New Configuration
O Open Existing Configuration
Go!
Cancel
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Chapter 5. Create and Run Configurations
5.1 Create New Configuration
If you choose to create a new configuration, there are two steps. First, fill out the Configuration
Settings form with the following options: the air quality grids for the Baseline File and the
Control File, the Pollutant, Population Year, Threshold, and whether BenMAP will run in
Point Mode or use the Latin Hypercube Points option. In the second step, specify the C-R
functions from lists of EPA Standard C-R Functions and your own C-R Functions.
^¦Configuration Settings
~0©
Select Air Quality Grids
Baseline File:
Control File:
Settings
Pollutant:
Population Year:
Latin Hypercube Points:
Run In Point Mode: | |
Threshold: 0.0
~ pen
Create
~ pen
Create
Map Grids
Cancel
Previous
Next
5.1.1 The Configuration Settings Form
This form opens after you select Create New Configuration and click Go!. In this form, you
first specify the air quality grids for the Baseline File and Control File. The baseline file
contains the air quality metrics for the scenario assumed to occur without any change in policy.
The control file specifies the air quality metrics assuming that some type of policy or change has
been implemented. The air quality grids should be of the same pollutant, and should also be based
on the same grid-type. If you choose a REMSAD grid for the baseline file, then a REMSAD grid
must be used in the control file. Conversely, it would not possible to use a REMSAD grid-type in
the baseline and a CMAO file in the control file. Similar rules hold for the other grid-types.
You may choose existing air quality grids, by clicking the Open button, and selecting an air quality
grid, which is designated with an "".aqg" extension. You may also create a new air quality grid by
clicking the New button. This will take you to window for Air Quality Grid Creation Method,
where you follow the same steps outlined in Chapter 4 for air quality grid creation.
The Pollutant you choose determines the suite of C-R functions available for the configuration.
BenMAP has C-R functions for ozone, PM2 5, PM10, and PMC. When specifying the Pollutant,
you typically choose the Pollutant known to have been used in the creation of the baseline and
control air quality grids. If PM2 5 data were used to create the grids, then you would specify the
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Chapter 5. Create and Run Configurations
Pollutant as PM2 5. However, occasionally you may want to specify a pollutant that differs from
that used in the air quality grid creation. For example, you may be interested in the change in
PM2 5 air quality, and estimate the impact of this change using PM10 functions. BenMAP allows
you to do this.
In choosing the Population Year, you specify the population data that will be used in the C-R
function. The values in the menu for the Population Year range between 1990 and 2025.
These years correspond to the range covered by the Census data and the population projections
built into BenMAP. You may also specify a year beyond 2025, by simply typing in the year
desired - in this case, BenMAP will calculate projected populations internally. Appendix B details
the sources for the population data used by BenMAP, and how the population projections are
generated.
Configuration Settings
Select Air Quality Grids
Baseline File:
-Settings—
Pollutant:
Population Year:
Latin Hypercube Points:
C:\Program Files\Abt Associates lnc\BenMAP^Air Quality Grids\P
Open
Create
C:\Prograrn Files\Abt Associates lnc\BenMAPV\ir Quality Grids\P
~pen
Create
Map Grids
PM2.5
2010
. Warning: when running in point mode, some pooling options will be unavailable at later
Run In Point Mode: stages of processing (e.g. Random / Fixed effects).
Threshold: 0.0
Cancel
Previous
Next
The Threshold indicates the minimum value that may be used in either the baseline or control air
quality metrics. That is, air quality metrics below the threshold will be replaced with the threshold
value. With a threshold of zero, there is no impact on the estimates generated by the C-R
functions. However, as the threshold increases, then it will have a progressively larger impact on
the incidence estimation. For most analyses, a threshold of zero is appropriate, as there is little
evidence suggesting the existence of a threshold. For particulate matter, a review of the recent
literature (Rossi et al., 1999; Daniels et al., 2000; Pope, 2000; Schwartz, 2000c) found that
PM-related health effects occurred down to the lowest measured levels. Nevertheless, the
Threshold option allows you to explore the impact of any given threshold on the incidence
estimation. This is also useful for scenarios where you might want to know the incidence
associated with changes in air quality occurring above a standard.
The Point Mode and Latin Hypercube Points options allow you to generate an average
incidence estimate, or a range of results that mirror the variability in the inputs to the C-R
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Chapter 5. Create and Run Configurations
functions. With the Point Mode option, BenMAP uses the mean values of the inputs to the C-R
functions, and generates a single "point estimate" of the change in adverse health effects.
With the Latin Hypercube Points option, you can generate a number of estimates that mirror
the variability in the inputs to the C-R functions. The Latin Hypercube Points option allows
you to generate specific percentiles along the estimated incidence distribution. For example, if
you specify 20 points, then BenMAP will generate estimates of the 2.5th percentile, 7.5th
percentile, and so on, up through the 97.5th percentile. The number of points suggested in the
drop down menu varies between 10 and 100. In addition, you can simply type in the desired
number of points. The greater the number of chosen points, the greater the time needed by
BenMAP to process the results. The relationship between the number of points and time needed
is essentially linear, so a doubling of the number of points would double the processing time.
If Point Mode is chosen, the number of Latin Hypercube
Points cannot be modified and will be ignored (treated as zero).
However, with the Latin Hypercube Points option, a point
estimate will still be generated. As discussed in Chapter 6 on
Aggregation, Pooling, and Valuation, by choosing the Point
Mode, you have fewer pooling options. You cannot conduct
fixed/random effects pooling, nor any other procedure that depends on knowing the distribution, or
the range of variability of the incidence estimates.
Pooling refers to combining of
different sets of data. BenMAP
has several pooling methods to
choose from.
5.1.2 Selecting C-R Functions for Configuration
The second step in creating a configuration is to select the C-R
functions. After filling in both the Select Air Quality Grids and
Settings of the Configuration Settings form, click Next to
select the C-R functions. You may choose from a list of EPA
Standard C-R Functions as well as any C-R functions that you
may have entered in the User C-R Functions window. (For
details on how to add your own C-R functions, see Chapter 8.)
An End point group represents a
broad class of adverse health
effects, such as premature
mortality, chronic bronchitis, and
hospital admissions.
BenMAP stores the C-R functions in a tree
structure that the user can expand and contract.
There are eleven endpoint groups, such as
Mortality and Hospital Admissions, Respiratory.
Within each endpoint group are sub-groups or
endpoints that are specific to each endpoint group,
such as Mortality, Long-Term, All Cause and
Mortality, Long-Term, Cardiopulmonary. In
cases where an endpoint group has just a single
endpoint, they share the same name. Exhibit 5-1
lists the endpoint groups and the associated
endpoints for the EPA Standard C-R functions.
EPA Standard C-R Functions
SI ^ute Bronchitis
IB Acute Myocardial Infarction
S3 Acute Respiratory Symptoms
IB Asthma Exacerbation
S) Chronic Bronchitis
SI Chronic Phlegm
SI Emergency Room Visits. Respiratory
S) Lower Respiratory Symptoms
SI Mortality
SI Work Loss Days
| Cancel | [ Previous | | Run |
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Chapter 5. Create and Run Configurations
Exhibit 5-1. Classification of C-R Functions Using Endpoint Groups and Endpoints
Endpoint group
Endpoint
Acute Bronchitis
Acute Myocardial Infarction
Acute Respiratory Symptoms
Asthma Exacerbation
Chronic Asthma
Chronic Bronchitis
Chronic Phlegm
Emergency Room Visits, Respiratory
Hospital Admissions, Cardiovascular
Hospital Admissions, Respiratory
Household Soiling Damage
Lower Respiratory Symptoms
Mortality
School Loss Days
Upper Respiratory Symptoms
Work Loss Days
Worker Productivity
Acute Bronchitis
Acute Myocardial Infarction, Nonfatal
Any of 19 Respiratory Symptoms
Minor Restricted Activity Days
Asthma Exacerbation, Asthma Attacks
Asthma Exacerbation, Cough
Asthma Exacerbation, Moderate or Worse
Asthma Exacerbation, One or More Symptoms
Asthma Exacerbation, Shortness of Breath
Asthma Exacerbation, Wheeze
Chronic Asthma
Chronic Bronchitis
Chronic Bronchitis, Reversals
Chronic Phlegm
Emergency Room Visits, Asthma
HA, All Cardiovascular
HA, Congestive Heart Failure
HA, Dysrhythmia
HA, Ischemic Heart Disease
HA, All Respiratory
HA, Asthma
HA, Chronic Lung Disease
HA, Chronic Lung Disease (less Asthma)
HA, Pneumonia
Household Soiling Damage
Lower Respiratory Symptoms
Mortality, Long-Term, All Cause
Mortality, Long-Term, Cardiopulmonary
Mortality, Long-Term, Infant
Mortality, Long-Term, Lung Cancer
Mortality, Short-Term, Chronic Lung
Mortality, Short-Term, Non-Accidental
School Loss Days, All Cause
School Loss Days, Illness-Related
School Loss Days, Respiratory-Related
Upper Respiratory Symptoms
Work Loss Days
Worker Productivity
Note: This exhibit includes the endpoint groups and endpoints for PM25, PM10, PMC, and ozone included in the EPA
Standard C-R functions.
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Chapter 5. Create and Run Configurations
To add studies to your configuration, simply highlight the C-R functions of interest and drag them
over to the right hand portion of the screen. You can do this for blocks of C-R functions by
dragging over an endpoint group or an endpoint, or drill down and drag individual studies.
OA
Configuration Settings
~us
Select C-R Functions
EPA Standard C-R Functions
Q Acute Bronchitis
B Acute Bronchitis
0 Dockery et al.
[• D ockery et al., 1996 I 8-12
=•••• Dockery et al., 1996 I <18
a McConnell et al.
S) Acute Myocardial Infarction
S) Acute Respiratory Symptoms
(j) Asthma Exacerbation
(j) Chronic Bronchitis
(j) Chronic Phlegm
(j) Emergency Room Visits, Respiratory
(j) Hospital Admissions, Cardiovascular
l+i H osoital Admissions. R esoiratorn
0
User C-R Functions
Endpoint Group
s> Acute Bronchitis
Endpoint
LowAge HighAge / | Race / | Gender i Author
Acute Bronchitis 8
12 All All
Dockery et al.
nil
_E
Cancel
Previous
Run
If you want to delete some of the C-R functions that you added to your configuration, just
highlight the studies of interest and hit the Delete key on your keyboard.
TIP; Highlighting Blocks of C-R Functions
To highlight blocks of C-R functions that you have added to your configuration, you may use two
approaches: (1) point your cursor at one end of the block, hold down the Shift key on your keyboard,
and then point your cursor at the C-R function on the other end of the block; or (2) hold down the
Ctrl-key on your keyboard, and point your cursor at all of the studies that you want to highlight - this
latter approach allows you to highlight discontinuous blocks of C-R functions.
When you drag over a study, BenMAP displays the study's endpoint group, endpoint, low age,
high age, race, gender, author, year, qualifier, location, and function. The low age and high age
define the bounds of the population of interest, and are inclusive, so bounds of eight and twelve
would include all children ages eight, nine, ten, eleven, and twelve. Race and gender also refer to
the population of interest for the C-R function. Author and year refer to the epidemiological study
on which the C-R function is based, and location gives the location of the study. The qualifier
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Chapter 5. Create and Run Configurations
helps to provide additional identifying information, such as when multiple C-R functions are
derived from the same study.
The display of these variables is designed to help identify the study. For additional details, point
your cursor at the C-R function and double-click your mouse - this will bring up a display box
titled C-R Function Data, with all of the details for that particular C-R function. Note that this
box is meant for information, and not for editing its contents. To edit the C-R functions that you
have added to BenMAP, see Chapter 8. However, as discussed next, you can make some
temporary edits right in the configuration screen.
^C-R Function Data
"EES
C-R Function Identification
Endpoint Group: Acute Bronchitis
Endpoint:
Acute Bronchitis
Author:
Year:
Pollutant:
Metric:
Qualifier:
Dockery et al.
1996
PM2.5
AnnualAverage
Low Age: 8
High Age:
Races:
Genders:
Location:
Other Pollutants:
12
All
All
24 communities
None
8-12
C-R Function and Parameters
Function:
-((lncidence/((1 -lncidence)*EXP(Beta*DELTAQ)+lncidence))-lncidence)"POP
Beta:
0.027212423
A:
0
B:
0
C:
0
Beta Distribution:
Normal
Name A:
Name B:
Name C:
PI Beta:
0.017095755
Incidence:
acuteBronchStol 2
P2Beta:
0
Incidence 2:
Prevalence:
Close
You can drag over the same study multiple times, and then make edits directly to the age, race
and gender variables displayed in the configuration screen, in order to be able to calculate the
impact of changes in these variables. To edit Low Age and HighAge, just highlight the
appropriate cell and type in the desired age values. Keep in mind that these age represent
inclusive age bounds, so if you type in 5 and 7 this will include all children ages five, six, and
seven years old. If you want just a single age year, then type the same year in both the LowAge
and the HighAge.
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TIP; Editing C-R Functions
You can edit the age, race, and gender variables in the right hand portion of the Configuration
Settings form. Any changes you make to the C-R functions will affect the current configuration
ONLY. If you save the current figuration, your edited version will appear next time you open it.
However, the underlying C-R function will not change.
If you want to edit C-R functions, you can create and then edit C-R functions using the Data menu.
You cannot edit the EPA Standard functions that come with BenMAP, but you can copy them, then
edit and save them as new functions in the User C-R Functions database.
^"Configuration Settings
Select C-R Functions
EPA Standard C-R Functions
~ Acute Bronchitis
El Acute Bronchitis
B Dockery et al.
i Dockery et al., 1996 I 8-12
; Dockery et al., 1996 I <18
E McConnell et al.
2) Acute Myocardial Infarction
2) Acute Respiratory Symptoms
S) Asthma Exacerbation
2) Chronic Bronchitis
2) Chronic Phlegm
2) Emergency Room Visits, Respiratory
2) Hospital Admissions, Cardiovascular
i+i Hospital Admissions. Respiratory
-
Endpoint Group V
Endpoint LowAge
HighAge
| Race
| Gender
Author
Acute Bronchitis
Acute Bronchitis 8
12
All
All
Dockery et al.
5>
Acute Bronchitis
Acute Bronchitis 5
i
All
All
Dockery et al.
User C-R Functions
Previous:
At the same time you can edit the race and gender variables, by clicking on the appropriate cell,
and then scrolling through the drop-down menu.
To sort the C-R functions in your configuration by a particular variable, just click the variable
name in the display that you want to sort. Click the variable name again to sort it in the opposite
direction (ascending or descending order).
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Chapter 5. Create and Run Configurations
Configuration Settings
Taas
Select C-R Functions
EPA Standard C-R Functions
Q Acute Bronchitis
B Acute Bronchitis
~ Dockery et al.
Dockery et al., 1996 I 8-12
!Dockery et al., 1996 I <18
E McConnell et al.
E) Acute Myocardial Infarction
E) Acute Respiratory Symptoms
E) Asthma Exacerbation
E) Chronic Bronchitis
E) Chronic Phlegm
El Emergency Room Visits, Respiratory
E) Hospital Admissions, Cardiovascular
[+] Hospital Admissions. Respiratory
0
User C-R Functions
Acute Bronchitis
Endpoint Group V Endpoint
Acute Bronchitis
Lo"AAge HighAge Race Gender Author
Acute Bronchitis 8
Acute Bronchitis
Acute Bronchitis Acute Bronchitis 5
Acute Bronchitis Acute Bronchitis 3
li]
12
All
12
Black
All
14
Black
All
All
Female
All
Asian
Black
Hispanic
Native American
Other
White
Dockery et al.
Dockery et al.
Dockery et al.
Female Dockery et al.
Previous
Special Note on Changing Demographic Patterns in the Future
Regarding BenMAP's population data, it is important to note that the projections used in
BenMAP allow for demographic changes up through the year 2025. However, the incidence
rates, as described in Appendix E, are fixed to the most recently available data, the period around
the year 2000. With the population getting older in the future, the incidence rate for broad age
groups, such as all individuals over the age of 30, will progressively differ the further out in the
future that you estimate adverse health effects. This happens because the incidence rates vary
by age, with some rates, such as mortality incidence, increasing significantly with age.
In the case of premature mortality, this can result in a large underestimate of incidence, if you
estimate the impacts in the future for a broad age group, such as all individuals over the age of 30.
To alleviate this problem, BenMAP provides C-R functions with relatively small age increments,
for those C-R functions that have incidence rates that vary by age. For example, instead of just a
single C-R function to estimate mortality for all persons 30 and older, BenMAP provides
premature mortality C-R functions for small age increments, such as individuals 30-34, 35-44, 45-
54, 55-64, and so on. You can then sum the results of the different age-group estimates, to
estimate premature mortality for persons ages 30 and over. Appendix E provides additional data
on the age groups available.
Nevertheless, this still leaves some residual problem owing to the fact that the incidence rates for
individual age groups are changing over time. To the extent that the life expectancy increases
over time, we will presumably see lower mortality rates in some age groups. The exact impact of
this impact is difficult to predict.
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Chapter 5. Create and Run Configurations
5.1.3 Running the Health Effects Incidence Configuration
To begin the calculation of incidence for the C-R
functions in the configuration, you click the Run
button on the bottom right-hand corner of the
Configuration Form that has the list of chosen
C-R functions. After clicking Run, BenMAP
allows you to save the configuration, or begin the
calculation. If you wish to save the configuration
for future use, click Save and specify a file with a
"\cfg" extension. When ready to generate
incidence estimates, click on the OK button.
BenMAP then requires that you specify a file in which to save the results, with a ".cfgr"
extension.
TIP: Saving a Configuration
When you click Run at the bottom of the Configuration Settings form, you will get a prompt that
says, "Ready to run configuration. If you wish to save this configuration, click the Save button.
When ready, click OK. If you are not ready to run this configuration, click Cancel." If you click
Save, you will be saving the configuration, i.e., the options and C-R functions that you have
selected, so that you can open the configuration and re-run it in the future. Once you are ready to
generate incidence estimates, click the OK button, and you will be prompted to save another file.
This second file is for the results of the configuration run, which you will then use for aggregation,
pooling and valuation and to generate reports.
5.2 Open Existing Configuration
To use the same settings as a previous BenMAP run, you can choose to open an existing
configuration. After clicking the Create and Run Configuration button, you simply choose
Open Configuration and click the Go! button. Once an existing configuration is open,, you can
do all the things with it that you would do with a newly created configuration - modify settings,
add and delete C-R functions, save it to a configuration file, generate results, etc.
5.3 Questions Regarding Configurations
Below are answers to some of the questions that may arise regarding the creation and use of
configurations.
^ Save Configuration
Ready to run configuration. If you wish to save
this configuration, click the Save button. When
ready, click OK. If you are not ready to run this
configuration, click Cancel.
|| Save || | Cancel | [ OK
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^Can I use air quality grids based on different Grid Types in the baseline and control
scenarios?
No. In any given analysis, you need to use the same Grid Type in the baseline and control
scenarios.
^Can I use air quality grids of the same Grid Type but based on different Grid Creation
Methods?
Yes. In any given analysis, you may use air quality grids made with different methods. Air
quality grids made with Model Direct, Monitor Direct, and Monitor and Model Relative may
be used interchangeably, if desired. Similarly, air quality grids made with different interpolation
methods may be compared. However, it generally is not recommended to create grids with
different methods and use them in the same analysis.
^Can I use air quality grids of the same Grid Type but with different Pollutants?
Yes. Air quality grids based on REMSAD and CMAQ grid-types, which have PM25, PM10, and
PMC data, may in theory be mixed in BenMAP. For this reason, you need to carefully name the
air quality grids, to avoid confusion. The air quality grids simply contain data for specific grid-
types, with certain air quality metrics, and BenMAP does not keep track whether the air pollution
data used in the generation of the air quality grids are PM2 5, PM10, or PMC. It is possible for a
user to accidentally mix air quality grids of different pollutant types, and still generate incidence
estimates. Generally, it is undesirable to mix air quality grids with different pollutants. However,
occasionally you may want to specify a pollutant that differs from that used in the air quality grid
creation. For example, you may be interested in the change in PM25 air quality, and estimate the
impact of this change using PM10 functions.
^Can I do I an analysis with multiple pollutants?
No. Currently BenMAP analyzes one pollutant at a time.
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In this chapter...
>- Use the Aggregation, Pooling,
and Valuation button to create a
new aggregation, pooling, and
valuation (APV) configuration.
>¦ Sort and pool incidence
results.
Learn the differences between
the available pooling methods.
Assign economic values to
incidence results.
>¦ Aggregate incidence results
and valuations.
CHAPTER 6
Aggregation,
Pooling, and
Valuation
>- Save and re-open APV
configurations.
Chapter Overview
6.1 Creating a New Configuration 6-1
6.1.1 Pooling Incidence Results 6-2
6.1.2 Valuing Pooled Incidence Results 6-13
6.1.3 APV Configuration Advanced Settings 6-16
6.3 Running the APV Configuration 6-17
6.4 Open Existing APV Configuration File 6-18
-------
6. Aggregation, Pooling, and Valuation
Once you have created a configuration results file with incidence results based on your two air
quality grids (using the Create and Run Configuration button), you can use the Aggregation,
Pooling, and Valuation button to combine the incidence results and place an economic value on
the combined results. You have two options.
Create a New Configuration for Aggregation, Pooling,
and Valuation. You can create a new type of configuration,
termed an Aggregation, Pooling, and Valuation (APV)
Configuration. This allows you to specify whether to aggregate
incidence results at the county, state or national level, or whether
to leave them at the grid cell level. In addition, you can specify
how you might want to combine or "pool" the incidence results,
using a variety of pooling options. Given the aggregated and
pooled incidence results, you then can specify how you might want to value them - typically there
are multiple valuations. These valuation results can then be further aggregated, and these
aggregated valuation results can be pooled. Having
made all of your selections, you may save this APV
Configuration file C'.ap\") for future use, and then
proceed to calculating the results, which are stored in
an APV Results file (".apvr).
^Open Existing Configuration for Aggregation,
Pooling, and Valuation. You can load an existing
APV Configuration file, edit the configuration, save it
with the same or a different name, and then proceed
to calculating the results.
6.1 Creating a New Configuration for
Aggregation, Pooling, and Valuation
To start you need to choose a configuration results file that contains incidence estimates at the
grid cell level (created by clicking the Create and Run Configuration button, see Chapter 5).
These incidence results are in files with a *.cfgr extension, and typically stored in the
Configuration Results folder. Once you open this file, you can begin creating your APV
configuration. You will start with selecting and pooling your incidence results, then move on to
valuation. These processes are described in detail below. Advanced functions, including
aggregation, are described in Section 6.14.
Aggregation refers to the
summing of grid cell level results
to the county, state or national
level. Pooling refers to
combining individual incidence
results or valuations into groups.
«>BcnMAP 2003 Beta 2.0
Data lools Help
Er
^ APV Configuration Creation Method
BUB
O Create New Configuration for Aggregation, Pooling, and Valuation.
O Open Existing Configuration file for Aggregation, Pooling, and Valuation (*.apv file).
Create Air Quality Grids
Create and Run
Configuration
Aggregation. Pooling,
and Valuation
Create Reports
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Chapter 6. Aggregation, Pooling and Valuation
Open a Configuration Results File
Look in: Configuration Results
^ © t ° H3-
My Recent
Documents
0
Desktop
3
My Documents
My Computer
<3
My Network
REMSAD direct example. cfgrj
File name:
Files of type:
PM25 2020 REMSAD direct example.cfgr
Configuration Results Files (x.cfgr)
Open
Cancel
6.1.1 Pooling Incidence Results
After opening the configuration results file, you will find a list of Available Incidence Results
on the left-hand side of the screen. The results are represented by the C-R Functions from which
they were created, and are displayed in a tree-structure with three levels, similar to the tree-
structure found in the Configuration Settings Form (see section 5.1.2). Endpoint Groups
occupy the top-most level, followed by Endpoints, and then individual C-R Function identifiers.
You will also see a pooling window at the right, where you can select pooling options.
There are several steps to pooling your incidence results:
Step 1. Select your default Advanced options
To select your default Advanced options, click on the Advanced button. There are two values
which you will want to set at this point - the Default Advanced Pooling Method and the
Default Monte Carlo Iterations. See Step 6, below, for a detailed discussion of these values.
It is important to set them first because these default values will be applied to all incidence results
added to the pooling window (see Step 2, below) after they are set. Once they are set, click
OK. If you decide not to change them, click Cancel.
Step 2. Add incidence results to the pooling window
In the Incidence and Pooling window, you will see all the incidence results generated from your
configuration (see Chapter 5) in the left hand column. You can drag individual incidence results,
or groups of results at any level of the tree and drag them over to the pooling window. Note that
once you drag a result or group of results into the pooling window it will still be displayed on the
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Chapter 6. Aggregation, Pooling and Valuation
left side. You do not have to drag all of your incidence results over into the pooling window, but
note that only those results showing in the pooling window will be included in the pooled incidence
or valuation results.
Incidence results are displayed in the pooling window in a tree structure determined by (1) the
order of the columns, and (2) the values of the identifying variables of the C-R Functions from
which the incidence results were generated (Endpoint Group, Endpoint, etc. - for a complete
list of variables and associated descriptions, see Exhibit 8.1).
Incidence Pooling and Aggregation
BBS
Available I ncidence R esults S elect Pooling M ethods
Q Hospital Admissions, Re
~ HA, Chronic Lung D
! Moolgavkar, 20
r Moolgavkar, 20
i Moolgavkar, 20
Moolgavkar, 20
El HA, Pneumonia
~ Hospital Admissions, Ca
|j] HA, Congestive He<
(i) HA, Dysrhythmia
(B HA, Ischemic Heart
EC
a
Pooling Window 1
Endpoint Group
Hospital Admissions, Respiratory
Endpoint
Author
Year Location
Low Age
HA, Chronic Lung Disease
Moolgavkar
2000
Los Angeles, CA
65
75
85
Pooling Method
No
- Window to Delete --
Delete
Add
Configuration Results Filename: C:\Program Files^Abt Associates lnc\BenMAP\Configuration Results\HospitalAdrnissionsExam I Browse
Advanced
Lanee I
Each line in the pooling window represents a node in the tree structure, with each node
representing either an individual incidence result or a collection of incidence results which have
common values for their leftmost identifying variables. The tree structure is generated by
comparing the leftmost values of the incidence result's identifying variables. High level nodes in
the tree are formed when results have common values for identifying variables, and branches in
the tree occur when the values differ.
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Chapter 6. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation 1 - ![~ 11^
E) Acute Bronchitis
E) Acute Myocardial Infarc
E) Acute Respiratory Symp
E) Asthma Exacerbation
El Asthma Exacerbatic
B Asthma Exacerbatic
i Vedal et aUffi
| Vedal et aL19<
Ostro et al., 20C
; Ostro et al., 20C
E) Asthma Exacerbatic
E) Asthma Exacerbatic
E) Asthma Exacerbatic
El Chronic Bronchitis
E) Chronic Phlegm
E) Emergency Room Visits
E) Hospital Admissions, Ca
/V
Pooling Window 1
Endpoint Group
Endpoint Author LowAge | High Age | Qualifier
Pooling Method
Asthma Exacerbation
Asthma Exacerbation, Cough
None
Vedal et al.
None
0
17
<18
6
13
6-13
Ostro et al.
8
13
None
8-13; Incidence
8-13; Prevalent
El Hospital Admissions, Re
El Household Soiling Dam.
Mnrtalitu —
< «» 1 B
< JUL I >
|--Window to Delete -- v [ Delete ][ Add
Configuration Results Filename: CAPrograrn FilesSAbt Associates lnc\BenMAP\Configuration ResultsSAIIPM 10Studies.cfgr [ Browse ]
[ Advanced j [ Cancel ] [ Next
In the above example, four incidence results have been dragged into the pooling window. Each of
the four C-R Functions has Endpoint Group Asthma Exacerbation and End point Asthma
Exacerbation, Cough. Thus, the top line, or root of the tree structure, represents all four
incidence results. A branch then occurs in the tree structure, because two studies have Author
Vedal et al. while the other two have Author Ostro et al. A further branch occurs within Vedal
et al. when the LowAge of the two incidence results differs. Similarly, a branch occurs within
Ostro et al. when the Qualifier of the two incidence results differs. Once a node has only a
single incidence result, no further branching can occur.
Step 3. Sort results
After dragging incidence results into the Pooling Window, you can rearrange the order of the
columns (variables), and thus change the tree structure. To do this, click on a column and hold
the button down as you drag it to its new location. Note that Endpoint Group is always the first
column, and Pooling Method is always the last column. All the other columns can be moved.
To see how the order of the columns in the pooling window affects the tree structure, consider
the following example:
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Chapter 6. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [- l[n]^j
B Acute Bronchitis
B • Acute Myocardial Infarc
B Acute Respiratory Symp:
0 Asthma Exacerbation
B Asthma E xacerbatic
B Asthma Exacerbatic
j-Vedal et aL 195
Vedal etal.,19<
~ stro et al., 20C
1 Ostro et al., 20C
B Asthma Exacerbatic
B Asthma E xacerbatic
B Asthma Exacerbatic
B Chronic Bronchitis
£) Chronic Phlegm
B Emergency Room Visits
B Hospital Admissions, Ca
-
Pooling Window 1
Endpoint Group
Qualifier Endpoint Author Low Age | Hit
Pooling Method
Asthma Exacerbation
None
<18
Asthma Exacerbation, Cough
Vedal et al.
0
17
6-13
Asthma Exacerbation, Cough
Vedal et al.
e
13
8-13; Incidence function
Asthma Exacerbation, Cough
~stro et al.
8
13
8-13; Prevalence function
Asthma Exacerbation, Cough
~stro et al.
8
13
B Hospital Admissions, Re
B Household Soiling Dam..- ,
(T1 Mnrtalitu V
< i r>i
< llll 1 >
--Window to Delete-- [jv [ Delete ] [ Add
Configuration Results Filename: C:\ProgramFiles\AbtAssociateslnc\BenMAP\ConfigurationResultsV\IIPM10Studies.cfgr [ Browse ]
| Advanced ] [ Cancel ] [ Next
This example uses exactly the same incidence results as the previous example, but with the
Qualifier column (variable) immediately after the Endpoint Group column. Because each
result has a unique value for the Qualifier variable, the first branch results in four children, which
each represent a single incidence result.
TIP; Sorting Incidence Results Prior to Pooling
Click and hold your cursor on a column (variable name) in the Pooling Window, then drag the
column either to the right or to the left. Release you cursor when you have moved the column over
the desired location - BenMAP will then rebuild thee tree structure using the newly specified variabl
order. The Endpoint Group is always on the far left-hand side and Pooling Method is always on
the right, but all of the other variables can be freely moved. Note that whenever you rearrange the
tree structure any Pooling Method values you may have selected are reset to None. It is thus
recommended that you sort your results prior to selecting Pooling Method values.
Step 4. Select pooling methods
Once the tree structure is set up in the Pooling Window, you are ready to select your pooling
methods. Essentially each pooling method involves a different method of combining input
incidence results to generate new incidence results. Results can be pooled any time a branch
occurs in the tree structure - that is, any time two or more results share common values for their
leftmost variables. BenMAP helps you to identify these spots by inserting a value of None in the
Pooling Method column at each spot where pooling is possible.
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Chapter 6. Aggregation, Pooling and Valuation
Exhibit 6-1 summarizes the different types of pooling approaches, and Appendix I provides a
detailed discussion of the approaches.
Exhibit 6-1. Pooling Approaches for Incidence and Valuation Results
Pooling
Approach
Description of Pooling Approach !
Availability
Point Latin
Mode Hypercube
None
Sum (Dependent)
Sum
(Independent)
Subtraction
(Dependent)
No pooling performed.
Results are summed assuming they are perfectly correlated. In Point Mode, this
is just a simple sum. In Latin Hypercube moed, BenMAP chooses the first
point from each result in the pooling and does a sipmle sum to generate the first
point in the pooled result, and so on for all of the points in the distribution of
results.
Results are summed assuming that they are independent. A Monte Carlo
simulation is used. At each iteration, a random point is chosen from the Latin
Hypercube of each result, and the sum of these values is put in a holding
container. After some number of iterations, the holding container is sorted low
to high and binned down to the appropriate number of Latin Hypercube points.
Results are subtracted assuming they are perfectly correlated. All subsequent
results are subtracted from the first result (the highest result in the display - to
reorder results, simply click and hold a result and then drag it to its new
position). In Point Mode, this is a simple subtraction. In Latin Hypercube
mode, BenMAP chooses the first point from each result in the pooling and does
a simple subtraction to generate the first point in the pooled result, and so on
for all of the points in the distribution of results.
Results are subtracted assuming that they are independent. A Monte Carlo
simulation is used. At each iteration, a random point is chosen from the Latin
Hypercube of the first result, and then random points are chosen from the Latin
Hypercube of each subsequent result and subtracted from the first. The result
is put into a holding container. After some number of iterations, the holding
container is sorted low to high and binned down to the appropriate number of
Latin Hypercube points.
Weights are specified by the user (see Step 5, below). In Point Mode, the new
result is generated by a simple weighted sum of the input results. In Latin
Hypercube mode, the results are combined using the user specified weights with
the "Round Weights to Two Digits" Advanced Pooling Method. See Step 6
below for details.
Pooling weights are generated automatically based on the inverse variance of
each input result, with the weights normalized to sum to one. Results with a
larger absolute variance get smaller weights. Results are then combined
according to the chosen Advanced Pooling Method.
Random / Fixed BenMAP first tests if random weights should be used. If not, BenMAP uses
Effects fixed effects weights. If yes, the weights take into account both the variance
within each set of results and the variance between sets of results. Results are
then combined according to the chosen Advanced Pooling Method.
Subtraction
(Independent)
Subjective
Weights
Fixed Effects
~
~
~
~
1 Appendix I details the different pooling approaches.
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Chapter 6. Aggregation, Pooling and Valuation
Note that some pooling methods are only available in Latin
Hypercube mode. This is because these pooling methods attempt
to combine distributions of results into new distributions, and no
distributional information is available in Point Mode. The Pooling
Method column will thus have different values in its drop down
list depending on the mode used to generate the incidence results
being pooled.
Step 5. Create additional pooling windows if needed
Within a given pooling window, you can have only one ordering of
the columns (variables). As we have seen, however, the ordering of the columns determines the
structure of the tree used to pool results. It may thus sometimes be necessary for analyses to
have multiple tree structures to handle the various pooling trees they require. To facilitate this,
BenMAP allows additional pooling windows to be added and deleted. To open a new pooling
window, simply click on the Add button. You may do this as many times as needed to
accommodate different sort orders. You can add the same incidence results to as many different
pooling windows as you like.
As needed you can also delete a pooling window by using the Window to Delete —" drop-
down menu to identify the pooling window, and then hitting the Delete button.
Latin Hypercube is a series of
points generated by using specified
percentiles in a given distribution,
such as that of a C-R coefficient. It
is a short-cut method designed to
represent a distribution, while at the
same time saving on computation
time. Using the Point Mode means
that BenMAP will use the mean
value of the coefficient in the C-R
function.
Incidence Pooling and Aggregation |- [ ~
~ Hospital Admissions, Re
E HA, Chronic Lung D
S HA, Pneumonia
~ Hospital Admissions, Ca
~ HA, Congestive He;
h Lippmann et al.,
Lippmann et al.,
Lippmann et al.,
E) HA, Dysrhythmia
Burnett et al., M
| " Burnett et al., 1J
Lippmann et al.,
Lippmann et al.,
Lippmann et al.,
© HA, Ischemic Heart
Pooling Window 1
Endpoint Group
Endpoint | Author Year Location Low Age
Pooling Method
Hospital Admissions, Cardiovascular
None
HA, Congestive Heart Failure
Lippmann et al.
2000
Detroit, Ml
None
65
75
85
HA, Dysrhythmia
Lippmann et al.
2000
Detroit, Ml
None
65
75
85
< mi | >
Pooling Window 2
Endpoint Group
Low Age
High Age | Endpoint Author |Ye;
Pooling Method
/V
Hospital Admissions, Cardiovascular
None
H
65
74
None
HA, Congestive Heart Failure
Lippmann et al.
200
HA, Dysrhythmia
Lippmann et al.
200
75
84
None
HA, Congestive Heart Failure
Lippmann et al.
200
HA, Dysrhythmia
Lippmann et al.
200
85
Max
None
HA, Congestive Heart Failure
Lippmann et al.
200
< mi | | >
Pooling Window 2| v
Delete
Add
< m —i r>i
Configuration Results Filename: C:\Program FilesSAbt Associates lnc\BenMAP\Configuration Results\h
ospitafedmissionsE xample. cf
f Browse
[ Advanced ] [ Cancel ] [ Next
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Chapter 6. Aggregation, Pooling and Valuation
Step 6. Advanced Pooling Methods, Monte Carlo Iterations
Some pooling methods have advanced options which should be set at this point. To set them,
double-click the Pooling Method column on the row for which you wish to select advanced
options. The advanced options available depend on the particular pooling method.
None, Sum (Dependent), Subtraction (Dependent). These pooling methods have no
advanced options associated with them.
Sum (Independent), Subtraction (Independent). These pooling methods have one advanced
option associated with them, Monte Carlo Iterations. As discussed in Exhibit 6-1 above and in
Appendix I, these two pooling methods involve a Monte Carlo simulation. This advanced option
specifies the number of iterations this simulation should go through in generating results. Its initial
value is set by the Default Monte Carlo Iterations value from the APV Configuration
Advanced Settings window (see Step 1, above).
Fixed Effects, Random / Fixed Effects. These pooling methods have two advanced options
associated with them - Advanced Pooling Method, and Monte Carlo Iterations. Advanced
Pooling Method can take on three different values, which are discussed next. Its initial value is
set by the Default Advanced Pooling Method value from the APV Configuration Advanced
Settings window (see Step 1, above).
Round weights to two digits. BenMAP rounds each weight to two digits (e.g. 0.73),
and then multiplies these weights by 100 to get two digit integers. Each entire distribution
(set of Latin Hypercube points) is then put into a holding container an integral number of
times, according to its integral weight. This holding container is then sorted low to high
and binned down to the appropriate number of Latin Hypercube points.
Round weights to three digits BenMAP rounds each weight to three digits (e.g. 0.732),
and then multiplies these weights by 1000 to get three digit integers. Each entire
distribution (set of Latin Hypercube points) is then put into a holding container an integral
number of times, according to its integral weight. This holding container is then sorted
low to high and binned down to the appropriate number of Latin Hypercube points.
Use exact weights for Monte Carlo. BenMAP uses exact weights and a Monte Carlo
simulation. On each iteration of the procedure, a particular result is chosen with a
probability equal to its weight. Once a result is chosen, one of its Latin Hypercube points
is chosen at random and put into a holding container. This is done some number of times
(see Monte Carlo Iterations, below), and the holding container is then sorted low to
high and binned down to the appropriate number of Latin Hypercube points.
Monte Carlo Iterations: This drop down list is only enabled when Use exact weights
for Monte Carlo is selected as the Advanced Pooling Method. It specifies the
number of iterations the Monte Carlo simulation should be run (see above). Its initial
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Chapter 6. Aggregation, Pooling and Valuation
value is set by the Default Monte Carlo Iterations value from the APV Advanced
Settings window (see Step 1, above).
Subjective Weights. Subjective weights pooling has no advanced options associated with it -
the advanced pooling method is always Round weights to two digits (see above). Double
clicking, however, brings up a dialog through which the weights for each result can be specified.
Alternatively, if you haven't set the weights before clicking the Next button the Select
Subjective Weights window will come up automatically.
<-
Incidence Pooling and Aggregation [- ltnJIfeSJ
(±1 Acute Myocardial Infarction
E Hospital Admissions, Cardiovascular
~ Hospital Admissions, Respiratory
E) HA, Asthma
~ HA, Chronic Lung Disease
j Lippmann et al., 2000165-74
[ Moolgavkar, 2000 165-74; C(
j Lippmann et al., 2000165+; (
j Moolgavkar, 2000 165+; CO
Lippmann et al., 20001 75-84
Moolgavkar, 2000 175-84; Q
Lippmann et al., 20001 85+; (
: Moolgavkar, 2000 185+; CD
Si HA, Chronic Lung Disease (less f-
S) HA, Pneumonia
B Acute Respiratory 5ymptoms
S) Mortality
E Work Loss Days
i< ... i m
Pooling Window 1
Endpoint Group
Author Endpoint Low Age High Age
Pooling Method
A
Hospital Admissions, Cardiovascular
Random / Fixed Effi
-
Moolgavkar
HA, All Cardiovascular
6um (Dependent)
65
74
75
84
85
Max
Lippmann et al.
Sum (Dependent)
HA, Congestive Heart Failure
Sum (Dependent)
65
74
75
84
85
Max
< . i
>
Pooling Window 2
Endpoint Group
Endpoint Author Low Age High Age Year
Pooling Method
Hospital Admissions, Respiratory
HA, Chronic Lung Disease
Random / Fixed Eff"
Lippmann et al.
Sum (Dependent)
65
74
2000
75
84
2000
85
Max
2000
Moolgavkar
Sum (Dependent)
65
74
2000
75
84
2000
85
Max
2000
< ¦ i i >
-- Window to Delete -- [ v
Delete ] [ Add
Pooling Window 1
Configuration Results Filename: | C:\Program FilesSAbt Associates lnc\BenMAP\Configuration Results\ClearSkie
sResults.cfgr
( Browse
[ Advanced ] [ Cancel ] [ Next
Note that the weights you enter need not add up to one - BenMAP will normalize them internally.
Also note that BenMAP initializes all the weights to 1 In, where n is the number of results being
pooled.
Example: Simple sorting and pooling of incidence results
If you add a single incidence result to the right-hand window, you will see just one line, and
therefore no opportunities to pool. This shown in the example below.
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Chapter 6. Aggregation, Pooling and Valuation
3* Incidence Pooling and Aggregation
utan
Available Incidence Results
Select Pooling Methods
0 HA, All Cardiovascular [ |
Moolgavkar, 2000118-24; CO; no ICD410
Moolgavkar, 2000 118-64; CO; no ICD410
Moolgavkar, 2000125-34; CO; no ICD410
Moolgavkar, 2000135-44; CO; no ICD410
Moolgavkar, 2000 145-54; CO; no ICD410
Moolgavkar, 2000155-G4; CO; no ICD410
Moolgavkar, 20031G5-74; CO; no ICD410
Moolgavkar, 20031G5+; no I CD 410
Moolgavkar, 2003 I 75-84; CO; no ICD410
Moolgavkar, 2003185+; CO; no I CD 410
El HA, Congestive Heart Failure
iLippmann et al., 20001G5-74; 03 j
Lippmann et ai., 2000165+; 03
Lippmann et al., 2000175-84; 03
Lippmann et al... 2000 I 85+.: 03 @
Endpoint Group
Author
Endpoint Low Age
High Age
Pooling Method
Hospital Admissions, Cardiovascular
Moolgavkar
HA, All Cardiovascular 65
74
Pooling Window 1
<
>
| - Window to Delete --
Configuration Results Filename: C:\Program FilesW)t Associates lnc\BenMAP\Configuration Results\ClearSkiesResults.cfgr
[ Cancel | [ Next
If you add a second incidence result to the window whose C-R Function has the same Endpoint
Group, but a different Author and Endpoint, you will then have a tree with two items in it. The
tree branches at the point where the two C-R Functions vary - at the Author column.
4^ Incidence Pooling and Aggregation f-
B H
B H
A, All Cardiovascular
Moolgavkar, 2000 118-24; CO; no ICD410
Moolgavkar, 2000118-64; CO; no ICD410
Moolgavkar, 2000125-34; CO; no ICD410
Moolgavkar, 20001 35-44; CO; no ICD410
Moolgavkar, 20001 45-54; CO; no ICD410
Moolgavkar, 2000155-64; CO; no ICD410
Moolgavkar, 2003165-74; CO; no ICD410
Moolgavkar, 20031 65+; no I CD410
Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 20031 85+; CO; no ICD410
A, Congestive Heart Failure
•Lippmann et al., 20001 65-74; 03 :
W
v.!
Pooling Window 1
Endpoint Group
Author Endpoint Low Age High Age
Pooling Method
Hospital Admissions, Cardiovascular
None
Moolgavkar
HA, All Cardiovascular
65
74
Lippmann et al.
HA, Congestive Heart Failure
65
74
< JIL I >
Lippmann et al., 2000 I 65+; 03
Lippmann et al., 2000 I 75-84; 03
Lippmann et al., 2000 185+; 03
-Window to Delete -- v [ Delete ] | Add
Configuration Results Filename: C:\Program Files\Abt Associates lnc\8enMAP\Configuration Results\ClearSkiesResults.cfgr | [ Browse ]
[ Advanced ] { Cancel ] [ Next
Note that a pooling method can now be selected for the two incidence results, since a branch has
appeared. If we desired to pool these two incidence results, we would end up with a pooled
result representing two Hospital Admissions (HA), Cardiovascular incidence results.
If you now add four more incidence results to the window whose C-R Functions have the same
Endpoint Group, you will see the following:
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Chapter 6. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [¦ ][DH£jM
Available Incidence Results
Select Pooling Methods
B H
QH
A, All Cardiovascular
Moolgavkar, 2000118-24; CO; no ICD410
Moolgavkar, 2000118-64; CO; no ICD410
Moolgavkar, 20001 25-34; CO; no ICD410
Moolgavkar, 20001 35-44; CO; no ICD410
Moolgavkar, 20001 45-54; CO; no ICD410
Moolgavkar, 20001 55-64; CO; no ICD410
Moolgavkar, 20031 65-74; CO; no I CD 410
Moolgavkar, 20031 65+; no I CD 410
Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 20031 85+; CO; no I CD 410
A, Congestive Heart Failure
Lippmann et al., 2000 165-74; 03
Lippmann et al., 2000 165+; 03
=
V|
Pooling Window 1
Endpoint Group
Author Endpoint Low Age
High Age
Pooling Method
Hospital Admissions, Cardiovascular
None
Moolgavkar
HA, All Cardiovascular
None
65
74
75
84
85
Max
Lippmann et al.
HA, Congestive Heart Failure
None
65
74
75
84
85
Max
<
a
iLippmann et al., 2000 I 85+; 03 •
|-- Window to Delete -- v | Delete | | Add
Configuration Results Filename: C:\ProgramFiles\AbtAssociateslnc\8enMAP\ConfigurationResults\ClearSkiesResults.cfgr [ Browse ]
[ Advanced ] f Cancel ] [ Next
Now you have many pooling options. Setting aside the issue of which pooling method to choose,
there are eight different pooling options at this point, since we have three places where we can
choose to pool or not to pool.
If you choose to pool at the two spots corresponding to Endpoints (HA, All Cardiovascular and
HA, Congestive Heart Failure) you would end up with two pooled results instead of six
individual incidence results.
If you choose to pool at the first place where the Pooling Method field says, None, the spot
corresponding to the Endpoint Group (Hospital Admissions), you will end up with a single result
representing all six of the original incidence results. However, you can also pool at the other
spots as well, and thereby impact the final pooled result:
If you pool at all three spots:
^ First. the three Moolgavkar results are pooled to a give a single HA, All
Cardiovascular result.
^Next, the three Lippmann results are pooled to give a single HA, Congestive Heart
Failure result.
^Finally, the two results generated in the previous steps are pooled to give a single
Hospital Admissions, Cardiovascular result.
If you pool at Hospital Admissions, Cardiovascular but not at the other two spots:
^ All six original results are pooled to give a single Hospital Admissions,
Cardiovascular result.
If you pool at Hospital Admissions, Cardiovascular and at HA, All Cardiovascular, but not at
HA, Congestive Heart Failure:
^ First. the three Moolgavkar results are pooled to a give a single HA, All
Cardiovascular result.
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Chapter 6. Aggregation, Pooling and Valuation
^The result generated in the previous step is pooled with the three HA. Congestive
Heart Failure results to give a single Hospital Admissions, Cardiovascular result.
These same principles apply no matter how many incidence results are being pooled, and
regardless of which pooling methods are selected.
Example: Using multiple pooling windows
As shown in the example above, there are many different ways to pool your incidence results.
Sometimes you may want to look at the same results in different ways, or you may just have
many results that need to be sorted by different variables. In these cases, you can open up
multiple pooling windows by clicking on the Add button.
For example, you might want to pool all results of C-R Functions by a particular author, rather
than pooling all results of C-R Functions of a particular endpoint. The examples below show the
same set of incidence results, first sorted by endpoint, then sorted by author. As you can see, the
pooling options are very different.
Incidence Pooling and Aggregation [-
Moolgavkar, 20001 55-64; CO; no ICD410
|- Moolgavkar, 20031 65-74; CO; no ICD410
I- Moolgavkar, 20031 65+; no I CD 410
|- Moolgavkar, 20031 75-84; CO; no ICD410
Moolgavkar, 2003185+; CO; no ICD410
El HA, Congestive Heart Failure
!¦¦¦ Lippmann et al., 2000 165-74; 03
| - Lippmann et al., 2000 165+; 03
Lippmann et al., 2000 175-84; 03
Lippmann et al., 2000 I 85+; 03
B HA, Dysrhythmia
j- Lippmann et al., 2000 I 65-74; 03
I" Lippmann et al., 2000 165+; 03
Lippmann et al., 2000 175-84; 03
Lippmann et al., 2000 I 85+; 03
B HA, Ischemic Heart Disease
Lippmann et al., 2000165-74; 03; no ICD41 (
Lippmann et al., 2000 165+; 03; no I CD410
Lippmann et al., 2000 175-84; 03; no ICD41 (
Lippmann et al., 2000 185+; 03; no I CD 410
El Hospital Admissions, Respiratory
E) Acute Respiratory Symptoms
El Mortality
El Work Loss Days
< "I! - I f>l
-
V
Pooling Window 1
Endpoint Group
Endpoint Author | Low Age
High Age
Pooling Method
Hospital Admissions, Cardiovascular
None
HA, All Cardiovascular
Moolgavkar
None
65
74
75
84
85
Max
HA, Congestive Heart Failure
Lippmann et al.
None
65
74
75
84
85
Max
HA, Dysrhythmia
Lippmann et al.
None
65
74
75
84
85
Max
HA, Ischemic Heart Disease
Lippmann et al.
None
65
74
75
84
85
Max
< , i i>
|--Window to Delete-- v [ Delete ][ Add
Configuration Results Filename: C:\Program Files^Abt Asso
:iates lnc\BenMAP\Configuration Results\ClearSkiesResults.cfgr | [ Browse j
[ Advanced ] [ Cancel ] | Next |]
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Chapter 6. Aggregation, Pooling and Valuation
Incidence Pooling and Aggregation [- ][nllESl
!¦¦¦ Moolgavkar, 2000 I 55-64; CO; no I CD 410
]¦¦ Moolgavkar, 2003165-74; CO; no ICD410
j- Moolgavkar, 2003165+; no I CD 410
Moolgavkar, 2003175-84; CO; no ICD410
-¦ Moolgavkar, 2003185+; CO; no I CD 410
Q HA, Congestive Heart Failure
{¦¦¦• Lippmann et al., 2000165-74; 03
j- Lippmann et al., 2000165+; 03
I- Lippmann et al., 2000175-84; 03
l- Lippmann et al., 2000185+; 03
~ HA, Dysrhythmia
j - Lippmann et al., 2000165-74; 03
j- Lippmann et al., 2000 I 65+; 03
j - Lippmann et al., 2000175-84; 03
i- Lippmann et al., 2000185+; 03
Q HA, Ischemic Heart Disease
j- Lippmann et al., 2000165-74; 03; no I CD 41
j- Lippmann et al., 2000 I 65+; 03; no I CD 410
j- Lippmann et al., 2000175-84; 03; no I CD 41
Lippmann et al., 2000185+; 03; no I CD 410
£)• Hospital Admissions, Respiratory
EB Acute Respiratory Symptoms
IS Mortality
S3-Work Loss Days
< ,,,, i [>
-
V
Pooling Window 1
Endpoint Group
Author Endpoint
Low Age | High Age
Pooling Method
Hospital Admissions, Cardiovascular
None
Moolgavkar
HA, All Cardiovascular
None
65
74
75
84
85
Max
Lippmann et al.
Nona
HA, Congestive Heart Failure
None
65
74
75
84
85
Max
HA, Dysrhythmia
None
65
74
75
84
85
Max
HA, Ischemic Heart Disease
None
65
74
75
84
85
Max
<
[>
— Window to Delete-- [v [ Delete | [ Add
Configuration R esults Filename: C: \Prograrn FilesVAbt Associates I nc\B enMAP\Configuration R esults\ClearS kiesR esults. cfgr [ B rowse ]
[ Advanced ] ( Cancel j [ Next
If you use two different pooling windows, each sorted as shown above, you can create results
pooled by Author, and results pooled by Endpoint.
6.1.2 Valuing Pooled Incidence Results
After you have specified your incidence pooling options you can hit the Next button and select
valuations and valuation pooling options. The Select Valuation Methods, Pooling and
Aggregation form appears after you click Next on the Incidence Pooling and Aggregation
form. This form should look quite similar to the Incidence Pooling and Aggregation form,
with two tree views on the left side representing EPA Standard Valuations and User
Valuations, and various pooling windows on the right side representing the selected valuations
and pooling options.
There will be one pooling window in the Select Valuation Methods, Pooling, and
Aggregation form for each pooling window in the Incidence Pooling and Aggregation form.
In each pooling window, there will be one result present for each incidence result left over after
all incidence pooling has occurred. Each of these results will be represented by a Select
value in the Valuation Method column.
The columns present in the Select Valuation Methods, Pooling, and Aggregation form are
determined by the incidence results left after all incidence pooling has occurred. There will be
exactly enough columns in each pooling window to represent the "least" pooled incidence result.
That is, the columns will be in the same order they were in the Incidence Pooling and
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Chapter 6. Aggregation, Pooling and Valuation
Aggregation form, but the only columns present will be those up to the level of the pooled
incidence result with the most columns left over after all pooling has occurred. Here is an
example:
^Select Valuation Methods, Pooling, and Aggregation [_ ][n]te^l
Valuation Methods
EPA Standard Valuations
Pooling Window 1
G) Hospital Admissions, Cardiovascular
User Valuations
Endpoint Group
Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular
None
HA, All Cardiovascular
--Select-
HA, Congestive Heart Failure
--Select--
HA, Dysrhythmia
-Select--
HA, Ischemic Heart Disease
--S elect-
[ Advanced ]
Cancel ] | Previous | | Run
There are several steps to take in the Select Valuation Methods, Pooling, and Aggregation
screen:
Step 1. Select your default Advanced options
To select your default Advanced options, click on the Advanced button. There are two values
which you will want to set at this point - the Default Advanced Pooling Method and the
Default Monte Carlo Iterations. See Step 6, above, for a detailed discussion of these values. It
is important to set them first because these default values will be applied to all valuation methods
added to the pooling window (see Step 2, below) after they are set. Once they are set, click
OK. If you decide not to change them, click Cancel. If you already set these values in Step 1
of pooling incidence results, you do not need to reset them here.
Step 2. Select your valuation methods
Valuation methods are specific to endpoint groups, and sometimes to endpoints as well. The only
valuation methods which appear in the left side tree views are those which have the same
endpoint group values as the pooled incidence results which are available to be valued. To select
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Chapter 6. Aggregation, Pooling and Valuation
a valuation method, select it in the left side tree views and drag and drop it onto the appropriate
incidence result in the pooling window. Note that BenMAP will only allow you to drop valuation
methods onto incidence results which have the same endpoint group value. For example,
BenMAP will not allow you to drop a Mortality valuation on a Hospital Admissions incidence
result. Note also that you can only drag and drop individual valuation methods, not entire groups
of them. For explanations of the various valuations, see Appendix I.
If you have added any of your own User Valuations, using the Data menu (see Chapter 8), you
can drag and drop them in the same way as the EPA Standard valuations.
Select Valuation Methods, Pooling, and Aggregation [- llnlfcj
Valuation Methods
EPA Standard Valuations
Pooling Window 1
S Hospital Admissions, Cardiovascular
B HA, All Cardiovascular
COI: med costs + wage loss I
COI: med costs + wage loss I
CGI: med costs + wage loss I
B HA, Congestive Heart Failure
COI: med costs + wage loss I
B HA, Dysrhythmia
Endpoint Group
Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular
None
HA, All Cardiovascular
COI: med costs + wage loss 165-Max
HA, Congestive Heart Failure
COI: med costs + wage loss 165-Max
HA, Dysrhythmia
COI: med costs + wage loss I 0-Max
iCOl: med costs + wage loss j
HA, Ischemic Heart Disease
B HA, Ischemic Heart Disease
: COI: med costs + wage loss I
< JUL _l &l\
User Valuations
COI: med costs + wage loss 165-Max
[ Advanced ] [ Cancel ] [ Previous j [ Run
When BenMAP runs the APV Configuration, it will generate a valuation result for each
valuation method you select by running the valuation method's valuation functions on the
incidence results for which they were selected. You do not need to select valuation methods for
every incidence result - incidence results without any valuation methods will simply be ignored
when valuation results are generated, aggregated, and pooled.
Because valuation functions have uncertainty associated with them, generating valuation results is
fairly complicated. The procedure used depends on whether the incidence results being used
were generated in Point Mode or with Latin Hypercube Points (see Chapter 5, above).
In Point Mode, BenMAP simply runs the valuation functions once using the point estimate of the
incidence result and the mean of the valuation function (see section 9.2) as inputs.
With Latin Hypercube Points, on the other hand, BenMAP generates one hundred percentile
points (from the 0.5th percentile to the 99.5th percentile) to represent the distribution of the inputs
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Chapter 6. Aggregation, Pooling and Valuation
to the valuation function. It then runs the valuation function once for each combination of values
from the incidence result, Latin Hypercube, and the hundred valuation points, putting the results
into a holding container. Finally, the holding container is sorted low to high and binned down to
the appropriate number of Latin Hypercube points, yielding a single valuation result.
Step 3. Sort results
Depending on how your incidence results were pooled, the columns in the valuation pooling
windows can be resorted in the same way as the incidence pooling window columns. This
resorting will have the same sort of impact on the tree structure of valuation results that it had on
the tree structure of incidence results. See Step 3 of Section 6.1.1, above, for more information.
Step 4. Select pooling methods
The same pooling methods are available for valuation results which were available for incidence
results. See Step 4 of Section 6.1.1, above, and Exhibit 6-1, above, for more details. You should
note that when more than one valuation method is selected for a particular pooled incidence
result, it is possible to pool the generated valuation results.
Select Valuation Methods, Pooling, and Aggregation j-
Valuation Methods
EPA Standard Valuations
Pooling Window 1
Q Hospital Admissions, Cardiovascular
0 HA, All Cardiovascular
COI: med costs + wage loss
j- COI: med costs + wage loss
COI: med costs + wage loss
Q HA, Congestive Heart Failure
: COI: med costs + wage loss
1=1 HA, Dysrhythmia
j - COI: med costs + wage loss
[=1 HA, Ischemic Heart Disease
COI: med costs + wage loss
< »" i i>
User Valuations
Endpoint Group
Author Endpoint
Valuation Method Pooling Method
Hospital Admissions, Cardiovascular
Random / Fixed Effe
Moolgavkar
COI: med costs + wage loss I 65-Max
Lippmann et al.
None
HA, Congestive Heart Failure
None
COI: med costs + wage loss I 65-Max
Sum (Independent)
Subtraction (Depender
Subtraction (Independe
Subjective Weights
Random / Fixed Effect
HA, Dysrhythmia
COI: med costs + wage loss I O-Max
HA, Ischemic Heart Disease
COI: med costs + wage loss 165-Max
[ Advanced j [ Cancel j [ Previous ] [ Run
Step 5. Advanced Pooling Methods, Monte Carlo Iterations
The same advanced pooling methods are available for valuation results which were available for
incidence results. See Step 6 of Section 6.1.1, above, for more details.
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Chapter 6. Aggregation, Pooling and Valuation
6.1.3 APV Configuration Advanced Settings
At any point when specifying the incidence and valuation
pooling options, you may click on the Advanced button
on the bottom-left of either the screen for Incidence
Pooling and Aggregation or Select Valuation
Methods, Pooling and Aggregation. This button will
open the APV Configuration Advanced Settings form.
The APV Configuration Advanced Settings form lets
you choose the level of aggregation for the incidence and
the valuation results. Since the valuation depends on the
incidence, the level of aggregation for the valuation must
equal or exceed that of incidence. For example, county-
level incidence aggregation may be combined with
national-level valuation aggregation, but not vice-versa.
As described above (see Step 6. Advanced Pooling Methods, Monte Carlo Iterations, for
more details), the APV Configuration Advanced Settings form also allows the specification of
Default Advanced Pooling Method and Default Monte Carlo Iterations values. It is
recommended that these be set before any incidence results are added to the Incidence Pooling
and Aggregation pooling windows.
The APV Configuration Advanced Settings form also allows the specification of a Dollar
Year - all valuation dollar figures will be reported in this years dollars. See Exhibit 8-5 for
descriptions of the variables used to adjust dollar figures to different years.
Finally, the APV Configuration Advanced Settings form allows the specification of a Random
Seed. As mentioned at the beginning of this chapter (see Open an APV Results File, above)
many of the pooling methods require the generation of sequences of random numbers (e.g.
choosing a random Latin Hypercube point during a Monte Carlo simulation). Providing a specific
Random Seed value allows the user to ensure that the same sequence of random numbers is
generated as in a previous analysis, thus allowing exact results to be reproduced.
If you do not set the Random Seed for a particular run, one will be generated automatically from
the system clock (the number generated will depend on the date and time, and should change
every minute). Normally, you should not set the Random Seed value. If you need to reproduce
a specific set of results, however, the random seed used to generate previous APV
Configuration Results can be obtained from an APV Configuration Result file (*.apvr) Audit
Trail Report (see Section 7.3, below).
6.2 Running the APV Configuration
After having specified the various pooling and aggregation options, you have the opportunity to
save your configuration for future use. The file that you save has an ""apv" extension. The
APV Configuration Advanced Settings
^¦:!0
Incidence Aggregation:
Aggregate to Nation
Hl,l
Valuation Aggregation:
Aggregate to Nation
M
Default Advanced Pooling Method:
| Round weights to two digits. [y]
Default Monte Carlo Iterations:
5000
|v|
Random Seed:
Random Integer
n
Dollar Year:
[2000
M
Cancel
1 OK |
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configuration that you have specified is similar in idea to the configuration that you developed for
choosing C-R functions. (That configuration has a "cfg" extension.) Both files allow you to save
choices that you have made, and re-run them at a later time.
You can save your APV configuration when you have finished making your valuation pooling
choices. Click the Run button, and then choose Save.
^Save Aggregation. Pooling, and Valuation Con... ¦BflB
Ready to run Aggregation, Pooling, and Valuation
Configuration. If you wish to save this configuration,
click the Save button. When ready, click OK. Ifyou
are not ready to run this configuration, click Cancel.
|j Save j| | Cancel | | OK
You then need to name your configuration (*.apv) file. We suggest that you save this in the
Configurations folder. When ready to generate APV Configuration results, click the OK button.
BenMAP then requires that you specify a file in which to save the results, with an ".apvr"
extension.
I IP! Naming APV Configuration and APV Configuration Results files
To keep track of your work, you may find it helpful to use the same name for your APV
Configuration (*.apv) and APV Configuration Results (*.apvr) files.
6.3 Open Existing Aggregation, Pooling, and Valuation (APV)
Configuration File
If you have an existing
configuration (*.apv) file, you can
open, and then edit it. If you have
only a few changes to make to an
existing configuration, it is typically
much quicker to open the previous
configuration, rather than entering
all of your choices again. Note
that the various parts of an APV
Configuration are quite
interdependent, so modifying part of the configuration may cause other parts to be reset. For
APV Configuration Creation Method
CJ Create New Configuration for Aggregation, Pooling, and Valuation.
© Open Existing Configuration file for Aggregation, Pooling, arid Valuation (*.apv file).
Cancel
Go!
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example, modifying the tree structure for incidence pooling will cause the valuation method
selection and valuation pooling tree structure to be cleared and reset. Changing the
Configuration Results Filename in the Incidence Pooling and Aggregation form will not
reset the incidence or valuation pooling trees as long as the new file contains incidence results
generated from the same C-R Functions as the old file. This can be quite helpful for generating
new A P V Configuration Results from several different Configuration Results files which were
generated from different baseline / control scenarios, but with the same set of C-R Functions.
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In this chapter...
Use the Create Reports button
to create incidence and valuation
reports.
Find the file formats, including
variable definitions and program
compatibility, for each report type.
Find out about using an Audit
Trail report to keep track of the
options and assumptions
underlying each analysis.
Chapter Overview
7.1 Incidence and Valuation Results 7-1
7.1.1 Incidence Results 7-2
7.1..2 Aggregated Incidence Results 7-5
7.1.3 Pooled Incidence Results 7-6
7.1.4 Valuation Results 7-6
7..1.5 Aggregated Valuation Results 7-8
7.1.6 Pooled Valuation Results 7-9
7.2 Raw Incidence Results 7-10
7.3 Audit Trail Reports 7-10
7.4 Questions Regarding Creating Reports 7-12
CHAPTER 7
Create
Reports
-------
7. Create Reports
There are three types of reports that you can access by clicking on the Create Reports button.
You will be asked which type of report you wish to create:
^ Incidence and Valuation Results: Raw, Aggregated, and Pooled use an Aggregation.
Pooling, and Valuation Results file (with the ".apvr" extension) to create report for incidence,
aggregated incidence, pooled incidence, valuation, aggregated valuation, or pooled valuation
results. These reports are comma separated values (CSV) files (*.csv) which can be read into
various spreadsheet and database programs, such as Microsoft Excel.
>*¦ Raw Incidence Results use a Configuration Results file (with the ".cfgr" extension) to
create reports for incidence results. These reports are CSV files.
^ Audit Trail Reports provides a summary of the assumptions underlying each of five types of
files generated by BenMAP: Air Quality Grids (with the "aqg" extension), Incidence
Configurations (with the "\cfg" extension), Configuration Results (with the '".cfgr" extension),
Aggregation, Pooling, and Valuation Configurations (with the "".apv" extension), and
Aggregation, Pooling, and Valuation Results (with the ".apvr" extension). These reports can
be viewed within BenMAP in an expandable tree structure, or can be exported to tab-delimited
text files.
Select Report Type mm\
0 Incidence and Valuation Results: Raw, Aggregated, and Pooled. (Created from x.apvr files)
o Raw Incidence Results. (Created from *.cfgr files)
O Audit Trail Reports (Created from x.aqg files, x.cfg files, x.cfgr files, x.apv files, or x.apvr files)
Cancel 1 OK
7.1 Incidence and Valuation Results: Raw, Aggregated, and Pooled
Using the results in the Aggregation, Pooling, and Valuation Res
file (".apvr" extension), you can create six types of reports: raw
incidence, aggregated incidence, pooled incidence, valuation (of
pooled incidence), aggregated valuation, and pooled valuation.
After you click on the Create Reports button and specify your
choice, you need to specify the APV Configuration Result File
that you want to use (see Chapter 6 for how to create an APV
result file). You then need to choose a Result Type. Exhibit 7-1
describes the results contained in each type of report.
suits
the
^Choose a Result Type ~US
Result Type
O Incidence Results
O Aggregated Incidence Results
O Pooled Incidence Results
O Valuation Results
O Aggregated Valuation Results
O Pooled Valuation Results
Cancel
OK
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Exhibit 7-1. Summary of the Reports Generated from APVR File
Result Type
Description
Incidence Results
Incidence results for each C-R function at the grid-cell level or aggregated at the county,
state, or national level.
Aggregated Incidence Results
Incidence results for each C-R function aggregated to the level you specified in the
Aggregation, Pooling, and Valuation configuration file.
Pooled Incidence Results
Incidence results aggregated and pooled as you specified in the Aggregation, Pooling, and
Valuation configuration file.
Valuation Results
Valuation results for the pooled and aggregated incidence results.
Aggregated Valuation Results
Valuation results aggregated to the level you specified in the Aggregation, Pooling, and
Valuation configuration file.
Pooled Valuation Results
Valuation results aggregated and pooled as you specified in the Aggregation, Pooling, and
Valuation configuration file.
7.1.1 Incidence and Valuation Results: Incidence Results
The Incidence Results report gives you the opportunity to examine the results of each C-R
function at the grid-cell level, or aggregate them to the county, state, or national level. Simply
select the options that you desire from the four main sections of the Configuration Results
Report form: Column Selection (these include Grid Fields, C-R Function Fields, and
Result Fields), Grouping Options, Display Options, and Advanced Options. As you
modify your choices, the Preview section will be updated accordingly.
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^Configuration Results Report
~(n]0
Column Selection
Grid Fields:
C-R Function Fields:
Result Fields:
0 Column
0 Row
D Endpoint Group
Metric
Version
@ Endpoint
Beta
Database
Pollutant
DistBeta
~ CornpiledFunction
5 Author
PIBeta
Incidence
~ Year
P2Beta
~ Incidence2
Qualifier
~ A
~ Prevalence
Location
NameA
Low Age
B
High Age
NameEl
Race
C
Gender
~ NameC
D Other Pollutants
Function
Point Estimate
0 Population
~ Delta
0 Mean
Standard Deviation
Variance
Latin Hypercube Points
Grouping Options
© Group by Gridcell, then by C-R function.
O Group by C-R function, then by Gridcell.
-Preview—
Display Options
Digits After Decimal Point:
Elements in Preview:
1X1
[~]
25
[~]
Advanced Options
Population Weighted Deltas: Q
Nation |>
Aggregation Level:
Column Row
Endpoint
Author Population Mean
u
hJ
1 1 Asthma Exacerbation, Asthma Attacks Whittemore and Korn 279581888.0 924090.2
1
1
Emergency Room Visits, Asthma
Weisel et al.
279581888.0
2098.2
1
1
HA, All Respiratory
Schwartz
18283208.0
2408.0
Cancel
OK
The Column Selection section allows you to choose the field names (and values) which will
appear in the report. The Grid Fields section allows the inclusion of Col and Row fields, which
can be helpful in identifying the grid-cell of a particular line in the report. These will not always
be necessary, however - for example, when results have been aggregated to the national level.
The C-R Function Fields section allows the inclusion of various fields which can be helpful in
identifying the C-R function of a particular line in the report. Almost all of the field names have
appeared previously in the preparation of a Aggregation, Pooling, and Valuation Results file.
Finally, the Result Fields section allows the inclusion of various types of results.
Exhibit 7-2 provides a summary of the variables available in this report format.
In the Grouping Options section, you can change the sorting of the results, by clicking the radio
buttons Group by Gridcell then by C-R function and Group by C-R function, then by
Gridcell.
In the Display Options section, you may set the number of digits that appear after the decimal
point, and you can set the number of rows that appear in the preview window.
In the Advanced Options, you can set the level of aggregation at the grid cell (none), county,
state, and national levels. You can also choose to generate Population Weighted Deltas, which
BenMAP calculates at the national level for each C-R function by weighting the change in the
pollution metric at each grid cell with the population of the grid cell. For example, if there are
large changes in highly populated urban grid cells and relatively small changes in lightly population
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Chapter 7. Create Report
rural grid cells, then the population-weighted change would reflect the large urban changes and be
relatively large.
Exhibit 7-2. Selected Variables in the Reports Based on the APVR file
Variable
Variable Description
Col
The column of the grid cell of the result. For grid cell level results, this is the column of the grid cell. For
county and state level results, this is the state FIPS code. For national results, this is always 1.
Row
The row of the grid cell of the result. For grid cell level results, this is the row of the grid cell. For
county results, this is the county FIPS code. For state and national results, this is always 1.
Endpoint Group
The endpoint group of the result (from the C-R function and/or valuation method). See Exhibit 5-1 for a
list of endpoint groups.
Endpoint
The endpoint of the result (from the C-R function and/or valuation method). See Exhibit 5-1 for a list of
endpoints.
Author
Author of the study used to develop the C-R function associated with the result.
Year
Year of the study used to develop the C-R function associated with the result.
Location
Location of the study used to develop the C-R function associated with the result.
Qualifier
For incidence results, the qualifier is a description that uniquely identifies a C-R function when combined
with the endpoint group, endpoint, author, and year. For valuation results, the qualifier is a description
that uniquely identifies a valuation function when combined with the endpoint group, endpoint, and age
range.
Other Pollutants
Other pollutants that were simultaneously included in the original study used to develop the C-R
function associated with the result.
Metric
Air quality metric used in the C-R function associated with the result.
Function
The C-R function associated with the result.
Compiled Function
To run faster, BenMAP uses C-R functions that have already been compiled. There are approximately
25 types of compiled in functions that are numbered starting with zero.
Version
The version of the C-R function associated with the result. Version indicates the number of times that a
particular C-R function has been included in a configuration. Typically the different versions of a C-R
function will have different population variables (Low Age, High Age, Race, and/or Gender).
ValuationMethod
A combination of the qualifier and age range of the result (from the associated valuation method(s)).
Population
Population provides the number of persons used in the C-R function calculation.
Delta
The difference between the baseline and control scenarios for the metric used in the C-R function.
Calculated by subtracting the metric value in the control scenario from the metric value in the baseline
scenario.
Point Estimate
The point estimate for this result.
Mean
Mean of the points in the Latin Hypercube for this result.
Std Dev
Standard deviation calculated based on the points in the Latin Hypercube for this result.
Latin Hypercube
The number of percentiles depends on the number of points in the Latin Hypercube for this result.
Points
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7.1.2 Incidence and Valuation Results: Aggregated Incidence Results
The Aggregated Incidence Results Report presents the
incidence results at the aggregation level that you have
previously specified in the Aggregation, Pooling, and Valuation
Configuration file. If you want to change the level of
aggregation, you need to revise your choices in the APV
Configuration Advanced Settings screen (see Chapter 6).
The Column Selection section looks largely the same as in
the Incidence Results Report, except that the Population
and Delta Result Fields are not available. The Grouping Options section and Display
Options section are exactly the same. The Advanced Options section no longer exists, as the
options in it do not apply to aggregated incidence results.
Aggregation refers to the
summing of grid cell level results
to the county, state or national
level. Pooling refers to
combining individual incidence
results or valuations into
composite results.
* APV Configuration Results Report
~[~0
Column Selection
Grid Fields:
0 Column
@ Row
C-R Function Fields:
Add Sums
Result Fields:
Endpoint Group
Metric
Version
15 Endpoint
~ Beta
~ Database
C Pollutant
~ DistEleta
~ CompiledFunction
5 Author
~ PI Beta
~ Incidence
] Year
~ P2E!eta
~ Incidence2
C Qualifier
~ A
~ Prevalence
C Location
~ NarneA
C Low Age
B
C High Age
~ NameB
C Race
~ C
~ Gender
~ NarneC
C Other Pollutants
~ Function
Point Estimate
0 Mean
~ Standard Deviation
Variance
~ Latin Hypercube Points
Grouping Options
Preview
Display Options
© Group by Gridcell, then by C-R Function.
Digits After Decimal Point:
1
IXI
[w]
O Group by C-R Function, then by Gridcell.
Elements in Preview:
50
m
u
Rc
Author M
1
0
Column
Endpoint
Asthma Exacerbation, Asthma Attacks Whittemore and Korn 14194.0
Emergency Room Visits, Asthma
HA, All Respiratory
Hi ill Rp^niralnm
Weisel et al.
Schwartz
^ r-kiiAiArl"?
33.7
37.2
Cancel
OK
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7.1.3 Incidence and Valuation Results: Pooled Incidence Results
The Pooled Incidence Results Report provides results aggregated and pooled to the level that
you previously specified in the Aggregation, Pooling, and Valuation Configuration file. This report
looks largely the same as the Aggregated Incidence Results Report, except that fewer C-R
Function Fields are available, and values for others will be blank. This is because after pooling,
only enough fields are retained to uniquely identify individual results.
' APV Configuration Results Report
Column Selection
Grid Fields:
0 Column
0 Row
Pooled C-Fi Function Fields:
Result Fields:
0 Endpoint Group
D Function
0 Endpoint
MD Version
Author
~ Year
~ Location
D Low Age
High Age
Qualifier
Race
Gender
~ Other Pollutants
Metric
Add Sunns
~ Point Estimate
0 Mean
D Standard Deviation
D Variance
0 Latin Hypercube Points
Grouping Options
© Group by Gridcell, then by Pooled C-R Function.
O Group by Pooled C-R Function, then by Gridcell.
Display Options—
Digits After Decimal Point:
Elements in Preview:
1
1^.1
M
25
ill
M
Preview
Column Row Endpoint Group
Endpoint Mean
Percentile 2.5
Percentile 7.5
Percenti e 12 A
Mortality
Hospital Admissions, Respiratory
F mprnpnn i R nnm \f i«iK- R p^nirahnn
Lance
7.1.4 Incidence and Valuation Results: Valuation Results
The Valuation Results report gives you the opportunity to examine the valuation results for the
pooled and aggregated incidence results. In the example below, the incidence results were
aggregated to the state level, so in the Column field, which represents the state FIPS code, you
can see the value changing from 1 (Alabama) to 4 (Arizona).
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APV Configuration Results Report
jn|@
Column Selection
Grid Fields:
Valuation Method Fields:
Result Fields:
^ Column
0 Row
0 Endpoint Group
Function
~ Endpoint
Version
Author
0 ValuationMethod
Year
Location
~ Low Age
~ High Age
~ Qualifier
Race
D Gender
~ Other Pollutants
~ Metric
~ Point Estimate
0 Mean
Standard Deviation
Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
Display Options
0 Group by Gridcell, then by Valuation Method.
Digits After Decimal Point:
0
IXI
M
Group by Valuation Method, then by Gridcell.
Elements in Preview:
25
tti
M
Preview
Column
Row
Endpoint Group
ValuationMethod
Mean
Percentile 0.5
A
1
1
Mortality
VSL, based on range from $1 to $10 million, normal distribution.
164367904
-228305072
1
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distribution
16454S928
-246053728
1
1
Mortality
VSL, based on range from $1 to $10 million. Beta distribution, rn
164394128
-240079168
1
1
Hospital Admissions, Resp
CO I: med costs + wage loss I 65-Max
2678033
53286
1
1
Emergency Room Visits, F
CO I: Smith etal. (1937) I O-Max
10490
5950
1
1
School Loss Days
010-17
1391972
365261
1
1
Acute Respiratory Sympto
WTP: 1 day, D/studies 118-Max
2895086
820284
1
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (2002) 118-Max
1044773
221077
4
1
Mortality
VSL, based on range from $1 to $10 million, normal distribution,
186764096
-234341664
4
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distribution
186868160
-252559616
4
1
Mortality
VSL, based on range from $1 to $10 million. Beta distribution, m
186812480
-246427072 v
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Chapter 7. Create Report
Note that the Add Sums button is only enabled for reports involving monetaiy valuations, not
those involving incidence estimates. Typically, incidence estimates should not be summed across
endpoint groups (for example, Mortality and Hospital Admissions, Respiratory). Within endpoint
groups, incidence estimates can be summed - you may do this in Aggregation, Pooling and
Valuation Configurations (see Chapter 6). Once results are in monetary values, however,
summing across endpoint groups can be useful in calculating aggregate benefits.
Add Valuation Sum [_ ][p]l63(
Pooling Window
Endpoint Group
ValuationMethod
Include in Total
1
Mortality
VSL, based on range from $1 to $10 million, r
h
1
Mortality
VSL, based on range from $1 to $10 million, L
~
1
Mortality
VSL, based on range from $1 to $10 million, E
~
1
Hospital Admissions, Respiratory
CO I: med costs + wage loss I 65-Max
a
1
Emergency Room Visits, Respiratory
COI: Smith etaL (1997) 10-Max
0
1
School Loss Days
010-17
0
1
Acute Respiratory Symptoms
WTP: 1 day, CV studies 118-Max
0
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (200<
0
< UN | | >1
Valuation Sum Identifier: Summation Type: Monte Carlo Iterations:
Total With Mortality Dependent v 15000 Cancel
OK
7.1.5 Incidence and Valuation Results: Aggregated Valuation Results
The Aggregated Valuation Results Report presents valuation results aggregated to the level
you specified in the Aggregation, Pooling, and Valuation configuration file. In the example below,
the valuation results are aggregated at the national level, so all of the results have the same value
(1) in the Column and Row fields. As with the Valuation Results Report, you can use the
Add Sums button to create totals with various valuation results.
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% APV Configuration Results Repoit
il@i
Column Selection
Grid Fields:
Valuation Method Fields:
Result Fields:
0 Column
0 Row
0 Eridpoint Group
Function
Endpoint
~ Version
[ Author
0 ValuationMethod
~ Year
Location
~ Low Age
High Age
~ Qualifier
Race
~ Gender
~ Other Pollutants
1 Metric
Point Estimate
0 Mean
Standard Deviation
Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
© Group by Gridcell, then by Valuation Method.
O Group by Valuation Method, then by Gridcell.
Display Options
Digits After Decimal Point:
Elements in Preview: 25
0
(XI
It]
25
IXI
M
Preview
Column
Row
Endpoint Group
ValuationMethod
Mean
Percentile 0.5
1 1
Mortality
VSL, based on range from $1 to $10 million, normal distributioi 9765866496
-12825786368
1
1
Mortality
VSL, based on range from $1 to $10 million. Uniform distributic
9774171136
-13822873800
1
1
Mortality
VSL, based on range from $1 to $10 million. Beta distribution.
9768701952
-13487233024
1
1
Hospital Admissions, Resp
CO I: med costs + wage loss I 65-Max
173828448
3458038
1
1
Emergency Room Visits, R
CO I: Smith etal. (1997) I O-Max
653580
370741
1
1
School Loss Days
010-17
93317712
24487106
1
1
Acute Respiratory Symptor
WTP: 1 day, CV studies 118-Max
209964112
57523848
1
1
Asthma Exacerbation
WTP: 1 symptom-day, Dickie and Ulery (2002) 118-Max
6801932S
14397180
a
Cancel
OK
7.1.6 Incidence and Valuation Results: Pooled Valuation Results
The Pooled Valuation Results Report presents valuation results aggregated and pooled to the
level you specified in the Aggregation, Pooling, and Valuation configuration file. (Recall that
aggregation refers to the geographic level that you have combined your results, and pooling refers
to how you have combined the results of different C-R functions / valuations.) As with the
Pooled Incidence Results Report, fewer Pooled Valuation Method Fields are available,
because only enough fields are retained to uniquely identify individual results.
In the example below, several different valuation approaches have been combined when valuing
mortality, so the Valuation Method field is blank. As with the other valuation reports, you can
use the Add Sums button to create totals with various valuation results.
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^ APV Configuration Results Report
"~US
Column Selection
Grid Fields:
Pooled Valuation Method Fields:
Result Fields:
0 Column
0 Row
0 Endpoint Group
0 ValuationMethod
~ Point Estimate
0 Mean
~ Standard Deviation
D Variance
0 Latin Hypercube Points
Add Sums
Grouping Options
© Group by Gridcell, then by Pooled Valuation Method.
O Group by Pooled Valuation Method, then by Gridcell.
Display Options
Digits After Decimal Point:
Elements in Preview: 25
m
M
25
m
M
Preview
Column
Row
Endpoint Group
ValuationMethod
Mean
Percentile 0.5
Percentile 1.5
Percentile
1
1 Mortality
9769579520
-13378630656
-10645738496
-83529630
1
1
Hospital Admissions, Respiratory
173828448
3458038
3456038
3458038
1
1
Emergency Room Visits, Respiratory
S53580
370741
397688
415920
1
1
School Loss Days
93317712
24487106
24487106
24487106
1
1
Acute Respiratory Symptoms
209964112
57523848
70813816
78359616
1
1
Asthma Exacerbation
68019328
14387180
15479634
16276288
<1 ""
t±l
Cancel
OK
7.2 Raw Incidence Results
The Raw Incidence Results report gives you the opportunity to examine the results of each C-R
function at the grid-cell level, county, state, or national level. It is based on the Configuration
Results file (with the *.cfgr extension), and is otherwise identical to the Incidence Results
Report generated from the Aggregation, Pooling, and Valuation results file (with the *.apvr
extension). BenMAP includes both versions to increase the reporting flexibility for users. (See
Section 7.1.1 for a description of the options available for this report type.)
7.3 Audit Trail Reports
The Audit Trail Reports provide a summary of the assumptions underlying the various parts of
the analysis. You may generate an audit trail with any of the file types used in BenMAP: Air
Quality Grids (with the "".aqg" extension), Incidence Configurations (with the "\cfg" extension),
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Chapter 7. Create Report
Configuration Results (with the ".cfgr" extension), Aggregation, Pooling, and Valuation
Configurations (with the "".apv" extension), and Aggregation, Pooling, and Valuation Results (with
the "".apvr" extension). The report itself has a tree structure that lets you easily find the
information that you are seeking. Below is an example of an Audit Trail Report for a
Aggregation, Pooling, and Valuation Results file.
Note that each successive step in an analysis contains a summary of its assumptions, and those of
each previous step in the analysis. For example, in the below report the assumptions of the
Configuration Results file used to generate the APV Results are present in the Configuration
Results node. Similarly, the assumptions of both the baseline and control air quality grids are
present under the Configuration Results node.
Note that you can export Audit Trail as a text file . Each branch in the tree structure will be
converted to a tab in the exported file, allowing for easy viewing in Excel, WordPad, and a
variety of other programs. Simply click on the Export button, name the file, and click Save.
•Tf Audit Trail Report ~as
Aggregation, Pooling, and Valuation Configuration Result: U:'¦Program Files'Wbt Associates lnc\BenMAP\Configuration Results\chap7_o3_e
E) Configuration Results: C:\Program Files'^Abt Associates lnc\BenMAP_new\Configuration Results\ozone_test.cfgr
El B aseline Air Q uality G rid: C:\Prograrn Files'Abt Associates I nc\B enM AP_newV\ir Quality G ridslio3_2002_county_closest_baseline. a<
El Control Air Q uality G rid: C: \Program Files'Abt Associates I nc'\B enM AP_newWir Q uality G rids\o3_2002_county_closest_1 Opct. aqg
Latin Flypercube Points: 20
Pollutant: D3
Year: 2000
Threshold: 0.000000
El Selected Studies
E) Advanced
- Incidence Pooling Windows
- Pooling Window 1
- Mortality, Mortality, Short-Term, Non-Accidental [Pooling Method: Random I Fined Effects] [Advanced Pooling Method: Round \A
El [Weight: 0.15, Mean: 3,528.23, StdDev: 1,355.66] Ito and Thurston, 1396, Chicago, IL [Pooling Method: Sum [Dependent]]
[j| [Weight: 0.21, Mean: 0.79, StdDev: 1,152.79] Kinney et al., 1995, Los Angeles, CA [Pooling Method: Sum [Dependent]]
El [Weight: 0.63, Mean: 1,953.77, StdDev: 670.24] Moolgavkar et al., 1995, Philadelphia, PA [Pooling Method: Sum (Depende
(±1 Hospital Admissions, Respiratory, HA, All Respiratory, Schwartz, 1995 [Pooling Method: Random / Fixed Effects] [Advanced Poc
Emergency Room Visits, Respiratory, Emergency Room Visits, Asthma, Weisel et al., 1995, New Jersey [Northern and Central), 0
School Loss Days, School Loss Days, All Cause, Chen et al., 2000, Washoe Co, NV, 6,11, 6-11, All, All, COPM10, OneFlourDa
Acute Respiratory Symptoms, Minor Restricted Activity Days, Dstro and Rothschild, 1989, nationwide, 18, 64,, All, All, PM2.5, T(
Asthma Exacerbation, Asthma Exacerbation, Asthma Attacks, Whitternore and Korn, 1980, Los Angeles, CA, 0, Max, All ages,AI
El Valuation Pooling Windows
< I >
Export OK
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Chapter 7. Create Report
7.4 Questions Regarding Creating Reports
Below are answers to some of the common questions that may arise in the creation of reports.
>^When creating reports from *.cfgr and *.apvr flies, why do some of the variables that I have
checked appear as blanks?
When results are pooled together, some of the identifying information for individual C-R functions
gets lost. For example, when pooling together endpoints within the same endpoint group, such as
"HA, Pneumonia" and "HA, Chronic Lung Disease" (both within "Hospital Admissions,
Respiratory"), there is no longer a unique endpoint name for the pooled result. So, BenMAP
would leave the endpoint name blank.
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In this chapter...
>- Learn about BenMAP's
mapping functions.
Map configuration inputs like
air quality grids.
Map incidence and valuation
results.
>- Map different variables and
modify the map display.
Chapter Overview
8.1 Overview of Mapping Features 8-1
8.1.1 Display Options 8-3
8.1.2 Taskbar Buttons 8-4
8.1.3 Mapping Different File Types with the Tools
Menu 8-6
8.2 Viewing Maps in a BenMAP Analysis 8-11
8.3 Questions Regarding Mapping 8-17
CHAPTER 8
Mapping
-------
8. Mapping
BenMAP features integrated mapping capabilities which you can access at several points in the
model. The main Mapping / GIS tool, available via the Tools drop-down menu in the main
screen, allows you to map all types of files and data associated with an analysis, including air
quality grid files, monitor data, population data, and incidence and valuation results. In addition, at
several points from within the program, you can view data being used in an analysis. You can
view maps when filtering monitor data, creating air quality grids, and when creating incidence
configuration files (with the *.cfg extension).
8.1 Overview of Mapping Features
To access the main mapping capabilities
within BenMAP, go to the Tools drop-down
menu, and choose the Mapping / GIS
option. A blank screen will appear, with
buttons at the top for managing files and
navigating the map. To see the name of
each button, simply hold the cursor over it.
Use the Open a file button in the top-left
corner to choose the file (or other type of
data) that you want to view. You can view
all of the files and data underlying an
analysis. Each map that you select and load
into the GIS viewer will appear on the left-
hand side of the screen as a layer. Exhibit
8-1 describes each type of map you can view.
Exhibit 8-1. Description of Mapping File Types
File Type
Variables Available
Source File Used
Notes
Air Quality
Grid
Annual summary values (average,
median or other metric where
available) within each grid cell.
Air quality grids
created by
BenMAP (*.aqg)
Monitors
Annual summary values (average,
median or other metric where
available) for each monitor.
Select monitors
from the BenMAP
library or use your
own by loading a
file.
Customize the monitor filtering options
or use the default settings.
mfnlfel
S # ©v
©x Q Q ^ Q j; | Albers Equal Area Conic [ v |- Reference Layer - v |
Air Quality Grid
Monitors
Population
County Data
Adjustment Grid
APV Results ~
CFG Results
| Close |
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Chapter 8. Mapping
File Type Variables Available Source File Used Notes
Population
Total population, and cross-
Internal BenMAP
Variable names for the cross-tabulations
tabulations of age and gender with
population files and
are abbreviated: for example,
race/hispanie origin within each grid
projections, based
a m 35to39 is Asian males 35 to 39
cell (for the Year and GridType you
on Census block-
years of age. Total population and single
specify).
level data.
variables are at the bottom of the list.
County Data
Incidence rates by age group; some
Internal BenMAP
Variables are listed in alphabetical order;
household, wage, income and
incidence data.
like variables are not grouped together.
employment data; total population.
At the county level.
Adjustment
PM grids: each variable represents a
BenMAP
Adjustment files are created using the
Grid
seasonal adjustment factor. Daily
adjustment files
Adjustment Factor Creator under the
average concentrations for each
(*.adj)
Tools menu, or during the Air Quality
season are sorted and placed in 5
Grid creation process using the Model
bins. The average of each bin is used
and Monitor Relative option.
as the adjustment factor: si bl is the
average for season l's first bin.
Ozone grids: each variable
represents one of 10 decile
adjustment factors.
APV Results:
For each incidence result, the
Aggregation,
Variables are as calculated between the
Incidence
ResultX variable is the mean increase
Pooling and
baseline and control scenarios before
in cases between the control and
Valuation Results
pooling and aggregation. Each incidence
baseline scenarios; the DELTA X
files (*.apvr)
result is given a number (e.g. ResultO,
variable is the difference between the
Resultl). Result variables can be
baseline and control scenarios for the
renamed in the Edit GIS Field Names
metric used in the C-R function; and
window.
the POP X variable is the number of
persons used in the C-R function.
APV Result:
For each aggregated incidence result,
Aggregation,
Results are aggregated to the level
Aggregated
the ResultX variable is the mean
Pooling and
specified in the APVR file. If results
Incidence
increase in cases between the control
Valuation Results
were aggregated to the national level, the
and baseline.
files (*.apvr)
US map will only show one color.
Variables can be renamed in the Edit GIS
Field Names window.
APV Results:
For each pooled incidence result, the
Aggregation,
Results are pooled and aggregated as
Pooled
ResultX variable is the mean
Pooling and
specified in the APVR file. Variables can
Incidence
increase in cases between the control
Valuation Results
be renamed in the Edit GIS Field
and baseline.
files (*.apvr)
Names window.
APV Results:
For each valuation result, the
Aggregation,
Only incidence results assigned a value in
Valuation
ResultX variable is the mean
Pooling and
the APVR file will appear in the variable
economic value placed on the increase
Valuation Results
list. You can create new groups of
in cases between the control and
files (*.apvr)
results and add their valuations together
baseline. You can also map any new
using the Add Sums buttons. Variables
sums created with the Add Sums
can be renamed in the Edit GIS Field
button.
Names window.
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Chapter 8. Mapping
File Type Variables Available Source File Used Notes
APV Results:
For each aggregated valuation result,
Aggregation,
Values are aggregated to the level
Aggregated
the ResultX variable is the mean
Pooling and
specified in the APVR file. Variables can
Valuation
economic value placed on the increase
Valuation Results
be renamed in the Edit GIS Field
in cases between the control and
files (*.apvr)
Names window.
baseline. You can also map any new
sums created with the Add Sums
button.
APV Results:
For each pooled valuation result, the
Aggregation,
Values are pooled and aggregated as
Pooled
ResultX variable is the mean
Pooling and
specified in the APVR file. Variables can
Valuation
economic value placed on the increase
Valuation Results
be renamed in the Edit GIS Field
in cases between the control and
files (*.apvr)
Names window.
baseline. You can also map any new
sums created with the Add Sums
button.
APV Results:
Variables as described above in each
Aggregation,
BenMAP opens each file type as a
All
report type.
Pooling and
separate layer. You can widen the left-
Valuation Results
hand panel to see the full layer names.
files (*.apvr)
CFG Results
Variables are the same as APV
Configuration
This produces the same map and
Results: Incidence.
Results files (*.cfgr)
variables as the Incidence option under
APV results. Variables can be renamed in
the Edit GIS Field Names window.
8.1.1 Display Options
After choosing the file or data type that you want to map, you must select the variable you want
to map, and how you want it displayed. Double-click on the layer name on the left-hand side of
the screen. A small box will appear with the Display Options for the layer. This box allows
you to set the following options:
Variable. The drop-down menu lists all of the variables contained in the layer you chose. Select
the variable that you want to view. If you want to view multiple variables in the same map at the
same time, you must load multiple versions of the same layer. Each layer can only show one
variable at a time.
Start Color and End Color. These are the colors that represent the gradations in the selected
variable. BenMAP uses 10 equal-sized increments for the variable, with a gradual transition
between the Start Color and the End Color. To change either color, click on the colored
square and select a new color.
Default Color. This is the color is used for values that fall outside of the range of the Min
Value and the Max Value.
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Chapter 8. Mapping
Min Value and the Max Value. These options define the range of the selected variable and are
automatically set to the minimum and maximum of the variable. However, you may wish to enter
other minimum and maximum values, such as in the case where there is one outlier that is
dominating the color scale. For example, some urban grid-cells such as those in Los Angeles
have extremely large values. Since BenMAP creates 10 equal-sized increments between the
minimum and the maximum, it is not uncommon to have most of the grid values in just a few of
the increments. If you change the minimum and/or maximum values to exclude outliers from the
color scale, the outliers will then appear in the Default color.
Decimal Digits. This specifies the number of digits used in displaying the results. The default is
two decimal points. However, for variables with values much smaller than one, such as some of
the incidence rate data, you will want to increase the number of digits that you display.
Grid Outline. This appears as a fine white line around the
border of each grid cell. For some grid types, this outline can
make the map too complicated, and you may want to uncheck
this option. An alternative is to use the Reference Layer drop-
down window, which lets you add a blue outline for the nation,
states, or counties.
Note that if you have specified point data, such as monitors, the
Display Options window includes the ability to set the size of
the points on the map. The Start Size, End Size, and Default
Size have a default value of 100, which you then edit. For example, if you want smaller values to
be represented by smaller points, you might leave the End Size unchanged, and reduce the value
for the Start Size.
Display Options DEB
Variable: | Total j v
Start Color: Min Value: 1G27.G4
End Color: j^H Max Value: 125779.40
Default Color:
Grid Outline: 0 Decimal Digits: 2 £j
Cancel | | OK
f Display Options
~SM
Variable:
Annual^ vg|
Start Size:
100
IXI
I*]
Start Color:
u
Min Value:
5.67
End Size:
100
m
It I
End Color:
~
Max Value:
139.39
Default Size:
100
IX)
It)
Default Color:
¦
Decimal Digits:
1 [~)
Cancel
OK
8.1.2 Taskbar Buttons
There are a number of standard buttons used in most map viewing programs which you can use
to navigate and customize the map view.
Open a file. Use this to open maps for viewing.
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Chapter 8. Mapping
~1
JMl
Save active layer to file. Creates a shape file for use in other map-viewing programs. The
active layer is the topmost visible layer.
Zoom to full extent. Allows you to view the whole map that you are viewing.
Increase zoom and Decrease zoom. Allows you to zoom in and out.
_
__
1 + 1
1 — 1
Q
Select a region for zooming. Allows you to select a region to view.
Drag mode. Allows you to manually move the map by clicking and dragging.
Click to display info for the cell under the mouse. Allows you to display info (all the
variable values) for individual cells or points by clicking on them.
O
Build Query. Allows you to view grid-cells that satisfy certain criteria. Hitting the Execute
button will produce a map of the cells that meet the criteria that you have specified. Below is
an example of how you might use this function.
- " ¦¦¦!! 1{ ¦'(" I
Fields
) Other
M ispanic
Male
Female
Total
T otal >= 50000
JQJ
Not || And j! Or
fT'l"]' :
Sample Values:
772 00
730.00
¦160.00 !
1322.00
'979.00
22497.99
S37S4.95
j Execute |
Cancel II! OK ;!
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Chapter 8. Mapping
£
Layer Statistics. Provides information about the active layer. In the Fields section simply
choose the variable of interest, and BenMAP will display statistics and sample values for that
variable.
Layer Statistics
n_m
Fields:
Statistics:
Sample Values:
Black
NatAmer
Asian
S|
Mean:
54109.40
Min:
0.00
Other
Hispanic
Max:
7208148.00
Male
Female
Total
StdDev:
208964.60
§
Sum:
2.795833E08
< 1 1111
1 l>
Count:
5167
772.00
-
730.00
R
160.00
1322.00
979.00
22497.99
83784.95
2888.00
810.00
25394.98
lotion qo
El)
OK
The drop-down menu provides alternative projections for displaying the data, with the default set
to Alters Equal. Area Conic.
8.1.3 Mapping Different File Types with the Tools Menu
The Open a file button gives the option to open a variety of file and data types. All of the
options are straightforward, though some require a few more steps than others. See Exhibit 8-1
above for information about each type. Below we give some step-by-step examples for some of
the types.
Example 1: Mapping Monitor Data
You can map monitor locations and concentration levels using
the Monitors option. Click the Open a file button and select
Monitors from the drop-down menu. A small window will
appear in which you can select a Monitor Source. You can
map data from the Library that comes with BenMAP, or you
may map your own monitor data, so long as it follows the
format in Exhibit 4-3.
** Choose Monitoi
Monitor Source
O Library
O File
Pollutant:
Monitor Year:
~[njQ
Cancel
~K
If you map data from the BenMAP I i bran', you need to specify
the Pollutant and the Monitor Year. Click OK. BenMAP
will then present the Advanced Options, where you need to
choose the filtering options that you want to use (See Section
4.4 for more information on filtering). Click Go!, and BenMAP will filter the data. Then click
OK, and BenMAP will generate a monitor layer that you can view.
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Chapter 8. Mapping
BenMAP GIS
Q[n|S
& U # ©s
Layers
Gv Q 0 0 2 Albers Equal Area Conic v States
PM2.5, 2002~
Tin
Close
In this map, each red square is a monitor location. To see the monitor values displayed with
colors varying by the level of the monitor, double-click on the layer on the left side of the map,
and follow the steps outlined above for setting the display options. Values shown using the
Monitors mapping option are annual average values (i-ig/m3 for PM and ppb for ozone).
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Chapter 8. Mapping
& y « ©s ©s q © ri © z Albers Equal Area Conic
r States)
Close
€* BenMAP GIS aaa
PM2.5,2002
3.96-6.31
B.31-RBG
8.66-11.01
11.01-13.36
13.36-15.70
15.70-18.05
18.05-20.40
20.40-22.75
22.75-25.10
25.10-27.45
Example 2: Mapping County Data
You can also map county incidence rates and demographic characteristics.
Included are incidence rates by age and race in some cases, total
population and population by age, and some housing and income variables.
Select Mapping / GIS from the Tools menu on the main BenMAP
screen. Click the Open a file button and select County Data from the
drop-dow n menu. A map of the continental United States will appear, with
all of the counties filled with gray. In the Layers field on the left side of
the screen, double-click on CountyIncidenceData label. In the next
screen, you can choose the variable of interest and your display options.
See Appendix E for more information on incidence data, including sources.
Variables for County
incidence Data are
shown in alphabetical
order by variable
name, not by type; for
instance, total
population is not
grouped with other
population variables.
Example 3: Mapping Aggregation, Pooling, and Valuation Results
You can map each of the six types of results stored in APV Results files (*.apvr): (1) Incidence,
(2) Aggregated Incidence, (3) Pooled Incidence, (4) Valuation, (5) Aggregated Valuation,
and (6) Pooled Valuation. Finally, you can select (7) All, if you wish to map all of the types of
results simultaneously. All of the options use the information contained in the Aggregation,
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Chapter 8. Mapping
Pooling and Valuation Results files (*.apvr), which you can create using the Aggregation,
Pooling, and Valuation button on the main screen.
BenMAP GIS
E? 13 ® ®s Q © *0 © s Albers Equal Area Conic ,v
States
Air Quality Grid
Monitors
Population
County Data
Adjustment Grid
APV Results
CFG Results
I 1 J. T U" I U.lJJ
18.05-20.40
20.40-22.75
22.75-25.10
25.10-27.45
Incidence
Aggregated Incidence
Pooled Incidence
Valuation
Aggregated Valuation
Pooled Valuation
All
^=7
Close
Click the Open a file button and select APV Results and one then one of the six results types
from the drop-down menu. A window will appear with identifiers for each of the variables. On
the right-hand side is the GIS Field Name. Mapping programs typically have a default length of
10 characters for variable names, so it is necessary to specify your own names or to use the
default name given in the GIS Field Name column.
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Chapter 8. Mapping
Edit GIS Field Names
Endpoint Group
Endpoint
Pollutant |Author
Year
Qualifier
Gis Field Name
A
-
B
Asthma Exacerbatio
Asthma Exacerbatio
~ 3
Whittemore and Kor
1980
All ages
ResultO
Emergency Room V
Emergency Room V
~ 3
Weisel et al.
1935
All ages
Resultl
Hospital Admissions,
HA, All Respiratory
~ 3
Schwartz
1995
65-74; New I-
Result2
Hospital Admissions,
HA, All Respiratory
~ 3
Schwartz
1935
65-74; Tacon
Result3
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1935
75-84; New I-
Result4
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1935
75-84; T aeon
Result5
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1935
85+; New Ha
Result6
Hospital Admissions,
HA, All Respiratory
03
Schwartz
1935
85+; T acoma
Result7
Acute Respiratory S.
Minor Restricted Act
03
Ostro and Rothschik
1989
Result8
Mortality
Mortality, Short-Terrr
03
Ito and Thurston
1936
<18; PM10
Result9
Mortality
Mortality, Short-T errr
03
Kinney et al.
1935
<18; PM10
Resultl 0
Mortality
Mortality, Short-T errr
03
Moolgavkar et al.
1995
<18
Resultl 1
< ¦> i [>
OK
For each of the three valuation options (Valuation,
Aggregated Valuation, and Pooled Valuation), a second
window appears that allows you to specify variables that you
want to add together (if you have selected Incidence, Pooled
Incidence, or Aggregated Incidence, BenMAP will go
directly to loading the new layer).
Click on the Add Sum button. Then in the far-right column,
Include in Total, check the variables that you want to include
in the total. In the lower-left corner, give a name (no longer
than 10 characters) for the total in the Valuation Sum
Identifier, and then choose whether to use a Dependent or
Independent sum. If you choose the latter, then you also need to choose the number of Monte
Carlo draws. To finish, click OK. You may repeat this step as many times as desired.
Valuation Sums Layer BOsJB
Sum Identifier | Gis Field Name |
[ Add Sum
| Cancel | | OK
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Chapter 8. Mapping
*&¦ Add Valuatio
n Sum
UJISIH
Pooling Window
Endpoint Group
Endpoint
Author
Year
Location
Include in Total
1
Mortality
~
1
Mortality
~
1
Mortality
~
1
Hospital Admissions, Respiratory
~
1
Emergency Room Visits, Respiratory
~
1
School Loss Days
~
1
Acute Respiratory Symptoms
~
1
Asthma Exacerbation
~
J
Valuation Sum Identifier: Summation Type: Monte f.'arlo Iterations:
Total 0 Dependent v [5000 Cancel
OK
Example 4: Mapping Multiple Layers of Data
It is possible to map multiple layers simultaneously. The layer that you have opened most recently
appears on the top of the list, and in the map its values lie on top of the other layers. By right-
clicking on any given layer, you can move its position within the list (select Move Up or Move
Down). By checking the box to the left of the layer name, you can turn a layer's visibility off and
on. For instance, if you want to see the second layer in the list, simply un-check the box next to
the first layer in the list. The second layer will then be on top and be visible. To delete a layer,
right-click on the layer name and select Delete.
8.2 Viewing Maps in a BenMAP Analysis
In addition to the Tools menu, BenMAP provides several places where you can map the input
data that you are using for a particular analysis. We provide examples of these mapping options
below.
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Chapter 8. Mapping
Example 5: Mapping Monitor Direct Air Quality Grids
When generating air quality grids there is an option
to map the grid and the data used to create it. For
example, when generating a Monitor Direct grid,
click on the Map button on the Monitor Direct
Settings page.
BenMAP will then generate the grid, generate a
map with both the monitors and the monitor data
interpolated to the grid cells. You can then use the
display options to generate the map you desire.
Monitor Direct Settings
aaa
Select Interpolation Method
O Closest Monitor
O Kriging [ Kriging Settings |
O Voronoi Neighbor Averaging (VNA)
0 Use Library Monitor Data
Grid Type:
Pollutant:
Library Monitor Year:
Monitor-File;
Advanced
Lane e!
BenMAP GIS
yisiB
Layers
Albers Equal Area Conic v Reference Layer --
3 Monitors
3 Air quality grid
_
>
vSSSEfBat>llS"1
niMiiiistv
HBliiin.!"'
i m
Close
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Example 6: Mapping Air Quality Monitors from the Advanced Monitor Filter
You can access mapping through the advanced monitor filter (discussed in Section 4.4) when
generating air quality grids. Click on the Create Air Quality Grids button and choose either
Monitor Direct, Monitor and Model Relative, or Monitor Rollback. On the Settings page,
click on the Advanced button, which brings up the Advanced Options page. Choose your
filtering options and click Go! Then click the Map button, which becomes active after filtering.
An initial map appears with each monitor location identified by a red square. To immediately
provide some context, you may want to choose, say, the States reference layer.
%BenMAPGIS
as? H
0v O ^ O j Albers Equal Area Conic v | States
ZH
Layers
3 PM2.5, 2002
\ fj
«*'¦*.* s
V' * -V.¥t55
*f -iSs
^7 i' ¦ ¦ TuQ
¦ vi: r*B2s
A
Close
Double-click on the layer to the left of the map.
Choose the display options that you would like,
and then click OK.
A map -with your display options will then
appear. You may then use the various mapping
options available, such as zooming in to an area
of interest, getting information on particular
monitors, querying the map to display monitors
with certain characteristics, and saving the map
as a shapefile and viewing it in another map viewer.
Display Options
~US
Variable: AnnuaiAvjl
M
Start Size: 100 [=j
Start Color:
u
Min Value:
5.S7
End Size: TOO [*]
End Color:
I I
Max Value:
139.39
Default Size: 1Q0 j^j
Default Color:
¦
Decimal Digits:
2 S
L
Cancel
QIC
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Chapter 8. Mapping
i r. ¦
5 PM2.5, 2002
3.96-6.31
6.31-3.66
8.66-11.01
11.01 -13.36
. 13.36-15.70
- 15.70-10.05
c IS.05-20.40
¦ 20.40-22.75
¦ - -
¦ 2510-27.45
Example 7: Mapping Monitor Rollback Inputs and Outputs
When using the Monitor Rollback option, you can generate two types of maps. First, you can
map the inputs to the rollback - the monitor data and any spatial adjustment file that you may
have used. Second, you can map the inputs and at the same time map the grid based on these
inputs. To start, click the Map button.
You will then have the option to map the inputs, or map
the inputs and the resulting grids.
In this example, BenMAP will produce a map with both
the inputs, as well as the baseline and control grids.
(The baseline grid gets created because we checked the
box for Make Baseline Grid (in addition to Control
Grid.) As before, you may use the various display
options to generate the map that you desire. Recall that
the topmost layer lies on top of the other mapped layers. To change the ordering of the layers,
simply right-click on the layer that you want to move (up or down).
Mapping Options
Mapping Options:
Map Grid Inputs (Adjustment files. Monitors).
. Map Grid Inputs and Create and Map Grid.
I OK
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Chapter 8. Mapping
*** Monitor Rollback Settings: (3) Additional Grid Settings
~ i
Select Interpolation Method
0 Closest Monitor
O Voronoi Neighbor Averaging (VNA)
O Kriging
Select Scaling Method
© None
O Spatial Only
Grid Type:
Adjustment File:
County
Browse
0 Make Baseline Grid (in addition to Control Grid).
Create
Advanced
Map
Cancel
Go!
** BenMAP GIS
# ©x ©s q e f? o 2
Albers Equal Area Conic
- Reference Layer -¦
Layers
3 Rolled back Grid
3 Baseline Grid
3 Rolled back Monitor
3 Baseline Monitors
•¦i...
Close
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Chapter 8. Mapping
Example 8: Mapping Air Quality Deltas
When developing a Configuration file (with the *.cfg extension), you can map the baseline and
control air quality grids, as well as the difference between the two. In the Con figuration
Settings window, after choosing the grids that you want to use, click on the Map Grids button.
^"Configuration Settings
Select Air Quality Grids
C:\Prograrm Files\Abt Associates lnc\BenMAP^Air Quality Grids\Ba
~ pen
Create
| C:\Program FilesNAbt Associates lnc\BenMAP^Air Quality Grids\Co
~ pen
Create
Map Grids
Settings
Pollutant:
Population Year:
PM10
2020
Latin Hypercube Points: 10
Run In Point Mode: Q
Threshold: 0.0
Cancel
Previous
Next
This will generate layers with the delta (baseline minus control), baseline, and control values for
each grid cell.
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Chapter 8. Mapping
P BenMAP GIS
\n})&
H
» e, Q, q e O O £
Albers Equal Area Conic |v
-- Reference Layer - | v
Layers
3 Delta
3 Control Grid
3 Baseline Grid
^ggjgBI
i^SisMIWiTii —*'—¦¦
% ^
Close
8.3 Questions Regarding Mapping
^Why is the Open a File menu button disabled?
This happened because you did not use the Tools button and choose Mapping / GIS. When
viewing maps while generating air quality grids, filtering monitor data, and other activities within
BenMAP you do not have access to other types of maps. This is to avoid too many competing
activities.
^All of the mapped values have the same color. How do I avoid this?
This can happen when the values are extremely small and you have not specified a sufficient
number of decimal points. Go to the Display Options window, and change the Decimal Points.
This can also happen when one of the grid cells is an outlier, with either a very low or very small
value. You can go to the Display Options window, and change either the Min Value or the
Max Value. Finally, this can happen if you have mapped national data. In this instance, you
should expect all areas to have the same color, since there is only a single national number for
display, such as when mapping national results, or mapping incidence rates that do not vary by
region (e.g., MRAD incidence rate).
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Chapter 8. Mapping
>^Can I map air quality for individual days?
No. BenMAP only maps annual averages. In the case of hourly metrics, such as the one-hour
daily maximum for ozone, BenMAP will map the average of the metric for the available days.
^ ( an I print maps in BenMAP?
No. BenMAP does not currently allow printing directly from the program. However, you can
export shapefiles, and then read these shapefiles into a program that does support printing, such
as ArcView.
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CHAPTER 9
In this chapter...
>- View EPA Standard and User-
defined C-R and valuation
functions.
Add and edit User-defined C-R
and valuation functions.
Find definitions for variables
used in C-R and valuation
functions.
Viewing and
Editing C-R
and Valuation
Functions
Chapter Overview
9.1 C-R Functions 9-1
8.1.1 Viewing C-R Functions 9-2
8.1.2 Adding C-R Functions 9-4
8.1.3 Editing C-R Functions 9-5
9.2 Valuation Functions 9-9
9.3 Frequently Asked Questions Regarding Viewing and
Editing C-R Functions and Valuations 9-13
-------
9. Viewing and Editing C-R and Valuation Functions
You can view the EPA Standard (or EPA
Approved) C-R and valuation functions by using the
Data drop-down menu on BenMAP's main screen.
The EPA Standard functions cannot be edited, but
you can create your own database of user C-R and
valuation functions which you can add to, view and
edit.
9.1 C-R Functions
BenMAP includes a large number of C-R functions
that quantify the relationship between the change in
exposure to air pollution and the adverse effects
caused by specific pollutants. In particular, BenMAP has C-R
functions for ozone, PM25, PM10, and PMC. See Appendices F
and G for details on the functions and their derivations.
The C-R functions are organized in the left part of the display into
two tree views - EPA Standard and User. Within each tree
view the C-R functions are organized by Endpoint Group, then
by Endpoint. C-R functions are represented within these groups
by their Study ID value, which combines the Author, Year, and
Qualifier fields.
BenMAP 2003 Beta 2.0
Data Tools Help
C-R Functions
Environmental Benefits Mapping and Analysis Program
Create Air Quality Grids
Create and Run
Configuration
Aggregation. Pooling,
and Valuation
Create Reports
A C-R (Concentration-Response)
Function calculates the change
in adverse health effects
associated with a change in
exposure to air pollution. A
typical C-R function has inputs
specifying the air quality metric
and pollutant, population
characteristics, and the
incidence rate of the health
effect.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
9.1.1 Viewing C-R Functions
To view a C-R function, locate and highlight it in the left tree view. Note that when an EPA
Standard C-R function is highlighted in this manner, the right side of the display is disabled. As
mentioned above, the EPA Standard functions cannot be edited. When a User C-R function is
highlighted, the right side of the display is enabled, and all values of User C-R functions can be
edited. Editing User-defined C-R functions is described below.
Exhibit 9-1 presents brief descriptions of the variables shown for each C-R function.
0" Edit C-R Functions
EPA Standard C-R Functions
Q Acute Bronchitis
S Acute Bronchitis
0 Dockery et al.
Dockery et al., 1
Dockery et al., 1
0 McConnell et al.
B Acute Myocardial Infarction
B Acute Respiratory Symptoms
B Asthma Exacerbation
EE) Chronic Asthma
B Chronic Bronchitis
+ Chronic Phlegm
B Emergency Room Visits, Res
B Hospital Admissions, Cardiov
B Hospital Admissions, Respira
B Household Soiling Damage
B Lower Respiratory Symptoms
B Mortality
S School Loss Days
B Upper Respiratory Symptoms
El Work Loss Days
r-T-i s. /-.i n i.—t:.
< m_
a"
0
User C-R Functions
Current C-R Function:
Endpoint Groups [Acute Bronchitis
Endpoint: [Acute Bronchitis
Metric: lAnnualAverage
Pollutant: | pm 2.5
Author:
O ther Pollutants:
Qualifier:
Dockery et al.
199B
None
Location: 24 communities
8-12
Low Age: [g
Race: All
High Age: |-|2
"±3
Gender [All
Function:
Beta:
P1 Beta:
k
-((I ncidence/([1 -I ncidence)*EXP(B eta*D E LT AO )+l ncidencefl-l n
Edit
0.027212
0.017096
0.000000
0.000000
DistBeta: [Normal
P2 Beta:
0.000000
0.000000
Name A
Name
Name C
Incidence; | acuteBronch8to12
Incidence 2:
Prevalence:
Cancel
Save Changes-
New
Clone I Edit
Cancel
OK
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Exhibit 9-1. Selected Variables in the C-R Function Database
Variable
Description
Endpoint Group An endpoint group represents a broad class of adverse health effects, such as premature mortality, chronic
bronchitis, and respiratory hospital admissions.
Endpoint An endpoint represents a relatively small class of adverse health effects, such as hospital admissions for
pneumonia. In some cases the endpoint and the endpoint group are the same.
Metric Air quality metric used in the C-R function.
Pollutant Pollutant used in C-R function to estimate adverse effect.
Author Author of the study used to develop the C-R function.
Year Year of the study used to develop the C-R function.
Other Pollutants Other pollutants that were simultaneously included in the original study used to develop the C-R function.
Location Location of the study used to develop the C-R function.
Qualifier Description that uniquely identifies a valuation function, when combined with the endpoint group,
endpoint, and age.
Low Age Lower bound of the age range included in the C-R function.
High Age Upper bound of the age range included in the C-R function.
Race Race of the population in the C-R function: American Indian, Asian American, Black, Other, and White.
Gender Gender of the population used in the C-R function.
Function Function refers to the form of the C-R function.
Beta Variable for C-R function.
Dist Beta Distribution for the variable Beta. The distribution may be chosen from a drop-down menu.
PI Beta Parameter used to describe distribution of Beta.
P2 Beta Parameter used to describe distribution of Beta.
A User-defined scalar.
Name A User-defined description for scalar A.
B User-defined scalar.
Name B User-defined description for scalar B.
C User-defined scalar.
Name C User-defined description for scalar C.
Incidence Number of adverse effects per unit time per person.
Incidence2 Number of adverse effects per unit time per person. In some instances, two incidence rates are required,
such as when calculating cardiovascular hospital admissions minus the contribution of myocardial hospital
admissions.
Prevalence Percentage of the population subject to being affected by adverse health effect (e.g., asthmatic population
subject to emergency room visits for asthma).
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Chapter 9. Viewing and Editing C-R and Valuation Functions
9.1.2 Adding C-R Functions
There are two approaches to adding C-R functions: (1) cloning existing C-R functions, and (2)
adding completely new C-R functions.
Cloning C-R Functions
You can clone, or copy, both EPA-Standard and User C-R functions in the same manner.
Cloning is a quick way to make minor changes to existing C-R functions. To clone a particular
function, first select the desired function as you would to view it (see above). On the bottom-left
of the screen, click the Clone / Edit button - the right side of the screen should now be enabled
for editing. Edit the values you wish to change (see below for information on editing a C-R
function), and then click the Save Changes button. This will save your newly cloned and edited
C-R function to the User C-R Functions database. Alternatively, click the upper Cancel button
to cancel your changes without saving them to the User C-R Functions database.
"a-* Edit C-R Functions
Q(nJ@
EPA Standard C-R Functions
Q Acute Bronchitis
~ Acute Bronchitis
~ Dockery et al.
Dockery et al., 1
Dockery et al., 1
(S McConnell et al.
© Acute Myocardial Infarction
© Acute Respiratory Symptoms
l±i Asthma Exacerbation
S3 Chronic Asthma
© Chronic Bronchitis
© Chronic Phlegm
El Emergency Room Visits, Res
© Hospital Admissions, Cardiov
© Hospital Admissions, Respira
© Household Soiling Damage
© Lower Respiratory Symptoms
© Mortality
© School Loss Days
© Upper Respiratory Symptoms
© Work Loss Days
<
nil
>
V
User C-R Functions
Current C-R Function:
Endpoint Group:
Endpoint:
Metric:
Acute Bronchitis
0
Acute Bronchitis
AnnualAverage
v Pollutant:
Author:
Other Pollutants:
Qualifier:
Dockery et al.
None
Year:
Location:
PM2.Ej
~E
03
PM10
PM2.5
PMC
8-12
Low Age: |g
High Age: 12"
Race:
Gender:
All
All
Function:
Beta:
P1 Beta:
A:
B:
C:
Incidence:
Incidence 2:
¦((I nciderice/((1 -I ncidence)*EXP(B eta"D E LT AQ ]+l ncidence)]-! n
Edit
0.027212
0.01703G
0.000000
0.000000
0.000000
acuteBrorich8to12
Dist Beta:
P2 Beta:
Name A:
Name B:
Name C:
Prevalence:
Normal
0.000000
Cancel
Save Changes
New
Clone / Edit
Cancel
OK
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Adding New C-R Functions
You can add completely new C-R functions by clicking the New button on the bottom-left of the
screen. The right side of the screen should then be enabled for editing, but all the values will be
blank. You can now fill in values for each field (certain fields must have values - Endpoint
Group, Endpoint, Metric, Pollutant, etc.) and click Save Changes or Cancel, as with Clone
/ Edit
9.1.3 Editing C-R Functions
You can edit C-R functions in various ways - by adding new functions (see above for the two
methods of adding new functions), or by selecting functions in the User C-R Functions database
and editing their values. The following section applies to functions edited in any of these ways.
When done editing, simply click Save Changes or Cancel to save the new/modified C-R
function to the User C-R Functions database or cancel changes, respectively.
Many C-R function variables can be edited directly by typing into the appropriate text boxes or
selecting values from the appropriate drop down lists. Examples of text boxes include the
Author and Qualifier variables. Examples of drop down lists include the Endpoint Group and
Pollutant variables.
The Beta variable and its associated variables, Dist Beta, PI Beta, and P2 Beta require some
special editing, as does the Function variable. Users are advised to familiarize themselves with
Appendix D before editing these variables.
The C-R function can have a single variable, Beta, which has a distribution associated with it.
This distribution is specified by the Dist Beta variable, and can take on sixteen different
distribution types: None (no uncertainty), Normal, Triangular, Poisson, Binomial, Log Normal,
Uniform, Exponential, Geometric, Weibull, Gamma, Logistic, Beta, Pareto, and Cauchy.
Each of these distributions takes different numbers and types of parameters which need to be put
into the PI Beta and P2 Beta variables. To facilitate this, BenMAP includes special dialogs for
each distribution type which contain the probability distribution function of the distribution, some
notes about the distribution type, and editable text boxes through which the parameters of the
distribution can be set. When you click OK after filling in values for each parameter in any of
these dialogs, BenMAP fills in the appropriate values for PI Beta and P2 Beta. Note that PI
Beta and P2 Beta cannot be edited directly.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
% r(lit C-R Functions
EPA Standard C-R Functions
~PS
B Acute Bronchitis
B Acute Bronchitis
B Dockery et al.
Dockery et al., 1
Dockery et al., 1
B McConnell et al.
B Acute Myocardial Infarc
B Acute Respiratory Symp
B Asthma Exacerbation
B Chronic Asthma
B Chronic Bronchitis
B Chronic Phlegm
B Emergency Room Visits.
S Hospital Admissions, Ca
B Hospital Admissions, Re
B Household Soiling Dam;
B Lower Respiratory Symp
B Mortality
S School Loss Days
B Upper Respiratory Symp
B Work Loss Days
Current C-R Function:
Endpoint Group:
Endpoint:
Metric:
Acute Bronchitis
Acute Bronchitis
AnnualAverage
Pollutant: PM2.5
User C-R Functions
Edit Distribution Values
~(n]@
Normal PDF:
Notes:
Mean Value:
1 / o\
The Normal distribution has two parameters - the
mean, nnu, and the standard deviation, sigrna.
0.027212
sigma:
0.017096
Cancel
OK
munities
C: 0.000000
Incidence:
N ame C:
v
:nce])-lri
Edit
DO
acuteBronch8to12
v Prevalence:
Incidence 2:
Cancel
Save Changes
New
Clone / Edit
Cancel
OK
The Function variable can be complicated to edit, so BenMAP includes a special function editing
dialog which can be accessed by clicking the Edit button to the right of the Function text box.
In general the function should be a mathematical expression which calculates the adverse effects
associated with a change in air quality. You can either select from a set of previously "compiled"
functions, or you can compose your own function. Choosing from among the 25 or so compiled
functions is the preferred option, since already compiled functions can be processed by BenMAP
significantly faster.
Note that if you make any changes to the compiled functions (adding variables, deleting variables,
etc.) your function will no longer be treated as compiled and will not have the speed advantages
associated with the compiled functions. Similarly, if you manually type in a function which looks
just like a compiled function it will not be treated as compiled.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Edit Function
U[n||(x)
Compose Function | Select Compiled Function
Available Compiled Functions:
¦((I ncidence/((1 -I ncidence)xE xp(B etaxD E LT AQ ]+l ncidence))-! ncidence)xPO P
Function:
v [ Select
•((lncidence/((1 -I ncidence)xEXP(BetaxDELTAQ)+l ncidence))-! ncidence)xPOP
Cancel
~K
If you choose to compose your own function string, you have a great deal of flexibility in choosing
from functions, variables, operators, and numbers. The functions available to be used in the
Function expression are listed in the leftmost of the upper windows in the function editing dialog.
They include ABS (absolute value), EXP (e to the x), IPOWER (x to the power v, y an integer),
LN (natural logarithm), POWER (x to the power v, y a real number), SQR (square), and SQRT
(square root). To see a description of one of the Available Functions, simply highlight it - a
description will appear below the Available Functions window. To insert the function into the
Function text box at the current cursor position, simply double-click it.
Edit Function
| Compose Function
Select Compiled Function
Available Functions:
£BSJx]
EXP(x]
IPOWER(x,y)
LN(x)
POWER (x,j>)
SOR(x)
SQRT(x)
Returns the absolute value of x.
ABS(x)
Function:
Available Variables:
¦yiaJB
Available County Variables:
Beta
A
A
l=fl
B
Lai
C
DELTAQ
nn n
0
< I
i m
county Name
A
tips
~
year
stateAbbr
latitude
|< |
ZJ L>]
-((I ncidence/((1 -I ncidence)"EXP(B etaKD E LT AQ )+l ncidence))-! ncidence)KPO P
Cancel
OK
The variables available to be used in the Function expression are listed in the two other upper
windows in the function editing dialog, Available Variables and Available County Variables.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
The variables in the Available Variables window are the main variables used in Function
expressions, and are described in Exhibit 9-2.
Exhibit 9-2. Available Variables
Variable
Description
Beta
The variable Beta specifies the C-R Function Database entry variable Beta, with its associated
distribution and parameters. As such, during a point-mode run this variable simply takes on the value
specified for Beta in the C-R Function Database. During an uncertainty run, however, this variable takes
on the various Latin Flypercube values from the distribution specified by Dist Beta, as described in
Chapter 5.
A
The variable A specifies the C-R Function Database entry variable A (a user-defined scalar).
B
The variable B specifies the C-R Function Database entry variable B (a user-defined scalar).
C
The variable C specifies the C-R Function Database entry variable C (a user-defined scalar).
DELTAQ
The variable DELTAQ represents the air quality delta (defined as baseline minus control), as defined by
the C-R Function Database entry Metric variable. This variable takes on different values for each grid
cell processed.
POP
The variable POP represents the population, as specified by the C-R Function Database entry variables
Low Age, High Age, Race, and Gender. This variable takes on different values for each grid cell
processed.
Incidence
The variable Incidence represents a single county incidence variable (see below for details on county
variables), and is mainly present for readability. It is replaced at runtime with the county incidence
variable specified by the C-R Function Database entry variable Incidence.
QO
The variable QO represents the control air quality value. This variable takes on different values for each
grid cell processed.
Qi
The variable Ql represents the baseline air quality value. This variable takes on different values for each
grid cell processed.
The variables in the Available County Variables window are treated differently from the
Available Variables. These variables are present in a database, and take on different values for
each county in the United States. This allows BenMAP to calculate more accurate estimates of
adverse effects. Exhibit 9-3 contains a selection of variables from the Available County
Variables and associated descriptions.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Exhibit 9-3. Selection of Available County Variables
Variable
Description
hospAllCardioUnderl 8
This variable represents the county-specific count of hospitalizations for cardiovascular
disease per person under the age of 18 per day.
mortAllCause25Up
This variable represents the county-specific count of all cause mortality per person over
the age of 25 per year.
count farm employed
This variable represents the county-specific count of persons employed on farms.
pctAsthma5to 17Black
This variable represents the county-specific percentage of total asthma cases which affect
black persons ages 5 to 17.
For a given grid cell, BenMAP calculates a value for each county variable in the following
manner:
^ BenMAP has a database which contains the percentages of each grid cell's population which
comes from each county. For example, the REMSAD grid cell (79, 26) gets 2.5% of its
population from county 01085, 47% from county 01047, and 50.5% from county 01001.
^ For each county which contributes population to a grid cell, BenMAP multiplies the
percentage of the population contributed by that county by that county's value for the county
variable.
^ The value for the grid cell is then the sum of these population-weighted, county-specific
values.
To see a description of any of the variables (both Available Variables and Available County
Variables), simply highlight it - a description will appear below the appropriate window. To insert
any of the variables into the Function text box at the current cursor position, simply double click
it.
9.2 Valuation Functions
BenMAP has a large number of valuation functions to estimate the economic value of adverse
effects. In particular, BenMAP has valuation functions for most of the Endpoint Groups for
which C-R functions exist, and for many of the specific Endpoints as well. See Exhibit 5-1 for a
list of the Endpoint Groups and Endpoints used in BenMAP.
The valuation functions are organized in the same way as the C-R functions (see above) - an
EPA Standard tree view and a User tree view. Again, within each tree view the valuation
functions are organized by Endpoint Group, then by Endpoint. Valuation functions are
represented within these groups by their Qualifier and age range values.
Valuation functions can be viewed, added, and edited in exactly the same way which C-R
functions are viewed, added, and edited (see above). The few differences will be highlighted
below.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Valuation functions have different variables than C-R functions. The variables present in the
Valuation Functions database are described in Exhibit 9-4.
Edit Valuations
Current Valuation:
Endpoint Group: [Acute Bronchitis
QSM
EPA Standard Valuations
B Acute Bronchitis
ED Acute Bronchitis
!¦ WTP: 1 day illness, [
WTP: 28 symptom-d.
WTP: G day illness, C
8 Acute Myocardial Infarction
B Acute Respiratory Symptoms
B Asthma Exacerbation
EE Chronic Asthma
B Chronic Bronchitis
+ E mergency R oom Visits, R es
El Hospital Admissions, Cardiov v
<
>
User Valuations
Low Age: Jo
Qualifier: WTP: 1 day illness, CV studies
v I Endpoint: lAcute Bronchitis
"3 High Age: [l7
Function: |A"CPI_AII
A::
59.308208
Name A:
Cancel
Edit
WTP in 2000$
Dist A:
Uniform
P1.A:
17.508855
P2A:
101.108567
B;
0.000000
Name B:
0
!Ef
0.000000
Name C:
0
O:
0.000000
NameD:
0
Save Changes
New Valuation Clone / Edit
Cancel
OK
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Exhibit 9-4. Selected Variables in the Valuation Database
Variable Description
Endpoint Group An endpoint group represents a broad class of adverse health effects, such as premature mortality,
chronic bronchitis, and respiratory hospital admissions.
Endpoint An endpoint represents a relatively small class of adverse health effects, such as hospital admissions
for pneumonia. In some cases the endpoint and the endpoint group are the same.
Low Age Lower bound of the age range included in the valuation function.
High Age Upper bound of the age range included in the valuation function.
Qualifier Description that uniquely identifies a valuation function, when combined with the endpoint group,
endpoint, and age.
Function Valuation function.
A Variable for valuation function.
Name A Name of varia
Dist A Distribution for the variable Beta. The distribution may be chosen from a drop-down menu.
PI A Parameter used to describe distribution of Beta.
P2 A Parameter used to describe distribution of Beta.
B User-defined scalar.
Name B User-defined description for scalar B.
C User-defined scalar.
Name C User-defined description for scalar C.
D User-defined scalar.
Name D User-defined description for scalar D.
When editing the Function variable of a Valuation Function database entry, an additional tab is
available - Select Compiled Functions. These functions are built into BenMAP and run much
more quickly than custom functions (which are not compiled). To select a compiled function,
simply click on the Select Compiled Functions tab, select the desired function in the drop down
list, click the Select button, and click OK. If you decide to exit the function editor without saving
your changes, click the Cancel button instead.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
^ Edit Function
~(US'
Compose Function Select Compiled Function
Available Compiled Functions:
A " CPI_ALL
V
Select
A * CPI ALL
a
A " CPI MED
—
A " B " CPI ALL
A * B " C * CPI ALL
=
A " CPI MED + B * EG WAGE
A " EXP(-B " (13-C))" CPI ALL
A " EXP(-B "12)" CPI ALL
A " CPI MED + B " Imedian income/f52"51)" ECI WAGE
V
Function:
A*CPI All
Cancel
OK
As with the compiled C-R functions, if you make any changes to the compiled valuation functions
(adding variables, deleting variables, etc.) your function will no longer be treated as compiled and
will not have the speed advantages associated with the compiled functions. Similarly, if you
manually type in a function which looks just like a compiled function it will not be treated as
compiled.
In the Compose Function tab of the function editor, almost everything looks just like the C-R
function editor. The only exception is the Available Variables window. Exhibit 9-5 presents the
variables available for valuation functions and associated descriptions.
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Chapter 9. Viewing and Editing C-R and Valuation Functions
Exhibit 9-5. Available Variables for Valuation Functions
Variable
Description
A The variable A specifies the Valuation Function Database entry variable A, with its associated
distribution and parameters. As such, during a point-mode run this variable simply takes on the value
specified for A in the Valuation Function Database. During an uncertainty run, however, this variable
takes on the various Latin Flypercube values from the distribution specified by Dist A, as described in
Chapter 6.
B The variable B specifies the Valuation Function Database entry variable B.
C The variable C specifies the Valuation Function Database entry variable C.
D The variable D specifies the Valuation Function Database entry variable D.
CPIALL The variable CPIALL specifies an inflation factor for generic dollar figures - the specific inflation factor
used depends on the Dollar Year selected (see Chapter 6 for details).
CPIMED The variable CPIMED specifies an inflation factor for medical dollar figures - the specific value used
depends on the Dollar Year selected.
ECIWAGE The variable ECIWAGE specifies an inflation factor for wages - the specific value used depends on the
Dollar Year selected.
9.3 Frequently Asked Questions Regarding Viewing and Editing C-R
Functions and Valuations
^Why can I not edit the EPA Standard functions?
BenMAP does not allow you to edit EPA Standard functions, so that you may have confidence
when you use them that they are indeed the standard versions, and have not been modified or
changed. If you want to use a function that is slightly different than an EPA Standard function,
simply clone the function and edit it in the user database.
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In this chapter...
Learn about the Tools menu.
>• Use the Adjustment Factor
Creator to generate adjustment
factor files.
Use the Neighbor File Creator
to extract information on
neighboring monitors from air
quality grids.
>- Use the CAMx I UAM-V Model
File Creator to create single
CAMx/UAM-V model files which
can be used by BenMAP.
CHAPTER 10
Chapter Overview
10.1 Mapping/GIS 10-1
10.2 Adjustment Factor Creator 10-1
10.3 Neighbor File Creator 10-2
10.4 CAMx I UAM-V Model File Creator 10-3
10.5 Questions Regarding Tool Menu 10-4
-------
10. Tools
The Tools drop-down menu gives you the ability to do tasks that are occasionally needed to
perform a standard analysis, or to better understand an analysis that you have already conducted.
In particular, the Tools menu gives you access to four options: (1) Mapping / GIS, (2)
Adjustment Factor Creator, (3) Neighbor File Creator, and (4) CAMx / UAM-V Model
File Creator.
10.1 Mapping / GIS
The Mapping / GIS option allows you to generate
a wide variety of maps, including maps of monitor
data, adjustment factors, air quality grids,
population data, county incidence rates, and both
incidence and valuation results. In addition, you
can export the maps you have generated and view
them in a shapefile viewer, such as Arcview.
Chapter 8 provides additional details on
BenMAP's mapping capabilities.
10.2 Adjustment Factor Creator
The Adjustment Factor Creator generates the
adjustment factor files needed for the Model and Monitor Relative air quality grid creation. To
use the Adjustment Factor Creator, you specify a model file, along with the grid type and
pollutant of that file. Supported combinations of grid type and pollutant are:
REMSAD, I'M,,, / PM25 / PMC
>• CAMx, 03
>• CMAQ, PM10 / PM2 S / PMC
Note that these are the same combinations
supported for Model Direct air quality grid
creation.
BenMAP includes the Adjustment Factor
Creator as a standalone tool in case you want to
simply generate adjustment factor files, without
generating air quality grids, or conducting other
parts of a typical analysis. Note that you can also
access the Adjustment Factor Creator during
<=» BenMAP 200 3 Beta 2 0
~ s
Data Tools Help
Mapping / GIS
Adjustment Factor Creator
Neighbor File Creator
CAMX I UAM-V Model File Creator
Environmental Benefits Mapping and Analysis Program
Create Air Quality Grids
Create and Run
Configuration
Aggregation, Pooling,
and Valuation
Create Reports
Create Adjustment Files
Model File:
Grid Type:
Pollutant:
Done
Go!
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Chapter 10. Tools
Model and Monitor Relative air quality grid creation by clicking the Create button for the Base
Year Adjustment File or Future Year Adjustment File. For more information on adjustment
factor files, see Chapter 4.
10.3 Neighbor File Creator
The Neighbor File Creator can be used to extract information from air quality grids created via
the Monitor Direct or Model and Monitor Relative methods. It generates a tab-delimited file
containing the neighbors of each grid cell, where "neighbors" refers to the monitors used to
generate the air quality metrics for the grid cell. Neighbors are identified by Monitor ID (see
Exhibit 1-2 for a description of this value). In addition, if the air quality grid was created using
VNA or Kriging interpolation (see Section 4.2 for more information on interpolation methods in
BenMAP) the file will also contain the weights associated with each neighbor.
¦^Create Neighbors File
Air Quality Grid:
Browse
Cancel
OK
Exhibit 10-1 presents the variables that are included in the files generated by the Neighbor File
Creator.
Exhibit 10-1. Variables in Data File Generated by the Neighbor File Creator
Variable
Description
Column
Column of grid cell.
Row
Row of grid cell.
Monitor ID
Monitor identifier.
Weight
Only present for grids created using IWA or Kriging interpolation. The weight used to generate air
quality metrics for the grid cell for this monitor.
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Chapter 10. Tools
10.4 CAMx / UAM-V Model File Creator
The CAMx / UAM-V Model File Creator is used to create single
CAMx/UAM-V model files which can be used by I Jen MAP in
Model Direct air quality grid creation, ox Adjustment Factor
creation. CAMx and UAM-V data comes in a series of individual
files (one file representing one day) for the Eastern United States
and a separate series of files for the Western United States.
BenMAP cannot read these files directly in Model Direct air quality
grid creation or in the creation of adjustment Factors. Instead, this
tool allows the selection of all Eastern domain files and all Western
domain files, and produces a single CAMx/UAM-V model file
(*.camx) which BenMAP can read directly.
^"Create a CAMx Model File
CAMx I UAM-V model data comes in sets of files, where each file
represents a single day's data for either the eastern half of the grid or the
western half of the grid. For mote information on the CAMx Model File
Creator, see the User Manual.
^"Create a CAMx Model File
Select Western Domain files:
To use the CAMx/UAM-V Model File Creator, you must first place all your Western domain
files in a common folder and all your Eastern domain files in a
common folder. You can then click the Select Western Domain
Files button and/or the Select Eastern Domain Files button - both
of these will bring up Open File dialogs. Within this dialog, select
all of your Western or Eastern files by clicking on them while
holding down the Ctrl key on your keyboard. Alternatively, you can
click on files while holding down the Shift key if you wish to select
entire ranges of files.
Open
(30
Look in: Western Domain Files
J o f «m*
III)
My Recent
Documents
&
My Documents
My Computer
File name:
My Network Files of type:
[May 19th.ascii
j^May 20th.ascii
| May 21st.ascii
^May 22nd.ascii
i May 23rd .ascii
^May 24th,ascii
jMay 25th,ascii
l^May 26th.ascii
IjMay 27th. ascii I
^May 28th.ascii
]^May 29th.ascii
"May 27th.ascii" "May 19th.ascii" "May 21 st.as v
Open
CAMx Files (x.ascii)
niaa-
ects\capms\capms_ui\deploy\Model DataSWestern Domain f
ects\capms\capms_ui\deplo.v\Model DataW/estem Domain F
ects\capms\capms_ui\deploy\Model Data\Western Domain I
ects\capms\capms_ui\deploy\Model Data Western Domain f
ects\capms\capms_ui\deploy\Model DataSWestem Domain f
Select Eastern Domain files:
CAMx t UAM-V model data comes in sets of files, where each file
represents a single day's data for either the eastern half of the grid or the
western half of the grid. For more information on the CAMx Model File
Creator, see the User Manual.
Done
Gol
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Chapter 10. Tools
When you have selected all your files, click the Open button. All your selected files should now
be listed in the window under the appropriate Select...Domain Files button.
At this point, you simply need to hit Go! and save your newly create CAMx/UAM-V model file.
When you are done using the CAMx/UAM-V Model File Creator, hit the Done button.
10.5 Questions Regarding Tool Menu
^Can I use the Neighbor File Creator on an air quality grid just based on model data, without any
monitor data?
No. Model Direct air quality grids have no neighboring monitors, by definition. BenMAP will
produce an Access Violation error if you attempt to create a neighbor file from a Model Direct
air quality grid. If this happens to you, just click OK in the Access Violation dialog and try again
with a Monitor Direct ox Monitor Model Relative air quality grid.
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Appendix A: Monitoring Data
BenMAP comes supplied with monitoring data for ozone, PM2 5, and PM10 for the years 1996
through 2002. This Appendix details how you may filter monitor data for use in an analysis. In addition,
this Appendix details the rollback procedures that you can perform on monitor data. The rollback
procedure is a quick way to determine the monitor levels that would exist under various kinds of changes
that you can specify. This includes three basic types of rollbacks: Percentage, Increment, and Rollback to
Standard.
Note that the monitor data have a particular format that we simply refer to as the BenMAP
format, and are derived from AMP500 and AMP501 files available from the U.S. Environmental
Protection Agency. This Appendix describes the BenMAP format, and how we used AMP500 and
AMP501 files to generate the BenMAP formatted files that come supplied with BenMAP. If you are
interested in using your own monitor data in an analysis, you simply use this same format, and follow the
directions for importing data detailed in Chapter 4. We have included the SAS (http://www.sas.com/)
code used to manipulate the AMP500 and AMP501 files.
A.l Monitoring Data Format and Variable Values
Monitoring data are available from the Air Quality System (AQS) maintained by the U.S.
Environmental Protection Agency (contact: Virginia Ambrose, email: ambrose.virginia@epa.gov'). In
order to import the data into BenMAP, we prepare the data with a fixed-field format that applies for both
hourly and daily data. Exhibit A-l lists the names and dates of the files that we received from EPA.
Exhibit A-2 lists the variables and a brief description of each, and Exhibit A-3 provides an example of
what a PM2 5 file would look like.
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Appendix A. Monitoring Data
Exhibit A-l. Format for Air Quality Monitoring Data
Pollutant File Type
File Name
Date Received
from EPA
Description
AMP500
AMP500.TXT
9/16/2002
AMP500 data for 1996
EG_500_SELPARM_1997_2002.TXT
6/19/2003
AMP500 data for 1997-2002
Ozone AMP501
Eg44201-1996.txt
9/30/2002
AMP501 data for 1996
Rd_501_44201_1997.txt
12/3/2002
AMP501 data for 1997
Rd_501_44201_1998.txt
12/3/2002
AMP501 data for 1998
Eg44201-1999.txt
9/30/2002
AMP501 data for 1999
Eg44201-2000.txt
9/30/2002
AMP501 data for 2000
Eg44201-2001.txt
9/30/2002
AMP501 data for 2001
ozone-reg 1 -3-2002.txt
3/27/2002
AMP501 data for 2002 - part 1
ozone-reg4-5-2002.txt
3/27/2002
AMP501 data for 2002 - part 2
ozone-reg6-8-2002.txt
3/27/2002
AMP501 data for 2002 - part 3
ozone-reg9-10-25-2002.txt
3/27/2002
AMP501 data for 2002 - part 4
PM25 AMP501
Eg81104-1996.txt
9/30/2002
AMP501 standard data for 1996
Eg 501 81104 pmlO nation 1997.txt
6/19/2003
AMP501 standard data for 1997
Eg 501 81104 pmlO nation 1998.txt
6/19/2003
AMP501 standard data for 1998
Eg81104-1999.txt
9/30/2002
AMP501 standard data for 1999
Eg81104-2000.txt
9/30/2002
AMP501 standard data for 2000
Eg81104-2001.txt
9/30/2002
AMP501 standard data for 2001
Eg 501 81104 pmlO nation 2002.txt
6/19/2003
AMP501 standard data for 2002
Eg88101-1996.txt
9/30/2002
AMP501 local data for 1996
Eg 501 88101 pmlO nation 1997.txt
6/19/2003
AMP501 local data for 1997
Eg 501 88101 pmlO nation 1998.txt
6/19/2003
AMP501 local data for 1998
Eg88101-1999.txt
9/30/2002
AMP501 local data for 1999
Eg88101-2000.txt
9/30/2002
AMP501 local data for 2000
Eg88101-2001.txt
9/30/2002
AMP501 local data for 2001
PM25 Continuous 2002.txt
5/5/2003
AMP501 continuous local data for 2002
pm25 daily2002.txt
5/1/2003
AMP501 daily local data for 2002
PM10 AMP501
Eg81102-1996.txt
9/30/2002
AMP501 standard data for 1996
Eg 501 81102 pmlO nation 1997.txt
6/19/2003
AMP501 standard data for 1997
Eg 501 81102 pmlO nation 1998.txt
6/19/2003
AMP501 standard data for 1998
Eg81102-1999.txt
9/30/2002
AMP501 standard data for 1999
Eg81102-2000.txt
9/30/2002
AMP501 standard data for 2000
Eg81102-2001.txt
9/30/2002
AMP501 standard data for 2001
Eg 501 81102 pmlO nation 2002.txt
6/19/2003
AMP501 standard data for 2002
Eg85101-1996.txt
9/30/2002
AMP501 local data for 1996
Eg 501 85101 pmlO nation 1997.txt
6/19/2003
AMP501 local data for 1997
Eg 501 85101 pmlO nation 1998.txt
6/19/2003
AMP501 local data for 1998
Eg85101-1999.txt
9/30/2002
AMP501 local data for 1999
Eg85101-2000.txt
9/30/2002
AMP501 local data for 2000
Eg85101-2001.txt
9/30/2002
AMP501 local data for 2001
Eg 501 85101 pmlO nation 2002.txt
6/19/2003
AMP501 local data for 2002
Files obtained from Virginia Ambrose, email: ambrose. virginia@epa. gov.
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Appendix A. Monitoring Data
Exhibit A-2. Format for Air Quality Monitoring Data
Variable a
Necessary for
BenMAP b
Description
year
yes
The year is a four-digit variable giving the year of the monitoring data.
monitor ID
yes
The monitor ID is a 15 character description of the monitor. It includes a state FIPS
code (2 characters), county FIPS code (3 characters), site ID (4 characters), pollutant
parameter (5 characters), and POC code (1 character).
latitude
yes
The latitude should be in decimal degrees.
longitude
yes
The longitude should be in decimal degrees. (Note that the longitude for the United
States has a negative sign.)
land use
no
Categorization of the prevalent land use within 1/4 mile of the Monitoring Site.
method
no
The method identifies the approach used to collect the monitor data. For example, the
Federal Reference Method for PM2 5 includes method codes 116-120 and 123.
location setting
no
A description of the environmental setting within which the Site is located.
probe location
no
Identification of the location of the sampling Probe
monitor objective
no
Identification of the reason for measuring air quality by the Monitor.
sample frequency
no
Indicates the scheduled elapsed time period between observations.
sample values
yes
Either 365 daily PM values or 8,760 hourly ozone values.
s Monitor data available from the EPA AQS (contact: Virginia Ambrose (ambrose. virginia@epa. gov-). Each monitor and method
represents a unique set of sample values, and occupies one line of data. BenMAP allows you to choose the desired methods, and
then averages the data so that a monitor ID occupies a single line of data. In those cases where, for a given monitor ID, there is
more than one land use, location setting, probe location, monitor objective, or sample frequency, then we flag the variable with a
value of "ZZ." This simply means that there is more than one value for that variable for a particular monitor ID.
b The year, monitor ID, latitude, longitude, and sample values are necessary for BenMAP to function. On the other hand, other
variables are not strictly necessary. You can filter the data using the method, but this variable is not strictly necessary. Some
missing sample values are allowable, and in fact, missing sample values is a common occurrence. Finally, the land use, location
setting, probe location, monitor objective, and sample frequency are not currently used in BenMAP, and are included now to
allow additional flexibility in future versions of BenMAP.
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Appendix A. Monitoring Data
Exhibit A-3. Sample PM2 5 File Format for User-Generated Monitor Text Files
Description
Sample Data"
List of variables
Sample daily data with some missing sample
values.
Sample daily data with some missing sample
values, as well as with missing land use,
location setting, probe location, monitor
objective, and sample frequency variables.
year, monitor ID, latitude, longitude, land use, method, location setting, probe
location, monitor objective, sample frequency, sample values
2002 , 010270001881011 , 33.281111, -85.802222, agricultural, 116, rural, side
of building, highest concentration, 3, 15.2, 18.7, . , . , 12.3, . , . , 22.8, . , .
10, etc.
2002, 010270001881011, 33.281111, -85.802222,
18.7, . , . , 12.3, . , . ,22.8, 10, etc.
116,
,3, 15.2,
1 Note that the data files need to be text files. However, the particular extension used for the files (e.g., *.txt, *.dat) is not
important. Each line represents a separate monitor observation. In this particular example, there are 18 slots for PM25 data, with
six of the slots occupied by non-missing sample values. An actual file would have 365 slots, with a number of these slots
occupied by missing values. Missing values should be delineated with
Data for a given pollutant can include a variety of units. Ozone data may be in parts per million,
parts per hundred million and parts per billion and micrograms per meter cubed. Similarly particulate
matter can include a variety of measurements. When reading in data, it is converted to common units.
Exhibit A-4 lists the measurement units for each of the pollutants and the measurement used in BenMAP,
and Exhibit A-5 provides a description of the variables used in BenMAP.
Exhibit A-4. Common Measurement Units Used for Monitoring Data in BenMAP
Pollutant
Common Measurements (AQS Code)
Measurement
in BenMAP
Notes
Ozone
1 = micrograms per cubic meter (|_ig/m3) at 25°C
7 = parts per million (ppm)
8 = parts per billion (ppb)
40 = parts per hundred million (pphm)
ppb
1 ppb = 0.51 ng/m3 at 25 Celsius and one
atmosphere.
pm2„
PM10
1 = ng/m3 at 25°C
05 = milligrams per meter cubed (mg/m3) at 25°C
105 = ng/m3 at local conditions.
Pg/m3
Some data have a units code of 83 which is
cubic meters per minute. Not clear what to
do with data, so it is dropped from the
analysis.
Source: U.S. EPA (U.S. EPA, 2002b). For more information on monitor data, please contact: Virginia Ambrose
(ambrose.virginia@epa.gov').
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Appendix A. Monitoring Data
Exhibit A-5. Description of Monitor Variables
Variable Name
Description
Variable Values "
method
The method identifies the approach used to
There are a large number of method code variables. For PM2 5
collect the monitor data. For example, the
the federal reference method codes are 116-120, and 123.
Federal Reference Method for PM2 5
includes method codes 116-120 and 123.
land use
Categorization of the prevalent land use
Agricultural; Blighted Areas; Commercial; Desert; Forest;
within 1/4 mile of the Monitoring Site.
Industrial; Military Reservation; Mobile; Residential
location setting
A description of the environmental setting
Rural; Suburban; Urban and Center City
within which the Site is located.
probe location
Identification of the location of the
Ground Level Support; Pole; Side of Building; Top of Building;
sampling Probe.
Tower; Other
monitor objective
Identification of the reason for measuring
Extreme Downwind; General/background; Highest
air quality by the Monitor.
Concentration; Invalid Code Test; Maximum Ozone
Concentration; Maximum Precursor Emissions Impact
Other; Population Exposure; Regional Transport;
Source Oriented; Upwind Background; Welfare Related Impacts
sample frequency
Indicates the scheduled elapsed time period
A = Daily: 24-1 Hour Samples
between observations.
B = Daily: 8-3 Hour Samples
C = Daily: 1-3 Hour Samples
D = Daily: 1-24 Hour Samples
E = Daily: 4-6 Hour Samples
F = Daily: 4-3 Hour Samples
G = Every 3rd Day: 24-1 Hour Samples
H = Every 3rd Day: 8-3 Hour Samples
I = Every 3rd Day: 1-3 Hour Samples
J = Every 3rd Day: 1-24 Hour Samples
K = Every 3rd Day: 4-6 Hour Samples
L = Every 3rd Day: 4-3 Hour Samples
M = Every 6th Day: 24-1 Hour Samples
N = Every 6th Day: 8-3 Hour Samples
O = Every 6th Day: 1-3 Hour Samples
P = Every 6th Day: 1- 24 Hour Samples
Q = Every 6th Day: 4-3 Hour Samples
S = Seasonal
T = 5 out of 7 Days
1 = Every Day
2 = Every Other Day
3 = Every 3rd Day
4 = Every 4th Day
5 = Every 5th Day
6 = Every 6th Day
7 = Every 12th Day
8 = Stratified Random
9 = Random
R = Episodic Sampling
s Source: U.S. EPA (U.S. EPA, 2002b). For more information on monitor data, please contact: Virginia Ambrose
(ambrose.virginia@epa.gov').
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Appendix A. Monitoring Data
A.2 How BenMAP Filters Monitor Data
For a given analysis, we typically choose a subset of the available monitors, using default options
typically used in EPA analyses. The Advanced button provides access to these default monitor filter option
options, and you can change them if you wish.
A.2.1 Filtering Particulate Matter Monitor Data
PM2 5 and PM10 data are measured under both standard and local conditions. Standard data give
readings based on conditions of 25 Celsius and 1 atmosphere of pressure; while local data give the reading
based on the temperature and pressure at the monitoring site at the time of the measurement. The pollutant
codes for standard and local data differ: local PM2 5 = 88101, standard PM2 5 = 81104, local PM10 = 85101,
and standard PM10 = 81102.
The monitor selection process can be summarized in the following steps:
(1) Determine whether a monitor is valid. You can specify the minimum number of daily
observations necessary for each quarter. Within this same calculation, you can specify:
• states;
• latitude and longitude; and
• POC codes. For PM10 the default is to exclude POC codes greater than 4. For PM2 5 the default is
to exclude POC codes greater than 2. For the POC codes kept in the universe of possible monitors,
you also need to specify a preference for POC codes. For PM10 the default preference is that
POC=l > POC=2 > POC=3 > POC=4. For PM25 the default preference is POC=l > POC=2.
(2) Choose whether to include both local data and standard data. If you choose both types of data,
then you need to specify a preference when both types of data are available at the same monitor 9-digit
monitor id. (A 9-digit id includes 2-digit state, 3-digit county and 4-digit site identifiers).
Note: to avoid potential confusion, we may not allow you to choose standard PM25 data. There are
relatively few standard PM25 monitors - on the order of ten or so each year - so the potential loss is small.
(3) Specify whether to have the data used in the model as standard or local data. Standard-to-local
conversion factors provided to Don McCubbin from Bryan Hubbell on April 2, 2002 are multiplied with
standard data to get local data; alternatively, to go from local to standard data, we divide by these
conversion factors. These conversion factors are available at the 9-digit monitor id level. In cases where
we do not have a conversion factor, we use a default value of 1.
(4) At the same 9-digit monitor id, there can be more than one monitor, identified by a 10-digit
monitor id - the difference being the POC code in the tenth digit. The preference identified in (1) above is
used to choose monitors.
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Appendix A. Monitoring Data
A.2.2 Filtering Ozone Monitor Data
The monitor selection process can be summarized in the following steps:
(1) Determine whether a day is valid. You can specify a window of time in each day that will be
used to determine if there are sufficient observations for the given day. Within your specified window (e.g.,
8:00am to 7:59pm), you can specify the minimum number of non-missing hours (e.g., 9 out of 12 possible).
Within this same calculation, you can specify:
• states;
• latitude and longitude; and
• POC codes. The default is to exclude POC codes greater than 4. For the POC codes kept in the
universe of possible monitors, you also need to specify a preference for POC codes. The default is
that POC=l > POC=2 > POC=3 > POC=4.
(2) Determine whether a monitor is valid. You can specify a window of time (e.g., May through
September) to check to see if there are a sufficient number of valid days. You can to specify the minimum
number of observations necessary during this user-specified window.
(3) At the same 9-digit monitor id, there can be more than one monitor, identified by a 10-digit
monitor id - the difference being the POC code in the tenth digit. The preference identified in (1) above
will be used to choose monitors.
A.3 Monitor Rollbacks
Once a set of monitors has been selected, the user may define one or more non-overlapping rollback
regions. A region is simply a set of states with an associated set of rollback parameter values. Three
rollback types are available - Percentage Rollback, Incremental Rollback, and Rollback to a Standard.
Each of these rollback types has different rollback parameters associated with it.
A.3.1 Percentage Rollback
Percentage Rollback involves setting only two parameters - a percentage and a background level.
The rollback procedure is similarly straightforward - each observation at each monitor in the region has the
portion of its value which is above background level reduced by percentage.
Example: Background Level: 35; Percentage: 25
Initial Observations at a monitor in rollback region:
20 20 25 59 35 51 83 35 30 67 87 79 63 35 35
If we select the background level of 35, we first calculate the portion of each observation that is
above background level, that is, we subtract the background level from the initial observation level.
Observations below background level are given a value of 0.
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Observation portions above background level:
0 0 0 24 0 16 48 0 0 32 52 44 28 0 0
When we apply the rollback percentage, each observation portion gets reduced by 25%.
Reduced portions above background level:
0 0 0 18 0 12 36 0 0 24 39 33 21 0 0
Then, each reduced portion is added to the background level of 35. Zero values are replaced by the
initial observations.
Reduced Observations:
20 20 25 53 35 47 71 35 30 59 74 68 56 35 35
A.3.2 Incremental Rollback
Incremental Rollback similarly involves setting only two parameters - an increment and a
background level. The rollback procedure is quite similar to the percentage rollback procedure - each
observation at each monitor in the region has the portion of its value which is above background level
reduced by increment. The reduced values are not allowed to become negative, however - that is, they are
truncated at zero.
Example: Background Level: 35; Increment: 25
Initial Observations:
20 20 25 59 35 51 83 35 30 67 87 79 63 35 35
Observation portions above background level:
0 0 0 24 0 16 48 0 0 32 52 44 28 0 0
Reduced portions above background level:
000000 23 007 27 19 30 0
Reduced Observations:
20 20 25 35 35 35 58 35 30 42 62 54 38 35 35
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A.3.3 Rollback to a Standard
Rollback to a Standard has two groups of parameters - those associated with the Attainment Test,
which determines whether a monitor is in attainment (meets the standard), and those associated with the
Rollback Methods, which are used to bring out of attainment monitors into attainment.
The Attainment Test parameters are Metric, Ordinality, and Standard. A monitor is considered
in attainment if the nth highest value of the metric specified by Metric is at or below the value specified by
Standard, where n is the value specified by Ordinality. For example, if Metric is
TwentyFourHourDailyAverage, Ordinality is two, and Standard is eighty five, a monitor will be
considered in attainment if the second highest value of TwentyFourHourDaily Average is at or below eighty
five.
Supported metrics for pollutants with hourly observations (Ozone) include FiveHourDailyAverage,
EightHourDaily Average, TwelveHourDaily Average, TwentyFourHourDaily Average, OneHourDailyMax,
and EightHourDailyMax. Supported metrics for pollutants with daily observations (PM10, PM2.5) include
TwentyFourHourDaily Average and Annual Average. For Annual Average, Ordinality does not apply,
since there is only a single metric value to work with.
The Rollback Method parameters are Interday Rollback Method, Interday Background Level,
Intraday Rollback Method, and Interday Background Level. These four parameters determine the
rollback procedures used to bring out of attainment monitors into attainment. The Interday Rollback
Method and Background Level are used to generate target values for the metric specified by the
Attainment Test. The Intraday Rollback Method and Background Level are used to adjust hourly
observations to meet the target metric values generated in the previous step. As such, the Intraday
Rollback Method and Background Level are used only for pollutants with hourly observations (ozone).
Interday Rollback - Generating Target Metric Values
Because standards are defined on metrics, not directly on observations, the first step in rolling back
out of attainment monitors is generating target metric values. There are four supported rollback methods
for Interday Rollbacks - Percentage, Incremental, Peak Shaving, and Quadratic. Each of these rollback
methods requires some preprocessing of the initial monitor metric values. We will discuss this
preprocessing first, and then go through Percentage, Incremental, and Peak Shaving rollbacks in turn.
Quadratic rollback is more complicated than these first three, and has its own section.
The Interday Background Level specifies the portion of each metric value which cannot be
affected by human intervention - we call this portion the non-anthropogenic portion. Whatever portion is
left over after subtracting out the background level is referred to as the anthropogenic portion. The
anthropogenic portion of the initial monitor metric values is the only part which will be affected by the
Interday Rollback Method.
BenMAP calculates an out of attainment value by determining the particular monitor metric value
which caused the monitor to be out of attainment - this value is the nth highest value of the metric specified
by the Attainment Test metric, where n is the Attainment Test ordinality. BenMAP then calculates an
anthropogenic out of attainment value by subtracting the Interday Background Level from the out of
attainment value. BenMAP also calculates an anthropogenic standard by subtracting the Interday
Background Level from the Attainment Test standard. Finally, BenMAP calculates a set of
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anthropogenic metric values and a set of non-anthropogenic metric values using the following procedure on
each initial monitor metric value:
IF the metric value is less than or equal to the Interday Background Level,
non-anthropogenic metric value = metric value
anthropogenic metric value = 0
ELSE
non-anthropogenic metric value = Interday Background Level
anthropogenic metric value = metric value - Interday Background Level
Interday Rollback - Percentage
To generate target metric values using Percentage rollback, BenMAP calculates the percentage
required to reduce the anthropogenic out of attainment value to exactly the anthropogenic standard. This
percentage reduction is then applied to all of the anthropogenic metric values. Finally, these reduced
anthropogenic metric values are added to the non-anthropogenic metric values to give the final target
metric values.
Example:
Initial Metric Values:
30 35 50 100 80 44 67 88 90 70 50 30 55 90 80 85
Attainment Test: Highest value of metric <= 70
Interday Background Level: 40
Out of Attainment Value: 100
Anthropogenic Out of Attainment Value: 60 (= 100 - 40)
Anthropogenic Standard: 30 (= 70 - 40)
Percentage Reduction Required: 50% (=(60-30)/60)
Non-Anthropogenic Metric Values:
30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40
Anthropogenic Metric Values:
0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45
Reduced Anthropogenic Metric Values:
0 0 5 30 20 2 14 24 25 15 5 0 8 25 20 23
Target Metric Values:
30 35 45 70 60 42 54 64 65 55 45 30 48 65 60 63
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Interday Rollback - Incremental
To generate target metric values using Incremental Rollback, BenMAP calculates the increment
required to reduce the anthropogenic out of attainment value to exactly the anthropogenic standard. This
incremental reduction is then applied to all of the anthropogenic metric values (but - they are not allowed to
fall below zero). Finally, these reduced anthropogenic metric values are added to the non-anthropogenic
metric values to give the final target metric values.
Example:
Initial Metric Values:
30 35 50 100 80 44 67 88 90 70 50 30 55 90 80 85
Attainment Test: Highest value of metric <= 70
Interday Background Level: 40
Interday Rollback Method: Incremental
Out of Attainment Value: 100
Anthropogenic Out of Attainment Value: 60
Anthropogenic Standard: 30 (=70 - 30)
Incremental Reduction Required: 30
Non-Anthropogenic Metric Values:
30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40
Anthropogenic Metric Values:
0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45
Reduced Anthropogenic Metric Values:
0 0 5 30 20 2 14 24 25 15 5 0 8 25 20 23
Target Metric Values:
30 35 45 70 60 42 54 64 65 55 45 30 48 65 60 63
Interday Rollback - Peak Shaving
To generate target metric values using Peak Shaving rollback, BenMAP simply truncates all
anthropogenic metric values at the anthropogenic standard. These reduced anthropogenic metric values
are added to the non-anthropogenic metric values to give the final target metric values.
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Example:
Initial Metric Values:
30 35 50 100 80 44 67 88 90 70 50 30 55 90 80 85
Attainment Test: Highest value of metric <= 70
Interday Background Level: 40
Interday Rollback Method: Peak Shaving
Anthropogenic Standard: 30
Non-Anthropogenic Metric Values:
30 35 40 40 40 40 40 40 40 40 40 30 40 40 40 40
Anthropogenic Metric Values:
0 0 10 60 40 4 27 48 50 30 10 0 15 50 40 45
Reduced Anthropogenic Metric Values:
0 0 10 30 30 4 27 30 30 30 10 0 15 30 30 30
Target Metric Values:
30 35 50 70 70 44 67 70 70 70 50 30 55 70 70 70
Intraday Rollback - Adjusting Hourly Observations
Once a set of target metric values has been calculated for a pollutant with hourly observations (e.g.,
Ozone), BenMAP must adjust the hourly observations so that they produce the target metric values. There
are three supported rollback methods for Intraday Rollback - Percentage, Incremental, and Quadratic.
Each of these rollback methods requires some preprocessing of the initial monitor observations, and each
can require multiple iterations to hit the target metric values. We will discuss this preprocessing and
iteration first, and then go through Percentage and Incremental rollbacks in turn. Quadratic rollback is
more complicated than these first two, and has its own section.
For various reasons, each of the Intraday Rollback methods can fail to hit the target metric values
during a single pass through the rollback procedure (these will be discussed in detail below). As such, each
of the rollback methods uses an iterative approach to get within a threshold of each of the target metric
values - currently this threshold is 0.05. The iterative approach works as follows:
For each target metric value, BenMAP calculates the current value of the Attainment Test metric.
For the first iteration, the metric value will be calculated using unadjusted hourly observations. For
subsequent iterations, the metric value will be calculated using the current values of the adjusted hourly
observations.
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If the difference between the metric value and the target metric value is less than or equal to 0.05,
the rollback procedure is finished. Otherwise, another iteration is required.
The Intraday Background Level specifies the portion of each observation which cannot be
affected by human intervention - we call this portion the non-anthropogenic portion. Whatever portion is
left over after subtracting out the background level is referred to as the anthropogenic portion. The
anthropogenic portion of the initial monitor observations is the only part which will be affected by the
Intraday Rollback Method.
In a way analogous to the Interday Rollback procedure, BenMAP calculates the twenty-four
hourly anthropogenic observations and the twenty-four hourly non-anthropogenic observations using the
following procedure for each hourly observation:
IF the current value of the observation is less than or equal to the Intraday Background Level,
non-anthropogenic observation = observation
anthropogenic observation = 0
ELSE
non-anthropogenic observation = Intraday Background Level
anthropogenic observation = observation - Intraday Background Level
Given (i) an Attainment Test Metric (e.g., EightHourDailyMax), (ii) an Intraday Background
Level, and (iii) a target metric value for the day, BenMAP proceeds to adjust hourly observations in the
following steps:
1. Calculate the Attainment Test metric (e.g., the 8-hour daily maximum);
2. Identify the "window" - i.e., the set of hours used to calculate the metric (e.g., if the 8-hour daily
maximum is achieved in the first 8 hours, then the window is comprised of the first 8 hours);
3. Calculate the non-anthropogenic hourly observations (=min(hourly observation, Intraday
Background Level));
4. Calculate the anthropogenic hourly observations (=hourly observation - Intraday Background
Level);
5. Calculate the non-anthropogenic metric value (= the metric using the non-anthropogenic hourly
observations in the "window");
6. Calculate the anthropogenic metric value (= the metric using the anthropogenic hourly
observations in the "window");
7. Calculate the anthropogenic target metric value (= the target metric value minus the non-
anthropogenic metric value);
8. Calculate the reduction required to get the anthropogenic metric value down to the anthropogenic
target metric value;
9. Adjust all anthropogenic hourly observations by the reduction calculated on the previous step;
10. Calculate the adjusted hourly observations (= the adjusted anthropogenic hourly observation + the
non-anthropogenic hourly observation).
Intraday Rollback - Percentage
Below, we present two examples of a percentage-based Intraday Rollback. In one example, a
single iteration is needed, and in the second example, two iterations are required because a number of the
monitor values fall below the assumed background level.
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Example: All Hourly Observations Exceed the Intraday Background (Single
Iteration)
If all of the hourly observations in a day are greater than the Intraday Background Level, then the
above procedure is straightforward and can be accomplished in a single iteration. We illustrate with the
following example. Suppose that:
Metric = EightHourDailyMax,
Target metric value for a given day = 85
Intraday Background Level = 40.
And that the hourly observations on that day are:
530 45 50 60 45 45 45 60 70 100 100 100 100 100 100 100 100 60 45 50 45 45 47 47
Based on these observations, we see that the 8-hour daily maximum = 110.
Assuming a background level of 40, then the Anthropogenic hourly observations are:
490 5 10 20 5 5 5 20 30 60 60 60 60 60 60 60 60 20 5 10 5 5 7 7
Then, we know:
Anthropogenic metric value = 70.
Non-anthropogenic metric value = 40.
Anthropogenic target metric value = 45.
Percentage reduction required = ((70-45)/70) = 35.7%
All of the hourly anthropogenic observations are reduced by 35.7%. The average of the first 8
values (the window on which the Test metric is based) will be exactly 45, the anthropogenic target metric
value. Finally, the adjusted hourly observations are calculated by adding the non-anthropogenic hourly
observation to the adjusted hourly anthropogenic observations.
Example: Some Hourly Observations are Below the Intraday Background (Multiple
Iterations Required)
In the above example, the anthropogenic target metric value was met on a single iteration because
all of the hourly observations were greater than the Intraday Background Level. In this case, a simple
percent reduction of all hourly values will produce an average in the window that is equal to the
anthropogenic target metric value. If some of the hourly observations in a day are less than or equal to the
Intraday Background Level, however, then BenMAP uses an iterative procedure. On each iteration, it
adjusts hourly observations using the 10-step method given above. It then compares the new metric value
to the target metric value. If the difference is less than or equal to 0.05 ppb, the rollback procedure is
finished. Otherwise, another iteration is required. The iterative procedure is illustrated in the following
example.
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Note that we are presenting an example below with an intraday background of 40 ppb. We only
use a non-zero intraday background as a sensitivity analysis in Exhibit 4-5, where we use intraday
backgrounds of 10, 20, 30, and 40. For the rest of our results we use an intraday background of 0 ppb.
Suppose that:
Metric = EightHourDailyMax,
Target metric value for a given day = 85
Intraday Background Level = 40.
Suppose also that the hourly observations on that day are:
530 20 25 60 35 35 40 60 70 100 100 100 100 100 100 100 100 60 33 40 30 30 25 20
Then, we know that the 8-hour daily maximum = 100.6.
Non-Anthropogenic Hourly Observations, Iteration One:
40 20 25 40 35 35 40 40 40 40 40 40 40 40 40 40 40 40 33 40 30 30 25 20
Anthropogenic Hourly Observations, Iteration One:
490 0 0 20 0 0 0 20 30 60 60 60 60 60 60 60 60 20 0 0 0 0 0 0
Non-Anthropogenic Metric Value: 34.4 (EightHourDailyMax - calculated over the same eight hour
window as the initial metric value was calculated over)
Anthropogenic Metric Value: 66.3
Anthropogenic Target Metric Value: 50.6
Percentage Reduction Required: 23.6%
Reduced Anthropogenic Hourly Observations, Iteration One:
374 0 0 15 0 0 0 15 23 46 46 46 46 46 46 46 46 15 0 0 0 0 0 0
Reduced Hourly Observations, Iteration One:
414 20 25 55 35 35 40 55 63 86 86 86 86 86 86 86 86 55 33 40 30 30 25 20
Reduced Metric Value (EightHourDailyMax): 85.8
Target Metric Value (EightHourDailyMax): 85
Non-Anthropogenic Hourly Observations, Iteration Two:
40 20 25 40 35 35 40 40 40 40 40 40 40 40 40 40 40 40 33 40 30 30 25 20
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Anthropogenic Hourly Observations, Iteration Two:
374 0 0 15 0 0 0 15 23 46 46 46 46 46 46 46 46 15 0 0 0 0 0 0
Non-Anthropogenic Metric Value: 40 (EightHourDailyMax - calculated over the same eight hour
window the initial metric value was calculated over)
Anthropogenic Metric Value: 45.8
Anthropogenic Target Metric Value: 45
Percentage Reduction Required: 1.9%
Reduced Anthropogenic Hourly Observations, Iteration Two:
368 0 0 15 0 0 0 15 23 45 45 45 45 45 45 45 45 15 0 0 0 0 0 0
Reduced Hourly Observations, Iteration Two:
408 20 25 55 35 35 40 55 63 85 85 85 85 85 85 85 85 55 33 40 30 30 25 20
Reduced Metric Value (EightHourDailyMax): 85
The above example, in addition to illustrating the Intraday Percentage Rollback, also illustrates
one reason why the iterative procedure can be necessary. When using the EightHourDailyMax metric in the
Attainment Test, it is possible for the window over which the maximum eight hour average occurs to
move after a single pass through the rollback procedure. When this happens, it becomes necessary to go
through additional iterations to hit the target metric value.
Intraday Rollback - Incremental
To adjust hourly observations using Incremental rollback, BenMAP calculates the increment
required to reduce the anthropogenic metric value to exactly the anthropogenic target metric value. This
incremental reduction is then applied to all of the anthropogenic observations (but - they are not allowed to
fall below zero). Finally, these reduced anthropogenic observations are added to the non-anthropogenic
observations to give the final reduced observations.
Example:
Initial Hourly Observations:
20 20 25 60 35 35 40 70 35 30 65 90 76 65 35 35 54 60 33 40 30 30 25 20
Initial Metric Value (EightHourDailyMax): 60
Target Metric Value (EightHourDailyMax): 55
Intraday Background Level: 40
Intraday Rollback Method: Incremental
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Non-Anthropogenic Hourly Observations, Iteration One:
20 20 25 40 35 35 40 40 35 30 40 40 40 40 35 35 40 40 33 40 30 30 25 20
Anthropogenic Hourly Observations, Iteration One:
0 0 0 20 0 0 0 30 0 0 25 50 36 25 0 0 14 20 0 0 0 0 0 0
Non-Anthropogenic Metric Value (EightHourDailyMax): 38.8
Anthropogenic Metric Value (EightHourDailyMax): 21.3
Anthropogenic Target Metric Value (EightHourDailyMax): 16.3
Incremental Reduction Required: 5.0
Reduced Anthropogenic Hourly Observations, Iteration One:
0 0 0 15 0 0 0 25 0 0 20 45 31 20 0 0 9 15 0 0 0 0 0 0
Reduced Hourly Observations, Iteration One:
20 20 25 55 35 35 40 65 35 30 60 85 71 60 35 35 49 55 33 40 30 30 25 20
Reduced Metric Value (EightHourDailyMax): 56.25
Target Metric Value (EightHourDailyMax): 55
Non-Anthropogenic Hourly Observations, Iteration Two:
20 20 25 40 35 35 40 40 35 30 40 40 40 40 35 35 40 40 33 40 30 30 25 20
Anthropogenic Hourly Observations, Iteration Two:
0 0 0 15 0 0 0 25 0 0 20 45 31 20 0 0 9 15 0 0 0 0 0 0
Non-Anthropogenic Metric Value (EightHourDailyMax): 38.8
Anthropogenic Metric Value (EightHourDailyMax): 17.5
Anthropogenic Target Metric Value (EightHourDailyMax): 16.3
Incremental Reduction Required: 1.25
Reduced Anthropogenic Hourly Observations, Iteration Two:
0 0 0 14 0 0 0 24 0 0 19 44 30 19 0 0 8 14 0 0 0 0 0 0
Reduced Hourly Observations, Iteration Two:
20 20 25 54 35 35 40 64 35 30 59 84 70 59 35 35 48 54 33 40 30 30 25 20
Reduced Metric Value (EightHourDailyMax): 55.3
Target Metric Value (EightHourDailyMax): 55
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This example should actually continue for one further iteration, with a new Incremental Reduction
of 0.3. This illustrates another reason why the iterative procedure can be necessary - for incremental
reductions, the prohibition against values becoming negative can cause target metric values to not be met.
Incremental reductions thus very often require multiple iterations.
Interday and Intraday Rollback - Quadratic
Quadratic rollback is based on an algorithm developed by Horst and Duff (1995). The idea behind
quadratic rollback is to reduce large values proportionally more than small values while just achieving the
standard - that is, the out-of-attainment value should be more or less at the standard after the rollback (some
small amount of error is involved).
The original quadratic rollback algorithm is designed to roll back hourly observations given a
desired peak value. That is, it assumes that the Attainment Test metric is the one-hour average and the
Attainment Test ordinality is one. As such, the algorithm was modified slightly to allow for ordinalities
other than one to be used.
The basic formula for quadratic rollback is:
Reduced Observation = [1-(A + B* Initial Observation ) ] * Initial Observation
where:
i ranges over the days being reduced.
A= 1 - V
V = Min( l,Vi)
V; = ( 2 * Maximum Observation Value * Standard) / X;
X; = ( 2 * Maximum Observation Value * Metricsl) - Metrics,2
B = Max( 0, [(V * Out of Attainment Value - Standard) / Out of Attainment Value2] )
Quadratic Rollback - Interday
Because Quadratic Rollback was originally designed to adjust hourly observations to meet a daily
metric standard, it is slightly complicated to use it to generate target metric values.
First, Quadratic Rollback calculates the anthropogenic out of attainment value by subtracting the
Intraday Background Level from the out of attainment value. Note that this differs from the other interday
rollback methods, which subtract the Interday Background Level from the out of attainment value.
Similarly, the anthropogenic standard is calculated by subtracting the Intraday Background Level from the
standard.
The anthropogenic observations and non-anthropogenic observations are then calculated. For
pollutants which have daily observations (PM10, PM2.5) the anthropogenic metric values are used (see
above for their calculation). For pollutants which have hourly observations (Ozone), Quadratic Rollback
loops through each metric value and calculates the twenty four corresponding anthropogenic observations
and non-anthropogenic observations as follows:
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IF the metric value is at or below the Interday Background Level,
For each observation,
non-anthropogenic observation = observation
anthropogenic observation = 0
ELSE
For each observation,
IF the observation is at or below the Intraday Background Level
non-anthropogenic observation = observation
anthropogenic observation = 0
ELSE
non-anthropogenic observation = Intraday Background Level
anthropogenic observation = observation - Intraday Background
Level
A new set of anthropogenic metric values is then calculated by generating the Attainment Test
metric from the anthropogenic observations. The Quadratic Rollback algorithm is then called, passing in
the anthropogenic metric values as Metrics, anthropogenic observations as Observations, anthropogenic
standard as Standard, and anthropogenic out of attainment value as Out of Attainment Value. The result is
a set of reduced anthropogenic observations. These are then added together with the non-anthropogenic
observations to give a final set of reduced observations.
Then, if Quadratic Rollback was also selected as the Intraday Rollback method, these observations
are used as the final reduced observations for the monitor. Otherwise, metric targets are generated from
these hourly observations, and the observations themselves are discarded.
Quadratic Rollback - Intraday
Quadratic Rollback can also be used to adjust hourly observations to meet metric targets generated
via a different rollback method. In this case, the algorithm is used to adjust each set of twenty four hourly
observations to meet the corresponding metric target. Intraday Quadratic Rollback uses the normal set of
anthropogenic observations as Observations, a single normal anthropogenic metric value as Metrics, and
the normal anthropogenic metric target as Standard. Intraday Quadratic Rollback tends to always slightly
miss its metric target, so it is not run in an iterative fashion as the other Intraday Rollback Methods are
(doing so would sometimes result in an infinite loop).
A.4 SAS Code Used to Prepare Monitor Data for BenMAP
In this sub-section, we present the SAS code that we used to process the data for BenMAP. In
preparing your own monitor data for BenMAP, you may want to use some of the algorithms presented here.
To ease the need of differentiating between leap and non-leap years, BenMAP assumes a year with
365 days. When incorporating monitoring data from a leap year (e.g., 2000), we average the values for
February 28 and 29.
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A.4.1 SAS Code for Processing Raw Particulate Matter Monitor Data for Input to BenMAP
title "file: amp500_501_pmdata_v3.sas";
02/12/03.
Etienne Gabel. Abt Associates.
This SAS code reads in from raw AMP500 file which contains monitor description data,
and from raw AMP501 file which contains formatted hourly pm2.5 and pmlO monitor sampling
data.
The code prepares a data set which contains the daily average data within a single array of
365 days. Each observation (row) corresponds to a unique monitor (including
poc code) and method pair.
For 1996 (leap year), February 28th values are set as the average of February 28th and 29th
sample values, and February 29th data are then dropped since CAPMS is hardwired to accept 365
days of pm data.
7/15/03 - Ed Al-Flussainy - changed code to read-in AMP500 data for 1997-2001 and merge it with the older 1996 data added
miami-dade county correction.
7/16/03 - Ed Al-Flussainy - changed macro to accomodate file format of the 2002 PM 2.5 (88101) source data file
(PM25_Continuous_2002.txt). Note that samplefreq is not available in 2002 PM 2.5 (88101) file, setting it = to 'blank' for
compatibility with rest of code.
options missing =
filename yr366 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\year_366_days.csv";
filename infilel "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG81104-1996.TXT";
filename infile2 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG_501 81104_NATION_1997.TXT";
filename infile3 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG_501 81104_NATION_1998.TXT";
filename infile4 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG81104-1999.TXT";
filename infile5 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG81104-2000.TXT";
filename infile6 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG81104-2001.TXT";
filename infile7 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG_501 81104_NATION_2002.TXT";
filename infile8 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG88101-1996.TXT";
filename infile9
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\RD_501 8810 l_PM25_NATION_1997.TXT";
filename infilelO
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\RD_501 8810 l_PM25_NATION_1998.TXT";
filename infilel 1 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG88101-1999.TXT";
filename infilel2 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG88101-2000.TXT";
filename infilel3 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\EG88101-2001.TXT";
filename infilel4 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM2.5\PM25_Continuous_2002.txt";
filename infilel5 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG81102-1996.TXT";
filename infilel6
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 81102_PM10_NATION_1997.TXT";
filename infilel7
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 81102_PM10_NATION_1998.TXT";
filename infilel8 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG81102-1999.TXT";
filename infilel9 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG81102-2000.TXT";
filename infile20 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG81102-2001.TXT";
filename infile21
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 81102_PM10_NATION_2002.TXT";
filename infile22 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG85101-1996.TXT";
filename infile23
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 8510 l_NATION_1997.TXT";
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filename infile24
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 8510 l_NATION_1998.TXT";
filename infile25 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG85101-1999.TXT";
filename infile26 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\data\PM10\EG85101-2000.TXT";
filename infile27 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG85101-2001.TXT";
filename infile28
"\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\data\PM10\EG_501 85101_NATION_2002.TXT";
* additional 2002 pm2.5 monitor data file for methods <701;
filename infile29 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\data\PM2.5\pm25_daily2002.txt";
filename outfl "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std96.TXT";
filename outf2 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM25std97.TXT";
filename outf3 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std98.TXT";
filename outf4 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM25std99.TXT";
filename outf5 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM25std00.TXT";
filename outf6 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std0 1 .TXT";
filename outf7 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std02.TXT";
filename outf8 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM251el96.TXT";
filename outf9 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM251el97.TXT";
filename outflO "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM251el98.TXT";
filename outfl 1 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM251el99.TXT";
filename outfl2 "\\BE5832\C$\capms_revision\data_bases\air_m onitor\sas_work\output\PM251el00.TXT";
filename outfl3 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251el01.TXT";
filename outfl4 "\\BE5832\C$\capms_revision\data_bases\air_m onitor\sas_work\output\PM251el02.TXT";
filename outfl5 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM10std96.TXT";
filename outfl6 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM10std97.TXT";
filename outfl7 "\\BE5832\C$\eapms_revision\data_bases\air_m onitor\sas_work\output\PM10std98.TXT";
filename outfl8 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM10std99.TXT";
filename outfl9 "\\BE5832\C$\capms_revision\data_bases\air_m onitor\sas_work\output\PM10std00.TXT";
filename outf20 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std01.TXT";
filename outf21 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std02.TXT";
filename outf22 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM101el96.TXT";
filename outf23 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101cl97.TXT";
filename outf24 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101el98.TXT";
filename outf25 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101el99.TXT";
filename outf26 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101cl00.TXT";
filename outf27 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101el01.TXT";
filename outf28 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101el02.TXT";
filename outjkl "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std96junk.TXT";
filename outjk2 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std97junk.TXT";
filename outjk3 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM25std98junk.TXT";
filename outjk4 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std99junk.TXT";
filename outjk5 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std00junk.TXT";
filename outjk6 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM25std01junk.TXT";
filename outjk7 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM25std02junk.TXT";
filename outjk8 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251el96junk.TXT";
filename outjk9 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM251el97junk.TXT";
filename outjklO "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251cl98junk.TXT";
filename outjkl 1 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251cl99junk.TXT";
filename outjkl2 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251el00junk.TXT";
filename outjkl3 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251el01junk.TXT";
filename outjkl4 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM251cl02junk.TXT";
filename outjkl5 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std96junk.TXT";
filename outjkl6 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std97junk.TXT";
filename outjkl7 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM 10std98junk.TXT";
filename outjkl8 "\\BE5832\C$\capms_revision\data_bases\air_monitor\sas_work\output\PM10std99junk.TXT";
filename outjkl9 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std00junk.TXT";
filename outjk20 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std01junk.TXT";
filename outjk21 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM10std02junk.TXT";
filename outjk22 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101el96junk.TXT";
filename outjk23 "\\BE5832\C$\eapms_revision\data_bases\air_monitor\sas_work\output\PM101cl97junk.TXT";
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filename outjk24
filename outjk25
filename outjk26
filename outjk27
filename outjk28
'\\BE5832\C$\eapms
'\\BE5832\C$\capms
'\\BE5832\C$\capms
'\\BE5832\C$\capms
'\\BE5832\C$\capms
revision\data_bases\air_monitor\sas_work\output\PM101cl98junk.TXT'
revision\data_bases\air_monitor\sas_work\output\PM101cl99junk.TXT'
revision\data_bases\air_monitor\sas_work\output\PM101cl00junk.TXT'
revision\data_bases\air_monitor\sas_work\output\PM101cl01junk.TXT'
revision\data_bases\air_monitor\sas_work\output\PM101cl02junk.TXT'
READ IN AMP500 FILE. CREATE MONITOR ID VARIABLE WITH STATE, COUNTY, AND SITE ID.
TRANSACTION CODE AA' INCLUDES MONID, LAND USE, LOCATION SETTING, LATITUDE AND LONGITUDE
VARIABLES. TRANSACTION CODE 'MA' INCLUDES MONID, POC AND PROBE LOCATION VARIABLES.
TRANSACTION CODE 'ME' INCLUDES MONID, POC AND MONITOR OBJECTIVE VARIABLES.
NOTE: PROBE LOCATION AND MONITOR OBJECTIVE ARE EQUIVALENT FOR PM25 STANDARD AND LOCAL, AND
ARE EQUIVALENT FOR PM10 STANDARD AND LOCAL.
* start of Ed's edit;
/* AMP 500 data */
%let netdata=H:\ENVR\MCCUBBIN\capms_revision\data_bases\air_monitor\sas_work\data\AMP500 file — contains monitor
description info;
filename amp500_l "&netdata\EG_500_SELP ARM_1997_2002.TXT";
filename amp500_2 "&netdata\AMP500.TXT";
* read-in new and old AMP500 data;
%macro read_amp500(param);
data tempAA_¶m;
infile amp500_¶m firstobs=l delimiter='|' missover lrecl = 6000 dsd;
length transactioncode $ 2 state $ 2 county $ 3 siteid $ 4 poc $ 1 xl-xl4 $ 5 landuse $ 20 locsetting $ 50
probelocation $ 20 monobjective $ 50 monid $ 9;
input transactioncode $ @;
if transactioncode="AA" then do;
input actioncode $ state $ county $ siteid $ latitude longitude xl-xl4 $ landuse $
locsetting $;
end;
if transactioncode="MA" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ xl-x4 $ probelocation $;
if probelocation="" then delete;
end;
if transactioncode="ME" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ monobjective $;
if monobjective="" then delete;
end;
drop xl-xl4 actioncode;
* Miami-Dade county correction;
if state="12" and county="025" then county="086";
monid = state || county || siteid;
if transactioncode in ("AA" "MA" "ME");
run;
proc sort data=tempAA_¶m;
by monid;
run;
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%mend read_amp500;
%read_amp500(l);
%read_amp500(2);
* merge new and old AMP500 data, separate data based on transactioncode and parameter;
data tempAA(keep=monid state county siteid latitude longitude landuse locsetting)
tempMA25(keep=monid state county siteid parameter poc probelocation)
tempMA10(keep=monid state county siteid parameter poc probelocation)
tempME25(keep=monid state county siteid parameter poc monobjective)
tempME10(keep=monid state county siteid parameter poc monobjective);
* note the merge order - tempAAl overwrites tempAA_2 in case of duplicate entries;
merge tempAA_2(in=dum2) tempAA_l(in=duml);
by monid;
if transactioncode="AA" then output tempAA;
else if transactioncode="MA" then do;
if parameter="88101" or parameter="81104" then output tempMA25;
if parameter="85101" or parameter="81102" then output tempMA 10;
end;
else if transactioncode="ME" then do;
if parameter="88101" or parameter="81104" then output tempME25;
if parameter="85101" or parameter="81102" then output tempMElO;
end;
run;
* end of Ed's edit;
proc sort data=tempMA25;
by monid poc;
proc sort data=tempME25;
by monid poc;
proc sort data=tempMA10;
by monid poc;
proc sort data=tempME10;
by monid poc;
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE LAND USE CODE. **/
proc sort data=tempAA out=tempAAl nodupkey;
by monid landuse;
proc summary data=tempAA 1;
by monid;
output out=tempAA2;
data tempAA3;
merge tempAA2 tempAA;
by monid;
if freq > 1 then landuse="ZZ";
keep monid latitude longitude landuse locsetting;
proc sort data=tempAA3 out=tempAA4 nodupkey;
by monid;
proc print data=tempAA4(obs=50);
where landuse="ZZ";
title2 "MORE THAN ONE LAND USE FOR A MONITOR IN AMP500 FILE, tempAA4";
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE LOCATION SETTING CODE. **/
proc sort data=tempAA out=tempAA5 nodupkey;
by monid locsetting;
proc summary data=tempAA5;
by monid;
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output out=tempAA6;
data tempAA7;
merge tempAA6(in=checkl) tempAA;
by monid;
if checkl=l and _freq_ > 1 then locsetting="ZZ";
keep monid latitude longitude landuse locsetting;
proe sort data=tempAA7 out=tempAA8 nodupkey;
by monid;
proe print data=tempAA8(obs=50);
where loesetting="ZZ";
title2 "MORE THAN ONE LOCATION SETTING FOR A MONITOR IN AMP500 FILE, tempAA8";
/** MAKE SINGLE FILE WITH TRANSACTION AA DATA, WITH FLAG FOR REPEATING LAND USE OR LOCATION
SETTING CODES. **/
data tempAA9;
merge tempAA4(drop=locsetting) tempAA8(drop=landuse);
by monid;
keep monid latitude longitude landuse locsetting;
/** ENTER FLAG "ZZ" FOR PM2.5 MONITORS IN AMP500 FILE WITH MORE THAN ONE PROBE LOCATION CODE. **/
proe sort data=tempMA25 out=tempMA25a nodupkey;
by monid poc probelocation;
proe summary data=tempMA25a;
by monid poc;
output out=tempMA25b;
data tempMA25c;
merge tempMA25b tempMA25;
by monid poc;
if _freq_ > 1 then probelocation—"ZZ";
keep monid poc probelocation;
proe sort data=tempMA25c out=tempMA25d nodupkey;
by monid poc;
proe print data=tempMA25d(obs=50);
where probelocation="ZZ";
title2 "MORE THAN ONE PROBE LOCATION FOR A PM2.5 MONITOR IN AMP500 FILE, tempMA25d";
/** ENTER FLAG "ZZ" FOR PM10 MONITORS IN AMP500 FILE WITH MORE THAN ONE PROBE LOCATION CODE. **/
proe sort data=tempMA10 out=tempMA10a nodupkey;
by monid poc probelocation;
proe summary data=tempMA10a;
by monid poc;
output out=tempMA10b;
data tempMAlOc;
merge tempMAlOb tempMAlO;
by monid poc;
if _freq_ > 1 then probelocation—"ZZ";
keep monid poc probelocation;
proe sort data=tempMA10c out=tempMA10d nodupkey;
by monid poc;
proe print data=tempMA10d(obs=50);
where probelocation="ZZ";
title2 "MORE THAN ONE PROBE LOCATION FOR A PM10 MONITOR IN AMP500 FILE, tempMAlOd";
/** ENTER FLAG "ZZ" FOR PM2.5 MONITORS IN AMP500 FILE WITH MORE THAN ONE MONITOR OBJECTIVE CODE.
**/
proe sort data=tempME25 out=tempME25a nodupkey;
by monid poc monobjective;
proe summary data=tempME25a;
by monid poc;
output out=tempME25b;
data tempME25c;
merge tempME25b tempME25;
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by monid poc;
if _freq_ > 1 then monobjective—"ZZ";
keep monid poc monobjective;
proc sort data=tempME25c out=tempME25d nodupkey;
by monid poc;
proc print data=tempME25d(obs=50);
where monobjective="ZZ";
title2 "MORE THAN ONE MONITOR OBJECTIVE FOR A MONITOR IN AMP500 FILE, tempME25d";
/** ENTER FLAG "ZZ" FOR PM10 MONITORS IN AMP500 FILE WITH MORE THAN ONE MONITOR OBJECTIVE CODE.
**/
proc sort data=tempME10 out=tempME10a nodupkey;
by monid poc monobjective;
proc summary data=tempME10a;
by monid poc;
output out=tempME10b;
data tempMElOc;
merge tempMElOb tempMElO;
by monid poc;
if _freq_ > 1 then monobjective—"ZZ";
keep monid poc monobjective;
proc sort data=tempME10c out=tempME10d nodupkey;
by monid poc;
proc print data=tempME10d(obs=50);
where monobjective="ZZ";
title2 "MORE THAN ONE MONITOR OBJECTIVE FOR A MONITOR IN AMP500 FILE, tempMElOd";
READ IN COMPLETE YEAR TEMPLATE FOR PM (366 DAYS - INCLUDES FEB 29TH FOR LEAP YEAR).
NOTE: CAPMS IS HARDWIRED TO ACCEPT 365 DAYS, SO FEB29 VALUES ARE AVERAGED WITH FEB28
VALUES FOR PM.
data tempO;
length day $ 4 monid $ 9;
infile yr366 firstobs=2 delimiter=',' missover;
input monid $ day $;
/** ADD ZERO TO MONID TO MAKE IT APPEAR FIRST IN THE MONITOR LIST AFTER THE MERGE BELOW. **/
monid = "0" || monid;
if length(day)=3 then day= "0" || day;
keep monid day;
proc sort;
by monid;
proc print data=temp0(obs=5);
title2 "TEMPLATE FOR FULL YEAR, tempO";
MACRO TO LOOP THROUGH THE DATA SETS.
%macro getdata;
%do i=l %to 28;
READ IN AMP501 FILES. NOTE THAT FOR SAMPLE DURATION: l=HOURLY DATA, AND 7=DAILY
AVERAGE DATA. WITHIN A GIVEN SAMPLE DURATION, AVERAGE OBSERVATIONS OVER THE COURSE
OF A DAY. THAT IS, IF WE HAVE TWO 24-HOUR AVERAGES FOR A GIVEN DAY, JUST TAKE THE
AVERAGE OF THESE TWO VALUES.
* start of Ed's edit;
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* note:
The format for the 2002 PM 2.5 (88101) comma delimited files is:
State Code, County Code, Site ID, Parameter, POC, Method, Unit, Sample Duration,
Date (YYYYMMDD), Start Time (Hour), Sample Value, Qualifier-1, Qualifier-2, Qualifier-3,
Qualifier-4, Qualifier-5, Qualifier-6, Qualifier-7, Qualifier-8, Qualifier-9, Qualifier-10 ;
* for 2002 PM2.5 lei data ( in loop), read in two source files (infile 14 and 29) and merge them;
%if &i=14 %then %do;
%do j=14 %to 29 %by 15;
data templ_t&j(keep=monid state county siteid poc parameter sampleduration unit method year
day samplefreq samplevalue)
junkl_t&j(keep=monid poc parameter method sampleduration day samplevalue);
infile infile&j firstobs=l missover lrecl=6000 dsd;
length state $ 2 county $ 3 siteid $ 4 parameter $ 5
poc $ 1 unit $ 3 date $ 8 year $ 4 starttime $ 5
samplefreq $ 8 monid $ 9 xl-xlO $ 2;
input state $ county $ siteid $ parameter $ poc $ method unit $ sampleduration
date $ starttime $ samplevalue xl-xlO $;
* note: samplefreq not available in input file, setting it = to 'blank' for compatibility with rest of code - ed;
samplefreq-';
/* KEEP DATA FOR RELEVANT PARAMETER. */
if parameter= "88101";
* Miami-Dade county correction;
if state="12" and county="025" then county="086";
/* CREATE MONITOR ID. DO NOT INCLUDE POC CODE. */
monid = state || county || siteid;
/* DROP MISSING OBSERVATIONS. */
if samplevalue=. then delete;
/* CALCULATE YEAR AND DAY, AND KEEP DATA FOR RELEVANT YEAR. */
year = substr(date,l,4);
day = substr(date,5,4);
if year="2002";
/* OUTPUT IF SAMPLE DURATION IS HOURLY (1) OR DAILY (7), WHERE DAILY IS NONNEGATIVE.*/
if sampleduration=l or (sampleduration=7 and samplevalue >= 0) then output templt&j;
else output junkl t&j;
run;
%end;
data templ_&i;
settempl_tl4 templ_t29;
run;
data junkl_&i;
set junkl_tl4 junkl_t29;
run;
%end;
* read-in and process all other data;
%else %do;
data templ_&i(keep=monid state county siteid poc parameter sampleduration unit method year
day samplefreq samplevalue)
junkl_&i(keep=monid poc parameter method sampleduration day samplevalue);
infile infile&i firstobs=l delimiter-|' missover lrecl = 6000 dsd;
length transactioncode $ 2 actioncode $ 1 state $ 2 county $ 3 siteid $ 4 parameter $ 5
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poc $ 1 unit $ 3 date $ 8 year $ 4 starttime $ 5
samplefreq $ 8 monid $ 9;
input transaction code $ @;
if transactioncode="RD" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ sampleduration unit $
method date $ starttime $ samplevalue xl $ samplefreq $;
/* KEEP DATA FOR RELEVANT PARAMETER. */
if &i < 8 then do;
if parameter="81104";
end;
if 8 <= &i < 15 then do;
if parameter="88101";
end;
if 15 <= &i < 22 then do;
if parameter="81102";
end;
if 22 <= &i then do;
if parameter="85101";
end;
* Miami-Dade county correction;
if state="12" and county="025" then county="086";
/* CREATE MONITOR ID. DO NOT INCLUDE POC CODE. */
monid = state || county || siteid;
/* DROP MISSING OBSERVATIONS. */
if samplevalue=. then delete;
/* CALCULATE YEAR AND DAY, AND KEEP DATA FOR RELEVANT YEAR. */
year = substr(date,l,4);
day = substr(date,5,4);
if &i in(l,8,15,22) then do;
if year="1996";
end;
if &i in(2,9,16,23) then do;
if year="1997";
end;
if&i in(3,10,17,24) then do;
if year="1998";
end;
if &i in(4,l 1,18,25) then do;
if year="1999";
end;
if &i in(5,12,19,26) then do;
if year="2000";
end;
if &i in(6,13,20,27) then do;
if year="2001";
end;
if&i in(7,14,21,28) then do;
if year="2002";
end;
/* OUTPUT IF SAMPLE DURATION IS HOURLY (1) OR DAILY (7), WHERE DAILY IS NONNEGATIVE.*/
if sampleduration=l or (sampleduration=7 and samplevalue >= 0) then output templ_&i;
else output junkl_&i;
run;
%end;
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* end of Ed's edit;
/* CHECK VARIOUS VARIABLES: SAMPLE DURATION, UNIT, METHOD, STATE, AND SAMPLE VALUES.*/
proc freq data=templ_&i;
tables sampleduration unit method state;
title2 "CHECK VARIOUS VARIABLES, tempi";
proc means data=templ_&i;
var samplevalue;
title2 "SAMPLEVALUE DISTRIBUTION, tempi";
/* CHECK IF MORE THAN ONE SAMPLE DURATION ON A GIVEN DAY FOR A GIVEN MONITOR+METHOD PAIR.*/
proc sort data=templ_&i;
by monid poc day method sampleduration;
proc summary data=templ_&i;
by monid poc day method sampleduration;
var samplevalue;
output out=newl_&i mean=;
proc summary data=newl_&i;
by monid poc day method;
var samplevalue;
output out=newlb_&i mean=;
proc print data=templ_&i(obs=5);
title2 "RAW DATA WITH BOTH HOURLY AND DAILY AVERAGE DATA, tempi";
proc print data=newl_&i(obs=5);
title2 "DAILY AVERAGE DATA, newl";
proc print data=newlb_&i(obs=5);
by _freq_;
title2 "CHECK IF MORE THAN ONE SAMPLE DURATION ON A GIVEN DAY, newlb";
run;
PUT DATA IN CORRECT UNITS (ug/m3) AND CHECK FOR REALLY LARGE OR NEGATIVE VALUES.
data temp2_&i(keep=monid poc method day sampleduration samplevalue)
junk2_&i(keep=monid poc method unit day samplevalue);
set newl_&i;
/* UNIT=105: mg/m3 (milligram) AT 25 C */
if unit = "005" then samplevalue=samplevalue*1000;
if unit in("001","005","105") then output temp2_&i;
else output junk2_&i;
proc print data=junk2_&i(obs=10);
title2 "DATA WITH STRANGE UNITS, SHOULD NOT PRINT, junk2";
data temp2b_&i;
set temp2_&i;
if samplevalue > 1000;
proc print data=temp2b_&i(obs=50);
title2 "SAMPLE OF LARGE VALUES, temp2b";
CHECK FOR MORE THAN ONE METHOD FOR EACH MONITOR, CHECK FOR MORE THAN ONE SAMPLE
FREQUENCY FOR EACH MONITOR+METHOD PAIR, AND CHECK FOR MORE THAN ONE SAMPLE DURATION FOR
EACH MONITOR+METHOD PAIR.
/** CHECK METHOD VARIABLE. **/
proc sort data=temp2_&i nodupkey out=temp2c_&i;
by monid poc method;
proc summary data=temp2c_&i;
by monid poc;
output out=temp2cc_&i;
data temp2ccc_&i;
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set temp2cc_&i;
if_freq_> 1;
proc print data=temp2ccc_&i(obs=50);
title2 "MORE THAN ONE METHOD FOR A MONITOR, temp2ccc";
/** CHECK SAMPLE FREQUENCY VARIABLE. **/
proc sort data=temp2_&i nodupkey out=temp2d_&i;
by monid poc method samplefreq;
proc summary data=temp2d_&i;
by monid poc method;
output out=temp2dd_&i;
run;
* a redundant sort procedure to eliminate merge error;
proc sort data=temp2_&i;
by monid poc method;
run;
data temp2ddd_&i;
merge temp2dd_&i temp2_&i;
by monid poc method;
if _freq_ > 1 then samplefreq— "ZZ";
proc sort data=temp2ddd_&i nodupkey;
by monid poc method;
proc print data=temp2ddd_&i(obs=50);
where samplefreq="ZZ";
title2 "MORE THAN ONE SAMPLE FREQUENCY FOR A MONITOR+METHOD PAIR, temp2ddd";
/** CHECK SAMPLE DURATION VARIABLE. **/
proc sort data=temp2_&i;
by monid poc method sampleduration;
proc summary data=temp2_&i;
by monid poc method sampleduration;
var samplevalue;
output out=temp2e_&i mean=;
proc summary data=temp2e_&i;
by monid poc method;
var samplevalue;
output out=temp2ee_&i n=;
data temp2eee_&i;
set temp2ee_&i;
if_freq_> 1;
proc print data=temp2eee_&i;
title2 "MORE THAN ONE SAMPLE DURATION FOR A MONITOR, temp2eee";
IF WE HAVE DAILY VALUES BASED ON MORE THAN ONE SAMPLE DURATION, CHOOSE SAMPLEDURATION=7
IN PREFERENCE TO SAMPLEDURATION= 1.
/** SEPERATE DAILY VALUES. **/
data temp3_&i;
set temp2_&i;
if sampleduration=7;
keep monid poc day sampleduration method samplevalue;
/** MAKE DATA SET OF HOURLY VALUES FOR DAYS AND MONITORS WHERE NO DAILY VALUES EXIST. **/
data temp4_&i;
merge temp3_&i(in=checkl) temp2_&i(in=check2);
by monid poc day method;
if checkl=0 and check2=l;
keep monid poc day sampleduration method samplevalue;
/** MERGE THE TWO. **/
data temp5_&i;
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merge temp3_&i temp4_&i;
by monid poe day method;
keep monid poe day sampleduration method samplevalue;
PUT IN FAKE MONITOR TO FILL OUT THE 366 DAYS OF DATA (FOR TRANSPOSE).
TRANSPOSE THE DATA. RESULTING FILE HAS VARIABLES MONID, POC, METHOD, AND DAYS 1 THROUGH 366.
DELETE DUMMY MONITOR.
CALCULATE FEBRUARY 28TH DATA AS AVERAGE OF FEBRUARY 28TH AND 29TH SAMPLING DATA, AND THEN
DELETE FEBRUARY 29TH DATA.
data temp6_&i;
merge temp5_&i tempO;
by monid;
keep monid poe method samplevalue day;
proc sort data=temp6_&i;
by monid poe method;
proc transpose data=temp6_&i out=new6_&i name=link;
by monid poe method;
id day;
data temp6b_&i(drop=_0229) junk6b_&i(keep=monid poe method 0229);
set new6_&i;
if monid = "010000000" then delete;
if 0228 ne . or 0229 ne . then _0228=mean(_0228, 0229);
MERGE IN LATITUDE AND LONGITUDE. CHECK FOR MISSING AND ZERO LATITUDE AND LONGITUDE VALUES.
data temp7_&i
junk7_&i (keep=monid poe latitude longitude method);
merge temp6b_&i(in=chekl) tempAA9(in=chek2 keep=monid latitude longitude);
by monid;
/** CHECK IF MISSING OR HAVE ZEROS FOR LATITUDE AND LONGITUDE DATA. **/
if (chekl=l and ehek2=0) or (chekl=l and ehek2=l and (latitude=0 or longitude=0 or latitudes or longitude=.)) then output
junk7_&i;
/** OUTPUT DATA OF INTEREST. **/
else if chekl=l then output temp7_&i;
proc means data=temp7_&i;
var latitude longitude;
title2 "LATITUDE AND LONGITUDE FOR MONITORS, temp7";
proc print data=junk7_&i;
var monid poc latitude longitude;
title2 "MISSING OR ZERO LATITUDE AND LONGITUDE, SHOULD NOT PRINT, junk7";
MERGE IN PROBE LOCATION, MONITOR OBJECTIVE, LAND USE, LOCATION SETTING, SAMPLING FREQUENCY.
MONITOR+METHOD PAIRS WITH MORE THAN ONE PROBE LOCATION, MONITOR OBJECTIVE, LAND USE,
LOCATION SETTING, AND/OR SAMPLING FREQUENCY HAVE 'ZZ' FLAG IN CORRESPONDING FIELD.
/** ADD IN PROBE LOCATION. **/
data temp8a_&i;
if &i<9 then do;
merge temp7_&i(in=checkl) tempMA25d(keep=monid poc probelocation);
by monid poc;
if checkl=l;
end;
if &i>8 then do;
merge temp7_&i(in=checkl) tempMA10d(keep=monid poc probelocation);
by monid poc;
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if checkl=l;
end;
/** ADD IN MONITOR OBJECTIVE. **/
data temp8b_&i;
if &i<15 then do;
merge temp8a_&i(in=checkl) tempME25d(keep=monid poc monobjective);
by monid poc;
if checkl=l;
end;
if &i>=15 then do;
merge temp8a_&i(in=checkl) tempME10d(keep=monid poc monobjective);
by monid poc;
if checkl=l;
end;
/** ADD IN LAND USE AND LOCATION SETTING. **/
data temp8c_&i;
merge temp8b_&i(in=checkl) tempAA9(keep=monid landuse locsetting);
by monid;
if checkl=l;
/** ADD IN SAMPLING FREQUENCY.
CREATE FINAL DATA FILE. PLACE BACK PARAMETER AND YEAR VARIABLES, AND CREATE SINGLE
VARIABLE MONID 15 WITH STATE CODE + COUNTY CODE + SITE ID + PARAMETER + POC. **/
data temp9_&i;
merge temp8c_&i(in=checkl) temp2ddd_&i(keep=monid poc method samplefreq);
by monid poc method;
if &i < 8 then parameter="81104";
if 8 <= &i < 15 then parameter="88101";
if 15 <= &i < 22 then parameter="81102";
if 22 <= &i then parameter="85101";
if &i in(l,8,15,22) then year="1996"
if &i in(2,9,16,23) then year="1997"
if &i in(3,10,17,24) then year="1998
if &i in(4,l 1,18,25) then year="1999
if &i in(5,12,19,26) then year="2000
if &i in(6,13,20,27) then year="2001
if &i in(7,14,21,28) then year="2002
monid 15 = monid || parameter || poc;
if checkl=l;
keep year monidl5 latitude longitude method landuse locsetting probelocation monobjective
samplefreq 0101- 0131 0201- 0228 0301- 0331 0401- 0430 0501- 0531 0601- 0630
0701- 0731 0801- 0831 0901- 0930 1001- 1031 1101- 1130 1201- 1231;
EXPORT FINAL DATA.
data _null_;
set temp9_&i;
file outf&i lrecl= 10000;
heading="year, monid, latitude, longitude, method, landuse, locationsetting, probelocation, monitorobjective, samplefrequency,
365dailyvalues, ";
if _n_=l then put heading;
put year monidl5 latitude longitude method
landuse locsetting probelocation monobjective samplefreq ( 0101- 0131) (',') ( 0201- 0228) (',')
( 0301- 0331) (',') ( 0401- 0430) (',') ( 0501- 0531) (',') ( 0601- 0630) (',') ( 0701- 0731) (',') ( 0801- 0831) (',')
( 0901- 0930) (',') ( 1001- 1031) (',') (_1101-1130) (',') ( 1201- 1231) (',');
EXPORT JUNK DATA.
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/** PUT ALL JUNK FILES IN SAME FORMAT WITH SAME VARIABLES: MONID, POC, LATITUDE, LONGITUDE,
METHOD, SAMPLE DURATION, UNIT, DAY AND SAMPLE VALUE. **/
data junk6b_&i;
set junk6b_&i;
if 0229 ne
day="0229";
data junk6b_&i;
set junk6b_&i;
sampleduration=.;
unit="";
latitudes;
longitude=.;
keep monid poc latitude longitude method sampleduration unit day;
proc sort data=junk7_&i nodupkey out=junk7_&i;
by monid poc method;
data _null_;
set junk6b_&i junkl_&i junk2_&i junk7_&i;
file outjk&i lrecl=5000;
heading="monid, poc, latitude, longitude, method, sampleduration, unit, day, samplevalue";
if _n_=l then put heading;
put monid poc latitude longitude method sampleduration unit
day samplevalue;
run;
%end;
%mend;
%getdata;
A.4.2 SAS Code for Processing Raw Ozone Monitor Data for Input to BenMAP
title "file: amp500_501_o3data.sas";
02/12/03.
Etienne Gabel.
This SAS code reads in from raw AMP500 file which contains monitor description data,
and from raw AMP501 file which contains formatted hourly ozone monitor sampling data.
The code prepares a data set which contains the hourly data within a single array of
24*365 = 8760 hours. Each observation (row) corresponds to a unique monitor (including
poc code) and method pair.
February 29th data from leap year are dropped, since CAPMS is hardwired to accept 8760
hours of ozone data.
7/16/03 - ed al-hussainy - added 1997, 1998 and 2002 data
note that 2002 input data is fragmented into 4 files
re-organized junk export macro
7/28/03 - ed - modified macro loop to prevent resource overload (too many temp files)
libname out "J:\Ozone\Output\";
filename yr8760 "J:\Ozone\Data\year_8760_hours.csv";
filename infilel "J:\Ozone\Data\EG44201-1996.TXT";
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filename infile2 "J:\Ozone\Data\EG44201-1999.TXT";
filename infile3 "J:\Ozone\Data\EG44201-2000.TXT";
filename infile4 "J:\Ozone\Data\EG44201-2001.TXT";
filename infile5 "J:\Ozone\Data\RD_501_44201_1997.TXT";
filename infile6 "J:\Ozone\Data\RD_501_44201_1998.TXT";
filename infile7 "J:\Ozone\Data\ozone-regl-3-2002.txt";
filename infile8 "J:\Ozone\Data\ozone-reg4-5-2002.txt";
filename infile9 "J:\Ozone\Data\ozone-reg6-8-2002.txt";
filename infilelO "J:\Ozone\Data\ozone-reg9-10-25-2002.txt";
filename outfl "J:\Ozone\Output\Ozone96.TXT'
filename outf2 "J:\Ozone\Output\Ozone99.TXT'
filename outfi "J:\Ozone\Output\OzoneOO.TXT'
filename outf4 "J:\Ozone\Output\OzoneOl.TXT'
filename outf5 "J:\Ozone\Output\Ozone97.TXT'
filename outf6 "J:\Ozone\Output\Ozone98.TXT'
filename outf7 "J:\Ozone\Output\Ozone02.TXT'
filename outjkl "J:\Ozone\Output\Ozone96junk.TXT'
filename outjk2 "J:\Ozone\Output\Ozone99junk.TXT'
filename outjk3 "J:\Ozone\Output\OzoneOOjunk.TXT'
filename outjk4 "J:\Ozone\Output\OzoneOljunk.TXT'
filename outjk5 "J:\Ozone\Output\Ozone97junk.TXT'
filename outjk6 "J:\Ozone\Output\Ozone98junk.TXT'
filename outjk7 "J:\Ozone\Output\Ozone02junk.TXT'
options missing =
READ IN AMP500 FILE. CREATE MONITOR ID VARIABLE WITH STATE, COUNTY, AND SITE ID.
TRANSACTION CODE AA' INCLUDES MONID, LAND USE, LOCATION SETTING, LATITUDE AND LONGITUDE
VARIABLES. TRANSACTION CODE 'MA' INCLUDES MONID, POC AND PROBE LOCATION VARIABLES.
TRANSACTION CODE 'ME' INCLUDES MONID, POC AND MONITOR OBJECTIVE VARIABLES.
* start of Ed's edit;
/* AMP 500 data */
%let netdata=H:\ENVR\MCCUBBIN\capms_revision\data_bases\air_monitor\sas_work\data\AMP500 file — contains monitor
description info;
filename amp500_l "&netdata\EG_500_SELP ARM_1997_2002.TXT";
filename amp500_2 "&netdata\AMP500.TXT";
* read-in new and old AMP500 data;
%macro read_amp500(param);
data tempAA_¶m;
infile amp500_¶m firstobs=l delimiter='|' missover lrecl = 6000 dsd;
length transactioncode $ 2 state $ 2 county $ 3 siteid $ 4 poc $ 1 xl-xl4 $ 5 landuse $ 20 locsetting $ 50
probelocation $ 20 monobjective $ 50 monid $ 9;
input transactioncode $ @;
if transactioncode="AA" then do;
input actioncode $ state $ county $ siteid $ latitude longitude xl-xl4 $ landuse $
locsetting $;
end;
if transactioncode="MA" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ xl-x4 $ probelocation $;
if probelocation="" then delete;
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end;
if transactioncode="ME" then do;
input aetioneode $ state $ county $ siteid $ parameter $ poc $ monobjective $;
if monobjective="" then delete;
end;
drop xl-xl4 aetioneode;
* Miami-Dade county correction;
if state="12" and county="025" then county="086";
monid = state || county || siteid;
if transactioncode in ("AA" "MA" "ME");
run;
proc sort data=tempAA_¶m;
by monid;
run;
%mend read_amp500;
%read_amp500(l);
%read_amp500(2);
* merge new and old AMP500 data, separate data based on transactioncode and parameter;
data tempAA(keep=monid state county siteid latitude longitude landuse locsetting)
tempMA(keep=monid state county siteid parameter poc probelocation)
tempME(keep=monid state county siteid parameter poc monobj ective);
* note the merge order - tempAAl overwrites tempAA_2 in case of duplicate entries;
merge tempAA_2(in=dum2) tempAA_l(in=duml);
by monid;
if transactioncode="AA" then output tempAA;
else if transactioncode="MA" then do;
if parameter= "44201" then output tempMA;
end;
else if transactioncode="ME" then do;
if parameter= "44201" then output tempME;
end;
run;
* end of Ed's edit;
proc sort data=tempAA;
by monid;
proc sort data=tempMA;
by monid poc;
proc sort data=tempME;
by monid poc;
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE LAND USE CODE. **/
proc sort data=tempAA out=tempAAl nodupkey;
by monid landuse;
proc summary data=tempAA 1;
by monid;
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output out=tempAA2;
data tempAA3;
merge tempAA2 tempAA;
by monid;
if _freq_> 1 then landuse="ZZ";
keep monid latitude longitude landuse locsetting;
proe sort data=tempAA3 out=tempAA4 nodupkey;
by monid;
proe print data=tempAA4(obs=50);
where landuse="ZZ";
title2 "MORE THAN ONE LAND USE FOR A MONITOR IN AMP500 FILE, tempAA4";
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE LOCATION SETTING CODE. **/
proe sort data=tempAA out=tempAA5 nodupkey;
by monid locsetting;
proe summary data=tempAA5;
by monid;
output out=tempAA6;
data tempAA7;
merge tempAA6(in=checkl) tempAA;
by monid;
if checkl=l and _freq_ > 1 then locsetting="ZZ";
keep monid latitude longitude landuse locsetting;
proe sort data=tempAA7 out=tempAA8 nodupkey;
by monid;
proe print data=tempAA8(obs=50);
where locsetting="ZZ";
title2 "MORE THAN ONE LOCATION SETTING FOR A MONITOR IN AMP500 FILE, tempAA8";
/** MAKE SINGLE FILE WITH TRANSACTION AA DATA, WITH FLAG FOR REPEATING LAND USE OR LOCATION
SETTING CODES. **/
data tempAA9;
merge tempAA4(drop=locsetting) tempAA8(drop=landuse);
by monid;
keep monid latitude longitude landuse locsetting;
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE PROBE LOCATION CODE. **/
proe sort data=tempMA out=tempMAl nodupkey;
by monid poc probelocation;
proe summary data=tempMA 1;
by monid poc;
output out=tempMA2;
data tempMA3;
merge tempMA2 tempMA;
by monid poc;
if _freq_ > 1 then probelocation—"ZZ";
keep monid poc probelocation;
proe sort data=tempMA3 out=tempMA4 nodupkey;
by monid poc;
proe print data=tempMA4(obs=50);
where probelocation="ZZ";
title2 "MORE THAN ONE PROBE LOCATION FOR A MONITOR IN AMP500 FILE, tempMA4";
/** ENTER FLAG "ZZ" FOR MONITORS IN AMP500 FILE WITH MORE THAN ONE MONITOR OBJECTIVE CODE. **/
proe sort data=tempME out=tempMEl nodupkey;
by monid poc monobjective;
proe summary data=tempME 1;
by monid poc;
output out=tempME2;
data tempME3;
merge tempME2 tempME;
by monid poc;
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if _freq_ > 1 then monobjective—"ZZ";
keep monid poe monobjective;
proc sort data=tempME3 out=tempME4 nodupkey;
by monid poe;
proc print data=tempME4(obs=50);
where monobjective="ZZ";
title2 "MORE THAN ONE MONITOR OBJECTIVE FOR A MONITOR IN AMP500 FILE, tempME4";
run;
READ IN COMPLETE YEAR TEMPLATE FOR OZONE (365*24 = 8760 HOURS). TEMPLATE EXCLUDES
FEBRUARY 29TH FOR LEAP YEARS, SINCE CAPMS IS HARDWIRED TO ACCEPT 8760 HOURS.
data tempO;
length monid $ 9 hour $ 8 sampletime $ 9;
infile yr8760 firstobs=2 delimiter=',' missover;
input monid $ day $ time $ sampletime $ hour $;
/** ADD ZERO TO MONID TO MAKE IT APPEAR FIRST IN THE MONITOR LIST AFTER THE MERGE BELOW. **/
monid = "0" || monid;
keep monid sampletime hour;
proc print data=temp0(obs=5);
title2 "TEMPLATE FOR FULL YEAR, tempO";
run;
* start of Ed's edit;
MACRO TO LOOP THROUGH DATA SETS.
%macro getdata;
%do i=l %to 7;
READ IN AMP501 FILES. NOTE: DATA ONLY HAS SAMPLE DURATION = 1 (=HOURLY DATA).
%* for 2002 data ( in loop), read in 4 source files (infile 7-10) and merge them;
%if &i=7 %then %do;
%do j=7 %to 10; %*read-in 2002 data (4 files);
data templ_&j(keep=monid state county siteid poc parameter sampleduration unit method year
samplefreq sampletime samplevalue)
junkl_&j(keep=monid poc parameter method sampleduration sampletime samplevalue);
infile infile&j firstobs=l delimiter='|' missover lrecl = 6000 dsd;
length transactioncode $ 2 actioncode $ 1 state $ 2 county $ 3 siteid $ 4 parameter $ 5
poc $ 1 sampleduration unit $ 3 method 8 date $ 8 year $ 4 starttime $ 5
samplefreq $ 8 monid $ 9;
input transactioncode $ @;
if transactioncode="RD" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ sampleduration unit $
method date $ starttime $ samplevalue xl $ samplefreq $;
if parameter="44201";
* Miami-Dade county correction;
if state="12" and county="025" then county="086";
/* CREATE MONITOR ID. MONID VARIABLE EXCLUDES POC CODE. */
monid = state || county || siteid;
/* DROP MISSING OBSERVATIONS. */
if samplevalue=. then delete;
/* CALCULATE YEAR AND KEEP DATA FOR RELEVANT YEAR. */
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year = substr(date,l,4);
if year="2002";
/* MAKE SAMPLE TIME A SINGLE VARIABLE (FOR TRANSPOSE BELOW) */
sampletime = substr(date,5,4) || starttime;
/* OUTPUT IF SAMPLE DURATION IS HOURLY (1).*/
if sampleduration=l then output tempi_&j;
else output junk l_&j;
end;
ran;
%end;
data temp 1;
settempl_7 templ_8 templ_9 templlO;
ran;
data junkl;
set junkl_7 junkl_8 junkl_9 junkllO;
ran;
%end;
* read-in and process all other data;
%if &i A= 7 %then %do;
data templ(keep=monid state county siteid poc parameter sampleduration unit method year
samplefreq sampletime samplevalue)
junkl(keep=monid poc parameter method sampleduration sampletime samplevalue);
infile infile&i firstobs=l delimiter-|' missover lrecl = 6000 dsd;
length transactioncode $ 2 actioncode $ 1 state $ 2 county $ 3 siteid $ 4 parameter $ 5
poc $ 1 sampleduration unit $ 3 method 8 date $ 8 year $ 4 starttime $ 5
samplefreq $ 8 monid $ 9;
input transactioncode $ @;
if transactioncode="RD" then do;
input actioncode $ state $ county $ siteid $ parameter $ poc $ sampleduration unit $
method date $ starttime $ samplevalue xl $ samplefreq $;
if parameter="44201";
%* Miami-Dade county correction;
if state="12" and county="025" then county="086";
/* CREATE MONITOR ID. MONID VARIABLE EXCLUDES POC CODE. */
monid = state || county || siteid;
/* DROP MISSING OBSERVATIONS. */
if samplevalue=. then delete;
/* CALCULATE YEAR AND KEEP DATA FOR RELEVANT YEAR. */
year = substr(date,l,4);
if &i= 1 then do;
if year="1996";
end;
if &i=2 then do;
if year="1999";
end;
if &i=3 then do;
if year="2000";
end;
if &i=4 then do;
if year="2001";
end;
if &i=5 then do;
if year="1997";
end;
if &i=6 then do;
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if year="1998";
end;
/* MAKE SAMPLE TIME A SINGLE VARIABLE (FOR TRANSPOSE BELOW) */
sampletime = substr(date,5,4) || starttime;
/* OUTPUT IF SAMPLE DURATION IS HOURLY (1).*/
if sampleduration=l then output tempi;
else output junkl;
end;
ran;
%end;
/** CHECK FOR MORE THAN ONE SAMPLE VALUE FOR A GIVEN MONITOR, DAY AND HOUR. **/
proc sort data=temp 1;
by monid poc method sampleduration unit samplefreq sampletime;
proc summary data=templ;
by monid poc method sampleduration unit samplefreq sampletime;
var samplevalue;
output out=newl mean=;
/*
proc print data=newl_&i;
where _freq_ > 1;
title2 "MORE THAN ONE SAMPLE VALUE FOR SAME DAY AND HOUR, SHOULD NOT PRINT, newl";
*/
PUT DATA IN CORRECT UNITS (PPB) AND CHECK FOR REALLY LARGE OR NEGATIVE VALUES.
data temp2(keep=monid poc method sampletime samplefreq samplevalue)
junk2(keep=monid poc method unit sampletime samplevalue);
set new 1;
/* UNIT=1: ug/m3 AT 25 C */
if unit = "001" then samplevalue=samplevalue*.51;
/* UNIT=40: PARTS PER HUNDRED MILLION */
if unit = "040" then samplevalue=samplevalue*10;
/* UNIT=007: PARTS PER MILLION */
if unit = "007" then samplevalue=samplevalue*1000;
if unit in("001","007","008","040") then output temp2;
else output junk2;
data temp2b;
set temp2;
if samplevalue > 1000;
/*
proc print data=temp2b_&i(obs=50);
title2 "SAMPLE OF LARGE VALUES, temp2b";
*/
CHECK FOR MORE THAN ONE METHOD FOR EACH MONITOR, AND CHECK FOR MORE THAN ONE SAMPLE
FREQUENCY FOR EACH MONITOR+METHOD PAIR.
/** CHECK METHOD VARIABLE. **/
proc sort data=temp2 nodupkey out=temp2c;
by monid poc method;
proc summary data=temp2c;
by monid poc;
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output out=temp2cc;
data temp2ccc;
set temp2cc;
if_freq_> 1;
/*
proc print data=temp2ccc_&i(obs=50);
title2 "MORE THAN ONE METHOD FOR A MONITOR, temp2ccc";
*/
/** CHECK SAMPLE FREQUENCY VARIABLE. **/
proc sort data=temp2 nodupkey out=temp2d;
by monid poc method samplefreq;
proc summary data=temp2d;
by monid poc method;
output out=temp2dd;
data temp2ddd;
merge temp2dd temp2;
by monid poc method;
if _freq_ > 1 then samplefreq— "ZZ";
proc sort data=temp2ddd nodupkey;
by monid poc method;
/*
proc print data=temp2ddd_&i(obs=50);
where samplefreq="ZZ";
title2 "MORE THAN ONE SAMPLE FREQUENCY FOR A MONITOR+METHOD PAIR, temp2ddd";
*/
MERGE WITH HOUR TEMPLATE. 365 DAYS*24 HOURS = 8760 HOURS. REPLACE SAMPLETIME VARIABLE (example
010100:00 for Jan lrst at midnight) WITH HOUR VARIABLE (example hourl for Jan lrst at midnight).
proc sort data=temp2 sortsize=8M;
by sampletime;
proc sort data=temp0;
by sampletime;
data temp3;
merge temp2(in=checkl) tempO(keep=sampletime hour);
by sampletime;
if checkl=l;
keep monid poc method sampletime hour samplevalue;
PUT IN FAKE MONITOR TO FILL OUT THE 8760 HOURS OF DATA (FOR TRANSPOSE). REMOVED LATER.
REMOVE DATA FROM FEBRUARY 29TH FOR LEAP YEAR.
proc sort data=temp3 sortsize=8M;
by monid poc method;
data temp3b;
set temp0(keep=monid hour) temp3;
keep monid poc method sampletime hour samplevalue;
data temp3c(keep=monid poc method hour samplevalue)
junk3(keep=monid poc method sampletime samplevalue);
set temp3b;
/** DROP LEAP YEAR FEBRUARY 29TH VALUES. **/
if hour="" then output junk3;
else output temp3c;
TRANSPOSE THE DATA. RESULTING FILE HAS VARIABLES MONID, POC, METHOD, AND HOUR 1 THROUGH 8760.
DELETE DUMMY MONITOR.
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/** TRANSPOSE DATA **/
proc transpose data=temp3c out=new3 name=link;
by monid poc method;
id hour;
data temp3d;
set new3;
/** DELETE DUMMY MONITOR **/
if monid = "010000000" then delete;
keep monid poc method hourl-hour8760;
MERGE IN LATITUDE AND LONGITUDE. CHECK FOR MISSING AND ZERO LATITUDE AND LONGITUDE VALUES.
data temp4 (keep=monid poc latitude longitude method hourl-hour8760)
junk4 (keep=monid poc latitude longitude method);
merge temp3d(in=chekl) tempAA9(in=chek2 keep=monid latitude longitude);
by monid;
/** CHECK IF MISSING OR HAVE ZEROS FOR LATITUDE AND LONGITUDE DATA. **/
if (chekl=l and chek2=0) or (chekl=l and chek2=l and (latitude=0 or longitude=0 or latitudes or longitude=.)) then output junk4;
/** OUTPUT DATA OF INTEREST. **/
else if chekl=l then output temp4;
proc univariate data=temp4;
var latitude longitude;
title2 "LATITUDE AND LONGITUDE FOR MONITORS, temp4";
proc print data=junk4_&i;
var monid poc latitude longitude;
title2 "MISSING OR ZERO LATITUDE AND LONGITUDE, SHOULD NOT PRINT, junk4";
MERGE IN PROBE LOCATION, MONITOR OBJECTIVE, LAND USE, LOCATION SETTING, SAMPLING FREQUENCY.
MONITOR+METHOD PAIRS WITH MORE THAN ONE PROBE LOCATION, MONITOR OBJECTIVE, LAND USE,
LOCATION SETTING, AND/OR SAMPLING FREQUENCY HAVE 'ZZ' FLAG IN CORRESPONDING FIELD.
/** ADD IN PROBE LOCATION. **/
data temp5a;
merge temp4(in=checkl) tempMA4(keep=monid poc probelocation);
by monid poc;
if checkl=l;
/** ADD IN MONITOR OBJECTIVE. **/
data temp5b;
merge temp5a(in=checkl) tempME4(keep=monid poc monobjective);
by monid poc;
if checkl=l;
/** ADD IN LAND USE AND LOCATION SETTING. **/
data temp5c;
merge temp5b(in=checkl) tempAA9(keep=monid landuse locsetting);
by monid;
if checkl=l;
/** ADD IN SAMPLING FREQUENCY.
CREATE FINAL DATA FILE. PLACE BACK PARAMETER AND YEAR VARIABLES, AND CREATE SINGLE
VARIABLE MONID 15 WITH STATE CODE + COUNTY CODE + SITE ID + PARAMETER + POC. **/
data temp6;
merge temp5c(in=checkl) temp2ddd(keep=monid poc method samplefreq);
by monid poc method;
parameter = "44201";
if &i=l then year="1996";
if &i=2 then year="1999"
if &i=3 then year="2000"
if &i=4 then year="2001"
if &i=5 then year="1997"
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if &i=6 then year="1998";
if &i=7 then year="2002";
if checkl=l;
monidl5 = monid || parameter || poe;
keep year monidl5 latitude longitude method landuse loesetting probelocation monobjective
samplefreq hourl-hour8760;
EXPORT FINAL DATA.
data _null_;
set temp6;
file outf&i lreel=500000;
heading="year, monid, latitude, longitude, method, landuse, locationsetting, probelocation, monitorobjective, samplefrequency,
8760hourlyvalues,
if _n_=l then put heading;
put year monidl5 latitude longitude method landuse loesetting
probelocation monobjective samplefreq (hourl-hour8760)
EXPORT JUNK DATA.
/** PUT ALL JUNK FILES IN SAME FORMAT WITH SAME VARIABLES: MONID, POC, LATITUDE, LONGITUDE,
METHOD, SAMPLE DURATION, UNIT, SAMPLE TIME AND SAMPLE VALUE. **/
proc sort data=junk4 nodupkey out=junk4;
by monid poc method;
data junk4;
set junk4;
sampleduration="";
unit="";
sampletime="";
samplevalue=.;
keep monid poc latitude longitude method sampleduration unit sampletime samplevalue;
run;
%end;
%mend getdata;
%macro dojunk;
%do j=l %to 7;
data _null_;
set junk&j .1 junk&j .2 junk&j .3 junk&j .4;
file outjk&j lrecl=5000;
heading="monid, poc, latitude, longitude, method, sampleduration, unit, sampletime, samplevalue";
if _n_= 1 then put heading;
put monid poc latitude longitude method sampleduration unit
sampletimesamplevalue;
run;
%end;
%mend dojunk;
%getdata;
%dojunk;
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Appendix B: Population Data
BenMAP calculates health impacts at the level of U.S. counties as well as for a variety of grid
structures used in air quality modeling (i.e., CMAQ 36 km, REMSAD 36km, REMSAD 12 km, and UAM-
V/CAMX 12km). In this description, we use the term "population grid-cells" to refer to counties or the
cells within an air quality modeling grid. The foundation for calculating the population level in the
population grid-cells is the 1990 and 2000 Census block data.1 A separate application developed by Abt
Associates, called "PopGrid," combines the Census block data with any user-specified set of population
grid-cells, so long as they are defined by a GIS shape file. Unfortunately, PopGrid relies on extremely large
census files that are too large to include with BenMAP. If you need a custom population grid please contact
Bryan Hubbell.2
If the geographic center of a Census block falls within a population grid-cell, PopGrid assigns the
block population to this particular population grid-cell. Note that the grid-cells in air quality model, such as
CMAQ, may cross multiple county boundaries. PopGrid keeps track of the total number of people by
county within a particular population grid-cell. Of course, when the population grid-cell is for U.S.
counties, then there is only a single county associated with the population grid-cell. With air quality
models, there can clearly be multiple counties in a population grid-cell. Keeping track of the total number
of people in a county is useful in the estimation of adverse health effects, where the calculation of
premature mortality depends on county-level mortality rates. It is also useful in the presentation of health
benefits, should you want state- and county-level estimates, as opposed to national estimates.
Within any given population grid-cell, BenMAP has 256 demographic variables, including 180
unique racial-gender-age groups: 19 age groups by gender by 5 racial groups (19*2*5=180). In addition
there is an Hispanic ethnicity variable, which includes a number of different racial groups, as well as a
number of variables that aggregate the population by race and gender. Exhibit B-l presents the 256
population variables available in BenMAP. As discussed below, these variables are available for use in
developing age estimates in whatever grouping desired by you.
Exhibit B-l Demographic Groups and Variables Available in BenMAP
Racial/Ethnic Group
Gender
Age
# Variables
White, African American, Asian,
American Indian, Other, Hispanic
Female,
Male
<1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+
228
All
-
<1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+
19
All
Female,
Male
-
2
White, African American, Asian,
American Indian, Other, Hispanic
-
-
6
All
-
-
1
^eolytics (2001a; 2002) provided the 1990 and 2000 census data.
2 Bryan Hubbell: C339-01, USEPA Mailroom, Research Triangle Park, NC 27711. Email:
hubbell,brvan@,epa.gov. Telephone: 919-541-0621.
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Appendix B. Population Data
B .1 Census Data 1990
In developing the 1990 Census data, we use block-level data in conjunction with detailed tract-level
data. The block data has 10 racial/ethnic groups, each divided between persons 17 and under and 18 and
older, to give a total of 20 variables (Exhibit B-2). The tract-level data has essentially all of the age
groupings of interest; some combining of variables is necessary to obtain the final set of variables used in
BenMAP (Exhibit B-3).
Exhibit B-2 Race, Ethnicity and Age Variables in 1990 Census Block Data
Race
Ethnicity
Age
White
Hispanic / Non-Hispanic
0-17, 18+
African American
Hispanic / Non-Hispanic
0-17, 18+
Asian & Pacific Islander
Hispanic / Non-Hispanic
0-17, 18+
Native American
Hispanic / Non-Hispanic
0-17, 18+
Other
Hispanic / Non-Hispanic
0-17, 18+
Source: Geolytics (2001a).
Exhibit B-3 Race, Ethnicity and Age Variables in 1990 Census Tract Data
Type
Race / Ethnicity
Gender
Age
Initial
Variables
White, African American, Asian,
American Indian, Other, Hispanic
Female,
Male
<1, 1-2, 3-4, 5, 6, 7-9, 10-11, 12-13, 14, 15, 16, 17, 18, 19, 20,
21, 22-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59,
60-61, 62-64, 65-69, 70-74, 75-79, 80-84, 85+
Final
Variables
White, African American, Asian,
American Indian, Other, Hispanic
Female,
Male
<1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44,
45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+
Source: Geolytics (2001b).
There is a trade-off between the geographic detail and the number of variables. To protect the
confidentiality of the respondents to the Census, the block data, which are the most detailed geographically,
have only two age groups. The Census groupings that cover a larger geographic area, such as the
blockgroup and tract, have many more demographic variables. For reasons discussed below, we do not use
the blockgroup data, and instead use the tract data to estimate the population distribution in each block. In
combining the information at the tract level with that at the block level, we assume that the age distribution
in a particular census tract is similar to all of the blocks comprising that tract.
The first step in the process of using the tract data is to combine the "initial" set of tract variables,
to form a set of "intermediate" variables that better matches the "final" set of variables desired and the
characteristics of the block data. Table B-3 lists the initial and final variables.
The goal is to estimate the 38 age-gender groups for each of the five racial groups and for the
Hispanic ethnic group. The approach for the five racial groups is similar, so we simply give a few examples
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Appendix B. Population Data
for the treatment of the Other group. Since Hispanics cross multiple racial groups, we give examples
specific for Hispanics.
B .1.1 Other Group Estimation with 1990 Census
The first step is to get a total estimate for the Other category at the block level:
°therbiock,0-17 = (Other Hisp agebIock 0_l7 + Other nonHisp agehiock 0 l7 )
°therbiock, i8+ = (Other Hisp ageblock 18+ + Other nonHisp agebIock K+)
We then use the intermediate variables from the tract data to apportion the two age groups in the
Other racial group. For the case of children ages 1 to 4, we calculate:
Othertract j_4
Other — Other ¦
Kyin(^r block, 1-4 block, 0-17 ^,7
For the case of children ages 15-19, we use both block variables:
Othertract i5_i7 OtherIraci l8 l9
Otherblock i5_i9 = Otherblock + Otherhktck l8+ ,
tract, 0-17 tract, 18+
The calculation of additional age and gender groupings is similar. However, it should be noted that
in some cases there are data available at the block data, but the larger tract data indicate that no one for a
particular demographic group is living there. In turn this leads to dividing by zero. This curiosity arises
because the tract data is based on a 10 percent sample of the population that fills out the long form, while
the block data are based on the 100 percent sample of the population that fills out the short form. In those
instances, where the tract data have zero population, we use county-level data:
Othert'„ounty, i_4
Otherblock l_4 = Otherblock 0_17 ¦
^>lnercOUnty, 0-17
Finally, we note that in a few exceptional cases we used state-level data.
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Appendix B. Population Data
B .1.2 Hispanic Group Estimation with 1990 Census
We use a similar process to estimate the Hispanic groups. We start by estimating the population at
the block level for ages 0 to 17 and for ages 18 and older:
5
Hisp age hloc:k l]_n = X Hispagehlock (i_n i
7 = 1
5
Hisp ageblock
, 18+ z Hisp ageblock
,18+,i
i= 1
where i=l to 5 refers to the five racial groups: White, African American, Asian, American Indian and Other.
To then estimate the 38 age-gender groupings for Hispanics, we then perform the same calculations
as when estimating the 38 age-gender groups for the five racial groups. For example, in calculating the
number of Hispanics ages 1 to 4:
HiSP tract, 1-4
HlSP block, 1-4 = HlSP block, 0-17 '77—:
HlSp'tract, 0-17
As with the five racial groups, the Hispanic ethnic group has instances where the tract data indicate
zero Hispanics while the block data has a positive number. To avoid dividing by zero, we then turn to the
county-level to provide information to apportion the block data between different age groups.
B .2 Census Data 2000
The 2000 Census allows respondents to choose more than one racial category, unlike the 1990
Census, which allowed only one choice. As a result there are seven racial categories in the 2000 Census
versus five in the 1990 Census. To make the 2000 Census data consistent with the 1990 Census, we
reduced the seven racial groups to the five used in the 1990 Census.
The initial data set at the block level includes 368 demographic groups: seven racial groups and
Hispanic ethnicity, by 23 pre-defined age groups by gender (Table B-4). As discussed below, we generated
for the 2000 Census the same 256 demographic variables that we generated for the 1990 Census.
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Appendix B. Population Data
Exhibit B-4 Race, Ethnicity and Age Variables in 2000 Census Block Data
Type
Race / Ethnicity
Gender
Age
Initial Variables White Alone, Black Alone, Native American
Male, Female 0-5, 5-10, 10-14, 15-17, 18-19, 20, 21,
Alone, Asian Alone, Pacific Islander /
Hawaiian Alone, Other Alone, Two or More
Alone, Hispanic (Non-Exclusive)
22-24, 25-29, 30-34, 35-39, 40-44, 45-
49, 50-54, 55-59, 60-61, 62-64, 65-66,
67-69, 70-74, 75-79, 80-84 85+
Final Variables White, African American, Asian & Pacific
(identical to Islander, American Indian, Other, Hispanic
1990 variables)
Female, Male <1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29,
30-34, 35-39, 40-44, 45-49, 50-54,
55-59, 60-64, 65-69, 70-74, 75-79,
80-84, 85+
Source: Geolytics (2002). Note: Some population values were errors in the original Census data (e.g., values of a billion or more).
Following personal communication with Geolytics, these were set to zero.
Because the 2000 Census includes somewhat different age groupings than that for the final set
generated for the 1990 Census. Age variables 15-17 and 18-19 are combined, 20, 21, and 22-24 are
combined, 60-61 and 62-24 are combined, and 65-66 and 67-69 are combined at the block level. One
variable, under 5 years, must be split into two variables (Under 1 and 1-4 years). Using the previous
assumption that population is uniformly distributed within age groups, we apply a factor of 1/5 to create the
<1 age group and 4/5 to create the 1-4 age group.
B .2.1 Matching Racial Categories in the 1990 and 2000 Censuses
Unlike the 1990 Census, respondents in the 2000 Census respondents could check more than one
box for race, so the reported results included a grouping of individuals that had checked two or more racial
categories. In addition, the 2000 Census separately reported the categories "Pacific Islander / Hawaiian
Along" and "Asian Alone." To make the racial groupings comparable with the 1990 Census, we first
combined Pacific Islander / Hawaiian Alone with the Asian Alone category to form the category Asian and
Pacific Islander category. Then we divided the category Two-or-More between the remaining five racial
categories.
Exhibit B-3 presents the estimated percentage of the national population by five racial groups: (1)
American Indian or Alaska Native, (2) Asian or Pacific Islander, (3) Black, (4) White, and (5) Other, as
well as for four combinations: (1) American Indian or Alaska Native (AIAN)/White, (2) Asian or Pacific
Islander (API)/White, (3) Black/White, and (4) Other combinations. Slightly over 98 percent of individuals
chose a single racial category, with 1.45 percent choosing three AIAN/White, API/White, and Black/White,
and 0.30 choosing other combinations (e.g., Black/Asian). Exhibit B-3 also presents the estimated primary
racial affiliation of individuals in these subcategories if they were to choose a single racial affiliation.
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Appendix B. Population Data
Exhibit B-5. Distribution of Racial Groups
Racial Category
% of T otal
U.S.
Population"
% of Population in
Sub-Groups by Primary Racial Affiliation b
AIAN API
Black
White
Other
All
American Indian or Alaska Native
(AIAN)
0.85
100
-
-
-
100
Asian or Pacific Islander (API)
3.35
100
-
-
-
100
Black
12.07
-
100
-
-
100
White
79.72
-
-
100
-
100
Other race
2.25
-
-
-
100
100
AIAN/White
0.89
12.4
-
80.9
6.7
100
API/White
0.30
34.6
-
46.9
18.4
100
Black/White
0.26
-
48.2
25.2
26.6
100
Other combinations c
0.30
-
-
-
100.0
100
Two-or-More Sub-Total d
1.75
6.3 5.9
7.2
52.9
27.7
100
1 All percentages weighted to be nationally representative. Percentages taken from Parker and Makuc (2001, Table 2), who cited the
National Health Interview Survey 1993-1995, APPENDIX: Percent Distribution (Standard Error) of Primary Racial Identification
for Selected Detailed Race Groups.
b Primary racial affiliation based on survey results from Parker and Makuc (2001, Appendix).
c Parker and Makuc (2001) did not provide an estimate of the primary racial affiliation for "Other combinations, so we assume that it
belongs to the "Other" category. Note that they did provide the primary racial affiliation for a fourth group "Black/AIAN:" 85.4%
Black, 7.0% AIAN, and 7.6% Other. However, we do not have an estimate of the relative abundance of Black/AIAN in the general
population, so we have dropped it from further consideration.
d As described in the text below, we calculated the percentages in this row from the percentages in the previous four rows for
AIAN/White, API/White, Black/White, and Other combinations.
To estimate how to assign a single racial group for individuals that chose two or more racial groups,
we used the results of Exhibit B-3 for the three main categories for which we an estimate of the primary
racial affiliation: AIAN/White, API/White, and Black/White. To account for the 0.30 percent of the
population in other combinations, we For each Census block, we assume that .89 / (.89+.30+.26+.30) =
50.8% of respondents in the Two or More category will fall into the AIAN / White category, and of these,
80.9% would primarily identify themselves as White if they were to choose a single racial category, 12.4%
would primarily identify themselves as American Indian or Alaska Native, and 6.7% would primarily
identify themselves as Other. Thus 0.508 * .809 = 41% of Two or More we will call White, 10% we
identify as Native American, and 5% as Other.
We did not attempt to predict what respondents in the 'Other Combinations' category would have
selected if they were to choose a single racial category, so we assume they are part of the "Other" category.
To estimate the number of individuals in each of the five races, we performed the following calculations:
NativeAmerican = NativeAmericanAlone Pop +TwoorMorePap
AIAN/ White
Nat%
MullipleRace.
¦ AIAN%
AIAN/White
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Appendix B. Population Data
Asian = AsianAlonePop + Pacificlslander / HawaiianPop +TwoorMorePop
API / White Na
MultipleRace A
API%,
Black = BlackAlonePop +TwoorMore Pop
Black / WhiteNat„/o
MultipleRaceNat% Black iwute ^
White = White AlonePop + Two orMorePop
ALAN I White
Nat%
MultipleRace Nat%
API / WhiteNat%
MultipleRace Nat%
Black / White
MultipleRace
¦ White% A1AN/White +
• White% API/white +
^ Nat %
White%
Black/White
Nat%
Other = Other AlonePop + Two or MorePop
( AIAN /White Natv \
Uu,,lpleRncC„ +
API / WhiteNc
MultipleRace h
Black / White
Other /o API^whjte +
NatVo
¦ Othcv^/a +
MultipleRace NaM Black! mite
Other Combinations „
MultipleRace
Nat%
This then reduces to:
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Appendix B. Population Data
Native AmericanPop = Native American AlonePop + (0.063)Two or MorePop
AsianPop = Asian AlonePop + Pacific Islander / HawaiianPop + (0.059)'/Yvo or MorePop
BlackPop = Black AlonePop + (0.072)Two or MorePop
WhitePop = White AlonePop + (0.530)'/Vvo or MorePop
OtherPop = White AlonePop + (0.276)Two or MorePop
B .3 Estimating Population Levels in Alternative Age Groups
In calculating the population in age groups that may include a portion of one of the pre-specified
demographic groups in Exhibit B-l, BenMAP assumes the population is uniformly distributed in the age
group. For example, to calculate the number of children ages 3 through 12, BenMAP calculates:
1 3
&8^3-12 — 2 aSe5-9 ^ ' ^8^10-14 '
B .4 Estimating Population Levels in Non-Census Years
To estimate population levels in non-Census years, BenMAP uses two basic approaches. To
estimate population between 1990 and 2000, BenMAP linearly interpolates between the two Census years
of 1990 and 2000. To forecast population levels beyond 2000, BenMAP scales the 2000 Census value with
the ratio of the county-level forecast for the future-year of interest and the county-level population in 2000.
Woods & Poole (2001) provides the county-level population forecasts used to calculate the scaling ratios.
Below we give examples with each approach.
B .4.1 Estimating Population Levels in 1991-1999
To estimate population levels between 1990 and 2000, BenMAP linearly interpolates between age
groupings of interest.
10-/ /
population i = —yyj— populationl990 + — popn/ation2uuu
where i is an index running from 0 (for 1990) through 10 (for 2000).
In the case where there is a single age group, such as determining the number of people ages 20
through 24 in 1996, BenMAP calculates:
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Appendix B. Population Data
_ 4_ 6_
aSe20-24, 1996 - aSe20-24, 1990 ^ ^q' aSe20-24,2000
The steps are essentially similar when more than one age goup is involved, such as determining the
number of people ages 20-27 in 1996. BenMAP first calculates the number of people ages 20-27 in 1990
and 2000, and then interpolates between the 1990 and 2000 values:
3
age20
-27,1990 = age20
-24, 1990
+ -ctge25
-29, 1990
3
age20
-27,2000 = age to -24, 2000
+ ~age25
-29, 2000
_ 4_ 6_
aSe20-27,1996 - jq ' a^g20-27, 1990 + j q ' aSe20-21, 2000 '
B .4.2 Forecasting Population Levels after 2000
To estimate population levels for the years after the last Census in 2000, BenMAP scales the 2000
Census-based estimate with the ratio of the county-level forecast for the future year of interest over the
2000 county-level population level. Woods & Poole (2001) provides the county-level population forecasts
used to calculate the scaling ratios; these data are discussed in detail in section B.5.
In the simplest case, where one is forecasting a single population variable, say, children ages 4 to 9
in the year 2010, CAMPS calculates:
age,
-9, county, 2010
age,
-9, g, 2010
age,
-9, g, 2000 '
aSe4-9, county, 2000
where the glh population grid-cell is wholly located within a given county.
In the case, where the glh grid-cell includes "n" counties in its boundary, the situation is somewhat
more complicated. BenMAP first estimates the fraction of individuals in a given age group (e.g., ages 4 to
9) that reside in the part of each county within the glh grid-cell. BenMAP calculates this fraction by simply
dividing the population all ages of a given county within the glh grid-cell by the total population in the glh
grid-cell:
, . , ageall
, g in countyc
fraction of age4_9
, g in countyc ruro
& all, g
Multiplying this fraction with the number of individuals ages 4 to 9 in the year 2000 gives an estimate of
the number of individuals ages 4 to 9 that reside in the fraction of the county within the glh grid-cell in the
year 2000:
aSe4-9, g in countyc, 2000 ~ aSS4-9, g, 2000 ' fi~aC^°n aSS4-9, g in countyc
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Appendix B. Population Data
To then forecast the population in 2010, we scale the 2000 estimate with the ratio of the county projection
for 2010 to the county projection for 2000:
age4
-9, countyc ,2010
age4
-9, g in countyc, 2010 = age4
-9, g in countyc, 2000 rirrp
o 4-9, countyc, 2000
Combining all these steps for "n" counties within the glh grid-cell, we forecast the population of persons
ages 4 to 9 in the year 2010 as follows:
\ total popg counjy^ age4
-9, countyc, 2010
^4-9.,, 2° 10 = ^ ^4-9. ,, 2(10(1 ' j ~
c=l IUIUI o 4-9, countyc, 2000
In the case where there are multiple age groups and multiple counties, BenMAP first calculates the
forecasted population level for individual age groups, and then combines the forecasted age groups. In
calculating the number of children ages 4 to 12, BenMAP calculates:
total pop !n county age4_
g in countyc 4-9, countyc, 2010
age4-9, g, 2010 I "^4-9, ,, 2000 p()p ^^
c=l ¥"¥ g 6c'4-9, countyc, 2000
tOtCll popg m COUnfyc age_10-14 , countyc, 2010
^10-14,*, 2010 = L ^10-14,^,2000 ' / ' ~
c=l IUIUI U6^10-14,countyc,2000
age4
-12, g, 2010
age4
-9,g, 2010 + 5*^10 -14, g, 2010
Since the Woods and Poole (2001) projections only extend through 2025, we used the existing
projections and constant growth factors to provide additional projections. To estimate population levels
beyond 2025, CAPMS linearly extrapolates from the final two years of data. For example, to forecast
population in 2030, CAPMS calculates:
age4_
9, 2030
— age49 2025 (2030 2025) • {^age4 9 2025 age4-9,2024) •
Exhibit B-6 summarizes the forecasted age-stratified, state-level populations for 2020 and 2030. In
addition, to provide a point of comparison, it includes population levels for year 2000.
Abt Associates Inc.
B-10
November 2003
-------
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S<
-------
Appendix B. Population Data
B .5 County Population Forecasts 2000-2025
Woods & Poole (2001) developed county-level forecasts for each year from 2000 through 2025, for
three racial groups "Black," "White," and "Other," and by age and by gender. For the Hispanic ethnic
group, Woods and Poole developed forecasts just for the total population, and not by age and gender. As
discussed in the section on population forecasts, CAPMS uses these forecasts to simply scale the 2000
Census block data, in order to estimate the population in the population grid-cells for any given year after
B .5.1 Aligning Woods & Poole FIPS Codes with BenMAP FIPS Codes
The county geographic boundaries used by Woods & Poole are somewhat more aggregated than the
county definitions used in the 2000 Census (and BenMAP), and the FIPS codes used by Woods and Poole
are not always the standard codes used in the Census. To make the Woods and Poole data consistent with
the county definitions in BenMAP, we disaggregated the Woods and Poole data and changed some of the
FIPS codes. Exhibit B-7 lists the discrepancies in the county definitions between Woods & Poole and those
used in BenMAP.
To assign the population in the more aggregated Woods & Poole county definitions to the more
disaggregated definitions used in BenMAP (and the U.S. Census), we used the total county population from
the 2000 U.S. Census. We then assumed that the age and racial groups were distributed uniformly across
the BenMAP counties contained within a Woods & Poole county definition. For example, in estimating the
population of children ages 4-9 in county "c" contained within a more broadly defined Woods & Poole
county, we would do the following:
After this factor was applied, we rounded the estimates to the nearest integer so as to avoid having
data with "partial people."
Exhibit B-7. Linkage Between Woods & Poole County Definitions and BenMAP County Definitions
2000.
age ati , countyc
age4_9
, countyc = age4_9
, W&.P county
aSeall ,W&.P county
Woods and Poole Counties (FIPS)
Counties in BenMAP (FIPS)
Northwest Arctic Borough, AK (02188)
Remainder of Alaska, AK (02999)
Aleutian Islands, AK (02010), Aleutian Islands East Borough, AK (02013),
Aleutian Islands West Census Area, AK (02016), Bethel Census Area, AK (02050),
Denali Borough, AK (02068), Dillingham Census Area, AK (02070), Haines
Borough, AK (02100), Kenai Peninsula Borough, AK (02122), Lake and Peninsula
Borough, AK (02164), North Slope Borough, AK (02185), Prince of Wales-Outer
Ketchikan, AK (02201), Sitka Borough, AK (02220), Skagway-Yukatat-Angoon,
AK (02231), Skagway-Hoonah-Angoon Census Area, AK (02232), Southeast
Fairbanks Census Area, AK (02240), Valdez-Cordova Census Area, AK (02261),
Wrangell-Petersburg Census Area, AK (02280), Yakutat Borough, AK (02282),
Yukon-Koyukuk, AK (02290)
Kobuk, AK (02140)
Yuma + La Paz, AZ (04027)
La Paz, AZ (04012), Yuma, AZ (04027)
Abt Associates Inc.
B-13
November 2003
-------
Appendix B. Population Data
Woods and Poole Counties (FIPS)
Counties in BenMAP (FIPS)
Miami-Dade, FL (12086)
Maui + Kalawao, HI (15901)
Fremont, ID (16043)
Park, MT (30067)
Valencia + Cibola, NM (35061)
Halifax, VA (51083)
Albemarle + Charlottesville, VA (51901)
Alleghany + Clifton Forge + Covington, VA
(51903)
Augusta + Staunton + Waynesboro, VA
(51907)
Bedford + Bedford City, VA (51909)
Campbell + Lynchburg, VA (51911)
Carroll + Galax, VA (51913)
Dinwiddie + Colonial Heights + Petersburg,
VA (51918)
Fairfax + Fairfax City + Falls Church City,
VA (51919)
Frederick + Winchester, VA (51921)
Greensville + Emporia, VA (51923)
Henry + Martinsville, VA (51929)
James City + Williamsburg, VA (51931)
Montgomery + Radford, VA (51933)
Pittsylvania + Danville, VA (51939)
Prince George + Hopewell, VA (51941)
Prince William + Manassas + Manassas Park,
VA (51942)
Roanoke + Salem, VA (51944)
Rockbridge + Buena Vista + Lexington, VA
(51945)
Rockingham + Harrisonburg, VA (51947)
Southampton + Franklin, VA (51949)
Spotsylvania + Fredericksburg, VA (51951)
Washington + Bristol, VA (51953)
Wise + Norton, VA (51955)
York + Poquoson, VA (51958)
Shawano (includes Menominee), WI (55901)
Dade, FL (12025)
Kalawao, HI (15005), Maui, HI (15009)
Fremont, ID (16043), Yellowstone Park, ID
Park, MT (30067), Yellowstone Park, MT (30113)
Cibola, NM (35006), Valencia, NM (35061)
Halifax, VA (51083), South Boston City, VA (51780)
Albemarle, VA (51003), Charlottesville City, VA (51540)
Alleghany, VA (51005), Clifton Forge City, VA (51560), Covington City, VA
(51580)
Augusta, VA (51015), Staunton City, VA (51790), Waynesboro City, VA (51820)
Bedford, VA (51019), Bedford City, VA (51515)
Campbell, VA (51031), Lynchburg City, VA (51680)
Carroll, VA (51035), Galax City, VA (51640)
Dinwiddie, VA (51053), Colonial Heights City, VA (51570), Petersburg City, VA
(51730)
Fairfax, VA (51059), Fairfax City, VA (51600), Falls Church City, VA (51610)
Frederick, VA (51069), Winchester City, VA (51840)
Greensville, VA (51081), Emporia City, VA (51595)
Henry, VA (51089), Martinsville City, VA (51690)
James City County, VA (51095), Williamsburg City, VA (51830)
Montgomery, VA (51121), Radford City, VA (51750)
Pittsylvania, VA (51143), Danville City, VA (51590)
Prince George, VA (51149), Hopewell City, VA (51670)
Prince William, VA (51153), Manassas City, VA (51683), Manassas Park City, VA
(51685)
Roanoke, VA (51161), Salem City, VA (51775)
Rockbridge, VA (51163), Buena Vista City, VA (51530), Lexington City, VA
(51678)
Rockingham, VA (51165), Harrisonburg City, VA (51660)
Southampton, VA (51175), Franklin City, VA (51620)
Spotsylvania, VA (51177), Fredericksburg City, VA (51630)
Washington, VA (51191), Bristol City, VA (51520)
Wise, VA (51195), Norton City, VA (51720)
York, VA (51199), Poquoson City, VA (51735)
Menominee, WI (55078), Shawano, WI (55115)
Abt Associates Inc.
B-14
November 2003
-------
Appendix B. Population Data
B .5.2 Age, Gender, Race, and Ethnicity
We generated the same 38 age and gender categories developed from the 1990 and 2000 Census
data. Since these projections are available for every year of age, it is a simple matter to sum the individual
years to get the same age categories used by BenMAP.
However, the only racial categories available are "White," "Black," and "Other." Since we do not
have an Asian or Native American group, or an Other group which is consistent with the definition used by
the 1990 and 2000 Census data, we assume that the projection data's Other category is representative of all
3 groups, and that they move together over time.
The county projections only forecast the Hispanic population of all ages, and does not have separate
gender and age forecasts. Lacking further information, we use the ratio of future-year all age population to
the year 2000 all age population when forecasting any particular age group of Hispanics. In effect, we
assume for all forecast years the same distribution of age and gender as found in the 2000 Census.
B .5.3 Creating Growth Ratios from Absolute Population Values
For each year from 2000 through 2025 and for each of the 256 demographic groups listed in
Exhibit B-l, BenMAP stores the ratio of the future-year to year 2000 county-level population projections.
As described below, these ratios are used to forecast population levels in the population grid-cells used by
BenMAP to health effects.
Note that there are a small number of cases were the 2000 county population for a specific
demographic group is zero, so the ratio of any future year to the year 2000 data is undefined. In these
relatively rare cases, we set the year 2000 ratio and all subsequent ratios to 1, assuming no growth.
There are an even smaller number of cases where a total population variable dwindles from some
non-zero number to zero, creating ratios of zero. Variables which represent a subpopulation of the first
variable may not be zero, however. In these cases, we set all subset population variables for that year to
zero.
For instance, if a county only had one person in it for the year 2000 - a 79 year old black male - we
set all variables (excluding total variables and BlackMale75to79) to a ratio of 1, because their 2000 values
of 0 produce undefined ratios. If the man dies at age 82, the total black population variable for years 2003
and beyond is calculated as 0/1 = 0. Thus for each of those years where the total black population is listed
as zero, we go back and set all black population variables to zero, to reflect the knowledge that the block is
empty. For all variables except the BlackMale75to79 age group (already zero), 1 becomes 0.
Abt Associates Inc.
B-15
November 2003
-------
Appendix C: Air Pollution Exposure Estimation Algorithms
BenMAP has grouped individuals into what we refer to as "population grid-cells," where the grid-
cells typically conform to some type of grid used in an air quality model, such as the REMSAD air quality
model, or just the counties of the United States. For each type of grid, the population is built in each grid-
cell by aggregating census block data. In the next step, BenMAP estimates the air pollution exposure for
each grid-cell, with the assumption that people living within a particular grid-cell experience the same air
pollution levels.
You have a variety of approaches to estimate the exposure to air pollution for the people living
within a given population grid-cell. Perhaps the simplest approach is to use model data directly, and to
assume that the people living within a particular model grid-cell experience the level estimated by the
model. An alternative approach is to use air pollution monitoring data, where you may choose the closest
monitor data to the center of a grid-cell, take an average of nearby monitors, or use kriging. In a third
general approach, you may combine both modeling and monitoring data to estimate exposure.
When combining modeling and monitoring data, BenMAP scales or adjusts the monitoring data
with modeling data. The advantage of modeling data is that they can provide predictions for years in which
monitoring data are not available, as well as to provide predictions in areas of the country for which
monitoring data are not available. And the advantage of monitor data is that they are based on actual
observations. Combining both sources of information, allows BenMAP to make more informed
predictions.
The goal of estimating exposure is to provide the necessary input for concentration-response
functions, so that BenMAP can estimate the impact of air pollution on adverse health effects. Exhibit C-l
lists the types of metrics commonly used in concentration-response functions. In the case of air pollution
metrics calculated on a daily basis, such as the one-hour maximum and the 24-hour average, it is often the
case that there are missing days of data. Air quality modeling is often conducted on a subset of the days in
the year, and air quality monitors often miss a number of observations through out the year. To account for
missing days, BenMAP represents the distribution of daily metrics with a certain number points or "bins,"
where each bin represents a certain range of the distribution, with the underlying assumption that missing
days have the same distribution as the available data. For ozone, we use 153 bins to represent the ozone
season from May through September, and for particulate matter we use 365 bins to represent the year. In
addition to being able to account for incomplete or missing data, and using bins to represent the distribution
provides a uniform approach that allows for easy comparison of different monitors.
Abt Associates Inc.
C-l
November 2003
-------
Appendix C. Air Pollution Exposure Estimation Algorithms
Exhibit C-l. Metrics Typically Used in Concentration-Response Functions for Criteria Air
Pollutants
Measurement
Frequency
Metric Name
Metric Description
Daily Daily Average
(e.g., PM25, PM10)
Annual Average
Annual Median
Daily average
Average of four quarterly averages. The four quarters are defined as: Jan-Mar,
April-June, Jul-Sep, Oct-Dec.
Median of values through out the year.
Hourly
(e.g., Ozone)
1-hour Daily Max
5-hour Daily Average
8-hour Daily Average
12-hour Daily Average
24-hour Daily Average
SUM06
Highest hourly value from 12:00 A.M. through 11:59 P.M.
Average of hourly values from 10:00 A.M. through 2:59 P.M.
Average of hourly values from 9:00 A.M. through 4:59 P.M.
Average of hourly values from 8:00 A.M. through 7:59 P.M.
Average of hours from 12:00 A.M. through 11:59 P.M.
SUM06 index is the sum of the ozone concentrations (measured in ppm) that
exceed 0.06 ppm between 8:00 am and 7:59 pm.
Note that the 8-hour daily average differs from the maximum 8-hour moving average described in the Federal Register (6 FR / Vol.
62, No. 138 / Friday, July 18, 1997 / Prepublication).
C .1 Direct Modeling
When using direct modeling data to estimate exposure, BenMAP assumes that the people living
within a particular air pollution model grid-cell experience the same air pollution levels. BenMAP then
estimates the air pollution metrics of interest. For pollutants measured hourly, such as ozone, these include
the one-hour maximum and 24-hour average, and for pollutants measured daily, such as particulate matter,
these include the annual mean and annual median.
Generally modeling data providing hourly observations are complete for any given day. However,
both hourly and daily often have missing days of observations. Given the estimated metrics, BenMAP then
represents the distribution of daily metrics with 153 ozone bins and 365 particulate matter bins. By
calculating bins with the available days, BenMAP assumes that the distribution of missing days is similar to
the distribution of available monitoring.
C .2 Closest Monitor
When using the closet monitor to represent air pollution levels at a population grid-cell, BenMAP
identifies the center of the population grid-cell, and then chooses the monitor that is closest to the center. In
the simplest case, BenMAP assigns the closest monitor to a population grid-cell, uses the monitoring data to
calculate the annual and daily air pollution metrics, and then calculates the bins that represent the
distribution of the daily metrics. The annual metrics and the binned daily metrics are then used in the
calculation of health effects.
The figure below presents nine population grid-cells and three monitors, with the focus on
identifying the monitor closest to grid-cell "E." In this example, the closest monitor happens to be 10 miles
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away from the center of grid-cell E, and the data from this monitor would be used to estimate air pollution
levels for the population in this grid-cell. An analogous procedure would be used to estimate air pollution
levels in the other grid-cells (A, B, C, D, F, G, H, and I).
A
B
15 miles
*
25 miles C
*
D
10 miles *
E
-#
F
* 15 miles
G
*
30 miles
H
*
20 miles
I
*
25 miles
# = Center Grid-Cell "E"
= Air Pollution Monitor
To capture some of the information generated by air pollution models, such as REMSAD, UAM-V
and others, BenMAP can also scale the data from the closest monitor with air pollution modeling data.
BenMAP includes two types of scaling - "temporal" and "spatial" scaling. We discuss each below.
C .2.1 Closest Monitor - Temporal Scaling
With temporal scaling, BenMAP scales monitoring data with the ratio of the future-year to base-
year modeling data, where the modeling data is from the modeling grid-cell containing the monitor. In the
case of pollutants typically measured hourly, such as ozone, BenMAP scales the hourly monitor values,
calculates the annual and daily metrics of interest, and then bins the daily metrics. In the case of pollutants
typically measured daily, BenMAP scales the daily values, calculates the annual metrics of interest, and
then bins the daily metric.
Consider the case in the figure below. To forecast air pollution levels for 2030, BenMAP would
multiply the 1995 monitor value (80 ppb) by the ratio of the 2030 model value (70 ppb) to the 1995 model
value (95 ppb):
Forecast2030 = Monitor Value 1995 * (Model Value D 2030 / Model Value D 1995)
Forecast2030 = 80 ppb * (70 ppb / 95 ppb) = 58.9 ppb.
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A
B
C
*
*
Model: d
E
F
1995 95 ppb
2030 70 ppb
~#
*
Monitor:
1995 80 ppb
G
H
I
*
*
*
# = Center Grid-Cell "E"
= Air Pollution Monitor
In this example, we have examined the adjustment of a single monitor value with the ratio of single
model values. The approach is essentially the same when there are multiple monitor values and multiple
model values. When there are multiple monitor values,
C .2.2 Closest Monitor - Spatial Scaling
With spatial scaling, we are estimating a monitor value for the center of each population grid-cell.
We start by choosing the closest monitor to the center of each population grid-cell, and then we scale this
closest monitor with modeling data. In particular, BenMAP multiplies the monitoring data with the ratio of
the base-year modeling data for the destination grid-cell to the base-year modeling data for grid-cell
containing the monitor. The spatial scaling occurs in the same fashion as with temporal scaling. In the case
of pollutants typically measured hourly, such as ozone, BenMAP scales the hourly monitor values,
calculates the annual and daily metrics of interest, and then bins the daily metrics. In the case of pollutants
typically measured daily, BenMAP scales the daily values, calculates the annual metrics of interest, and
then bins the daily metric.
To estimate air pollution levels for 1995 in grid-cell "E" below, BenMAP would multiply the 1995
closest monitor value (80 ppb) by the ratio of the 1995 model value for grid-cell "E" (70 ppb) to the 1995
model value for grid-cell "D" (95 ppb):
Forecast1995 = Monitor Value1995 * (Model Value E 1995 / Model Value D 1995)
Forecast1995 = 80 ppb * (85 ppb / 95 ppb) = 71.6 ppb.
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A
B
C
*
*
Model: D
Model: E
F
1995 95 ppb
1995 85 ppb
~ #
*
Monitor:
1995 80 ppb
G
H
I
*
*
*
# = Center Grid-Cell "E"
= Air Pollution Monitor
C .2.3 Closest Monitor - Temporal and Spatial Scaling
Combining both temporal and spatial scaling, BenMAP first multiplies monitoring data with both
the ratio of the future-year to base-year modeling data, where the modeling data is from the modeling grid-
cell containing the monitor. This gives a temporary forecast for 2030. BenMAP then multiplies this
temporary forecast with the ratio of the future-year modeling data for the destination grid-cell to the future-
year modeling data for grid-cell containing the monitor. As seen below, this simplifies to multiplying
monitoring data with both the ratio of future-year modeling data from the destination grid-cell to the base-
year modeling data from the grid-cell containing the monitor. Again, as described for temporal and spatial
scaling, BenMAP first scales the hourly and daily values, generates the metrics of interest and then bins the
daily metrics.
To forecast air pollution levels for 2030 in the figure below, BenMAP would multiply the 1995
monitor value (80 ppb) by the ratio of the 2030 model value (70 ppb) to the 1995 model value (95 ppb):
Temporary Forecast2030 = Monitor Value 1995 * (Model Value D 2030 / Model Value D 1995)
Temporary Forecast2030 = 80 ppb * (70 ppb / 95 ppb) = 58.9 ppb.
Forecast 2030 = Temporary Forecast 2030 * (Model Value E 2030 / Model Value D 2030)
Forecast 2030 = 58.9 ppb * (60 ppb / 70 ppb) = 50.5 ppb.
Note that through cancellation, this equation simplifies to:
Forecast 2030 = Monitor Value 1995 * (Model Value E 2030 / Model Value D 1995)
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A
B
*
C
*
Model: d
1995 95 ppb
2030 70 ppb
Model
1995
2030
E
85 ppb
60 ppb
#
F
*
Monitor: *
1995 80 ppb
G
H
I
*
*
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
C .3 Voronoi Neighbor Averaging (VNA)
Like the closest monitor approach, the Voronoi Neighbor Averaging (VNA) algorithm uses monitor
data directly or in combination with modeling data. However, instead of using the single closest monitor to
estimate exposure at a population grid-cell, the VNA algorithm interpolates air quality at every population
grid cell by first identifying the set of monitors that best "surround" the center of the population grid-cell.
*
*
*
#
*
*
*
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
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In particular, BenMAP identifies the nearest monitors, or "neighbors," by drawing a polygon, or
"Voronoi" cell, around the center of each BenMAP grid cell. The polygons have the special property that
the boundaries are the same distance from the two closest points.
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
We then choose those monitors that share a boundary with the center of grid-cell "E." These are
the nearest neighbors, we use these monitors to estimate the air pollution level for this grid-cell.
15 miles
* 15 miles
20 miles
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
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To estimate the air pollution level in each grid-cell, BenMAP calculates the annual and the binned
daily metrics for each of the neighboring monitors, and then calculates an inverse-distance weighted
average of the metrics. The further the monitor is from the BenMAP grid-cell, the smaller the weight.
In the figure below, the weight for the monitor 10 miles from the center of grid-cell E is calculated
as follows:
J_
weishtWmiieS = ~7~~y pr= 035 •
V10+ 15 + 15 + 20/
The weights for the other monitors would be calculated in a similar fashion. BenMAP would then
calculate an inverse-distance weighted average for 1995 air pollution levels in grid-cell E as follows:
Forecast 1995 = 0.35*80 ppb + 0.24*90 ppb+ 0.24*60 ppb + 0.18*100 ppb = 81.2 ppb .
A
B
Monitor:
1995 90 ppb ^
15 miles
C
*
D
Monitor: *
1995 80 ppb
10 miles
LD
F
*
Monitor:
1995 60 ppb
15 miles
G
*
/ H
*
Monitor:
1995 100 ppb
20 miles
I
*
# = Center Grid-Cell "E"
= Air Pollution Monitor
Note that BenMAP is calculating an inverse-distance weighted average of the annual metrics and
the binned daily metrics. Alternatively, BenMAP could calculate an inverse-distance weighted average of
the hourly and daily observations, calculated the annual and daily metrics, and then binned the daily
metrics.
C .3.1 Voronoi Neighbor Averaging (VNA) - Temporal Scaling
As with forecasting air pollution levels by temporally scaling the closest monitor, BenMAP can
combine VNA with temporal scaling. BenMAP temporally scales all of the neighboring monitors,
calculates the metrics of interest, and then calculates an inverse distance-weighted average of the metrics.
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Consider the example in the figure below. To forecast air pollution levels for 2030, BenMAP
would multiply the 1995 monitor value by the ratio of the 2030 model value to the 1995 model value:
Y" Kdodclj 2Q3Q
Forecast9n,n = >, Weight, * Monitor, * —
1 " ' ' K A rule /
i= 1 lVlUUVli 1995
70^ f 100) ( 80^ f 120^|
Forecast2030 = [ 0.35*80*—J + 0.24*90*+ (^0.24*60*—J + ^0.18* 100*= 64Ippb
A
Model: b
1995 100 ppb
2030 85 ppb
Monitor:
1995 90 ppb ^
15 miles
C
*
Model: d
1995 95 ppb
2030 70 ppb
Monitor: *
1995 80 ppb
10 miles
LD
Model: p
1995 80 ppb
2030 60 ppb
*
Monitor:
1995 60 ppb
15 miles
G
*
Model: H
199? 120 ppb
2030 100 ppb
Monitor:
1995 100 ppb
20 miles
I
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
C .3.2 Voronoi Neighbor Averaging (VNA) - Spatial Scaling
BenMAP can also combine VNA with spatial scaling. For each of the neighbor monitors, BenMAP
multiplies the monitoring data with the ratio of the base-year modeling data for the destination grid-cell to
the base-year modeling data for grid-cell containing the monitor. The spatial scaling occurs in the same
fashion as with temporal scaling. In the case of pollutants typically measured hourly, such as ozone,
BenMAP scales the hourly monitor values, calculates the annual and daily metrics of interest, and then bins
the daily metrics. In the case of pollutants typically measured daily, BenMAP scales the daily values,
calculates the annual metrics of interest, and then bins the daily metric.
Consider the example in the figure below. To forecast air pollution levels for 1995, BenMAP
would multiply the 1995 monitor value by the ratio of the 1995 model value to the 1995 model value:
4 Model j 1995
Forecast1QQ5 = / Weight, * Monitor * —
£! Model, 1995
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85^1 ( 85 ^ ( 85^ ( 85 ,
Forecastigg5 = 0.35*80*— + 0.24*90*—— + 0.24*60*— + 0.18*100*——] = 10.8ppb
95/ V 100/ V 80/ V 120
A
Model: B
1995 100 ppb
Monitor:
1995 90 ppb ^
15 miles
C
*
Model: D
1995 95 ppb
Monitor: *
1995 80 ppb
10 miles
Model: / E
1995 85/ppb
-#
/
Model: F
1995 80 ppb
*
Monitor:
1995 60 ppb
15 miles
G
*
Mod^l: H
1995 120 ppb
*
Monitor:
1995 100 ppb
20 miles
I
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
C .3.3 Voronoi Neighbor Averaging (VNA) - Temporal & Spatial Scaling
Combining both temporal and spatial scaling, BenMAP multiplies monitoring data with the ratio of
the future-year to base-year modeling data, where the future-year modeling data are from the destination
grid-cell and the base-year modeling data are from the grid-cell containing the monitor. One the hourly and
daily monitoring data are scaled, BenMAP generates the metrics of interest, bins the daily metrics, and then
uses the metrics to estimate adverse health effects in the population grid-cell.
The figure below gives an example of combining temporal and spatial scaling.
4
Forecast2030 = ^ Weightt * Monitor ¦¦
ModelE 2030
Modelt 1995
60^| ( 60 ^ ( 60^| ( 60 ^
Forecast,mn = | 0.35*80*— + 0.24*90*—— + 0.24*60*— + 0.18*100*—— = 50.0
95/ V 100/ V 80/ V 120/
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A
Model: b
1995 100 ppb
2030 85 ppb
Monitor:
1995 90 ppb ^
15 miles
C
*
Model: d
1995 95 ppb
2030 70 ppb
Monitor: *
1995 80 ppb
10 miles
Model: / £
1995 85rfpb
2030 6(#pbb
~#
Model: f
1995 80 ppb
2030 60 ppb
*
Monitor:
1995 60 ppb
15 miles
G
*
Model: H
199? 120 ppb
2030 100 ppb
Monitor:
1995 100 ppb
20 miles
I
*
# = Center Grid-Cell "E"
*
= Air Pollution Monitor
C .4 Kriging
Following the same approach as the closest monitor approach and VNA, the kriging algorithm can
use monitor data directly or in combination with modeling data. BenMAP includes two types: Ordinary
Kriging and Block Kriging. Below, we discuss each type, and then briefly discuss their use in BenMAP.
C .4.1 Ordinary Kriging
Ordinary Kriging estimates an unknown value using a linear combination of available sample data.
It is a "best linear unbiased estimator". Unbiased, because it tries to have the residual mean equal to 0, best
because it minimizes the variance <7R of the errors.
The estimate v can be expressed as linear combination of true values.
n
V = £ w J ¦ V
j=1
Defining the error r as the difference between the estimated value and the true value allows us to
write the error of the i-th estimate as
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The average error or residual mean can therefore be written as
1 k 1 k
m = —V r. = —V v. - V-
Since the estimate is unbiased and a stationary random model function is assumed, the following
holds true3:
n
Z wi =1
7 = 1
2
At this point we can write the error variance (JR of a set of k estimates as:
= tX (n - mr)2
K 7=1
Assuming a mean error of 0 simplifies this equation to:
K 7=1
Unfortunately, the true values v are unknown. However, applying a stationary random function
model allows us to rewrite (6) as
7 = 1 j= 1 7-1
with (72 being the variance and ( ' being the covariances.
V
In order to solve this equation an additional term is introduced into the equation.
<^R-= Z Z w7 Q - 2Z w, Ci0 + 2/
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Due to the unbiasedness assumption the last term in (8) equals 0. Furthermore, setting the first
partial derivative with respect to jj to 0 reproduces the unbiasedness condition.
Since we assumed that the error variance will be minimal, the partial derivatives with respect to
wt will be calculated and set to 0.
This approach leads us to:
n
T,wAj+M=ci0 v/=wi
J=1
and
n
7=1
This system of equations can be written in matrix notation (referred to as the Ordinary Kriging
System)
Cw=D
or
C C 1
*-11 ••• 1
~wl~
•• ^
o
1
C C 1
1 * * * ^mr 1
Wn
—
r
n0
1 ... 1 0
-V-
1
In order to solve this equation for the weights, we multiply this equation with the inverse of the
covariance matrix C (assuming that C is positive definite):
w = C_1 • D
C .4.2 Block Kriging
Block Kriging is a simple extension to the Ordinary Kriging system. It allows the computation of a
mean value over a local area. Since any linear combination of random variables is also a random variable,
the mean value over an area can be described as follows:
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Introducing a point to block covariance:
- 1 ^ ~
cu = c,
I ^1 jeA
we can rewrite the ordinary kriging equation:
C CI
^11 ••• ^1 n 1
c c
^«1 •••
1 0
w i
A
c
10
c,
1
as follows:
C C 1
^11 ••• 1
1
c,
nn
1
Wi
C
1A
C rtA
1
C .4.3 Kriging in BenMAP
BenMAP allows you to enter the empirically determined values for the "nugget" and the covariance
function that is to be used during the interpolation process. At the current stage these need to be computed
using external programs. After entering these values the program requires you to hit the validate button
before allowing you to commit the newly entered values. This ensures that the entered covariance function
can be interpreted at runtime.
Block Kriging is similar to Ordinary Kriging and introduces only one new set of parameters. In the
Block Kriging options you can enter the matrix parameters for the support points that are to be overlaid on
top of each grid cell. These support points are used to compute the point to block covariances introduced in
Note for shapefile grids that the support points will be computed by superimposing the bounding
box of the shape element with the matrix of supporting cells. Supporting points that fall outside the shape
element are deleted. Should all supporting points be eliminated, the system will default to the shape's
centerpoint for the covariance calculation.
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BenMAP also allows the user to save the current system settings (saved user default settings will be
restored the next time BenMap is started). Note that all changes to the Kriging settings will persist for the
entire work session with BenMap. Should it become necessary, the user can restore the original system
settings using the Restore Original Default button.
Finally, as noted above, you may use kriging with modeling to scale the monitor data, in the same
that you can do scaling with the closest monitor and VNA interpolation options.
C .5 Temporal and Spatial Scaling Adjustment Factors
As presented in the preceding examples of temporal and spatial scaling, both closest monitor and
VNA use model data to scale monitor observations. In the examples, we scaled single monitor values with
the ratio of single model values. In fact, however, the scaling involves multiple monitor values and
multiple model values.
To proceed with the scaling, BenMAP takes the modeling values and splits them into groups. For
ozone, we use 10 adjustment factors for the ozone season, where the first group represents the first 10
percent of the model observations; the second group represents the observations between the 10th and 20th
percentile; and so on through the tenth group, which represents the observations between the 90th and 100th
percentiles. BenMAP then averages the values in each group. For particulate matter, there are five
adjustment factors for each of the four seasons in the year, where the first group in each season represents
the first 20 percent of the model observations; the second group represents the observations between the
20th and 40th percentiles; and so on. Then, as for ozone model values, BenMAP averages the particulate
matter model values in each group.
BenMAP treats the monitor values in a similar way. It sorts the monitor values from low to high,
and divides them into the same number groups as there are scaling factors. Exhibit C-2 summarizes some
of the types of analyses that have been conducted recently using scaling factors.
Exhibit C-2. Types of Analyses Using Scaling Factors
Pollutant
Daily Time Period
Season
# Scaling Factors
Each Season
Ozone
Daylight hours: 9:00 am -
8:59 pm
May 1 - September 30
10
Particulate
Matter
-
Four seasons: January-March, April-June,
July-September, October-December
5
C .5.1 Calculation of Scaling Factors
In developing scaling factors, BenMAP sorts the modeling data into either 10 groups or 20 groups,
depending on the pollutant (10 for ozone and for particulate five for each of the four seasons). Given the
number of groups, then BenMAP determines how to assign the model values. In determining to which
group a value belongs, BenMAP assigns a two-digit "percentile" to each value. With values in a given
grid-cell sorted from low to high, the percentile for each value will equal: (the observation rank number
minus 0.5) divided by (the total number of values) multiplied by (100). If there are 250 hourly values, the
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first hourly value will have a percentile = (l-0.5)/(250)*(100) = 0.20%; the 27th value will have a percentile
= (27-0.5)/(250)*(100) = 10.60%; and so on.
Each data group is represented by "group-lo" and "group-hi" values. These are the minimum and
the maximum percentiles in each group, where group-lo equals: (group rank minus 1) multiplied by (100)
divided by (the number of groups); and group-hi equals: (group rank) multiplied by (100) divided by (the
number of groups) minus 0.001. If there are ten groups: the first group will have: group-lo = (1-1)/100* 10
= 0.000%, and group-hi = (1/100* 10)-0.001 = 9.999% ; the second group will have: group-lo =
(2-1)/100* 10 = 10.000%, and group-hi = (2/100* 10)-0.001 = 19.999% ; and so on to the tenth group, which
will have: group-lo = (10-1)/100* 10 = 90.000%, and group-hi = (10/100* 10)-0.001 = 99.999%. BenMAP
assigns each observation to a particular group with the following algorithm: if "group-lo" <"percentile" <
"group-hi", then assign the observation to that data group.
C .5.2 How BenMAP Scales PM and Ozone Monitor Data
Below we give the equations that BenMAP uses when scaling particulate matter and ozone monitor
values.
Scaling Particulate Matter Monitor Values
After preparing the model and monitor data, BenMAP calculates the following:
, , RFMSADJJcJiltm
adjusted monitor. . ,. = monitor. ., • „
J i,j,future i,j ,base /y>/A/ZtS/l / )
where:
adjusted monitor = predicted daily PM2 5 level, after adjustment by model data (|ig/m3)
monitor = observed daily PM2 5 monitor level (|ig/m ')
i = day identifier
j = model season/quintile group (1 to 20)
k = grid cell identifier for population grid cell
1 = grid cell identifier for grid cell containing monitor
base = base-year (e.g., 2000)
future = future-year (e.g., 2020)
REMSAD = representative model season/quintile value (|ig/m3)
After adjusting the monitor values to reflect air quality modeling, BenMAP calculates for each
monitor the PM25 metrics needed to estimate adverse health effects. In the case of Voronoi Neighbor
Averaging, BenMAP then calculates an inverse-distance weighted average of the neighbors identified for
each population grid cell:
n
population grid cell^ adjusted monitorm future ¦ weightm ¦
m= 1
where:
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population grid cell
adjusted monitor
m
future
weight
= inverse distance-weighted PM25 metric at population grid cell (|ig/m3)
= predicted PM25 metric, after adjustment by model data (|ig/m3)
= monitor identifier
= future-year (e.g., 2020)
= inverse-distance weight for monitor
After generating the bins for both the baseline and control scenarios, BenMAP can use these to
calculate the change in air quality needed in most C-R functions to calculate the change in adverse health
effects. To calculate the change in air quality, BenMAP subtracts the baseline value in the first bin from the
control value in the first bin, and so on for each of the 365 bins created for the daily PM25 average.
Scaling Ozone Monitor Values
After preparing the model and monitor data, BenMAP calculates the following:
CAMXj k future
adjusted monitor^ future = momIori j hasc • ^
where:
adjusted monitor = predicted hourly ozone level, after adjustment by model data (ppb)
monitor = observed hourly ozone monitor level (ppb)
i = hour identifier
j = model decile group (1 to 10)
k = grid cell identifier for population grid cell
1 = grid cell identifier for grid cell containing monitor
base = base-year (e.g., 1996)
future = future-year (e.g., 2030)
CAMX = representative model decile value (ppb)
After adjusting the monitor values to reflect air quality modeling, BenMAP calculates for each
monitor the ozone metrics needed to estimate adverse health effects. In the case of Voronoi Neighbor
Averaging, BenMAP then calculates an inverse-distance weighted average of the neighbors identified for
each population grid cell:
n
population grid cellfuture = £ adjusted monitorm future ¦ w eightm ¦
m= 1
where:
population grid cell
adjusted monitor
m
future
weight
: inverse distance-weighted ozone metric at population grid cell (ppb)
: predicted ozone metric, after adjustment by model data (ppb)
: monitor identifier
: future-year (2020, 2030)
: inverse-distance weight for monitor
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After generating the bins for both the baseline and control scenarios, BenMAP can use these to
calculate the change in air quality needed in most C-R functions to calculate the change in adverse health
effects. To calculate the change in air quality, BenMAP subtracts the baseline value in the first bin from the
control value in the first bin, and so on for each of the 153 bins created for the daily ozone metrics.
C .6 Binned Metrics
When estimating air pollution exposure, BenMAP calculates both daily metrics, such as the 24-
hour daily average, and annual metrics, such as the annual mean. Because daily metrics are often not
available for the entire year, BenMAP calculates representative values or bins with the available daily
metrics, under the assumption that the missing days have a similar distribution. Each bin represents a day.
In the case where there are 365 bins, the set of bins represents the entire year.
When combining air pollution metrics from multiple monitors, BenMAP first calculates the bins for
the daily metrics, and then combines the bins, such as with some form of VNA. Once BenMAP has
calculated binned exposure measures for both a baseline and a control scenario, BenMAP then takes the
difference between the two scenarios for each bin - taking the difference between the baseline value in the
first bin and the control value in the first bin, and so on for each of the bins.
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This Appendix provides of an overview regarding the concentration-response (C-R) functions that
BenMAP uses to estimate the impact of a change in air pollution on adverse health effects. It provides a
description of the particular types of C-R functions that BenMAP uses. And then summarizes the approach
used to choose the C-R functions included in BenMAP, and presents some issues associated with the use of
C-R functions.
D .1 Overview
The relationship between the concentration of a pollutant, x, and the population response, y, is
called the concentration-response (C-R) function. For example, the concentration of the pollutant may be
fine particulate matter (PM2 5) in |ig/m3 per day, and the population response may be the number of
premature deaths per 100,000 population per day. C-R functions are estimated in epidemiological studies.
A functional form is chosen by the researcher, and the parameters of the function are estimated using data
on the pollutant (e.g., daily levels of PM25) and the health response (e.g., daily mortality counts). There are
several different functional forms, discussed below, that have been used for C-R functions. The one most
commonly used is the log-linear form, in which the natural logarithm of the health response is a linear
function of the pollutant concentration.
For the purposes of estimating benefits, we are not interested in the C-R function itself, however,
but the relationship between the change in concentration of the pollutant, Ax, and the corresponding change
in the population health response, Ay. We want to know, for example, if the concentration of PM25 is
reduced by 10 |ig/nr\ how many premature deaths will be avoided? The relationship between Ax and Ay
can be derived from the C-R function, as described below.
Many epidemiological studies, however, do not report the C-R function, but instead report some
measure of the change in the population health response associated with a specific change in the pollutant
concentration. The most common measure reported is the relative risk associated with a given change in
the pollutant concentration. A general relationship between Ax and Ay can, however, be derived from the
relative risk. The relative risk and similar measures reported in epidemiological studies are discussed in the
sections below. The derivation of the relationship of interest for BenMAP - the relationship between Ax
and Ay - is discussed in the subsequent sections.
D .1.1 Review Relative Risk and Odds Ratio
The terms relative risk and odds ratio are related but distinct. Exhibit D-l provides the basis for
demonstrating their relationship.
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Exhibit D-l. Relative Risk and Odds Ratio Notation
The "risk" that people with baseline pollutant exposure will be adversely affected (e.g., develop
chronic bronchitis) is equal to y0, while people with control pollutant exposure face a risk, yc, of being
adversely affected. The relative risk (RR) is simply:
Jo
Exposure
Fraction of Population
Adverse Effect Measure
Affected
Not Affected
Relative Risk
Odds
Baseline Pollutant Exposure
Yo
1-Yo
Yc/O-Yc)
Yc/Yc
Control Pollutant Exposure
Yc
1-Yc
y
1 >
1- y 1-y
yc RR- c
i-jv v
As the risk associated with the specified change in pollutant exposure gets small (i.e., both y0 and yc
approach zero), the ratio of (l-yc) to (l-y0) approaches one, and the odds ratio approaches the relative risk.
This relationship can be used to calculate the pollutant coefficient in the C-R function from which the
reported odds ratio or relative risk is derived, as described below.
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D .2 The estimation of health effect incidence change
The functional form of the relationship between the change in pollutant concentration, Ax, and the
change in population health response (usually an incidence rate), Ay depends on the functional form of the
C-R function from which it is derived, and this depends on the underlying relationship assumed in the
epidemiological study chosen to estimate a given effect. For expository simplicity, the following
subsections refer simply to a generic adverse health effect, "y" and uses particulate matter (PM) as the
pollutant - that is, Ax = APM - to illustrate how the relationship between Ax and Ay is derived from each
of several different C-R functions.
Estimating the relationship between APM and Ay can be thought of as consisting of three steps:
(1) choosing a functional form of the relationship between PM and y (the C-R function),
(2) estimating the values of the parameters in the C-R function assumed, and
(3) deriving the relationship between APM and Ay from the relationship between PM and y (the C-
R function).
Epidemiological studies have used a variety of functional forms for C-R functions. Some studies
have assumed that the relationship between adverse health and pollution is best described by a linear form,
where the relationship between y and PM is estimated by a linear regression in which y is the dependent
variable and PM is one of several independent variables. Log-linear regression4 and logistic regression are
other common forms.
D .2.1 Linear Model
A linear relationship between the rate of adverse health effects (incidence rate) and various
explanatory variables is of the form:
y = a + {3¦ PM
where a incorporates all the other independent variables in the regression (evaluated at their mean values,
for example) times their respective coefficients. The relationship between the change in the rate of the
adverse health effect from the baseline rate (y0) to the rate after control (yc) associated with a change from
PM0 to PMC is then:
t±y=yc-y,=P-{PMc-PM,)=($kPM.
4The log-linear form used in the epidemiological literature on ozone- and PM-related health
effects is often referred to as "Poisson regression" because the underlying dependent variable is a count
(e.g., number of deaths), believed to be Poisson distributed. The model may be estimated by regression
techniques but is often estimated by maximum likelihood techniques. The form of the model, however, is
still log-linear.
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For example, Ostro et al. (1991, Table 5) reported a PM25 coefficient of 0.0006 (with a standard
error of 0.0003) for a linear relationship between asthma and PM2 5 exposure.5
The lower and upper bound estimates for the PM25 coefficient are calculated as follows:
=P~ (l-96o> )= 0.0006- (1.96 0.0003)= 1.2105
Apper bound--- P+ (1.96 -ap )= 0.0006+ (1.96 0.0003)= 0.00119
It is then straightforward to calculate lower and upper bound estimates of the change in asthma.
D .2.2 Log-linear Model
The log-linear relationship defines the incidence rate (y) as:
y=B-e"™
or, equivalently,
\n(y)=a+fiPM,
where the parameter B is the incidence rate of y when the concentration of PM is zero, the parameter p is
the coefficient of PM, ln(y) is the natural logarithm of y, and a = ln(B).6
The relationship between APM and Ay is:
Ay=yc-y0 = Be^-Be^.
This may be rewritten as:
Ay=ePpM° ^eKPM^PM^-\yy,-{e^M-X),
where y0 is the baseline incidence rate of the health effect (i.e., the incidence rate before the change in PM).
5Ostro et al. (1991) happen to use the natural logarithm of PM25.
6 Other covariates besides pollution clearly affect mortality. The parameter B might be thought of
as containing these other covariates, for example, evaluated at their means. That is, B = B0exp{PjXj + ...
+ pMxM}. where B0 is the incidence of y when all covariates in the model are zero, and x,. .... xM arc the
other covariates evaluated at their mean values. The parameter B drops out of the model, however, when
changes in y are calculated, and is therefore not important.
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The change in the incidence of adverse health effects can then be calculated by multiplying the
change in the incidence rate, Ay, by the relevant population (e.g., if the rate is number per 100,000
population, then the relevant population is the number of 100,000s in the population).
When the PM coefficient (P) and its standard error (op) are published (e.g., Ostro et al., 1989), then
the coefficient estimates associated with the lower and upper bound may be calculated easily as follows:
Plower bound ~ P~ (1-96- <7)
fiupperboun^fi+i196^)-
Epidemiological studies often report a relative risk for a given APM, rather than the coefficient, p
(e.g., Schwartz et al., 1995, Table 4). Recall that the relative risk (RR) is simply the ratio of two risks:
RR=—=e/3APM
yc
Taking the natural log of both sides, the coefficient in the C-R function underlying the relative risk
can be derived as:
InCm
APM
The coefficients associated with the lower and upper bounds (e.g., the 2.5 and 97.5 percentiles) can
be calculated by using a published confidence interval for relative risk, and then calculating the associated
coefficients.
Because of rounding of the published RR and its confidence interval, the standard error for the
coefficient implied by the lower bound of the RR will not exactly equal that implied by the upper bound, so
an average of the two estimates is used. The underlying standard error for the coefficient (op) can be
approximated by:
.5 percentile
^'/?, 2.5 percentile
1.96
$91.5 percentile ft
®fi, 97.5 percentile
1.96
_____ ^P, 2.5 percentile ^ft, 97.5 percentile
= O
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D .2.3 Logistic Model
In some epidemiological studies, a logistic model is used to estimate the probability of an
occurrence of an adverse health effect. Given a vector of explanatory variables, X, the logistic model
assumes the probability of an occurrence is:
prob{
occurrence
x-p)
f ex'p ^
TT^
where p is a vector of coefficients.7 This may be rewritten as:
exp ex>
y-
1 + e p e~x'p 1+e
-x-p
The odds of an occurrence is:
odds =
y
i-y
1+ e
-x-p
1-
1+ e
rX-P
odds =
1+e
-x-p
1+e
-x-p
1-
/ e-x.p
1+e
-x-p
,-X-P
¦ - 0x-P
1+e
-x-p
ln(odds) = X p .
The odds ratio for the control scenario (oddsc) versus the baseline (odds0) is then:
,, oddsc \l-yc) \e Xc'p) eXc'P
oaasratio = = — = — = ^ _ .
°d< ( _J^_) f M *X'f
1 - J'ci
e
'Greene (1997, Chapter 19) presents models with discrete dependent variables; in particular, page
874 presents the logit model. See also Judge et al. (1985, p. 763).
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The change in the probability of an occurrence from the baseline to the control (Ay), assuming that
all the other covariates remain constant, may be derived from this odds ratio:
Jc
odds ratio =
i -yc
y0
1- Jo
eXJ er-ePM<>
eW er . e™ol
= e
APM-/3
jc
1 -yc
j0
.l-JcJ
¦e
APM-P
yc = (!-jc)-
Jo ^
U-y0)
¦ e
APM-P
yc + yc-
r y0
.i-y0J
APM-fi _
r Jo
• 1-JV
APM-p
yc
i +
Jo
.1 -JV
APM-fi
_ Jo
U-J0,
APM-P
yc
i +
Jo
.1-Jo.
Jo
APM-fi
y0-e
-1 _ Jo-
PM-fi l -y0 + y0-e
A PM-p
Multiplying by:
gives:
j-APMp
-APM-P '
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v = *
Sc \ -APMp
(l-y0).e-W + y0
Ay = yc-y0=- , \M.P y0-
(1 -y0)-e-™*+y0
The change in the number of cases of the adverse health effect is then obtained by multiplying by
the relevant population:
Is. Incidence = A_y • pop =
yo
/I \ -A PM-R . y 0
(\-y0)-e p + y0
¦ pop.
When the coefficient (P) and its standard error (op) are published (e.g., Pope et al., 1991, Table 5),
then the coefficient estimates associated with the lower and upper bound may be calculated easily as
follows:
Plower bound = P ~ (1-96 • <7g)
Pupper bound P 1.96 (Xg ) .
Often the logistic regression coefficients are not published, and only the odds ratio corresponding to
a specified change in PM is presented (e.g., Schwartz et al., 1994). It is easy to calculate the underlying
coefficient as follows:
1 Vi(odds ratio) = A PM (5
ln(odds ratio)
P =
KPM
The coefficients associated with the lower and upper bound estimates of the odds ratios are
calculated analogously.
The underlying standard error for the coefficient (op) can be approximated by:
_P~P2 .5 percentile
2.5 percentile
^f3, 97.5 percentile
1.96
097. 5 percentile P
1.96
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_ ^P, 2.5 percentile ^fi. 91.5 percentile
an = ~
Sometimes, however, the relative risk is presented. The relative risk does not equal the odds ratio,
and a different procedure should be used to estimate the underlying coefficient.8
The relative risk (RR) is simply:
RR = ^~,
where y0 is the risk (i.e., probability of an occurrence) at the baseline PM exposure and yc is the risk at the
control PM exposure.
When the baseline incidence rate (y0) is given, then it is easy to solve for the control incidence rate
(yc):
y =^L
RR
The odds ratio, may then be calculated:
odds ratio
y o
_ l-Jo
i-yc
Given the odds ratio, the underlying coefficient (P) may be calculated as before:
In {odds ratio)
P =
APM
The odds ratio and the coefficient calculated from it are dependent on the baseline and control
incidence rates. Unfortunately, it is not always clear what the baseline and control incidence rates should
be. Abbey et al. (1995b, Table 2) reported that there are 117 new cases of chronic bronchitis out of a
sample of 1,631, or a 7.17 percent rate. In addition, they reported the relative risk (RR = 1.81) for a new
case of chronic bronchitis associated with an annual mean concentration "increment" of 45 /ug/m3 of PM2 5
exposure.
Assuming that the baseline rate for chronic bronchitis (y0) should be 7.17 percent, the question
becomes whether the "increment" of 45 /ug/m3 should be added to or subtracted from the existing PM2 5
8Note that ESEERCO (1994, p. V-21) calculated (incorrectly) the underlying regression
coefficient for Abbey et al. (1993, Table 5) by taking the logarithm of the relative risk and dividing by the
change in TSP.
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concentration. If added the control incidence rate (yc) would be greater than the baseline rate (y0), while
subtraction would give a control rate less than the incidence rate. In effect, one might reasonably derive
two estimates of the odds ratio:
y0 ) ( 1.81 0.0717
.1 -yj U-(1.81-0.0717);
oddsratiol = r- = — N = 1.931
y I ( 0.0717
1 -yc) VI-0.0717.
y0 { 0.0717
oddsratio2 = j ^V= 7—00717—Y=
yc
i-x
1.81
0.0717
1_^r
lnO931) = 001462
45
„ ln(1.873)
B, = — -= 0.01394.
H2 45
An alternative is to simply assume that the relative risk (1.81) is reasonably close to the odds ratio
and calculate the underlying coefficient. It is easy to show that the relative risk equals:
. _ yo _ (i \ -APM-fl
RR = ^=(l-y0)-e-APM^+y0 .
yc
Assuming that:
e-APM^(l-y0)-e-APM?+y0
=^> RR = e~KPMp .
It is then possible to calculate the underlying coefficient:
In (RR) „
-APM
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12(1S1) = 001319
45
Since this coefficient estimate is based on the assumption that
-ya).e-^+ya ,
it should be used in a C-R function that maintains this assumption. In effect, it should be applied to a log-
linear C-R function:
fi-APM
Ay= J*, ¦(*'•-!)
Using the formula for the change in the incidence rate and assuming a 10 fj.g/m3 decline in PM2 5, it
is shown that this results in changes within the bounds suggested by the two estimates based on using the
estimated odds ratios:
0717
Ay, = -: —————— 0.0717 = -0.00914
(1 - 0.0717)-e10 0 01462 + 0.07 1 7
0717
Ay2 =7 , " 10001394 0.0717 = -0.00874
(1 - 0.0717)-e10 0 01394 + 0.07 1 7
Ay3 = 0.0717 • (e~10'°-01319 - l) = -0.00886 .
In this instance, it seems that simply using the relative risk to estimate the underlying coefficient
results in a good approximation of the change in incidence. Since it is unclear which of the two other
coefficients (P, or p2) should be used ~ as the published work was not explicit - the coefficient based on the
relative risk and the log-linear functional form seems like a reasonable approach.
D .2.4 Cox proportional Hazards Model
Use of a Cox proportional hazards model in an epidemiological study results in a C-R function that
is log-linear in form. It is often used to model survival times, and as a result, this discussion focuses on
mortality impacts.
The Cox proportional hazards model is based on a hazard function, defined as the probability that
an individual dies at time t, conditional on having survived up to time t (Collet, 1994, p. 10). More
formally, the hazard function equals the probability density function for the risk of dying divided by one
minus the cumulative probability density function:
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h(X,t) =
1 -F(X,t)
The proportional hazards model takes the form:
h(X,t) = h0(t)ex 13
where X is a vector of explanatory variables, p is a vector of coefficients, and h0(t) is the so-called "baseline
hazard" rate.9 This terminology differs from that used in most of this discussion: this "baseline hazard" is
the risk when all of the covariates (X) are set to zero; this is not the risk in the baseline scenario.
Taking the ratio of the hazard functions for the baseline and control scenarios gives the relative
risk:
oo _ h(X0,t) _ h0(t)e ^ apmjs
~ h(Xc,t) " h0(t)ex^ ~
where it is assumed that the only difference between the baseline and control is the level of PM pollution.
The relative risk is often presented rather than the coefficient p, so it is necessary to estimate p in
order to develop functional relationship between APM and Ay, as described previously for log-linear C-R
functions.
D .3 General Issues in Estimating Health & Welfare Benefits
Changes in air pollution result in changes in a number of health and welfare effects, or "endpoints,"
that society values. This chapter discusses key issues in their estimation. The first part of this section
discusses the development of C-R functions, based on the results from epidemiological studies, and the
second part discusses some general issues that arise with C-R functions.
D .3.1 Choosing Epidemiological Studies and Developing Concentration-Response Functions
This section reviews the steps we performed in selecting and developing C-R functions for
inclusion in BenMAP. The first section of this appendix describes how we chose studies from the
epidemiological literature for use in the present analysis. In any given study, there are often a large number
of estimated relationships between air pollution and adverse health effects, because the estimated
relationship can depend on the number and types of pollutants included in the model, among other reasons.
We then describe how we chose the specific estimated relationships, or models, from among the potentially
large number available in any given study. And then we briefly discuss how we convert the estimated
9The Cox proportional hazards model is sometimes termed a "semi-parametric" model, because
the baseline hazard rate is calculated using a non-parametric method, while the impact of explanatory
variables is parameterized. Collet (1994) details the estimation of Cox proportional hazards models; in
particular, see Collet's discussion (pp. 95-97) of nonparametric estimation of the baseline hazard.
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model into C-R functions, which then allow us to quantify the change in adverse health effects due to a
change in air pollution exposure.
Study Selection
We relied on an up-to-date assessment of the published scientific literature to ascertain the
relationship between particulate matter and ozone exposure and adverse human health effects. We
evaluated studies using a variety of selection criteria, including: its location and design, the characteristics
of the study population, and whether the study was peer-reviewed (Exhibit D-2).
In selecting studies for use in this analysis, priority was given to studies that focused on PM2 5 and
ozone, given that the emissions reductions from nonroad sources are likely to result primarily in reduced
ambient PM25 and ozone levels. For a given health effect, if sufficient PM25 studies were available, we
selected them rather than PM10 studies in the base analysis. In addition, results from several recent studies
allowed for the inclusion of new health effects, such as myocardial infarction for PM2 5 and school loss days
for ozone.
While a broad range of serious health effects have been associated with exposure to elevated ozone
and PM levels, we include only a subset of health effects in this quantified benefit analysis. Health effects
are excluded from this analysis for three reasons: (i) the possibility of double counting (such as hospital
admissions for specific respiratory diseases); (ii) uncertainties in applying effect relationships based on
clinical studies to the affected population; or (iii) a lack of an established C-R relationship.
A more detailed description of the studies and health effects included in this analysis are presented
in Appendices F and G.
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Exhibit D-2. Summary of Considerations Used in Selecting C-R Functions
Consideration
Comments
Peer reviewed
research
Study type
Study period
Population attributes
Study size
Study location
Pollutants included
in model
Measure of PM
Economically
valuable health
effects
Non-overlapping
endpoints
Peer reviewed research is preferred to research that has not undergone the peer review process.
Among studies that consider chronic exposure (e.g., over a year or longer) prospective cohort studies are
preferred over cross-sectional studies because they control for important individual-level confounding
variables that cannot be controlled for in cross-sectional studies.
Studies examining a relatively longer period of time (and therefore having more data) are preferred, because
they have greater statistical power to detect effects. More recent studies are also preferred because of
possible changes in pollution mixes, medical care, and life style over time. However, when there are only a
few studies available, studies from all years will be included.
The most technically appropriate measures of benefits would be based on C-R functions that cover the entire
sensitive population, but allow for heterogeneity across age or other relevant demographic factors. In the
absence of C-R functions specific to age, sex, preexisting condition status, or other relevant factors, it may be
appropriate to select C-R functions that cover the broadest population, to match with the desired outcome of
the analysis, which is total national-level health impacts.
Studies examining a relatively large sample are preferred because they generally have more power to detect
small magnitude effects. A large sample can be obtained in several ways, either through a large population,
or through repeated observations on a smaller population, i.e. through a symptom diary recorded for a panel
of asthmatic children.
U.S. studies are more desirable than non-U.S. studies because of potential differences in pollution
characteristics, exposure patterns, medical care system, population behavior and life style.
When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is important to use
properly specified C-R functions that include both pollutants. Use of single pollutant models in cases where
both pollutants are expected to affect a health outcome can lead to double-counting when pollutants are
correlated.
For this analysis, C-R functions based on PM2 5 are preferred to PM10 because reductions in emissions from
diesel engines are expected to reduce fine particles and not have much impact on coarse particles. Where
PM2 5 functions are not available, PM10 functions are used as surrogates, recognizing that there will be
potential downward (upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction.
Some health effects, such as forced expiratory volume and other technical measurements of lung function,
are difficult to value in monetary terms. These health effects are not quantified in this analysis.
Although the benefits associated with each individual health endpoint may be analyzed separately, care must
be exercised in selecting health endpoints to include in the overall benefits analysis because of the possibility
of double counting of benefits. Including emergency room visits in a benefits analysis that already considers
hospital admissions, for example, will result in double counting of some benefits if the category "hospital
admissions" includes emergency room visits.
Model Selection
For any given study selected for use in this analysis, there are often multiple models quantifying the
relationship between air pollution exposure and adverse health. For each model, we needed to identify the
specific models that we would use to develop C-R functions.
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Single Pollutant versus Multipollutant Models
Many of the epidemiological studies present results both for the case where only one pollutant is
entered into the health effects model, or single-pollutant models, and where two or more pollutants are
entered into the health effects model, or multi-pollutant models. When attempting to quantify the impact of
a single pollutant, such as PM2 5, on adverse health effects, the use of single-pollutant models may result in
biased estimates. For example, to the extent that any of the co-pollutants present in the ambient air may
have contributed to the health effects attributed to PM2 5 in single pollutant models, risks attributed to PM2 5
might be overestimated where C-R functions are based on single-pollutant models.
In multi-pollutant models, it may be difficult to sort out which pollutants are exerting an
independent effect when pollutants in a given location are highly correlated. As discussed in the 2002 draft
PM CD (U.S. EPA, 2002a), inclusion of pollutants that are highly correlated with one another can lead to
misleading conclusions in identifying a specific causal pollutant. When collinearity exists, multi-pollutant
models would be expected to produce unstable and statistically insignificant effects estimates for both PM
and the co-pollutants (U.S. EPA, 2002a, p.9-130).
Single- and multi-pollutant models each have potential advantages and disadvantages, with neither
type clearly preferable over the other, however, the regulatory focus of this analysis is on PM and ozone.
For regulatory analyses which consider two pollutants together, adding incidence changes for a given health
endpoint, based on a single-pollutant PM model, to the incidence changes based on a single-pollutant ozone
model could result in an overestimate of incidence change, if both have an effect on the health endpoint and
there is some collinearity between the two pollutants.
As a result, our first choice for this analysis is to use multi-pollutant models with both PM and
ozone, rather than single-pollutant models and multi-pollutant models with other pollutants. If multi-
pollutant models with both PM and ozone were not available, then models with other co-pollutants were
preferred to single-pollutant models. In the absence of multi-pollutant models from a given study, single
pollutant models were selected for use in the analysis.
Model Selection Criteria
In many epidemiological studies of air pollution and health, researchers estimate and present
numerous single pollutant and multi-pollutant models for the same pollutant and health endpoint. These
models may differ from each other in a number of characteristics, including: the functional form of the
model, the covariates included in the model, the pollutant exposure metric, the lag structure, and the study
population.
For the purposes of estimating health benefits associated with pollutant changes, it is neither
realistic nor advantageous to include every model presented in each study. However, it is important that a
relatively objective process be used to select among models. Described below are the criteria that were
used as guidance in the selection of a particular model from among several models presented in a study. It
is not possible in all cases to select a model using a completely objective and mechanical process. In many
cases, professional judgement and an understanding of the study context are necessary as well to select the
most appropriate models. Exhibit D-3 summarizes the selection criteria that we used.
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Exhibit D-3. Description of Selection Criteria
Selection Criteria
Description
Goodness-of-fit statistics
If an appropriate measure for model selection is reported for each of several models in a study, then
this measure may be used as the basis on which to select a model.
Best captures distributed
Select the model that appears to best capture a distributed lag effect: If multiple single-lag models
lag
and/or moving average models are specified, select the model with the largest effect estimate, all
else equal.
Best set of control variables
Select the model which includes temporal variables (i.e. season, weather patterns, day of the week)
and other known non-pollutant confounders, all else equal. Select the model which uses the most
sophisticated methods of capturing the relationship between these variables and the dependent
variable (e.g., affords the most flexibility in fitting possible nonlinear trends).
Useful for health effects
The model must be in a form that is useful for health effects modeling (e.g., the pollutant variable
modeling
should be a continuous variable rather than a categorical variable).
Biologically plausible
Select only those models that are biologically plausible.
Sample size
Select the model with the larger sample size, all else equal.
Goodness-of-Fit Statistics
Model specification (or mis-specification) is one of the most important issues confronting
researchers - and those who apply the results of their research. The goal is to select the "right model" - i.e.,
the model that has included all the variables that should be in the model (i.e., are relevant) and has not
included any variables that should not be in the model (i.e., that are irrelevant). However, is not often
known which model is the "right model." There are several ways of selecting one model from among
several. One way is to use a goodness of fit measure, which provides a measure of how well a model fits
the data. There are a variety of goodness of fit measures available, but use of such measures can at times be
misleading. In order to select models based on a goodness of fit criterion, it is important to understand the
meaning behind typical goodness of fit measures.
One of the most common goodness of fit measures is R2, often called the "explained variance" or
the "coefficient of multiple determination." R2 measures the proportion of the total variability in the
dependent variable (e.g., the daily incidence of a health effect) that is explained by the linear regression.
The closer R2 is to 1.0, the greater this proportion. The problem with R2, however, is that it can be
increased simply by adding more independent variables to the model, regardless of the variables' relevance
to predicting the dependent variable. In the extreme, if there are N observations in the dataset, a model with
N explanatory variables will result in an R2 of 1, but it would be a meaningless model with respect to
predictive value. If several models are reported in a study, all with the same number of variables, this
drawback is avoided. In that case, if R2 is reported for each estimated model, this may be a reasonable
measure of the relative goodness of fit of the models and an acceptable way to select a single model from
among several.
In many cases, however, R2 is a problematic measure of goodness of fit, for the reason stated above.
In view of the drawback of R2, several alternative measures of goodness of fit have been developed which
essentially penalize the model for additional variables - or, equivalently, give "points" for parsimony. Two
of the more commonly used of these measures are the adjusted R2 and Akaike's Information Criterion
(AIC). The selection criterion, using the adjusted R2, is to select the model with the largest adjusted R2; the
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selection criterion, using the AIC, is to select the model with the smallest AIC. Both of these measures
offset the incremental "fit" gained by including variables in the model with a "penalty" for increasing the
number of explanatory (independent) variables. It should be noted, however, that several such measures
have been suggested, and there is no clear way to determine which of these measures to select the "best"
model is itself the "best" measure. Nevertheless, if a measure of goodness of fit (particularly, one of the
measures that consider parsimony) is presented in a paper, this provides a reasonable means by which to
select one model out of several.
Often, however, no goodness of fit measure is presented in a paper. A common approach for
deciding the appropriate set of independent variables to be included in a model is to include a variable if its
t-value exceeds the critical value for testing whether the variable's coefficient is significantly different from
zero at the 5 percent level. Several variables, or an entire model, can similarly be tested with an F-test. (If
all the coefficients are being tested jointly, the null hypothesis being tested is that all the coefficients are
zero, in which case the model has no more predictive value than the mean of the dependent variable.) In
some cases, a comparison of F-statistics (or their corresponding p-values) can be used to select from among
several models - in particular, if the F-test for one model suggests that one cannot reject the null hypothesis
at the five percent level whereas the F-test for another model suggests that one should reject the null
hypothesis.10
For example, Stieb et al. (1996) estimated the association between ozone and ER visit rates using
both a linear model (in which ER visit rate was a linear function of ozone level) and a quadratic model (in
which ER visit rate was a linear function of ozone level squared). No goodness of fit measure was reported
in the paper. However, model p-values were reported. The linear model was not statistically significant at
the 5% level, whereas the quadratic model was highly significant. This suggests that the linear model does
no better in predicting ER visits than the mean of ER visits, whereas the quadratic model has predictive
value. The authors of the study themselves noted that "only ozone appeared to have a nonlinear
relationship with visit rates" (Stieb et al., 1996, p. 1356) and that "quadratic, linear-quadratic, and indicator
models consistently fit the data better than the linear model..." (Stieb et al., 1996, p. 1358). Based on the
relative model p-values presented in the paper, corroborated by the authors' observations, the quadratic
model was selected for inclusion in this analysis.
Best Captures a Distributed Lag Effect.
The question of lags and the problems of correctly specifying the lag structure in a model has been
discussed extensively (U.S. EPA, 2002a, Section 8.4.4). In many time-series studies, after the basic model
is fit (before considering the pollutant of interest), several different lags are typically fit in separate single-
lag models and the most significant lag is chosen. The 2002 draft PM CD notes that "while this practice
may bias the chance of finding a significant association, without a firm biological reason to establish a fixed
pre-determined lag, it appears reasonable" (U.S. EPA, 2002a, p. 8-237).
There is recent evidence (Schwartz, 2000b) that the relationship between PM and health effects
may best be described by a distributed lag (i.e., the incidence of the health effect on day n is influenced by
PM concentrations on day n, day n-1, day n-2 and so on). If this is the case, a model that includes only a
single lag (e.g., a 0-day lag or a 1-day lag) is likely to understate the total impact of PM. The 2002 draft
10 If F-statistics are both (or all) greater than the critical value, it is less clear that a comparison of
these F-statistics would be a good way to select a model.
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PM CD makes this point, noting that "if one chooses the most significant single lag day only, and if more
than one lag day shows positive (significant or otherwise) associations with mortality, then reporting a RR
[relative risk] for only one lag would also underestimate the pollution effects" (U.S. EPA, 2002a, p. 8-241).
The same may hold true for other pollutants that have been associated with various health effects.
Several studies report similar models with different lag structures. For example, Moolgavkar
(2000c) studied the relationship between air pollution and respiratory hospital admissions in three U.S.
metropolitan areas. The author reports models with PM lagged from zero to five days. Since the lagging of
PM was the only difference in the models and the relationship is probably best described using a distributed
lag model, any of single-lag effect estimates are likely to underestimate the full effect. Therefore, we
selected the model with the largest effect estimate.
Most Sophisticated Model That Includes Temporal Variables
A correctly specified model for evaluating air pollution and health would include all variables that
are relevant independent predictors of the health outcome and none that are not. If there are variables that
are known from prior literature to be associated with both air pollution and the health endpoint (e.g.
temperature or season), then omitting these variables is likely to result in biased effect estimates - the C-R
function would attribute too much or too little of the health effect to the pollutant. Since temporal and
weather patterns are known to confound the relationship between air pollution and health, we selected the
models which, all else equal, adjusted for these factors over those that did not.
Useful for Health Effects Modeling
In order for a model to be selected for use, the pollutant must be a continuous variable, so that
changes in incidence of the health effect can be predicted to result from any change in pollutant
concentration. Those models which examine the effects of being above or below a pollutant threshold or
those that look at changes in health associated with categories of pollutant levels are not useful for this
purpose.
For example, in a study of the association between air pollution and emergency room (ER) visits,
Stieb et al. (1996) estimated several different models. One of these models relates ER visit rates to being
above or below the 95th percentile value of ozone (that is, it essentially estimates an average ER visit rate
for days above the 95th percentile value of ozone and a different average ER visit rate for days below it). In
another study, Peters et al. (2001) estimated a model using quintiles of PM levels. None of these models is
appropriate for use in CAPMS. Instead, we selected models which associate the incidence (rate) of a health
effect with the pollutant concentration.
Biologically Plausible.
If a model includes a relationship that simply doesn't make biological sense, it is probably mis-
specified and should not be used for predictive purposes. It is sometimes not clear, however, what is
biologically plausible and what is not. For example, Stieb et al. (1996) estimated a linear-quadratic model -
i.e., a model which included both ozone and the square of ozone as independent variables - in a study of air
pollution and ER visits. The coefficient of the linear term in this model was negative, while the coefficient
of the quadratic term was positive. A graph of the model showed a curve which "dips" at low levels of
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ozone - i.e. at low ozone levels, increases in ozone are associated with decreases in risk. Since ozone is not
likely to be beneficial at any levels, this model is not considered to be biologically plausible.
Sample Size
Several studies report the results of an analysis using different population subsets. All else equal,
the model based on the larger population, which results in more statistical power, is selected. For example,
Pope et al. (1991) studied the association between PM and respiratory health in children. The authors
report results for a school-based sample of 34 children and a patient-based sample of 21 residents. Since
there are many more observations in the school-based sample (3,096 versus 1,912) and no other significant
differences between the models, the model estimated from the school-based sample is used.
Chen et al. (2000) examined the association between air pollution and school absenteeism. The
authors reported results by elementary school grade and for all grades combined. With all else equal in the
models, the C-R function with the larger number of observations was selected (i.e., the model based on all
grades combined).
As discussed above, these criteria are used as general guidance when it is not obvious which model
should be chosen from a particular study. The purpose of this process is to provide as objective a protocol
as possible for selecting C-R functions. However, model selection can never be a completely mechanical
and objective process because it often depends on the specific context of the particular study. In some
cases, consideration of several of the aforementioned criteria must be weighed before selecting C-R
functions for use in the analysis. The C-R functions selected for use in this analysis are described below
with study summaries and a description of which model was selected, when multiple models are available.
D .4 Issues in Using Concentration-Response Functions
This section briefly summarizes some of the issues that arise when using C-R functions.
D .4.1 S-Plus Issue
Recently, the Health Effects Institute (HEI) reported findings by health researchers at Johns
Hopkins University and others that have raised concerns about aspects of the statistical methods used in a
number of recent time-series studies of short-term exposures to air pollution and health effects (Greenbaum,
2002). The estimates derived from the long-term exposure studies, which typically account for a major
share of the economic benefits, are not affected. Similarly, the time-series studies employing generalized
linear models (GLMs) or other parametric methods, as well as case-crossover studies, are not affected.
As discussed in HEI materials provided to EPA and to CASAC (Greenbaum, 2002), researchers
working on the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) found problems in the
default "convergence criteria" used in Generalized Additive Models (GAM) and a separate issue first
identified by Canadian investigators about the potential to underestimate standard errors in the same
statistical package. These and other scientists have begun to reanalyze the results of several important time
series studies with alternative approaches that address these issues and have found a downward revision of
some results. For example, the mortality risk estimates for short-term exposure to PM10 from NMMAPS
were overestimated (this study was not used in this benefits analysis of fine particle effects). However,
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both the relative magnitude and the direction of bias introduced by the convergence issue is case-specific.
In the C-R functions described in detail in Appendices F and G, we have included the available reanalyses
of previous studies, such as those collected in a recent document from the Health Effects Institute (2003).
D .4.2 Thresholds
When conducting clinical (chamber) and epidemiological studies, C-R functions may be estimated
with or without explicit thresholds. Air pollution levels below the threshold are assumed to have no
associated adverse health effects. When a threshold is not assumed, as is often the case in epidemiological
studies, any exposure level is assumed to pose a non-zero risk of response to at least one segment of the
population.
The possible existence of an effect threshold is a very important scientific question and issue for
policy analyses. The EPA Science Advisory Board Advisory Council for Clean Air Compliance, which
provides advice and review of EPA's methods for assessing the benefits and costs of the Clean Air Act
under Section 812 of the Clean Air Act, has advised EPA that there is currently no scientific basis for
selecting a threshold of 15 |ig/m3 or any other specific threshold for the PM-related health effects
considered in typical benefits analyses (U.S. EPA, 1999b). This is supported by the recent literature on
health effects of PM exposure (Rossi et al., 1999; Daniels et al., 2000; Pope, 2000; Schwartz, 2000c) which
finds in most cases no evidence of a non-linear concentration-response relationship and certainly does not
find a distinct threshold for health effects. The most recent draft of the EPA Air Quality Criteria for
Particulate Matter (U.S. EPA, 2002a) reports only one study, analyzing data from Phoenix, AZ, that
reported even limited evidence suggestive of a possible threshold for PM2 5 (Smith et al., 2000).
Recent cohort analyses by the Health Effects Institute (Krewski et al., 2000) and Pope et al. (Pope
et al., 2002) provide additional evidence of a quasi-linear concentration-response relationship between long-
term exposures to PM25 and mortality. According to the latest draft PM criteria document, Krewski et al.
"found a visually near-linear relationship between all-cause and cardiopulmonary mortality residuals and
mean sulfate concentrations, near-linear between cardiopulmonary mortality and mean PM2 5, but a
somewhat nonlinear relationship between all-cause mortality residuals and mean PM2 5 concentrations that
flattens above about 20 |ig/m\ The confidence bands around the fitted curves are very wide, however,
neither requiring a linear relationship nor precluding a nonlinear relationship if suggested by reanalyses."
The Pope et al. analysis, which represented an extension to the Krewski et al. analysis, found that the
concentration-response relationships relating PM2 5 and mortality "were not significantly different from
linear associations."
Daniels et al. (2000) examined the presence of threshold in PM10 concentration-response
relationships for daily mortality using the largest 20 U.S. cities for 1987-1994. The results of their models
suggest that the linear model was preferred over spline and threshold models. Thus, these results suggest
that linear models without a threshold may well be appropriate for estimating the effects of PM10 on the
types of mortality of main interest. Schwartz and Zanobetti (2000) investigated the presence of threshold
by simulation and actual data analysis of 10 U.S. cities. In the analysis of real data from 10 cities, the
combined concentration-response curve did not show evidence of a threshold in the PM10-mortality
associations. Schwartz et al. (2002) investigated thresholds by combining data on the PM2 5-mortality
relationships for six cities and found an essentially linear relationship down to 2 |ig/nr\ which is at or below
anthropogenic background in most areas. They also examined just traffic related particles and again found
no evidence of a threshold. The Smith et al. (2000) study of associations between daily total mortality and
PM2 5 and PM10_2 5 in Phoenix, AZ (during 1995-1997) also investigated the possibility of a threshold using
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a piecewise linear model and a cubic spline model. For both the piecewise linear and cubic spline models,
the analysis suggested a threshold of around 20 to 25 |ig/nr\ However, the concentration-response curve
for PM2 5 presented in this publication suggests more of a U- or V-shaped relationship than the usual
"hockey stick" threshold relationship.
Finally, in a recent review of methods for estimating the public health benefits of air pollution
regulations, National Research Council (2002) concluded that there is no evidence for any departure from
linearity in the observed range of exposure to PM10 or PM2 5, nor any indication of a threshold. They cite
the weight of evidence available from both short and long term exposure models and the similar effects
found in cities with low and high ambient concentrations of PM.
D .4.3 Degree of Prematurity of Mortality
It is possible that the short-term studies are detecting an association between air pollution and
mortality that is primarily occurring among terminally ill people. Critics of the use of short-term studies for
policy analysis purposes correctly point out that an added risk factor that results in terminally ill people
dying a few days or weeks earlier than they otherwise would have (referred to as "short-term harvesting") is
potentially included in the measured PM mortality "signal" detected in such a study. While some of the
detected excess deaths may have resulted in a substantial reduction in lifespan, others may have resulted in
a relatively small decrease in lifespan. Studies by Spix et al (1993) and Pope et al. (1992) yield conflicting
evidence, suggesting that harvesting may represent anywhere from zero to 50 percent of the deaths
estimated in short-term studies. However, recent work by Zeger et al. (1999), Schwartz (2000a), and
Zanobetti et al. (2002) that focused exclusively on this issue, reported that short-term harvesting does not
play a major role in the PM-mortality relationship.11
Moreover, it is not likely that the excess mortality reported in a long-term prospective cohort study
like Pope et al. (1995) contains any significant amount of this short-term harvesting. The Cox proportional
hazard statistical model used in the Pope study examines the question of survivability throughout the study
period (ten years). Deaths that are premature by only a few days or weeks within the ten-year study period
(for example, the deaths of terminally ill patients, triggered by a short duration PM episode) are likely to
have little impact on the calculation of the average probability of surviving the entire ten-year interval.
D .4.4 Estimating Effects for Multiple Age Groups
For analyses focusing on a year well past the year 2000, you should note that the population age
distribution is expected to change over time, with a greater percentage of the population moving into older
age categories. Because baseline incidence rates for older populations tend to exceed those for younger
populations for several health endpoints (most importantly, for mortality), this demographic shift has
important implications for the estimation of future-year incidence change. If you apply a C-R function to
an entire population, using one average baseline incidence, this demographic shift would be missed, and the
future-year incidence change would be significantly underestimated.
nZeger et al. (1999, p. 171) reported that: "The TSP-mortality association in Philadelphia is
inconsistent with the harvesting-only hypothesis, and the harvesting-resistant estimates of the TSP
relative risk are actually larger - not smaller - than the ordinary estimates."
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To take into account projected demographic shifts and the corresponding implications for predicted
incidence change, we have included C-R functions for separate age groups within the entire population to
which a C-R function is applicable, using projected populations in each age group. Projected baseline
incidences (incidence rates times populations) used in the calculation of future-year pollutant-related
incidence change therefore better reflect the expected demographic shifts.
The ideal approach would be have future-year incidence rates. However, these are not available.
Thus to the extent that you use baseline incidence rates (which may decline slightly over time for younger
age groups and increase for the oldest groups), you may be mis-estimating incidence change for particular
age groups to the extent that baseline incidence rates change over time.
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Concentration-Response (C-R) functions developed from log-linear or logistic models estimate the
percent change in an adverse health effect associated with a given pollutant change. In order to estimate the
absolute change in incidence using these functions, we need the baseline incidence rate of the adverse
health effect. This appendix describes the data used to estimate baseline incidence rates for the health
effects considered in this analysis.
E .1 Mortality
Age, cause, and county-specific mortality rates were obtained from the U.S. Centers for Disease
Control (CDC) for the years 1996 through 1998. CDC maintains an online data repository of health
statistics, CDC Wonder, accessible at http://wonder.cdc.gov/. The mortality rates provided are derived
from U.S. death records and U.S. Census Bureau postcensal population estimates. Mortality rates were
averaged across three years (1996 through 1998) to provide more stable estimates. When estimating rates
for age groups that differed from the CDC Wonder groupings, we assumed that rates were uniform across
all ages in the reported age group. For example, to estimate mortality rates for individuals ages 30 and up,
we scaled the 25-34 year old death count and population by one-half and then generated a population-
weighted mortality rate using data for the older age groups. Population-weighted national mortality rates
are presented in Exhibit E-l.
Exhibit E-l. National Mortality Rates for Selected Conditions, by Age Group
Mortality Category
(ICD codes)
Mortality Rate by Age Group (deaths per 100 people per year)
0-17
18-24
25-29
30-34
35-44
45-54
55-64
65-74
75-84
85+
All-Cause
0.045
0.093
0.119
0.119
0.211
0.437
1.056
2.518
5.765
15.160
Non-Accidental (ICD <800)
0.025
0.022
0.057
0.057
0.150
0.383
1.006
2.453
5.637
14.859
Chronic Lung Disease (ICD
490-496)
0.000
0.001
0.001
0.001
0.002
0.009
0.046
0.166
0.367
0.561
Cardio-Pulmonary
0.004
0.005
0.013
0.013
0.044
0.143
0.420
1.163
3.179
9.846
Source: We obtained data from 1996-1998 from the CDC Wonder (http://wonder.ede.gov/). County-specific rates are used in the C-
R functions.
E .2 Hospitalizations
Regional hospitalization counts were obtained from the National Center for Health Statistics'
(NCHS) National Hospital Discharge Survey (NHDS). NHDS is a sample-based survey of non-Federal,
short-stay hospitals (<30 days)12, and is the principal source of nationwide hospitalization data. The survey
collects data on patient characteristics, diagnoses, and medical procedures.
12
The following hospital types are excluded from the survey: hospitals with an average patient length of stay of greater
than 30 days, federal, military, Department of Veterans Affairs hospitals, institutional hospitals (e.g. prisons), and hospitals with
fewer than six beds.
E-l
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Public use data files for the year 1999 survey were downloaded13 and processed to estimate
hospitalization counts by region. NCHS groups states into four regions using the following groupings
defined by the U.S. Bureau of the Census:
• Northeast - Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New
York, New Jersey, Pennsylvania
• Midwest - Ohio, Indiana, Illinois, Michigan, Wisconsin, Minnesota, Iowa, Missouri, North
Dakota, South Dakota, Nebraska, Kansas
• South - Delaware, Maryland, District of Columbia, Virginia, West Virginia, North Carolina,
South Carolina, Georgia, Florida, Kentucky, Tennessee, Alabama, Mississippi, Arkansas,
Louisiana, Oklahoma, Texas
• West - Montana, Idaho, Wyoming, Colorado, New Mexico, Arizona, Utah, Nevada, Washington,
Oregon, California, Alaska, Hawaii
We calculated per capita hospitalization rates, by dividing these counts by the estimated regional
population estimates for 1999 that we derived from the U.S. Bureau of the Census and the population
projections used by NHDS to generate the counts. Note that NHDS started with hospital admission counts,
based on a sample of admissions, and then they used population estimates to generate population-weighted
hospital admission counts that are representative of each region. This weighting used forecasts of 1999
population data. Ideally, we would use these same forecasts to generate our admission rates. However,
while NHDS presented counts of hospital admissions with a high degree of age specificity, it presented
regional population data for only four age groups: 0-14, 15-44, 45-64, and 65+.14 Using only the NHDS
data, we would be limited to calculating regional admission rates for four groups. Because we are
interested in a broader range of age groups, we turned to 2000 Census.
We used the 2000 Census to obtain more age specificity, and then corrected the 2000 Census
figures so that the total population equaled the total for 1999 forecasted by NHDS. That is, we sued the
following procedure: (1) we calculated the count of hospital admissions by region in 1999 for the age
groups of interest, (2) we calculated the 2000 regional populations corresponding to these age groups, (3)
calculated regional correction factors, that equal the regional total population in 1999 divided by the
regional total population in 2000 by region, (4) multiplied the 2000 population estimates by these correction
factors, and (5) divided the 1999 regional count of hospital admissions by the estimated 1999 population.
The endpoints in hospitalization studies are defined using different combinations of ICD codes.
Rather than generating a unique baseline incidence rate for each ICD code combination, for the purposes of
this analysis, we identified a core group of hospitalization rates from the studies and applied the appropriate
combinations of these rates in the C-R functions:
• all respiratory (ICD-9 460-519)
• chronic lung disease (ICD-9 490-496)
• asthma (ICD-9 493)
• pneumonia (ICD-9 480-487)
• acute bronchitis (ICD-9 466)
• acute laryngitis (ICD-9 464)
• all cardiovascular (ICD-9 390-459)
• ischemic heart disease (ICD-9 410-414)
13 Data are available at ftp://ftp.cde.gov/pub/Health_Statisties/NCHS/Datasets/NHDS/
14 See: 1999nhds_summary.pdf (p. 187) for published regional population estimates for 1999.
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• dysrhythmia (ICD-9 427)
• congestive heart failure (ICD-9 428)
For each C-R function, we selected the baseline rate or combination of rates that most closely
matches to the study endpoint definition. For studies that define chronic lung disease as ICD 490-492, 494-
496, we subtracted the incidence rate for asthma (ICD 493) from the chronic lung disease rate (ICD 490-
496). In some cases, the baseline rate will not match exactly to the endpoint definition in the study. For
example, Burnett et al. (2001) studied the following respiratory conditions in infants <2 years of age: ICD
464.4, 466, 480-486, 493. For this C-R function we apply an aggregate of the following rates: ICD 464,
466, 480-487, 493. Although they do not match exactly, we assume that relationship observed between the
pollutant and study-defined endpoint is applicable for the additional codes. Exhibit E-2 presents a summary
of the national hospitalization rates for 1999 from NHDS.
Exhibit E-2. Hospitalization Rates, by Region and Age Group
Hospitalization Rate by Age Group
Hospitalization Category (admissions per 100 people per year)
0-18
18-24
25-34
35-44
45-54
55-64
65+
Respiratory
all respiratory
460-519
1.066
0.271
0.318
0.446
0.763
1.632
5.200
acute laryngitis
464
0.055
0.002
0.001
0.002
0.008
0.000
0.005
acute bronchitis
466
0.283
0.017
0.014
0.017
0.027
0.040
0.156
pneumonia
480-487
0.308
0.069
0.103
0.155
0.256
0.561
2.355
asthma
493
0.281
0.081
0.110
0.099
0.144
0.161
0.205
chronic lung disease
490-496
0.291
0.089
0.124
0.148
0.301
0.711
1.573
Cardiovascular
all cardiovascular
390-459
0.043
0.084
0.206
0.678
1.926
4.389
11.629
ischemic heart disease
410-414
0.004
0.008
0.031
0.231
0.902
2.021
3.708
dysrhythmia
427
0.011
0.017
0.027
0.076
0.158
0.392
1.387
congestive heart failure
428
0.003
0.005
0.011
0.011
0.160
0.469
2.167
Source: As described in the text, we obtained the regional count of hospital admissions from National Hospital Discharge Survey
(NHDS), and we obtained the population data from the 2000 U.S. Census and NHDS.
E .3 Emergency Room Visits for Asthma
Regional asthma emergency room visit counts were obtained from the National Hospital
Ambulatory Medical Care Survey (NHAMCS). NHAMCS is a sample-based survey, conducted by NCHS,
designed to collect national data on ambulatory care utilization in hospital emergency and outpatient
departments of non-Federal, short-stay hospitals (<30 days).15
15 The target universe of the NHAMCS is in-person visits made in the United States to emergency and outpatient
departments of non-Federal, short-stay hospitals (hospitals with an average stay of less than 30 days) or those whose specialty is
general (medical or surgical) or children's general.
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Appendix E. Sources of Prevalence and Incidence Data
Public use data files for the year 2000 survey were downloaded16 and processed to estimate
hospitalization counts by region. We obtained population estimates from the 2000 U.S. Census. The
NCHS regional groupings described above were used to estimate regional emergency room visit rates.
Exhibit E-3 presents the estimated asthma emergency room rates by region.
Exhibit E-3. Emergency Room Visit Rates for Asthma, by Region and Age Group
ER Category
ICD-9 Code
Region
0-18
ER Visit Rate
(visits per 100 people per year)
18-64
65+
asthma
493
Northeast
0.761
0.802
0.300
Midwest
1.476
0.877
0.334
South
1.243
0.420
0.192
West
0.381
0.381
0.137
Source: We obtained ER visit counts for the year 2000 from the National Hospital Ambulatory Medical Care Survey (NHAMCS)
and population data were obtained from the 2000 U.S. Census.
E .4 Nonfatal Heart Attacks
The relationship between short-term particulate matter exposure and heart attacks was quantified in
a case-crossover analysis by Peters et al. (2001). The study population was selected from heart attack
survivors in a medical clinic. Therefore, the applicable population to apply to the C-R function is all
individuals surviving a heart attack in a given year. Several data sources are available to estimate the
number of heart attacks per year. For example, several cohort studies have reported estimates of heart
attack incidence rates in the specific populations under study. However, these rates depend on the specific
characteristics of the populations under study and may not be the best data to extrapolate nationally. The
American Heart Association reports approximately 540,000 new heart attacks per year using data from a
multi-center study (Haase, 2002, to be published in the American Heart Association's 2003 Statistical
Handbook). Exclusion of heart attack deaths reported by CDC Wonder yields approximately 330,000
nonfatal cases per year.
An alternative approach to the estimation of heart attack rates is to use data from the National
Hospital Discharge Survey, assuming that all heart attacks that are not instantly fatal will result in a
hospitalization. According to the National Hospital Discharge Survey, in 1999 there were approximately
829,000 hospitalizations due to heart attacks (acute myocardial infarction: ICD-9 410) (Popovic, 2001,
Table 8). We used regional hospitalization rates over estimates extrapolated from cohort studies because
the former is part of a nationally representative survey with a larger sample size, which is intended to
provide reliable national estimates. As additional information is provided regarding the American Heart
Association methodology, we will evaluate the usefulness of this estimate of heart attack incidence.
Rosamond et al. (1999) reported that approximately six percent of male and eight percent of female
hospitalized heart attack patients die within 28 days (either in or outside of the hospital). We, therefore,
applied a factor of 0.93 to the count of hospitalizations to estimate the number of nonfatal heart attacks per
16 Data are available at ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHAMCS/
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Appendix E. Sources of Prevalence and Incidence Data
year. To estimate the rate of nonfatal heart attack, we divided the count by the population estimate for
2000 from the U.S. Census. Exhibit E-4 presents the regional nonfatal heart attack incidence rates.
Exhibit E-4. Nonfatal Heart Attack Rates, by Region and Age Group
Endpoint (ICD codes)
Region
0-18
Nonfatal Heart Attack Rate
(cases per 100 people per year) "
18-64
65+
nonfatal heart attacks (ICD-9 410)
Northeast
0.0000
0.2167
1.6359
Midwest
0.0003
0.1772
1.4898
South
0.0006
0.1620
1.1797
West
0.0000
0.1391
1.1971
s Rates are based on data from the 1999 National Hospital Discharge Survey (NHDS) and an estimate from Rosamond et al. (1999)
that approximately 7% of individuals hospitalized for a heart attack die within 28 days.
E .5 School Loss Days
Epidemiological studies have examined the relationship between air pollution and a variety of
measures of school absence. These measures include: school loss days for all causes, illness-related, and
respiratory illness-related. We have two sources of information. The first is the National Center for
Education Statistics, which provided an estimate of all-cause school loss days, and the other is the National
Health Interview Survey (Adams et al., 1999, Table 47), which has data on different categories of acute
school loss days. Exhibit E-5 presents the illness-related rates used in this analysis.
E .5.1 All-Cause School Loss Rates
Based on data from the U.S. Department of Education (1996, Table 42-1), the National Center for
Education Statistics estimates that for the 1993-1994 school year, 5.5 percent of students are absent from
school on a given day. This estimate is comparable to study-specific estimates from Chen et al. (2000) and
Ransom and Pope (1992), which ranged from 4.5 to 5.1 percent.
We use the total or all-cause school absence rate in C-R functions based on studies by Chen et al.
(2000), Gilliland et al. (2001) and Ransom et al. (1992). We also use the all-cause school absence rate as a
population adjustment in C-R functions derived from Gilliland et al. (2001), for which it is necessary to
estimate the average proportion of children attending school on a given day. This is described in more
detail in the specific C-R function summaries.
E .5.2 Illness-Related School Loss Rates
The National Health Interview Survey (NHIS) has regional estimates of school loss days due to a
variety of acute conditions (Adams et al., 1999). NHIS is a nationwide sample-based survey of the health
of the noninstitutionalized, civilian population, conducted by NCHS. The survey collects data on acute
conditions, prevalence of chronic conditions, episodes of injury, activity limitations, and self-reported
health status. However, it does not provide an estimate of all-cause school loss days.
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Appendix E. Sources of Prevalence and Incidence Data
In estimating illness-related school loss days, we started with school loss days due to acute
problems (Adams et al., 1999, Table 47) and subtracted lost days due to injuries, in order to match the
definition of the study used in the C-R function to estimate illness-related school absences (Gilliland et al.,
2001). We then divided by 180 school days per to estimate /7/«t.v.v-rclated school absence rates per school
day. Similarly, when estimating respiratory illness-related school loss days, we use data from Adams et al.
(1999, Table 47). Note that we estimated 180 school days in a year to calculate respiratory illness-related
school absence rates per year.
Exhibit E-5. School Loss Day Rates
Type of School Loss Day *
Northeast
Absence Rate by Region
(cases per 100 students per year)
Midwest South
West
Respiratory illness-related absences
131.4
165.6
109.8
223.2
Illness-related absences
244.8
262.8
255.6
370.8
All-cause
990.0
990.0
990.0
990.0
s We based illness-related school loss day rates on data from the 1996 NHIS (Adams et al., 1999, Table 47) and an estimate of 180
school days per year. This excludes school loss days due to injuries. We based the all-cause school loss day rate on data from the
National Center for Education Statistics (U.S. Department of Education, 1996, Table 42-1).
E .6 Other Acute and Chronic Effects
For many of the minor effect studies, baseline rates from a single study are often the only source of
information, and we assume that these rates hold for locations in the U.S. The use of study-specific
estimates are likely to increase the uncertainty around the estimate because they are often estimated from a
single location using a relatively small sample. These endpoints include: acute bronchitis, chronic
bronchitis, upper respiratory symptoms, lower respiratory symptoms. Exhibit E-6 presents a summary of
these baseline rates.
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Appendix E. Sources of Prevalence and Incidence Data
Exhibit E-6. Selected Acute and Chronic Effects Rates
Endpoint
Age
Parameter"
Rate
Source
Acute Bronchitis
8-12
Incidence
4.300
(American Lung Association, 2002a,
Table 11)
Chronic Bronchitis
27+
Incidence
0.378
(Abbey et al., 1993, Table 3)
Chronic Bronchitis
18+
Prevalence
4.43%
18-44
45-64
3.67%
5.05%
(American Lung Association, 2002b,
Table 4)
65+
5.87%
Lower Respiratory Symptoms
(LRS)
7-14
Incidence
43.8
(Schwartz et al., 1994, Table 2)
Minor Restricted Activity Days
(MRAD)
18-64
Incidence
780.0
(Ostro and Rothschild, 1989, p. 243)
Work Loss Day (WLD)
18-64
Incidence
217.2
18-24
25-44
197.1
247.5
(Adams et al., 1999, Table 41); (U.S.
Bureau of the Census, 1997, No. 22)
45-64
179.6
s The incidence rate is the number of cases per 100 people per year. Prevalence refers to the fraction of people that have a particular
illness during a particular time period.
E .6.1 Acute Bronchitis
The annual rate of acute bronchitis for children ages 5 to 17 was obtained from the American Lung
Association (2002a, Table 11). The authors reported an annual incidence rate per person of 0.043, derived
from the 1996 National Health Interview Survey.
E .6.2 Chronic Bronchitis Incidence Rate
The annual incidence rate for chronic bronchitis is estimated from data reported by Abbey et
al.(1993, Table 3). The rate is calculated by taking the number of new cases (234), dividing by the number
of individuals in the sample (3,310), dividing by the ten years covered in the sample, and then multiplying
by one minus the reversal rate (estimated to be 46.6% based on Abbey et al. (1995a, Table 1)). We then
multiplied this result by 100 to calculate an annual incidence rate per 100 people of 0.378.
Age-specific incidence rates are not available. Abbey et al. (1995a, Table 1) did report the
incidences by three age groups (25-54, 55-74, and 75+) for "cough type" and "sputum type" bronchitis.
However, they did not report an overall incidence rate for bronchitis by age-group. Since, the cough and
sputum types of bronchitis overlap to an unknown extent, we did not attempt to generate age-specific
incidence rates for the over-all rate of bronchitis.
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E .6.3 Chronic Bronchitis Prevalence Rate
We obtained the annual prevalence rate for chronic bronchitis from the American Lung Association
(2002b, Table 4). Based on an analysis of 1999 National Health Interview Survey data, they estimated a
rate of 0.0443 for persons 18 and older, they also reported the following prevalence rates for people in the
age groups 18-44, 45-64, and 65+: 0.0367, 0.0505, and 0.0587, respectively.
E .6.4 Lower Respiratory Symptoms
Lower respiratory symptoms (LRS) are defined as two or more of the following: cough, chest pain,
phlegm, wheeze. The proposed yearly incidence rate for 100 people, 43.8, is based on the percentiles in
Schwartz et al. (Schwartz et al., 1994, Table 2). The authors did not report the mean incidence rate, but
rather reported various percentiles from the incidence rate distribution. The percentiles and associated per
person per day values are 10th = 0 percent, 25th = 0 percent, 50th = 0 percent, 75th = 0.29 percent, and 90th =
0.34 percent. The most conservative estimate consistent with the data are to assume the incidence per
person per day is zero up to the 75th percentile, a constant 0.29 percent between the 75th and 90th percentiles,
and a constant 0.34 percent between the 90th and 100th percentiles. Alternatively, assuming a linear slope
between the 50th and 75th, 75th and 90th, and 90th to 100th percentiles, the estimated mean incidence rate per
person per day is 0.12 percent.17 We used the latter approach in this analysis, and then multiplied by 100
and by 365 to calculate the incidence rate per 100 people per year.
E .6.5 Minor Restricted Activity Days (MRAD)
Ostro and Rothschild (1989, p. 243) provide an estimate of the annual incidence rate of MRADs
(7.8). We multiplied this estimate by 100 to get an annual rate per 100 people.
E .6.6 Work Loss Days
The yearly work-loss-day incidence rate per 100 people is based on estimates from the 1996
National Health Interview Survey (Adams et al., 1999, Table 41). They reported a total annual work loss
days of 352 million for individuals ages 18 to 65. The total population of individuals of this age group in
1996 (162 million) was obtained from (U.S. Bureau of the Census, 1997, No. 22). The average annual rate
of work loss days per individual (2.17) was multiplied by 100 to obtain the average yearly work-loss-day
rate of 217 per 100 people. Using a similar approach, we calculated work-loss-day rates for ages 18-24, 25-
44, and 45-64, respectively.
E .7 Asthma-Related Health Effects
Several studies have examined the impact of air pollution on asthma development or exacerbation.
Many of the baseline incidence rates used in the C-R functions are based on study-specific estimates. The
baseline rates for the various endpoints are described below and summarized in Exhibit E-7.
17 For example, the 62.5th percentile would have an estimated incidence rate per person per day of 0.145 percent.
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Appendix E. Sources of Prevalence and Incidence Data
Exhibit E-7. Asthma-Related Health Effects Rates
Endpoint
Age
Parameter"
Rate
Source
Acute Bronchitis
9-15
Incidence
32.6
(McConnell et al., 1999, Table 2)
Asthma Attacks
18+
Incidence
2008
1999 National Health Interview Survey
Asthma Exacerbation, Shortness of
Breath, African American
8-13
8-13
Incidence
Prevalence
1351
7.40%
(Ostro et al., 2001, p.202)
Asthma Exacerbation, Wheeze,
African American
8-13
8-13
Incidence
Prevalence
2774
17.30%
(Ostro et al., 2001, p.202)
Asthma Exacerbation, Cough, African
American
8-13
8-13
Incidence
Prevalence
2446
14.50%
(Ostro et al., 2001, p.202)
Asthma Exacerbation, Cough
6-13
Incidence
3139
(Vedal et al., 1998, Table 1 p. 1038)
Asthma Exacerbation, One or more
symptoms
5-13
Incidence
21900
(Yu et al., 2000, Table 2 p. 1212)
Chronic Asthma, Male
27+
Incidence
0.219
(McDonnell et al., 1999, Table 4)
Phlegm
9-15
Incidence
25.7
(McConnell et al., 1999, Table 2)
Upper Respiratory Symptoms (URS)2
9-11
Incidence
12479
(Pope et al., 1991, Table 2)
s The incidence rate is the number of cases per 100 people per year. Prevalence refers to the fraction of people that have a particular
illness during a particular time period.
E .7.1 Asthma Attacks
The annual rate of asthma attacks among asthmatics is estimated from the 1999 National Health
Interview Survey. Individuals with asthma were asked about the number of wheezing attacks per year. The
average number of wheezing attacks per year was multiplied by 100 to obtain a wheezing attack rate per
year per 100 people for individuals 18 and older. We assume that this rate of wheezing attacks can be used
as a surrogate for asthma attacks.
Note that the same survey examined wheezing attacks for children. However, the number of
wheezing attacks per year were censored at 12 (compared to censoring at 95 for adults). Due to the
potential for underestimation of the number of children's wheezing attacks, we used the adult rate for all
individuals.
E .7.2 Asthma Exacerbation
There are a variety of types of symptoms for asthma exacerbation. We calculated rates for
shortness of breath, wheeze, cough, and other asthma related effects.
E .7.3 Shortness of Breath
To estimate the annual rate of new shortness of breath episodes among African-American
asthmatics, ages 8-13, we used the rate reported by Ostro et al. (2001, p.202). We estimated the daily
prevalence of shortness of breath episodes among African-American asthmatics, ages 8-13, by taking a
weighted average of the reported rates in Ostro et al. (2001, p.202).
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Appendix E. Sources of Prevalence and Incidence Data
E .7.4 Wheeze
The daily rate of new wheeze episodes among African-American asthmatics, ages 8-13, is reported
by Ostro et al. (2001, p.202) as 0.076. We multiplied this value by 100 and by 365 to get the annual
incidence rate per 100 people. The daily rate of prevalent wheeze episodes (0.173) among African-
American asthmatics, ages 8-13, is estimated by taking a weighted average of the reported rates in Ostro et
al. (2001, p.202).
E .7.5 Cough
The daily rate of new cough episodes among African-American asthmatics, ages 8-13, is reported
by Ostro et al. (2001, p.202) as 0.067. We multiplied this value by 100 and by 365 to get the annual
incidence rate per 100 people. The daily rate of prevalent cough episodes (0.145) among African-American
asthmatics, ages 8-13, is estimated by taking a weighted average of the reported rates in Ostro et al. (2001,
p.202).
E .7.6 One or More Symptoms
Yu et al. (2000, Table 2, p. 1212) reported a daily rate of at least one asthma episode per asthmatic
child ages 5-13. An asthma episode is defined as at least one of the following asthma symptoms: wheezing,
coughing, chest tightness, or shortness of breath.
E .7.7 Chronic Asthma
We derived the annual incidence rate per 100 people by taking the number of new cases (32),
dividing by the number of individuals in the sample (972), as reported by (McDonnell et al., 1999, Table 4),
and then dividing by the 15 years in the sample. We then multiplied by 100 to get the annual incidence rate
per 100 people.
E .7.8 Upper Respiratory Symptoms
Upper Respiratory Symptoms are defined as one or more of the following: runny or stuffy nose;
wet cough; burning, aching, or red eyes. Using the incidence rates for upper respiratory symptoms among
asthmatics, published in Pope et al. (1991, Table 2), we calculated a sample size-weighted average
incidence rate.
E .7.9 Asthma Population Estimates
In studies examining the association between air pollution and the development or exacerbation of
asthma, often times an estimate of the percent of the population with asthma is required. Asthma
percentages were obtained either directly from the National Health Interview Survey (NHIS) or an
American Lung Association (2002c) report summarizing data from NHIS. Exhibit E-8 presents asthma
prevalence rates used define asthmatic populations in the C-R functions.
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Appendix E. Sources of Prevalence and Incidence Data
Exhibit E-8. Asthma Prevalence Rates Used to Estimate Asthmatic Populations
Population Group
Prevalence
Source
All Ages
3.86%
<18
5.27%
5-17
5.67%
American Lung Association (2002c, Table 7)s
18-44
3.71%
45-64
3.33%
65+
2.21%
African-American, 5 to 17
7.26%
American Lung Association (2002c, Table 9) 1
African-American, <18
7.35%
Male, 27+
2.10%
2000 NHIS public use data filesb
1 The work by the American Lung Association is based on the 1999 National Health Interview Survey.
b See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/
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Appendix F: Particulate Matter Concentration-Response Functions
In this Appendix, we present the concentration-response (C-R) functions used to estimate PM-
related adverse health effects. Each sub-section has an Exhibit with a brief description of the C-R function
and the underlying parameters. Following each Exhibit, we present a brief summary of each of the studies
and any items that are unique to the study.
Note that the main text describes the methods that we used to choose these C-R functions from the
wide range available in the literature. In addition, Appendix D mathematically derives the standard types of
C-R functions that we encountered in the epidemiological literature, such as, log-linear, logistic and linear,
so we simply note here the type of functional form. Finally, Appendix E presents a description of the
sources for the incidence and prevalence data used in these C-R functions.
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Appendix F. Particulate Matter C-R Functions
F .1 Long-term Mortality
There are two types of exposure to PM that may result in premature mortality. Short-term exposure
may result in excess mortality on the same day or within a few days of exposure. Long-term exposure over,
say, a year or more, may result in mortality in excess of what it would be if PM levels were generally lower,
although the excess mortality that occurs will not necessarily be associated with any particular episode of
elevated air pollution levels. In other words, long-term exposure may capture a facet of the association
between PM and mortality that is not captured by short-term exposure.
F .1.1 Mortality - Mean, All Cause (Krewski et al., 2000) - Reanalysis of Pope et al. (1995)
The Krewski et al. (2000) reanalysis of Pope et al. (1995) used a Cox proportional hazard model to
estimate the impact of long-term PM exposure. The original investigation followed 295,223 individuals18
ages 30 and over in 50 cities from September 1, 1982 to December 31, 1989, and related their survival to
median PM25 concentrations for 1979 to 1983. Krewski et al. (2000) independently estimated city-specific
annual mean values from EPA's Inhalable Particle Monitoring Network (IPMN) for the same years (1979-
1983). Krewski et al. (2000) followed Pope et al. (1995, Table 2) and reported results for all-cause deaths,
lung cancer (ICD-9 code: 162), cardiopulmonary deaths (ICD-9 codes: 401-440 and 460-519), and "all
other" deaths,19 and found that mean PM2 5 is significantly related to all-cause and cardiopulmonary
mortality. Krewski et al. included only PM, so it is unclear to what extent it may be including the impacts
of ozone or other gaseous pollutants.
Pope et al. (1995) is the better of the two published prospective cohort studies: it has a larger
population and includes more cities than the prospective cohort study by Dockery et al. (1993). Pope et
al.'s study has several further advantages. The population followed in this study was largely Caucasian and
middle class, decreasing the likelihood that interlocational differences in premature mortality were due in
part to differences in race, socioeconomic status, or related factors. In addition, the PM coefficient in Pope
et al. is likely to be biased downward, counteracting a possible upward bias associated with historical air
quality trends discussed earlier. One source of this downward bias is the generally healthier and study
population, in comparison to poorer minority populations. Krewski et al. (2000, Part II - Table 52) found
that educational status was a strong effect modifier of the PM - mortality relationship in both studies, with
the strongest effect seen among the less educated. In fact, much of the differences in magnitude of effect
between the studies was made up when assessing risk across comparable levels of educational attainment.
Another source of downward bias is that intercity movement of cohort members was not considered
in the original study and therefore could not be evaluated in the reanalysis. Migration across study cities
would result in exposures of cohort members being more similar than would be indicated by assigning city-
specific annual average pollution levels to each member of the cohort. The more intercity migration there
is, the more exposure will tend toward an intercity mean. If this is ignored, differences in exposure levels,
that are proxied by differences in city-specific annual average PM levels, will be exaggerated, and will
result in a downward bias of the PM coefficient (because a given difference in mortality rates is being
associated with a larger difference in PM levels than is actually the case).
18 The total study population was 552,138 in 151 cities, however, only 295,223 individuals resided in 50 cities with fine
particle data.
19
All-cause mortality includes accidents, suicides, homicides and legal interventions. The category "all other" deaths is
all-cause mortality less lung cancer and cardiopulmonary deaths.
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Appendix F. Particulate Matter C-R Functions
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.12) and 95% confidence
interval (1.06-1.19) associated with a change in annual mean PM2 5 exposure of 24.5 |ig/nr' (based on the
range from the original ACS study) (Krewski et al., 2000, Part II - Table 31).
Functional Form: Log-linear
Coefficient: 0.004626
Standard Error: 0.001205
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.2 Mortality - Median, All Cause (Krewski et al., 2000) - Reanalysis of Pope et al. (1995)
Krewski et al. (2000) performed an analysis of Pope et al. (2000) using independently estimated
city-specific annual median values as well. Fine particle estimates were obtained from EPA's Inhalable
Particle Monitoring Network (IPMN) for the years 1979-1983 for the same 50 cities. Overall, the estimates
showed good agreement with the median values used in the original investigation with one exception. The
median fine particle concentration for Denver dropped from 16.1 to 7.8 |ig/nr\ resulting in a larger range
between the least and most polluted cities and a reduced relative risk. Since the original estimate could not
be audited, Denver is included in the subsequent C-R function as there is no reason to believe that the
monitoring data is invalid.
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.14) and 95% confidence
interval (1.06-1.22) associated with achange in annual median PM25 exposure of24.5 |ig/m3 (based on the
range from the original ACS study) (Krewski et al., 2000, Part II - Table 31).
Functional Form: Log-linear
Coefficient: 0.005348
Standard Error: 0.001464
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.3 Mortality - Median, Random Effects with Regional Adjustment (Krewski et al., 2000)
- Reanalysis of Pope et al. (1995)
Krewski et al. (2000) also performed an analysis of Pope et al. (2000) using
a random effects model to estimate a regionally-adjusted relative risk. The authors used an indicator
variable representing seven regions of the U.S. The regionally-adjusted estimate was comparable with the
results from the standard Cox Proportional Hazards Model, which assumes that all observations are
statistically independent.
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Appendix F. Particulate Matter C-R Functions
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.16) and 95% confidence
interval (0.99-1.37) associated with a change in annual median PM2 5 exposure of 24.5 |ig/m' (based on the
range from the original ACS study) (Krewski et al., 2000, Part II - Table 46).
Functional Form: Log-linear
Coefficient: 0.006058
Standard Error: 0.003383
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.4 Mortality - Median, Random Effects with Independent Cities (Krewski et al., 2000) -
Reanalysis of Pope et al. (1995)
Krewski et al. (2000) also performed an analysis of Pope et al. (2000) using a random effects
approach to estimate an independent cities model. This approach incorporates between-city variation into
second-stage modeling weights, thereby avoiding the assumption of independent observations. However,
potential regional patterns in mortality may be overlooked, because the approach assumes that city-specific
mortality rates are statistically independent. The independent cities estimate is considerably larger than the
standard Cox Proportional Hazards Model, which assumes that all observations are statistically
independent.
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.29) and 95% confidence
interval (1.12-1.48) associated with achange in annual median PM25 exposure of24.5 |ig/nr' (based on the
range from the original ACS study) (Krewski et al., 2000, Part II - Table 46).
Functional Form: Log-linear
Coefficient: 0.010394
Standard Error: 0.002902
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.5 Mortality (Krewski et al., 2000) - Reanalysis of Dockery et al. (1993)
Krewski et al. (2000) performed a validation and replication analysis of Dockery et al. (1993). The
originial investigators examined the relationship between PM exposure and mortality in a cohort of 8,111
individuals aged 25 and older, living in six U.S. cities. They surveyed these individuals in 1974-1977 and
followed their health status until 1991. While they used a smaller sample of individuals from fewer cities
than the study by Pope et al., they used improved exposure estimates, a slightly broader study population
(adults aged 25 and older; a higher proportion without a high school education), and a follow-up period
nearly twice as long as that of Pope et al. (1995). Krewski et al. (2000, Part II - Table 52) found that
educational status was a strong effect modifier of the PM - mortality relationship in both studies, with the
strongest effect seen among the less educated. Perhaps because of these differences, Dockery et al. study
found a larger effect of PM on premature mortality than that found by Pope et al.
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Appendix F. Particulate Matter C-R Functions
After an audit of the air pollution data, demographic variables, and cohort selection process,
Krewski et al. (2000) noted that a small portion of study participants were mistakenly censored early. The
following C-R function is based on the risk estimate from the audited data, with the inclusion of those
person-years mistakenly censored early.
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.28) and 95% confidence
interval (1.10-1.48) associated with a change in annual mean PM25 exposure of 18.6 |ig/m3 to 29.6 |ig/m3
(Krewski et al., 2000, Part I - Table 19c).
Functional Form: Log-linear
Coefficient: 0.013272
Standard Error: 0.004070
Incidence Rate: county-specific annual all cause mortality rate per person ages 25 and older
Population: population of ages 25 and older
F .1.6 Mortality, All Cause (Pope et al., 1995)
Pope et al. (1995) used a Cox proportional hazard model to estimate the impact of long-term PM
exposure. They followed 295,223 individuals20 ages 30 and over in 50 cities from September 1, 1982 to
December 31, 1989, and related their survival to median PM25 concentrations for 1979 to 1983. Pope et al.
(1995, Table 2) reported results for all-cause deaths, lung cancer (ICD-9 code: 162), cardiopulmonary
deaths (ICD-9 codes: 401-440 and 460-519), and "all other" deaths,21 and found that median PM25 is
significantly related to all-cause and cardiopulmonary mortality. Pope et al. included only PM, so it is
unclear to what extent it may be including the impacts of ozone or other gaseous pollutants.
Pope et al. (1995) is the better of the two published prospective cohort studies: it has a larger
population and includes more cities than the prospective cohort study by Dockery et al. (1993). Pope et
al.'s study has several further advantages. The population followed in this study was largely Caucasian and
middle class, decreasing the likelihood that interlocational differences in premature mortality were due in
part to differences in race, socioeconomic status, or related factors. In addition, the PM coefficient in Pope
et al. is likely to be biased downward, counteracting a possible upward bias associated with historical air
quality trends discussed earlier. One source of this downward bias is the generally healthier study
population, in comparison to poorer minority populations. Another source of downward bias is that
intercity movement of cohort members was not considered in this study. Migration across study cities
would result in exposures of cohort members being more similar than would be indicated by assigning city-
specific annual average pollution levels to each member of the cohort. The more intercity migration there
is, the more exposure will tend toward an intercity mean. If this is ignored, differences in exposure levels,
that are proxied by differences in city-specific annual average PM levels, will be exaggerated, and will
result in a downward bias of the PM coefficient (because a given difference in mortality rates is being
associated with a larger difference in PM levels than is actually the case).
20
The total study population was 552,138 in 151 cities, however, only 295,223 individuals resided in 50 cities with fine
particle data.
21
All-cause mortality includes accidents, suicides, homicides and legal interventions. The category "all other" deaths is
all-cause mortality less lung cancer and cardiopulmonary deaths.
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Appendix F. Particulate Matter C-R Functions
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.17) and 95% confidence
interval (1.09-1.26) associated with a change in annual median PM25 exposure of 24.5 |ig/m' (Pope et al.,
1995, Table 2).
Functional Form: Log-linear
Coefficient: 0.006408
Standard Error: 0.001509
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.7 Mortality, All Cause (Dockery et al., 1993)
Dockery et al. (1993) examined the relationship between PM exposure and mortality in a cohort of
8,111 individuals aged 25 and older, living in six U.S. cities. They surveyed these individuals in 1974-1977
and followed their health status until 1991. While they used a smaller sample of individuals from fewer
cities than the study by Pope et al., they used improved exposure estimates, a slightly broader study
population (adults aged 25 and older), and a follow-up period nearly twice as long as that of Pope et al.
(1995). Perhaps because of these differences, Dockery et al. study found a larger effect of PM on premature
mortality than that found by Pope et al.
Single Pollutant Model
The coefficient and standard error are estimated from the relative risk (1.26) and 95% confidence
interval associated (1.08-1.47) with a change in annual mean PM25 exposure of 18.6 |ig/m3 (Dockery et al.,
1993, Tables 1 and 5).
Functional Form: Log-linear
Coefficient: 0.012425
Standard Error: 0.004228
Incidence Rate: county-specific annual all cause mortality rate per person ages 25 and older
Population: population of ages 25 and older
F .1.8 Mortality, All Cause (Pope et al., 2002) - Based on ACS Cohort
The Pope et al. (2002) analysis is a longitudinal cohort tracking study that uses the same American
Cancer Society (ACS) cohort as the original Pope et al. (1995) study, and the Krewski et al. (2000)
reanalysis. Pope et al. (2002) analyzed survival data for the cohort from 1982 through 1998, 9 years longer
than the original Pope study. Pope et al. (2002) also obtained PM2 5 data in 116 metropolitan areas collected
in 1999, and the first three quarters of 2000. This is more metropolitan areas with PM2 5 data than was
available in the Krewski reanalysis (61 areas), or the original Pope study (50 areas), providing a larger size
cohort.
They used a Cox proportional hazard model to estimate the impact of long-term PM exposure using
three alternative measures of PM2 5 exposure; metropolitan area-wide annual mean PM levels from the
beginning of tracking period ('79-'83 PM data, conducted for 61 metropolitan areas with 359,000
individuals), annual mean PM from the end of the tracking period ('99-'00, for 116 areas with 500,000
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Appendix F. Particulate Matter C-R Functions
individuals), and the average annual mean PM levels of the two periods (for 51 metropolitan areas, with
319,000 individuals). PM levels were lower in '99-00 than in '79 - '83 in most cities, with the largest
improvements occurring in cities with the highest original levels.
Pope et al. (2002) followed Krewski et al. (2000) and Pope et al. (1995, Table 2) and reported
results for all-cause deaths, lung cancer (ICD-9 code: 162), cardiopulmonary deaths (ICD-9 codes: 401-440
and 460-519), and "all other" deaths.22 Like the earlier studies, Pope et al. (2002) found that mean PM2 5 is
significantly related to all-cause and cardiopulmonary mortality. In addition, Pope et al. (2002) found a
significant relationship with lung cancer mortality, which was not found in the earlier studies. None of the
three studies found a significant relationship with "all other" deaths.
Pope et al. (2002) obtained ambient data on gaseous pollutants routinely monitored by EPA during
the 1982-1998 observation period, including S02, N02, CO, and ozone. They did not find significant
relationships between N02, CO, and ozone and premature mortality, but there were significant relationships
between S04 (as well as S02), and all-cause, cardiopulmonary, lung cancer and "all other" mortality.
PM2 5 Function(s)
'79-'83 Exposure
The coefficient and standard error for PM25 using the '79-'83 PM data are estimated from the
relative risk (1.041) and 95% confidence interval (1.008-1.075) associated with a change in annual mean
exposure of 10.0 |ig/nr\ Pope et al. (2002, Table 2).23
Functional Form: Log-linear
Coefficient: 0.004018
Standard Error: 0.001642
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
Average of'79-'83 and '99-'00 Exposure
The coefficient and standard error for PM25 using the average of '79-'83 and '99-'00 PM data are
estimated from the relative risk (1.062) and 95% confidence interval (1.016-1.110) associated with a change
in annual mean exposure of 10.0 |ig/nr\ Pope et al. (2002, Table 2).24
Functional Form: Log-linear
Coefficient: 0.006015
Standard Error: 0.002257
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
22
All-cause mortality includes accidents, suicides, homicides and legal interventions. The category "all other" deaths is
all-cause mortality less lung cancer and cardiopulmonary deaths.
23
Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
24
Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
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Appendix F. Particulate Matter C-R Functions
S04 Function(s)
The coefficient and standard error for S04 using '80-'81 data are estimated from the relative risk
(1.060) and 95% confidence interval (1.036-1.084) associated with a change in annual mean exposure of
6.5 |ig/m3. The relative risk and confidence interval were provided by C.A. Pope III over the phone.
Functional Form: Log-linear
Coefficient: 0.008964
Standard Error: 0.001778
Incidence Rate: county-specific annual all cause mortality rate per person ages 30 and older
Population: population of ages 30 and older
F .1.9 Mortality, Cardiopulmonary (Pope et al., 2002) - Based on ACS Cohort
Pope et al. (2002) followed Krewski et al. (2000) and Pope et al. (1995, Table 2) and reported
results for all-cause deaths, lung cancer (ICD-9 code: 162), cardiopulmonary deaths (ICD-9 codes: 401-440
and 460-519), and "all other" deaths.25 Like the earlier studies, Pope et al. (2002) found that mean PM2 5
and S04 (as well as S02) is significantly related to all-cause and cardiopulmonary mortality. In addition,
Pope et al. (2002) found a significant relationship with lung cancer mortality, which was not found in the
earlier studies. None of the three studies found a significant relationship with "all other" deaths.
PM2 5 Function(s)
'79-'83 Exposure
The coefficient and standard error for PM25 using the '79-'83 PM data are estimated from the
relative risk (1.059) and 95% confidence interval (1.015-1.105) associated with a change in annual mean
exposure of 10.0 |ig/m3. Pope et al. (2002, Table 2).26
Functional Form: Log-linear
Coefficient: 0.005733
Standard Error: 0.002167
Incidence Rate: county-specific annual cardiopulmonary mortality rate (ICD codes 401-440, 460-519) per
person ages 30 and older
Population: population of ages 30 and older
25
All-cause mortality includes accidents, suicides, homicides and legal interventions. The category "all other" deaths is
all-cause mortality less lung cancer and cardiopulmonary deaths.
26 Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
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Appendix F. Particulate Matter C-R Functions
Average of'79-'83 and '99-'00 Exposure
The coefficient and standard error for PM25 using the average of '79-'83 and '99-'00 PM data are
estimated from the relative risk (1.093) and 95% confidence interval (1.033-1.158) associated with a change
in annual mean exposure of 10.0 |ig/nr\ Pope et al. (2002, Table 2).27
Functional Form: Log-linear
Coefficient: 0.008893
Standard Error: 0.002914
Incidence Rate: county-specific annual cardiopulmonary mortality rate (ICD codes 401-440, 460-519) per
person ages 30 and older
Population: population of ages 30 and older
S04 Function(s)
The coefficient and standard error for S04 using '80-'81 data are estimated from the relative risk
(1.050) and 95% confidence interval (1.015-1.087) associated with a change in annual mean exposure of
6.5 |ig/nr\ The relative risk and confidence interval were provided by C.A. Pope III over the phone.
Functional Form: Log-linear
Coefficient: 0.007506
Standard Error: 0.002690
Incidence Rate: county-specific annual cardiopulmonary mortality rate (ICD codes 401-440, 460-519) per
person ages 30 and older
Population: population of ages 30 and older
F .1.10 Mortality, Lung Cancer (Pope et al., 2002) - Based on ACS Cohort
Pope et al. (2002) followed Krewski et al. (2000) and Pope et al. (1995, Table 2) and reported
results for all-cause deaths, lung cancer (ICD-9 code: 162), cardiopulmonary deaths (ICD-9 codes: 401-440
and 460-519), and "all other" deaths.28 Like the earlier studies, Pope et al. (2002) found that mean PM2 5
S04 (as well as S02) is significantly related to all-cause and cardiopulmonary mortality. In addition, Pope
et al. (2002) found a significant relationship with lung cancer mortality, which was not found in the earlier
studies. None of the three studies found a significant relationship with "all other" deaths.
27
Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
28 All-cause mortality includes accidents, suicides, homicides and legal interventions. The category "all other" deaths is
all-cause mortality less lung cancer and cardiopulmonary deaths.
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Appendix F. Particulate Matter C-R Functions
PM2 5 Function(s)
'79-'83 Exposure
The coefficient and standard error for PM25 using the '79-'83 PM data are estimated from the
relative risk (1.082) and 95% confidence interval (1.011-1.158) associated with a change in annual mean
exposure of 10.0 |ig/nr\ Pope et al. (2002, Table 2).29
Functional Form: Log-linear
Coefficient: 0.007881
Standard Error: 0.003463
Incidence Rate: county-specific annual lung cancer mortality rate (ICD code 162) per person ages 30 and
older
Population: population of ages 30 and older
Average of'79-'83 and '99-'00 Exposure
The coefficient and standard error for PM25 using the average of '79-'83 and '99-'00 PM data are
estimated from the relative risk (1.135) and 95% confidence interval (1.044-1.234) associated with a change
in annual mean exposure of 10.0 |ig/nr\ Pope et al. (2002, Table 2).30
Functional Form: Log-linear
Coefficient: 0.012663
Standard Error: 0.004265
Incidence Rate: county-specific annual lung cancer mortality rate (ICD code 162) per person ages 30 and
older
Population: population of ages 30 and older
S04 Function(s)
The coefficient and standard error for S04 using '80-'81 data are estimated from the relative risk
(1.095) and 95% confidence interval (1.040-1.153) associated with a change in annual mean exposure of
6.5 |ig/m3. The relative risk and confidence interval were provided by C.A. Pope III over the phone.
Functional Form: Log-linear
Coefficient: 0.013962
Standard Error: 0.004048
Incidence Rate: county-specific annual lung cancer mortality rate (ICD code 162) per person ages 30 and
older
Population: population of ages 30 and older
29
Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
30
Note that we used an unpublished, final version of the paper that presents the relative risks with one more significant
digit than that found in the published version. We chose to use this extra information to increase the precision of our estimates.
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Appendix F. Particulate Matter C-R Functions
F .1.11 Infant Mortality (Woodruff et al., 1997)
In a study of four million infants in 86 U.S. metropolitan areas conducted from 1989 to 1991,
Woodruff et al. (1997) found a significant link between PM10 exposure in the first two months of an infant's
life with the probability of dying between the ages of 28 days and 364 days. PM10 exposure was significant
for all-cause mortality. PM10 was also significant for respiratory mortality in average birth-weight infants,
but not low birth-weight infants.
In addition to the work by Woodruff et al., work in Mexico City (Loomis et al., 1999), the Czech
Republic (Bobak and Leon, 1992), Sao Paulo (Saldiva et al., 1994; Pereira et al., 1998), and Beijing (Wang
et al., 1997) provides additional evidence that particulate levels are significantly related to infant or child
mortality, low birth weight or intrauterine mortality.
Conceptually, neonatal or child mortality could be added to the premature mortality predicted by
Pope et al. (1995), because the Pope function covers only the population over 30 years old.31 However, the
EPA Science Advisory Board recently advised the Agency not to include post-neonatal mortality in this
analysis because the study is of a new endpoint and the results have not been replicated in other studies
(U.S. EPA, 1999a, p. 12). The estimated avoided incidences of neonatal mortality are estimated and
presented as a sensitivity analysis, and are not included in the primary analysis.
Single Pollutant Model
The coefficient and standard error are based on the odds ratio (1.04) and 95% confidence interval
(1.02-1.07) associated with a 10 /ig/m3 change in PM10 (Woodruff et al., 1997, Table 3).
Functional Form: Logistic
Coefficient: 0.003922
Standard Error: 0.001221
Incidence Rate: county-specific annual postneonatal32 infant deaths per infant under the age of one
Population: population of infants under one year old
31
Predicted neonatal mortality could not be added to the premature mortality predicted by the daily (short-term exposure)
mortality studies, however, because these studies cover all ages.
32
Post-neonatal refers to infants that are 28 days to 364 days old.
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November 2003
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Appendix F. Particulate Matter C-R Functions
F .2 Short-term Mortality
Short-term mortality studies are those that typically link daily air pollution levels with daily
changes in mortality rates.
F .2.1 Short-Term Mortality, Non-Accidental (Fairley, 2003)
Using data from 1989-1996 in Santa Clara County, California, Fairley et al. (1999) examined the
relationship between daily non-accidental mortality and fluctuations in a variety of pollutants, including
PM25, coarse PM10 (i.e., PM25_10), nitrate (N03), S04, coefficient of haze (COH), ozone, CO, and N02.
They reported that PM2 5 and N03 were significant in single-pollutant models, as well as two-pollutant
models. PM25 was only insignificant when paired with PM10 and N03 and N03 was only insignificant
when paired with PM2 5. The other pollutants were insignificant when paired with either PM2 5 or N03.
The analysis by Fairly et al. (1999) relied on a generalized additive model based on the Splus
software. Because of potential bias from using Splus, Fairley (2003) conducted a reanalysis, and reported
that the conclusions of the original study were unchanged. Both PM2 5 and N03 appear significantly related
to non-accidental mortality.
Single Pollutant Model
The coefficient and standard error for PM2 5 are estimated from the relative risk (1.092) and 95%
confidence interval (1.018-1.172) for a 28 |ig/m3 increase in PM2 5 in the 0-day lag GAM stringent ('New
GAM') model (Fairley, 2003, Table la).
Functional Form: Log-linear
Coefficient: 0.003143
Standard Error: 0.001283
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Multipollutant Model (PM2 5 and ozone)
In a model with 8-hour averaged ozone, the coefficient and standard error for PM2 5 are estimated
from the relative risk (1.100) and 95% confidence interval (1.024-1.181) for a 28 |ig/m3 increase in PM25 in
the 0-day lag GAM stringent ('New GAM') model (Fairley, 2003, Table lb).
Functional Form: Log-linear
Coefficient: 0.003404
Standard Error: 0.001300
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
F .2.2 Short-Term Mortality, Non-Accidental (Ito, 2003)
Ito (2003) reanalyzed a study by Lippmann et al. (2000) who examined the associations between
PM components and daily mortality and elderly hospital admissions in Detroit, Michigan. The reanalysis
Abt Associates Inc.
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November 2003
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Appendix F. Particulate Matter C-R Functions
by Ito reported that more generalized additive models with stringent convergence criteria and generalized
linear models resulted in smaller relative risk estimates.
Single Pollutant Model
The coefficient and standard error for PM2 5 are estimated from the relative risk (1.027) and 95%
confidence interval (0.974-1.083) for a 36 |ig/m3 increase in PM25 in the 3-day lag GAM stringent model
(Ito, 2003, Table 4).
Functional Form: Log-linear
Coefficient: 0.000740
Standard Error: 0.000752
Incidence Rate: region-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
F .2.3 Short-Term Mortality, Non-Accidental (Klemm and Mason, 2003)
Klemm and Mason (2003) reanalyzed a prior study by Klemm and Mason (2000), who conducted a
replication of work by Schwartz et al. (1996). In the updated work using more stringent convergence
criteria and generalized linear models, Klemm and Mason (2003) reported a generally smaller relationship
between daily PM25 levels and premature mortality.
Single Pollutant Model
The coefficient and standard error for PM2 5 are estimated from the relative risk (1.012) and 95%
confidence interval (0.008-1.016) for a 10 |ig/m3 increase in PM25 in the 0-day lag GAM stringent ('GAM
2002') model (Klemm and Mason, 2003, Table 1).
Functional Form: Log-linear
Coefficient: 0.001193
Standard Error: 0.000202
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
F .2.4 Short-Term Mortality, Non-Accidental (Moolgavkar, 2003)
Moolgavkar (2003) reanalyzed a study by Moolgavkar (2000b) who examined the relationships
between daily mortality and hospital admissions in Los Angeles and Cook Counties. The reanalysis by
Moolgavkar reported that more generalized additive models with stringent convergence criteria and
generalized linear models generally resulted in smaller relative risk estimates, and that gases such as CO
were often more closely associated with health endpoints than particulate matter.
Single Pollutant Model
The coefficient and standard error for PM2 5 are estimated from the relative risk (1.0059) and the t-
statistic (1.96) for a 10 |ig/m3 increase in PM2 5 in the 1-day lag GAM-30df stringent (10~8) model
(Moolgavkar, 2003, Table 1)
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November 2003
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.000588
Standard Error: 0.000300
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
F .2.5 Short-Term Mortality, Non-Accidental (Schwartz et al., 1996)
Schwartz et al. (1996) pooled the results from six cities in the U.S. and found a significant
relationship between daily PM25 concentration and non-accidental mortality.33 Abt Associates Inc. (1996b,
p. 52) used the six PM2 5 relative risks reported by Schwartz et al. in a three-step procedure to estimate a
pooled PM2 5 coefficient and its standard error. The first step estimates a random-effects pooled estimate of
P; the second step uses an "empirical Bayes" procedure to reestimate the p for each study as a weighted
average of the p reported for that location and the random effects pooled estimate; the third step estimates
the underlying distribution of p, and uses a Monte Carlo procedure to estimate the standard error (Abt
Associates Inc., 1996a, p. 65).
Single Pollutant Model
The C-R function to estimate the change in mortality associated with daily changes in PM2 5 is:
Functional Form: Log-linear
Coefficient: 0.001433 (Abt Associates Inc., 1996a, Exhibit 7.2)
Standard Error: 0.000129 (Abt Associates Inc., 1996a, Exhibit 7.2)
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Single Pollutant Model, Lag Adjusted
Recent studies have found that an increase in PM levels on a given day can elevate mortality for
several days following the exposure (Samet et al., 2000; Schwartz, 2000b). These studies have reported the
results of distributed lag models for the relationship between PM10 and daily mortality. Schwartz (2000b)
examined the relationship between PM10 and daily mortality and reported results both for a single day lag
model and an unconstrained distributed lag model. The unconstrained distributed lag model coefficient
estimate is 0.0012818 and the single-lag model coefficient estimate is 0.0006479. A distributed lag
adjustment factor can be constructed as the ratio of the estimated coefficient from the unconstrained
distributed lag model to the estimated coefficient from the single-lag model reported in Schwartz (2000).
The ratio of these estimates is 1.9784. In order to estimate the full impact of daily PM levels on daily
mortality, we applied this ratio to the coefficient obtained from Schwartz et al. (1996) for the association
between PM25 and daily mortality.
In applying the ratio derived from a PM10 study to PM25, we assume that the same relationship
between the distributed lag and single day estimates would hold for PM2 5. Effect estimates for the PM10-
daily mortality relationship tend to be lower in magnitude than for PM2 5, because fine particles are believed
to be more closely associated with mortality than the coarse fraction of PM. If most of the increase in
33
Schwartz et al. (1996, p. 929) defined non-accidental mortality as all-cause mortality less deaths due to accidents and
other external causes (ICD-9 codes: 800-999). Other external causes includes suicide, homicide, and legal intervention (National
Center for Health Statistics, 1994).
Abt Associates Inc. F-16 November 2003
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Appendix F. Particulate Matter C-R Functions
mortality is expected to be associated with the fine fraction of PM10, then it is reasonable to assume that the
same proportional increase in risk would be observed if a distributed lag model were applied to the PM2 5
data.
The distributed lag model coefficient is estimated by multiplying the distributed lag adjustment
factor of 1.9784 with the pooled PM2 5 coefficient. Note that the distributed lag adjustment C-R function is
only run for the point estimate, as the standard error of this modified coefficient has not been estimated.
Functional Form: Log-linear
Coefficient: 0.001433
Lag Adjustment: 1.9784
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
F .2.6 Short-Term Mortality, Non-Accidental (Schwartz, 2003)
Schwartz et al. (1996) pooled the results from six cities in the U.S. and found a significant
relationship between daily PM2 5 concentration and non-accidental mortality.34 In a reanalysis of this work,
Schwartz (2003) reported that the coefficients are somewhat smaller and less stable, but that the overall
relationship between PM2 5 and mortality remained unchanged.
Single Pollutant Model
The coefficient and standard error are provided Schwartz (2003, Table 1) (see: combined estimate,
mean of lag 0 and 1, New Convergence - GAM stringent).
Functional Form: Log-linear
Coefficient: 0.00137
Standard Error: 0.0002
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Single Pollutant Model, Lag Adjusted
As noted in the section on the Schwartz et al. (1996) C-R function, we have added a distributed lag
adjustment factor. The distributed lag model coefficient is estimated by multiplying the distributed lag
adjustment factor of 1.9784 with the PM2 5 coefficient. Note that the distributed lag adjustment C-R
function is only run for the point estimate, as the standard error of this modified coefficient has not been
estimated.
Functional Form: Log-linear
Coefficient: 0.00137 (Schwartz, 2003, Table 1)
Lag Adjustment: 1.9784
Incidence Rate: county-specific annual daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
34 Schwartz et al. (1996, p. 929) defined non-accidental mortality as all-cause mortality less deaths due to accidents and
other external causes (ICD-9 codes: 800-999). Other external causes includes suicide, homicide, and legal intervention (National
Center for Health Statistics, 1994).
Abt Associates Inc. F-17 November 2003
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Appendix F. Particulate Matter C-R Functions
F .2.7 Short-Term Mortality, Chronic Lung Disease - Lag Adjusted (Schwartz et al., 1996)
Schwartz et al. (1996) evaluated the relationship between daily PM25 levels and short-term
mortality in six U.S. cities. Schwartz pooled results across the six cities and found statistically significant
associations between daily PM2 5 levels and non-accidental mortality (ICD codes <800), along with
mortality for ischemic heart disease (ICD codes 410-414), COPD (ICD codes 490-496), and pneumonia
(ICD codes 480-486). A smaller association was found for PM10 and no significant associations were
reported for PM10_2 5. The C-R function for chronic lung disease mortality is based on the results of a single
pollutant model using a two-day average of PM2 5 (Schwartz et al., 1996, Table 7). In order to estimate the
impact of daily PM2 5 levels on daily mortality if a distributed lag model had been fit, the PM2 5 coefficient
is adjusted as described below.
Single Pollutant Model
The PM25 coefficient is based on a reported 3.3% increase in COPD mortality associated with a 10
/ig/m3 change in two-day average PM2 5 levels (Schwartz et al., 1996, Table 7). This coefficient (0.003247)
is then multiplied by the distributed lag adjustment factor of 1.9784 to estimate a distributed lag model
coefficient.
Functional Form: Log-linear
Coefficient: 0.003247
Lag Adjustment: 1.9784
Incidence Rate: county-specific annual daily chronic lung disease mortality rate (ICD codes 490-496)
Population: population of all ages
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November 2003
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Appendix F. Particulate Matter C-R Functions
F .3 Chronic Illness
Schwartz (1993) and Abbey et al. (1993; 1995c) provide evidence that PM exposure over a number
of years gives rise to the development of chronic bronchitis in the U.S., and a recent study by McDonnell et
al. (1999) provides evidence that ozone exposure is linked to the development of asthma in adults. These
results are consistent with research that has found chronic exposure to pollutants leads to declining
pulmonary functioning (Detels et al., 1991; Ackermann-Liebrich et al., 1997; Abbey et al., 1998).35
F .3.1 Chronic Bronchitis (Abbey et al., 1995c, California)
Abbey et al. (1995c) examined the relationship between estimated PM25 (annual mean from 1966 to
1977), PM10 (annual mean from 1973 to 1977) and TSP (annual mean from 1973 to 1977) and the same
chronic respiratory symptoms in a sample population of 1,868 Californian Seventh Day Adventists. The
initial survey was conducted in 1977 and the final survey in 1987. To ensure a better estimate of exposure,
the study participants had to have been living in the same area for an extended period of time. In single-
pollutant models, there was a statistically significant PM2 5 relationship with development of chronic
bronchitis, but not for AOD or asthma; PM10 was significantly associated with chronic bronchitis and AOD;
and TSP was significantly associated with all cases of all three chronic symptoms. Other pollutants were
not examined. The C-R function is based on the results of the single pollutant model presented in Table 2.
Single Pollutant Model (Chronic Bronchitis)
The estimated coefficient (0.0137) is presented for a one /ig/nr' change in PM2 5 (Abbey et al.,
1995c, Table 2). The standard error is calculated from the reported relative risk (1.81) and 95% confidence
interval (0.98-3.25) for a 45 /ig/m3 change in PM25 (Abbey et al., 1995c, Table 2).
Functional Form: Logistic
Coefficient: 0.0137
Standard Error: 0.00680
Incidence Rate: annual bronchitis incidence rate per person (Abbey et al., 1993, Table 3) = 0.00378
Population: population of ages 27 and older36 without chronic bronchitis = 95.57%37 of population 27+
Single Pollutant Model (Chronic Bronchitis, Reversals)
In developing the C-R function for chronic bronchitis, it is necessary to estimate its annual
incidence rate. The annual incidence rate is derived by taking the number of new cases (234), dividing by
the number of individuals in the sample (3,310), as reported by Abbey et al.(1993, Table 3), dividing by the
ten years covered in the sample, and then multiplying by one minus the reversal rate.38 Reversals refer to
35
There are a limited number of studies that have estimated the impact of air pollution on chronic bronchitis. An
important hindrance is the lack of health data and the associated air pollution levels over a number of years.
36 Using the same data set, Abbey et al. (1995a, p. 140) reported that the respondents in 1977 ranged in age from 27 to
95.
37
The American Lung Association (2002b, Table 4) reports a chronic bronchitis prevalence rate for ages 18 and over of
4.43% (American Lung Association, 2002b, Table 4).
38The percentage of reversals is estimated to be 46.6% based on Abbey et al. (1995a, Table 1).
Abt Associates Inc. F-20 November 2003
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Appendix F. Particulate Matter C-R Functions
those cases of chronic bronchitis that were reported at the start of the Abbey et al. survey, but were
subsequently not reported at the end of the survey. Since we assume that chronic bronchitis is a permanent
condition, we subtract these reversals from the C-R function for chronic bronchitis. Nevertheless, reversals
may likely represent a real effect that should be included in an analysis.
The estimated coefficient (0.0137) is presented for a one /jg/m3 change in PM2 5 (Abbey et al.,
1995c, Table 2). The standard error is calculated from the reported relative risk (1.81) and 95% confidence
interval (0.98-3.25) for a 45 //g/m3 change in PM25 (Abbey et al., 1995c, Table 2).
Functional Form: Logistic
Coefficient: 0.0137
Standard Error: 0.00680
Incidence Rate: annual bronchitis incidence rate per person for chronich bronchitis that eventually resolves
itself. Based on the percentage of reversals (46.6%) from Abbey et al. (1995a, Table 1) and chronic
bronchitis cases from (Abbey et al., 1993, Table 3) = 0.00325
Population: population of ages 27 and older39 without chronic bronchitis = 95.57%40 of population 27+
F .3.2 Chronic Bronchitis (Schwartz, 1993)
Schwartz (1993) examined survey data collected from 3,874 adults ranging in age from 30 to 74,
and living in 53 urban areas in the U.S. The survey was conducted between 1974 and 1975, as part of the
National Health and Nutrition Examination Survey, and is representative of the non-institutionalized U.S.
population. Schwartz (1993, Table 3) reported chronic bronchitis prevalence rates in the study population
by age, race, and gender. Non-white males under 52 years old had the lowest rate (1.7%) and white males
52 years and older had the highest rate (9.3%). The study examined the relationship between the
prevalence of reported chronic bronchitis, asthma, shortness of breath (dyspnea) and respiratory illness41,
and the annual levels of TSP, collected in the year prior to the survey (TSP was the only pollutant examined
in this study). TSP was significantly related to the prevalence of chronic bronchitis, and marginally
significant for respiratory illness. No effect was found for asthma or dyspnea. The C-R function for PM10
is estimated from the results of the single pollutant model reported for TSP.
Single Pollutant Model
The estimated coefficient is based on the odds ratio ( 1.07) associated with 10 |ig/m3 change in TSP
(Schwartz, 1993, p. 9). Assuming that PM10 is 55 percent of TSP42 and that particulates greater than ten
micrometers are harmless, the coefficient is calculated by dividing the TSP coefficient by 0.55. The
standard error for the coefficient is calculated from the 95% confidence interval for the odds ratio (1.02 to
1.12) (Schwartz, 1993, p. 9).
39
Using the same data set, Abbey et al. (1995a, p. 140) reported that the respondents in 1977 ranged in age from 27 to
95.
40 The American Lung Association (2002b, Table 4) reports a chronic bronchitis prevalence rate for ages 18 and over of
4.43% (American Lung Association, 2002b, Table 4).
41 Respiratory illness defined as a significant condition, coded by an examining physician as ICD-8 code 460-519.
Al
The conversion of TSP to PM10 is from ESEERCO (1994, p. V-5), who cited studies by EPA (1986) and the California
Air Resources Board (1982).
Abt Associates Inc. F-21 November 2003
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Appendix F. Particulate Matter C-R Functions
Schwartz (1993) examined the prevalence of chronic bronchitis, not its incidence. To use
Schwartz's study and still estimate the change in incidence, there are at least two possible approaches. The
first is to simply assume that it is appropriate to use the baseline incidence of chronic bronchitis in a C-R
function with the estimated coefficient from Schwartz's study, to directly estimate the change in incidence.
The second is to estimate the percentage change in the prevalence rate for chronic bronchitis using the
estimated coefficient from Schwartz's study in a C-R function, and then to assume that this percentage
change applies to a baseline incidence rate obtained from another source. (That is, if the prevalence
declines by 25 percent with a drop in PM, then baseline incidence drops by 25 percent with the same drop
in PM.) This analysis is using the latter approach, and estimates a percentage change in prevalence which is
then applied to a baseline incidence rate. The scaling factor used in the C-R function is the ratio of chronic
bronchitis incidence rate (estimated from Abbey et al. (1993)) to chronic bronchitis prevalence rate
(estimated from American Lung Association (2002b, Table 4)).
Functional Form: Logistic
Coefficient: 0.0123
Standard Error: 0.00434
Prevalence Rate: annual chronic bronchitis prevalence rate per person (American Lung Association,
2002b, Table 4) = 0.0443
Population: population of ages 30 and older without chronic bronchitis = 95.57%43 of population 30+
Adjustment Factor: ratio of chronic bronchitis incidence to chronic bronchitis prevalence =
0.00378/0.0443 = 0.085 (Abbey et al., 1993, Table 3; American Lung Association, 2002b, Table 4)
43 The American Lung Association (2002b, Table 4) reports a chronic bronchitis prevalence rate for ages 18 and over of
4.43% (American Lung Association, 2002b, Table 4).
Abt Associates Inc. F-22 November 2003
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-------
Appendix F. Particulate Matter C-R Functions
F .4 Hospitalizations
F .4.1 Hospital Admissions for All Respiratory (Burnett et al., 1997, Toronto)
Burnett et al. (1997) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada during the summers of 1992-1994. In a Poisson regression, all
respiratory admissions (ICD codes 464-466,480-486,490-494,496) were linked to coefficient of haze
(COH) and ozone; other PM measures were less strongly linked. In two pollutant models, they found that
CO, N02, and S02 were not significant, controlling for COH. They found that ozone was still significant,
controlling for COH. In multipollutant models with COH, 03, N02, and S02, both ozone and COH
remained signifcant. None of the other PM measures (PM10, PM10_2 5, PM2 5) were significant in four-
pollutant models. The PM C-R functions are based on results from single and multipollutant models.
PM2 5 Function(s)
Single Pollutant Model (PM2 5)
In a single pollutant model with adjustment for temperature and dew point, the PM2 5 coefficient
and standard error are based on a relative risk of 1.037 (t-statistic 3.29) for an 11 |ig/m3 increase in four-
day average PM25 (Burnett et al., 1997, Table 2, p. 617).
Functional Form: Log-linear
Coefficient: 0.003303
Standard Error: 0.001004
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM2 5 and ozone)
In a model with ozone, the PM2 5 coefficient and standard error are based on a relative risk of 1.027
(t-statistic 2.33) for an 11 |ig/m3 increase in four-day average PM25 (Burnett et al., 1997, Table 4, p. 618).
Functional Form: Log-linear
Coefficient: 0.002422
Standard Error: 0.001039
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM2 5, N02, ozone, and S02)
In a four-pollutant model with N02, 03, and S02, the PM2 5 coefficient and standard error are based
on a relative risk of 0.999 (t-statistic 0.10) for an 11 |ig/m3 increase in four-day average PM25 (Burnett et
al., 1997, Table 6, p. 618).
Abt Associates Inc.
F-29
November 2003
-------
Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: -0.000091
Standard Error: 0.000910
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
PM10 2 5 Function(s)
Single Pollutant Model
In a single pollutant model with adjustment for temperature and dew point, the PM10_2 5 coefficient
and standard error are based on a relative risk of 1.023 (t-statistic 3.41) for a 4.75 |ig/m3 increase in five-
day average PM10_25 (Burnett et al., 1997, Table 2, p. 617).
Functional Form: Log-linear
Coefficient: 0.004787
Standard Error: 0.001404
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM10_2 5 and ozone)
In a model with ozone, the PM10_2 5 coefficient and standard error are based on a relative risk of
1.020 (t-statistic 3.04) for a 4.75 |ig/m3 increase in five-day average PM10_25 (Burnett et al., 1997, Table 4,
p. 618).
Functional Form: Log-linear
Coefficient: 0.004169
Standard Error: 0.001371
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM10_2 5, N02, ozone, and S02)
In a four-pollutant model with N02, ozone, and S02, the PM10_2 5 coefficient and standard error are
based on a relative risk of 1.007 (t-statistic 0.82) for a 4.75 |ig/m3 increase in five-day average PM10_2 5
(Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: 0.001469
Standard Error: 0.001791
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Abt Associates Inc.
F-30
November 2003
-------
Appendix F. Particulate Matter C-R Functions
PM10 Function(s)
Single Pollutant Model
In a single pollutant model with adjustment for temperature and dew point, the PM10 coefficient and
standard error are based on a relative risk of 1.03 (t-statistic 3.42) for a 14.25 |ig/m3 increase in five-day
average PM10 (Burnett et al., 1997, Table 2, p. 617).
Functional Form: Log-linear
Coefficient: 0.002074
Standard Error: 0.000607
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM10 and ozone)
In a model with ozone, the PM10 coefficient and standard error are based on a relative risk of 1.027
(t-statistic 3.16) for a 14.25 |ig/m3 increase in five-day average PM10 (Burnett et al., 1997, Table 4, p. 618).
Functional Form: Log-linear
Coefficient: 0.001870
Standard Error: 0.000592
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (PM10, N02, ozone, and S02)
In a four-pollutant model with N02, ozone, and S02, the PM10 coefficient and standard error are
based on a relative risk of 1.004 (t-statistic 0.36) for a 14.25 |ig/m3 increase in five-day average PM10
(Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: 0.000280
Standard Error: 0.000778
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person (ICD
codes 464, 466, 480-487, 490-496)
Population: population of all ages
F .4.2 Hospital Admissions for All Respiratory (Burnett et al., 2001, Toronto)
Burnett et al. (2001) studied the association between air pollution and acute respiratory hospital
admissions (ICD codes 493, 466, 464.4, 480-486) in Toronto from 1980-1994, among children <2 years of
age. They collected hourly concentrations of the gaseous pollutants, CO, N02, S02, and ozone. Daily
measures of particulate matter were estimated for the May to August period of 1992-1994 using TSP,
sulfates, and coefficient of haze data. The authors report a positive association between ozone in the May
through August months and respiratory hospital admissions, for several single days after elevated ozone
levels. The strongest association was found using a five-day moving average of ozone. No association was
Abt Associates Inc. F-31 November 2003
-------
Appendix F. Particulate Matter C-R Functions
found in the September through April months. In co-pollutant models with a particulate matter or another
gaseous pollutant, the ozone effect was only slightly diminished. The effects for PM and gaseous pollutants
were generally significant in single pollutant models but diminished in co-pollutant models with ozone,
with the exception of CO. The C-R functions for PM10_2 5 are based on a single pollutant and co-pollutant
model, using the four-day moving average of PM10_2 5. The C-R functions for PM2 5 are based on a single
pollutant and co-pollutant model, using the four-day moving average of PM2 5.
PM2 5 Function(s)
Single Pollutant Model
The single pollutant coefficient and standard error are based on a percent increase of 15.8 (t-stat
3.29) for an 18.0 |ig/m3 increase in four-day average PM25 (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.008150
Standard Error: 0.002477
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person <2 (ICD
codes 464, 466, 480-487, 493)
Population: population of ages under 2
Multipollutant Model (PM2 5 and ozone)
In a model with ozone, the coefficient and standard error are based on a percent increase of 1.4 (t-
stat 0.24) for an 18.0 |ig/m3 increase in four-day average PM25 (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.000772
Standard Error: 0.003218
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person <2 (ICD
codes 464, 466, 480-487, 493)
Population: population of ages under 2
PM10 2 5 Function(s)
Single Pollutant Model
The single pollutant coefficient and standard error are based on a percent increase of 18.3 (t-stat
3.77) for a 16.2 |ig/m3 increase in four-day average PM10_25 (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.010374
Standard Error: 0.002752
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person <2 (ICD
codes 464, 466, 480-487, 493)
Population: population of ages under 2
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Appendix F. Particulate Matter C-R Functions
Multipollutant Model (PM10_2 5 and ozone)
In a model with ozone, the coefficient and standard error are based on a percent increase of 4.5 (t-
stat 0.72) for a 16.2 |ig/m3 increase in four-day average PM10_25 (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.002717
Standard Error: 0.003774
Incidence Rate: region-specific daily hospital admission rate for all respiratory disease per person <2 (ICD
codes 464, 466, 480-487, 493)
Population: population of ages under 2
F .4.3 Hospital Admissions for All Respiratory (Schwartz, 1995, New Haven)
Schwartz (1995) examined the relationship between air pollution and respiratory hospital
admissions (ICD codes 460-519) for individuals 65 and older in New Haven, Connecticut, from January
1988 to December 1990. In single-pollutant models, PM10 and S02 were significant, while ozone was
marginally significant. In two-pollutant models, ozone was significant in a model with PM10 and not
significant in a model with S02, but had relatively stable coefficient estimates. PM10 was significant in two-
pollutant models with ozone and S02. S02 was significant only in the co-pollutant model with PM10. The
PM10 C-R functions are based on results from a single pollutant and two-pollutant model (PM10 and ozone).
Single Pollutant Model
In a single-pollutant model, the coefficient and standard error are calculated from the relative risk
(1.06) and 95% confidence interval (1.00-1.13) for a 50 |ig/m ' increase in average daily PM10 levels
(Schwartz, 1995, Table 3, p. 534).
Functional Form: Log-linear
Coefficient: 0.001165
Standard Error: 0.000624
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
Multipollutant Model (PM10 and ozone)
In a model with ozone, the coefficient and standard error are estimated from the relative risk (1.09)
and 95% confidence interval (1.00-1.20) for a 50 |ig/m3 increase in average daily PM10 levels (Schwartz,
1995, Table 3, p. 534).
Functional Form: Log-linear
Coefficient: 0.001724
Standard Error: 0.000930
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
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Appendix F. Particulate Matter C-R Functions
F .4.4 Hospital Admissions for All Respiratory (Schwartz, 1995, Tacoma)
Schwartz (1995) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Tacoma, Washington, from January 1988 to December 1990. In single-
pollutant models, PM10, ozone, and S02 were all significant. Ozone remained significant in two-pollutant
models with PM10 and S02, and had stable coefficient estimates. PM10 was significant in a two-pollutant
model with S02, but not in a model with ozone, although the central estimate remained stable. S02 was not
significant in two-pollutant models with ozone or PM10. The PM10 C-R functions are based on results from
a single pollutant and two-pollutant model (PM10 and ozone).
Single Pollutant Model
In a single-pollutant model, the coefficient and standard error are calculated from the relative risk
(1.10) and 95% confidence interval (1.03-1.17) for a 50 |ig/m3 increase in average daily PM10 levels
(Schwartz, 1995, Table 6, p. 535).
Functional Form: Log-linear
Coefficient: 0.001906
Standard Error: 0.000650
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
Multipollutant Model (PM10 and ozone)
In a model with PM10, the coefficient and standard error are estimated from the relative risk (1.12)
and 95% CI (0.97-1.29) for a 50 |ig/m ' increase in average daily PM10 levels (Schwartz, 1995, Table 6, p.
535).
Functional Form: Log-linear
Coefficient: 0.002267
Standard Error: 0.001455
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
F .4.5 Hospital Admissions for All Respiratory (Thurston et al., 1994, Toronto)
Thurston et al. (1994) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada, for six weeks in July and August 1986-1988. In single-pollutant
linear regression models, ozone and various measures of PM were linked to all respiratory admissions (ICD
codes 466, 480-482, 485, 490-493). In two-pollutant models, ozone was still significant, but measures of
PM were often not significant; only H+ was significant. The C-R functions for PM2 5 and PM10 are based on
results from the reported single pollutant models and co-pollutant models with ozone. For PM10_25, results
are reported only from a single pollutant model.
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Appendix F. Particulate Matter C-R Functions
PM2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM2 5 coefficient (0.0828) and standard error (0.0367) are reported
in Table 3 (Thurston et al., 1994, p. 281) for a unit |ig/m3 increase daily average PM25 levels.
Functional Form: Linear
Coefficient: 0.0828
Standard Error: 0.0367
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (PM2 5 and ozone)
In a model with ozone, the PM2 5 coefficient (0.0434) and standard error (0.0429) are reported in
Table 3 (Thurston et al., 1994, p. 281) for a unit |ig/m3 increase daily average PM2 5 levels.
Functional Form: Linear
Coefficient: 0.0434
Standard Error: 0.0429
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
PM10 2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10_25 coefficient (0.1228) and standard error (0.0895) are
reported in Table 3 (Thurston et al., 1994, p. 281) for a unit |ig/m3 increase daily average PM10_2 5 levels.
Functional Form: Linear
Coefficient: 0.1228
Standard Error: 0.0895
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10 coefficient (0.0642) and standard error (0.0290) are reported
in Table 3 (Thurston et al., 1994, p. 281) for a unit |ig/m3 increase daily average PM10 levels.
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Functional Form: Linear
Coefficient: 0.0642
Standard Error: 0.0290
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (PM10 and ozone)
In a model with ozone, the PM10 coefficient (0.0339) and standard error (0.0344) are reported in
Table 3 (Thurston et al., 1994, p. 281) for a unit |ig/m3 increase daily average PM10 levels.
Functional Form: Linear
Coefficient: 0.0339
Standard Error: 0.0344
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
F .4.6 Hospital Admissions for Asthma (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada froml980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found all significantly associated with
asthma except S02. They estimated multiple pollutant models, where pollutants for best fitting model were
chosen using stepwise regression based on AIC criterion. Asthma admissions were linked to ozone, CO,
and PMI0_2 5. The C-R functions for PM10_2 5 are based on the results of a single pollutant model and three-
pollutant model (03, CO, PM10_2 5). The C-R functions for PM2 5 and PM10are based on the results of a
single pollutant model.
PM2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (4.60) and t-statistic (3.22)
reported in Table 3 (Burnett et al., 1999, p. 133) for an 18.0 |ig/m3 increase in three-day average PM25
levels.
Functional Form: Log-linear
Coefficient: 0.002499
Standard Error: 0.000776
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person (ICD code
493)
Population: population of all ages
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Appendix F. Particulate Matter C-R Functions
PM10 2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(5.25) and t-statistic (4.20) reported in Table 3 (Burnett et al., 1999, p. 133) for a 12.2 |ig/m3 increase in
three-day average PM10_2 5 levels.
Functional Form: Log-linear
Coefficient: 0.004194
Standard Error: 0.000999
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person (ICD code
493)
Population: population of all ages
Multipollutant Model (PM10_2 5, CO, and ozone)
In a model with ozone and CO, the PM10_2 5 coefficient and standard error are based on the percent
increase (4.00) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the authors
(3.04)44 for a 12.2 |ig/m3 increase in three-day average PM10_25 levels.
Functional Form: Log-linear
Coefficient: 0.003215
Standard Error: 0.001058
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person (ICD code
493)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(5.27) and t-statistic (3.39) reported in Table 3 (Burnett et al., 1999, p. 133) for a 30.2 i-ig/m3 increase in
three-day average PM10 levels.
Functional Form: Log-linear
Coefficient: 0.001701
Standard Error: 0.000502
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person (ICD code
493)
Population: population of all ages
44 Rick Burnett (co-author), personal communication.
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F .4.7 Hospital Admissions for Asthma (Lin et al., 2002, Toronto)
Lin et al. (2002) examined the association between ambient particulate matter in Toronto and
asthma hospitalizations in children (ages 6-12) between 1981 and 1993. The authors collected PM data
measured every six days for the period of 1984 to 1990.45 The authors analyzed the PM-asthma
hospitalization association using a case-crossover analysis (with unidirectional and bidirectional controls)46
and a time series analysis with moving averages of PM ranging from 1 day to 7 days . They estimated the
effects on boys and girls separately and found an increasing association between PM10_2 5 and asthma
hospitalizations as averaging time increased, with a leveling off around six or seven days. This effect
remained significant in a model with CO, N02, S02, and ozone. Results for gaseous pollutants were not
reported. They did not find a significant association for PM2 5 or PM10 in models other than the
unidirectional case-crossover analysis. The authors suggest that estimates from a unidirectional case-
crossover analysis may be significantly biased when time trends are present. The considerable difference
between the results from this model and the bidirectional and time series analyses suggest that this may be
the case.
The C-R functions for PM are based on the time series analysis rather than the bidirectional case-
crossover because the time series produces more stable estimates (i.e., the 95% confidence intervals are
always narrower than those from the case-crossover design) and this design is more commonly used in air
pollution epidemiology. The reported relative risks for PM10_2 5 increase as the number of days included in
the moving average increases - up through 7 days (the maximum number of days considered). This
suggests that the multi-day averages are capturing to some extent what is essentially a distributed lag effect
- that is, that PM10_2 5 even 7 days earlier has some impact on asthma hospitalization rates. We therefore
selected the model with the 7-day average for use in the single pollutant C-R functions. In multipollutant
models, only results using 5- and 6-day averages were reported, so the C-R functions are based on 6-day
averages.
PM2 5 Function(s) - Boys
Single Pollutant Model
The single pollutant coefficient and standard error for boys are based on the relative risk (0.96) and
95% confidence interval (0.91-1.02) for a 9.3 |ig/m3 increase in 7-day average PM25 (Lin et al., 2002, Table
3, p. 579)
Functional Form: Log-linear
Coefficient: -0.004389
Standard Error: 0.003130
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
45 For the remaining days, they estimated PM using TSP, sulfate, and coefficient of haze data.
46 In the case-crossover analysis, the same individual serves as a case and control. In the unidirectional model, the case
period is during the hospital visit and the control period is at some point well in advance of the case period. In the bidirectional
model, there are two control periods for each visit, one before the case period and one after the case period.
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Multipollutant Model (PM2 5, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for boys are based on
the relative risk (0.94) and 95% confidence interval (0.88-1.01) for a 9.3 |ig/m3 increase in 6-day average
PM25(Lin et al., 2002, Table 5, p. 580).
Functional Form: Log-linear
Coefficient: -0.006653
Standard Error: 0.003779
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
PM2 5 Function(s) - Girls
Single Pollutant Model
The single pollutant coefficient and standard error for girls are based on the relative risk (1.06) and
95% confidence interval (0.98-1.13) for a 9.3 |ig/m3 increase in 7-day average PM25(Lin et al., 2002, Table
4, p. 580)
Functional Form: Log-linear
Coefficient: 0.006265
Standard Error: 0.008377
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
Multipollutant Model (PM2 5, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for girls are based on
the relative risk (0.98) and 95% confidence interval (0.90-1.08) for a 9.3 |ig/m3 increase in 6-day average
PM25(Lin et al., 2002, Table 5, p. 580).
Functional Form: Log-linear
Coefficient: -0.002172
Standard Error: 0.005001
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
PM10 2 5 Function(s) - Boys
Single Pollutant Model
The single pollutant coefficient and standard error for boys are based on the relative risk (1.12) and
95% confidence interval (1.04-1.20) for an 8.4 |ig/m3 increase in 7-day average PM10_25 (Lin et al., 2002,
Table 3, p. 579)
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Functional Form: Log-linear
Coefficient: 0.013492
Standard Error: 0.004346
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
Multipollutant Model (PM10_2 5, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for boys are based on
the relative risk (1.15) and 95% confidence interval (1.06-1.25) for an 8.4 |ig/m3 increase in 6-day average
PM10,, (Lin et al., 2002, Table 5, p. 580).
Functional Form: Log-linear
Coefficient: 0.016638
Standard Error: 0.005007
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
PM10 2 5 Function(s) - Girls
Single Pollutant Model
The single pollutant coefficient and standard error for girls are based on the relative risk (1.20) and
95% confidence interval (1.09-1.31) for an 8.4 |ig/m3 increase in 7-day average PM10_25 (Lin et al., 2002,
Table 4, p. 580)
Functional Form: Log-linear
Coefficient: 0.021705
Standard Error: 0.005583
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
Multipollutant Model (PM10_2 5, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for girls are based on
the relative risk (1.15) and 95% confidence interval (1.03-1.29) for an 8.4 |ig/m3 increase in 6-day average
PM10_25 (Lin et al., 2002, Table 5, p. 580).
Functional Form: Log-linear
Coefficient: 0.016638
Standard Error: 0.006836
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
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Appendix F. Particulate Matter C-R Functions
PM10 Function(s) - Boys
Single Pollutant Model
The single pollutant coefficient and standard error for boys are based on the relative risk (1.01) and
95% confidence interval (0.95-1.08) for a 14.8 |ig/m3 increase in 7-day average PM10(Lin et al., 2002,
Table 3, p. 579)
Functional Form: Log-linear
Coefficient: 0.000672
Standard Error: 0.002211
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
Multipollutant Model (PM10, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for boys are based on
the relative risk (1.02) and 95% confidence interval (0.94-1.11) for a 14.8 |ig/m3 increase in 6-day average
PM10(Lin et al., 2002, Table 5, p. 580).
Functional Form: Log-linear
Coefficient: 0.001338
Standard Error: 0.002865
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per male child ages 6-
12 (ICD code 493)
Population: population of males, ages 6 through 12
PM10 Function(s) - Girls
Single Pollutant Model
The single pollutant coefficient and standard error for girls are based on the relative risk (1.07) and
95% confidence interval (0.98-1.16) for a 14.8 |ig/m3 increase in 7-day average PM10(Lin et al., 2002,
Table 4, p. 580)
Functional Form: Log-linear
Coefficient: 0.004572
Standard Error: 0.002906
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
Multipollutant Model (PM10, CO, N02, ozone, and S02)
In a model with CO, N02, ozone, and S02, the coefficient and standard error for girls are based on
the relative risk (1.03) and 95% confidence interval (0.93-1.15) for a 14.8 |ig/m3 increase in 6-day average
PM10(Lin et al., 2002, Table 5, p. 580).
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Functional Form: Log-linear
Coefficient: 0.001997
Standard Error: 0.003660
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per female child ages
6-12 (ICD code 493)
Population: population of females, ages 6 through 12
F .4.8 Hospital Admissions for Asthma (Sheppard et al., 1999; Sheppard, 2003)
Sheppard et al. (1999) studied the relation between air pollution in Seattle and nonelderly (<65)
hospital admissions for asthma from 1987 to 1994. They used air quality data for PM10, PM25, coarse
PM1010_2 5, S02, ozone, and CO in a Poisson regression model with control for time trends, seasonal
variations, and temperature-related weather effects.47 They found asthma hospital admissions associated
with PM10, PM2 5, PMj0_25, CO, and ozone. They did not observe an association for S02. They found PM
and CO to be jointly associated with asthma admissions. The best fitting co-pollutant models were found
using ozone. However, ozone data was only available April through October, so they did not consider
ozone further. For the remaining pollutants, the best fitting models included PM25 and CO. Results for
other co-pollutant models were not reported.
In response to concerns that the work by Sheppard et al. (1999) may be biased because of the Splus
issue (discussed in Appendix D of this User Manual), Sheppard (2003) reanalyzed some of this work, in
particular Sheppard reanalyzed the original study's PM2 5 single pollutant model.
PM2 5 Function(s)
Single Pollutant Model (Sheppard, 2003)
The coefficient and standard error are based on the relative risk (1.04) and 95% confidence interval
(1.01-1.06) for a 11.8 |ig/m3 increase in PM25 in the 1-day lag GAM stringent model (Sheppard, 2003, pp.
228-229).
Functional Form: Log-linear
Coefficient: 0.003324
Standard Error: 0.001045
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person <65 (ICD
code 493)
Population: population of ages 65 and under
Multipollutant Model (PM2 5 and CO) (Sheppard et al., 1999)
The coefficient and standard error for the co-pollutant model with CO are calculated from a relative
risk of 1.03 (95% CI 1.01-1.06) for an 11.8 i-ig/m3 increase48 in PM25 (Sheppard et al., 1999, p. 28).
47 PM2 5 levels were estimated from light scattering data.
48 The reported IQR change in the abstract and text is smaller than reported in Table 3.
in the abstract and text to be correct because greater number of significant figures are reported.
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Functional Form: Log-linear
Coefficient: 0.002505
Standard Error: 0.001045
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person <65 (ICD
code 493)
Population: population of ages 65 and under
PM10 2 5 Function(s)
Single Pollutant Model (Sheppard et al., 1999)
The single pollutant coefficient and standard error are calculated from a relative risk of 1.04 (95%
CI 1.01-1.07) for a 9.3 |ig/m3 increase49 in PM10_2 5 (Sheppard et al., 1999, p. 27).
Functional Form: Log-linear
Coefficient: 0.004217
Standard Error: 0.001583
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person <65 (ICD
code 493)
Population: population of ages 65 and under
PM10 Function(s)
Single Pollutant Model (Sheppard et al., 1999)
The single pollutant coefficient and standard error are calculated from a relative risk of 1.05 (95%
CI 1.02-1.08) for a 19 |ig/m3 increase50 in PM10 (Sheppard et al., 1999, p. 27).
Functional Form: Log-linear
Coefficient: 0.002568
Standard Error: 0.000767
Incidence Rate: region-specific daily hospital admission rate for asthma admissions per person <65 (ICD
code 493)
Population: population of ages 65 and under
F .4.9 Hospital Admissions for Asthma (Thurston et al., 1994, Toronto)
Thurston et al. (1994) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada, for six weeks in July and August 1986-1988. In single-pollutant
linear regression models, ozone was strongly associated with asthma admissions (ICD code 493) and
various measures of PM were marginally significant. In two-pollutant models, ozone remained significant,
49
The reported IQR change in the abstract and text is smaller than reported in Table 3. We assume the change reported
in the abstract and text to be correct because greater number of significant figures are reported.
50 The reported IQR change in the abstract and text is smaller than reported in Table 3.
in the abstract and text to be correct because greater number of significant figures are reported.
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Appendix F. Particulate Matter C-R Functions
but measures of PM were often not significant. The C-R functions for PM2 5 and PM10 are based on results
from the reported single pollutant models and co-pollutant models with ozone. For PM10_2 5, results are
reported only from a single pollutant model.
PM2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM2 5 coefficient (0.0334) and standard error (0.0241) are reported
in Table 4 (Thurston et al., 1994, p. 282) for a unit |ig/m3 increase daily average PM25 levels.
Functional Form: Linear
Coefficient: 0.0334
Standard Error: 0.0241
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (PM2 5 and ozone)
In a model with ozone, the PM2 5 coefficient (0.0132) and standard error (0.0273) are reported in
Table 4 (Thurston et al., 1994, p. 282) for a unit |ig/m3 increase daily average PM2 5 levels.
Functional Form: Linear
Coefficient: 0.0132
Standard Error: 0.0273
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
PM10 2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10_25 coefficient (0.0670) and standard error (0.0571) are
reported in Table 4 (Thurston et al., 1994, p. 282) for a unit |ig/m3 increase daily average PM10_2 5 levels.
Functional Form: Linear
Coefficient: 0.0670
Standard Error: 0.0571
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10 coefficient (0.0248) and standard error (0.0180) are reported
in Table 4 (Thurston et al., 1994, p. 282) for a unit |ig/m3 increase daily average PM10 levels.
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Functional Form: Linear
Coefficient: 0.0248
Standard Error: 0.0180
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (PM10 and ozone)
In a model with ozone, the PM10 coefficient (0.0039) and standard error (0.0208) are reported in
Table 4 (Thurston et al., 1994, p. 282) for a unit |ig/m3 increase daily average PM10 levels.
Functional Form: Linear
Coefficient: 0.0039
Standard Error: 0.0208
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
F .4.10 Hospital Admissions for Chronic Lung Disease (Lippmann et al., 2000; Ito,
2003)
Lippmann et al. (2000) studied the association between particulate matter and daily mortality and
hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study periods, 1985-
1990 and 1992-1994. The 1992-1994 study period had a greater variety of data on PM size and was the
main focus of the report. The authors collected hospitalization data for a variety of cardiovascular and
respiratory endpoints. They used daily air quality data for PM10, PM2 5, and PM10_2 5 in a Poisson regression
model with generalized additive models (GAM) to adjust for nonlinear relationships and temporal trends.
In single pollutant models, all PM metrics were statistically significant for pneumonia (ICD codes 480-
486), PM10_2 5 and PM10 were significant for ischemic heart disease (ICD code 410-414), and PM2 5 and PM10
were significant for heart failure (ICD code 428). There were positive, but not statistically significant
associations, between the PM metrics and COPD (ICD codes 490-496) and dysrhythmia (ICD code 427).
In separate co-pollutant models with PM and either ozone, S02, N02, or CO, the results were generally
comparable. The PM2 5 C-R functions are based on results of the single pollutant model and co-pollutant
model with ozone.
In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.
(2000). The reanalysis by Ito reported that more generalized additive models with stringent convergence
criteria and generalized linear models resulted in smaller relative risk estimates. Not all of the original
results were replicated, so we present here a mix of C-R functions from the reanalysis and from the original
study (when the reanalyzed results were not available).
Single Pollutant Model (Ito, 2003)
The coefficient and standard error are based on the relative risk (1.043) and 95% confidence
interval (0.902-1.207) for a 36 |ig/nr' increase in PM25 in the 3-day lag GAM stringent model (Ito, 2003,
Table 8).
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Functional Form: Log-linear
Coefficient: 0.001169
Standard Error: 0.002064
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and ozone) (Lippmann et al., 2000)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.040 (95% CI
0.877-1.234) for a 36 i-ig/m3 increase in PM25 (Lippmann et al., 2000, Table 14, p. 26).
Functional Form: Log-linear
Coefficient: 0.001089
Standard Error: 0.002420
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
F .4.11 Hospital Admissions for Chronic Lung Disease (Moolgavkar, 2000c;
Moolgavkar, 2003)
Moolgavkar (2000c) examined the association between air pollution and COPD hospital
admissions (ICD 490-496) in the Chicago, Los Angeles, and Phoenix metropolitan areas. He collected
daily air pollution data for ozone, S02, N02, CO, and PM10 in all three areas. PM2 5 data was available
only in Los Angeles. The data were analyzed using a Poisson regression model with generalized additive
models to adjust for temporal trends. Separate models were run for 0 to 5 day lags in each location.
Among the 65+ age group in Chicago and Phoenix, weak associations were observed between the gaseous
pollutants and admissions. No consistent associations were observed for PM10. In Los Angeles,
marginally significant associations were observed for PM2 5, which were generally lower than for the
gases. In co-pollutant models with CO, the PM25 effect was reduced. Similar results were observed in the
0-19 and 20-64 year old age groups.
In response to concerns with the Splus issue, Moolgavkar (2003) reanalyzed his earlier study. In
the reanalysis, he reported that more generalized additive models with stringent convergence criteria and
generalized linear models resulted in smaller relative risk estimates. Not all of the original results were
replicated, so we present here a mix of C-R functions from the reanalysis and from the original study
(when the reanalyzed results were not available).
The PM2 5 C-R functions for the 65+ age group are based on the reanalysis in Moolgavkar
(Moolgavkar, 2003) of the single and co-pollutant models (PM2 5 and CO). The PM2 5 C-R functions for
the 20-64 age group are based on the original study's single and co-pollutant models (PM25 and CO).
Since the true PM effect is most likely best represented by a distributed lag model, then any single lag
model should underestimate the total PM effect. As a result, we selected the lag models with the greatest
effect estimates for use in the C-R functions.
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Ages 65 and older
Single Pollutant Model (Moolgavkar, 2003)
The coefficient and standard error are calculated from an estimated percentage change of 1.8551
and t-statistic of 3.53 for a 10 |ig/m3 increase in PM25 in the 2-day lag GAM-30df stringent (10~8) model
(Moolgavkar, 2003, Table 17).
Functional Form: Log-linear
Coefficient: 0.001833
Standard Error: 0.000519
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and N02) (Moolgavkar, 2003)
In a model with PM2 5 and N02, the coefficient and standard error are calculated from the
estimated percentage change of 0.4240 and t-statistic of 0.62 for a 10 |ig/m3 increase in PM2 5 in the 0-day
lag GAM-lOOdf stringent (10~8) model (Moolgavkar, 2003, Table 19).
Functional Form: Log-linear
Coefficient: 0.000419
Standard Error: 0.000676
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and CO) (Moolgavkar, 2000c)
In a model with CO, the coefficient and standard error are calculated from an estimated percent
change of 0.852 and t-statistic of 0.8 for a 10 |ig/m3 increase in PM25 in the two-day lag model
(Moolgavkar, 2000c, Table 3, p. 80).
Functional Form: Log-linear
Coefficient: 0.0008
Standard Error: 0.001000
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
51 In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 2.0 would result in a relative risk of 1.020 and coefficient of 0.001980. The "estimated"
percent change, as reported by Moolgavkar, of 2.0 results in a relative risk of 1.020201 and coefficient of 0.002.
52
In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 0.8 would result in a relative risk of 1.008 and coefficient of 0.000797. The "estimated"
percent change, as reported by Moolgavkar, of 0.8 results in a relative risk of 1.008032 and coefficient of 0.0008.
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Ages 18 to 6453
Single Pollutant Model (Moolgavkar, 2000c)
The single pollutant coefficient and standard error are calculated from an estimated percent change
of2.254 and t-statistic of 3.0 for a 10 |ig/nr' increase in PM25 in the two-day lag model (Moolgavkar,
2000c, Table 4, p. 81).
Functional Form: Log-linear
Coefficient: 0.0022
Standard Error: 0.000733
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 18-64 (ICD codes 490-492, 494-496)55
Population: population of ages 18 to 64
Multipollutant Model (PM2 5 and CO) (Moolgavkar, 2000c)
In a model with CO, the coefficient and standard error are calculated from an estimated percent
change of 2.056 and t-statistic of 2.2 for a 10 |ig/m3 increase in PM25 in the two-day lag model
(Moolgavkar, 2000c, Table 4, p. 81).
Functional Form: Log-linear
Coefficient: 0.0020
Standard Error: 0.000909
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 18-64 (ICD codes 490-492, 494-496)57
Population: population of ages 18 to 64
53
Although Moolgavkar (2000c) reports results for the 20-64 year old age range, for comparability to other studies, we
apply the results to the population of ages 18 to 64.
54 In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 2.2 would result in a relative risk of 1.022 and coefficient of 0.002176. The "estimated"
percent change, as reported by Moolgavkar, of 2.2 results in a relative risk of 1.022244 and coefficient of 0.0022.
55 Moolgavkar (2000c) reports results for ICD codes 490-496. In order to avoid double counting non-elderly asthma
hospitalizations (ICD code 493) with Sheppard et al. (1999) in a total benefits estimation, we have excluded ICD code 493 from the
baseline incidence rate used in this function.
56 In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 2.0 would result in a relative risk of 1.020 and coefficient of 0.001980. The "estimated"
percent change, as reported by Moolgavkar, of 2.0 results in a relative risk of 1.020201 and coefficient of 0.002.
57
Moolgavkar (2000c) reports results for ICD codes 490-496. In order to avoid double counting non-elderly asthma
hospitalizations (ICD code 493) with Sheppard et al. (1999) in a total benefits estimation, we have excluded ICD code 493 from the
baseline incidence rate used in this function.
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F .4.12 Hospital Admissions for Chronic Lung Disease (Moolgavkar et al., 1997,
Minneapolis)
Moolgavkar et al. (1997) examined the relationship between air pollution and chronic lung disease
hospital admissions (ICD codes 490-496) for individuals 65 and older in Minneapolis-St. Paul, Minnesota,
from January 1986 to December 1991. In a Poisson regression, they found no significant effect for any of
the pollutants (PM10, ozone, or CO). The effect for ozone was marginally significant. The PM10 C-R
function is based on the results from a three-pollutant model (ozone, CO, PM10) to estimate chronic lung
disease incidence. The model with a 100 df smoother was reported to be optimal (p. 368).
Multipollutant Model (PM10, CO, and ozone)
In a model with ozone and CO, the estimated PM10 coefficient and standard error are based on a
1.77 percent increase in admissions (95% CI -1.3, 4.9) due to a PM10 change of 20 |ig/m3 (Moolgavkar et
al., 1997, Table 4 and p. 366).
Functional Form: Log-linear
Coefficient: 0.000877
Standard Error: 0.000777
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
F .4.13 Hospital Admissions for Chronic Lung Disease (Schwartz, 1994a,
Minneapolis)
Schwartz (1994c) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Minneapolis, MN, from January 1986 to December 1989. In single-pollutants
models, PM10 was significantly related to chronic lung disease. Ozone was not significantly linked to
chronic lung disease and the results were not reported. The PM10 C-R function is based on the results of
the single-pollutant model with "spline" smoothing.
Single Pollutant Model
In a model with spline functions to adjust for time and weather, the coefficient and standard error
are based on the relative risk (1.47) and 95% confidence interval (1.10-1.95) associated with a 100 |ig/m3
increase in two-day average PM10 levels (Schwartz, 1994c, Table 4, p. 369) .
Functional Form: Log-linear
Coefficient: 0.003853
Standard Error: 0.001461
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 65+ (ICD codes 490-496)
Population: population of ages 65 and older
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F .4.14 Hospital Admissions for Chronic Lung Disease (less Asthma) (Burnett et al.,
1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada froml980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM25, CO, N02, S02, and ozone and found PM10_2 5, PM10, and ozone
significantly associated with chronic lung disease (ICD codes 490-492, 496). They estimated multiple
pollutant models, where pollutants for the best fitting model were chosen using stepwise regression based
on AIC criterion. In a three pollutant model, admissions for chronic obstructive pulmonary disease
(COPD) were linked to ozone and PM10_25. A non-significant association was found with CO. The C-R
functions for PM2 5 and PM10 are based on the results of a single pollutant model. The C-R functions for
PM10_2 5 are based on the results of a single pollutant model and three-pollutant model (03, CO, PM10_2 5).
PM2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (3.42) and t-statistic (1.89)
reported in Table 3 (Burnett et al., 1999, p. 133) for an 18.0 |ig/m3 increase in two-day average PM2 5
levels.
Functional Form: Log-linear
Coefficient: 0.001868
Standard Error: 0.000988
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person (ICD codes 490-492, 494-496)
Population: population of all ages
PM10_2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(6.07) and t-statistic (3.26) reported in Table 3 (Burnett et al., 1999, p. 133) for a 12.2 |ig/m3 increase in
three-day average PM10_25 levels.
Functional Form: Log-linear
Coefficient: 0.004830
Standard Error: 0.001482
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person (ICD codes 490-492, 494-496)
Population: population of all ages
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Multipollutant Model (PM10_2 5, CO, and ozone)
In a model with ozone and CO, the PM10_2 5 coefficient and standard error are based on the percent
increase (3.86) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (1.90)58 for a 12.2 |ig/m ' increase in three-day average PM10_2 5 levels.
Functional Form: Log-linear
Coefficient: 0.003104
Standard Error: 0.001634
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person (ICD codes 490-492, 494-496)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(4.11) and t-statistic (2.44) reported in Table 3 (Burnett et al., 1999, p. 133) for a 30.2 |ig/m3 increase in
three-day average PM10 levels.
Functional Form: Log-linear
Coefficient: 0.001334
Standard Error: 0.000547
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person (ICD codes 490-492, 494-496)
Population: population of all ages
F .4.15 Hospital Admissions for Chronic Lung Disease (less Asthma) (Moolgavkar,
2000c)
Moolgavkar (2000c) examined the association between air pollution and COPD hospital
admissions (ICD 490-496) in the Chicago, Los Angeles, and Phoenix metropolitan areas. He collected
daily air pollution data for ozone, S02, N02, CO, and PM10 in all three areas. PM2 5 data was available
only in Los Angeles. The data were analyzed using a Poisson regression model with generalized additive
models to adjust for temporal trends. Separate models were run for 0 to 5 day lags in each location.
Among the 65+ age group in Chicago and Phoenix, weak associations were observed between the gaseous
pollutants and admissions. No consistent associations were observed for PM10. In Los Angeles,
marginally significant associations were observed for PM2 5, which were generally lower than for the
gases. In co-pollutant models with CO, the PM25 effect was reduced. Similar results were observed in the
0-19 and 20-64 year old age groups.
The PM2 5 C-R functions are based on the single and co-pollutant models (PM2 5 and CO) reported
for the 20-64 and 65+ age groups. Since the true PM effect is most likely best represented by a distributed
58 Rick Burnett (co-author), personal communication.
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lag model, then any single lag model should underestimate the total PM effect. As a result, we selected the
lag models with the greatest effect estimates for use in the C-R functions.
Ages 18 to 6459
Multipollutant Model (PM2 5 and CO)
In a model with CO, the coefficient and standard error are calculated from an estimated percent
change of 2.060 and t-statistic of 2.2 for a 10 |ig/m3 increase in PM25 in the two-day lag model
(Moolgavkar, 2000c, Table 4, p. 81).
Functional Form: Log-linear
Coefficient: 0.0020
Standard Error: 0.000909
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease admissions per
person 18-64 (ICD codes 490-492, 494-496)61
Population: population of ages 18 to 64
F .4.16 Hospital Admissions for Chronic Lung Disease (less Asthma) (Samet et al.,
2000,14 Cities)
Samet et al. (2000) examined the relationship between air pollution and hospital admissions for
individuals of ages 65 and over in 14 cities across the country.62 Cities were selected on the basis of
available air pollution data for at least four years between 1985 and 1994 during which at least 50% of
days had observations between the city-specific start and end of measurements. Hospital admissions were
obtained from the Health Care Financing Administration (HCFA) for the years 1992 and 1993. Poisson
regression was used in the analysis with unconstrained distributed lag models to examine the possibility
that air pollution affects hospital admissions on not only the same day but on later days as well. The use of
unconstrained distributed lags has the advantages of (1) not inappropriately biasing down risk estimates
due to tight constraints (e.g. one day lag) and (2) not leaving the often arbitrary choice of lag period to the
investigator's discretion. The C-R functions are based on the pooled estimate across all 14 cities, using the
unconstrained distributed lag model and fixed or random effects estimates, depending on the results of a
test for heterogeneity.
For this analysis, the unadjusted, base models for the effect of PM10 on hospital admissions were
used. The authors performed a second-stage regression to estimate the impact of S02 and 03 on the PM10 -
59
Although Moolgavkar (2000c) reports results for the 20-64 year old age range, for comparability to other studies, we
apply the results to the population of ages 18 to 64.
60 In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 2.0 would result in a relative risk of 1.020 and coefficient of 0.001980. The "estimated"
percent change, as reported by Moolgavkar, of 2.0 results in a relative risk of 1.020201 and coefficient of 0.002.
61 Moolgavkar (2000c) reports results for ICD codes 490-496. In order to avoid double counting non-elderly asthma
hospitalizations (ICD code 493) with Sheppard et al. (1999) in a total benefits estimation, we have excluded ICD code 493 from the
baseline incidence rate used in this function.
62 The cities under investigation include: Birmingham, Boulder, Canton, Chicago, Colorado Springs, Detroit,
Minneapolis/St. Paul, Nashville, New Haven, Pittsburgh, Provo/Orem, Seattle, Spokane, Youngstown.
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hospitalization effect. For ozone, the PM10 effect in each city was regressed on the correlation between
ozone and particulate matter (the slope of a PM10 vs. 03 regression) in that city. The fitted line for this
regression will have a slope of zero if there is no relationship, meaning that the effect of PM10 is not
dependent on the correlation between PM10 and 03. The adjusted point estimate was obtained by
determining the PM10 effect when the correlation between the pollutants is zero (i.e. the y-intercept of the
fitted line). The effect of 03 adjustment on the PM10 - hospitalization relationship appeared to be minimal
except for the case of COPD. In this case, adjustment increased the point estimate of the independent
particulate matter effect. The variance of this estimate, however, was quite large and the confidence
intervals of the adjusted and unadjusted estimates overlapped substantially. For these reasons, there
appeared to be little impact of 03 adjustment.63 Furthermore, the statistical power and robustness of this
second-stage approach to co-pollutant adjustment are in question because of the small number of
observations used in the regression (14 cities) and the potential for one or two observations to dramatically
impact the results.64 Finally, for the case of COPD, adjustment led to an increased PM10 independent
effect, meaning that if the adjustment is valid, the impact on hospital admissions will be underestimated
rather than overestimated.
Single Pollutant Model
The estimated PM10 coefficient is based on a 2.88 percent increase (RR =
due to a PM10 change of 10.0 ng/m3 (Samet et al., 2000, Part II - Table 14)65. The
estimated from the reported lower (0.19 percent) and upper bounds (5.64 percent)
(Samet et al., 2000, Part II - Table 14).
Functional Form: Log-linear
Coefficient: 0.002839
Standard Error: 0.001351
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person 65+
(ICD codes 490-492, 494-496)
Population: population of ages 65 and older
1.0288) in admissions
standard error is
of the percent increase
F .4.17 Hospital Admissions for Chronic Lung Disease (less Asthma) (Schwartz,
1994b, Detroit)
Schwartz (1994b) examined the relationship between air pollution and hospital admissions (ICD
codes 491-492, 494-496) for individuals 65 and older in Detroit, Michigan, from January 1986 to
December 1989. In a two-pollutant Poisson regression model, Schwartz found both PM10 and ozone
significantly linked to pneumonia and COPD. The authors state that effect estimates were relatively
unchanged compared to the unreported single pollutant models. No significant associations were found
between either pollutant and asthma admissions. The C-R function for chronic lung disease incidence is
based on the results of the "basic" co-pollutant model (PM10 and ozone) presented in Table 4 (p. 651).
63 Joel Schwartz (co-author), personal communication.
64 Commentary from the Health Review Committee (Samet et al., 2000, p.77) states that "[w]hile the approach used in
the morbidity analysis is novel...the question arises as to the adequacy of statistical power for performing these analyses."
65 The random effects estimate of the unconstrained distributed lag model was chosen for COPD admissions since the
chi-square test of heterogeneity was significant (see Samet et al., 2000, Part II - Table 15).
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Multipollutant Model (PM10 and ozone)
The PM10 coefficient and standard error are reported in Table 4 (Schwartz, 1994b, p. 651) for a
one |ig/m3 increase in daily average PM10 levels.
Functional Form: Log-linear
Coefficient: 0.00202
Standard Error: 0.00059
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person 65+
(ICD codes 490-492, 494-496)
Population: population of ages 65 and older
F .4.18 Hospital Admissions for Pneumonia (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada from 1980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found all significantly associated
with pneumonia and other respiratory infections (ICD codes 464,466,480-487,494). They estimated multi-
pollutant models, where pollutants for the best fitting model were chosen using stepwise regression based
on AIC criterion. Respiratory infection admissions were linked to ozone, N02, and PM2 5. The C-R
functions for PM10_2 5 and PM10 are based on the results of a single pollutant model. The C-R functions for
PM2 5 are based on the results of a single pollutant model and three-pollutant model (ozone, N02, PM2 5).
PM2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(7.64) and t-statistic (6.09) reported in Table 3 (Burnett et al., 1999, p. 133) for an 18.0 |ig/m3 increase in
three-day average PM25 levels.
Functional Form: Log-linear
Coefficient: 0.004090
Standard Error: 0.000672
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
Multipollutant Model (PM2 5, N02, and ozone)
In a model with ozone and N02, the PM2 5 coefficient and standard error are based on the percent
increase (6.08) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (4.46)66 for an 18.0 |ig/m3 increase in three-day average PM25 levels.
66 Rick Burnett (co-author), personal communication.
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Functional Form: Log-linear
Coefficient: 0.003279
Standard Error: 0.000735
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
PM10 2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (4.44) and t-statistic (4.00)
reported in Table 3 (Burnett et al., 1999, p. 133) for a 12.2 |ig/m3 increase in three-day average PM10_2 5
levels.
Functional Form: Log-linear
Coefficient: 0.003561
Standard Error: 0.000890
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (8.35) and t-statistic (5.96)
reported in Table 3 (Burnett et al., 1999, p. 133) for a 30.2 |ig/m3 increase in three-day average PM10
levels.
Functional Form: Log-linear
Coefficient: 0.002656
Standard Error: 0.000446
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
F .4.19 Hospital Admissions for Pneumonia (Lippmann et al., 2000; Ito, 2003)
Lippmann et al. (2000) studied the association between particulate matter and daily mortality and
hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study periods,
1985-1990 and 1992-1994. The 1992-1994 study period had a greater variety of data on PM size and was
the main focus of the report. The authors collected hospitalization data for a variety of cardiovascular and
respiratory endpoints. They used daily air quality data for PM10, PM2 5, and PM10_2 5 in a Poisson regression
model with generalized additive models (GAM) to adjust for nonlinear relationships and temporal trends.
In single pollutant models, all PM metrics were statistically significant for pneumonia (ICD codes 480-
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486), PM10_2 5 and PM10 were significant for ischemic heart disease (ICD code 410-414), and PM25 and
PM10 were significant for heart failure (ICD code 428). There were positive, but not statistically
significant associations, between the PM metrics and COPD (ICD codes 490-496) and dysrhythmia (ICD
code 427). In separate co-pollutant models with PM and either ozone, S02, N02, or CO, the results were
generally comparable.
In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.
(2000). The reanalysis by Ito reported that more generalized additive models with stringent convergence
criteria and generalized linear models resulted in smaller relative risk estimates. Not all of the original
results were replicated, so we present here a mix of C-R functions from the reanalysis and from the
original study (when the reanalyzed results were not available). The PM2 5 C-R functions are based on
results of the single pollutant model and co-pollutant model with ozone.
Single Pollutant Model (Ito, 2003)
The estimated PM2 5 coefficient and standard error are based on a relative risk of 1.154 (95% CI -
1.027, 1.298) due to a PM25 change of 36 |ig/m3 in the 1-day lag GAM stringent model (Ito, 2003, Table
7).
Functional Form: Log-linear
Coefficient: 0.003979
Standard Error: 0.001659
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and ozone) (Lippmann et al., 2000)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.175 (95% CI
1.026-1.345) for a 36 i-ig/m3 increase in PM25 (Lippmann et al., 2000, Table 14, p. 26).
Functional Form: Log-linear
Coefficient: 0.004480
Standard Error: 0.001918
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older
F .4.20 Hospital Admissions for Pneumonia (Moolgavkar et al., 1997, Minneapolis)
Moolgavkar et al. (1997) examined the relationship between air pollution and hospital admissions
for individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January 1986 to December 1991. In
a four pollutant Poisson model examining pneumonia admissions (ICD codes 480-487) in Minneapolis,
ozone was significant, while N02, S02, and PM10 were not significant. The PM10 C-R function is based on
the results from the four-pollutant model to estimate pneumonia incidence. The model with a 130 df
smoother was reported to be optimal (p. 368).
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Multipollutant (PM10, N02, ozone, and S02)
In a model with N02 and ozone, the estimated PM10 coefficient and standard error are based on a
1.00 percent increase in admissions (95% CI -1.0, 3.0) due to a PM10 change of 20 |ig/m3 (Moolgavkar et
al., 1997, Table 4, p. 366)
Functional Form: Log-linear
Coefficient: 0.000498
Standard Error: 0.000505
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older
F .4.21 Hospital Admissions for Pneumonia (Samet et al., 2000,14 Cities)
Samet et al. (2000) examined the relationship between air pollution and hospital admissions for
individuals of ages 65 and over in 14 cities across the country.67 Cities were selected on the basis of
available air pollution data for at least four years between 1985 and 1994 during which at least 50% of
days had observations between the city-specific start and end of measurements. Hospital admissions were
obtained from the Health Care Financing Administration (HCFA) for the years 1992 and 1993. Poisson
regression was used in the analysis with unconstrained distributed lag models to examine the possibility
that air pollution affects hospital admissions on not only the same day but on later days as well. The use of
unconstrained distributed lags has the advantages of (1) not inappropriately biasing down risk estimates
due to tight constraints (e.g. one day lag) and (2) not leaving the often arbitrary choice of lag period to the
investigator's discretion. The C-R functions are based on the pooled estimate across all 14 cities, using the
unconstrained distributed lag model and fixed or random effects estimates, depending on the results of a
test for heterogeneity.
For this analysis, the unadjusted, base models for the effect of PM10 on hospital admissions were
used. The authors performed a second-stage regression to estimate the impact of S02 and 03 on the PM10 -
hospitalization effect. For ozone, the PM10 effect in each city was regressed on the correlation between
ozone and particulate matter (the slope of a PM10 vs. 03 regression) in that city. The fitted line for this
regression will have a slope of zero if there is no relationship, meaning that the effect of PM10 is not
dependent on the correlation between PM10 and 03. The adjusted point estimate was obtained by
determining the PM10 effect when the correlation between the pollutants is zero (i.e. the y-intercept of the
fitted line). The effect of 03 adjustment on the PM10 - hospitalization relationship appeared to be minimal
except for the case of COPD. In this case, adjustment increased the point estimate of the independent
particulate matter effect. The variance of this estimate, however, was quite large and the confidence
intervals of the adjusted and unadjusted estimates overlapped substantially. For these reasons, there
appeared to be little impact of 03 adjustment.68 Furthermore, the statistical power and robustness of this
second-stage approach to co-pollutant adjustment are in question because of the small number of
observations used in the regression (14 cities) and the potential for one or two observations to dramatically
67The cities under investigation include: Birmingham, Boulder, Canton, Chicago, Colorado Springs, Detroit,
Minneapolis/St. Paul, Nashville, New Haven, Pittsburgh, Provo/Orem, Seattle, Spokane, Youngstown.
68 Joel Schwartz (co-author), personal communication.
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impact the results.69 Finally, for the case of COPD, adjustment led to an increased PM10 independent
effect, meaning that if the adjustment is valid, the impact on hospital admissions will be underestimated
rather than overestimated.
Single Pollutant Model
The estimated PM10 coefficient is based on a 2.07 percent increase (RR =
due to a PM10 change of 10.0 ng/m3 (Samet et al., 2000, Part II - Table 14)70. The
estimated from the reported lower (0.94 percent) and upper bounds (3.22 percent)
(Samet et al., 2000, Part II - Table 14).
Functional Form: Log-linear
Coefficient: 0.002049
Standard Error: 0.000570
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person 65+ (ICD codes
480-487)
Population: population of ages 65 and older
1.0207) in admissions
standard error is
of the percent increase
F .4.22 Hospital Admissions for Pneumonia (Schwartz, 1994a, Minneapolis)
Schwartz (1994a) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January 1986 to December 1989. In
single-pollutant Poission regression models, both ozone and PM10 were significantly associated with
pneumonia admissions. In a two-pollutant model, Schwartz found PM10 significantly related to
pneumonia; ozone was weakly linked to pneumonia. The results were not sensitive to the methods used to
control for seasonal patterns and weather. The PM10 C-R functions are based on the results of the single
pollutant model and the two-pollutant model (PM10 and ozone) with "spline" smoothing.
Single Pollutant Model
The single pollutant coefficient and standard error are based on the relative risk (1.17) and 95%
confidence interval (1.03-1.33) for a 100 |ig/m3 increase in daily average PM10 levels (Schwartz, 1994a, p.
369).
Functional Form: Log-linear
Coefficient: 0.001570
Standard Error: 0.000652
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older.
69
Commentary from the Health Review Committee (Samet et al., 2000, p.77) states that "[w]hile the approach used in
the morbidity analysis is novel...the question arises as to the adequacy of statistical power for performing these analyses."
70
The random effects estimate of the unconstrained distributed lag model was chosen for pneumonia admissions since
the chi-square test of heterogeneity was significant (see Samet et al., 2000, Part II - Table 15).
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Multipollutant Model (PM10 and ozone)
In a model with ozone and spline functions to adjust for time and weather, the coefficient and
standard error are based on the relative risk (1.18) and 95% confidence interval (1.03, 1.36) for a 100
|ig/m ' increase in daily average PM10 levels (Schwartz, 1994a, Table 4).
Functional Form: Log-linear
Coefficient: 0.001655
Standard Error: 0.000709
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older
F .4.23 Hospital Admissions for Pneumonia (Schwartz, 1994b, Detroit)
Schwartz (1994b) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Detroit, Michigan, from January 1986 to December 1989. In a two-pollutant
Poisson regression model, Schwartz found both PM10 and ozone significantly linked to pneumonia and
COPD. The authors state that effect estimates were relatively unchanged compared to the unreported
single pollutant models. No significant associations were found between either pollutant and asthma
admissions. The PM10 C-R function for pneumonia incidence is based on results of the co-pollutant model
(PM10 and ozone).
Multipollutant Model (PM10 and ozone)
The PM10 coefficient and standard error are reported in Table 4 (Schwartz, 1994b, p. 651) for a
one |ig/m3 increase in daily average PM10 levels.
Functional Form: Log-linear
Coefficient: 0.00115
Standard Error: 0.00039
Incidence Rate: region-specific daily hospital admission rate for pneumonia admissions per person 65+
(ICD codes 480-487)
Population: population of ages 65 and older
F .4.24 Hospital Admissions for All Cardiovascular (Burnett et al., 1997, Toronto)
Burnett et al. (1997) examined the relationship between air pollution and cardiac hospital
admissions (ICD codes 410-414,427,428) for individuals of all ages in Toronto, Canada during the
summers of 1992-1994. In a Poisson regression, cardiac admissions were linked to coefficient of haze
(COH) and ozone; other PM measures were less strongly linked. In two pollutant models, they found that
CO, N02, and S02 were not significant, controlling for COH. They found that ozone was still significant,
controlling for COH. In multi-pollutant models with COH, ozone, N02, and S02, both ozone and COH
remained significant. None of the other PM measures (PM10, PM10_2 5, PM2 5) were significant in four-
pollutant models. PM C-R functions are based on the results of single and multipollutant models.
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PM2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM2 5 coefficient and standard error are based on a relative risk of
1.031 (t-statistic 1.8) for an 11 |ig/m3 increase in four-day average PM2 5 (Burnett et al., 1997, Table 2, p.
617).
Functional Form: Log-linear
Coefficient: 0.002775
Standard Error: 0.001542
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (PM2 5 and ozone)
In a model with ozone, the PM2 5 coefficient and standard error are based on a relative risk of
1.014 (t-statistic 0.78) for an 11 |ig/m3 increase in four-day average PM25 (Burnett et al., 1997, Table 5, p.
618).
Functional Form: Log-linear
Coefficient: 0.001264
Standard Error: 0.001620
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (PM2 5, N02, ozone, and S02)
In a four-pollutant model with N02, ozone, and S02, the PM2 5 coefficient and standard error are
based on a relative risk of 0.993 (t-statistic 0.33) for an 11 |ig/m3 increase in four-day average PM2 5
(Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: -0.000639
Standard Error: 0.001935
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
PM10 2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10_2 5 coefficient and standard error are based on a relative risk
of 1.036 (t-statistic 3.41) for a 4.75 |ig/m3 increase in four-day average PM10 25 (Burnett et al., 1997, Table
2, p. 617).
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Functional Form: Log-linear
Coefficient: 0.007446
Standard Error: 0.002183
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (PM10_2 5 and ozone)
In a model with ozone, the PM10_2 5 coefficient and standard error are based on a relative risk of
1.034 (t-statistic 3.28) for a 4.75 |ig/m3 increase in four-day average PM10_25 (Burnett et al., 1997, Table 5,
p. 618).
Functional Form: Log-linear
Coefficient: 0.007039
Standard Error: 0.002146
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (PM10_2 5, N02, ozone, and S02)
In a four-pollutant model with N02, ozone, and S02, the PM10_2 5 coefficient and standard error are
based on a relative risk of 1.022 (t-statistic 1.68) for a 4.75 |ig/m3 increase in four-day average PM10_2 5
(Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: 0.004581
Standard Error: 0.002727
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
In a single pollutant model, the PM10 coefficient and standard error are based on a relative risk of
1.033 (t-statistic 2.24) for a 14.25 |ig/m3 increase in four-day average PM10 (Burnett et al., 1997, Table 2,
p. 617).
Functional Form: Log-linear
Coefficient: 0.002278
Standard Error: 0.001017
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
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Multipollutant Model (PM10 and ozone)
In a model with ozone, the PM10 coefficient and standard error are based on a relative risk of
1.025 (t-statistic 1.68) for a 14.25 |ig/m3 increase in four-day average PM10 (Burnett et al., 1997, Table 5,
p. 618).
Functional Form: Log-linear
Coefficient: 0.001733
Standard Error: 0.001031
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (PM10, N02, ozone, and S02)
In a four-pollutant model with N02, ozone, and S02, the PM10 coefficient and standard error are
based on a relative risk of 0.996 (t-statistic 0.23) for a 14.25 |ig/m3 increase in four-day average PM10
(Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: -0.000281
Standard Error: 0.001223
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
F .4.25 Hospital Admissions for All Cardiovascular (Moolgavkar, 2000b;
Moolgavkar, 2003)
Moolgavkar (2000a) examined the association between air pollution and cardiovascular hospital
admissions (ICD 390-448) in the Chicago, Los Angeles, and Phoenix metropolitan areas. He collected
daily air pollution data for ozone, S02, N02, CO, and PM10 in all three areas. PM2 5 data was available
only in Los Angeles. The data were analyzed using a Poisson regression model with generalized additive
models to adjust for temporal trends. Separate models were run for 0 to 5 day lags in each location.
Among the 65+ age group, the gaseous pollutants generally exhibited stronger effects than PM10 or PM2 5.
The strongest overall effects were observed for S02 and CO. In a single pollutant model, PM25 was
statistically significant for lag 0 and lag 1. In co-pollutant models with CO, the PM2 5 effect dropped out
and CO remained significant. For ages 20-64, S02 and CO exhibited the strongest effect and any PM2 5
effect dropped out in co-pollutant models with CO.
In response to concerns with the Splus issue, Moolgavkar (2003) reanalyzed his earlier study. In
the reanalysis, he reported that more generalized additive models with stringent convergence criteria and
generalized linear models resulted in smaller relative risk estimates. Not all of the original results were
replicated, so we present here a mix of C-R functions from the reanalysis and from the original study
(when the reanalyzed results were not available). The PM2 5 C-R functions are based on single pollutant
and co-pollutant (PM2 5 and CO) models.
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Ages 65 and older
Single Pollutant Model (Moolgavkar, 2003)
The single pollutant coefficient and standard error are calculated from an estimated percent change
of 1.5871 and t-statistic of 4.59 for a 10 |ig/m3 increase in PM25 in the 0-day lag GAM-30df stringent (10~8)
model (Moolgavkar, 2003, Table 12).
Functional Form: Log-linear
Coefficient: 0.001568
Standard Error: 0.000342
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular admissions per person
65+ (ICD codes 390-429)
Population: population of ages 65 and older.
Multipollutant Model (PM2 5 and CO) (Moolgavkar, 2003)
In a model with PM2 5 and CO, the single pollutant coefficient and standard error are calculated
from an estimated percent change of 0.3959 and t-statistic of 0.92 for a 10 |ig/nr' increase in PM2 5 in the 0-
day lag GAM-lOOdf stringent (10~8) model (Moolgavkar, 2003, Table 14).
Functional Form: Log-linear
Coefficient: 0.000389
Standard Error: 0.000423
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular admissions per person
65+ (ICD codes 390-429)
Population: population of ages 65 and older
Ages 18 to 6472
Single Pollutant Model (Moolgavkar, 2000b)
The single pollutant coefficient and standard error are calculated from an estimated percent change
of 1.473 and t-statistic of 4.1 for a 10 |ig/m ' increase in PM25 in the zero lag model (Moolgavkar, 2000a,
Table 4, p. 1203).
71
In a log-linear model, the percent change is equal to (RR - 1) * 100. In this study, Moolgavkar defines and reports the
"estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and log RR are essentially the same.
For example, a true percent change of 2.2 would result in a relative risk of 1.022 and coefficient of 0.002176. The "estimated"
percent change, as reported by Moolgavkar, of 2.2 results in a relative risk of 1.022244 and coefficient of 0.0022.
72
Although Moolgavkar (2000a) reports results for the 20-64 year old age range, for comparability to other studies, we
apply the results to the population of ages 18 to 64.
73
In a log-linear model, the percent change is equal to (RR - 1) * 100. In a similar hospitalization study by Moolgavkar
(2000c), he defines and reports the "estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and
log RR are essentially the same. For example, a true percent change of 1.4 would result in a relative risk of 1.014 and coefficient of
0.00139. Assuming that the 1.4 is the "estimated" percent change described previously would result in a relative risk of 1.014098
and coefficient of 0.0014. We assume that the "estimated" percent changes reported in this study reflect the definition from
(Moolgavkar, 2000c).
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Functional Form: Log-linear
Coefficient: 0.0014
Standard Error: 0.000341
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular admissions per person
ages 18 to 64 (ICD codes 390-409, 411-459)74
Population: population of ages 18 to 64
Multipollutant Model (PM2 5 and CO) (Moolgavkar, 2000b)
In a model with CO, the coefficient and standard error are calculated from an estimated percent
change of 0.975 and t-statistic of 1.8 for a 10 |ig/m3 increase in PM25 in the zero lag model (Moolgavkar,
2000a, Table 4, p. 1203).
Functional Form: Log-linear
Coefficient: 0.0009
Standard Error: 0.000500
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular admissions per person
ages 18 to 64 (ICD codes 390-409, 411-459)76
Population: population of ages 18 to 64
F .4.26 Hospital Admissions for All Cardiovascular (Samet et al., 2000,14 Cities)
Samet et al. (2000) examined the relationship between air pollution and hospital admissions for
individuals of ages 65 and over in 14 cities across the country.77 Cities were selected on the basis of
available air pollution data for at least four years between 1985 and 1994 during which at least 50% of
days had observations between the city-specific start and end of measurements. Hospital admissions were
obtained from the Health Care Financing Administration (HCFA) for the years 1992 and 1993. Poisson
regression was used in the analysis with unconstrained distributed lag models to examine the possibility
that air pollution affects hospital admissions on not only the same day but on later days as well. The use of
unconstrained distributed lags has the advantages of (1) not inappropriately biasing down risk estimates
74 Moolgavkar (2000a) reports results that include ICD code 410 (heart attack). In the benefits analysis, avoided
nonfatal heart attacks are estimated using the results reported by Peters et al. (2001). The baseline rate in the Peters et al. function
is a modified heart attack hospitalization rate (ICD code 410), since most, if not all, nonfatal heart attacks will require
hospitalization. In order to avoid double counting heart attack hospitalizations, we have excluded ICD code 410 from the baseline
incidence rate used in this function.
75
In a log-linear model, the percent change is equal to (RR - 1) * 100. In a similar hospitalization study by Moolgavkar
(2000c), he defines and reports the "estimated" percent change as (log RR * 100). Because the relative risk is close to 1, RR-1 and
log RR are essentially the same. For example, a true percent change of 0.9 would result in a relative risk of 1.009 and coefficient of
0.000896. Assuming that the 0.9 is the "estimated" percent change described previously would result in a relative risk of 1.009041
and coefficient of 0.0009. We assume that the "estimated" percent changes reported in this study reflect the definition from
(Moolgavkar, 2000c).
76 Moolgavkar (2000a) reports results that include ICD code 410 (heart attack). In the benefits analysis, avoided
nonfatal heart attacks are estimated using the results reported by Peters et al. (2001). The baseline rate in the Peters et al. function
is a modified heart attack hospitalization rate (ICD code 410), since most, if not all, nonfatal heart attacks will require
hospitalization. In order to avoid double counting heart attack hospitalizations, we have excluded ICD code 410 from the baseline
incidence rate used in this function.
77 ..... .
The cities under investigation include: Birmingham, Boulder, Canton, Chicago, Colorado Springs, Detroit,
Minneapolis/St. Paul, Nashville, New Haven, Pittsburgh, Provo/Orem, Seattle, Spokane, Youngstown.
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due to tight constraints (e.g. one day lag) and (2) not leaving the often arbitrary choice of lag period to the
investigator's discretion. The C-R functions are based on the pooled estimate across all 14 cities, using the
unconstrained distributed lag model and fixed or random effects estimates, depending on the results of a
test for heterogeneity.
For this analysis, the unadjusted, base models for the effect of PM10 on hospital admissions were
used. The authors performed a second-stage regression to estimate the impact of S02 and 03 on the PM10 -
hospitalization effect. For ozone, the PM10 effect in each city was regressed on the correlation between
ozone and particulate matter (the slope of a PM10 vs. 03 regression) in that city. The fitted line for this
regression will have a slope of zero if there is no relationship, meaning that the effect of PM10 is not
dependent on the correlation between PM10 and 03. The adjusted point estimate was obtained by
determining the PM10 effect when the correlation between the pollutants is zero (i.e. the y-intercept of the
fitted line). The effect of 03 adjustment on the PM10 - hospitalization relationship appeared to be minimal
except for the case of COPD. In this case, adjustment increased the point estimate of the independent
particulate matter effect. The variance of this estimate, however, was quite large and the confidence
intervals of the adjusted and unadjusted estimates overlapped substantially. For these reasons, there
appeared to be little impact of 03 adjustment.78 Furthermore, the statistical power and robustness of this
second-stage approach to co-pollutant adjustment are in question because of the small number of
observations used in the regression (14 cities) and the potential for one or two observations to dramatically
impact the results.79 Finally, for the case of COPD, adjustment led to an increased PM10 independent
effect, meaning that if the adjustment is valid, the impact on hospital admissions will be underestimated
rather than overestimated.
Single Pollutant Model
The estimated PM10 coefficient is based on a 1.19 percent increase (RR =
due to a PM10 change of 10.0 ng/m3 (Samet et al., 2000, Part II - Table 14)80. The
estimated from the reported lower (0.97 percent) and upper bounds (1.41 percent)
(Samet et al., 2000, Part II - Table 14).
Functional Form: Log-linear
Coefficient: 0.001183
Standard Error: 0.000111
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person 65+
(ICD codes 390-459)
Population: population of ages 65 and older
F .4.27 Hospital Admissions for Dysrhythmias (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada froml980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and 03 and found PM2 5, PM10, and CO significantly
78 Joel Schwartz (co-author), personal communication.
79
Commentary from the Health Review Committee (Samet et al., 2000, p.77) states that "[w]hile the approach used in
the morbidity analysis is novel...the question arises as to the adequacy of statistical power for performing these analyses."
1.0119) in admissions
standard error is
of the percent increase
80 The fixed effects estimate of the unconstrained distributed lag model was chosen for CVD admissions since the chi-
square test of heterogeneity was non-significant (see Samet et al., 2000, Part II - Table 15).
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associated with admissions. They estimated multiple pollutant models, where pollutants for best fitting
model were chosen using stepwise regression based on AIC criterion. The final model for dysrhythmias
admissions included 03, CO, and PM2 5. CO was significantly associated with admissions, while 03 and
PM2 5 were marginally significant. The C-R functions are based on the reported single pollutant and
multipollutant models for PM2 5 and single pollutant models for other PM measures.
PM2 5 Function(s)
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(4.33) and t-statistic (2.91) reported in Table 3 (Burnett et al., 1999, p. 133) for an 18.0 |ig/m3 increase in
daily average PM2 5 concentration.
Functional Form: Log-linear
Coefficient: 0.002355
Standard Error: 0.000809
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia disease per person (ICD
code 427)
Population: population of all ages
Multipollutant Model (PM2 5, CO, and ozone)
In a model with ozone and CO, the PM2 5 coefficient and standard error are based on the percent
increase (2.47) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (1.49)81 for an 18.0 |ig/nr' increase in daily average PM25 concentration.
Functional Form: Log-linear
Coefficient: 0.001356
Standard Error: 0.000910
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia disease per person (ICD
code 427)
Population: population of all ages
PM10_2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (2.47) and t-statistic (1.88)
reported in Table 3 (Burnett et al., 1999, p. 133) for a 12.2 |ig/m3 increase in daily average PM10_2 5
concentration.
81 Rick Burnett (co-author), personal communication.
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.002000
Standard Error: 0.001064
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia disease per person (ICD
code 427)
Population: population of all ages
PM10 Function(s)
Single Pollutant Model
The coefficient and standard error are based on the percent increase (5.00) and t-statistic (4.25)
reported in Table 3 (Burnett et al., 1999, p. 133) for a 30.2 |ig/nr' increase in daily average PM10
concentration.
Functional Form: Log-linear
Coefficient: 0.001616
Standard Error: 0.000533
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia disease per person (ICD
code 427)
Population: population of all ages
F .4.28 Hospital Admissions for Dysrhythmia (Lippmann et al., 2000; Ito, 2003)
Lippmann et al. (2000) studied the association between particulate matter and daily mortality and
hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study periods,
1985-1990 and 1992-1994. The 1992-1994 study period had a greater variety of data on PM size and was
the main focus of the report. The authors collected hospitalization data for a variety of cardiovascular and
respiratory endpoints. They used daily air quality data for PM10, PM2 5, and PM10_2 5 in a Poisson regression
model with generalized additive models (GAM) to adjust for nonlinear relationships and temporal trends.
In single pollutant models, all PM metrics were statistically significant for pneumonia (ICD codes 480-
486), PM10_2 5 and PM10 were significant for ischemic heart disease (ICD code 410-414), and PM2 5 and
PM10 were significant for heart failure (ICD code 428). There were positive, but not statistically
significant associations, between the PM metrics and COPD (ICD codes 490-496) and dysrhythmia (ICD
code 427). In separate co-pollutant models with PM and either ozone, S02, N02, or CO, the results were
generally comparable.
In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.
(2000). The reanalysis by Ito reported that more generalized additive models with stringent convergence
criteria and generalized linear models resulted in smaller relative risk estimates. Not all of the original
results were replicated, so we present here a mix of C-R functions from the reanalysis and from the
original study (when the reanalyzed results were not available).
Single Pollutant Model (Ito, 2003)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.046 (95% CI
0.906-1.207) for a 36 |ig/in3 increase in PM25 in the 1-day lag GAM stringent model (Ito, 2003, Table 10).
Abt Associates Inc.
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.001249
Standard Error: 0.002033
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia admissions per person 65+
(ICD code 427)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and ozone) (Lippmann et al., 2000)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.080 (95% CI
0.904-1.291) for a 36 i-ig/m3 increase in PM25 (Lippmann et al., 2000, Table 14, p. 27).
Functional Form: Log-linear
Coefficient: 0.002138
Standard Error: 0.002525
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia admissions per person 65+
(ICD code 427)
Population: population of ages 65 and older
F .4.29 Hospital Admissions for Congestive Heart Failure (Lippmann et al., 2000; Ito,
2003)
Lippmann et al. (2000) studied the association between particulate matter and daily mortality and
hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study periods,
1985-1990 and 1992-1994. The 1992-1994 study period had a greater variety of data on PM size and was
the main focus of the report. The authors collected hospitalization data for a variety of cardiovascular and
respiratory endpoints. They used daily air quality data for PM10, PM2 5, and PM10_2 5 in a Poisson regression
model with generalized additive models (GAM) to adjust for nonlinear relationships and temporal trends.
In single pollutant models, all PM metrics were statistically significant for pneumonia (ICD codes 480-
486), PM10_2 5 and PM10 were significant for ischemic heart disease (ICD code 410-414), and PM2 5 and
PM10 were significant for congestive heart failure (ICD code 428). There were positive, but not
statistically significant associations, between the PM metrics and COPD (ICD codes 490-496) and
dysrhythmia (ICD code 427). In separate co-pollutant models with PM and either ozone, S02, N02, or
CO, the results were generally comparable.
In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.
(2000). The reanalysis by Ito reported that more generalized additive models with stringent convergence
criteria and generalized linear models resulted in smaller relative risk estimates. Not all of the original
results were replicated, so we present here a mix of C-R functions from the reanalysis and from the
original study (when the reanalyzed results were not available).
Single Pollutant Model (Ito, 2003)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.117 (95% CI
1.020-1.224) for a 36 |ig/m3 increase in PM2 5 in the 1-day lag GAM stringent model (Ito, 2003, Table 11).
Abt Associates Inc.
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November 2003
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.003469
Standard Error: 0.001293
Incidence Rate: region-specific daily hospital admission rate for congestive heart failure admissions per
person 65+ (ICD code 428)
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and ozone) (Lippmann et al., 2000)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.183 (95% CI
1.053-1.329) for a 36 i-ig/m3 increase in PM25 (Lippmann et al., 2000, Table 14, p. 27).
Functional Form: Log-linear
Coefficient: 0.004668
Standard Error: 0.001650
Incidence Rate: region-specific daily hospital admission rate for congestive heart failure admissions per
person 65+ (ICD code 428)
Population: population of ages 65 and older
F .4.30 Hospital Admissions for Ischemic Heart Disease (Lippmann et al., 2000; Ito,
2003)
Lippmann et al. (2000) studied the association between particulate matter and daily mortality and
hospitalizations among the elderly in Detroit, MI. Data were analyzed for two separate study periods,
1985-1990 and 1992-1994. The 1992-1994 study period had a greater variety of data on PM size and was
the main focus of the report. The authors collected hospitalization data for a variety of cardiovascular and
respiratory endpoints. They used daily air quality data for PM10, PM2 5, and PM10_2 5 in a Poisson regression
model with generalized additive models (GAM) to adjust for nonlinear relationships and temporal trends.
In single pollutant models, all PM metrics were statistically significant for pneumonia (ICD codes 480-
486), PM10_2 5 and PM10 were significant for ischemic heart disease (ICD code 410-414), and PM2 5 and
PM10 were significant for heart failure (ICD code 428). There were positive, but not statistically
significant associations, between the PM metrics and COPD (ICD codes 490-496) and dysrhythmia (ICD
code 427). In separate co-pollutant models with PM and either ozone, S02, N02, or CO, the results were
generally comparable.
In response to concerns with the Splus issue, Ito (2003) reanalyzed the study by Lippmann et al.
(2000). The reanalysis by Ito reported that more generalized additive models with stringent convergence
criteria and generalized linear models resulted in smaller relative risk estimates. Not all of the original
results were replicated, so we present here a mix of C-R functions from the reanalysis and from the
original study (when the reanalyzed results were not available).
Single Pollutant Model (Ito, 2003)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.053 (95% CI
0.971-1.143) for a 36 |ig/m3 increase in PM2 5 in the 1-day lag GAM stringent model (Ito, 2003, Table 9)
Abt Associates Inc.
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November 2003
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.001435
Standard Error: 0.001156
Incidence Rate: region-specific daily hospital admission rate for ischemic heart disease admissions per
person 65+ (ICD codes 411-414)82
Population: population of ages 65 and older
Multipollutant Model (PM2 5 and ozone) (Lippmann et al., 2000)
The co-pollutant coefficient and standard error are calculated from a relative risk of 1.041 (95% CI
0.947-1.144) for a 36 i-ig/m3 increase in PM25 (Lippmann et al., 2000, Table 14, p. 27).
Functional Form: Log-linear
Coefficient: 0.001116
Standard Error: 0.001339
Incidence Rate: region-specific daily hospital admission rate for ischemic heart disease admissions per
person 65+ (ICD codes 411-414)83
Population: population of ages 65 and older
82 Lippmann et al. (2000) reports results for ICD codes 410-414. In the benefits analysis, avoided nonfatal heart attacks
are estimated using the results reported by Peters et al. (2001). The baseline rate in the Peters et al. function is a modified heart
attack hospitalization rate (ICD code 410), since most, if not all, nonfatal heart attacks will require hospitalization. In order to
avoid double counting heart attack hospitalizations, we have excluded ICD code 410 from the baseline incidence rate used in this
function.
83 Lippmann et al. (2000) reports results for ICD codes 410-414. In the benefits analysis, avoided nonfatal heart attacks
are estimated using the results reported by Peters et al. (2001). The baseline rate in the Peters et al. function is a modified heart
attack hospitalization rate (ICD code 410), since most, if not all, nonfatal heart attacks will require hospitalization. In order to
avoid double counting heart attack hospitalizations, we have excluded ICD code 410 from the baseline incidence rate used in this
function.
Abt Associates Inc. F-70 November 2003
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Appendix F. Particulate Matter C-R Functions
F .5 Emergency Room Visits
F .5.1 Emergency Room Visits for Asthma (Norris et al., 1999)
Norris et al. (1999) examined the relation between air pollution in Seattle and childhood (<18)
hospital admissions for asthma from 1995 to 1996. The authors used air quality data for PM10, light
scattering (used to estimate fine PM), CO, S02, N02, and 03 in a Poisson regression model with
adjustments for day of the week, time trends, temperature, and dew point. They found significant
associations between asthma ER visits and light scattering (converted to PM2 5), PM10, and CO. No
association was found between 03, N02, or S02 and asthma ER visits, although 03 had a significant
amount of missing data. In multipollutant models with either PM metric (light scattering or PM10) and
N02 and S02, the PM coefficients remained significant while the gaseous pollutants were not associated
with increased asthma ER visits. The PM C-R functions are based on results of the single and
multipollutant models reported.
PM2 5 Function(s)
Single Pollutant Model
The single pollutant coefficient and standard error are calculated from a relative risk of 1.15 (95%
CI 1.08-1.23) for a 9.5 i-ig/m3 increase in PM25 (Norris et al., 1999, Table 4, p. 492).
Functional Form: Log-linear
Coefficient: 0.014712
Standard Error: 0.003492
Incidence Rate: region-specific daily emergency room rate for asthma admissions per person <18 (ICD
code 493)
Population: population of ages under 18
Multipollutant Model (PM2 5, N02 and S02)
In a model with N02 and S02, the PM2 5 coefficient and standard error are calculated from a
relative risk of 1.17 (95% CI 1.08-1.26) for a 9.5 i-ig/m3 increase in PM25 (Norris et al., 1999, p. 491).
Functional Form: Log-linear
Coefficient: 0.016527
Standard Error: 0.004139
Incidence Rate: region-specific daily emergency room rate for asthma admissions per person <18 (ICD
code 493)
Population: population of ages under 18
PM10 Function(s)
Single Pollutant Model
The single pollutant coefficient and standard error are calculated from a relative risk of 1.14 (95%
CI 1.05-1.23) for an 11.6|_ig/m3 increase in PM10 (Norris etal., 1999, Table 4, p. 492).
Abt Associates Inc.
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.011296
Standard Error: 0.003480
Incidence Rate: region-specific daily emergency room rate for asthma admissions per person <18 (ICD
code 493)
Population: population of ages under 18
Multipollutant Model (PM10, N02; and S02)
In a model with N02 and S02, the PM10 coefficient and standard error are calculated from a
relative risk of 1.14 (95%CI 1.04-1.26) for an 11.6|_ig/m3 increase in PM10 (Norris et al., 1999,p.491).
Functional Form: Log-linear
Coefficient: 0.011296
Standard Error: 0.004220
Incidence Rate: region-specific daily emergency room rate for asthma admissions per person <18 (ICD
code 493)
Population: population of ages under 18
F .5.2 Emergency Room Visits for Asthma (Schwartz et al., 1993, Seattle)
Schwartz et al. (1993) examined the relationship between air quality and emergency room visits
for asthma (ICD codes 493,493.01,493.10,493.90,493.91) in persons under 65 and 65 and over, living in
Seattle from September 1989 to September 1990. Using single-pollutant models they found daily levels of
PM10 linked to ER visits in individuals ages under 65, and they found no effect in individuals ages 65 and
over. They did not find a significant effect for S02 and ozone in either age group. The results of the single
pollutant model for PM10 are used in this analysis.
Single Pollutant Model
The PM10 coefficient and standard error are reported by Schwartz et al. (1993, p. 829) for a unit
|ig/nr' increase in four-day average PM10 levels.
Functional Form: Log-linear
Coefficient: 0.00367
Standard Error: 0.00126
Incidence Rate: region-specific daily emergency room rate for asthma admissions per person <65 (ICD
code 493)
Population: population of ages under 65
Abt Associates Inc.
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November 2003
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Appendix F. Particulate Matter C-R Functions
F .6 Acute Effects
F .6.1 Acute Bronchitis (Dockery et al., 1996)
Dockery et al. (1996) examined the relationship between PM and other pollutants on the reported
rates of asthma, persistent wheeze, chronic cough, and bronchitis, in a study of 13,369 children ages 8-12
living in 24 communities in U.S. and Canada. Health data were collected in 1988-1991, and single-
pollutant models were used in the analysis to test a number of measures of particulate air pollution.
Dockery et al. found that annual level of sulfates and particle acidity were significantly related to
bronchitis, and PM21 and PM10 were marginally significantly related to bronchitis.84 They also found
nitrates were linked to asthma, and sulfates linked to chronic phlegm. It is important to note that thestudy
examined annual pollution exposures, and the authors did not rule out that acute (daily) exposures could be
related to asthma attacks and other acute episodes. Earlier work, by Dockery et al. (1989), based on six
U.S. cities, found acute bronchitis and chronic cough significantly related to PM15. Because it is based on
a larger sample, the Dockery et al. (1996) study is the better study to develop a C-R function linking PM2 5
with bronchitis.
Bronchitis was counted in the study only if there were "reports of symptoms in the past 12
months" (Dockery et al., 1996, p. 501). It is unclear, however, if the cases of bronchitis are acute and
temporary, or if the bronchitis is a chronic condition. Dockery et al. found no relationship between PM
and chronic cough and chronic phlegm, which are important indicators of chronic bronchitis. For this
analysis, we assumed that the C-R function based on Dockery et al. is measuring acute bronchitis. The C-
R function is based on results of the single pollutant model reported in Table 1.
Single Pollutant Model
The estimated logistic coefficient and standard error are based on the odds ratio (1.50) and 95%
confidence interval (0.91-2.47) associated with being in the most polluted city (PM21 = 20.7 |ig/m3) versus
the least polluted city (PM21 = 5.8 |ig/m3) (Dockery et al., 1996, Tables 1 and 4). The original study used
PM21, however, we use the PM21 coefficient and apply it to PM2 5 data.
Functional Form: Logistic
Coefficient: 0.027212
Standard Error: 0.017096
Incidence Rate: annual bronchitis incidence rate per person = 0.043 (American Lung Association, 2002a,
Table 11)
Population: population of ages 8-12
F .6.2 Acute Myocardial Infarction (Heart Attacks), Nonfatal (Peters et al., 2001)
Peters et al. (2001) studied the relationship between increased particulate air pollution and onset of
heart attacks in the Boston area from 1995 to 1996. The authors used air quality data for PM10, PM10_2 5,
PM25,"black carbon", 03, CO, N02, and S02 in a case-crossover analysis. For each subject, the case
period was matched to three control periods, each 24 hours apart. In univariate analyses, the authors
observed a positive association between heart attack occurrence and PM2 5 levels hours before and days
84 The original study measured PM21, however when using the study's results we use PM25.
difference, assuming that the adverse effects of PM2 j and PM2 5 are comparable.
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Appendix F. Particulate Matter C-R Functions
before onset. The authors estimated multivariate conditional logistic models including two-hour and
twenty-four hour pollutant concentrations for each pollutant. They found significant and independent
associations between heart attack occurrence and both two-hour and twenty-four hour PM2 5 concentrations
before onset. Significant associations were observed for PM10 as well. None of the other particle measures
or gaseous pollutants were significantly associated with acute myocardial infarction for the two hour or
twenty-four hour period before onset.
The patient population for this study was selected from health centers across the United States.
The mean age of participants was 62 years old, with 21% of the study population under the age of 50. In
order to capture the full magnitude of heart attack occurrence potentially associated with air pollution and
because age was not listed as an inclusion criteria for sample selection, we apply an age range of 18 and
over in the C-R function. According to the National Hospital Discharge Survey, there were no
hospitalizations for heart attacks among children <15 years of age in 1999 and only 5.5% of all
hospitalizations occurred in 15-44 year olds (Popovic, 2001, Table 10).
PM2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are calculated from an odds ratio of 1.62 (95% CI 1.13-2.34) for
a 20 |ig/m3 increase in twenty-four hour average PM25 (Peters et al., 2001, Table 4, p. 2813).
Functional Form: Logistic
Coefficient: 0.024121
Standard Error: 0.009285
Incidence Rate: region-specific daily nonfatal heart attack rate per person 18+ = 93% of region-specific
daily heart attack hospitalization rate (ICD code 410)85
Population: population of ages 18 and older
PM10_2 5 Function(s)
Single Pollutant Model
The coefficient and standard error are calculated from an odds ratio of 1.39 (95% CI 0.89-2.15) for
a 15 |ig/m3 increase in twenty-four hour average PM10_2 5 (Peters et al., 2001, Table 4, p. 2813).
Functional Form: Logistic
Coefficient: 0.021954
Standard Error: 0.015000
Incidence Rate: region-specific daily nonfatal heart attack rate = 93% of region-specific daily heart attack
hospitalization rate (ICD code 410)86
Population: population of all ages
85 This estimate assumes that all heart attacks that are not instantly fatal will result in a hospitalization. In addition,
Rosamond et al. (1999) report that approximately six percent of male and eight percent of female hospitalized heart attack patients
die within 28 days (either in or outside of the hospital). We applied a factor of 0.93 to the number of hospitalizations to estimate
the number of nonfatal heart attacks per year.
86 This estimate assumes that all heart attacks that are not instantly fatal will result in a hospitalization. In addition,
Rosamond et al. (1999) report that approximately six percent of male and eight percent of female hospitalized heart attack patients
die within 28 days (either in or outside of the hospital). We applied a factor of 0.93 to the number of hospitalizations to estimate
the number of nonfatal heart attacks per year.
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Appendix F. Particulate Matter C-R Functions
PM10 Function(s)
Single Pollutant Model
The coefficient and standard error are calculated from an odds ratio of 1.66 (95% CI 1.11-2.49) for
a 30 |ig/m3 increase in twenty-four hour average PM10 (Peters et al., 2001, Table 4, p. 2813).
Functional Form: Logistic
Coefficient: 0.016894
Standard Error: 0.006870
Incidence Rate: region-specific daily nonfatal heart attack rate = 93% of region-specific daily heart attack
hospitalization rate (ICD code 410)87
Population: population of all ages
F .6.3 Any of 19 Respiratory Symptoms (Krupnick et al., 1990)
Krupnick et al. (1990) estimated the impact of air pollution on the incidence of any of 19
respiratory symptoms or conditions in 570 adults and 756 children living in three communities in Los
Angeles, California from September 1978 to March 1979. Krupnick et al. (1990) listed 13 specific
"symptoms or conditions": head cold, chest cold, sinus trouble, croup, cough with phlegm, sore throat,
asthma, hay fever, doctor-diagnosed ear infection, flu, pneumonia, bronchitis, and bronchiolitis. The other
six symptoms or conditions are not specified.
In their analysis, they included COH, ozone, N02, and S02, and they used a logistic regression
model that takes into account whether a respondent was well or not the previous day. A key difference
between this and the usual logistic model, is that the model they used includes a lagged value of the
dependent variable. In single-pollutant models, daily 03, COH, and S02 were significantly related to
respiratory symptoms in adults. Controlling for other pollutants, they found that ozone was still
significant. The results were more variable for COH and S02, perhaps due to collinearity. N02 had no
significant effect. No effect was seen in children for any pollutant. The results from the two-pollutant
model with COH and ozone are used to develop a C-R function.
Multipollutant Model (PM10 and ozone)
The C-R function used to estimate the change in ARD2 associated with a change in daily average
PM10 concentration is based on Krupnick et al. (1990, p. 12):88
kARD2= hPMlo pop,
87 This estimate assumes that all heart attacks that are not instantly fatal will result in a hospitalization. In addition,
Rosamond et al. (1999) report that approximately six percent of male and eight percent of female hospitalized heart attack patients
die within 28 days (either in or outside of the hospital). We applied a factor of 0.93 to the number of hospitalizations to estimate
the number of nonfatal heart attacks per year.
88 Krupnick and Kopp (1988, p. 2-24) and ESEERCO (1994, p. V-32) used the same C-R functional form as that used
here.
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Functional Form: Linear
Coefficient: first derivative of the stationary probability = 0.000461
Standard Error: 0.000239
Population: population of ages 18-64 years89
The logistic regression model used by Krupnick et al. (1990) takes into account whether a
respondent was well or not the previous day. Following Krupnick et al. (p. 12), the probability that one is
sick is on a given day is:
Pn
probability (ARD2)=-
l-Pi + Po
probability(ARD2\sicknessor not t_Y )= —pa+^.ARD2t t+x-p >for /= 0,1 •
where:
X = the matrix of explanatory variables
p0 = the probability of sickness on day t, given wellness on day t-1, and
Pi = the probability of sickness on day t, given sickness on day t-1.
In other words, the transition probabilities are estimated using a logistic function; the key
difference between this and the usual logistic model, is that the model includes a lagged value of the
dependent variable.
To calculate the impact of COH (or other pollutants) on the probability of ARD2, it is possible, in
principle, to estimate ARD2 before the change in COH and after the change:
AARD2= ARD2after - ARD2hefc
However the full suite of coefficient estimates are not available.90 Rather than use the full suite of
coefficient values, the impact of COH on the probability of probability of ARD2 may be approximated by
the derivative of ARD2 with respect to COH:
89 Krupnick et al. (1990, Table 1) reported the age distribution in their complete data, but they did not report the ages of
individuals that were considered "adult." This analysis assumes that individuals 18 and older were considered adult. Only a small
percentage (0.6%) of the study population is above the age of 60, so the C-R function was limited to the adult population, up
through the age of 65.
90 The model without N02 (Krupnick et al., 1990, Table V equation 3) was used in this analysis, but the full suite of
coefficient estimates for this model were not reported. Krupnick et al. (1990, Table IV) reported all of the estimated coefficients for
a model of children and for a model of adults when four pollutants were included (ozone, COH, S02, and N02). However, because
of high collinearity between N02 and COH, N02 was dropped from some of the reported analyses (Krupnick et al., p. 10), and the
resulting coefficient estimates changed substantially (see Krupnick et al., 1990, Table IV). Both the ozone and COH coefficients
dropped by about a factor of two or more.
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dprobability(ARD2) /v('~Pi)A-o//{/;i + ('~A>)
^ = (1-a+a)2 :
where PC0H is the reported logistic regression coefficient for COH. Since COH data are not available for
the benefits analysis, an estimated PM10 logistic regression coefficient is used based on the following
assumed relationship between PM10, COH, and TSP:
COH=O.W6TSP
PMW = 0.55 TSP
=> COH = 0.2109 PMW
=> j3PMm = 0.2109-j3COH = 0.21090.0088= 0.001856.
This analysis uses PC0H = 0.0088 (Krupnick et al., 1990, Table V equation 3). The conversion
from COH to TSP is based on study-specific information provided to ESEERCO (1994, p. V-32). The
conversion of TSP to PM10 is from also from ESEERCO (1994, p. V-5), which cited studies by EPA
(1986) and the California Air Resources Board (1982).
The change in the incidence of ARD2 associated with a given change in COH is then estimated
by:
r)A RD2 A A RD2
cPMw ~ APMjq
KARD2 ,
A.PM10 ~ PMl°
^^ARD2=/3;Mio^PM10.
This analysis uses transition probabilities obtained from Krupnick et al. as reported by ESEERCO
(1994, p. V-32), for the adult population: p: = 0.7775 and p0 = 0.0468. This implies:
0.0468( 1- 0.7775)0.00185610.7775, + (1- 0.0468)1
Ppm = ; ^ -0.000461.
10 1-0.7775+0.0468
The standard error for the coefficient is derived using the reported standard error of the logistic
regression coefficient in Krupnick et al. (1990, Table V):
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' PpMl0,MSh= 0-2109high = 0.2109 (0.0088+( 1.960.0046))= 0.003757
0.0468 (1- 0.7775) 0.003757 [0.7775+ (1- 0.0468)1
:> /? = —= 0000934
(1-0.7775+0.0468)2
0PMlo,high PpM10 (0.000934-0.000461)
0„ , = = =0.000236
PMgh I96 I96
Am,/ow = 0-2109-^co///ow = 0.2109-(0.0088-(1.960.0046))=-4.55510"
0.0468 (1-0.7775) (-4.55510"s) [0.7775+(1-0.0468)]
I0,/ow" (1-0.7775+0.0468)2 ~ 113210
/ML (0.000461+1.13210"5)
=> ao , = =~ = 0.000241
^ 1.96 1.96
+ 0^000239.
F .6.4 Lower Respiratory Symptoms (Schwartz and Neas, 2000)
Schwartz et al. (2000) replicated a previous analysis (Schwartz et al., 1994) linking PM levels to
lower respiratory symptoms in children in six cities in the U.S. The original study enrolled 1,844 children
into a year-long study that was conducted in different years (1984 to 1988) in six cities. The students were
in grades two through five at the time of enrollment in 1984. By the completion of the final study, the
cohort would then be in the eighth grade (ages 13-14); this suggests an age range of 7 to 14. The previous
study focused on PM10, acid aerosols, and gaseous pollutants, although single-pollutant PM25 results were
reported. Schwartz et al. (2000) focused more on the associations between PM25 and PM10_25 and lower
respiratory symptoms. In single and co-pollutant models, PM2 5 was significantly associated with lower
respiratory symptoms, while PM10_2 5 was not. PM10_2 5 exhibited a stronger association with cough than did
PM25. The PM2 5 C-R functions for lower respiratory symptoms are based on the results of the reported
single pollutant and co-pollutant model (PM2 5 and PM10_2 5).
Single Pollutant Model
The coefficient and standard error are calculated from the reported odds ratio (1.33) and 95%
confidence interval (1.11-1.58) associated with a 15 ng/m3 change in PM25 (Schwartz and Neas, 2000,
Table 2).
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Functional Form: Logistic
Coefficient: 0.019012
Standard Error: 0.006005
Incidence Rate: daily lower respiratory symptom incidence rate per person = 0.0012 (Schwartz et al.,
1994, Table 2)
Population: population of ages 7 to 14
Multipollutant Model (PM2 5 and PM10_2 5)
In a model with PM10_2 5, the PM2 5 coefficient and standard error are calculated from the reported
odds ratio (1.29) and 95% confidence interval (1.06-1.57) associated with a 15 ng/m3 change in PM25
(Schwartz and Neas, 2000, Table 2).
Functional Form: Logistic
Coefficient: 0.016976
Standard Error: 0.006680
Incidence Rate: daily lower respiratory symptom incidence rate per person = 0.0012 (Schwartz et al.,
1994, Table 2)
Population: population of ages 7 to 14
F .6.5 Lower Respiratory Symptoms (Schwartz et al., 1994)
Schwartz et al. (1994) used logistic regression to link lower respiratory symptoms in children with
S02, N02, ozone, PM10, PM2 5, sulfate and H+ (hydrogen ion). Children were selected for the study if they
were exposed to indoor sources of air pollution: gas stoves and parental smoking. The study enrolled
1,844 children into a year-long study that was conducted in different years (1984 to 1988) in six cities.
The students were in grades two through five at the time of enrollment in 1984. By the completion of the
final study, the cohort would then be in the eighth grade (ages 13-14); this suggests an age range of 7 to
14.
In single pollutant models S02, N02, PM2 5, and PM10 were significantly linked to cough. In two-
pollutant models, PM10 had the most consistent relationship with cough; ozone was marginally significant,
controlling for PM10. In models for upper respiratory symptoms, they reported a marginally significant
association for PM10. In models for lower respiratory symptoms, they reported significant single-pollutant
models, using S02, 03, PM2 5, PM10, S04, and H+. The PM2 5 C-R function is based on the single pollutant
model reported in Table 5.
Single Pollutant Model
The coefficient and standard error are calculated from the reported odds ratio (1.44) and 95%
confidence interval (1.15-1.82) associated with a 20 //g/m3 change in PM25 (Schwartz et al., 1994, Table
5).
Functional Form: Logistic
Coefficient: 0.018232
Standard Error: 0.005856
Incidence Rate: daily lower respiratory symptom incidence rate per person = 0.0012 (Schwartz et al.,
1994, Table 2)
Population: population of ages 7 to 14
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F .6.6 Minor Restricted Activity Days: Ostro and Rothschild (1989)
Ostro and Rothschild (1989) estimated the impact of PM2 5 and ozone on the incidence of minor
restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a national
sample of the adult working population, ages 18 to 65, living in metropolitan areas.91 The annual national
survey results used in this analysis were conducted in 1976-1981. Controlling for PM2 5, two-week
average ozone has highly variable association with RRADs and MRADs. Controlling for ozone, two-week
average PM2 5 was significantly linked to both health endpoints in most years. The C-R function for PM is
based on this co-pollutant model.
The study is based on a "convenience" sample of non-elderly individuals. Applying the C-R
function to this age group is likely a slight underestimate, as it seems likely that elderly are at least as
susceptible to PM as individuals under 65. The elderly appear more likely to die due to PM exposure than
other age groups (e.g., Schwartz, 1994d, p. 30) and a number of studies have found that hospital
admissions for the elderly are related to PM exposures (e.g., Schwartz, 1994b; Schwartz, 1994c).
Multipollutant Model (PM2 5 and ozone)
Using the results of the two-pollutant model, we developed separate coefficients for each year in
the analysis, which were then combined for use in this analysis. The coefficient is a weighted average of
the coefficients in Ostro and Rothschild (1989, Table 4) using the inverse of the variance as the weight:
( 1981 R \
y A_
(J2
i=\976u fai
1981 i
V 1=1976 u y
= 0.00741.
The standard error of the coefficient is calculated as follows, assuming that the estimated year-
specific coefficients are independent:
( 1981
a\=var
V
CJ
/=1976 fal
1981 i
V i=1976 fa J
( 1981
V
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.00741
Standard Error: 0.00070
Incidence Rate: daily incidence rate for minor restricted activity days (MRAD) = 0.02137 (Ostro and
Rothschild, 1989, p. 243)
Population: adult population ages 18 to 64
F .6.7 School Loss Days, All Cause (Chen et al., 2000)
Chen et al. (2000) studied the association between air pollution and elementary school absenteeism
(grades 1-6)92 in Washoe County, Nevada. Daily absence data were available for all elementary schools in
the Washoe Country School District. The authors regressed daily total absence rate on the three air
pollutants, meteorological variables, and indicators for day of the week, month, and holidays. They
reported statistically significant associations between both ozone and CO and daily total absence rate for
grades one through six. PM10 was negatively associated with absence rate, after adjustment for ozone, CO,
and meteorological and temporal variables. The C-R function for PM is based on the results from a
multiple linear regression model with CO, ozone, and PM10.
Multipollutant Model (PM10, CO, and ozone)
The coefficient and standard error are presented in Table 3 (Chen et al., 2000, p. 1008) for a unit
|ig/nr' increase in daily PM10 concentration.
The reported coefficient represents an absolute increase in absenteeism rate for a unit increase in
PM10. If we apply this study to other locations, we assume that the same absolute increase will occur for a
unit increase in PM10, regardless of the baseline rate. If the study location has a particularly high baseline
rate, we may be overestimating decreases in absenteeism nationally, and vice-versa. As an example,
consider if the baseline absenteeism rate were 10% in the study and 5% nationally. An absolute increase
in absence rate of 2% associated with a given increase in PM10 reflects a relative increase in absence rate of
20% for the study population. However, in the national estimate, we would assume the same absolute
increase of 2%, but this would reflect a relative increase in the absenteeism rate of 40%.
An alternative approach is to estimate apply the relative increase in absenteeism rate in the C-R
function by adjusting the results by the ratio of the national absenteeism rate to the study-specific rate. As
a result, the percent increase in absenteeism rate associated with an increase in PM10 is extrapolated
nationally rather than the absolute increase in absenteeism rate. The incidence derivation section above
describes the data used to estimate national and study-specific absence rates.
In addition to this scaling factor, there are two other scaling factors which are applied to the
function. A scaling factor of 0.01 is used to convert the beta from a percentage (x 100) per unit increase of
PM10 to a proportion per unit increase of PM10. As a result it can be applied directly to the national
population of school children ages 6 through 11 to estimate the number of absences avoided.
The final scaling factor is used to adjust for the proportion of school days in the full year. In the
modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
92
Assuming that most children start kindergarten at age 5, the corresponding ages for grades 1 through 6 would be 6
through 11.
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49.3% of the days in a given year are school days (180/365). The C-R function parameters are shown
below.
Functional Form: Linear
Coefficient: -0.015400
Standard Error: 0.004400
Population: population of children ages 6-11
Scaling Factor 1: Ratio of national school absence rate to study-specific school absence rate93 = 1.081
Scaling Factor 2: Convert beta in percentage terms to a proportion = 0.01
Scaling Factor 3: Proportion of days in the year that are school days94 = 0.493
F .6.8 School Loss Days, All Cause (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences. The C-R function for PM10 is based on the
results of the single pollutant model.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the
previous day and the incidence rate as the number of incident absences on a given day over the population
at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since
school absences due to air pollution may last longer than one day, an estimate of the average duration of
school absences could be used to calculated the total avoided school loss days from an estimate of avoided
new absences. A simple ratio of the total absence rate divided by the new absence rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Duration=
newAbsences
A TotalAbsences = -[incidence ¦ (e ^
1)] • duration ¦ pop
93
National school absence rate of 5.50% obtained from the U.S. Department of Education (1996, Table 42-1). Study-
specific school absence rate of 5.09% obtained from Chen et al. (2000, Table 1).
Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
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Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For all absences, the coefficient and standard error are based on a percent increase of 22.8 percent
(95% CI 11.6 percent, 35.2 percent) associated with a 10 ng/m3 increase in daily average PM10
concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
49.3% of the days in a given year are school days (180/365).
In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Functional Form: Log-linear
Coefficient: 0.020539
Standard Error: 0.004894
Incidence Rate: daily school absence rate = 0.055 (U.S. Department of Education, 1996, Table 42-1)
Population: population of children ages 9-10 not absent from school on a given day95 = 94.5% of children
ages 9-10
Scaling Factor: Proportion of school days in a year96 = 0.493
F .6.9 School Loss Days, All Cause (Ransom and Pope, 1992, Provo)
Ransom and Pope (1992) studied the relationship between particulate air pollution and elementary
school absenteeism (grades 1-6)97 in Utah Valley from 1985 to 1990. The authors identified school
absences using weekly attendance data from the Provo School District and daily attendance data from an
elementary school in Orem, Utah. The authors regressed school absence rates on PM10,weather variables,
day of the week, month of the year, and indicators for holidays or extended weekends. The authors report
that a four week moving average of PM10 provided the best model fit. They found a statistically significant
95 The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
96
Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
97
Assuming that most children start kindergarten at age 5, the corresponding ages for grades 1 through 6 would be 6
through 11.
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association between increases in PM10 and absence rates in Provo and Orem, after adjustment for weather
variables and temporal trends. The C-R function for PM10 is based on results of the linear regression
model in the Provo School District for grades 1-6 (Ransom and Pope, 1992, Table 3, p. 211).
Single Pollutant Model
For Provo, the coefficient and standard error for a 100 |ig/m3 increase in four-week average PM10
concentration are reported as 2.1921 and 0.4610, respectively (Ransom and Pope, 1992, Table 3, p. 211).
The reported coefficient represents an absolute increase in absenteeism rate for a unit increase in
PM10. If we apply this study to other locations, we assume that the same absolute increase will occur for a
unit increase in PM10, regardless of the baseline rate. If the study location has a particularly high baseline
rate, we may be overestimating decreases in absenteeism nationally, and vice-versa. As an example,
consider if the baseline absenteeism rate were 10% in the study and 5% nationally. An absolute increase
in absence rate of 2% associated with a given increase in PM10 reflects a relative increase in absence rate of
20% for the study population. However, in the national estimate, we would assume the same absolute
increase of 2%, but this would reflect a relative increase in the absenteeism rate of 40%.
An alternative approach is to estimate apply the relative increase in absenteeism rate in the C-R
function by adjusting the results by the ratio of the national absenteeism rate to the study-specific rate. As
a result, the percent increase in absenteeism rate associated with an increase in PM10 is extrapolated
nationally rather than the absolute increase in absenteeism rate. The incidence derivation section above
describes the data used to estimate national and study-specific absence rates.
An additional scaling factor is used to adjust for the proportion of school days in the full year. In
the modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
49.3% of the days in a given year are school days (180/365). The C-R function parameters are shown
below.
Functional Form: Linear
Coefficient: 0.021921
Standard Error: 0.00461
Population: population of children ages 6-11
Scaling Factor 1: Ratio of national school absence rate to study-specific school absence rate98 = 1.211
Scaling Factor 2: Proportion of school days in a year" = 0.493
F .6.10 School Loss Days, All Cause (Ransom and Pope, 1992, Orem)
Ransom and Pope (1992) studied the relationship between particulate air pollution and elementary
school absenteeism (grades 1-6)100 in Utah Valley from 1985 to 1990. The authors identified school
98 National school absence rate of 5.5% obtained from the U.S. Department of Education (1996, Table 42-1). Study-
specific school absence rate of 4.54% obtained from Ransom and Pope (1992, Table 2).
99
Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
100 Assuming that most children start kindergarten at age 5, the corresponding ages for grades 1 through 6 would be 6
through 11.
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absences using weekly attendance data from the Provo School District and daily attendance data from an
elementary school in Orem, Utah. The authors regressed school absence rates on PM10,weather variables,
day of the week, month of the year, and indicators for holidays or extended weekends. The authors report
that a four week moving average of PM10 provided the best model fit. They found a statistically significant
association between increases in PM10 and absence rates in Provo and Orem, after adjustment for weather
variables and temporal trends. The C-R function for PM10 is based on results of the linear regression
model for grades 1-6 in Orem, Utah (Ransom and Pope, 1992, Table 4, p. 212).
Single Pollutant Model
For Orem, the coefficient and standard error for a 100 |ig/m3 increase in four-week average PM10
concentration are reported as 2.115 and 0.4600, respectively (Ransom and Pope, 1992, Table 4, p. 212).
The reported coefficient represents an absolute increase in absenteeism rate for a unit increase in
PM10. If we apply this study to other locations, we assume that the same absolute increase will occur for a
unit increase in PM10, regardless of the baseline rate. If the study location has a particularly high baseline
rate, we may be overestimating decreases in absenteeism nationally, and vice-versa. As an example,
consider if the baseline absenteeism rate were 10% in the study and 5% nationally. An absolute increase
in absence rate of 2% associated with a given increase in PM10 reflects a relative increase in absence rate of
20% for the study population. However, in the national estimate, we would assume the same absolute
increase of 2%, but this would reflect a relative increase in the absenteeism rate of 40%.
An alternative approach is to estimate apply the relative increase in absenteeism rate in the C-R
function by adjusting the results by the ratio of the national absenteeism rate to the study-specific rate. As
a result, the percent increase in absenteeism rate associated with an increase in PM10 is extrapolated
nationally rather than the absolute increase in absenteeism rate. The incidence derivation section above
describes the data used to estimate national and study-specific absence rates.
An additional scaling factor is used to adjust for the proportion of school days in the full year. In
the modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
49.3% of the days in a given year are school days (180/365). The C-R function parameters are shown
below.
Functional Form: Linear
Coefficient: 0.02115
Standard Error: 0.00460
Population: population of children ages 6-11
Scaling Factor 1: Ratio of national school absence rate to study-specific school absence rate101 = 1.076
Scaling Factor 2: Proportion of school days in a year102 = 0.493
101 National school absence rate of 5.50% obtained from the U.S. Department of Education (1996, Table 42-1). Study-
specific school absence rate of 5.11% obtained from Ransom and Pope (1992, Table 1).
1 02
Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
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F .6.11 School Loss Days, Illness-Related (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences. The C-R function for PM10 is based on the
results of the single pollutant model.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the
previous day and the incidence rate as the number of incident absences on a given day over the population
at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since
school absences due to air pollution may last longer than one day, an estimate of the average duration of
school absences could be used to calculated the total avoided school loss days from an estimate of avoided
new absences. A simple ratio of the total absence rate divided by the new absence rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Duration = ;
newAbsences
A TotalAbsences = -\incidence (e-P,APMw _ ] )j duration pop
Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For total illness-related absences, the coefficient and standard error are based on a percent increase
of 5.7 percent (95% CI -12.1 percent, 27.0 percent) associated with a 10 ng/m3 increase in daily average
PM10 concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
49.3% of the days in a given year are school days (180/365).
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In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Functional Form: Log-linear
Coefficient: 0.005543
Standard Error: 0.009387
Incidence Rate: region-specific daily illness-related school absence rate (Adams et al., 1999, Table 47),
assuming 180 school days per year.
Population: population of children ages 9-10 not absent from school on a given day103 = 94.5% of
children ages 9-10
Scaling Factor: Proportion of school days in a year104 = 0.493
F .6.12 School Loss Days, Respiratory Illness-Related (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the
previous day and the incidence rate as the number of incident absences on a given day over the population
at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since
school absences due to air pollution may last longer than one day, an estimate of the average duration of
school absences could be used to calculated the total avoided school loss days from an estimate of avoided
new absences. A simple ratio of the total absence rate divided by the "incident" rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Duration = —
newAbsences
A TotalAbsences = -[incidence ¦ - 1)] • duration ¦ pop
103
The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
104 Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
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Appendix F. Particulate Matter C-R Functions
Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For respiratory illness-related absences, the coefficient and standard error are based on a percent
increase of -4.3 percent (95% CI -32.2 percent, 35.0 percent) associated with a 10 ng/m3 increase in daily
average PM10 concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the year, however, in reality, school absences
will be avoided only on school days. Using an estimate of 180 school days per year, we estimate that
49.3% of the days in a given year are school days (180/365).
In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Functional Form: Log-linear
Coefficient: -0.004395
Standard Error: 0.017569
Incidence Rate: region-specific daily respiratory illness-related school absence rate (Adams et al., 1999,
Table 47), assuming 180 school days per year.
Population: population of children ages 9-10 not absent from school on a given day105 = 94.5% of
children ages 9-10
Scaling Factor: Proportion of school days in a year106 = 0.493
F .6.13 Work Loss Days (Ostro, 1987)
Ostro (1987) estimated the impact of PM2 5 on the incidence of work-loss days (WLDs), restricted
activity days (RADs), and respiratory-related RADs (RRADs) in a national sample of the adult working
population, ages 18 to 65, living in metropolitan areas.107 The annual national survey results used in this
105 The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
106 Using an estimate of 180 school days per year, we estimate that 49.3% of the days in a given year are school days
(180/365).
107
The study population is based on the Health Interview Survey (HIS), conducted by the National Center for Health
Statistics. In publications from this ongoing survey, non-elderly adult populations are generally reported as ages 18-64. From the
study, it is not clear if the age range stops at 65 or includes 65 year olds. We apply the C-R function to individuals ages 18-64 for
consistency with other studies estimating impacts to non-elderly adult populations.
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Appendix F. Particulate Matter C-R Functions
analysis were conducted in 1976-1981. Ostro reported that two-week average PM2 5 levels108 were
significantly linked to work-loss days, RADs, and RRADs, however there was some year-to-year
variability in the results. Separate coefficients were developed for each year in the analysis (1976-1981);
these coefficients were pooled. The coefficient used in the concentration-response function presented here
is a weighted average of the coefficients in Ostro (1987, Table III) using the inverse of the variance as the
weight.
The study is based on a "convenience" sample of non-elderly individuals. Applying the C-R
function to this age group is likely a slight underestimate, as it seems likely that elderly are at least as
susceptible to PM as individuals under 65. The elderly appear more likely to die due to PM exposure than
other age groups (e.g., Schwartz, 1994d, p. 30) and a number of studies have found that hospital
admissions for the elderly are related to PM exposures (e.g., Schwartz, 1994b; Schwartz, 1994c). On the
other hand, the number of workers over the age of 65 is relatively small; it was approximately 3% of the
total workforce in 2001(U.S. Bureau of the Census, 2002, Table 561).
Single Pollutant Model
The coefficient used in the C-R function is a weighted average of the coefficients in Ostro (1987,
Table III) using the inverse of the variance as the weight:
The standard error of the coefficient is calculated as follows, assuming that the estimated year-
specific coefficients are independent:
V 1=1976 v y
\i=\916°'pi ) V
This eventually reduces down to:
0.00036
108 The study used a two-week average pollution concentration; the C-R function uses a daily average, which is assumed
to be a reasonable approximation.
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Appendix F. Particulate Matter C-R Functions
Functional Form: Log-linear
Coefficient: 0.0046
Standard Error: 0.00036
Incidence Rate: daily work-loss-day incidence rate per person ages 18 to 64 = 0.00595 (U.S. Bureau of
the Census, 1997, No. 22; Adams et al., 1999, Table 41)
Population: adult population ages 18 to 64
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Appendix F. Particulate Matter C-R Functions
F .7 Asthma-Related Effects
F .7.1 Acute Bronchitis (McConnell et al., 1999)
McConnell et al. (1999) examined the relationship between air pollution and bronchitic symptoms
among asthmatic 4th, 7th, and 10th grade children in southern California.109 The authors collected
information on the prevalence of bronchitis, chronic cough, and chronic phlegm among children with and
without a history of asthma and/or wheeze. They used annual measurements of ozone, PM10, PM25, N02,
and acids in a logistic regression model with adjustments for personal covariates. Neither bronchitis,
cough, or phlegm were associated with any of the pollutants among children with no history of wheeze or
asthma or a history of wheeze without diagnosed asthma. Among asthmatics, PM10 was significantly
associated with bronchitis and phlegm; PM2 5 was significantly associated with phlegm and marginally
associated with bronchitis; N02 and acids were both significantly associated with phlegm; and ozone was
not significantly associated with any of the endpoints.
Bronchitis was defined in the study by the question: "How many times in the past 12 months did
your child have bronchitis?" (McConnell et al., 1999, p. 757). It is unclear, however, if the cases of
bronchitis are acute and temporary, or if the bronchitis is a chronic condition. McConnell et al. found a
relationship between PM and chronic phlegm but none with chronic cough, each of which may be
indicators of chronic bronchitis. For this analysis, we assumed that the C-R function based on McConnell
et al. is measuring acute bronchitis. The PM C-R functions for bronchitis among asthmatics are based on
the results of the single pollutant model reported in Table 3.
pm25
Single Pollutant Model
The estimated logistic coefficient and standard error are based on the odds ratio (1.4) and 95%
confidence interval (0.9-2.3) associated with an increase in yearly mean 2-week average PM2 5 of 15
|ig/m3. (McConnell et al., 1999, Table 3)
Functional Form: Logistic
Coefficient: 0.022431
Standard Error: 0.015957
Incidence Rate: annual incidence rate of one or more episodes of bronchitis per asthmatic = 0.326
(McConnell et al., 1999, Table 2)
Population: population of asthmatics ages 9 to 15 = 5.67%110 of population ages 9 to 15
109 Assuming that a child enters kindergarten at age 5, 4th grade corresponds to age 9 and 10th grade corresponds to age
15. We therefore applied the results of this study to children ages 9 to 15.
110 The American Lung Association (2002c, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%
(based on data from the 1999 National Health Interview Survey).
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Appendix F. Particulate Matter C-R Functions
PM10
Single Pollutant Model
The estimated logistic coefficient and standard error are based on the odds ratio (1.4) and 95%
confidence interval (1.1-1.8) associated with an increase in annual average PM10 of 19 |ig/m\ (McConnell
etal., 1999, Table 3)
Functional Form: Logistic
Coefficient: 0.017709
Standard Error: 0.006612
Incidence Rate: annual incidence rate of one or more episodes of bronchitis per asthmatic = 0.326
(McConnell et al., 1999, Table 2)
Population: population of asthmatics ages 9 to 15 = 5.67%m of population ages 9 to 15
F .7.2 Asthma Attacks (Whittemore and Korn, 1980)
Whittemore and Korn (1980) examined the relationship between air pollution and asthma attacks
in a survey of 443 children and adults, living in six communities in southern California during three 34-
week periods in 1972-1975. The analysis focused on TSP and oxidants (Ox). Respirable PM, N02, S02
were highly correlated with TSP and excluded from the analysis. In a two pollutant model, daily levels of
both TSP and oxidants were significantly related to reported asthma attacks. The results from this model
were used, and the oxidant result was adjusted so it may be used with ozone data.
Multipollutant Model (PM10 and ozone)
The PM10 C-R function is based on the results of a co-pollutant model of TSP and ozone
(Whittemore and Korn, 1980, Table 5). Assuming that PM10 is 55 percent of TSP112 and that particulates
greater than ten micrometers are harmless, the coefficient is calculated by dividing the TSP coefficient
(0.00079) by 0.55. The standard error is calculated from the two-tailed p-value (<0.01) reported by
Whittemore and Korn (1980, Table 5), which implies a t-value of at least 2.576 (assuming a large number
of degrees of freedom).
Functional Form: Logistic
Coefficient: 0.001436
Standard Error: 0.000558
Incidence Rate: daily incidence of asthma attacks = 0.0550113
Population: population of asthmatics of all ages = 3.86% of the population of all ages (American Lung
Association, 2002c, Table 7)
111 The American Lung Association (2002c, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%
(based on data from the 1999 National Health Interview Survey).
112 The conversion of TSP to PM10 is from ESEERCO (1994, p. V-5), who cited studies by EPA (1986) and the
California Air Resources Board (1982).
113
Based on an analysis of the 1999 National Health Interview Survey, the daily incidence of wheezing attacks for adult
asthmatics is estimated to be 0.0550. In the same survey, wheezing attacks for children were examined, however, the number of
wheezing attacks per year were censored at 12 (compared to censoring at 95 for adults). Due to the potential for underestimation of
the number of children's wheezing attacks, we used the adult rate for all individuals.
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Appendix F. Particulate Matter C-R Functions
F .7.3 Asthma Exacerbation, Cough (Ostro et al., 2001)
Ostro et al. (2001) studied the relation between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and 03 in a logistic regression model with control for age, income,
time trends, and temperature-related weather effects.114 Asthma symptom endpoints were defined in two
ways: "probability of a day with symptoms" and "onset of symptom episodes". New onset of a symptom
episode was defined as a day with symptoms followed by a symptom-free day. The authors found cough
prevalence associated with PM10 and PM25 and cough incidence associated with PM2 5 PM10, and N02.
Ozone was not significantly associated with cough among asthmatics. The PM C-R functions are based on
the results of single pollutant models looking at both the probability of symptoms and the onset of new
symptoms.
PM2 5 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.03 (95% CI 0.98-1.07) for a 30
|ig/m3 increase in 12-hour average PM25 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.000985
Standard Error: 0.000747
Incidence Rate: daily cough rate per person (Ostro et al., 2001, p.202) = 0.145
Population: asthmatic African-American population ages 8 to 13 = 7.26%115 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.10 (95% CI 1.03-1.18) for a 30
|ig/m3 increase in 12-hour average PM2 5 concentration.
The C-R function based on this model will estimate the number of new onset episodes of cough
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For cough, this ratio is 2.2 (14.5% divided by 6.7%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of cough, as defined by the study. On
average, 14.5% of African-American asthmatics have cough on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.145 =
114 The authors note that there were 26 days in which PM25 concentrations were reported higher than PM10
concentrations. The majority of results the authors reported were based on the full dataset. These results were used for the basis for
the C-R functions.
115 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix F. Particulate Matter C-R Functions
85.5%). As a result, a factor of 85.5% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new cough episode.
Functional Form: Logistic
Coefficient: 0.003177
Standard Error: 0.001156
Incidence Rate: daily new onset cough (incidence) rate per person (Ostro et al., 2001, p.202) = 0.067
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of cough =
6.21% of African-American population ages 8 to 13 multiplied (85.5% at-risk116 times 7.26% asthmatic117)
Adjustment Factor: average number of consecutive days with a cough episode (days) = 2.2
PM10 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.10 (95% CI 1.04-1.16) for a 17
|ig/m ' increase in daily average PM10 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.005606
Standard Error: 0.001639
Incidence Rate: daily cough rate per person (Ostro et al., 2001, p.202) = 0.145
Population: asthmatic African-American population ages 8 to 13 = 7.26%118 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.25 (95% CI 1.12-1.39) for a 17
l-ig/m3 increase in daily average PM10 concentration (Ostro et al., 2001, Table 5, p.204).
The C-R function based on this model will estimate the number of new onset episodes of cough
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For cough, this ratio is 2.2 (14.5% divided by 6.7%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of cough, as defined by the study. On
average, 14.5% of African-American asthmatics have cough on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.145 =
85.5%). As a result, a factor of 85.5% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new cough episode.
116 On average, 17.3% of African-American asthmatics have cough episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.145 = 85.5%) are at-risk for a new onset episode.
117.
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
118 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Functional Form: Logistic
Coefficient: 0.013126
Standard Error: 0.003241
Incidence Rate: daily new onset cough (incidence) rate per person (Ostro et al., 2001, p.202) = 0.067
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of cough =
6.21% of African-American population ages 8 to 13 multiplied (85.5% at-risk119 times 7.26% asthmatic120)
Adjustment Factor: average number of consecutive days with a cough episode (days) = 2.2
F .7.4 Asthma Exacerbation, Cough (Vedal et al., 1998)
Vedal et al. (1998) studied the relationship between air pollution and respiratory symptoms among
asthmatics and non-asthmatic children (ages 6 to 13) in Port Alberni, British Columbia, Canada. Four
groups of elementary school children were sampled from a prior cross-sectional study: (1) all children with
current asthma, (2) children without doctor diagnosed asthma who experienced a drop in FEV after
exercise, (3) children not in groups 1 or 2 who had evidence of airway obstruction, and (4) a control group
of children with matched by classroom. The authors used logistic regression and generalized estimating
equations to examine the association between daily PM10 levels and daily increases in various respiratory
symptoms among these groups. In the entire sample of children, PM10 was significantly associated with
cough, phlegm, nose symptoms, and throat soreness. Among children with diagnosed asthma, the authors
report a significant association between PM10 and cough symptoms, while no consistent effects were
observed in the other groups. Since the study population has an over-representation of asthmatics, due to
the sampling strategy, the results from the full sample of children are not generalizeable to the entire
population. The C-R function presented below is based on results among asthmatics only.
Single Pollutant Model
The PM10 coefficient and standard error are based on an increase in odds of 8% (95% CI 0-16%)
reported in the abstract for a 10 |ig/m3 increase in daily average PM10.
Functional Form: Logistic
Coefficient: 0.007696
Standard Error: 0.003786
Incidence Rate: daily cough rate per person (Vedal et al., 1998, Table 1, p. 1038) = 0.086
Population: asthmatic population ages 6 to 13 = 5.67%121 of population ages 6 to 13
F .7.5 Asthma Exacerbation, Moderate or Worse (Ostro et al., 1991)
Ostro et al. (1991) examined the effect of air pollution on asthmatics, ages 18 to 70, living in
Denver, Colorado from December 1987 to February 1988. The respondents in this study were asked to
119 On average, 17.3% of African-American asthmatics have cough episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.145 = 85.5%) are at-risk for a new onset episode.
120
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
121
The American Lung Association (2002c, Table 7) estimates asthma prevalence for children 5-17 at 5.67% (based on
data from the 1999 National Health Interview Survey).
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Appendix F. Particulate Matter C-R Functions
record daily a subjective rating of their overall asthma status each day (0=none, l=mild, 2=moderate,
3=severe, 4=incapacitating). Ostro et al. then examined the relationship between moderate (or worse)
asthma and H+, sulfate, S02, PM25, estimated PM25, PM10, nitrate, and nitric acid. Daily levels of H+ were
linked to cough, asthma, and shortness of breath. PM2 5 was linked to asthma. Sulfate was linked to
shortness of breath. No effects seen for other pollutants. The C-R function is based on a single-pollutant
linear regression model where the log of the pollutant is used.
Single Pollutant Model
Two PM2 5 coefficients are presented, both equal 0.0006, however only one is significant. The
coefficient based on data that does not include estimates of missing PM2 5 values is not significant (std
error = 0.0053); the coefficient that includes estimates of missing PM2 5 values (estimated using a function
of sulfate and nitrate) is significant at p < 0.5 (std error = 0.0003). The latter coefficient is used here. The
C-R function to estimate the change in the number of days with moderate (or worse) asthma is as follows:
A Days Moderate/Worse Asthma= - /?ln
pm2, A
PM 25 before J
¦pop,
Functional Form: Linear (using log of the pollutant)
Coefficient: 0.0006
Standard Error: 0.0003
Population: population of asthmatics of all ages122 = 3.86% of the population of all ages (American Lung
Association, 2002c, Table 7)
F .7.6 Asthma Exacerbation, One or More Symptoms (Yu et al., 2000)
Yu et al. (2000) examined the association between air pollution and asthmatic symptoms among
mild to moderate asthmatic children ages 5-13 in Seattle. They collected air quality data for CO, S02,
PM10, and PMj 0and asked study subjects to record symptoms daily. They used logistic regression models
with generalized estimating equations in two different approaches. A "marginal approach" was used to
estimate the impact of air pollution on asthma symptoms and a "transition approach" was used to estimate
the association conditioned on the previous day's outcome. The primary endpoint, odds of at least one
asthma symptom, was significantly associated with CO, PM10, and PMj 0 in single pollutant models. In
multipollutant models, CO remained significant while PM effects declined slightly. The magnitude of the
effects were similar between the "marginal" and "transition" approaches. The C-R function is based on the
results of the "transition approach," where the previous day's symptoms is an explanatory variable.
Single Pollutant Model
The single pollutant PM10 coefficient and standard error are based on the odds ratio (1.10) and
95% confidence interval (1.03-1.16) for a 10 |ig/m3 increase in one-day lagged daily average PM10 (Yu et
al., 2000, Table 4, p. 1212).
122
The C-R function is applied to asthmatics of all ages, although the study population consists of asthmatics between the
ages of 18 and 70. It seems reasonable to assume that individuals over the age of 70 are at least as susceptible as individuals in the
study population. It also seems reasonable to assume that individuals under the age of 18 are also susceptible. For example,
controlling for oxidant levels, Whittemore and Korn (1980) found TSP significantly related to asthma attacks in a study population
comprised primarily (59 percent) of individuals less than 16 years of age.
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Functional Form: Logistic
Coefficient: 0.009531
Standard Error: 0.003032
Incidence Rate: daily rate of at least one asthma episode per person (Yu et al., 2000, Table 2, p. 1212) =
0.60
Population: asthmatic population ages 5 to 13 = 5.67%123 of population ages 5 to 13
Multipollutant Model (PM10, CO, S02)
The C-R function is based on the results of the "transition approach," where the previous day's
symptoms is an explanatory variable. The multipollutant PM10 coefficient and standard error are based on
the odds ratio (1.05) and 95% confidence interval (0.95-1.16) for a 10 |ig/m3 increase in one-day lagged
daily average PM10 (Yu et al., 2000, Table 4, p. 1212).
Functional Form: Logistic
Coefficient: 0.004879
Standard Error: 0.005095
Incidence Rate: daily rate of at least one asthma episode per person (Yu et al., 2000, Table 2, p. 1212) =
0.60
Population: asthmatic population ages 5 to 13 = 5.67%124 of population ages 5 to 13
F .7.7 Asthma Exacerbation, Shortness of Breath (Ostro et al., 1995)
Using a logistic regression estimation, Ostro et al. (1995) estimated the impact of PM10, ozone,
N02, and S02 on the incidence of coughing, shortness of breath, and wheezing in 83 African-American
asthmatic children ages 7-12 living in Los Angeles from August through September 1992. Regression
results show both PM10 and ozone significantly linked to shortness of breath; the beginning of an asthma
episode was also significantly linked to ozone. No effect was seen for N02 and S02. Results for single-
pollutant models only were presented in the published paper. The C-R function is based on the model with
adjustment for respiratory infection, temperature, and outdoor mold levels.
Single Pollutant Model
The PM10 coefficient and standard error are based on the odds ratio (1.60) and 95% confidence
interval (1.07-2.37) (Ostro et al., 1995, Table 3) associated with a change in daily mean PM10 of 55.87
/ig/nr' (Ostro et al., 1995, Table 2).
123
The American Lung Association (2002c, Table 7) estimates asthma prevalence for children 5 to 17 at 5.67% (based on
data from the 1999 National Health Interview Survey).
124
The American Lung Association (2002c, Table 7) estimates asthma prevalence for children 5 to 17 at 5.67% (based on
data from the 1999 National Health Interview Survey).
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Functional Form: Logistic
Coefficient: 0.008412
Standard Error: 0.003631
Incidence Rate: daily shortness of breath incidence rate per person (Ostro et al., 1995, p. 715) = 0.056
Population: asthmatic African-American population ages 7 to 12 = 7.26%125 of African-American
population ages 7 to 12
F .7.8 Asthma Exacerbation, Shortness of Breath (Ostro et al., 2001)
Ostro et al. (2001) studied the relationship between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and ozone in a logistic regression model with control for age,
income, time trends, and temperature-related weather effects. Asthma symptom endpoints were defined in
two ways: "probability of a day with symptoms" and "new onset of a symptom episode". New onset of a
symptom episode was defined as a day with symptoms followed by a symptom-free day. The authors
found that both the prevalent and incident episodes of shortness of breath were associated with PM2 5 and
PM10. Neither ozone nor N02 were significantly associated with shortness of breath among asthmatics.
The PM C-R functions are based on the results of single pollutant models looking at both the probability of
symptoms and the onset of new symptoms.
PM2 5 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 1.00-1.17) for a 30
|ig/m3 increase in 12-hour average PM25 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.002565
Standard Error: 0.001335
Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p.202) = 0.074
Population: asthmatic African-American population ages 8 to 13 = 7.26%126 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.10 (95% CI 1.00-1.20) for a 30
|ig/m3 increase in 12-hour average PM25 concentration (Ostro et al., 2001, Table 5, p.204).
The C-R function based on this model will estimate the number of new onset episodes of shortness
of breath avoided. In order to convert this estimate to the total number of episodes avoided, the results are
adjusted by an estimate of the duration of symptom episodes. The average duration can be estimated from
Ostro et al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset
episode. For shortness of breath, this ratio is 2.0 (7.4% divided by 3.7%) (Ostro et al., 2001, p.202).
125
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
126 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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In addition, not all children are at-risk for a new onset of shortness of breath, as defined by the
study. On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given
day (Ostro et al., 2001, p.202). Only those who are symptom-free on the previous day are at-risk for a new
onset episode (1-0.074 = 92.6%). As a result, a factor of 92.6% is used in the function to estimate the
population of African-American 8 to 13 year old children at-risk for a new shortness of breath episode.
Functional Form: Logistic
Coefficient: 0.003177
Standard Error: 0.001550
Incidence Rate: daily new onset shortness of breath (incidence) rate per person (Ostro et al., 2001, p.202)
= 0.037
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of shortness of
breath = 6.72% of African-American population ages 8 to 13 multiplied (92. 6% at-risk127 times 7.26%
asthmatic128)
Adjustment Factor: average number of consecutive days with a shortness of breath episode (days) = 2.0
PM10 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.14 (95% CI 1.04-1.24) for a 17
|ig/nr' increase in daily average PM10 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.007708
Standard Error: 0.002639
Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p.202) = 0.074
Population: asthmatic African-American population ages 8 to 13 = 7.26%129 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.20 (95% CI 1.06-1.37) for a 17
l-ig/m3 increase in daily average PM10 concentration (Ostro et al., 2001, Table 5, p.204).
The C-R function based on this model will estimate the number of new onset episodes of shortness
of breath avoided. In order to convert this estimate to the total number of episodes avoided, the results are
adjusted by an estimate of the duration of symptom episodes. The average duration can be estimated from
Ostro et al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset
episode. For shortness of breath, this ratio is 2.0 (7.4% divided by 3.7%) (Ostro et al., 2001, p.202).
127
On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given day (Ostro et al.,
2001, p.202). Only those who are symptom-free on the previous day (1-0.074 = 92.6%) are at-risk for a new onset episode.
128 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
129
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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In addition, not all children are at-risk for a new onset of shortness of breath, as defined by the
study. On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given
day (Ostro et al., 2001, p.202). Only those who are symptom-free on the previous day are at-risk for a new
onset episode (1-0.074 = 92.6%). As a result, a factor of 92.6% is used in the function to estimate the
population of African-American 8 to 13 year old children at-risk for a new shortness of breath episode.
Functional Form: Logistic
Coefficient: 0.010725
Standard Error: 0.003850
Incidence Rate: daily new onset shortness of breath (incidence) rate per person (Ostro et al., 2001, p.202)
= 0.037
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of shortness of
breath = 6.72% of African-American population ages 8 to 13 multiplied (92. 6% at-risk130 times 7.26%
asthmatic131)
Adjustment Factor: average number of consecutive days with a shortness of breath episode (days) = 2.0
F .7.9 Asthma Exacerbation, Wheeze (Ostro et al., 2001)
Ostro et al. (2001) studied the relation between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and 03 in a logistic regression model with control for age, income,
time trends, and temperature-related weather effects. Asthma symptom endpoints were defined in two
ways: "probability of a day with symptoms" and "onset of symptom episodes". New onset of a symptom
episode was defined as a day with symptoms followed by a symptom-free day. The authors found both the
prevalence and incidence of wheeze associated with PM2 5 PM10, and N02. Ozone was not significantly
associated with wheeze among asthmatics. The PM C-R functions are based on the results of single
pollutant models looking at both the probability of symptoms and the onset of new symptoms.
PM2 5 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.06 (95% CI 1.01-1.11) for a 30
|ig/m3 increase in 12-hour average PM25 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.001942
Standard Error: 0.000803
Incidence Rate: daily wheeze rate per person (Ostro et al., 2001, p.202) = 0.173
Population: asthmatic African-American population ages 8 to 13 = 7.26%132 of African-American
population ages 8 to 13
130
On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given day (Ostro et al.,
2001, p.202). Only those who are symptom-free on the previous day (1-0.074 = 92.6%) are at-risk for a new onset episode.
131
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
1 32
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.08 (95% CI 1.01-1.14) for a 30
|ig/m3 increase in 12-hour average PM2 5 concentration (Ostro et al., 2001, Table 5, p.204).
The C-R function based on this model will estimate the number of new onset episodes of wheeze
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For wheeze, this ratio is 2.3 (17.3% divided by 7.6%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of wheeze, as defined by the study. On
average, 17.3% of African-American asthmatics have wheeze on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.173 =
82.7%). As a result, a factor of 82.7% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new wheeze episode.
Functional Form: Logistic
Coefficient: 0.002565
Standard Error: 0.001030
Incidence Rate: daily new onset wheeze (incidence) rate per person (Ostro et al., 2001, p.202) = 0.076
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of wheeze =
6.00% of African-American population ages 8 to 13 multiplied (82.7% at-risk133 times 7.26% asthmatic134)
Adjustment Factor: average number of consecutive days with a wheeze episode (days) = 2.3
PM10 Function(s)
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on an odds ratio of 1.04 (95% CI 0.98-1.10) for a 17
|ig/m3 increase in daily average PM10 concentration (Ostro et al., 2001, Table 4, p.204).
Functional Form: Logistic
Coefficient: 0.002307
Standard Error: 0.001733
Incidence Rate: daily wheeze rate per person (Ostro et al., 2001, p.202) = 0.173
Population: asthmatic African-American population ages 8 to 13 = 7.26%135 of African-American
population ages 8 to 13
133
On average, 17.3% of African-American asthmatics have wheeze episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.173 = 82.7%) are at-risk for a new onset episode.
134 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
135
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on an odds ratio of 1.12 (95% CI 1.01-1.23) for a 17
|ig/m3 increase in daily average PM10 concentration (Ostro et al., 2001, Table 5, p.204).
The C-R function based on this model will estimate the number of new onset episodes of wheeze
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For wheeze, this ratio is 2.3 (17.3% divided by 7.6%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of wheeze, as defined by the study. On
average, 17.3% of African-American asthmatics have wheeze on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.173 =
82.7%). As a result, a factor of 82.7% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new wheeze episode.
Functional Form: Logistic
Coefficient: 0.006666
Standard Error: 0.002957
Incidence Rate: daily new onset wheeze (incidence) rate per person (Ostro et al., 2001, p.202) = 0.076
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of wheeze =
6.00% of African-American population ages 8 to 13 multiplied (82.7% at-risk136 times 7.26% asthmatic137)
Adjustment Factor: average number of consecutive days with a wheeze episode (days) = 2.3
F .7.10 Chronic Phlegm (McConnell et al., 1999)
McConnell et al. (1999) examined the relationship between air pollution and bronchitic symptoms
among asthmatic 4th, 7th, and 10th grade children in southern California.138 The authors collected
information on the prevalence of bronchitis, chronic cough, and chronic phlegm among children with and
without a history of asthma and/or wheeze. They used annual measurements of ozone, PM10, PM25, N02,
and acids in a logistic regression model with adjustments for personal covariates. Neither bronchitis,
cough, or phlegm were associated with any of the pollutants among children with no history of wheeze or
asthma or a history of wheeze without diagnosed asthma. Among asthmatics, PM10 was significantly
associated with bronchitis and phlegm; PM2 5 was significantly associated with phlegm and marginally
associated with bronchitis; N02 and acids were both significantly associated with phlegm; and ozone was
not significantly associated with any of the endpoints.
Phlegm was defined in the study by the question: "Other than with colds, does this child usually
seem congested in the chest or bring up phlegm?" (McConnell et al., 1999, p. 757). The authors refer to
this definition as "chronic phlegm" and we also assume that the term "usually" refers to chronic, rather
136 On average, 17.3% of African-American asthmatics have wheeze episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.173 = 82.7%) are at-risk for a new onset episode.
1 37
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
138 Assuming that a child enters kindergarten at age 5, 4th grade corresponds to age 9 and 10th grade corresponds to age
15. We therefore applied the results of this study to children ages 9 to 15.
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than acute, phlegm. The PM C-R functions for chronic phlegm among asthmatics are based on the results
of the single pollutant model reported in Table 3.
pm25
Single Pollutant Model
The estimated logistic coefficient and standard error are based on the odds ratio (2.6) and 95%
confidence interval (1.2-5.4) associated with an increase in yearly mean 2-week average PM25 of 15
|ig/nr\ (McConnell et al., 1999, Table 3)
Functional Form: Logistic
Coefficient: 0.063701
Standard Error: 0.025580
Incidence Rate: annual incidence rate of phlegm per asthmatic = 0.257 (McConnell et al., 1999, Table 2)
Population: population of asthmatics ages 9 to 15 = 5.67%139 of population ages 9 to 15
PM10
Single Pollutant Model
The estimated logistic coefficient and standard error are based on the odds ratio (2.1) and 95%
confidence interval (1.4-3.3) associated with an increase in annual average PM10 of 19 |ig/nr\ (McConnell
et al., 1999, Table 3)
Functional Form: Logistic
Coefficient: 0.039049
Standard Error: 0.011512
Incidence Rate: annual incidence rate of phlegm per asthmatic = 0.257 (McConnell et al., 1999, Table 2)
Population: population of asthmatics ages 9 to 15 = 5.67%140 of population ages 9 to 15
F .7.11 Upper Respiratory Symptoms (Pope et al., 1991)
Using logistic regression, Pope et al. (1991) estimated the impact of PM10 on the incidence of a
variety of minor symptoms in 55 subjects (34 "school-based" and 21 "patient-based") living in the Utah
Valley from December 1989 through March 1990. The children in the Pope et al. study were asked to
record respiratory symptoms in a daily diary. With this information, the daily occurrences of upper
respiratory symptoms (URS) and lower respiratory symptoms (LRS) were related to daily PM10
concentrations. Pope et al. describe URS as consisting of one or more of the following symptoms: runny
or stuffy nose; wet cough; and burning, aching, or red eyes. Levels of ozone, N02, and S02 were reported
low during this period, and were not included in the analysis. The sample in this study is relatively small
and is most representative of the asthmatic population, rather than the general population. The school-
based subjects (ranging in age from 9 to 11) were chosen based on "a positive response to one or more of
139
The American Lung Association (2002c, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%
(based on data from the 1999 National Health Interview Survey).
140 The American Lung Association (2002c, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%
(based on data from the 1999 National Health Interview Survey).
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three questions: ever wheezed without a cold, wheezed for 3 days or more out of the week for a month or
longer, and/or had a doctor say the 'child has asthma' (Pope et al., 1991, p. 669)." The patient-based
subjects (ranging in age from 8 to 72) were receiving treatment for asthma and were referred by local
physicians. Regression results for the school-based sample (Pope et al., 1991, Table 5) show PM10
significantly associated with both upper and lower respiratory symptoms. The patient-based sample did
not find a significant PM10 effect. The results from the school-based sample are used here.
Single Pollutant Model
The coefficient and standard error for a one /ig/m3 change in PM10 is reported in Table 5.
Functional Form: Logistic
Coefficient: 0.0036
Standard Error: 0.0015
Incidence Rate: daily upper respiratory symptom incidence rate per person = 0.3419 (Pope et al., 1991,
Table 2)
Population: asthmatic population ages 9 to 11 = 5.67%141 of population ages 9 to 11
141 The American Lung Association (2002c, Table 7) estimates asthma prevalence for children ages 5 to 17 at 5.67%
(based on data from the 1999 National Health Interview Survey).
Abt Associates Inc. F-108 November 2003
-------
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Appendix F. Particulate Matter C-R Functions
F .8 Welfare Effects
F .8.1 Household Soiling Damage (ESEERCO, 1994)
Particulate matter air pollution has been shown to result in dirtier clothes, which in turn results in
higher annual cleaning costs for consumers. One benefit of reduced particulate matter, then, is the
consequent reduction in cleaning costs for consumers. Several studies have provided estimates of the cost
to households of PM soiling. The study that is cited by ESEERCO (1994) as one of the most sophisticated
and is relied upon by EPA in its 1988 Regulatory Impact Analysis for S02 is Manuel et al. (1982). Using a
household production function approach and household expenditure data from the 1972-73 Bureau of
Labor Statistics Consumer Expenditure Survey for over twenty cities in the United States, Manuel et al.
estimated the annual cost of cleaning per |ig/m3 PM per household as $1.55 ($0.59 per person times 2.63
persons per household). This estimate is low compared with others (e.g., estimates provided by Cummings
et al. (1985) and Watson and Jaksch (1982) are about eight times and five times greater, respectively). The
ESEERCO report notes, however, that the Manuel estimate is probably downward biased because it does
not include the time cost of do-it-yourselfers. Estimating that these costs may comprise at least half the
cost of PM-related cleaning costs, they double the Manuel estimate to obtain a point estimate of $3.10
(reported by ESEERCO in 1992 dollars as $2.70).
The Manuel et al. (1982) study measured particulate matter as TSP rather than PM10 or PM2 5. If a
one |ig/m3 increase in TSP causes $1.55 worth of cleaning expenses per household, the same unit dollar
value can be used for PM10 (or PM2 5) only if particle size doesn't matter ~ i.e., only if particles of all sizes
are equally soiling. Suppose, for example, that PM10 is 75% of TSP and that all particles are equally
soiling. Then 75% of the damage caused by a one |ig/m3 increase in TSP is due to PM10. This is
(0.75)($ 1.55) = $1.16. However, this corresponds to a 0.75 |ig/m3 increase in PM10. A one |ig/m3 increase
in PM10 would therefore yield a dollar soiling damage of $1.16/0.75 = $1.55.
Suppose, however, that only PM10 matters. Then the $1.55 underestimates the impact of a one
|ig/m3 increase in PM10, because it corresponds to a less than one |ig/m3 increase in PM10 (e.g., a 0.75
|ig/m3 increase in PM10). In this case, the correct unit value per unit of PM10 would be ($1.55)/0.75 =
$2.07. If only PM10 matters, then either (1) the dollar value can be adjusted by dividing it by the
percentage of TSP that is PM10 and PM10 can be used in the soiling damage function, or (2) the dollar value
can be left unadjusted and TSP, rather than PM10, can be used in the soiling damage function.
Finally, it is possible that, while both PM10 and PM25 are components of TSP that cause consumer
cleaning costs, the remaining portion of TSP has a greater soiling capability than either the PM10 or PM2 5
component. In this case, using either PM10 or PM2 5 air quality data with a household soiling function
based on TSP would yield overestimates of the PM10- or PM2 5-related consumer cleaning costs avoided by
reductions in concentration of these pollutants.
There is, however, insufficient information on the relative soiling capabilities of the different
components of TSP. We have assumed that all components of TSP have an equivalent soiling capacity.
Abt Associates Inc.
F-110
November 2003
-------
Appendix G: Ozone Concentration-Response Functions
In this Appendix, we present the concentration-response (C-R) functions used to estimate ozone-
related adverse health effects. Each sub-section has an Exhibit with a brief description of the C-R function
and the underlying parameters. Following each Exhibit, we present a brief summary of each of the studies
and any items that are unique to the study.
Note that the main text describes the methods that we used to choose these C-R functions from the
wide range available in the literature. In addition, Appendix D mathematically derives the standard types
of C-R functions that we encountered in the epidemiological literature, such as, log-linear, logistic and
linear, so we simply note here the type of functional form. Finally, Appendix E presents a detailed
description of the sources for the incidence and prevalence data used in these C-R functions.
Abt Associates Inc.
G-l
November 2003
-------
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Appendix G. Ozone C-R Functions
G .1 Short-term Mortality
Exhibit G-l summarizes the C-R functions used to estimate the relationship between ozone and
short-term mortality. Detailed summaries of each of the studies used to generate the functions are
described below, along with the parameters used in each of the functions.
G .1.1 Short-Term Mortality, Non-Accidental (Fairley, 2003)
Using data from 1989-1996 in Santa Clara County, California, Fairley et al. (1999) examined the
relationship between daily non-accidental mortality and fluctuations in a variety of pollutants, including
PM2 5, coarse PM10 (i.e., PM2 5_10), nitrate (N03), S04, coefficient of haze (COH), ozone, CO, and N02.
They reported that PM2 5 and N03 were significant in single-pollutant models, as well as two-pollutant
models. PM2 5 was only insignificant when paired with PM10 and N03 and N03 was only insignificant
when paired with PM25. The other pollutants were insignificant when paired with either PM25 or N03.
The analysis by Fairly et al. (1999) relied on a generalized additive model based on the Splus
software. Because of potential bias from using Splus, Fairley (2003) conducted a reanalysis, and reported
that the conclusions of the original study were unchanged. Both PM2 5 and N03 appear significantly
related to non-accidental mortality.
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the relative risk (1.031)
and 95% confidence interval (0.997-1.066) reported for a 19.6 ppb increase in daily 8-hour maximum
ozone concentration in the 0-day lag GAM stringent ('New GAM') model (Fairley, 2003, Table la).
Functional Form: Log-linear
Coefficient: 0.001558
Standard Error: 0.000871
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Multipollutant Model (ozone and PM2 5)
In a co-pollutant model with PM2 5, the coefficient and standard error are based on the relative risk
(1.057) and 95% confidence interval (0.954-1.171) reported for a 19.6 ppb increase in daily 8-hour
maximum ozone concentration in the 0-day lag GAM stringent ('New GAM') model (Fairley, 2003, Table
lb).
Functional Form: Log-linear
Coefficient: 0.002828
Standard Error: 0.002668
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Abt Associates Inc.
G-3
November 2003
-------
Appendix G. Ozone C-R Functions
G .1.2 Short-Term Mortality, Non-Accidental (Ito and Thurston, 1996, Chicago)
Ito and Thurston (1996) examined the relationship between daily non-accidental mortality and air
pollution levels in Cook County, Illinois from 1985 to 1990. They examined daily levels of ozone, PM10,
S02, and CO, and found a significant relationship for ozone and PM10 with both pollutants in the model; no
significant effects were found for S02 and CO. In single pollutant models the effects were slightly larger.
The C-R functions for ozone are based on results from both the single and co-pollutant models.
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the relative risk (1.10)
and 95% confidence interval (1.06-1.15) reported for a 100 ppb increase in daily one-hour maximum
ozone concentration (Ito and Thurston, 1996, p. 87).
Functional Form: Log-linear
Coefficient: 0.000953
Standard Error: 0.000208
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Multipollutant Model (ozone and PM10)
In a co-pollutant model with PM10, the coefficient (0.000634) and standard error (0.000251) were
obtained directly from the author because the published paper reported incorrect information.
Functional Form: Log-linear
Coefficient: 0.000634
Standard Error: 0.000251
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
G .1.3 Short-Term Mortality, Non-Accidental (Kinney et al., 1995, Los Angeles)
Kinney et al. (1995) examined the relationship between daily non-accidental mortality and air
pollution levels in Los Angeles, California from 1985 to 1990. They examined ozone, PM10, and CO, and
found a significant relationship for each pollutant in single pollutant models. The effect for ozone dropped
to zero with the inclusion of PM10 in the model, while the effect for CO and PM10 appeared co-pollutant
ozone models.
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the relative risk (1.02)
and 95% confidence interval (1.00-1.05) reported for a 143 ppb increase in daily one-hour maximum
ozone levels (Kinney et al., 1995, Table 2, p. 64).
Functional Form: Log-linear
Coefficient: 0.000138
Standard Error: 0.000087
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Abt Associates Inc.
G-4
November 2003
-------
Appendix G. Ozone C-R Functions
Multipollutant Model (ozone and PM10)
In a model with PM10, the coefficient and standard error are based on the relative risk (1.00) and
95% confidence interval (0.94-1.06) reported for a 143 ppb increase in daily one-hour maximum ozone
concentration (Kinney et al., 1995, Table 2, p. 64).
Functional Form: Log-linear
Coefficient: 0
Standard Error: 0.000214
Incidence: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
G .1.4 Short-Term Mortality, Non-Accidental (Moolgavkar et al., 1995, Philadelphia)
Moolgavkar et al. (1995) examined the relationship between daily non-accidental mortality and air
pollution levels in Philadelphia, Pennsylvania from 1973 to 1988. They examined ozone, TSP, and S02 in
a three-pollutant model, and found a significant relationship for ozone and S02; TSP was not significant.
In season-specific models, ozone was significantly associated with mortality only in the summer months.
The C-R function for ozone is based on the full-year three-pollutant model reported in Table 5
(Moolgavkar et al., 1995, p. 482).
Multipollutant Model (ozone, S02, TSP)
The coefficient and standard error are based on the relative risk (1.063) and 95% confidence
interval (1.018-1.108) associated with a 100 ppb increase in daily average ozone (Moolgavkar et al., 1995,
p. 482, Table 5).
Functional Form: Log-linear
Coefficient: 0.000611
Standard Error: 0.000216
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
G .1.5 Short-Term Mortality, Non-Accidental (Samet et al., 1997, Philadelphia)
Samet et al. (1997) examined the relationship between daily non-accidental mortality and air
pollution levels in Philadelphia, Pennsylvania from 1974 to 1988. They examined ozone, TSP, S02, N02,
and CO in a Poisson regression model. In single pollutant models, ozone, S02, TSP, and CO were
significantly associated with mortality. In a five-pollutant model, they found a positive statistically
significant relationship for each pollutant except N02. The C-R functions for ozone are based on the
single pollutant and five-pollutant model (ozone, CO, N02, S02, and TSP) reported in Table 9 (Samet et
al., 1997, p. 20).
Abt Associates Inc.
G-5
November 2003
-------
Appendix G. Ozone C-R Functions
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(2.28) and t-statistic (3) associated with a 20.219 ppb increase in two-day average ozone (Samet et al.,
1997, p. 20, Table 9).
Functional Form: Log-linear
Coefficient: 0.001115
Standard Error: 0.000372
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
Multipollutant Model (ozone, CO, N02, S02, and TSP)
In a model with CO, N02, S02, and TSP, the ozone coefficient and standard error are based on the
percent increase (1.91) and t-statistic (3) associated with a 20.219 ppb increase in two-day average ozone
(Samet et al., 1997, p. 20, Table 9).
Functional Form: Log-linear
Coefficient: 0.000936
Standard Error: 0.000312
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages
G .1.6 Short-Term Mortality, Non-Accidental (World Health Organization (WHO)
Working Group, 2003, Europe)
The World Health Organization (WHO 2003, p. 44) conducted a meta-analysis of time-series
studies conducted between 1996 and 2001. The results of the analysis are preliminary, so we have
presented it as an alternative estimate of the relationship between ozone and premature mortality. We
consider two sets of results: one based on the 1-hour maximum and the other based on the 8-hour average.
1-Hour Maximum Model
In a model with the daily 1-hour maximum the relative risk is 1.004 associated with a 10 ug/m3
change in ozone, with a 5th and 95th estimate of 1.001 and 1.006 (WHO 2003, p. 44). In calculating the
coefficient and standard error for the C-R function, we assume a conversion of 1.963 ug/m3 per ppb. This
is the standard conversion at 25° C and one atmosphere. We have used this function with a population of
all ages, as well as just the population ages 65 and up.
Functional Form: Log-linear
Coefficient: 0.000784
Standard Error: 0.000250
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages & ages 65+
Abt Associates Inc.
G-6
November 2003
-------
Appendix G. Ozone C-R Functions
8-Hour Average Model
In a model with the daily 8-hour average the relative risk is 1.006 associated with a 10 ug/m3
change in ozone, with a 5th and 95th estimate of 1.003 and 1.009 (WHO 2003, p. 44). The paper is not
completely clear on how the 8-hour average should be calculated, so we have assumed the average
between the hours of 9:00 am and 4:59 pm. In calculating the coefficient and standard error for the C-R
function, we assume a conversion of 1.963 ug/m3 per ppb. This is the standard conversion at 25° C and
one atmosphere. We have used this function with a population of all ages, as well as just the population
ages 65 and up.
Functional Form: Log-linear
Coefficient: 0.001174
Standard Error: 0.000299
Incidence Rate: county-level daily non-accidental mortality rate (ICD codes <800) per person
Population: population of all ages & ages 65+
Abt Associates Inc.
G-7
November 2003
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Appendix G. Ozone C-R Functions
G .3 Hospital Admissions
Exhibit G-3 summarizes the C-R functions used to estimate the relationship between ozone and
hospital admissions. Detailed summaries of each of the studies used to generate the functions are
described below, along with the parameters used in each of the functions.
G .3.1 Hospital Admissions for All Respiratory (Burnett et al., 1997, Toronto)
Burnett et al. (1997) examined the relationship between air pollution and hospital admissions (ICD
codes 464-466, 480-486, 490-494, 496) for individuals of all ages in Toronto, Canada during the summers
of 1992-1994. In a Poisson regression model, all respiratory admissions were linked to coefficient of haze
(COH) and ozone; other PM measures were less strongly linked. In two pollutant models with COH, they
found that CO, N02, and S02 were not significant, while ozone remained significant. In multipollutant
models with COH, ozone, N02, and S02, both ozone and COH remained significant. None of the other
PM measures (PM10, PM10_2 5, PM25) were significant in four-pollutant models. The ozone C-R functions
are based on the results from the single pollutant model and multipollutant models with PM co-pollutants.
Single Pollutant Model
In a single pollutant model with adjustment for temperature and dew point, the coefficient and
standard error are based on the relative risk (1.064) and t-statistic (5.13) reported for an 11.5 ppb increase
in 12-hour average ozone (1997, Table 2, p. 617).
Functional Form: Log-linear
Coefficient: 0.005394
Standard Error: 0.001052
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person
(ICD 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (ozone and PM2 5)
In a model with PM25, the coefficient and standard error are based on the relative risk (1.059) and
t-statistic (4.56) reported for an 11.5 ppb increase in 12-hour average ozone (1997, Table 4, p. 618).
Functional Form: Log-linear
Coefficient: 0.004985
Standard Error: 0.001093
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person
(ICD 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (ozone and PM10_2 5)
In a model with PM10_2 5, the coefficient and standard error are based on the relative risk (1.062)
and t-statistic (4.89) reported for an 11.5 ppb increase in 12-hour average ozone (1997, Table 4, p. 618).
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Functional Form: Log-linear
Coefficient: 0.005231
Standard Error: 0.001070
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person
(ICD 464, 466, 480-487, 490-496)
Population: population of all ages
Multipollutant Model (ozone, N02, PM2 5, and S02)
In a four-pollutant model with N02, PM2 5, and S02, the coefficient and standard error are based on
the relative risk (1.059) and t-statistic (4.66) reported for an 11.5 ppb increase in 12-hour average ozone
(1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: 0.004985
Standard Error: 0.001070
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person
(ICD 464, 466, 480-487, 490-496)
Population: population of all ages
G .3.2 Hospital Admissions for All Respiratory (Burnett et al., 2001, Toronto)
Burnett et al. (2001) studied the association between air pollution and acute respiratory hospital
admissions (ICD codes 493, 466, 464.4, 480-486) in Toronto from 1980-1994, among children less than 2
years of age. They collected hourly concentrations of the gaseous pollutants, CO, N02, S02, and ozone.
Daily measures of particulate matter were estimated for the May to August period of 1992-1994 using
TSP, sulfates, and coefficient of haze data. The authors report a positive association between ozone in the
May through August months and respiratory hospital admissions, for several single days after elevated
ozone levels.
The strongest association was found using a five-day moving average of ozone. No association
was found in the September through April months. In co-pollutant models with a particulate matter or
another gaseous pollutant, the ozone effect was only slightly diminished. The effects for PM and gaseous
pollutants were generally significant in single pollutant models but diminished in co-pollutant models with
ozone, with the exception of CO. The C-R functions for ozone are based on a single pollutant and two co-
pollutant models, using the five-day moving average of one-hour max ozone.
Single Pollutant Model144
The single pollutant coefficient and standard error are based on a percent increase (34.8) and 95%
confidence interval of the percent increase (19.3 percent, 52.3 percent) for a 45.2 ppb change in the five-
day moving average of one-hour max ozone (Burnett et al., 2001, Table 2 and p. 448).
144 The authors present seven single-pollutant models: the first six of these use single lags of 0 days, 1 day, ..., up to 5
days. The seventh model uses a 5-day moving average of 0-day, 1-day, 2-day, 3-day and 4-day lagged 1-hour maximum ozone
concentrations. The authors describe the 5-day moving average model as an attempt to "more fully characterize this pattern of
temporally distributed effects" (p. 448). It shows a percentage increase of 34.8%, substantially larger than the percentage increase
from any of the single lag models. This suggests that the 5-day moving average is indeed capturing some of the effect of each of
the days that were shown to have an effect, individually, in the single lag models.
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Functional Form: Log-linear
Coefficient: 0.006607
Standard Error: 0.0001378
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person less
than 2 years of age (ICD codes 464, 466, 480-487, 493)
Population: population less than 2 years of age
Multipollutant Model (ozone and PM2 5)
In a model with PM25, the coefficient and standard error are based on the percent increase (33.0)
and t-statistic (3.44) associated with a 45.2 ppb increase in the five-day moving average of one-hour max
ozone (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.006309
Standard Error: 0.001834
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person less
than 2 years of age (ICD codes 464, 466, 480-487, 493)
Population: population less than 2 years of age
Multipollutant Model (ozone and PM10_2 5)
In a model with PM10_2 5, the coefficient and standard error are based on the percent increase (29.4)
and t-statistic (3.00) associated with a 45.2 ppb increase in the five-day moving average of one-hour max
ozone (Burnett et al., 2001, Table 3).
Functional Form: Log-linear
Coefficient: 0.005702
Standard Error: 0.001901
Incidence Rate: region-specific daily hospital admission rate for all respiratory admissions per person less
than 2 years of age (ICD codes 464, 466, 480-487, 493)
Population: population less than 2 years of age
G .3.3 Hospital Admissions for All Respiratory (Schwartz, 1995, New Haven)
Schwartz (1995) examined the relationship between air pollution and respiratory hospital
admissions (ICD codes 460-519) for individuals 65 and older in New Haven, Connecticut, from January
1988 to December 1990. In single-pollutant models, PM10 and S02 were significant, while ozone was
marginally significant. In a co-pollutant model with ozone and PM10, both pollutants were significant.
PM10 remained significant in a model with S02, while ozone was marginally significant when adjusted for
S02. S02 was significant in a co-pollutant model with PM10 but not with ozone. The ozone C-R functions
are based on results from the single pollutant model and co-pollutant model with PM10.
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Appendix G. Ozone C-R Functions
Single Pollutant Model
In a single-pollutant model, the coefficient and standard error are calculated from the relative risk
(1.03) and 95% confidence interval (1.02-1.05) for a 50 |ig/m3 increase in average daily ozone levels
(Schwartz, 1995, Table 3, p. 534).145
Functional Form: Log-linear
Coefficient: 0.002284
Standard Error: 0.001323
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
Multipollutant Model (ozone and PM10)
In a model with PM10, the coefficient and standard error are estimated from the relative risk (1.07)
and 95% confidence interval (1.00-1.15) for a 50 |ig/m3 increase in average daily ozone levels (Schwartz,
1995, Table 3, p. 534).146
Functional Form: Log-linear
Coefficient: 0.002652
Standard Error: 0.001398
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
G .3.4 Hospital Admissions for All Respiratory (Schwartz, 1995, Tacoma)
Schwartz (1995) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Tacoma, Washington, from January 1988 to December 1990. In single-
pollutant models, PM10, ozone, and S02 were all significant. Ozone remained significant in separate co-
pollutant models with PM10 and S02. PM10 remained significant in a co-pollutant model with S02, but not
in a co-pollutant model with ozone. S02 was not significant in either of the co-pollutant models. The
ozone C-R functions are based on results from the single pollutant model and co-pollutant model with
PM10.
145 To calculate the coefficient, a conversion of 1.96 ng/m3 per ppb is used, based on a density of ozone of 1.96 grams per
liter (at 25 degrees Celsius). Since there are 1000 liters in a cubic meter and a million |_ig in a gram, this density means that there
are 1.96 billion |_ig of ozone in a cubic meter of ozone. If a cubic meter has just one ppb of ozone, then this means that this
particular cubic meter has 1.96 |_ig of ozone (i.e., one ppb = 1.96 |_ig/m3).
146 To calculate the coefficient, a conversion of 1.96 ng/m3 per ppb is used, based on a density of ozone of 1.96 grams per
liter (at 25 degrees Celsius). Since there are 1000 liters in a cubic meter and a million |_ig in a gram, this density means that there
are 1.96 billion |_ig of ozone in a cubic meter of ozone. If a cubic meter has just one ppb of ozone, then this means that this
particular cubic meter has 1.96 |_ig of ozone (i.e., one ppb = 1.96 |_ig/m3).
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Appendix G. Ozone C-R Functions
Single Pollutant Model
In a single-pollutant model, the coefficient and standard error are calculated from the relative risk
(1.21) and 95% confidence interval (1.06-1.38) for a 50 |ig/m3 increase in average daily ozone levels
(Schwartz, 1995, Table 6, p. 535)147
Functional Form: Log-linear
Coefficient: 0.007472
Standard Error: 0.002638
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
Multipollutant Model (ozone and PM10)
In a model with PM10, the coefficient and standard error are estimated from the relative risk (1.20)
and 95% confidence interval (1.06-1.37) for a 50 |ig/m3 increase in average daily ozone levels (Schwartz,
1995, Table 6, p. 535).148
Functional Form: Log-linear
Coefficient: 0.007147
Standard Error: 0.002565
Incidence Rate: region-specific daily hospital admission rate for respiratory admissions per person 65+
(ICD codes 460-519)
Population: population of ages 65 and older
G .3.5 Hospital Admissions for All Respiratory (Thurston et al., 1994, Toronto)
Thurston et al. (1994) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada, for six weeks in July and August 1986-1988. In single-pollutant
linear regression models, ozone and various measures of PM were linked to all respiratory admissions
(ICD codes 466, 480-482, 485, 490-493). In two-pollutant models, ozone was still significant, but
measures of PM were often not significant; only H+ was significant. The C-R functions for ozone are
based on results from single and multipollutant models.
Single Pollutant Model
In a single pollutant model, the ozone coefficient (0.0528) and standard error (0.0197) are reported
in Table 3 (Thurston et al., 1994, p. 281) for a unit ppb increase in one-hour maximum ozone levels.
147 To calculate the coefficient, a conversion of 1.96 ng/m3 per ppb is used, based on a density of ozone of 1.96 grams per
liter (at 25 degrees Celsius). Since there are 1000 liters in a cubic meter and a million |_ig in a gram, this density means that there
are 1.96 billion |_ig of ozone in a cubic meter of ozone. If a cubic meter has just one ppb of ozone, then this means that this
particular cubic meter has 1.96 |_ig of ozone (i.e., one ppb = 1.96 |_ig/m3).
148To calculate the coefficient, a conversion of 1.96 ng/m3 per ppb is used, based on a density of ozone of 1.96 grams per
liter (at 25 degrees Celsius). Since there are 1000 liters in a cubic meter and a million |_ig in a gram, this density means that there
are 1.96 billion |_ig of ozone in a cubic meter of ozone. If a cubic meter has just one ppb of ozone, then this means that this
particular cubic meter has 1.96 |_ig of ozone (i.e., one ppb = 1.96 |_ig/m3).
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Appendix G. Ozone C-R Functions
Functional Form: Linear
Coefficient: 0.0528
Standard Error: 0.0197
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (ozone and PM2 5)
In a model with PM25, the ozone coefficient (0.0404) and standard error (0.0233) are reported in
Table 3 (Thurston et al., 1994, p. 281) for a unit ppb increase in one-hour maximum ozone levels.
Functional Form: Linear
Coefficient: 0.0404
Standard Error: 0.0233
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (ozone and PM10)
In a model with PM10, the ozone coefficient (0.0388) and standard error (0.0241) are reported in
Table 3 (Thurston et al., 1994, p. 281) for a unit ppb increase in one-hour maximum ozone levels.
Functional Form: Linear
Coefficient: 0.0388
Standard Error: 0.0241
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
G .3.6 Hospital Admissions for Asthma (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada from 1980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found all significantly associated
with asthma except S02. They estimated multi-pollutant models, where pollutants for best fitting model
were chosen using stepwise regression based on AIC criterion. Asthma admissions were linked to ozone,
CO, and PM10_2 5. The C-R functions for ozone are based on the results of a single pollutant model and
three pollutant model (ozone, CO, PM10_2 5).149
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(6.32) and t-statistic (4.63) reported in Table 3 (Burnett et al., 1999, p. 133) for a 19.5 ppb increase in
three-day average ozone concentration.
149
Burnett et al. (1999) reports results for co-pollutant models with ozone and various PM metrics as well, however,
standard errors were not provided so these estimates were not used to derive C-R functions.
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Appendix G. Ozone C-R Functions
Functional Form: Log-linear
Coefficient: 0.003143
Standard Error: 0.000679
Incidence Rate: region-specific daily hospital admission rate for asthma per person (ICD code 493)
Population: population of all ages
Multipollutant Model (ozone, CO, and PM10_2 5)
In a model with PM10_2 5 and CO, the ozone coefficient and standard error are based on the percent
increase (4.99) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (3.48)150 for a 19.5 ppb increase in three-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.002497
Standard Error: 0.000718
Incidence Rate: region-specific daily hospital admission rate for asthma per person
(ICD code 493)
Population: population of all ages
G .3.7 Hospital Admissions for Asthma (Sheppard et al., 1999, Seattle)
Sheppard et al. (1999) studied the relationship between air pollution in Seattle and nonelderly
(<65) hospital admissions for asthma from 1987 to 1994. They used air quality data for PM10, PM25, PM10_
2 5, S02, ozone, and CO in a Poisson regression model with control for time trends, seasonal variations, and
temperature-related weather effects.151 They found asthma hospital admissions associated with PM10,
PM25, PM10_25, CO, and ozone. They did not observe an association for S02. They found PM and CO to be
jointly associated with asthma admissions. The best fitting co-pollutant models were found using ozone.
However, ozone data was only available April through October, so they did not consider ozone further.
For the remaining pollutants, the best fitting models included PM2 5 and CO. Results for other co-pollutant
models were not reported. The ozone C-R function is based on the results of a single pollutant model.
Single Pollutant Model
The single pollutant coefficient and standard error are calculated from the relative risk (1.06) and
95% confidence interval (1.02-1.11) associated with a 20 ppb increase in eight-hour average ozone
(Sheppard et al., 1999, p. 27).
Functional Form: Log-linear
Coefficient: 0.002913
Standard Error: 0.001079
Incidence Rate: region-specific daily hospital admission rate for asthma per person <65 (ICD code 493)
Population: population of ages 65 and under
150 Rick Burnett (co-author), personal communication.
151 PM25 levels were estimated from light scattering data.
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Appendix G. Ozone C-R Functions
G .3.8 Hospital Admissions for Asthma (Thurston et al., 1994, Toronto)
Thurston et al. (1994) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada, for six weeks in July and August 1986-1988. In single-pollutant
linear regression models, ozone was strongly associated with asthma admissions (ICD code 493) and
various measures of PM were marginally significant. In two-pollutant models, ozone remained significant,
but measures of PM were often not significant. The C-R functions for ozone are based on results from
single and multipollutant models.
Single Pollutant Model
In a single pollutant model, the ozone coefficient (0.0346) and standard error (0.0124) are reported
in Table 4 (Thurston et al., 1994, p. 282) for a unit ppb increase in one-hour maximum ozone levels.
Functional Form: Linear
Coefficient: 0.0346
Standard Error: 0.0124
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (ozone and PM2 5)
In a model with PM25, the ozone coefficient (0.0265) and standard error (0.0142) are reported in
Table 4 (Thurston et al., 1994, p. 282) for a unit ppb increase in one-hour maximum ozone levels.
Functional Form: Linear
Coefficient: 0.0265
Standard Error: 0.0142
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
Multipollutant Model (ozone and PM10)
In a model with PM10, the ozone coefficient (0.0290) and standard error (0.0146) are reported in
Table 4 (Thurston et al., 1994, p. 282) for a unit ppb increase in one-hour maximum ozone levels.
Functional Form: Linear
Coefficient: 0.0290
Standard Error: 0.0146
Baseline Pop: baseline population in Toronto = 2,400,000 (U.S. EPA, 1997, Table D-7)
Population: population of all ages
G .3.9 Hospital Admissions for Chronic Lung Disease (Moolgavkar et al., 1997,
Minneapolis)
Moolgavkar et al. (1997) examined the relationship between air pollution and hospital admissions
(ICD codes 490-496) for individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January 1986
to December 1991. In a Poisson regression, they found no significant effect for any of the pollutants
(PM10, ozone, or CO). The effect for ozone was marginally significant. The model with a 100 df smoother
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Appendix G. Ozone C-R Functions
was reported to be optimal (p. 368). The C-R function is based on the results from a three-pollutant model
(ozone, CO, PM10) using the 100 df smoother.
Multipollutant Model (ozone, CO, PM10)
In a model with CO and PM10, the estimated coefficient and standard error are based on the
percent increase (4.2) and 95% confidence interval of the percent increase (-1.0-9.4) associated with a
change in daily average ozone levels of 15 ppb (Moolgavkar et al., 1997, Table 4 and p. 366).
Functional Form: Log-linear
Coefficient: 0.002743
Standard Error: 0.001699
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person 65+
(ICD codes 490-496)
Population: population of ages 65 and older
G .3.10 Hospital Admissions for Chronic Lung Disease (less Asthma) (Burnett et al.,
1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada from 1980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found PM10_2 5, PM10, and ozone
significantly associated with chronic lung disease (ICD codes 490-492, 496). They estimated multi-
pollutant models, where pollutants for the best fitting model were chosen using stepwise regression based
on AIC criterion. In a three pollutant model, admissions for chronic obstructive pulmonary disease
(COPD) were linked to ozone and PM10_25. A non-significant association was found with CO. The C-R
functions for ozone are based on the results of a single pollutant model and three-pollutant model (ozone,
CO, PM10_25).152
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(7.29) and t-statistic (4.23) reported in Table 3 (Burnett et al., 1999, p. 133) for a 19.5 ppb increase in
three-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.003608
Standard Error: 0.000853
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person (ICD
codes 490-492, 494-496)
Population: population of all ages
152
Burnett et al. (1999) reports results for co-pollutant models with ozone and various PM metrics as well, however,
standard errors were not provided so these estimates were not used to derive C-R functions.
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Appendix G. Ozone C-R Functions
Multipollutant Model (ozone, CO, and PM10_2 5)
In a model with PM10_2 5 and CO, the ozone coefficient and standard error are based on the percent
increase (6.08) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (2.74)153 for a 19.5 ppb increase in three-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.003027
Standard Error: 0.001105
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person (ICD
codes 490-492, 494-496)
Population: population of all ages
G .3.11 Hospital Admissions for Chronic Lung Disease (less Asthma) (Schwartz,
1994b, Detroit)
Schwartz (1994b) examined the relationship between air pollution and hospital admissions (ICD
codes 491-492, 494-496) for individuals 65 and older in Detroit, Michigan, from January 1986 to
December 1989. In a two-pollutant Poisson regression model, Schwartz found both PM10 and ozone
significantly linked to pneumonia and COPD. The authors state that effect estimates were relatively
unchanged compared to the unreported single pollutant models. No significant associations were found
between either pollutant and asthma admissions. The C-R function for chronic lung disease incidence is
based on the results of the "basic" co-pollutant model (ozone and PM10) presented in Table 4 (p. 651).154
Multipollutant Model (ozone and PM10)
The coefficient and standard error for the "basic" model are reported in Table 4 (Schwartz, 1994b,
p.651) for a one ppb change in daily average ozone.
Functional Form: Log-linear
Coefficient: 0.00549
Standard Error: 0.00205
Incidence Rate: region-specific daily hospital admission rate for chronic lung disease per person 65+
(ICD codes 490-492, 494-496)
Population: population of ages 65 and older
G .3.12 Hospital Admissions for Pneumonia (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions for
individuals of all ages in Toronto, Canada from 1980 to 1994. The authors examined single pollutant log-
linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found all significantly associated
with pneumonia and other respiratory infections (ICD codes 464, 466, 480-487, 494). They estimated
multipollutant models, where pollutants for the best fitting model were chosen using stepwise regression
153
Rick Burnett (co-author), personal communication.
154 Schwartz (1994b) also reports results using generalized additive models to fit time and temperature variables, however
no standard error or confidence intervals were reported.
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Appendix G. Ozone C-R Functions
based on AIC criterion. Pneumonia and respiratory infection admissions were linked to ozone, N02, and
PM25. The C-R functions for ozone are based on the results of a single pollutant model and three-pollutant
model (ozone, N02, PM25).155
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(4.42) and t-statistic (4.29) reported in Table 3 (Burnett et al., 1999, p. 133) for a 19.5 ppb increase in two-
day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.002218
Standard Error: 0.000517
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
Multipollutant Model (ozone, N02, PM2 5)
In a model with PM2 5 and N02, the ozone coefficient and standard error are based on the percent
increase (3.93) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (3.80)156 for a 19.5 ppb increase in two-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.001977
Standard Error: 0.000520
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person (ICD codes 464,
466, 480-487)
Population: population of all ages
G .3.13 Hospital Admissions for Pneumonia (Moolgavkar et al., 1997, Minneapolis)
Moolgavkar et al. (1997) examined the relationship between air pollution and pneumonia hospital
admissions (ICD 480-487) for individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January
1986 to December 1991. In a four pollutant Poisson model examining pneumonia admissions in
Minneapolis, ozone was significant, while N02, S02, and PM10 were not significant. The model with a 130
df smoother was reported to be optimal (p. 368). The ozone C-R function is based on the results from the
four-pollutant model with a 130 df smoother.
Multipollutant Model (ozone, N02, PM10,and S02)
In a model with N02, PM10,and S02, the estimated coefficient and standard error are based on the
percent increase (5.7) and 95% confidence interval of the percent increase (2.5-8.9) associated with an
increase in daily average ozone levels of 15 ppb (Moolgavkar et al., 1997, Table 4 and p. 366).
155 Burnett et al. (1999) reports results for co-pollutant models with ozone and various PM metrics as well, however,
standard errors were not provided so these estimates were not used to derive C-R functions.
156 Rick Burnett (co-author), personal communication.
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Appendix G. Ozone C-R Functions
Functional Form: Log-linear
Coefficient: 0.003696
Standard Error: 0.00103
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person 65+ (ICD codes
480-487)
Population: population of ages 65 and older
G .3.14 Hospital Admissions for Pneumonia (Schwartz, 1994b, Detroit)
Schwartz (1994b) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Detroit, Michigan, from January 1986 to December 1989. In a two-pollutant
Poisson regression model, Schwartz found both PM10 and ozone significantly linked to pneumonia and
COPD. The authors state that effect estimates were relatively unchanged compared to the unreported
single pollutant models. No significant associations were found between either pollutant and asthma
admissions. The PM10 C-R function for pneumonia incidence is based on results of the "basic" co-
pollutant model (ozone and PM10).157
Multipollutant Model (ozone and PM10)
The ozone C-R function for pneumonia incidence is based on the coefficient and standard error for
the "basic" co-pollutant model presented in Table 4 (Schwartz, 1994b, p. 651).
Functional Form: Log-linear
Coefficient: 0.00521
Standard Error: 0.0013
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person 65+ (ICD codes
480-487)
Population: population of ages 65 and older
G .3.15 Hospital Admissions for Pneumonia (Schwartz, 1994a, Minneapolis)
Schwartz (1994a) examined the relationship between air pollution and hospital admissions for
individuals 65 and older in Minneapolis-St. Paul, Minnesota, from January 1986 to December 1989. In
single-pollutant Poisson regression models, both ozone and PM10 were significantly associated with
pneumonia admissions. In a two-pollutant model, Schwartz found PM10 significantly related to
pneumonia; ozone was weakly linked to pneumonia. The results were not sensitive to the methods used to
control for seasonal patterns and weather. The ozone C-R functions are based on the results of the single
pollutant model and the two-pollutant model (PM10 and ozone) with spline smoothing for temporal
patterns and weather.
Single Pollutant Model
The single pollutant coefficient and standard error are based on the relative risk (1.19) and 95%
confidence interval (1.02-1.40) for a 50 ppb increase in daily average ozone levels (Schwartz, 1994a, p.
369).
157
Schwartz (1994b) also reports results using generalized additive models to fit time and temperature variables, however
no standard error or confidence intervals were reported.
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Appendix G. Ozone C-R Functions
Functional Form: Log-linear
Coefficient: 0.003479
Standard Error: 0.001616
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person 65+ (ICD codes
480-487)
Population: population of ages 65 and older
Multipollutant Model (ozone and PM10)
In a model with PM10 and spline functions to adjust for time and weather, the coefficient and
standard error are based on the relative risk (1.22) and 95% confidence interval (1.02, 1.47) for a 50 ppb
increase in daily average ozone levels (Schwartz, 1994a, Table 4).
Functional Form: Log-linear
Coefficient: 0.003977
Standard Error: 0.001865
Incidence Rate: region-specific daily hospital admission rate for pneumonia per person 65+ (ICD codes
480-487)
Population: population of ages 65 and older
G .3.16 Hospital Admissions for All Cardiovascular (Burnett et al., 1997, Toronto)
Burnett et al. (1997) examined the relationship between air pollution and cardiac hospital
admissions (ICD codes 410-414, 427, 428) for individuals of all ages in Toronto, Canada during the
summers of 1992-1994. In a Poisson regression model, cardiac admissions were linked to coefficient of
haze (COH) and ozone; other PM measures were less strongly linked. In two pollutant models, they found
that CO, N02, and S02 were not significant, controlling for COH. They found that ozone was still
significant, controlling for COH. In multi-pollutant models with COH, ozone, N02, and S02, both ozone
and COH remained significant. None of the other PM measures (PM10, PM10_2 5, PM2 5) were significant in
four-pollutant models. The ozone C-R functions are based on the results from the single pollutant model
and multipollutant models with PM co-pollutants.
Single Pollutant Model
In a single pollutant model with adjustment for temperature and dew point, the ozone coefficient
and standard error are based on the relative risk (1.074) and t-statistic (3.85) reported for an 11.5 ppb
increase in the three-day average of 12-hour average ozone (Burnett et al., 1997, Table 2, p. 617).
Functional Form: Log-linear
Coefficient: 0.006208
Standard Error: 0.001612
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (ozone and PM2 5)
In a model with PM2 5, the ozone coefficient and standard error are based on the relative risk
(1.062) and t-statistic (3.48) reported for an 11.5 ppb increase in the three-day average of 12-hour average
ozone (Burnett et al., 1997, Table 5, p. 618).
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Appendix G. Ozone C-R Functions
Functional Form: Log-linear
Coefficient: 0.005231
Standard Error: 0.001503
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (ozone and PM10_2 5)
In a model with PM10_2 5, the ozone coefficient and standard error are based on the relative risk
(1.063) and t-statistic (3.74) reported for an 11.5 ppb increase in the three-day average of 12-hour average
ozone (Burnett et al., 1997, Table 5, p. 618).
Functional Form: Log-linear
Coefficient: 0.005313
Standard Error: 0.001421
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
Multipollutant Model (ozone, N02, PM2 5, S02)
In a four-pollutant model with PM2 5, N02, and S02, the ozone coefficient and standard error are
based on the relative risk (1.067) and t-statistic (3.73) reported for an 11.5 ppb increase in the three-day
average of 12-hour average ozone (Burnett et al., 1997, Table 6, p. 618).
Functional Form: Log-linear
Coefficient: 0.005639
Standard Error: 0.001512
Incidence Rate: region-specific daily hospital admission rate for all cardiovascular disease per person
(ICD codes 410-414, 427, 428)
Population: population of all ages
G .3.17 Hospital Admissions for Dysrhythmia (Burnett et al., 1999, Toronto)
Burnett et al. (1999) examined the relationship between air pollution and hospital admissions (ICD
427) for individuals of all ages in Toronto, Canada from 1980 to 1994. The authors examined single
pollutant log-linear models for PM10, PM10_2 5, PM2 5, CO, N02, S02, and ozone and found PM2 5, PM10, and
CO significantly associated with admissions. They estimated multiple pollutant models, where pollutants
for best fitting model were chosen using stepwise regression based on AIC criterion. The final model for
dysrhythmia admissions included ozone, CO, and PM25. CO was significantly associated with admissions,
while ozone and PM2 5 were marginally significant. The C-R functions for ozone are based on the results
of a single pollutant model and three-pollutant model (ozone, CO, and PM2 5).158
158 Burnett et al. (1999) reports results for co-pollutant models with ozone and various PM metrics as well, however,
standard errors were not provided so these estimates were not used to derive C-R functions.
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Appendix G. Ozone C-R Functions
Single Pollutant Model
In a single pollutant model, the coefficient and standard error are based on the percent increase
(3.51) and t-statistic (1.71) reported in Table 3 (Burnett et al., 1999, p. 133) for a 19.5 ppb increase in
three-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.001769
Standard Error: 0.001035
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia per person (ICD code 427)
Population: population of all ages
Multipollutant Model (ozone, CO, PM2 5)
In a model with PM2 5 and CO, the ozone coefficient and standard error are based on the percent
increase (3.34) reported in Table 5 (Burnett et al., 1999, p. 135) and the t-statistic obtained from the
authors (1.63)159 for a 19.5 ppb increase in three-day average ozone concentration.
Functional Form: Log-linear
Coefficient: 0.001685
Standard Error: 0.001034
Incidence Rate: region-specific daily hospital admission rate for dysrhythmia per person (ICD code 427)
Population: population of all ages
159 Rick Burnett (co-author), personal communication.
Abt Associates Inc. G-26
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Appendix G. Ozone C-R Functions
G .4 Emergency Room Visits
Exhibit G-4 summarizes the C-R functions used to estimate the relationship between ozone and
emergency room visits. Detailed summaries of each of the studies used to generate the functions are
described below, along with the parameters used in each of the functions.
G .4.1 Emergency Room Visits for Asthma (Cody et al., 1992, Northern NJ)
Cody et al. (1992) examined the relationship between ER visits and air pollution for persons of all
ages in central and northern New Jersey, from May to August in 1988-1989. In a two pollutant multiple
linear regression model, ozone was linked to asthma visits, and no effect was seen for S02. They modeled
PM10 in separate analysis because of limited (every sixth day) sampling. No significant effect was seen for
PM10. The C-R function for ozone is based on results of a co-pollutant model with S02 (Cody et al., 1992,
Table 6, p. 191).
Multipollutant Model (ozone and S02)
The ozone coefficient and standard error are reported per 1 ppm increment of five-hour ozone
levels, which are converted to a 1 ppb increment by dividing by 1,000 (Cody et al., 1992, Table 6, p. 191).
Functional Form: Linear
Coefficient: 0.0203
Standard Error: 0.00717
Baseline Population: baseline population of Northern New Jersey160 = 4,436,976
Population: population of all ages
G .4.2 Emergency Room Visits for Asthma (Jaffe et al., 2003)
Jaffe et al. (2003) examined the relationship between ER visits and air pollution for persons ages
5-34 in Cleveland, Columbus, and Cincinnati, Ohio, from 1991 through 1996. In single-pollutant Poisson
regression models, ozone and S02 were linked to asthma visits, and no significant effect was seen for N02
and PM10.
Single Pollutant Model
The ozone coefficient and standard error are reported per 10 ppb increment of the maximum daily
8-hour average ozone level (Jaffe et al., 2003, Table 3). We used the results from the three cities
combined. The relative risk is 1.03, with a 95 percent confidence interval of 1.00 to 1.06.
Functional Form: Log-linear
Coefficient: 0.002956
Standard Error: 0.001486
Incidence: asthma ER rate for ages 0-17, 18-24, and 25-34
Population: population of ages from 5 to 34
160 The population estimate is based on the 1990 population for the eight counties containing hospitals or in the central
core of the study. Cody et al. (1992, Figure 1) presented a map of the study area; the counties are: Bergen, Essex, Hudson,
Middlesex, Morris, Passaic, Somerset, and Union.
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Appendix G. Ozone C-R Functions
G .4.3 Emergency Room Visits for Asthma (Norris et al., 1999)
Norris et al. (1999) examined the relation between air pollution in Seattle and childhood (<18)
hospital admissions for asthma from 1995 to 1996. The authors used air quality data for PM10, light
scattering (used to estimate fine PM), CO, S02, N02, and ozone in a Poisson regression model with
adjustments for day of the week, time trends, temperature, and dew point. They found significant
associations between asthma ER visits and light scattering (converted to PM2 5), PM10, and CO. No
association was found between ozone, N02, or S02 and asthma ER visits, although ozone had a significant
amount of missing data. In multi-pollutant models with either PM metric (light scattering or PM10) and
N02 and S02, the PM coefficients remained significant while the gaseous pollutants were not associated
with increased asthma ER visits. The C-R function for ozone is based on the result of a single pollutant
model.
Single Pollutant Model
The coefficient and standard error are calculated from a relative risk of 1.02 (95% CI 0.98-1.05)
for a 4.6 ppb increase in maximum eight-hour ozone levels (Norris et al., 1999, p. 491).
Functional Form: Log-linear
Coefficient: 0.004305
Standard Error: 0.003826
Incidence Rate: region-specific daily emergency room rate for asthma per person <18 (ICD code 493)
Population: population of ages under 18
G .4.4 Emergency Room Visits for Asthma (Schwartz et al., 1993, Seattle)
Schwartz et al. (1993) examined the relationship between air quality and emergency room visits
for asthma (ICD codes 493, 493.01, 493.10, 493.90, 493.91) in persons under 65 and 65 and over, living in
Seattle from September 1989 to September 1990. Using single-pollutant models they found daily levels of
PM10 linked to ER visits in individuals ages under 65, and they found no effect in individuals ages 65 and
over. They did not find a significant effect for S02 and ozone in either age group. The C-R function is
based on the results of the single pollutant model for ozone.
Single Pollutant Model
The ozone coefficient and standard error are based on the relative risk (0.97) and 95% confidence
interval (0.89-1.05) for a 15 ppb increase in daily ozone levels (Schwartz et al., 1993, p. 829).
Functional Form: Log-linear
Coefficient: -0.002031
Standard Error: 0.002812
Incidence Rate: region-specific daily emergency room rate for asthma per person <65 (ICD code 493)
Population: population of ages under 65
G .4.5 Emergency Room Visits for Asthma (Stieb et al., 1996, New Brunswick)
Stieb et al. (1996) examined the relationship between ER visits and air pollution for persons of all
ages in St. John, New Brunswick, Canada, from May through September in 1984-1992. Ozone was
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November 2003
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Appendix G. Ozone C-R Functions
significantly linked to ER visits, especially when ozone levels exceeded 75 ppb. The authors reported
results from a linear model, quadratic model, and linear-quadratic model using daily average and 1-hour
maximum ozone. In the linear model, ozone was borderline significant. In the quadratic and linear-
quadratic models, ozone was highly significant. This is consistent with the author's conclusion that "only
ozone appeared to have a nonlinear relationship with visit rates" (p. 1356) and that "quadratic, linear-
quadratic, and indicator models consistently fit the data better than the linear model..." (p. 1358). The
linear term in the linear-quadratic model is negative, implying that at low ozone levels, increases in ozone
are associated with decreases in risk. Since this does not seem biologically plausible, the ozone C-R
functions described here are based on the results of the quadratic regression models presented in Table 2
(Stiebetal., 1996, p. 1356).
Single Pollutant Model (one-hour max ozone)
The coefficient and standard error of the quadratic model are reported in Table 2 (Stieb et al.,
1996, p. 1356) for a 1 ppb increase in 1-hour daily maximum ozone levels. The C-R function to estimate
avoided emergency visits derived from a quadratic regression model is shown below:
8 9 9
A Asthma ER Visits= BasePop\-i°XbaSeUne ) - (03 control ) } P°P ,
Functional Form: Quadratic
Coefficient: 0.00004
Standard Error: 0.00002
Baseline Population: baseline population of St. John, New Brunswick (Stieb et al., 1996, p. 1354) =
125,000
Population: population of all ages
Single Pollutant Model (daily average ozone)
The coefficient and standard error of the quadratic model are reported in Table 2 (p. 1356) for
a 1 ppb increase in daily average ozone levels. The C-R function to estimate avoided emergency visits
derived from a quadratic regression model is shown below:
3 9 9
A Asthma ER Visits= BasePop\.(°* jbaseime) - (°3,control) }P°P,
Functional Form: Quadratic
Coefficient: 0.0001
Standard Error: 0.00004
Baseline Population: baseline population of St. John, New Brunswick (Stieb et al., 1996, p. 1354) =
125,000
Population: population of all ages
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Appendix G. Ozone C-R Functions
G .4.6 Emergency Room Visits for Asthma (Weisel et al., 1995, Northern NJ)
Weisel et al. (1995) examined the relationship between ER visits and air pollution for persons of
all ages in central and northern New Jersey, from May to August in 1986-1990. A significant relationship
was reported for ozone. The C-R function is based on the results of the single pollutant models reported
by Weisel et al. (1995, Table 2).
Single Pollutant Model
The coefficient (P) used in the C-R function is a weighted average of the coefficients in Weisel et
al. (1995, Table 2) using the inverse of the variance as the weight:
( 1990
P=
y A_
(72
i=\9S6u
1990 i
V /=1986 y
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The standard error of the coefficient (ap) is calculated as follows, assuming that the estimated year-
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( 1990
(7fj = var
P,
( 1990
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1990
V; = 1986 ^ J
Pi
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Z var
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Functional Form: Linear
Coefficient: 0.0443
Standard Error: 0.00723
Baseline Population: baseline population of Northern New Jersey161 = 4,436,976
Population: population of all ages
161 The population estimate is based on the 1990 population for the eight counties containing hospitals or in the central
core of the study. Cody et al. (1992, Figure 1) presented a map of the study area; the counties are: Bergen, Essex, Hudson,
Middlesex, Morris, Passaic, Somerset, and Union.
Abt Associates Inc. G-31 November 2003
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Appendix G. Ozone C-R Functions
G .5 Acute Morbidity
Exhibit G-5 summarizes the C-R functions used to estimate the relationship between ozone and
acute morbidity. Detailed summaries of each of the studies used to generate the functions are described
below, along with the parameters used in each of the functions.
G .5.1 Any of 19 Respiratory Symptoms: Krupnick (1990)
Krupnick et al. (1990) estimated the impact of air pollution on the incidence of any of 19
respiratory symptoms or conditions in 570 adults and 756 children living in three communities in Los
Angeles, California from September 1978 to March 1979. Krupnick et al. (1990) listed 13 specific
"symptoms or conditions": head cold, chest cold, sinus trouble, croup, cough with phlegm, sore throat,
asthma, hay fever, doctor-diagnosed ear infection, flu, pneumonia, bronchitis, and bronchiolitis. The other
six symptoms or conditions are not specified.
In their analysis, they included coefficient of haze (COH, a measure of particulate matter
concentrations), ozone, N02, and S02, and they used a logistic regression model that takes into account
whether a respondent was well or not the previous day. A key difference between this and the usual
logistic model, is that the model they used includes a lagged value of the dependent variable. In single-
pollutant models, daily ozone, COH, and S02 were significantly related to respiratory symptoms in adults.
Controlling for other pollutants, they found that ozone was still significant. The results were more variable
for COH and S02, perhaps due to collinearity. N02 had no significant effect. No effect was seen in
children for any pollutant. The results from the two-pollutant model with COH and ozone are used to
develop a C-R function.
Multipollutant Model (ozone and coefficient of haze)
The C-R function used to estimate the change in ARD2 associated with a change in daily one-hour
maximum ozone162 is based on Krupnick et al. (1990, p. 12):163
A A lil)2= 0 A ()-. pop,
Functional Form: Linear
Coefficient: first derivative of the stationary probability = 0.000137
Standard Error: 0.0000697
Population: population of ages 18-64 years164
The logistic regression model used by Krupnick et al. (1990) takes into account whether a
respondent was well or not the previous day. Following Krupnick et al. (p. 12), the probability that one is
sick is on a given day is:
162Krupnick et al. (1990) used parts per hundred million (pphm) to measure ozone; the coefficient used here is based on
ppb.
163Krupnick and Kopp (1988, p. 2-24) and ESEERCO (1994, p. V-32) used the same C-R functional form as that used
here.
164The coefficient estimates are based on the sample of "adults," and assumes that individuals 18 and older were
considered adult. According to Krupnick et al. (1990, Table 1), about 0.6 percent of the study sample was over the age of 60. This
is a relatively small fraction, so it is further assumed that the results do not apply to individuals 65 years of age and older.
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Appendix G. Ozone C-R Functions
Pn
probability(ARD2)=
1 -Pi + Po
1
p] = probabiHtyi A Rl)2 \ sickness or not t_, )=-—l+x-p 'for /= 04 •
where:
X = the matrix of explanatory variables
p0 = the probability of sickness on day t, given wellness on day t-1, and
Pi = the probability of sickness on day t, given sickness on day t-1.
In other words, the transition probabilities are estimated using a logistic function; the key
difference between this and the usual logistic model, is that the model includes a lagged value of the
dependent variable.
To calculate the impact of ozone (or other pollutants) on the probability of ARD2, it is possible, in
principle, to estimate ARD2 before the change in ozone and after the change:
KARD2=ARD2after-ARD2before .
However the full suite of coefficient estimates are not available.165 Rather than use the full suite of
coefficient values, the impact of ozone on the probability of ARD2 may be approximated by the derivative
of ARD2 with respect to ozone:166
c^rob ability (ARD2) +
dO-,
where P is the reported logistic regression coefficient for ozone. The change in the incidence of ARD2
associated with a given change in ozone is then estimated by:
dARD2 A A RD2
d03 = A 03
AARD2 _
^ AO,
165The model without N02 (Krapnick et al., 1990, Table V equation 3) was used in this analysis, but the full suite of
coefficient estimates for this model were not reported. Krapnick et al. (Table IV) reported all of the estimated coefficients for a
model of children and for a model of adults when four pollutants were included (ozone, COH, S02, and N02). However, because of
high collinearity between N02 and COH, N02 was dropped from some of the reported analyses (Krapnick et al., p. 10), and the
resulting coefficient estimates changed substantially (see Krapnick et al., Table V). Both the ozone and COH coefficients dropped
by about a factor of two or more.
166The derivative result is reported by Krapnick et al. (1990, p. 12).
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Appendix G. Ozone C-R Functions
=> AARD2= j8* A 03 .
This analysis uses transition probabilities obtained from Krupnick et al. as reported by ESEERCO
(1994, p. V-32) for the adult population: p: = 0.7775 and p0 = 0.0468. This implies:
. 0.0468(1-0.7775) 0.00055[0.7775+(1-0.0468)1
8 = ;— - -T—^ —=0.000137 .
(1-0.7775+0.0468)
The standard error for the coefficient is derived using the reported standard error of the logistic
regression coefficient in Krupnick et al. (1990, Table V):
P = 0.00055+ (1.960.00027)= 0.00108
0, 0.0468(1-0.7775)0.00108-[0.7775+(1-0.0468)]
> Pu u = ; ^ =0.000268
h'sh (1-0.7775+0.0468)2
Pt»gh-P (0.000268-0.000137)
(7a hlph = —2 = -= 0.0000668
P.hgh 196 196
0.00055- (1.960.00027)= 0.0000208
- 0.0468(1- 0.7775} 0.0000208{0.7775+ (1-0.0468)] _6
> HInw — / — 5.17'10
(1-0.7775+0.0468)
P~ Pi™ (0.000137+ 5.17 10"6)
a, , = Hlow = = 0.0000725
1.96 1.96
= °P'high + (J,3Jow = 0.0000697.
G .5.2 Minor Restricted Activity Days: Ostro and Rothschild (1989)
Ostro and Rothschild (1989) estimated the impact of PM2 5 and ozone on the incidence of minor
restricted activity days (MRADs) and respiratory-related restricted activity days (RRADs) in a national
sample of the adult working population, ages 18 to 65, living in metropolitan areas.167 The annual national
167 The study population is based on the Health Interview Survey (HIS), conducted by the National Center for Health
Statistics. In publications from this ongoing survey, non-elderly adult populations are generally reported as ages 18-64. From the
study, it is not clear if the age range stops at 65 or includes 65 year olds. We apply the C-R function to individuals ages 18-64 for
Abt Associates Inc. G-35 November 2003
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Appendix G. Ozone C-R Functions
survey results used in this analysis were conducted in 1976-1981. Controlling for PM25, two-week
average ozone had a highly variable association with RRADs and MRADs. Controlling for ozone, two-
week average PM2 5 was significantly linked to both health endpoints in most years. The C-R function for
ozone is based on the co-pollutant model with PM2 5.
The study is based on a "convenience" sample of non-elderly individuals. Applying the C-R
function to this age group is likely a slight underestimate, as it seems likely that elderly are at least as
susceptible to ozone as individuals under 65. A number of studies have found that hospital admissions for
the elderly are related to ozone exposures (e.g., Schwartz, 1994b; Schwartz, 1995).
Multipollutant Model (ozone and PM2 5)
The coefficient and standard error used in the C-R function are based on a weighted average of the
coefficients in Ostro and Rothschild (1989, Table 4). The derivation of these estimates is described below.
Functional Form: Log-linear
Coefficient: 0.00220
Standard Error: 0.000658
Incidence Rate: daily incidence rate for minor restricted activity days (MRAD) = 0.02137 (Ostro and
Rothschild, 1989, p. 243)
Population: adult population ages 18 to 64
The coefficient used in the C-R function is a weighted average of the coefficients in Ostro and
Rothschild (1989, Table 4) using the inverse of the variance as the weight:168
The standard error of the coefficient is calculated as follows, assuming that the estimated year-
specific coefficients are independent:
0.00220
V 1 = 1976 V pi
c72p = var
This reduces down to:
consistency with other studies estimating impacts to non-elderly adult populations.
168 The calculation of the MRAD coefficient and its standard error is exactly analogous to the calculation done for the
work-loss days coefficient based on Ostro (1987).
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Appendix G. Ozone C-R Functions
-=0.000658.
7
G .5.3 School Loss Days, All Cause (Chen et al., 2000)
Chen et al. (2000) studied the association between air pollution and elementary school absenteeism
(grades 1-6)169 in Washoe County, Nevada. Daily absence data were available for all elementary schools
in the Washoe Country School District. The authors regressed daily total absence rate on the three air
pollutants, meteorological variables, and indicators for day of the week, month, and holidays. They
reported statistically significant associations between both ozone and CO and daily total absence rate for
grades one through six. PM10 was negatively associated with absence rate, after adjustment for ozone, CO,
and meteorological and temporal variables. The C-R function for ozone is based on the results from a
multiple linear regression model with CO, ozone, and PM10.
Multipollutant Model (ozone, CO, and PM10)
The coefficient and standard error are presented in Table 3 (Chen et al., 2000, p. 1008) for a unit
ppm increase in the two-week average of daily one-hour maximum ozone concentration. This is converted
to unit ppb increase by dividing by 1,000.
The reported coefficient represents an absolute increase in absenteeism rate for a unit increase in
ozone. If we apply this study to other locations, we assume that the same absolute increase will occur for a
unit increase in ozone, regardless of the baseline rate. If the study location has a particularly high baseline
rate, we may be overestimating decreases in absenteeism nationally, and vice-versa. As an example,
consider if the baseline absenteeism rate were 10% in the study and 5% nationally. An absolute increase
in absence rate of 2% associated with a given increase in ozone reflects a relative increase in absence rate
of 20% for the study population. However, in the national estimate, we would assume the same absolute
increase of 2%, but this would reflect a relative increase in the absenteeism rate of 40%.
An alternative approach is to estimate apply the relative increase in absenteeism rate in the C-R
function by adjusting the results by the ratio of the national absenteeism rate to the study-specific rate. As
a result, the percent increase in absenteeism rate associated with an increase in ozone is extrapolated
nationally rather than the absolute increase in absenteeism rate. The incidence derivation section above
describes the data used to estimate national and study-specific absence rates.
In addition to this scaling factor, there are two other scaling factors which are applied to the
function. A scaling factor of 0.01 is used to convert the beta from a percentage (x 100) per unit increase of
ozone to a proportion per unit increase of ozone. As a result it can be applied directly to the national
population of school children ages 6 through 11 to estimate the number of absences avoided.
The final scaling factor adjusts for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the ozone season (May 1 - September 30),
however, in reality, school absences will be avoided only on school days. We assume that children are in
school during weekdays for all of May, two weeks in June, one week in August, and all of September.
169
Assuming that most children start kindergarten at age 5, the corresponding ages for grades 1 through 6 would be 6
through 11.
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Appendix G. Ozone C-R Functions
This corresponds to approximately 2.75 months out of the 5 month season, resulting in an estimate of
39.3% of days (2.75/5*5/7). The C-R function parameters are shown below.
Functional Form: Linear
Coefficient: 0.013247
Standard Error: 0.004985
Population: population of children ages 6-11
Scaling Factor 1: Ratio of national school absence rate to study-specific school absence rate170 = 1.081
Scaling Factor 2: Convert beta in percentage terms to a proportion = 0.01
Scaling Factor 3: Proportion of days that are school days in the ozone season171 = 0.393
G .5.4 School Loss Days, All Cause (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences. The C-R function for ozone is based on the
results of the single pollutant model.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the
previous day and the incidence rate as the number of incident absences on a given day over the population
at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since
school absences due to air pollution may last longer than one day, an estimate of the average duration of
school absences could be used to calculated the total avoided school loss days from an estimate of avoided
new absences. A simple ratio of the total absence rate divided by the new absence rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Duration= —
new Absences
A TotalAbsences= - [incidence(e ^ A°3 -1)\duration pop
170
National school absence rate of 5.5% obtained from the U.S. Department of Education (1996, Table 42-1). Study-
specific school absence rate of 5.09% obtained from Chen et al. (2000, Table 1).
171
Ozone is modeled for the 5 months from May 1 through September 30. We assume that children are in school during
weekdays for all of May, 2 weeks in June, 1 week in August, and all of September. This corresponds to approximately 2.75 months
out of the 5 month season, resulting in an estimate of 39.3% of days (2.75/5*5/7).
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Appendix G. Ozone C-R Functions
Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For all absences, the coefficient and standard error are based on a percent increase of 16.3 percent
(95% CI -2.6 percent, 38.9 percent) associated with a 20 ppb increase in 8-hour average ozone
concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the ozone season (May 1 - September 30),
however, in reality, school absences will be avoided only on school days. We assume that children are in
school during weekdays for all of May, two weeks in June, one week in August, and all of September.
This corresponds to approximately 2.75 months out of the 5 month season, resulting in an estimate of
39.3% of days (2.75/5*5/7).
In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Functional Form: Log-linear
Coefficient: 0.007550
Standard Error: 0.004527
Incidence Rate: daily school absence rate = 0.055 (U.S. Department of Education, 1996, Table 42-1)
Population: population of children ages 9-10 not absent from school on a given day172 = 94.5% of
children ages 9-10
Scaling Factor: Proportion of days that are school days in the ozone season173 = 0.393
G .5.5 School Loss Days, Illness-Related (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
172
The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
173
Ozone is modeled for the 5 months from May 1 through September 30. We assume that children are in school during
weekdays for all of May, 2 weeks in June, 1 week in August, and all of September. This corresponds to approximately 2.75 months
out of the 5 month season, resulting in an estimate of 39.3% of days (2.75/5*5/7).
Abt Associates Inc. G-39 November 2003
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Appendix G. Ozone C-R Functions
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences. The C-R function for ozone is based on the
results of the single pollutant model.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the
previous day and the incidence rate as the number of incident absences on a given day over the population
at risk for an absence on a given day (i.e. those children who were not absent on the previous day). Since
school absences due to air pollution may last longer than one day, an estimate of the average duration of
school absences could be used to calculated the total avoided school loss days from an estimate of avoided
new absences. A simple ratio of the total absence rate divided by the new absence rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Durations
newAbsences
A TotalAbsences=-[incidence (e~13 A°3 -1)\duration pop
Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For total illness-related absences, the coefficient and standard error are based on a percent increase
of 62.9 percent (95% CI 18.4 percent, 124.1 percent) associated with a 20 ppb increase in 8-hour average
ozone concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the ozone season (May 1 - September 30),
however, in reality, school absences will be avoided only on school days. We assume that children are in
school during weekdays for all of May, two weeks in June, one week in August, and all of September.
This corresponds to approximately 2.75 months out of the 5 month season, resulting in an estimate of
39.3% of days (2.75/5*5/7).
In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Abt Associates Inc.
G-40
November 2003
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Appendix G. Ozone C-R Functions
Functional Form: Log-linear
Coefficient: 0.024398
Standard Error: 0.008138
Incidence Rate: region-specific daily illness-related school absence rate (Adams et al., 1999, Table 47),
assuming 180 school days per year.
Population: population of children ages 9-10 not absent from school on a given day174 = 94.5% of
children ages 9-10
Scaling Factor: Proportion of days that are school days in the ozone season175 = 0.393
G .5.6 School Loss Days, Respiratory Illness-Related (Gilliland et al., 2001)
Gilliland et al. (2001) examined the association between air pollution and school absenteeism
among 4th grade school children (ages 9-10) in 12 southern Californian communities. The study was
conducted from January through June 1996. The authors used school records to collect daily absence data
and parental telephone interviews to identify causes. They defined illness-related absences as respiratory
or non-respiratory. A respiratory illness was defined as an illness that included at least one of the
following: runny nose/sneezing, sore throat, cough, earache, wheezing, or asthma attack. The authors used
15 and 30 day distributed lag models to quantify the association between ozone, PM10, and N02 and
incident school absences. Ozone levels were positively associated with all school absence measures and
significantly associated with all illness-related school absences (non-respiratory illness, respiratory illness,
URI and LRI). Neither PM10 nor N02 was significantly associated with illness-related school absences,
but PM10 was associated with non-illness related absences. The C-R function for ozone is based on the
results of the single pollutant model.
Gilliland et al. (2001) defines an incident absence as an absence that followed attendance on the previous
day and the incidence rate as the number of incident absences on a given day over the population at risk
for an absence on a given day (i.e. those children who were not absent on the previous day). Since school
absences due to air pollution may last longer than one day, an estimate of the average duration of school
absences could be used to calculated the total avoided school loss days from an estimate of avoided new
absences. A simple ratio of the total absence rate divided by the new absence rate would provide an
estimate of the average duration of school absences, which could be applied to the estimate of avoided new
absences as follows:
totalAbsences
Duration=
newAbsences
A TotalAbsences=-[incidence(e~/3AO} -1)]' duration pop
Since the function is log-linear, the baseline incidence rate (in this case, the rate of new absences)
is multiplied by duration, which reduces to the total school absence rate. Therefore, the same result would
be obtained by using a single estimate of the total school absence rate in the C-R function. Using this
174 The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
175
Ozone is modeled for the 5 months from May 1 through September 30. We assume that children are in school during
weekdays for all of May, 2 weeks in June, 1 week in August, and all of September. This corresponds to approximately 2.75 months
out of the 5 month season, resulting in an estimate of 39.3% of days (2.75/5*5/7).
Abt Associates Inc. G-41 November 2003
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Appendix G. Ozone C-R Functions
approach, we assume that the same relationship observed between pollutant and new school absences in
the study would be observed for total absences on a given day. As a result, the total school absence rate is
used in the function below. The derivation of this rate is described in the section on baseline incidence rate
estimation.
Single Pollutant Model
For respiratory illness-related absences, the coefficient and standard error are based on a percent
increase of 82.9 percent (95% CI 3.9 percent, 222.0 percent) associated with a 20 ppb increase in 8-hour
average ozone concentration (2001, Table 6, p. 52).
A scaling factor is used to adjust for the number of school days in the ozone season. In the
modeling program, the function is applied to every day in the ozone season (May 1 - September 30),
however, in reality, school absences will be avoided only on school days. We assume that children are in
school during weekdays for all of May, two weeks in June, one week in August, and all of September.
This corresponds to approximately 2.75 months out of the 5 month season, resulting in an estimate of
39.3% of days (2.75/5*5/7).
In addition, not all children are at-risk for a new school absence, as defined by the study. On
average, 5.5% of school children are absent from school on agiven day (U.S. Department of Education,
1996, Table 42-1). Only those who are in school on the previous day are at risk for a new absence (1-
0.055 = 94.5%). As a result, a factor of 94.5% is used in the function to estimate the population of school
children at-risk for a new absence.
Functional Form: Log-linear
Coefficient: 0.030188
Standard Error: 0.014436
Incidence Rate: region-specific daily respiratory illness-related school absence rate (Adams et al., 1999,
Table 47), assuming 180 school days per year.
Population: population of children ages 9-10 not absent from school on a given day176 = 94.5% of
children ages 9-10
Scaling Factor: Proportion of days that are school days in the ozone season177 = 0.393
G .5.7 Worker Productivity: Crocker and Horst (1981)
To monetize benefits associated with increased worker productivity resulting from improved
ozone air quality, we used information reported in Crocker and Horst (1981) and summarized in EPA
(1994). Crocker and Horst examined the impacts of ozone exposure on the productivity of outdoor citrus
workers. The study measured productivity impacts as the change in income associated with a change in
ozone exposure, given as the elasticity of income with respect to ozone concentration (-0.1427).178 The
176 The proportion of children not absent from school on a given day (5.5%) is based on 1996 data from the U.S.
Department of Education (1996, Table 42-1).
177
Ozone is modeled for the 5 months from May 1 through September 30. We assume that children are in school during
weekdays for all of May, 2 weeks in June, 1 week in August, and all of September. This corresponds to approximately 2.75 months
out of the 5 month season, resulting in an estimate of 39.3% of days (2.75/5*5/7).
178 The relationship estimated by Crocker and Horst between wages and ozone is a log-log relationship,
elasticity of wages with respect to ozone is a constant, equal to the coefficient of the log of ozone in the model.
Abt Associates Inc. G-42
Therefore the
November 2003
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Appendix G. Ozone C-R Functions
reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in income. Given
the national median daily income for outdoor workers engaged in strenuous activity reported by the U.S.
Census Bureau (2002), $68 per day (2000$),179 a ten percent reduction in ozone yields about $0.97 in
increased daily wages. We adjust the national median daily income estimate to reflect regional variations
in income using a factor based on the ratio of county median household income to national median
household income. No information was available for quantifying the uncertainty associated with the
central valuation estimate. Therefore, no uncertainty analysis was conducted for this endpoint.
Single Pollutant Model
The C-R function for estimating changes in worker productivity is shown below:
Q\— Qo
A productivity= fy dailyincome- pop-,
0i
Functional Form: Linear
Coefficient: 0.1427
Daily Income: median daily income for outdoor workers180
Population: population of adults 18 to 64 employed as farm workers.
179
The national median daily income for workers engaged in "farming, forestry, and fishing" from the U.S. Census
Bureau (2002, Table 621, p. 403) is used as a surrogate for outdoor workers engaged in strenuous activity.
180 The national median daily income for workers engaged in "farming, forestry, and fishing" was obtained from the U.S.
Census Bureau (2002, Table 621, p. 403) and is used as a surrogate for outdoor workers engaged in strenuous activity. This
national median daily income ($68) is then scaled by the ratio of national median income to county median income to estimate
county median daily income for outdoor workers.
Abt Associates Inc. G-43 November 2003
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Appendix G. Ozone C-R Functions
G .6 Asthma-Related Effects
Exhibit G-6 summarizes the C-R functions used to estimate the relationship between ozone and
asthma-related effects. Detailed summaries of each of the studies used to generate the functions are
described below, along with the parameters used in each of the functions.
G .6.1 Asthma Attacks (Whittemore and Korn, 1980)
Whittemore and Korn (1980) examined the relationship between air pollution and asthma attacks
in a survey of 443 children and adults, living in six communities in southern California during three 34-
week periods in 1972-1975. The analysis focused on TSP and oxidants (Ox). Respirable PM, N02, S02
were highly correlated with TSP and excluded from the analysis. In a two pollutant model, daily levels of
both TSP and oxidants were significantly related to reported asthma attacks. The results from this model
were used, and the oxidant result was adjusted so it may be used with ozone data.
Multipollutant Model (ozone and PM10)
The daily one-hour ozone coefficient is based on an oxidant coefficient (1.66) estimated from data
expressed in ppm. The coefficient is converted to ppb by dividing by 1,000 and to ozone by multiplying
by 1.11,181 The standard error is calculated from the two-tailed p-value (<0.01) reported by Whittemore
and Korn (1980, Table 5), which implies a t-value of at least 2.576 (assuming a large number of degrees of
freedom).
Functional Form: Logistic
Coefficient: 0.001843
Standard Error: 0.000715
Incidence Rate: daily incidence of asthma attacks = 0.0550182
Population: population of asthmatics of all ages = 3.86% of the population of all ages (American Lung
Association, 2002c, Table 7)
G .6.2 Asthma Exacerbation, Cough (Ostro et al., 2001)
Ostro et al. (2001) studied the relation between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and 03 in a logistic regression model with control for age, income,
181 The study used oxidant measurements in ppm (Whittemore and Korn, 1980, p. 688); these have been converted to
ozone measurements in ppb, assuming ozone comprises 90% of oxidants (i.e., 1.11 *ozone=oxidant). It is assumed that the harm of
oxidants is caused by ozone. The view expressed in the Ozone Staff Paper (U.S. EPA, 1996, p.164) is consistent with assuming
that ozone is the oxidant of concern at normal ambient concentrations: "Further, among the photochemical oxidants, the acute-
exposure chamber, field, and epidemiological human health data base raises concern only for ozone at levels of photochemical
oxidants commonly reported in ambient air. Thus, the staff recommends that ozone remain as the pollutant indicator for protection
of public health from exposure to all photochemical oxidants found in the ambient air."
182 Based on an analysis of the 1999 National Health Interview Survey, the daily incidence of wheezing attacks for adult
asthmatics is estimated to be 0.0550. In the same survey, wheezing attacks for children were examined, however, the number of
wheezing attacks per year were censored at 12 (compared to censoring at 95 for adults). Due to the potential for underestimation of
the number of children's wheezing attacks, we used the adult rate for all individuals.
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Appendix G. Ozone C-R Functions
time trends, and temperature-related weather effects.183 Asthma symptom endpoints were defined in two
ways: "probability of a day with symptoms" and "onset of symptom episodes". New onset of a symptom
episode was defined as a day with symptoms followed by a symptom-free day. The authors found cough
prevalence associated with PM10 and PM25 and cough incidence associated with PM2 5 PM10, and N02.
Ozone was not significantly associated with cough among asthmatics. The ozone C-R functions are based
on the results of single pollutant models looking at both the probability of symptoms and the onset of new
symptoms.
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on the odds ratio (0.93) and 95% confidence interval
(0.87-0.99) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 4,
p.204).
Functional Form: Logistic
Coefficient: -0.001814
Standard Error: 0.000824
Incidence Rate: daily cough rate per person (Ostro et al., 2001, p.202) = 0.145
Population: asthmatic African-American population ages 8 to 13 = 7.26%184 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on the odds ratio (0.88) and 95% confidence interval
(0.78-0.98) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 5,
p.204).
The C-R function based on this model will estimate the number of new onset episodes of cough
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For cough, this ratio is 2.2 (14.5% divided by 6.7%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of cough, as defined by the study. On
average, 14.5% of African-American asthmatics have cough on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.145 =
85.5%). As a result, a factor of 85.5% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new cough episode.
183 The authors note that there were 26 days in which PM25 concentrations were reported higher than PM10
concentrations. The majority of results the authors reported were based on the full dataset. These results were used for the basis for
the C-R functions.
184 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix G. Ozone C-R Functions
Functional Form: Logistic
Coefficient: -0.003196
Standard Error: 0.001456
Incidence Rate: daily new onset cough rate per person (Ostro et al., 2001, p.202) = 0.067
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of cough =
6.21% of African-American population ages 8 to 13 (85.5% at-risk185 times 7.26% asthmatic186)
Scaling Factor: average number of consecutive days with a cough episode (days) = 2.2
G .6.3 Asthma Exacerbation, Shortness of Breath (Ostro et al., 1995)
Using a logistic regression estimation, Ostro et al. (1995) estimated the impact of PM10, ozone,
N02, and S02 on the incidence of coughing, shortness of breath, and wheezing in 83 African-American
asthmatic children ages 7-12 living in Los Angeles from August through September 1992. Regression
results show both PM10 and ozone significantly linked to shortness of breath; the beginning of an asthma
episode was also significantly linked to ozone. No effect was seen for N02 and S02. Results for single-
pollutant models only were presented in the published paper. The C-R function is based on the model with
adjustment for respiratory infection, temperature, and outdoor mold levels (Ostro et al., 1995, Table 3).
Single Pollutant Model
The ozone coefficient and standard error are based on the odds ratio (1.36) and 95% confidence
interval (1.02-1.83) (Ostro et al., 1995, Table 3) associated with a change in one-hour daily maximum
ozone of 8.02 pphm (80.2 ppb) (Ostro et al., 1995, Table 2).
Functional Form: Logistic
Coefficient: 0.003834
Standard Error: 0.001859
Incidence Rate: daily shortness of breath incidence rate per person (Ostro et al., 1995, p. 715) = 0.056
Population: asthmatic African-American population ages 7 to 12 = 7.26%187 of African-American
population ages 7 to 12
G .6.4 Asthma Exacerbation, Shortness of Breath (Ostro et al., 2001)
Ostro et al. (2001) studied the relationship between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and ozone in a logistic regression model with control for age,
income, time trends, and temperature-related weather effects. Asthma symptom endpoints were defined in
two ways: "probability of a day with symptoms" and "new onset of a symptom episode". New onset of a
symptom episode was defined as a day with symptoms followed by a symptom-free day. The authors
185 On average, 14.5% of African-American asthmatics have cough episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.145 = 85.5%) are at-risk for a new onset episode.
186 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
187 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix G. Ozone C-R Functions
found that both the prevalent and incident episodes of shortness of breath were associated with PM2 5 and
PM10. Neither ozone nor N02 were significantly associated with shortness of breath among asthmatics.
The ozone C-R functions are based on the results of single pollutant models looking at both the probability
of symptoms and the onset of new symptoms.
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on the odds ratio (1.01) and 95% confidence interval
(0.92-1.10) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 4,
p.204).
Functional Form: Logistic
Coefficient: 0.000249
Standard Error: 0.001140
Incidence Rate: daily shortness of breath rate per person (Ostro et al., 2001, p.202) = 0.074
Population: asthmatic African-American population ages 8 to 13 = 7.26%188 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on the odds ratio (1.00) and 95% confidence interval
(0.87-1.16) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 5,
p.204).
The C-R function based on this model will estimate the number of new onset episodes of shortness
of breath avoided. In order to convert this estimate to the total number of episodes avoided, the results are
adjusted by an estimate of the duration of symptom episodes. The average duration can be estimated from
Ostro et al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset
episode. For shortness of breath, this ratio is 2.0 (7.4% divided by 3.7%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of shortness of breath, as defined by the
study. On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given
day (Ostro et al., 2001, p.202). Only those who are symptom-free on the previous day are at-risk for a new
onset episode (1-0.074 = 92.6%). As a result, a factor of 92.6% is used in the function to estimate the
population of African-American 8 to 13 year old children at-risk for a new shortness of breath episode.
Functional Form: Logistic
Coefficient: 0
Standard Error: 0.001835
Incidence Rate: daily new onset shortness of breath rate per person (Ostro et al., 2001, p.202) = 0.037
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of shortness of
breath = 6.72% of African-American population ages 8 to 13 (92.6% at-risk189 times 7.26% asthmatic190)
Scaling Factor: average number of consecutive days with a shortness of breath episode (days) = 2.0
188 The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
189
On average, 7.4% of African-American asthmatics have shortness of breath episodes on a given day (Ostro et al.,
2001, p.202). Only those who are symptom-free on the previous day (1-0.074 = 92.6%) are at-risk for a new onset episode.
190
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix G. Ozone C-R Functions
G .6.5 Asthma Exacerbation, Wheeze (Ostro et al., 2001)
Ostro et al. (2001) studied the relation between air pollution in Los Angeles and asthma
exacerbation in African-American children (8 to 13 years old) from August to November 1993. They used
air quality data for PM10, PM2 5, N02, and 03 in a logistic regression model with control for age, income,
time trends, and temperature-related weather effects. Asthma symptom endpoints were defined in two
ways: "probability of a day with symptoms" and "onset of symptom episodes". New onset of a symptom
episode was defined as a day with symptoms followed by a symptom-free day. The authors found both the
prevalence and incidence of wheeze associated with PM2 5 PM10, and N02. Ozone was not significantly
associated with wheeze among asthmatics. The ozone C-R functions are based on the results of single
pollutant models looking at both the probability of symptoms and the onset of new symptoms.
Single Pollutant Model (probability of symptoms)
The coefficient and standard error are based on the odds ratio (0.94) and 95% confidence interval
(0.88-1.00) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 4,
p.204).
Functional Form: Logistic
Coefficient: -0.001547
Standard Error: 0.000815
Incidence Rate: daily wheeze rate per person (Ostro et al., 2001, p.202) = 0.173
Population: asthmatic African-American population ages 8 to 13 = 7.26%191 of African-American
population ages 8 to 13
Single Pollutant Model (probability of a new onset of symptoms)
The coefficient and standard error are based on the odds ratio (0.95) and 95% confidence interval
(0.86-1.04) reported for a 40 ppb increase in one-hour maximum ozone levels (Ostro et al., 2001, Table 5,
p.204).
The C-R function based on this model will estimate the number of new onset episodes of wheeze
avoided. In order to convert this estimate to the total number of episodes avoided, the results are adjusted
by an estimate of the duration of symptom episodes. The average duration can be estimated from Ostro et
al. (2001) using the ratio of the probability of a symptom episode to the probability of a new onset episode.
For wheeze, this ratio is 2.3 (17.3% divided by 7.6%) (Ostro et al., 2001, p.202).
In addition, not all children are at-risk for a new onset of wheeze, as defined by the study. On
average, 17.3% of African-American asthmatics have wheeze on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day are at-risk for a new onset episode (1-0.173 =
82.7%). As a result, a factor of 82.7% is used in the function to estimate the population of African-
American 8 to 13 year old children at-risk for a new wheeze episode.
191
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix G. Ozone C-R Functions
Functional Form: Logistic
Coefficient: -0.001282
Standard Error: 0.001212
Incidence Rate: daily new onset wheeze rate per person (Ostro et al., 2001, p.202) = 0.076
Population: asthmatic African-American population ages 8 to 13 at-risk for a new episode of wheeze =
6.00% of African-American population ages 8 to 13 (82.7% at-risk192 times 7.26% asthmatic193)
Scaling Factor: average number of consecutive days with a wheeze episode (days) = 2.3
192
On average, 17.3% of African-American asthmatics have wheeze episodes on a given day (Ostro et al., 2001, p.202).
Only those who are symptom-free on the previous day (1-0.173 = 82.7%) are at-risk for a new onset episode.
193
The American Lung Association (2002c, Table 9) estimates asthma prevalence for African-American children ages 5
to 17 at 7.26% (based on data from the 1999 National Health Interview Survey).
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Appendix H. Economic Valuation of Health Effects
Appendix H: Economic Value of Health Effects
This appendix first presents an overview of valuation, and then presents the unit values that are
available in BenMAP for each of the health endpoints included in the current suite of C-R functions.
Wherever possible, we present a distribution of the unit value, characterizing the uncertainty surrounding
any point estimate. The mean of the distribution is taken as the point estimate of the unit value, and the
distribution itself is used to characterize the uncertainty surrounding the unit value, which feeds into the
uncertainty surrounding the monetary benefits associated with reducing the incidence of the health
endpoint. Below we give detailed descriptions of the derivations of unit values and their distributions, as
well as tables listing the unit values and their distributions, available for each health endpoint. The
definitions of the distributions and their parameters is given in Exhibit H-l 1, at the end of this Appendix.
H.l Overview of Valuation
Reductions in ambient concentrations of air pollution generally lower the risk of future adverse
health affects by a fairly small amount for a large population. A lower risk for everyone means that fewer
cases of the adverse health effect are expected, although we don't know ex ante which cases will be
avoided. For example, the analysis may predict 100 hospital admissions for respiratory illnesses avoided,
but the analysis does not estimate which individuals will be spared those cases of respiratory illness that
would have required hospitalization. The health benefits conferred on individuals by a reduction in
pollution concentrations are, then, actually reductions in the risk of having to endure certain health
problems. These benefits (reductions in risk) may not be the same for all individuals (and could be zero
for some individuals). Likewise, the WTP for a given benefit is likely to vary from one individual to
another. In theory, the total social value associated with the decrease in risk of a given health problem
resulting from a given reduction in pollution concentrations is generally taken to be the sum of everyone's
WTP for the benefits they receive:
where B is the benefit (i.e., the reduction in risk of having to endure the health problem conferred on the ilh
individual by the reduction in pollution concentrations, WTP^B,) is the ilh individual's WTP for that
benefit, and N is the number of people exposed to the pollution.194 If a reduction in pollution
concentrations affects the risks of several health endpoints, the total health-related social value of the
reduction in pollution concentrations is:
where BtJ is the benefit related to the jlh health endpoint (i.e., the reduction in risk of having to endure the jlh
health problem) conferred on the ilh individual by the reduction in pollution concentrations, and WTP^B^)
is the ilh individual's WTP for that benefit.
The reduction in risk of each health problem for each individual is not known, however (nor is
each individual's WTP for each possible risk reduction he or she might receive). Instead, epidemiological
194 WTP may also include altruism - that is, a person may be WTP not only for his own benefits,
but for the benefits that would be enjoyed by others.
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Appendix H. Economic Valuation of Health Effects
studies allow us to estimate the number of cases of an adverse health effect that would be avoided by a
given reduction in pollutant concentrations. Therefore, in practice, benefit analyses take an ex post
approach and estimate the value of a statistical health problem avoided. If we have an estimate of the
average individual's WTP for the risk reduction conferred upon him, we can derive from that an estimate
of the value of a statistical case avoided. Suppose, for example, that a given reduction in pollutant
concentrations results in a decrease in mortality risk of 1/10,000. Then for every 10,000 individuals, one
individual would be expected to die in the absence of the reduction in pollutant concentrations (who would
not be expected to die in the presence of the reduction in pollutant concentrations). If the average
individual's WTP for this 1/10,000 decrease in mortality risk is $100, then the value of a statistical life is
10,000 x $100, or $1 million. In general, the ex ante WTP for a risk reduction of x can be converted into
an ex post value of a statistical case avoided by dividing the average individual's WTP for the risk
reduction of x by x (e.g. $100/0.0001 = $1,000,000). The same type of calculation can produce values for
statistical incidences of other health endpoints.
The value of a statistical case avoided is referred to here as a "unit value." The total dollar value
for a specific health effect is the number of statistical cases of the health effect avoided times the unit value
for that health effect. Whereas ideally the unit value would reflect the underlying WTP for the ex ante risk
reduction (as in the above example), in practice we usually have estimates of the value of the ex post
statistical case avoided. Sometimes those values come from contingent valuation studies, in which study
participants are queried about their WTP to avoid a specific adverse health effect. Sometimes, when WTP
estimates are not available, WTP is approximated by other measures, most notably cost of illness
measures.
An individual's WTP to avoid an adverse health effect will include, at a minimum, the amount of
money he would have to pay for medical expenses associated with the illness. Because medical
expenditures are to a significant extent shared by society, via medical insurance, Medicare, etc., however,
the medical expenditures actually incurred by the individual are likely to be less than the total medical cost
to society. The total value to society of an individual's avoidance of an adverse health effect, then, might
be thought of as having two components: (1) the cost of the illness (COI) to society, including the total
value of the medical resources used (some portion of which will be paid by the individual), plus the value
of the lost productivity, as well as (2) the WTP of the individual, as well as that of others, to avoid the pain
and suffering resulting from the illness.
These two components might be rephrased as (1) the market component and (2) the non-market
component. When an individual becomes ill, there is some amount of resources (medical goods and
services) that are used to address the illness. The value of those resources, whoever pays the bill, is the
market component of the value of avoiding the illness - i.e., the value of the resources that would not have
to be used up if the individual had not incurred the illness. This may be a small value - e.g., the cost of
aspirin used for a headache, or a very large value - e.g., the value of medical goods and services used to
treat someone who goes to the hospital with a life-threatening illness. The COI approach attempts to
estimate the total value of the medical resources used up as well as the value of the individual's time lost
as a result of the illness. Because this method does not include the value of avoiding the pain and suffering
resulting from the illness (a potentially large component), it is generally believed to underestimate the total
value of avoiding the illness, perhaps substantially.
The contingent valuation method attempts to elicit from people what they would be willing to pay
to avoid the illness. Because of the distortion in the market for medical goods and services, whereby
individuals generally do not pay the full value of the medical resources used to address their illnesses,
however, this method too is likely to understate the total value of avoiding the illness.
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Appendix H. Economic Valuation of Health Effects
Although the COI and contingent valuation approaches to valuing health effects avoided are the
two most common methods, other methods have been used in certain circumstances. The method the
benefit analyst chooses to value a particular health endpoint will depend in part on what is available.
Benefit analysts typically do not do primary research to generate data for valuation to be used in a benefit
analysis - it is too expensive and time consuming. Instead, the benefit analyst uses data or estimates that
have been collected or generated by researchers and can be readily obtained in publicly available databases
or in the open literature. The unit values currently available for use in BenMap are all of this type.
Sometimes more than one estimate of a unit value for a health effect is available. For chronic
bronchitis, for example, we have both a WTP estimate and COI estimates. In that case, you may select one
or pool two or more estimates. As research continues and new unit values become available, the database
of unit values available for use in BenMAP will be updated. The discussion below refers to the set of unit
values that are currently available for use in BenMAP. Unless otherwise stated, all unit values are in
2000$.
H.2 Mortality
The economics literature concerning the appropriate method for valuing reductions in premature
mortality risk is still developing. The adoption of a value for the projected reduction in the risk of
premature mortality is the subject of continuing discussion within the economics and public policy
analysis communities. Issues such as the appropriate discount rate and whether there are factors, such as
age or the quality of life, that should be taken into consideration when estimating the value of avoided
premature mortality are still under discussion. BenMAP currently offers a variety of options reflecting the
uncertainty surrounding the unit value for premature mortality.
H.2.1 Value of a Statistical Life Based on 26 Studies
One unit value available in BenMAP is $6.3 million. This estimate is the mean of a distribution
fitted to 26 "value of statistical life" (VSL) estimates that appear in the economics literature and that have
been identified in the Section 812 Reports to Congress as "applicable to policy analysis." This represents
an intermediate value from a variety of estimates, and it is a value EPA has frequently used in Regulatory
Impact Analyses (RIAs) as well as in the Section 812 Retrospective and Prospective Analyses of the Clean
Air Act.
The VSL approach and the set of selected studies mirrors that of Viscusi (1992) (with the addition
of two studies), and uses the same criteria as Viscusi in his review of value-of-life studies. The $6.3
million estimate is consistent with Viscusi's conclusion (updated to 2000$) that "most of the reasonable
estimates of the value of life are clustered in the $3.8 to $8.9 million range." Five of the 26 studies are
contingent valuation (CV) studies, which directly solicit WTP information from subjects; the rest are
wage-risk studies, which base WTP estimates on estimates of the additional compensation demanded in
the labor market for riskier jobs. Because this VSL-based unit value does not distinguish among people
based on the age at their death or the quality of their lives, it can be applied to all premature deaths.
H.2.2 Value of a Statistical Life Based on Selected Studies
In addition to the value of a statistical based on the results of 26 studies, we have included four
alternatives based loosely on the results of recent work by Mrozek and Taylor (2002) and Viscusi and
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Appendix H. Economic Valuation of Health Effects
Aldy (2003). Each of the four alternatives has a mean value of $5.5 million (2000$), but with a different
distributions: normal, uniform, triangular, and beta. Exhibit H-l presents the distribution parameters for
the suite of mortality valuations currently available in BenMAP.
Exhibit H-l. Unit Values Available for Mortality
Basis for Estimate" Age Range at Unit Value Distribution of Parameters of
Death (VSL) Unit Value** Distribution
(200°S) P1 P7
mm. max. PI F2
VSL, based on 26 value-of-life studies.
0
99
$6,324,101
Weibull
5.32E-6
1.509588
VSL based on range from $1 million to $10 million -
95% CI of assumed normal distribution.
0
99
$5,500,000
Normal
2,295,960.54
-
VSL based on range from $1 million to $10 million -
assumed uniform distribution.
0
99
$5,500,000
Uniform
1,000,000
10,000,000
VSL based on range from $1 million to $10 million -
assumed triangular distribution.
0
99
$5,500,000
Triangular
1,000,000
10,000,000
VSL based on range from $1 million to $10 million -
95% CI of assumed beta distribution. B
0
99
$5,500,000
Beta
1.95
1.95
1 The original value of a statistical life was calculated in 1990 $. We have used a factor of 1.3175, based on the All-Items CPI-U.
b The Beta distribution in this instance also has a scale parameter equal to 10993993.6.
H.3 Chronic Illness
This sub-section presents the unit values developed for chronic bronchitis, chronic asthma, and
non-fatal myocardial infarctions.
H.3.1 Chronic Bronchitis
PM-related chronic bronchitis is expected to last from the initial onset of the illness throughout the
rest of the individual's life. WTP to avoid chronic bronchitis would therefore be expected to incorporate
the present discounted value of a potentially long stream of costs (e.g., medical expenditures and lost
earnings) as well as WTP to avoid the pain and suffering associated with the illness. Both WTP and COI
estimates are currently available in BenMAP.
Unit Value Based on Two Studies of WTP
Two contingent valuation studies, Viscusi et al. (1991) and Krupnick and Cropper (1992), provide
estimates of WTP to avoid a case of chronic bronchitis. Viscusi et al. (1991) and Krupnick and Cropper
(1992) were experimental studies intended to examine new methodologies for eliciting values for
morbidity endpoints. Although these studies were not specifically designed for policy analysis, they can
be used to provide reasonable estimates of WTP to avoid a case of chronic bronchitis. As with other
contingent valuation studies, the reliability of the WTP estimates depends on the methods used to obtain
the WTP values. The Viscusi et al. and the Krupnick and Cropper studies are broadly consistent with
current contingent valuation practices, although specific attributes of the studies may not be.
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The study by Viscusi et al. (1991) uses a sample that is larger and more representative of the
general population than the study by Krupnick and Cropper (1992), which selects people who have a
relative with the disease. However, the chronic bronchitis described to study subjects in the Viscusi study
is severe, whereas a pollution-related case may be less severe.
The relationship between the severity of a case of chronic bronchitis and WTP to avoid it was
estimated by Krupnick and Cropper (1992). We used that estimated relationship to derive a relationship
between WTP to avoid a severe case of chronic bronchitis, as described in the Viscusis study, and WTP to
avoid a less severe case. The estimated relationship (see Table 4 in Krupnick and Cropper) can be written
as:
\n(WTP)= a+ /3*sev
where a denotes all the other variables in the regression model and their coefficients, p is the coefficient of
sev, estimated to be 0.18, and sev denotes the severity level (a number from 1 to 13). Let x (< 13) denote
the severity level of a pollution-related case of chronic bronchitis, and 13 denote the highest severity level
(as described in Viscusi et al., 1991). Then
\vl(WTP^ )= (X+ fi* 13
and
ln( WTPx )= (X+ /3*x.
Subtracting one equation from the other,
1 ^ )- ln(WTPX )= f3*(l 3- x)
or
In
f wtp13^
WTP„
= J3*(\3-jc) .
Exponentiating and rearranging terms,
WTPX = WTP13 .
There is uncertainty surrounding the exact values of WTP13; x, and P, and this uncertainty can be
incorporated in the equation, if you request that the analysis be carried out in "uncertainty mode." The
distribution of WTP to avoid a severe case of chronic bronchitis, WTP 13 ,is based on the distribution of
WTP responses in the Viscusi et al. (1991) study. The distribution of x, the severity level of an average
case of pollution-related chronic bronchitis, is modeled as a triangular distribution centered at 6.5, with
endpoints at 1.0 and 12.0. And the distribution of P is normal with mean = 0.18 and std. dev.= 0.0669 (the
estimate of b and standard error reported in Krupnick and Cropper, 1992).
In uncertainty mode, BenMAP uses a Monte Carlo approach. On each Monte Carlo iteration,
random draws for these three variables are made, and the resulting WTPX is calculated from the equation
above. Because this function is non-linear, the expected value of WTP for a pollution-related case of CB
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Appendix H. Economic Valuation of Health Effects
cannot be obtained by using the expected values of the three uncertain inputs in the function (doing that
will substantially understate mean WTP). A Monte Carlo analysis suggests, however, that the mean WTP
to avoid a case of pollution-related chronic bronchitis is about $340,000. Therefore, if you request that the
analysis be carried out in "point estimate" mode, that is the unit value that is used.
Alternative Cost of Illness Estimates
Cost of illness estimates for chronic bronchitis were derived from estimates of annual medical
costs and annual lost earnings by Cropper and Krupnick (1990). This study estimated annual lost earnings
resulting from chronic bronchitis as a function of age at onset of the illness, for the following age
categories: 25-43, 35-44, 45-54, and 55-65 (see Cropper and Krupnick, Table 8). Annual medical
expenses were estimated for 10-years age groups (0-9, 10-19, 20-29, ..., 80-89). We derived estimates of
the present discounted value of the stream of medical and opportunity costs for people whose age of onset
is 30, 40, 50, 60, 70, and 80. Medical costs (which are in 1977$ in the Cropper and Krupnick study) were
inflated to 2000$ using the CPI-U for medical care; lost earnings (opportunity costs) were inflated to
2000$ using the Employment Cost Index for Wages and Salaries. Life expectancies were assumed to be
unaffected by the illness.195 For example, an individual at age 70 has a life expectancy of 14.3 more years,
and we assumed that someone whose age of onset of chronic bronchitis is 70 will also live for 14.3 more
years. We also assumed that opportunity costs at ages 66 and over were zero. Present discounted values
were calculated using three and seven percent discount rates.
For each of the two discount rates, there are three cost of illness unit values for chronic bronchitis
available in BenMAP, for the following age categories: 27-44, 45-64, and 65+. These are the age
categories that were used in the epidemiological study that estimated a concentration-response function for
chronic bronchitis (Abbey et al., 1995b). The estimate for the 27-44 age group is an average of the
present discounted values calculated for ages 30 and 40; the estimate for the 45-64 age category is an
average of the present discounted values calculated for ages 50 and 60; and the estimate for the 65+ age
category is an average of the present discounted values calculated for ages 70 and 80. The suite of unit
values available for use in BenMAP are shown in Exhibit H-2 below.
Exhibit H-2. Unit Values Available for Chronic Bronchitis
Basis for Estimate
Age of
Onset
min
max.
Present
Discounted
Value of
Medical
Costs
Present
Discounted
Value of
Opportunity
Costs
Unit
Value
Distribution
WTP: average severity
30 99
N/A
N/A
$340,482
custom
COI: med costs + wage loss, 3% DR
27 44
$18,960
$135,463
$154,422
none
45 64
$23,759
$76,029
$99,788
none
65 99
$11,088
$0
$11,088
none
COI: med costs + wage loss, 7% DR
27 44
$7,886
$80,444
$88,331
none
45 64
$14,390
$59,577
$73,967
none
65 99
$9,030
$0
$9,030
none
195 Source of life expectancies: National Vital Statistics Reports, Volume 47, No. 19, June 30,
1999. Table 5: "Life expectancy at selected ages by race and sex: United States, 1997.
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H.3.2 Chronic Bronchitis Reversals
The unit value for chronic bronchitis reversals assumes that this is chronic bronchitis with a
severity level of 1. The method for generating a distribution of unit values in BenMAP is therefore the
same as the WTP-based unit value method for chronic bronchitis (see above), with x=l. The mean of this
distribution is $150,221.
H.3.3 Chronic Asthma
Two studies have estimated WTP to avoid chronic asthma in adults. Blumenschein and
Johannesson (1998) used two different contingent valuation (CV) methods, the dichotomous choice
method and a bidding game, to estimate mean willingness to pay for a cure for asthma. The mean WTP
elicited from the bidding game was $189 per month, or $2,268 per year (in 1996$). The mean WTP
elicited from the dichotomous choice approach was $343 per month, or $4,116 per year (in 1996$). Using
$2,268 per year, a three percent discount rate, and 1997 life expectancies for males in the United States
(National Center for Health Statistics, 1999, Table 5), the present discounted value of the stream of annual
WTPs is $47,637 (in 2000$).
O'Conor and Blomquist (1997) estimated WTP to avoid chronic asthma from estimates of risk-
risk tradeoffs. Combining the risk-risk tradeoffs with a statistical value of life, the annual value of
avoiding asthma can be derived. Assuming a value of a statistical life of $6 million, they derived an
annual WTP to avoid asthma of $1500 (O'Connor and Blomquist, 1997, p. 677). For a value of a
statistical life of $5,894,400 (in 1997 $), the corresponding implied annual value of avoiding chronic
asthma, based on O'Conor and Blomquist would be $1,474. Assuming athree percent discount rate and
1997 life expectancies for males in the United States, the present discounted value of the stream of annual
WTPs would be $30,257 (in 2000$). A unit value, based on athree percent discount rate, is the average of
the two estimates, or $38,947. Following the method used for the §812 Prospective analysis, the
uncertainty surrounding the WTP to avoid a case of chronic asthma among adult males was characterized
by a triangular distribution on the range determined by the two study-specific WTP estimates.
A second unit value, using a seven percent discount rate, is also available for use in BenMAP.
The method used to derive this unit value is the same as that described above for the three percent discount
rate unit value. The unit values available for use in BenMAP are summarized in Exhibit H-3 below.
Exhibit H-3. Unit Values Available for Chronic Asthma
Basis for Estimate
Age Range
min. max.
Unit V alue
Distribution of
Unit Value
Parameters of Distribution
PI P2
WTP: 3% DR (Discount Rate)
27
99
$38,947
triangular
$30,257
$47,637
WTP: 7% DR
27
99
$25,357
triangular
$19,699
$31,015
H.3.4 Non-Fatal Myocardial Infarctions (Heart Attacks)
In the absence of a suitable WTP value for reductions in the risk of non-fatal heart attacks, there
are a variety of cost-of-illness unit values available for use in BenMAP. These cost-of-illness unit values
incorporate two components: the direct medical costs and the opportunity cost (lost earnings) associated
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Appendix H. Economic Valuation of Health Effects
with the illness event. Because the costs associated with a heart attack extend beyond the initial event
itself, the unit values include costs incurred over five years. Using age-specific annual lost earnings
estimated by Cropper and Krupnick (1990), and a three percent discount rate, we estimated the following
present discounted values in lost earnings over 5 years due to a heart attack: $8,774 for someone between
the ages of 25 and 44, $12,932 for someone between the ages of 45 and 54, and $74,746 for someone
between the ages of 55 and 65. The corresponding age-specific estimates of lost earnings using a seven
percent discount rate are $7,855, $11,578, and $66,920, respectively. Cropper and Krupnick do not
provide lost earnings estimates for populations under 25 or over 65. As such we do not include lost
earnings in the cost estimates for these age groups.
We have found three possible sources of estimates of the direct medical costs of a myocardial
infarction (MI) in the literature:
Wittels et al. (1990) estimated expected total medical costs of MI over 5 years to be $51,211 (in
1986$) for people who were admitted to the hospital and survived hospitalization. (There does not
appear to be any discounting used.) Wittels et al. was used to value coronary heart disease in the
812 Retrospective Analysis of the Clean Air Act. Using the CPI-U for medical care, the Wittels
estimate is $109,474 in year 2000$. This estimated cost is based on a medical cost model, which
incorporated therapeutic options, projected outcomes and prices (using "knowledgeable
cardiologists" as consultants). The model used medical data and medical decision algorithms to
estimate the probabilities of certain events and/or medical procedures being used. The authors
note that the average length of hospitalization for acute MI has decreased overtime (from an
average of 12.9 days in 1980 to an average of 11 days in 1983). Wittels et al. used 10 days as the
average in their study. It is unclear how much further the length of stay (LOS) for MI may have
decreased from 1983 to the present. The average LOS for ICD code 410 (MI) in the year-2000
AHQR HCUP database is 5.5 days. However, this may include patients who died in the hospital
(not included among our non-fatal MI cases), whose LOS was therefore substantially shorter than
it would be if they hadn't died.
Eisenstein et al. (2001) estimated 10-year costs of $44,663, in 1997$ (using a three percent
discount rate), or $49,651 in 2000$ for MI patients, using statistical prediction (regression) models
to estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were included.
Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal MI of $15,540 (in
1995$), and $1,051 annually thereafter. Converting to year 2000$, that would be $18,880 for a 5-
year period, using a three percent discount rate, or $17,850, using a seven percent discount rate.
The age group-specific estimates of opportunity cost over a five-year period are combined with the
medical cost estimates from each of the three studies listed above. Because opportunity costs are derived
for each of five age groups, there are 3 x 5 = 15 unit values for each of 2 discount rates, or 30 unit values
available for use in BenMAP.196 These are given in Exhibit H-4 below.
196 We were unable to achieve complete consistency, unfortunately, because of limitations in the
input studies. For example, although we calculated opportunity costs over a five-year period using a 3
percent and a 7 percent discount rate, we were not able to do the same for medical costs, except for the
medical costs estimated by Russell et al. (in which they estimate an annual cost). Wittels et al. appear to
have used no discounting in their estimate; Eisenstein et al. used a 3 percent discount rate. Similarly,
although almost all cost estimates (opportunity costs and medical costs) are for a 5-year period, the
medical cost estimate reported by Eisenstein et al. is for a 10-year period. There was no reasonable
method for inferring from that study what costs over a 5-year period would be.
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Appendix H. Economic Valuation of Health Effects
Exhibit H-4 Unit Values Available for Myocardial Infarction
Basis of Estimate
Age Range
Min Max
Medical Cost"
Opportunity
Cost b
Total Cost
COI: 5 yrs med, 5 yrs wages, 3% DR,
0
24
$109,474
$0
$109,474
Wittels (1990)
25
44
$109,474
$9,033
$118,507
45
54
$109,474
$13,313
$122,787
55
65
$109,474
$76,951
$186,425
66
99
$109,474
$0
$109,474
COI: 10 yrs med, 5 yrs wages, 3% DR,
0
24
$49,651
$0
$49,651
Eisenstein (2001)
25
44
$49,651
$9,033
$58,683
45
54
$49,651
$13,313
$62,964
55
65
$49,651
$76,951
$126,602
66
99
$49,651
$0
$49,651
COI: 5 yrs med, 5 yrs wages, 3% DR,
0
24
$22,331
$0
$22,331
Russell (1998)
25
44
$22,331
$9,033
$31,363
45
54
$22,331
$13,313
$35,644
55
65
$22,331
$76,951
$99,281
66
99
$22,331
$0
$22,331
COI: 5 yrs med, 5 yrs wages, 7% DR,
0
24
$109,474
$0
$109,474
Wittels (1990)
25
44
$109,474
$8,087
$117,561
45
54
$109,474
$11,919
$121,393
55
65
$109,474
$68,894
$178,368
66
99
$109,474
$0
$109,474
COI: 10 yrs med, 5 yrs wages, 7% DR,
0
24
$49,651
$0
$49,651
Eisenstein (2001)
25
44
$49,651
$8,087
$57,738
45
54
$49,651
$11,919
$61,570
55
65
$49,651
$68,894
$118,545
66
99
$49,651
$0
$49,651
COI: 5 yrs med, 5 yrs wages, 7% DR,
0
24
$21,113
$0
$21,113
Russell (1998)
25
44
$21,113
$8,087
$29,200
45
54
$21,113
$11,919
$33,032
55
65
$21,113
$68,894
$90,007
66
99
$21,113
$0
$21,113
sFrom Cropper and Krupnick (1990). Present discounted value of 5 yrs of lost earnings, at 3% and 7% discount rate, adjusted
from 1977$ to 2000$ using CPI-U "all items".
b An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998). Note that Wittels et al. appears not to
have used discounting in deriving a 5-year cost of $109,474; Russell et al. estimated first-year direct medical costs and annual
costs thereafter. The resulting 5-year cost is $22,331, using a 3% discount rate, and $21,113, using a 7% discount rate. Medical
costs were inflated to 2000$ using CPI-U for medical care.
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Appendix H. Economic Valuation of Health Effects
H.4 Hospital Admissions & Emergency Room Visits
This section presents the values for avoided hospital admissions, as well as avoided emergency
room visits. We assume that hospital admissions due to acute exposure to air pollution pass through the
emergency room. However, the value of hospital admissions that we have calculated here does not
account for the cost incurred in the emergency room visit.
H.4.1 Hospital Admissions
As suggested above, the total value to society of an individual's avoidance of a hospital admission
can be thought of as having two components: (1) the cost of illness (COI) to society, including the total
medical costs plus the value of the lost productivity, as well as (2) the WTP of the individual, as well as
that of others, to avoid the pain and suffering resulting from the illness.
In the absence of estimates of social WTP to avoid hospital admissions for specific illnesses
(components 1 plus 2 above), estimates of total COI (component 1) are available for use in BenMAP as
conservative (lower bound) estimates. Because these estimates do not include the value of avoiding the
pain and suffering resulting from the illness (component 2), they are biased downward. Some analyses
adjust COI estimates upward by multiplying by an estimate of the ratio of WTP to COI, to better
approximate total WTP. Other analyses have avoided making this adjustment because of the possibility of
over-adjusting ~ that is, possibly replacing a known downward bias with an upward bias. Based on
Science Advisory Board (SAB) advice, the COI values currently available for use in BenMAP are not
adjusted.
Unit values are based on ICD-code-specific estimated hospital charges and opportunity cost of
time spent in the hospital (based on the average length of a hospital stay for the illness). The opportunity
cost of a day spent in the hospital is estimated as the value of the lost daily wage, regardless of whether or
not the individual is in the workforce.
For all hospital admissions endpoints available in BenMAP, estimates of hospital charges and
lengths of hospital stays were based on discharge statistics provided by the Agency for Healthcare
Research and Quality's Healthcare Utilization Project (2000). The total COI for an ICD-code-specific
hospital stay lasting n days is estimated as the mean hospital charge plus n times the daily lost wage. Year
2000 county-specific median annual wages197 divided by (52*5) were used to estimate county-specific
median daily wages. Because wage data used in BenMAP are county-specific, the unit value for a hospital
admission varies from one county to another.
Most hospital admissions categories considered in epidemiological studies consisted of sets of ICD
codes. The unit value for the set of ICD codes was estimated as the weighted average of the ICD-code-
specific COI estimates. The weights were the relative frequencies of the ICD codes among hospital
discharges in the United States, as estimated by the National Hospital Discharge Survey (Owings and
Lawrence, 1999, Table 1). The hospital admissions for which unit values are available in BenMAP are
given in Exhibit H-5. Although unit values available for use in BenMAP are county-specific, the national
median daily wage was used to calculate opportunity costs and total costs for the table below, to give a
general idea of the cost of illness estimates for the different hospital admissions endpoints.
197 Source: U.S. Year 2000 Census, compiled by Geolytics.
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The mean hospital charges and mean lengths of stay provided by (AHRQ 2000) are based on a
very large nationally representative sample of about seven million hospital discharges, and are therefore
the best estimates of mean hospital charges and mean lengths of stay available, with negligible standard
errors.
Exhibit H-5. Unit Values Available for Hospital Admissions
EndPoint I CD Codes Age Range Mean Mean Total Cost of
Hospital Length of Illness (Unit
min.
max.
Charge 11
Stay (days) ¦
Value) b
HA, All Cardiovascular
390-429
65
99
$20,607
5.07
$21,191
HA, All Cardiovascular
390-429
0
99
$20,873
4.71
$21,415
HA, All Cardiovascular
390-429
20
64
$22,300
4.15
$22,778
HA, Congestive Heart Failure
428
65
99
$14,573
5.60
$15,218
HA, Dysrhythmia
427
0
99
$14,811
3.70
$15,237
HA, Ischemic Heart Disease
410-414
65
99
$25,322
4.81
$25,876
HA, All Respiratory
460-519
65
99
$17,600
6.88
$18,393
HA, All Respiratory
460-519
0
99
$14,999
5.63
$15,647
HA, All Respiratory
460-519
0
2
$7,416
2.97
$7,759
HA, Asthma
493
0
64
$7,448
2.95
$7,788
HA, Asthma
493
65
99
$11,417
4.99
$11,991
HA, Asthma
493
0
99
$8,098
3.30
$8,478
HA, Chronic Lung Disease
490-496
65
99
$12,781
5.59
$13,425
HA, Chronic Lung Disease
490-496
0
99
$10,882
4.59
$11,412
HA, Chronic Lung Disease
490-496
20
64
$10,194
4.04
$10,660
HA, Chronic Lung Disease (less Asthma)
490-492, 494-496
65
99
$12,993
5.69
$13,648
HA, Chronic Lung Disease (less Asthma)
490-492, 494-496
0
99
$12,742
5.45
$13,370
HA, Chronic Lung Disease (less Asthma)
490-492, 494-496
20
64
$11,820
4.48
$11,820
HA, Pneumonia
480-487
65
99
$17,030
7.07
$17,844
HA, Pneumonia
480-487
0
99
$14,693
5.92
$15,375
s Source of hospital charges and lengths of stay: Agency for Healthcare Research and Quality. 2000. HCUPnet, Healthcare Cost
and Utilization Project. http://www.agrg.gov/data/hcup/hcupnet.htm .
b The opportunity cost of a day spent in the hospital was estimated, for this exhibit, at the median daily wage of all workers,
$115.20, regardless of age. The median daily wage was calculated by dividing the median weekly wage ($576 in 2000$) by 5.
The median weekly wage was obtained from U.S. Census Bureau, Statistical Abstract of the United States: 2001, Section 12, Table
621: "Full-Time Wage and Salary Workers - Numbers and Earnings: 1985 to 2000." Actual unit values used in BenMAP are
based on county-specific wages, and are therefore county-specific.
H.4.2 Emergency Room Visits for Asthma
Two unit values are currently available for use in BenMAP for asthma emergency room (ER)
visits. One is $311.55, from Smith et al., 1997, who reported that there were approximately 1.2 million
asthma-related ER visits made in 1987, at a total cost of $186.5 million, in 1987$. The average cost per
visit was therefore $155 in 1987$, or $311.55 in 2000 $ (using the CPI-U for medical care to adjust to
2000$). The uncertainty surrounding this estimate, based on the uncertainty surrounding the number of
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ER visits and the total cost of all visits reported by Smith et al. is characterized by a triangular distribution
centered at $311.55, on the interval [$230.67, $430.93],
A second unit value is $260.67 from Stanford et al. (1999). This study considered asthmatics in
1996-1997, in comparison to the Smith et al. (1997) study, which used 1987 National Medical Expenditure
Survey (NMES) data). In comparing their study, the authors note that the 1987 NMES, used by Smith et
al., "may not reflect changes in treatment patterns during the 1990s." In addition, its costs are the costs to
the hospital (or ER) for treating asthma rather than charges or payments by the patient and/or third party
payer. Costs to the ER are probably a better measure of the value of the medical resources used up on an
asthma ER visit (see above for a discussion of costs versus charges).
The unit values and the corresponding distributions available in BenMAP for asthma-related ER
visits are summarized in Exhibit H-6.
Exhibit H-6. Unit Values Available for Asthma-Related ER Visits
Basis for Estimate
Age Range
Unit
Distribution of Unit
Parameters of Distribution
Value
Value
mm.
max.
PI
P2
COI: Smith et al. (1997)
0
99
$312
triangular
$231
$431
COI: Standford et al. (1999)
0
99
$261
normal
5.22
-
H.5 Acute Symptoms and Illness Not Requiring Hospitalization
Several acute symptoms and illnesses have been associated with air pollution, including acute
bronchitis in children, upper and lower respiratory symptoms, and exacerbation of asthma (as indicated by
one of several symptoms whose occurrence in an asthmatic generally suggests the onset of an asthma
episode). In addition, several more general health endpoints which are associated with one or more of
these acute symptoms and illnesses, such as minor restricted activity days, school loss days, and work loss
days, have also been associated with air pollution. We briefly discuss the derivation of the unit values for
each of these acute symptoms and illnesses, and then present all of these unit values in exhibits H-9 and H-
10 at the end of this section.
For several of the acute symptoms and illnesses for which more than one unit value is available in
BenMAP, one of these is the value that EPA used in several recent benefits analyses. These "original" unit
values were all based on a set of three CV studies, in which respondents were asked their WTP to avoid a
day of specific symptoms. These study- and symptom-specific WTP estimates, along with the
recommended midrange estimates derived by IEc (1993) on which the original unit values were based, are
presented in Exhibit H-7 below.
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Exhibit H-7. Median WTP Estimates and Derived Midrange Estimates (in 1999 $)
Symptom"
Dickie et al. (1987)
Tolley et al. (1986)
Loehman et al.
(1979)
Mid-Range
Estimate
Throat congestion
4.81
20.84
-
12.75
Head/sinus congestion
5.61
22.45
10.45
12.75
Coughing
1.61
17.65
6.35
8.93
Eye irritation
-
20.03
-
20.03
Headache
1.61
32.07
-
12.75
Shortness of breath
0.00
-
13.47
6.37
Pain upon deep inhalation (PDI)
5.63
-
-
5.63
Wheeze
3.21
-
-
3.21
Coughing up phlegm
3.51b
-
-
3.51
Chest tightness
8.03
-
-
8.03
s All estimates are WTP to avoid one day of symptom. Midrange estimates were derived by IEc (1993).
b 10% trimmed mean.
H.5.1 Acute Bronchitis in Children
Estimating WTP to avoid a case of acute bronchitis is difficult for several reasons. First, WTP to
avoid acute bronchitis itself has not been estimated. Estimation of WTP to avoid this health endpoint
therefore must be based on estimates of WTP to avoid symptoms that occur with this illness. Second, a
case of acute bronchitis may last more than one day, whereas it is a day of avoided symptoms that is
typically valued. Finally, the C-R function used in the benefit analysis for acute bronchitis was estimated
for children, whereas WTP estimates for those symptoms associated with acute bronchitis were obtained
from adults.
Three unit values are available in BenMAP for acute bronchitis in children. In previous benefits
analyses, EPA used a unit value of $59.31. This is the midpoint between a low estimate and a high
estimate. The low estimate is the sum of the midrange values recommended by IEc (1994) for two
symptoms believed to be associated with acute bronchitis: coughing and chest tightness. The high
estimate was taken to be twice the value of a minor respiratory restricted activity day. For a more
complete description of the derivation of this estimate, see Abt Associates (2000, p. 4-30).
The above unit value assumes that an episode of acute bronchitis lasts only one day. However,
this is generally not the case. More typically, it can last for 6 or 7 days. A simple adjustment, then, would
be to multiply the original unit value of $59.31 by 6 or 7. A second unit value of $356 (=$59.31x6) was
therefore derived.
Finally, as noted above, the epidemiological study relating air pollution to the incidence of acute
bronchitis referred to children specifically. The value of an avoided case should therefore be WTP to
avoid a case in a child, which may be different from WTP to avoid a case in an adult. Recent work by
Dickie and Ulery (2002) suggests, in fact, that parents are generally willing to pay about twice as much to
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avoid sickness in their children as in themselves.198 In one of several models they estimated, the natural
logarithm of parents' WTP was related both to the number of symptom-days avoided and to whether it was
their child or themselves at issue. Dickie and Ulery noted that "experiencing all of the symptoms
[considered in their study - cough and phlegm, shortness of breath/wheezing, chest pain, and fever] for 7
days, or 28 symptom-days altogether, is roughly equivalent to a case of acute bronchitis ..." Using this
model, and assuming that a case of acute bronchitis can be reasonably modeled as consisting of 28
symptom-days, we estimated parents' WTP to avoid a case of acute bronchitis in a child to be $374.199
This is the third unit value available in BenMAP.
H.5.2 Upper Respiratory Symptoms (URS) in Children
In past benefits analyses, EPA based willingness to pay to avoid a day of URS on symptom-
specific WTPs to avoid those symptoms identified as part of the URS complex of symptoms. Pope et al.
(1991) defined a day of URS as consisting of one or more of the following symptoms: runny or stuffy
nose; wet cough; and burning, aching, or red eyes. The three contingent valuation (CV) studies shown in
Exhibit H-7 above have estimated WTP to avoid various morbidity symptoms that are either within the
URS symptom complex defined by Pope et al., or are similar to those symptoms. The three individual
symptoms that were identified as most closely matching those listed by Pope et al. for URS are cough,
head/sinus congestion, and eye irritation, corresponding to "wet cough," "runny or stuffy nose," and
"burning, aching or red eyes," respectively. A day of URS could consist of any one of the seven possible
"symptom complexes" consisting of at least one of these three symptoms. The original unit value for URS
was based on the assumption that each of these seven URS complexes is equally likely. This unit value for
URS, $24.64, is just an average of the seven estimates of mean WTP for the different URS complexes.
The WTP estimates on which the first unit value is based were elicited from adults, whereas the
health endpoint associated with air pollution in the epidemiological study is in children. As noted above,
recent research by Dickie and Ulery (2002) suggests that parental WTP to avoid symptoms and illnesses in
their children is about twice what it is to avoid those symptoms and illnesses in themselves. We therefore
derived a second unit value of $49.28 (=2 x $24.64) from the first unit value.
A third unit value was derived by using Model 1, Table III in Dickie and Ulery (2002) (the same
model used for acute bronchitis), assuming that a day of URS consists of 2 symptoms. As noted above,
this model relates parental WTP to the number of symptom-days avoided and to whether it is the parent or
the child at issue. The unit value derived from this model is $187.200
198 This is, to our knowledge, the only estimate, based on empirical data, of parental WTP for
their children versus themselves.
199 The mean household income among participants in the Dickie and Ulery CV survey was
slightly higher than the national average. We therefore adjusted all WTP estimates that resulted from
their models downward slightly, using an income elasticity of WTP of 0.147, the average of the income
elasticities estimated in the four models in the study. The adjustment factor thus derived was 0.9738.
200 A WTP estimate elicited from parents concerning their WTP to avoid symptoms in their
children may well include some calculation of lost earnings resulting from having to lose a day of work.
Estimates from the Dickie and Ulery model therefore (appropriately) probably include not only their
WTP to have their children avoid the pain and suffering associated with their illness, but also the
opportunity cost of a parent having to stay home with a sick child.
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Appendix H. Economic Valuation of Health Effects
H.5.3 Lower Respiratory Symptoms (LRS) in Children
The three unit values for LRS in children currently available in BenMAP follow the same pattern
as those for URS in children. In past benefits analyses, EPA based willingness to pay to avoid a day of
LRS on symptom-specific WTPs to avoid those symptoms identified as part of the LRS complex of
symptoms. Schwartz et al. (1994) defined a day of LRS as consisting of at least two of the following
symptoms: cough, chest tightness, coughing up phlegm, and wheeze. Of the symptoms for which WTP
estimates are available (listed in Exhibit H-7), those that most closely match the symptoms listed by
Schwartz et al. are coughing, chest tightness, coughing up phlegm, and wheeze. A day of LRS, as defined
by Schwartz et al., could consist of any one of 11 possible combinations of at least two of these four
symptoms. In the absence of any further information, each of the 11 possible "symptom clusters" was
considered equally likely. The original unit value for LRS, $15.57, is just an average of the eleven
estimates of mean WTP for the different LRS symptom clusters.
A second unit value is twice the original unit value, or $31.15, based on the evidence from Dickie
and Ulery (2002) that parents are willing to pay about twice as much to avoid symptoms and illness in
their children as in themselves. The third unit value is based on Model 1, Table III in Dickie and Ulery,
assuming that, as for URS, a day of LRS consists of 2 symptoms. As noted above, this model relates
parental WTP to the number of symptom-days avoided and to whether it is the parent or the child at issue.
The unit value derived from this model is $187.
H.5.4 "Any of 19 Respiratory Symptoms"
The presence of "any of 19 acute respiratory symptoms" is a somewhat subjective health effect
used by Krupnick et al. (1990). Moreover, not all 19 symptoms are listed in the Krupnick et al. study. It is
therefore not clear exactly what symptoms were included in the study. Even if all 19 symptoms were
known, it is unlikely that WTP estimates could be obtained for all of the symptoms. Finally, even if all 19
symptoms were known and WTP estimates could be obtained for all 19 symptoms, the assumption of
additivity of WTPs becomes tenuous with such a large number of symptoms. The likelihood that all 19
symptoms would occur simultaneously, moreover, is very small.
Acute respiratory symptoms must be either upper respiratory symptoms or lower respiratory
symptoms. In the absence of further knowledge about which of the two types of symptoms is more likely
to occur among the "any of 19 acute respiratory symptoms," we assumed that they occur with equal
probability. Because this health endpoint may also consist of combinations of symptoms, it was also
assumed that there is some (smaller) probability that upper and lower respiratory symptoms occur together.
To value avoidance of a day of "the presence of any of 19 acute respiratory symptoms" we therefore
assumed that this health endpoint consists either of URS, or LRS, or both. We also assumed that it is as
likely to be URS as LRS and that it is half as likely to be both together. That is, it was assumed that "the
presence of any of 19 acute respiratory symptoms" is a day of URS with 40 percent probability, a day of
LRS with 40 percent probability, and a day of both URS and LRS with 20 percent probability. Using the
point estimates of WTP to avoid a day of URS and LRS derived above, the point estimate of WTP to avoid
a day of "the presence of any of 19 acute respiratory symptoms" is:
(0.40)($24.64) + (0.40)($15.57) + (0.20)($24.64 + $15.57) = $24.12.
Because this health endpoint is only vaguely defined, and because of the lack of information on the
relative frequencies of the different combinations of acute respiratory symptoms that might qualify as "any
of 19 acute respiratory symptoms," the unit dollar value derived for this health endpoint must be
considered only a rough approximation.
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Appendix H. Economic Valuation of Health Effects
H.5.5 Work Loss Days (WLDs)
Work loss days are valued at a day's wage. BenMAP calculates county-specific median daily
wages from county-specific annual wages by dividing by (52*5), on the theory that a worker's vacation
days are valued at the same daily rate as work days.
H.5.6 Minor Restricted Activity Days (MRADs)
Two unit values are currently available in BenMAP for MRADs. No studies are reported to have
estimated WTP to avoid a minor restricted activity day (MRAD). However, IEc (1993) derived an estimate
of WTP to avoid a minor respiratory restricted activity day (MRRAD), using WTP estimates from Tolley
et al. (1986) for avoiding a three-symptom combination of coughing, throat congestion, and sinusitis. This
estimate of WTP to avoid a MRRAD, so defined, is $38.37 (1990 $). Although Ostro and Rothschild
(1989) estimated the relationship between PM2 5 and MRADs, rather than MRRADs (a component of
MRADs), it is likely that most of the MRADs associated with exposure to PM2 5 are in fact MRRADs. The
original unit value, then, assumes that MRADs associated with PM exposure may be more specifically
defined as MRRADs, and uses the estimate of mean WTP to avoid a MRRAD.
Any estimate of mean WTP to avoid a MRRAD (or any other type of restricted activity day other
than WLD) will be somewhat arbitrary because the endpoint itself is not precisely defined. Many different
combinations of symptoms could presumably result in some minor or less minor restriction in activity.
Krupnick and Kopp (1988) argued that mild symptoms will not be sufficient to result in a MRRAD, so that
WTP to avoid a MRRAD should exceed WTP to avoid any single mild symptom. A single severe
symptom or a combination of symptoms could, however, be sufficient to restrict activity. Therefore WTP
to avoid a MRRAD should, these authors argue, not necessarily exceed WTP to avoid a single severe
symptom or a combination of symptoms. The "severity" of a symptom, however, is similarly not precisely
defined; moreover, one level of severity of a symptom could induce restriction of activity for one
individual while not doing so for another. The same is true for any particular combination of symptoms.
Given that there is inherently a substantial degree of arbitrariness in any point estimate of WTP to
avoid a MRRAD (or other kinds of restricted activity days), the reasonable bounds on such an estimate
must be considered. By definition, a MRRAD does not result in loss of work. WTP to avoid a MRRAD
should therefore be less than WTP to avoid a WLD. At the other extreme, WTP to avoid a MRRAD
should exceed WTP to avoid a single mild symptom. The highest IEc midrange estimate of WTP to avoid
a single symptom is $20.03 (1999 $), for eye irritation. The point estimate of WTP to avoid a WLD in the
benefit analysis is $83 (1990 $). If all the single symptoms evaluated by the studies are not severe, then
the estimate of WTP to avoid a MRRAD should be somewhere between $16 and $83. Because the IEc
estimate of $38 falls within this range (and acknowledging the degree of arbitrariness associated with any
estimate within this range), the IEc estimate is used as the mean of a triangular distribution centered at $38,
ranging from $16 to $61. Adjusting to 2000 $, this is a triangular distribution centered at $50.55, ranging
from $21 to $80.
A second unit value is based on Model 1, Table III in Dickie and Ulery (2002). This model
estimates the natural logarithm of parents' WTP to avoid symptoms as a linear function of the natural
logarithm of the number of symptom-days avoided and whether or not the person avoiding the symptoms
is the parent or the child. The unit value derived from this model, assuming that an MRAD consists of one
day of 3 symptoms in an adult, is $98.
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Appendix H. Economic Valuation of Health Effects
H.5.7 Asthma Exacerbation
Several respiratory symptoms in asthmatics or characterizations of an asthma episode have been
associated with exposure to air pollutants. All of these can generally be taken as indications of an asthma
exacerbation ("asthma attack") when they occur in an asthmatic. BenMAP therefore uses the same set of
unit values for all of the variations of "asthma exacerbation" that appear in the epidemiological literature.
Two unit values are currently available in BenMAP for asthma exacerbation in adults, and three
are currently available for asthma exacerbation in children. In past benefits analyses, EPA based
willingness to pay to avoid an asthma exacerbation on four WTP estimates from Rowe and Chestnut
(1986) for avoiding a "bad asthma day." The mean of the four average WTPs is $32 (1990 $), or $43 in
2000$. The uncertainty surrounding this estimate was characterized by a continuous uniform distribution
on the range defined by the lowest and highest of the four average WTP estimates from Rowe and
Chestnut, [$12, $54] in 1990$, or [$16, $71] in 2000 $. This unit value is available for both adults and
children.
A second unit value for adults was derived by using Model 1, Table III in Dickie and Ulery (2002)
(the same model used for acute bronchitis, LRS, and URS), assuming that an asthma exacerbation consists
of 1 symptom-day. As noted above, this model relates parental WTP to the number of symptom-days
avoided and to whether it is the parent or the child at issue. The unit value derived from this model for
adults is $74.
Two additional unit values are available for children. One of these is twice the original unit value,
or $86, based on the evidence from Dickie and Ulery (2002) that parents are willing to pay about twice as
much to avoid symptoms and illness in their children as in themselves. The third unit value is based on
Model 1, Table III in Dickie and Ulery (the same model used for asthma exacerbation in adults, only now
with the "adult or child" variable set to 1 rather than 0). The unit value derived from this model is $156.
H.5.8 School Loss Days
There is currently one unit value available in BenMAP for school loss days, based on (1) the
probability that, if a school child stays home from school, a parent will have to stay home from work to
care for the child, and (2) the value of the parent's lost productivity. We first estimated the proportion of
families with school-age children in which both parents work, and then valued a school loss day as the
probability of a work loss day resulting from a school loss day (i.e., the proportion of households with
school-age children in which both parents work) times a measure of lost wages.
From the U.S. Bureau of the Census (2002) we obtained (1) the numbers of single, married, and
"other" (i.e., widowed, divorced, or separated) women with children in the workforce, and (2) the rates of
participation in the workforce of single, married, and "other" women with children. From these two sets of
statistics, we calculated a weighted average participation rate of 72.85 percent, as shown in Exhibit H-8.
Our estimated daily lost wage (if a mother must stay at home with a sick child) is based on the
median weekly wage among women age 25 and older in 2000 (U.S. Bureau of the Census, 2002, Table
621). This median weekly wage is $551. Dividing by 5 gives an estimated median daily wage of $103.
The expected loss in wages due to a day of school absence in which the mother would have to stay home
with her child is estimated as the probability that the mother is in the workforce times the daily wage she
would lose if she missed a day = 72.85% of $103, or $75. We currently have insufficient information to
characterize the uncertainty surrounding this estimate.
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Appendix H. Economic Valuation of Health Effects
Exhibit H-8. Women with Children: Number and Percent in the Labor Force, 2000, and Weighted
Average Participation Rate
Category
Women in Labor
Force
(millions) a
(1)
Participation Rate
(%)a
(2)
Implied Total
Number in
Population (in
millions)
(3) = (l)/(2)
Implied Percent in
Population
(4)
Population-
Weighted Average
Participation Rate
[=sum (2) *(4) over
rows]
Single
3.1
73.9%
4.19
11.84%
-
Married
18.2
70.6%
25.78
72.79%
-
Other b
4.5
82.7%
5.44
15.36%
-
Total
--
-
35.42
-
72.85%
s Source: U.S. Bureau of the Census (2002, Table 577).
b Widowed, divorced, or separated.
A unit value based on the approach described above is likely to understate the value of a school
loss day in two ways. First, it omits WTP to avoid the symptoms/illness which resulted in the school
absence. Second, it effectively gives zero value to school absences which do not result in a work loss day.
The unit value of $75 is therefore considered an "interim" value until such time as alternative means of
estimating this unit value become available.
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Appendix H. Economic Valuation of Health Effects
Exhibit H-9. Unit Values Available for Acute Symptoms and Illnesses
Parameters of
, , ,, , „ ... ... ,. Age Kange Unit Distribution nistrihntion
Health Endpoint Basis for Estimate ... „TT , uistriDunon
V alue of Unit V alue
min.
max.
PI
P2
Acute Bronchitis
WTP: 1 day illness, CV studies
0
17
$59
uniform
17.5099
101.107
WTP: 6 day illness, CV studies
0
17
$356
uniform
105.059
606.639
WTP: 28 symptom-days, Dickie and
Ulery (2002)
0
17
$374
lognormal
5.9470
0.0907
Any of 19
Respiratory
Symptoms
WTP: 1 day illness, CV studies
18
65
$24
uniform
0
48.2476
Minor Restricted
Activity Days
WTP: 1 day, CV studies
WTP: 3 symptoms 1 day, Dickie and
Ulery (2002).
18
18
99
99
$51
$98
triangular
lognormal
20.7114
4.60884
80.3688
0.06486
Lower Respiratory
WTP: 1 day, CV studies
0
17
$16
uniform
6.94334
24.4664
Symptoms
WTP: 2 symptoms 1 day, Dickie and
Ulery (2002).
0
17
$187
lognormal
5.2556
0.07048
WTP: 2 x 1 day, CV studies
0
17
$31
uniform
13.8867
48.9327
School Loss Days
0
0
17
$75
none
N/A
N/A
Upper Respiratory
WTP: 1 day, CV studies
0
17
$25
uniform
9.22265
43.1093
Symptoms
WTP: 2 symptoms 1 day, Dickie and
Ulery (2002)
0
17
$187
lognormal
5.2556
0.07048
WTP: 2x1 day, CV studies
0
17
$49
uniform
18.4453
86.2186
Work Loss Days b
Median daily wage, county-specific
18
65
$115
none
N/A
N/A
"All unit values pulled from a lognormal distribution from Model 1, Table III in Dickie and Ulery (2002) are multiplied by
0.973811 to adjust for a difference in mean household income between the study participants and the general population. The unit
values shown here have already been adjusted.
bUnit values for work loss days are county-specific, based on county-specific median wages. The unit value shown here is the
national median daily wage, given for illustrative purposes only.
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Appendix H. Economic Valuation of Health Effects
Exhibit H-10. Unit Values Available for Asthma-related Acute Symptoms and Illnesses
Health
Endpoint
Basis for Estimate*
Age Range
mm. max.
Unit
Value
Unit Value
Distribution
Parameters of
Distribution
PI P2
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
Asthma
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
Attacks
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
Cough
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
Moderate or
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
Worse
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
One or More
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
Symptoms
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
Shortness of
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
Breath
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
Asthma
WTP: bad asthma day, Rowe and Chestnut (1986)
18
99
$43
uniform
15.5599
70.8826
Exacerbation,
Wheeze
WTP: 1 symptom-day, Dickie and Ulery (2002)
18
99
$74
lognormal
4.321
0.09569
WTP: bad asthma day, Rowe and Chestnut (1986)
0
17
$43
uniform
15.5599
70.8826
WTP: 2 x bad asthma day, Rowe and Chestnut
0
17
$86
uniform
31.1198
141.765
(1986)
WTP: 1 symptom-day, Dickie and Ulery (2002)
0
17
$156
lognormal
5.074
0.09253
*A11 unit values pulled from a lognormal distribution from Model 1, Table III in Dickie and Ulery, 2002, are multiplied by
0.973811 to adjust for a difference in mean household income between the study participants and the general population. The unit
values shown here have already been adjusted.
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Appendix H. Economic Valuation of Health Effects
Exhibit H-ll. Unit Value Uncertainty Distributions and Their Parameters
Distribution"
Parameter 1 (PI)
Parameter 2 (P2)
Normal
standard deviation
-
Triangular
minimum value
maximum value
Lognormal b
mean of corresponding normal
distribution
standard deviation of corresponding
normal distribution
Uniform
minimum value
maximum vaue
Weibullc
a
P
s In all cases, BenMAP calculates the mean of the distribution, which is used as the "point estimate" of the unit value.
b If Y is a normal random variable, and Y = logeX, then X is lognormally distributed. Equivalently, X is lognormally distributed
if X = eY, where Y is normally distributed.
\ A-i
a)
: The Weibull distribution has the following probability density function: ^ ^
a¦
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Appendix I. Uncertainty & Pooling
Appendix I: Uncertainty & Pooling
This Appendix discusses the treatment of uncertainty in BenMAP, both for incidence changes and
associated dollar benefits. Some background is then given on pooling methodology. Finally, the
mechanics of the various Pooling Methods available in BenMAP are discussed in detail, including
Subjective Weight based pooling, Fixed Effects pooling, Random/Fixed Effects pooling, and independent
and dependent Sum and Subtraction.
1.1 Uncertainty
Although there are several sources of uncertainty affecting estimates of incidence changes and
associated benefits, the sources of uncertainty that are most readily quantifiable in benefits analyses are
uncertainty surrounding the C-R relationships and uncertainty surrounding unit dollar values. The total
dollar benefit associated with a given endpoint group depends on how much the endpoint group will
change in the control scenario (e.g., how many premature deaths will be avoided) and how much each unit
of change is worth (e.g., how much a statistical death avoided is worth).
Both the uncertainty about the incidence changes and uncertainty about unit dollar values can be
characterized by distributions. Each "uncertainty distribution" characterizes our beliefs about what the
true value of an unknown (e.g., the true change in incidence of a given health effect) is likely to be, based
on the available information from relevant studies.201 Unlike a sampling distribution (which describes the
possible values that an estimator of an unknown value might take on), this uncertainty distribution
describes our beliefs about what values the unknown value itself might be. Such uncertainty distributions
can be constructed for each underlying unknown (such as a particular pollutant coefficient for a particular
location) or for a function of several underlying unknowns (such as the total dollar benefit of a regulation).
In either case, an uncertainty distribution is a characterization of our beliefs about what the unknown (or
the function of unknowns) is likely to be, based on all the available relevant information. Uncertainty
statements based on such distributions are typically expressed as 90 percent credible intervals. This is the
interval from the fifth percentile point of the uncertainty distribution to the ninety-fifth percentile point.
The 90 percent credible interval is a "credible range" within which, according to the available information
(embodied in the uncertainty distribution of possible values), we believe the true value to lie with 90
percent probability. The uncertainty surrounding both incidence estimates and dollar benefits estimates
can be characterized quantitatively in BenMAP. Each is described separately below.
1.1.1 Characterization of Uncertainty Surrounding Incidence Changes
To calculate point estimates of the changes in incidence of a given adverse health effect associated
with a given set of air quality changes, BenMAP performs a series of calculations at each grid-cell. First,
it accesses the C-R functions needed for the analysis, and then it accesses any data needed by the C-R
functions. Typically, these include the grid-cell population, the change in population exposure at the grid-
cell, and the appropriate baseline incidence rate. BenMAP then calculates the change in incidence of
adverse health effects for each selected C-R function. This is described more fully in Chapter 5. The
201 Although such an "uncertainty distribution" is not formally a Bayesian posterior distribution, it
is very similar in concept and function (see, for example, the discussion of the Bayesian approach in
Kennedy 1990, pp. 168-172).
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Appendix I. Uncertainty & Pooling
resulting incidence change is stored, and BenMAP proceeds to the next grid-cell, where the above process
is repeated.
In Latin Hypercube mode (see Chapter 5), BenMAP reflects the uncertainty surrounding estimated
incidence changes (resulting from the sampling uncertainty surrounding the pollutant coefficients in the C-
R functions used) by producing a distribution of possible incidence changes rather than a single point
estimate. To do this, it uses the distribution (DistBeta) associated with the pollutant coefficient (Beta, or
P), and potentially the point estimate (Beta) and two parameters (PIBeta, P2Beta). Typically, pollutant
coefficients are normally distributed, with mean Beta and standard deviation PI Beta. See Chapter 8 for
more information on these C-R Function variables.
BenMAP uses an N-point Latin Hypercube202 to represent the underlying distribution of P and to
create a corresponding distribution of incidence changes in each population grid cell, where N is
specified by you (as Latin Hypercube Points - see Chapter 5). The Latin Hypercube method represents an
underlying distribution by N percentile points of the distribution, where the n'h percentile point is equal to:
, 100 100
{n-1)- +
N 2N
Suppose, for example, that you elect to use a 20-point Latin Hypercube. BenMAP would then represent
the distribution of P by 20 percentile points, specifically the 2.5th, 7.5th, ..., 97.5th. To do this, the inverse
cumulative distribution function specified by the distribution of P is called with the input probability equal
to each the 20 percentile points. BenMAP then generates an estimate of the incidence change in a grid-cell
for each of these values of P, resulting in a distribution of N incidence changes. This distribution is stored,
and BenMAP proceeds to the next population grid-cell, where the process is repeated.
1.1.2 Characterization of Uncertainty Surrounding Dollar Benefits
The uncertainty distribution of the dollar benefits associated with a given health or welfare effect
is derived from the two underlying uncertainty distributions - the distribution of the change in incidence of
the effect (number of cases avoided) and the distribution of the value of a case avoided (the "unit value").
The derivation of the uncertainty distribution for incidence change is described above. The distributions
used to characterize the uncertainty surrounding unit values are described in detail in Appendix H. As
noted in that Appendix, a variety of distributions have been used to characterize the uncertainty of unit
values, including uniform, triangular, normal, and Weibull.
To represent the underlying distribution of uncertainty surrounding unit values, a 100-point Latin
Hypercube is generated in the same way described in the previous section for the distribution of p. That is,
the unit value distribution is represented using the 0.5th, 1.5th, ..., and 99.5th percentile values of its
distribution.
202The Latin Hypercube method is used to enhance computer processing efficiency. It is a
sampling method that divides a probability distribution into intervals of equal probability, with an
assumption value for each interval assigned according to the interval's probability distribution.
Compared with conventional Monte Carlo sampling, the Latin Hypercube approach is more precise over a
fewer number of trials because the distribution is sampled in a more even, consistent manner
(Decisioneering, 1996, pp. 104-105).
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A distribution of the uncertainty surrounding the dollar benefits associated with a given endpoint
is then derived from latin hypercube values generated to represent the change in incidence and the latin
hypercube values generated to represent the unit value distribution. To derive this new distribution, each
of the 100 unit values is multiplied by each of the N incidence change values, yielding a set of 100 * N
dollar benefits. These values are sorted low to high and binned down to a final distribution of N dollar
benefit values.
1.2 Pooling
There is often more than one study that has estimated a C-R function for a given pollutant-health
endpoint combination. Each study provides an estimate of the pollutant coefficient, P, in the C-R function,
along with a measure of the uncertainty of the estimate. Because uncertainty decreases as sample size
increases, combining data sets is expected to yield more reliable estimates of p, and therefore more reliable
estimates of the incidence change predicted using p. Combining data from several comparable studies in
order to analyze them together is often referred to as meta-analysis.
For a number of reasons, including data confidentiality, it is often impractical or impossible to
combine the original data sets. Combining the results of studies in order to produce better estimates of p
provides a second-best but still valuable way to synthesize information (DerSimonian and Laird, 1986).
This is referred to as pooling. Pooling P's requires that all of the studies contributing estimates of p use
the same functional form for the concentration-response function. That is, the P's must be measuring the
same thing.
It is also possible to pool the study-specific estimates of incidence change derived from the C-R
functions, instead of pooling the underlying P's themselves. For a variety of reasons, this is often possible
when it is not feasible to pool the underlying P's. For example, if one study is log-linear and another is
linear, we could not pool the P's because they are not different estimates of a coefficient in the same C-R
function, but are instead estimates of coefficients in different C-R functions. We can, however, calculate
the incidence change predicted by each C-R function (for a given change in pollutant concentration and,
for the log-linear function, a given baseline incidence rate), and pool these incidence changes. BenMAP
allows the pooling of incidence changes predicted by several studies for the same pollutant-health endpoint
group combination. It also allows the pooling of the corresponding study-specific estimates of monetary
benefits.
As with estimates based on only a single study, BenMAP allows you to characterize the
uncertainty surrounding pooled estimates of incidence change and/or monetary benefit. To do this,
BenMAP pools the study-specific distributions of incidence changes (or monetary benefit) to derive a
pooled distribution. This pooled distribution incorporates information from all the studies used in the
pooling procedure.
1.2.1 Weights Used for Pooling
The relative contribution of any one study in the pooling process depends on the weight assigned
to that study. A key component of the pooling process, then, is the determination of the weight given to
each study. There are various methods that can be used to assign weights to studies (these are three of the
Pooling Methods - see Chapter 6 for more information). Below we discuss the possible weighting
schemes that are available in BenMAP.
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Subjective (User-specified) Weights
BenMAP allows you the option of specifying the weights to be used. Suppose, for example, you
want to simply average all study-specific results. You would then assign a weight of 1/N to each of the N
study-specific distributions that are to be pooled. Note that subjective weights are limited to two decimal
places, and are normalized if they do not sum to one.
Automatically Generated Weights
A simple average has the advantage of simplicity but the disadvantage of not taking into account
the uncertainty of each of the estimates. Estimates with great uncertainty surrounding them are given the
same weight as estimates with very little uncertainty. A common method for weighting estimates involves
using their variances. Variance takes into account both the consistency of data and the sample size used to
obtain the estimate, two key factors that influence the reliability of results. BenMAP has two methods of
automatically generating pooling weights using the variances of the input distributions - Fixed Effects
Pooling and Random /Fixed Effects Pooling.
The discussion of these two weighting schemes is first presented in terms of pooling the pollutant
coefficients (the P's), because that most closely matches the discussion of the method for pooling study
results as it was originally presented by DerSimonian and Laird (1986). We then give an overview of the
analogous weighting process used within BenMAP to generate weights for incidence changes rather than
P's.
Fixed Effects Weights
The fixed effects model assumes that there is a single true concentration-response relationship and
therefore a single true value for the parameter p that applies everywhere. Differences among P's reported
by different studies are therefore simply the result of sampling error. That is, each reported P is an
estimate of the same underlying parameter. The certainty of an estimate is reflected in its variance (the
larger the variance, the less certain the estimate). Fixed effects pooling therefore weights each estimate
under consideration in proportion to the inverse of its variance.
Suppose there are n studies, with the ith study providing an estimate P, with variance v, (I = 1, ...,
n). Let
denote the sum of the inverse variances. Then the weight, \v,, given to the ith estimate, p,, is
1/v,
w. =
S
This means that estimates with small variances (i.e., estimates with relatively little uncertainty surrounding
them) receive large weights, and those with large variances receive small weights.
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The estimate produced by pooling based on a fixed effects model, then, is just a weighted average
of the estimates from the studies being considered, with the weights as defined above. That is,
p/e = E w. * p. .
The variance associated with this pooled estimate is the inverse of the sum of the inverse variances:
i
Exhibit 1-2 shows the relevant calculations for this pooling for three sample studies.
Exhibit 1-2. Example of Fixed Effects Model Calculations
Study
Pi
Vj
1/v,
W;
w,*p,
1
0.75
0.1225
8.16
0.016
0.012
2
1.25
0.0025
400
0.787
0.984
3
1.00
0.0100
100
0.197
0.197
Sum
£ = 508.16
£= 1.000
£= 1.193
The sum of weighted contributions in the last column is the pooled estimate of p based on the
fixed effects model. This estimate (1.193) is considerably closer to the estimate from study 2 (1.25) than is
the estimate (1.0) that simply averages the study estimates. This reflects the fact that the estimate from
study 2 has a much smaller variance than the estimates from the other two studies and is therefore more
heavily weighted in the pooling.
The variance of the pooled estimate, vfe, is the inverse of the sum of the variances, or 0.00197.
(The sums of the P, and v, are not shown, since they are of no importance. The sum of the 1 / v is S, used to
calculate the weights. The sum of the weights, w;, i=l,..., n, is 1.0, as expected.)
Random / Fixed Effects Weights
An alternative to the fixed effects model is the random effects model, which allows the possibility
that the estimates P, from the different studies may in fact be estimates of different parameters, rather than
just different estimates of a single underlying parameter. In studies of the effects of PM10 on mortality, for
example, if the composition of PM10 varies among study locations the underlying relationship between
mortality and PM10 may be different from one study location to another. For example, fine particles make
up a greater fraction of PM10 in Philadelphia than in El Paso. If fine particles are disproportionately
responsible for mortality relative to coarse particles, then one would expect the true value of p in
Philadelphia to be greater than the true value of p in El Paso. This would violate the assumption of the
fixed effects model.
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The following procedure can test whether it is appropriate to base the pooling on the random
effects model (vs. the fixed effects model):
A test statistic, Qw , the weighted sum of squared differences of the separate study estimates from
the pooled estimate based on the fixed effects model, is calculated as:
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Appendix I. Uncertainty & Pooling
The estimate produced by pooling based on the random effects model, then, is just a weighted
average of the estimates from the studies being considered, with the weights as defined above. That is,
B.^ = v w/ * B. .
The variance associated with this random effects pooled estimate is, as it was for the fixed effects
pooled estimate, the inverse of the sum of the inverse variances:
i
^rand
S 1/v/
The weighting scheme used in a pooling based on the random effects model is basically the same
as that used if a fixed effects model is assumed, but the variances used in the calculations are different.
This is because a fixed effects model assumes that the variability among the estimates from different
studies is due only to sampling error (i.e., each study is thought of as representing just another sample from
the same underlying population), while the random effects model assumes that there is not only sampling
error associated with each study, but that there is also between-study variability ~ each study is estimating
a different underlying p. Therefore, the sum of the within-study variance and the between-study variance
yields an overall variance estimate.
Fixed Effects and Random / Fixed Effects Weighting to Pool Incidence Change Distributions and
Dollar Benefit Distributions
Weights can be derived for pooling incidence changes predicted by different studies, using either
the fixed effects or the fixed / random effects model, in a way that is analogous to the derivation of weights
for pooling the P's in the C-R functions. As described above, BenMAP generates a latin hypercube
representation of the distribution of incidence change corresponding to each C-R Function selected. The
means of those study-specific latin hypercube distributions of incidence change are used in exactly the
same way as the reported P's are used in the calculation of fixed effects and random effects weights
described above. The variances of incidence change are used in the same way as the variances of the P's.
The formulas above for calculating fixed effects weights, for testing the fixed effects hypothesis, and for
calculating random effects weights can all be used by substituting the mean incidence change for the ith C-
R Function for p and the variance of incidence change for the ith C-R Function for vr203
Similarly, weights can be derived for dollar benefit distributions. As described above, BenMAP
generates a latin hypercube representation of the distribution of dollar benefits . The means of those latin
hypercube distributions are used in exactly the same way as the reported P's are used in the calculation of
fixed effects and random effects weights described above. The variances of dollar benefits are used in the
same way as the variances of the P's. The formulas above for calculating fixed effects weights, for testing
203 There may be a problem with transferring the fixed effects hypothesis test to "incidence
change space." The test statistic to test the fixed effects model is a chi-squared random variable. In the
original paper on this pooling method, DerSimonian and Laird, 1986, were discussing the pooling of
estimates of parameters, which are generally normally distributed. The incidence changes predicted from
a C-R function will not be normally distributed if the C-R function is not a linear function of the pollutant
coefficient, which, in most cases it is not. (Most C-R functions are log-linear.) In that case, the test
statistic may not be chi-square distributed. However, most log-linear C-R functions are nearly linear
because their coefficients are very small. In that case the test statistic is likely to be nearly chi-square
distributed.
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the fixed effects hypothesis, and for calculating random effects weights can all be used by substituting the
mean dollar benefit change for the ith valuation for p and the variance of dollar benefits for the ith
valuation for vr
BenMAP always derives Fixed Effects and Random /Fixed Effects weights using nationally
aggregated results, and uses those weights for pooling at each grid cell (or county, etc. if you choose to
aggregate results prior to pooling). This is done because BenMAP does not include any regionally based
uncertainty - that is, all uncertainty is at the national level in BenMAP, and all regional differences
(population, for example) are treated as certain.
1.2.2 The Mechanics of Pooling in BenMAP
Once weights are generated for each input distribution, BenMAP has three options for using these
weights to combine the input distributions into a single new distribution. These options are referred to as
Advanced Pooling Methods (see Chapter 6 for more details).
Round Weights to Two Digits
This is BenMAP's default Advanced Pooling Method, and is always the method used when
Subjective Weights are used. The first step is converting the weights to two digit integers by multiplying
them by 100 and rounding to the nearest integer. If all the integral weights thus generated are divisible by
the smallest weight, they are each divided by that smallest weight. For example, if the original weights
were 0.1, 0.2, 0.3, and 0.4, the resulting integral weights would be 10/10, 20/10, 30/10, and 40/10 (or 1, 2,
3, and 4).
BenMAP then creates a new distribution by sampling each entire input distribution according to its
weight. That is, in the above example the first distribution would be sampled once, the second distribution
twice, and so forth. The advantage of sampling whole distributions is that it preserves the characteristics
(i.e., the moments - the mean, the variance, etc.) of the underlying distributions. Assuming n latin
hypercube points, the resulting distribution will contain a maximum of 100 * n values, which are then
sorted low to high and binned down to n values, which will represent the new, pooled distribution.
Round Weights to Three Digits
This Advanced Pooling Method is essentially the same as rounding weights to two digits, except
that the weights are converted to three digit integers, and so forth. That is, the weights are multiplied by
1000 and rounded to the nearest integer. Again, if all the integral weights thus generated are divisible by
the smallest weight, they are each divided by that smallest weight. Assuming n latin hypercube points, the
resulting distribution with this Advanced Pooling Method can contain a maximum of 1000 * n values,
which are sorted low to high and binned down to n values, which represent the new, pooled distribution.
Exact Weights for Monte Carlo
This Advanced Pooling Method uses a Monte Carlo method to combine the input distributions.
Using this method, on each of many iterations, (1) an input distribution is selected (with the probability of
selection equal to the weight assigned to the distribution), and (2) a value is randomly drawn from that
distribution. Values chosen in this way are placed into a temporary pooled distribution, which will have
one point per iteration of the Monte Carlo method. The number of iterations is specified by the user (see
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Chapter 6), and defaults to 5,000. After the temporary distribution is fully generated, it is sorted low to
high and binned down to n values (where n is the number of Latin Hypercube Points chosen for the
analysis - see Chapter 5).
1.2.3 Summing Distributions
Sometimes rather than pooling distributions we want to add them. For example, some studies
have estimated a C-R function for hospital admissions for COPD and another C-R function for hospital
admissions for pneumonia. From each of these C-R functions, BenMAP can derive the corresponding
distributions for incidence change. Hospital admissions for COPD and pneumonia are two of the most
important components of respiratory hospital admissions, and we may want to estimate the number of
cases of "respiratory hospital admissions," as characterized by being either COPD or pneumonia. To do
this we would add the two distributions.
Summing across distributions can be done in one of two ways: We can assume the two
distributions are independent of each other or dependent. Which is the more reasonable assumption
depends on the particulars of the distributions being summed.
Assuming Independence
This is the Sum (Independent) Pooling Method (see Chapter 6 for details). To sum two
distributions that are independent, on each of many iterations of a Monte Carlo procedure, BenMAP (1)
randomly selects a value from the first input distribution, (2) randomly selects a value from the second
input distribution, and (3) adds the two values together. To sum N distributions that are independent,
BenMAP follows an analogous procedure in which, on each iteration it makes a random selection from
each of the input distributions and then adds the results together. When the Monte Carlo procedure is
completed, all such generated results are sorted low to high and binned down to the appropriate number of
latin hypercube points. The number of iterations is determined by the Monte Carlo Iterations setting (see
Chapter 6).
Assuming Dependence
This is the Sum (Dependent) Pooling Method (see Chapter 6 for details). Recall that the
uncertainty distributions in BenMAP are latin hypercube representations, consisting of N percentile points.
To sum two distributions assumed to be dependent, BenMAP simply generates a new N point latin
hypercube where each point is the sum of the corresponding points from the input latin hypercubes. That
is, the first point in the new latin hypercube is the sum of the first points in the two input latin hypercubes,
and so forth. To sum n distributions that are assumed to be dependent, BenMAP follows an analogous
procedure in which each point in the new latin hypercube is the sum of the corresponding points from each
of the input latin hypercubes.
1.2.4 Subtracting Distributions
In some cases, you may want to subtract one or more distribution(s) from another. For example,
one study may have estimated a C-R function for minor restricted activity days (MRADs), and another
study may have estimated a C-R function for asthma "episodes." You may want to subtract the change in
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incidence of asthma episodes from the change in incidence from MRADs before estimating the monetary
value of the MRADs, so that the monetary value of asthma episodes avoided will not be included.
Subtracting across distributions can be done in one of two ways: we can assume the two
distributions are independent of each other or dependent. Which is the more reasonable assumption
depends on the particulars of the distributions being subtracted.
Assuming Independence
This is the Subtraction (Independent) Pooling Method. To subtract one distribution from another,
assuming independence, on each of many iterations of a Monte Carlo procedure, BenMAP (1) randomly
selects a value from the first input distribution, (2) randomly selects a value from the second input
distribution, and (3) subtracts the second value from the first. To subtract N distributions from another
distribution, assuming independence, BenMAP follows an analogous procedure in which, on each iteration
it makes a random selection from each of the input distributions and then subtracts the second through the
Nth from the first. When the Monte Carlo procedure is completed, all such generated results are sorted
low to high and binned down to the appropriate number of latin hypercube points. The number of
iterations is determined by thq Monte Carlo Iterations setting (see Chapter 6).
Assuming Dependence
This is the Subtraction (Dependent) Pooling Method (see Chapter 6 for details). Recall that the
uncertainty distributions in BenMAP are latin hypercube representations, consisting of N percentile points.
To subtract one distribution from another, assuming them to be dependent, BenMAP simply generates a
new N point latin hypercube where each point is the result of subtracting the corresponding point of the
second input latin hypercube from the corresponding point of the first input latin hypercube. That is, the
first point in the new latin hypercube is the result of subtracting the first point in the second latin
hypercube from the first point of the first latin hypercube, and so forth. To subtract n distributions from
another distribution, assuming dependence, BenMAP follows an analogous procedure in which each point
in the new latin hypercube is the result of subtracting the corresponding points of the second through the
Nth input latin hypercubes from the corresponding point of the first.
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References
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