United States EPA Science Advisory EPA-SAB-EC-01-008
Environmental Board (1400A) August 2001
Protection Agency Washington, DC
www. epa.sov/sab
vs/EBA ARSENIC RULE
BENEFITS ANALYSIS:
AN SAB REVIEW
A REVIEW BY THE ARSENIC
RULE BENEFITS REVIEW
PANEL (ARBRP) OF THE US
EPA SCIENCE ADVISORY
BOARD (SAB)
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
WASHINGTON, D.C. 20460
August 3 0,2001
OFFICE OF THE ADMINISTRATOR
SCIENCE ADVISORY BOARD
EPA-SAB-EC-01-008
Honorable Christine Todd Whitman
Administrator
U.S. Environmental Protection Agency
1200 Pennsylvania Avenue, NW
Washington, DC 20460
Subject: Arsenic Rule Benefits Analysis; An EPA Science Advisory Board Review
Dear Governor Whitman:
On July 19 and 20, 2001 the Arsenic Rule Benefits Review Panel (ARBRP) of the US
EPA Science Advisory Board (SAB) met to review the EPA report Arsenic in Drinking Water
Rule Economic Analysis (EPA 815-R-00-026). As part of the review process, the Panel
responded to five charge questions:
Charge Question 1: How should latency be addressed in the benefits estimates when
existing literature does not provide specific quantitative estimates of latency periods
associated with exposure to arsenic in drinking water?
Charge Question 2: How should health endpoints (other than bladder and lung cancer) be
addressed in the analysis, when [existing] literature does not provide specific
quantification, to ensure appropriate consideration by decision makers and the public?
Charge Question 3: Should reduction/elimination of exposure be evaluated as a separate
benefits category, in addition to or in conjunction with mortality and morbidity
reduction?
Charge Question 4: How should total benefits and costs and incremental benefits and
costs be addressed in analyzing regulatory alternatives to ensure appropriate
consideration by decision makers and the public?
Charge Question 5: How should uncertainties be addressed in the analysis to ensure
appropriate consideration by decision makers and the public?
Detailed answers to these questions are found in the body of the report. The major
findings and recommendations are:
-------
1. Charge Question 1
In evaluating the health benefits of a reduction in exposure to a carcinogen, what matters
is the cessation-lag between a reduction in exposure and a reduction in risk. While 'latency' is
the term used in the charge, in fact, 'cessation-lag' is the more appropriate term, and the two are
not necessarily equivalent. In other words, time between initiation of exposure and the increase
in risk (latency) does not necessarily equal time between cessation of exposure and the reduction
in risk.
The length of the cessation-lag determines the number of cancer cases avoided each year
after a policy is implemented. If, for example, people previously exposed to 50 jig/L of arsenic
in drinking water are exposed, beginning in 2006, to only 10 |ig/L, cancer risks in the population
will eventually decline to a steady-state level associated with a lifetime of exposure to 10 |ig/L.
How fast this reduction in risk occurs depends on the cessation-lag following reduction in
exposure. If the cessation-lag is zero, this steady-state level will be reached immediately.
We believe that the current arsenic benefits analysis is flawed for two reasons: (a) the
primary analysis considers only the case of a zero cessation-lag; (b) when the analysis considers
alternate 'latency periods' it incorrectly assumes no reduction in cancer cases until the end of the
latency period. The correct approach is to clearly identify the assumption of a zero cessation-lag
as an upper bound to benefits and to consider alternate, plausible cessation-lags in the primary
benefits analysis. In the report, we suggest ways in which the length of the cessation-lag could
be estimated. To each assumption there corresponds a time path of cancer cases avoided that
gradually approaches the steady-state number of cancer cases avoided.
2. Charge Question 2
The scientific literature on health effects due to arsenic exposure includes studies of a
number of endpoints other than cancer, as well as studies of several cancer sites for which the
risks/benefits have not been quantified (USEPA 2000). The quality of these studies varies, as does the
strength of evidence they provide. Specifically, it appears to us that it should be
possible to quantify mortality from ischemic heart disease, diabetes mellitus, hypertension and
skin cancer, and that the evidence is reasonably strong relating arsenic to these endpoints.
Although the strength of evidence is lower, the Panel recommends serious consideration be
given to quantification of benefits from reductions in prostate cancer, nephritis and nephrosis,
hypertensive heart disease and non-malignant respiratory disease. The literature that would
permit quantification of cases avoided for these endpoints is discussed in Section 2.2.2 of the
report.
Ideally, quantification would take the form of a dose response function that would permit
the Agency to estimate the number of cases of mortality and morbidity avoided by the
regulation. If, however, the shape of the dose-response function cannot reliably be estimated at
doses relevant to the regulation considered, it would be useful to compare benchmark doses for
the non-quantified endpoints (e.g., the ED01) with benchmark doses for the quantified
endpoints.1 This will indicate whether non-quantified effects are, in fact, seen at similar
exposures in the study populations as the bladder and lung cancer outcomes.
1 The ED01 is that dose which produces a response in 1% of the population. It is equivalent to a
1 in 100 risk.
-------
In addition to these comparisons, the type of information that should be provided in a
benefit-cost analysis about endpoints that have not been quantified is described in tables, such as
those presented in Appendix 2.2 of this report. Studies must first be selected according to well-
defined criteria. The information that should be provided for each study (grouped by health
endpoint of interest) includes:
a) Nature of the study design
b) How exposure was measured
c) Range of exposures observed
d) What type of statistical analysis was conducted and what confounding factors
were controlled for in the analysis
e) Measure of association (e.g., odds ratio) and level of statistical significance of
the association
In some cases the literature may be so extensive that a summary of results is required in
the text of the report. As much as possible, this summary should focus on clinical measures that
are clear indications of morbidity and that affect individuals' well-being and activities so as to
make it possible to link these endpoints to the available data on individuals' valuations of
improvements in health. It should also provide some discussion of the mechanism by which the
toxin would be expected to exert an effect. The summary should also indicate the level at which
effects were observed in the studies reported (including benchmark doses where possible) and
should comment on the likelihood of observing these effects at the levels relevant to the
regulatory decision.
3. Charge Question 3
Regarding Charge Question 3, we believe that reductions in exposure in this case should
not be considered a separate category of benefits in a benefit cost analysis. The damage function
approach to valuing benefits currently used by the Agency separates the measurement of the
relationship between exposure and response (e.g., risk of fatal or non-fatal cancer) from the
valuation of reductions in risk of death or illness. Epidemiologists estimate dose-response
functions and economists measure the value people place on reductions in risk of death or illness
associated with them. To add a separate value for reductions in exposure to arsenic per se would
double count the health benefits estimated using the damage function approach.
We do recognize that some people may value the existence of lower levels of arsenic in
drinking water, possibly for psychological reasons (e.g., dread of being exposed), and we believe
that existence values are a legitimate category of benefits. Existence values are not
accommodated within a damage function approach to benefit quantification. Reliable estimates
of these values would need to identify the marginal benefit to individuals associated with a
change in concentration, separate from the change in health risks associated with the change in
exposure. We found no empirical evidence to support or contradict such a relationship in the
case of arsenic. In the absence of any empirical data, there is no basis for estimating an
existence value in this case.
4. Charge Question 4
We applaud the Agency for presenting the costs and benefits associated with various
possible maximum contaminant levels rather than presenting only the costs and benefits
associated with a single standard that the Agency proposes to implement. We believe, however,
that in the primary analysis (and in the Executive Summary) benefits and costs should be
calculated on a water supply system basis, with the results summarized in a format that breaks
-------
them down by system size. Because of the large economies of scale associated with drinking
water treatment, the net benefits (benefits minus costs) are likely to vary substantially by system
size, and this information should be made clear to policy makers and the public.
Such an analysis would allow decision makers to evaluate a range of alternative
strategies rather than a one-size-fits-all approach. The high cost of arsenic control is driven by
the tail of a distribution involving a number of small systems. The analysis needs to make this
clear so that decision makers can consider this fact in formulating an appropriate response. For
example, other policy measures that could be considered include efforts to promote the
consolidation of very small systems, or the provision of bottled water by very small systems to
meet their customers' needs for potable water.
We also believe that benefits (and incremental benefits associated with different
maximum contaminant levels) should be presented in terms of cases of morbidity and mortality
avoided as well as in monetary terms, and that the age distribution of cases avoided should be
presented whenever possible. The description of cases avoided allows readers to consider
alternatives to monetization of benefits. Information about the age distribution of health benefits
is important in evaluating the incidence of regulations, and benefit-cost analyses should make
this task as easy as possible.
5. Charge Question 5
Benefit-cost analyses of drinking water regulations are likely to entail uncertainties in the
(a) measurement of exposure, (b) measurement of dose-response, (c) valuation of health
outcomes and (d) measurement of costs. The sources of these uncertainties include
measurement error (uncertainty about the average level of arsenic in tap water or of the amount
of tap water consumed) as well as uncertainty about which model to use in describing the
relationship between exposure and response at low doses. In general, there are two approaches
to handling these sources of uncertainty—sensitivity analysis and Monte Carlo simulation. In a
sensitivity analysis various assumptions are made about the correct model (e.g., dose response
function) or parameter (e.g., discount rate) to use in the analysis and results are presented for
each set of assumptions. In a Monte Carlo analysis a distribution is assumed for a key parameter
or set of parameters (e.g., the slope of the dose-response function) and several thousand draws
are made from this distribution. Benefits are calculated for each value of the parameter drawn.
This yields a probability distribution of benefits, whose parameters (e.g., the 10th and 90th
percentiles) can be reported.
We believe that, in the case of model uncertainty, it is appropriate to rely on sensitivity
analysis; however, the assumptions underlying each sensitivity analysis should be clearly spelled
out when presenting results. It is particularly inappropriate to present only the highest and
lowest numbers associated with a set of sensitivity analyses, which may give the reader the false
impression that these constitute the upper and lower bounds of a uniform distribution. For
parameters for which it is possible to specify a distribution, Monte Carlo analysis is desirable
(for example, in the case of the slope of the dose-response function).
6. General Comments on the Benefit-Cost Analysis for Arsenic
The document Arsenic in Drinking Water Rule: Economic Analysis makes a serious
attempt at analyzing the benefits and costs of alternate MCLs for arsenic in drinking water.
Many aspects of the analysis deserve commendation. These include calculating benefits and
costs for different possible MCLs, presenting some breakdown of benefits and costs by system
size, and presenting cost-effectiveness information (cost per cancer case avoided) that would
-------
enable the drinking water standard for arsenic to be compared to other public health programs.
We do, however, have certain criticisms of the computation of the benefits, the
computation of the costs, and with the presentation of the results, especially as they appear in the
Executive Summary.
a) Computation of Benefits
(1) In calculating cancer cases avoided, the primary (central case) analysis
assumes no cessation-lag between reduction in exposure to arsenic and
reduction in cancer risk. This assumption yields an upper bound to the
number of cancer cases avoided by any MCL. It should be noted that this
assumption produces an upper bound to benefits. Furthermore, alternate
assumptions regarding the length of the cessation-lag should be included
in the primary analysis and reported in the Executive Summary.
(2) Estimates of cancer cases avoided should be broken down by age. The
underlying dose-response function (Morales et al. 2000) predicts
reductions in risk by age group; hence cancer cases avoided can be broken
down by age group. It is important for policy makers and the public to
know how many beneficiaries of a regulation are seven years old and how
many are 70.
(3) We believe that it is possible to quantify more health endpoints than lung
and bladder cancers. Specifically, it appears to us that the data permit
quantification of mortality from ischemic heart disease, diabetes mellitus,
hypertension and skin cancer, for which substantial evidence supports an
association, as well as for prostate cancer, nephritis and nephrosis,
hypertensive heart disease and non-malignant respiratory disease, for
which some evidence points to an association with arsenic exposure.
However, this recommendation should be considered in light of the more
definitive analysis by the NAS Arsenic Subcommittee.
(4) The benefit analysis should present detailed information on non-quantified
health effects in the manner suggested in this report (see Section 2.2 and
Appendix 2.2), rather than simply listing possible health effects.
(5) Estimates of avoided non-fatal cancers and other non-fatal diseases should
be computed in the same fashion as estimates of avoided fatal cancers.
The length of the cessation-lag should also be treated in a parallel fashion.
(6) To value non-fatal bladder cancers, the Agency used a value for chronic
bronchitis provided by Viscusi, et al. (1991). This study is based on a
small sample and values a different kind of health endpoint. There is one
study (Magat et al. 1996) that values a different form of non-fatal cancer
(non-fatal lymphoma), but it is also based on a relatively small and
probably not representative sample. We recommend that the value used in
the report and the alternative we have identified be used as bounds in an
uncertainty analysis.
(7) We believe that the central estimate of $6.1 million for the value of a
statistical life (VSL) is appropriate. On the question of whether to add a
-------
value for cancer morbidity before death, we do not believe that there is an
adequate basis in the literature for doing this. But we can endorse adding
estimates of the medical costs of treatment and amelioration for fatal
cancers to the VSL as a lower bound on the true value of avoiding fatal
cancers.
b) Computation of Costs
(1) Costs should be computed using data for the systems affected by the
proposed arsenic standard(s) rather than national cost data.
(2) The costs of complying with the proposed MCLs may be overstated to the
extent that (a) removal of arsenic may also remove other toxic substances;
(b) possibilities for combining ground and surface water to meet the MCL
have been overlooked.
(3) The capital costs of drinking water treatment should be amortized using
the interest rate that each water system must pay to borrow money, not at
the rate of 7% (or 3%) used in the current analysis.
c) Presentation ofResults_
(1) The Executive Summary should clearly state the size of the population
affected by each MCL considered in the analysis, as well as the number of
systems affected.
(2) The Executive Summary should present benefits in terms of cases of
mortality and morbidity avoided, as well as in monetary terms, including
the age distribution of avoided cancers (and other health endpoints, if
possible).
(3) The primary case analysis should include sensitivity to the length of the
cessation-lag, and this should be reported in the Executive Summary.
(4) Benefits and costs should be broken down and compared by system size.
We recommend that the Agency modify its analysis to take account of the issues we have
raised regarding the computation of benefits and costs associated with the arsenic standard.
This report was reviewed and approved by the SAB Executive Committee in a public
meeting held on August 27, 2001. We appreciate the opportunity to review and provide advice
on this important report. The EPA Science Advisory Board would be pleased to expand on any
of the findings described in our report, and we look forward to your response.
Sincerely,
/S/ /S/
Dr. William H. Glaze, Chair Dr. Maureen Cropper, Chair
EPA Science Advisory Board Arsenic Rule Benefits Review Panel
EPA Science Advisory Board
-------
NOTICE
This report has been written as part of the activities of the EPA Science Advisory Board,
a public advisory group providing extramural scientific information and advice to the
Administrator and other officials of the Environmental Protection Agency. The Board is
structured to provide balanced, expert assessment of scientific matters related to problems facing
the Agency. This report has not been reviewed for approval by the Agency and, hence, the
contents of this report do not necessarily represent the views and policies of the Environmental
Protection Agency, nor of other agencies in the Executive Branch of the Federal government, nor
does mention of trade names or commercial products constitute a recommendation for use.
Distribution and Availability: This EPA Science Advisory Board report is provided to the EPA
Administrator, senior Agency management, appropriate program staff, interested members of the
public, and is posted on the SAB website (www.epa.gov/sab). Information on its availability is
also provided in the SAB's monthly newsletter (Happenings at the Science Advisory Board).
Additional copies and further information are available from the SAB Staff [US EPA Science
Advisory Board (1400A), 1200 Pennsylvania Avenue, NW, Washington, DC 20460-0001; 202-
564-4546].
-------
U.S. Environmental Protection Agency
EPA Science Advisory Board
Arsenic Rule Benefits Review Panel*
CHAIR
Dr. Maureen L. Cropper, Lead Economist, The World Bank, Washington, DC and Professor of
Economics, University of Maryland;
Member: Advisory Council on Clean Air Compliance Analysis
OTHER SAB MEMBERS
Dr. Richard Bull, Consulting lexicologist, MoBull Consulting, Kennewick, WA
Member: Research Strategies Advisory Committee and Drinking Water Committee
Dr. W. Michael Hanemann, Professor, University of California, Berkeley, CA
Member: Environmental Economics Advisory Committee
Dr. V. Kerry Smith, University Distinguished Professor, Department of Agricultural and
Resource Economics, North Carolina State University, Raleigh, NC
Member: Advisory Council on Clean Air Compliance Analysis
CONSULTANTS
Dr. A. Myrick Freeman, Professor, Department of Economics, Bowdoin College, Brunswick,
ME
Dr. Dale Hattis, Research Associate Professor, Center for Technology, Environment, and
Development, Clark University, Worcester, MA
Dr. Irva Hertz-Picciotto, Professor, Department of Epidemiology, University of North Carolina,
Chapel Hill, NC.
SCIENCE ADVISORY BOARD STAFF
Mr. Thomas Miller, Designated Federal Officer, 1200 Pennsylvania Avenue, NW, Washington,
DC
Ms. Rhonda Fortson, Management Assistant, 1200 Pennsylvania Avenue, NW, Washington, DC
Ms. Wanda Fields, Management Assistant, 1200 Pennsylvania Avenue, NW, Washington, DC
-------
TABLE OF CONTENTS
1. INTRODUCTION 1
1.1 Background 1
1.2 Charge to the Panel 1
2. RESPONSES TO CHARGE QUESTIONS 3
2.1 The impact of timing of exposure on avoided cancers (Charge Question 1) 3
2.1.1. Introduction 3
2.1.2. Calculation of reduced cancer fatalities associated with reduced
exposure to a carcinogen 4
2.1.2.1 The timing of the exposure-response relationship 4
2.1.2.2 Calculating the time path of cancer cases avoided 5
2.1.3 Quantifying the relationship between exposure and mortality risk 5
2.2 Characterization of non-quantified health endpoints (Charge Question 2) 7
2.2.1 Overview 7
2.2.2. Quantifiability of particular health endpoints 9
2.2.2.1 Cardiovascular disease endpoints 9
2.2.2.2 Diseases of the endocrine system 10
2.2.2.3 Other cancer sites 11
2.2.2.4 Non-malignant respiratory diseases 11
2.2.2.5 Reproductive effects 11
2.2.2.6 Neurologic and neurodevelopmental endpoints 12
2.2.3 Valuation of non-quantified health endpoints 12
2.3 Exposure reduction as a benefit category (Charge Question 3) 12
2.4 Comparison of benefits and costs (Charge Question 4) 13
2.4.1 Comparison of benefits and costs by system size 13
2.5 Incorporation of uncertainty into benefits measures (Charge Question 5) 14
3. GENERAL COMMENTS ON THE ECONOMIC ANALYSIS 16
3.1 Comments on exposure assessment 16
3.1.1 Characterization of U.S. population exposure in the analysis 16
3.2 Comments on the computation of benefits 16
3.2.1 Treatment of latency 16
3.2.2 Treatment of age 17
3.2.3 Valuing avoided cancer morbidity 17
3.2.4 Valuing avoided cancer morality 17
3.3 Comments on the computation of costs 18
3.3.1 Factors that may cause costs to be overstated and/or benefits to be understatedl8
3.3.2 Amortization of costs 18
3.3.3 Unanticipated costs 19
3.3.4 Policy implications of regulatory costs 20
-------
REFERENCES R-l
APPENDICES
Appendix 1 - Background; NDWAC Benefits Working Group Comments A-1
Appendix 2 - Supplementary Information A-2
Appendix 2.1 Appendix to charge question 1 A-2
Appendix 2.2 Appendix to charge question 2 A-13
-------
1. INTRODUCTION
1.1 Background
According to information provided by EPA (letter from Diane Regas, June 9, 2001),
studies have linked long-term exposure to arsenic in drinking water to cancer of the bladder,
lungs, skin, kidney, nasal passages, liver, and prostate. Non-cancer effects of ingesting arsenic
include cardiovascular, pulmonary, immunological, neurological, and endocrine (e.g., diabetes).
The current standard of 50 jig/L was set by EPA in 1975, based on a Public Health Service
standard originally established in 1942. A March 1999 report by the National Academy of
Sciences concluded that the current standard does not achieve EPA's goal of protecting public
health and should be lowered as soon as possible.
The Safe Drinking Water Act (SDWA) requires EPA to revise the existing 50
microgram per liter (|ig/L) arsenic standard. In response to this mandate, the Agency published
a standard of 10 |ig/L to protect consumers against the effects of long-term, chronic exposure to
arsenic in drinking water on January 22, 2001. The rule is significant in that it is the second
drinking water regulation for which EPA has used the discretionary authority under §1412(b)(6)
of the SDWA to set the Maximum Contaminant Level (MCL) higher than the technically
feasible level, which is 3 jig/L for arsenic — based on a determination that the costs would not
justify the benefits at this level. The January 22, 2001 arsenic rule is based on the conclusion
that a 10 jig/L MCL maximizes health risk reduction at a cost justified by the benefits.
Key stakeholder concerns about the benefits component of the economic analysis include
the following issues: (a) the timing of health benefits accrual; (b) the use of the Value of
Statistical Life as a measure of health benefits; (c) the use of alternative methodologies for
benefits estimation; (d) how the Agency considered non-quantifiable benefits in its regulatory
decision-making process; (e) the analysis of incremental costs and benefits; and (f) the Agency's
assumption that health risk reduction benefits will begin to accrue at the same time costs begin to
accrue.
The January 22, 2001 rule will apply to all 54,000 community water systems and requires
compliance by 2006. A community water system is a system that serves 15 locations or 25
residents year-round, and includes most cities and towns, apartments, and mobile home parks
with their own water supplies. EPA estimates that roughly five percent, or 3000, of community
water systems, serving 11 million people, will have to take corrective action to lower the current
levels of arsenic in their drinking water. The new standard will also apply to 20,000 "non-
community" water systems that serve at least 25 of the same people more than six months of the
year, such as schools, churches, nursing homes, and factories. EPA estimates that five percent,
or 1,100, of these water systems, serving approximately 2 million people, will need to take
measures to comply with the January 22, 2001 rule. Of all of the affected systems, 97 percent
are small systems that serve fewer than 10,000 people each.
1.2 Charge to the Panel
The Science Advisory Board (SAB) was asked to conduct a review of the benefits
analysis prepared by EPA in support of the arsenic drinking water standard which is contained in
its regulatory support document Arsenic in Drinking Water Rule Economic Analysis (USEPA
2000). The Agency asked that the Panel evaluate whether the components, methodology, criteria
and estimates reflected in EPA's analysis are reasonable and appropriate in light of (1) the
Science Advisory Board's (SAB) benefits transfer report (SAB 2000; Report on EPA's White
Paper, Valuing the Benefits of Fatal Cancer Risk Reduction)., (2) EPA Guidelines for Preparing
-------
Economic Analyses (USEPA 2000a), (3) relevant requirements of SDWA, (4) the Report of the
Benefits Working Group of the National Drinking Water Advisory Council (NDWAC
unpublished, October 1998), and (5) recent literature. Specifically, the Agency asked that the
Panel consider the following issues:
Charge Question 1: How should latency be addressed in the benefits estimates when
existing literature does not provide specific quantitative estimates of latency periods
associated with exposure to arsenic in drinking water?
Charge Question 2: How should health endpoints (other than bladder and lung cancer)
be addressed in the analysis, when [existing] literature does not provide specific
quantification, to ensure appropriate consideration by decision makers and the public?
Charge Question 3: Should reduction/elimination of exposure be evaluated as a
separate benefits category, in addition to or in conjunction with mortality and morbidity
reduction?
Charge Question 4: How should total benefits and costs and incremental benefits and
costs be addressed in analyzing regulatory alternatives to ensure appropriate
consideration by decision makers and the public?
Charge Question 5: How should uncertainties be addressed in the analysis to ensure
appropriate consideration by decision makers and the public?
Responses to these questions, and to other issues the Committee wishes to address, are provided
to the Agency below.
-------
2. RESPONSE TO THE CHARGE QUESTIONS
2.1 The Impact of the Timing of Exposure on Avoided Cancers
Charge Question 1: How should latency be addressed in the benefits estimates when
existing literature does not provide specific quantitative estimates of latency periods
associated with exposure to arsenic in drinking water?
2.1.1. Introduction
A central component in analyzing the benefits of reduced exposure to a carcinogen is the
prediction of the annual reduction in cancer cases following reduction in exposure. If a
population previously exposed to 50 |ig/L of arsenic in drinking water is exposed, beginning in
2006, to only 10 |ig/L, cancer risks in the population will eventually decline to a steady-state
level associated with a lifetime of exposure to 10 |ig/L. How fast this reduction in risk occurs
depends on the cessation-lag following reduction in exposure. We believe that this is more
appropriately termed a "cessation-lag," rather than "latency." This distinction is clarified below.
In order to explain what should be done when the length of this cessation-lag is unknown,
we must describe how the timing of the relationship between exposure and response (death due
to cancer) should be treated in a benefits analysis. We emphasize that we believe that this is how
such an analysis is conducted; it does not refer to the approach taken in the arsenic benefits
analysis. As in the case of arsenic, we analyze a policy that would reduce exposure from a
current level of d° (e.g., 50 |ig/L) to d* (e.g., 10 |ig/L). We assume that this policy would
continue into the indefinite future.
For a benefits analysis we would like to:
a) Calculate the expected number of cancer fatalities avoided each year, as a result
of the policy, beginning with the year in which the policy is implemented and
continuing into the future.
If benefits are to be monetized in accordance with conventional economic practice:
b) The expected number of cancer fatalities avoided each year should be multiplied
by the value of a statistical life in that year. This will give the dollar value of
benefits each year, beginning with the year in which the policy in implemented.
The dollar value of benefits in each year should be discounted to the year in
which the policy is implemented and summed. The present discounted value of
benefits, so calculated, should be compared with the present discounted value of
costs, calculated over the same period.
The timing of the relationship between exposure and cancer mortality is implicit in the
calculations in (a). As described more fully below, if the lag between reduction in exposure and
reduction in risk of death is long, there will be fewer cancer fatalities avoided in years
immediately following the policy than if the lag were shorter. Uncertainties in the timing of the
exposure-response relationship will be reflected in uncertainties in the number of cancer
fatalities reduced each year after the policy is implemented. These uncertainties should be
treated as described in the answer to Charge Question 5.
-------
2.1.2 Calculation of Reduced Cancer Fatalities Associated with Reduced Exposure to
a Carcinogen
The approach taken here is to relate the age-adjusted risk of death due to cancer to the
history of exposure to the carcinogen. This relationship, together with information on the age
distribution of the population affected by the policy, can be used to calculate the expected
number of cancer fatalities avoided by the policy.
The epidemiology underlying the arsenic benefits analysis (Morales et al. 2000) assumes
that the conditional probability of dying from cancer at age t, h(t) is related to cumulative
exposure to a carcinogen as of age t, X;, by a proportional hazard model:
(1) h(t,x) = ho(t)gfo)
where hg(t) = baseline risk of dying from cancer at age t (assuming survival to age t) and g(x;)
represents the impact of exposure incurred up to age t on risk of death.2
2.1.2.1 The Timing of the Exposure-Response Relationship
The key question is how cumulative exposure (x;) depends on the dose of arsenic
received at ages 0 through t. Let d; = dose received at age i. A general form that this
relationship could take is3:
(2) xt = ft(d0,db...,dt)
The exact form of this function reflects the answers to the following four questions (Tollerud et
al. 1999):
(a) How long does it take after an exposure until an increase in risk is observed?
(b) How long does the effect of an exposure last after exposure has terminated?
(c) How does the effect of exposure vary by the age at which it was received?
(d) Does the exposure act at an early or late stage in the carcinogenic process?
The relevant questions for the implementation of changes in the drinking water standard
for arsenic are questions (b)-(d). In contrast, most of the epidemiologic literature addressing the
issue of latency has focused on question (a), which is the usual definition of latency. The
committee wishes to underscore that data addressing question (a) do not necessarily provide
information answering questions (b)-(d). Unfortunately, much less work has been done to
evaluate questions (b)-(d) in the epidemiologic literature in general, and in the research on
arsenic carcinogenicity in particular.
The NAS report Veterans and Agent Orange: Update 1998 (Tollerud et al. 1999)
addresses the second question, regarding how long effects last after cessation of exposure. With
2 A proportional hazard model (Pope et al. 1995) is also used to measure the association between
particulate matter and all-cause mortality in The Benefits and Costs of the Clean Air Act 1970-
1990 (USEPA 1997) and The Benefits and Costs of the Clean Air Act 1990-2010 (USEPA 1999).
The issue of the length of the cessation-lag after a reduction in exposure also arises in these
studies.
3The function f. ( ) could also be conditioned on other factors such as smoking.
-------
respect to arsenic in drinking water, the charge of our committee is an expansion of this
question: when does the excess risk (compared to a lifetime of exposure to d* (e.g., 10 |ig/L))
begin to attenuate and how long does it take until all of the excess is eliminated? Since the term
latency has a traditional usage that is not the charge given to this committee, we have coined the
phrase "cessation-lag" to clarify and emphasize the difference.
An important point is that the time to benefits from reducing arsenic in drinking water
may not equal the estimated time since first exposure to an adverse effect. A good example is
cigarette smoking: the latency between initiation of exposure and an increase in lung cancer risk
is approximately 20 years. However, after cessation of exposure, risk for lung cancer begins to
decline rather quickly. A benefits analysis of smoking cessation programs based on the observed
latency would greatly underestimate the actual benefits. We return to the issue of how to
estimate the length of the cessation-lag below.
2.1.2.2 Calculating the Time Path of Cancer Cases Avoided
If the relationships in (1) and (2) are known, it is, in principle, a simple matter to
compute the expected number of cancer fatalities avoided at age t (and, by analogy, for all other
ages) in each year following the policy. In the first year of the policy it is only the most recent
dose of the carcinogen (dt for persons who are age t in the year the policy is implemented) that
is affected by the policy. The expected reduction in risk of death due to cancer at age t in the
first year of the policy is:
(3) h3(tMW,di°,..,dt°)) - g(ft(d0°,d1°,...,dt-))]
where the superscripts ° and • refer to doses with and without the policy, respectively. In the
second year of the policy, for persons of age t, both dM and dt are affected by the policy, and the
formula in (3) would be adjusted accordingly. Eventually, a steady-state will be reached in
which persons of age t face the same mortality risk from cancer as people who have been
exposed to the lower level of the carcinogen (d*) throughout their lifetime.
In each year, the number of fatalities avoided by the policy among persons of age t would
be the expression similar to (3) multiplied by the number of persons of age t. Similar
computations would be performed for persons of all ages. In this manner, it should be possible
to compute the expected number of fatalities avoided, by age (or age-group), in each year
following the implementation of the policy. Because the age distribution of avoided cancer
fatalities is calculated, it should be reported in a benefits analysis even if information on the age
distribution of avoided fatalities is not used in valuing avoided mortality.
2.1.3 Quantifying the Relationship Between Exposure and Mortality Risk
Most epidemiologic studies ignore the time pattern of exposure in estimating the
proportional hazard model in equation (1). For example, Morales et al. (2000) effectively
assume that
(4) *= • dj.
i=0
-------
Given sufficient data, the time pattern of exposure and effect can be estimated in the
context of equations (1) and (2).4 In order to properly study effects of protracted exposures,
detailed exposure histories for each study subject, including the dates and ages when the
individual was exposed and the level of exposure at all points in time, are needed. Appropriate
statistical methods have been developed for an investigation of the effect of exposure accrued as
a function of time since that exposure (Thomas 1983; Breslow and Day 1987; Thomas 1988). In
general, the ability to investigate the issues of timing of exposure in a given data set will depend
on the quality of the exposure measure, the quality of the timing of exposure information, the
number of people developing the disease of interest, and variation of exposure over time within
the study group. These aspects of study quality are, of course, important in evaluating any
epidemiologic investigation. But there are special problems that arise in the evaluation of time-
related factors (Enterline and Henderson 1973; Thomas 1987).
If possible, it would be desirable to use information about the mechanism by which
cancer occurs in estimating the length of the cessation-lag.5 For example, if arsenic primarily
exerts a late-stage effect in the cancer formation process, the cessation-lag will be snorter than if
arsenic primarily exerts an early-stage effect. Appendix 2.1 to this report discusses how the time
pattern of exposure and response could be estimated in the context of the multi-stage model of
cancer formation.
In addition, two published studies have attempted to address either latency or cessation-
lag, or the stage at which arsenic acts in the carcinogenic pathway. Brown and Chu (1983, 1987)
attempted an analysis based on one of the arsenic-exposed occupational cohorts and
demonstrated that two models provided good fit to the data: one with only a late-stage effect and
the other with both an early- and late-stage effect. There was a slightly better fit for the model
with only a late-stage effect but the difference in fit was not sufficient to exclude an early-stage
effect. A more recent analysis (Hazelton et al. 2000) examined an occupational cohort with
exposures to arsenic, radon and tobacco using biologically based models. They evaluated the
time between generation of the first malignant cell and death from lung cancer. This would
appear to assume an early-stage effect only; nevertheless, it is notable that the best fit was given
for a gamma distribution of lags that had a mean of 4.1 years and a variance of 2.9 years. Under
this distribution, which is consistent with a minimal first stage effect of arsenic, the bulk of the
benefit following cessation would be expected to occur within the first five years after exposure
is reduced.
It thus appears that some information about the length of the cessation-lag is available in
the case of arsenic. Additional information on the length of the cessation-lag could be evaluated
from data on arsenic-exposed populations in Taiwan and Chile, and we urge that such research
be undertaken. In Taiwan, the water supply was changed in the early 1970's, thereby eliminating
the arsenic exposure. In Antofagasta, Chile, water treatment beginning in 1970 reduced the
arsenic concentration from 800 to 110 |ig/L within a short time, and over a few more years to 40-
50|ig/L.
If, however, such information were not available (as the charge question assumes), what
could be done? One extreme assumption that would yield an upper bound io the benefits of the
program is to assume that the program immediately attains the steady-state result, i.e., that the
4Latencies and cessation-lags would be expected to vary by cancer site, would probably be
shorter for cardiovascular disease than for cancer, and may be shortest for reproductive effects.
5 We emphasize that the same model should be used to estimate the time pattern of exposure and
response as is used to estimate the potency of the carcinogen.
-------
reduction in the age-t mortality rate is given by:
(5)
This is the assumption made in the Agency's primary analysis.
If it should prove infeasible to estimate the cessation-lag and account for it as described
above, it would still be desirable to examine the influence of this lag by performing sensitivity
analyses similar to those carried out for the PM-mortality relationship in the Agency's analysis
of The Benefits and Costs of the Clean Air Act: 1990-2010 (USEPA 1999). In the context of the
multi-stage model described in Appendix 2.1, we would suggest that the testing of extreme cases
of potential mechanisms (i.e., arsenic's effects being exerted entirely at an early stage v. all at a
late stage) be done as part of the uncertainty analysis.
2.2. Characterization of Non-Quantified Health Endpoints
Charge Question 2: How should health endpoints (other than bladder and lung cancer)
be addressed in the analysis, when [existing] literature does not provide specific
quantification, to ensure appropriate consideration by decision makers and the public?
2.2.1 Overview
The scientific literature on health effects due to arsenic exposure includes studies of a
number of endpoints other than cancer, as well as studies of several cancer sites for which the
risks/benefits have not been quantified (USEPA 2000). The quality of these studies varies, as
does the strength of evidence they provide. Nevertheless, this body of evidence is relevant for
the determination of an MCL and needs to be addressed more fully. In some cases, the non-
quantified effects can and should be quantified. In other words, the lack of quantification by
EPA, to date, of these effects should not be construed to mean that they are not quantifiable.
Of the 49 non-quantified non-carcinogenic health effects listed in the Benefits Analysis
(USEPA 2000), some would not be relevant at low exposure levels, e.g., at or below the current
standard. These would include gangrene in adults or children, hepatic enlargement, Raynaud's
syndrome and others. The main categories for which there may be concern at lower exposure
levels are: several cardiovascular and cerebrovascular diseases, endocrine effects (diabetes
mellitus), reproductive health outcomes, and non-malignant respiratory diseases. Some data
have emerged for neurologic or neurodevelopmental outcomes, but this evidence is currently
somewhat sparse.
Studies addressing the major categories of both non-cancer outcomes and other cancer
sites of concern (besides lung and bladder) at lower exposure levels are listed in the tables in
Appendix 2.2 (which are not comprehensive, but rather, representative). These studies
demonstrate a broad array of related endpoints and indicate the range and weight of evidence,
qualitatively, as well as the consistency with which these effects are related to arsenic exposure.
Such consistency, particularly when at least some of the studies are of high quality and have
adjusted for individual-level confounders, strengthens the evidence for causality.
Given (a) the consistency of results, including supportive in vivo animal experiments; (b)
epidemiologic studies with individual level data on exposure, outcomes, and confounders; and
(c) evidence suggesting plausibility of effects at low exposures: the Panel finds that for several
of these health endpoints, the benefits can and should be quantified. These include, at a
minimum, mortality from ischemic heart disease, diabetes mellitus, hypertension and skin
-------
cancer. Serious consideration should also be given to prostate cancer, nephritis and nephrosis,
hypertensive heart disease, and non-malignant respiratory disease, for which there is some
evidence of an association and data that would permit quantification of effects. The discussion
below briefly assesses the broad groupings of outcomes, highlighting those for which
quantification appears to be eminently feasible.6
By 'quantification' we mean estimation of a dose-response function that would permit
the Agency to predict the number of cases of cancer and non-cancer effects avoided by the
regulation. When the shape of the dose-response function cannot reliably be estimated at doses
relevant to the regulation, it may be possible to suggest the importance of non-quantified health
effects in other ways. For example, Appendix 2.2 compares the total non-cancer mortality and
mortality from cancers other than bladder and lung associated with arsenic exposure in Taiwan
with excess deaths due to lung and bladder cancer. These data indicate the total excess cancer
mortality to be about double that of lung and bladder alone; the numbers are similar for males
and for females. The excess from non-cancer endpoints is between 75% and 95% of that from
lung and bladder cancers combined. This calculation gives a very approximate example of how
important the other mortality endpoints could be, and indicates that the total excess mortality
might be as high as three times that from lung and bladder cancer alone.
Another approach is to compare the benchmark doses at which effects of arsenic have
been found in other studies (for example, in producing mortality from ischemic heart disease and
diabetes) with the benchmark doses in the studies for lung and bladder cancer. This allows one
to determine whether non-quantified effects have occurred at similar doses as cancer endpoints.
Other approaches are possible (Hattis et al. 1999, 2001).
In addition to these comparisons, the type of information that should be provided in a
benefit-cost analysis about endpoints that have not been quantified is listed in the tables in
Appendix 2.2. For each health endpoint (e.g., cardiovascular morbidity), studies that pass
certain scientific criteria should be listed.7 The information that should be provided for each
study includes:
(a) Nature of the study design
(b) How exposure was measured
(c) Range of exposures observed
(d) What type of statistical analysis was conducted and what confounding factors
were controlled for in the analysis
(e) Measure of association (e.g., odds ratio) and level of statistical significance of the
association
In some cases the literature may be so extensive that a summary of results is required in
the text of the report. This summary should focus on health endpoints that have meaning to
6Notably, these outcomes are not all independent. For instance, arsenic is associated with
increased prevalence of hypertension, and with increased incidence of ischemic heart disease.
Within the studies assessing the latter, hypertension was a strong risk factor. Thus, hypertension
may be one step along one or more pathways by which arsenic increases risk for ischemic heart
disease. Nonetheless, hypertension can itself be a cause of death, though this occurs much more
rarely than death due to ischemic heart disease.
7 For an example of such criteria see Table 5-2 in The Benefits and Costs of the Clean Air Act
1990-2010 (USEPA 1999) which lists the criteria used to select studies that examine the health
effects of the criteria air pollutants.
-------
humans, and should provide some discussion of the mechanism by which the toxin would be
expected to exert an effect. The summary should also indicate the level at which effects were
observed in the studies reported and should comment on the likelihood of observing these effects
at the levels relevant to the regulatory decision.
2.2.2 Quantifiability of Particular Health Endpoints
2.2.2.1 Cardiovascular Disease Endpoints (see Tables I, II, and III in
Appendix 2.2)
Both human and animal studies provide evidence that arsenic affects the cardiovascular
system, possibly via several mechanisms. The human studies have included both occupational
cohorts for which exposure is primarily by inhalation, and communities for which exposure is
primarily via drinking water. Both morbidity (Lagerkvist et al. 1986; Chen et al. 1988; Chen et
al. 1995, Tseng et al. 1996, Chiou et al. 1997, Rahman et al. 1999, Hsueh et al. 1998, Tsai et al.
1999), and mortality (Axelson et al. 1978; Wu et al. 1989; Engel et al. 1994; Chen et al. 1996;
Tsai et al. 1999; Lewis et al. 1999; Hertz-Picciotto et al. 2000) have been addressed in these
investigations. Several tables in Appendix 2.2 illustrate the range of types of studies and
exposure levels at which these effects have been observed.
The Taiwanese study by Chen et al. (1996) on mortality from ischemic heart disease is
particularly interesting, in that a wide range of individual-level confounding factors were
adjusted in the analysis, including age, sex, smoking, body mass index, serum cholesterol level,
serum triglyceride level, blackfoot disease, hypertension and diabetes. Their adjustment for the
latter two chronic diseases that may themselves contribute to ischemic heart disease risk could
have attenuated the effects, although the relative risks are reduced only modestly by the
inclusion of the confounders other than blackfoot disease. Nevertheless, there is a strong dose-
response relationship, rising from 2-fold to 5-fold increased risks according to the cumulative
exposure level.
Another study from Taiwan, by Tsai et al. (1999), relied on vital statistics, and hence did
not collect the individual-level confounding data included by Chen and colleagues. However,
these authors present analyses for a broader list of causes of mortality, including diabetes,
hypertension, pulmonary heart disease, cerebrovascular disease, liver cirrhosis, and a host of
other non-cancer and cancer endpoints. The findings on lung and bladder cancer confirm those
of numerous other investigators; results for ischemic heart disease are similarly consistent with
those of Chen et al. (1996) and others. Additionally, the study presents information on some
health outcomes not previously observed in arsenic-exposed populations.
Whereas most of the studies on cardiovascular endpoints have been conducted in
communities with long and heavy exposures, a few were conducted in a population with more
relevant levels. For instance, Lewis et al. (1999) examined records from the Mormon Church
from towns in Utah with concentrations in drinking water of 18-164 |ig/L. These authors found
mortality due to hypertensive heart disease to be elevated in both males and females. Although
individual-level confounder data were not available, the church's prohibitions on consumption of
alcohol and caffeine would tend to minimize this problem; the extremely low rates of respiratory
cancer and non-malignant respiratory disease attest to the low level of smoking in this
population, which may also explain the low incidence of ischemic heart disease.
In another study relevant for evaluating the plausibility of effects at low level exposures,
Gomez-Caminero (2001) examined several biomarkers of subclinical cardiovascular damage
comparing a population exposed at 45 |ig/L in drinking water to one with negligible exposures
-------
(<2 |ig/L). Among pregnant women residing in the exposed community, the levels of von
Willebrand factor were significantly reduced as compared with those in unexposed pregnant
women.8 The important point is that these data suggest damage to the endothelium of the arterial
walls at levels just under the current standard of 50 jig/L. The vascular endothelium serves as a
barrier between blood plasma and the arterial smooth muscle and regulates the flow of
lipoproteins between these compartments. Arsenic may damage the endothelium directly or
restrict its repair or regenerative capacity, by inhibiting endothelial cell hyperplasia. Reduced
von Willebrand factor could play a role in this process.
It is also notable that, in the past, clinical cardiovascular effects normally only seen in
adults were observed in children at very high exposure levels. The possibility that subclinical
damage to the cardiovascular system occurs in early life, setting the stage for severe and
potentially fatal events at older ages, should be considered.
The Panel concludes that cardiovascular effects of arsenic could be occurring at current
levels in drinking water. Despite uncertainty in the shape of the dose-response curve, a
benchmark dose approach would be a reasonable starting point for incorporating these benefits
into the risk/benefit analysis associated with reduction of the MCL. To place the epidemiologic
findings with regard to ischemic heart disease in context, over 500,000 deaths occurred in the
U.S. in 1999 due to this cause, or 22% of all deaths. Undoubtedly the overwhelming majority of
these are not due to arsenic. However, the same can be said for lung and bladder cancer in the
general population. Given the large background incidence of ischemic heart disease, the
committee believes these effects/benefits should be quantified. A similar argument would apply
to the morbidity and mortality from hypertension.
Peripheral vascular disease is a well-established effect of high exposures to arsenic, to
the extent that the presence of one severe form of this condition, blackfoot disease, has been
used as an indicator of exposure. There is probably little direct relevance of the extreme
manifestations of this condition for lower exposures. The likelihood of less severe conditions at
low exposures is uncertain.
2.2.2.2 Diseases of the Endocrine System (see Table IV, Appendix 2.2).
Most of the epidemiologic literature demonstrating increased risk of diabetes in
association with arsenic exposure has been published in the last five years (Tsai et al. 1999; Lai
et al. 1994; Tseng et al. 2000; Rahman et al. 1998). Studies include occupational and drinking
water sources for exposure, and both mortality and morbidity studies have found significant
excesses. Generally speaking, because diabetes is not a common cause of death, mortality
studies would be expected to observe only the tip of the iceberg in terms of increased incidence.
However, even when not fatal, diabetes engenders large medical costs and has a serious, lifelong
impact on the quality of life.
Besides overt clinical disease, subclinical indicators potentially relevant to the
development of diabetes have been examined in studies of arsenic-exposed populations.
Specifically, glucosuria and elevated glycosylated hemoglobin have both been found in
association with arsenic exposure (Jensen and Hansen 1998; Rahman et al. 1999; Gomez-
Caminero 2001). These are biologically significant markers of impaired glucose metabolism.
Glycosylated hemoglobin represents an indicator of long-term glycemic control. The Chilean
population examined by Gomez-Caminero (2001), for which exposures were -45 |ig/L, was
The von Willebrand factor is a protein that promotes normal clotting of the blood.
10
-------
found to have significantly elevated glycosylated hemoglobin, both when this biomarker was
treated as a continuous measure (% of hemoglobin glycosylated), and when it was dichotomized
(>6.5% vs. <6.5%). Since these women were pregnant, the age range was fairly young and
therefore the majority were born after levels were reduced to about 110 jig/L, which occurred
around 1970 (Hopenhayn-Rich et al. 2000). As the risk of diabetes increases with age, the
findings may indicate that the effects of arsenic on glycemic status could begin early, laying the
basis for clinical disease that manifests primarily beyond the reproductive years (i.e., Type n
diabetes).
Evidence for the diabetogenicity of arsenic is mounting, plausible mechanisms have been
shown, subclinical markers of altered glycemic control have been observed, and there appears to
be relevance at low exposures. Diabetes was directly responsible for 68,000 deaths in the U.S. in
1999, representing 2.9% of deaths, more than five times as many as occurred due to bladder
cancer. Quantification of the benefits of reducing the arsenic MCL in terms of diabetes
mortality, as well as the multidimensional costs associated with chronic illness, is appropriate.
Any effect that arsenic has in increasing the incidence or advancing the onset of Type n diabetes
will contribute to the risks of many other diseases associated with arsenic exposure (e.g.
hypertension, cardiovascular disease, liver cancer, peripheral vascular disease).
2.2.2.3 Other Cancer Sites (see Table V, Appendix 2.2).
Increased risks for kidney, liver, skin, bone, prostate, laryngeal, nasal and other sites are
observed to occur in populations exposed to arsenic through ingestion (Lewis et al. 1999; Smith
et al. 1992; Tsai et al. 1999). A comprehensive accounting of benefits from the reduction in the
arsenic MCL should quantitate at least the strongest of these effects, accounting for uncertainty.
Recent studies on the mechanisms for arsenic carcinogenicity do not suggest that lung and
bladder would be the only sites affected. An excess of prostate cancer was associated with
cumulative arsenic exposures above 1 mg/L year in Utah.
2.2.2.4 Non-malignant Respiratory Diseases (see Table VI, Appendix 2.2).
The increased incidence of bronchitis, emphysema, respiratory symptoms, and chronic
airway obstruction are surprising for exposures that do not occur via inhalation. At high
exposures, strong dose-response relationships were found for respiratory symptoms (Mazumder
et al. 2000). Plausibility for these effects at low exposures is uncertain. Shortness of breath was
elevated at 50-199 jig/L in West Bengal (Mazumder et al. 2000), and an ecologic study in the
U.S. found mortality was increased from chronic airways obstruction and emphysema at levels
as low as 5-10 |ig/L, with the highest risk at >20 jig/L (Engel and Smith 1994). This latter
finding suggests the possibility that communities with somewhat higher arsenic concentrations in
drinking water (e.g., >20 |ig/L) may also include a higher proportion of smokers. Two concerns
are: first, that smoking could be a confounder, and second, that smoking and arsenic could have
synergistic effects. Since smoking acts synergistically with arsenic in producing lung cancer
(Hertz-Picciotto et al. 1992), a similar interaction for non-malignant respiratory diseases is
possible. Although smoking is a voluntary risk, smokers do constitute a susceptible subgroup.
2.2.2.5 Reproductive Effects (see Table VII, Appendix 2.2).
Few reproductive endpoints have been examined in more than one study. Most of the
spontaneous abortion studies were conducted in populations with high exposures; those that were
not did not have individual data on confounders, and hence little confidence can be placed in the
results. The time trend analyses by Hopenhayn-Rich et al. (2000) suggest that stillbirths and
postneonatal mortality are increased at high exposures but not at levels between 40 and 70 |ig/L;
n
-------
the decline in rates in the exposed region after arsenic levels are reduced may be partially
attributable to other improvements in water quality and standard of living. In contrast, an effect
on birth weight may be seen at lower levels, based on the studies to date. Transfer of arsenic to
the fetus has been shown; interestingly, blood plasma arsenic was essentially all in the form of
DMA, and pregnant women had a higher proportion of their urinary arsenic as DMA than
nonpregnant women (Concha et al. 1998), suggesting more efficient methylation during
pregnancy.
2.2.2.6 Neurologic and Neurodevelopmental Endpoints (see Table VIII,
Appendix 2.2).
There have been studies indicating associations between environmental exposures and
pathologies, symptoms, and developmental deficit.
2.2.3 Valuation of Non-Quantified Health Endpoints
The preceding discussion suggests that some health endpoints affected by arsenic
exposure, including skin cancer and ischemic heart disease could be quantified. That is, the
expected reduction in cases could be calculated for each endpoint (possibly by age group) for
each year following the reduction in exposure. If the magnitudes of these effects can be
characterized, valuation should be done in the same way as for bladder and lung cancers. (See
Charge Question 1.)
Two issues, however, arise: (a) Do unit values exit for all of the health endpoints that
can be quantified? (b) Should valuation be done if effects cannot be quantified?
To answer the first question, unit values that measure what individuals would pay to
avoid adverse health effects (Willingness-to-Pay estimates) do not exist for all health endpoints
mentioned in our answer to Charge Question 2. The Benefits and Costs of the Clean Air Act
1990-2010 (USEPA 1999) contains a recent review of the available data for at least some of the
relevant endpoints. Where only cost of illness estimates are available, they can be used but
should be clearly described as providing lower bounds on true willingness to pay (Freeman
1993). The EPA Cost of Illness Handbook is a recent source of cost of illness data for some
relevant endpoints (USEPA 200la)
To make economic valuation possible, it is important to express and characterize these
other endpoints in terms of effects on people's activity levels and sense of well-being, as much
as possible. There is a fairly extensive body of data on the economic values of reducing days
experiencing various symptoms, restricted activity days, hospitalizations, required treatments,
etc. It would be difficult to use this body of data to value many of the health effects listed in
Exhibit 5-1 (p. 5-4 of the arsenic economic analysis) such as hepatic enlargement, anemia,
leukopenia, peripheral neuropathy, since the clinical significance and impact on individuals'
activities of these effects may vary significantly.
To answer the second question raised above, it is not possible to value health effects that
have not been quantified.
2.3 Exposure Reduction as a Benefit Category
Charge Question 3: Should reduction/elimination of exposure be evaluated as a
separate benefits category, in addition to or in conjunction with mortality and morbidity
reduction?
12
-------
Regarding Charge Question 3, the Panel believes in this case that reductions in exposure
should not be considered a separate category of benefits in a benefit cost analysis. The Agency
has adopted a damage function approach to quantifying the benefits associated with reducing
people's exposure to arsenic. The damage function framework to estimating benefits separates
the measurement of the relationship between exposure and response (e.g., the risk of fatal or
non-fatal cancer) from the valuation of reductions in the risk of each of these health endpoints.
Under the damage function approach, epidemiologists estimate dose-response functions
and economists measure the value people place on reductions in risk of death or illness.
Reductions in exposure are therefore already valued when one values the reductions in the risk
of death or illness associated with those exposures under the damage function approach. Adding
a separate value for reductions in exposure to arsenic per se would require that the be associated
with some additional source of benefits.
We do recognize that some people may value the existence of lower levels of arsenic in
drinking water, possibly for psychological reasons (e.g., dread of being exposed), and we believe
that existence values are a legitimate category of benefits. Existence values are not
accommodated within a damage function approach to benefit quantification. Reliable estimates
of these values would need to identify the marginal benefit to individuals associated with a
change in concentration, separate from the change in health risks associated with the change in
exposure. We found no empirical evidence to support or contradict such a relationship in the
case of arsenic. In the absence of any empirical data, there is no basis for estimating an
existence value in this case.
2.4 Comparison of Benefits and Costs
Charge Question 4: How should total benefits and costs and incremental benefits and
costs be addressed in analyzing regulatory alternatives to ensure appropriate
consideration by decision makers and the public?
2.4.1 Comparison of Benefits and Costs by System Size
One noteworthy feature of the arsenic in drinking water problem is that for the most part,
those who would receive the health benefits from reductions in the concentrations of arsenic in
drinking water will also bear the costs of achieving them. These costs will take the form of
higher rates and prices for water supply and/or higher taxes to cover these costs. Thus it is
important to try to determine whether those who receive these benefits would be willing to bear
the costs of reducing arsenic concentrations in their drinking water. This is the question that
benefit-cost analysis attempts to answer, because in principle the benefits of a program are
defined as the sum of the affected individuals' willingness to pay for these improvements. If all
benefits and costs of a regulation are measured accurately, and if the benefits received by the
members of a group are less than the costs paid by the members of the group, this is a signal that
the members of the group would consider themselves to be made worse off by the regulation.
Conversely, if benefits exceed costs, the policy would make the members of the group better off.
For this reason, we recommend that benefits and costs should be calculated on a water
supply system basis. Specifically, we recommend that total benefits and costs and marginal
benefits and costs be calculated for all the systems that are affected by the standard, and the
system-level results then be aggregated to the national level. Because of the large economies of
scale associated with drinking water treatment, the net benefits (benefits minus costs) are likely
to vary substantially by system size, and this information should be made clear to policy makers
and the public. Such an analysis would allow decision makers to evaluate a range of alternative
13
-------
strategies rather than a one-size-fits-all approach. The high cost of arsenic control is driven by
the tail of a distribution involving a number of small systems. The analysis needs to make this
clear so that decision makers can consider this fact in formulating an appropriate policy
response.
When there are too many affected systems to perform a separate cost analysis tailored to
the specific circumstances of every system, some data aggregation may be necessary.
Nevertheless, the existing cost analysis appears to be too generic and too little tailored to the
specific circumstances of the particular utilities affected by arsenic regulation (e.g., water supply
systems in the west and southwest that use groundwater). Rather than using national cost
functions, an attempt should be made to employ cost functions tailored to these affected utilities.
Grouping utilities into size classes and conducting an analysis by size class is acceptable if this is
done with specific reference to size classes that are meaningful for the systems affected by the
arsenic regulation and using data specific to these systems. In the existing analysis, individual
cost analyses were performed only for water utilities that serve more than a million people
("very large systems"); we recommend lowering the threshold population size for performing
individual cost analyses, for example to a service population of 250,000 or more.
2.5 Incorporation of Uncertainty into Benefits Measures
Charge Question 5: How should uncertainties be addressed in the analysis to ensure
appropriate consideration by decision makers and the public?
Doing one's best to understand and communicate uncertainty is a basic obligation of
technical analysts to their audience. Ideally, the goal should be to enable the audience to make
as informed a choice among risk acceptance/risk control options as if the audience members
themselves had been able to go through the process of analysis. Good uncertainty assessments
help decision-makers take appropriate precautions, where indicated, against the possibility that
future improved data will alter the balance of benefits and costs projected from current
information. If applied consistently and comparably across different types of information (i.e.
costs and benefits of various types) uncertainty analyses also can help planners make judgments
about the relative productivity of investments in different kinds of information-gathering
activities for future regulatory choices (including, for example, the timing of implementation
measures).
Benefit-cost analyses of drinking water regulations are likely to entail uncertainties in the
(a) measurement of exposure, (b) measurement of dose-response, (c) valuation of health
outcomes and (d) measurement of costs. The sources of these uncertainties include
measurement error (uncertainty about the average level of arsenic in tap water or of the amount
of tap water consumed) as well as uncertainty about which model to use in describing the
relationship between exposure and response at low doses. In general, there are two approaches
to handling these sources of uncertainty—sensitivity analysis and Monte Carlo simulation. In a
sensitivity analysis various assumptions are made about the correct model (e.g., dose response
function) or parameter (e.g., discount rate) to use in the analysis and results are presented for
each set of assumptions. In a Monte Carlo analysis a distribution is assumed for a key parameter
or set of parameters (e.g., the Value of a Statistical Life) and several hundred draws are made
from this distribution. Benefits are calculated for each value of the parameters drawn. This
yields a probability distribution of benefits, whose parameters (e.g., the 10th and 90th percentiles)
can be reported.
We believe that, in the case of model uncertainty, it is appropriate to rely on sensitivity
analysis; however, the assumptions underlying each sensitivity analysis should be clearly spelled
14
-------
out when presenting results. It is particularly inappropriate to present only the highest and
lowest numbers associated with a set of sensitivity analyses, which may give the reader the false
impression that these constitute the upper and lower bounds of a uniform distribution. For
parameters for which it is possible to specify a distribution, Monte Carlo analysis is desirable
(for example, in the case of the slope of the dose-response function).
The EPA analysis of the Arsenic in Drinking Water Rule discusses some of the sources
of uncertainty in benefit estimates and handles them by performing sensitivity analyses.
Specifically, it focuses on the impact of alternate assumptions about the parameters of the dose-
response function, which will vary depending on what fraction of arsenic in the Taiwanese
population (the population used to estimate the dose response function) is assumed to come from
drinking water. A "high" and "low" estimate of benefits are generated based on alternate
assumptions about the sources of arsenic exposure in Taiwan.
The other set of sensitivity analyses that are performed pertain to the Value of a
Statistical Life (VSL). This is varied to allow for (a) changes in the VSL as incomes grow, (b)
the involuntary nature of drinking water risks and (c) the length of the latency period. As we
explain in more detail in the next section, latency (or, more correctly, the cessation-lag between
reduction in exposure and reduction in risk) is not handled correctly in the arsenic benefits
analysis. We also have a criticism of the treatment of the adjustment for the involuntary nature
of drinking water risks. In principle, however, there is nothing wrong with handling these
sources of uncertainty through a sensitivity analysis. The choice of discount rate is also
correctly handled via sensitivity analysis.
The report could, however, improve in its reporting of the results of these sensitivity
analyses in two ways. First, the presentation of the details of the analysis in the Executive
Summary and in the body of the report does not provide a sufficiently clear description of the
specific details of all aspects of the uncertainty analysis. With considerable effort it is possible
to develop a more complete understanding of how the analysis was undertaken by studying the
appendices to the report. Second, when the results of two alternate assumptions are presented,
for example, the "high" and "low" benefit estimates in the Executive Summary, it is important to
state that these are not the endpoints of a uniform distribution.
15
-------
3. GENERAL COMMENTS ON THE ECONOMIC ANALYSIS
3.1 Comments on Exposure Assessment
3.1.1 Characterization of U.S. Population Exposure in the Analysis
There are a few opportunities to improve the presentation of arsenic exposures in the
benefits analysis. First, although the report gives national estimates of the proportion of water
systems of various types that exceed various average arsenic levels, and Tables IHC-5 and C-6
give helpful breakdowns by geographic region and the system size (population served per
system), there does not appear to be an accessible presentation of the national or regional
numbers of people or population aggregate exposures broken down in the same ways. A
breakdown of the numbers of people in these categories is important for understanding the
distributional burdens of both current arsenic exposures/health harm and the prospective
compliance costs. A breakdown of the amounts of population aggregate exposure in these
categories is very important for understanding the extent to which the national aggregate arsenic-
in-drinking water problem would be reduced by different MCLs.
3.2 Comments on the Computation of Benefits
3.2.1 Treatment of 'Latency'
As the answer to Charge Question 1 implies, we do not believe that the lag between
reduction in exposure and reduction in fatal cancers has been treated correctly in the benefits
analysis. The correct approach is to predict the number of fatal cancers avoided each year based
on an assumption about the percent of the steady-state reduction in cancer cases that will be
achieved each year following the policy. For example, in The Benefits and Costs of the Clean
Air Act 1990-2010 (USEPA 1999), it was assumed that 25% of the steady-state benefits from
reducing air pollution would be achieved in the first year of the policy, 50% by the second year,
and (increasing gradually), 100% of the benefits by the end of the 5th year of the policy.
Once this time path is established, the number of fatal cancers avoided in year t should
be multiplied by the Value of a Statistical Life in year t and the result discounted to the first
year of the policy. The sum of these present discounted values over the horizon of the analysis
yields the present discounted value of benefits of the policy. It is, of course, possible to
annualize this number by calculating the constant annual value of benefits that produces the
same present discounted value of benefits.
In its primary analysis the Agency makes no adjustment for the cessation-lag in its
calculation of cancer mortalities avoided. It simply assumes that the cancer mortality risk will
drop immediately to the new steady-state level upon implementation of the new standard. Then
in a sensitivity analysis (Section 5.5), it accounts for the cessation-lag not with alternative
calculations of cancer mortalities avoided, but by discounting the Value of a Statistical Life
applied to these avoided deaths for three alternative lag periods, 5, 10, and 20 years. In terms of
the calculated monetary benefits, this is equivalent to assuming there is no reduction in cancer
mortalities avoided for the first 5, 10, 20 years after the regulation is implemented, after which
the cancer mortality risk drops immediately to the new steady-state level.
In valuing avoided nonfatal cancers, the cessation-lag should be taken into account in
estimating the numbers of cases avoided in the same way that we described for fatal cancers in
Section 2.1.
16
-------
3.2.2 Treatment of Age
There is sufficient information in the dose-response function in Morales et al. (2000) to
calculate cancer cases avoided by age group. We believe that this should be done. The dose-
response function used to compute the number of cancer cases avoided in the benefits analysis
(Model 1 of Morales et al. 2000) is a special case of equation (1) in which "the relative risk of
mortality at any time is assumed to increase exponentially with a linear function of dose and a
quadratic function of age (p. B-7)." Instead of using this equation to predict risks by age group,
the information contained in the equation is aggregated to compute a lifetime cancer risk.
3.2.3 Valuing Avoided Cancer Morbidity
To value nonfatal bladder cancers, the Agency used a value for avoiding a statistical case
of chronic bronchitis obtained by Viscusi, Magat, and Huber (1991). We have two reservations
about this. First, this study used a small sample obtained in a shopping mall in North Carolina
and thus may not be representative of either the U.S. population as a whole or the population of
individuals at risk of bladder cancer. Second, we have no basis for determining that avoiding a
case of chronic bronchitis has the same value as avoiding a nonfatal case of bladder cancer.
On this second point, there is one study of willingness to pay to avoid a nonfatal case on
one type of cancer. Magat, Viscusi, and Huber estimated the willingness to pay to avoid a case
of nonfatal lymphoma to be $3.6 million (Magat, et al. 1996). This value was obtained from a
similar shopping mall intercept survey with a substantially larger sample size. So, although the
endpoint being valued more nearly corresponds to nonfatal bladder cancer, there is still the
question of the representativeness of the sample. We also note that the value obtained is at least
20 times larger than the cost of illness for nonfatal bladder cancer cited in Exhibit 5-10. Thus
we do not have a lot of confidence in this number. Therefore, we recommend that the value used
in the report and the alternative discussed here be used as bounds in an uncertainty analysis.
However, this range should be clearly identified as displaying the two extreme estimates
available in the literature so it is not misconstrued as a confidence interval.
3.2.4 Valuing Avoided Cancer Mortality
The Agency should recognize the uncertainty in the estimated VSL used to value fatal
cancers either by sensitivity analysis or incorporating the uncertainty in Monte Carlo analyses.
The committee believes that the adjustments to the VSL for the voluntariness/control-
lability of risk does not conform to standard economic practice. The SAB Review of the EPA's
White Paper, Valuing the Benefits of Fatal Cancer Risk Reductions recommended that no such
adjustments be made.
We believe that the central estimate of about $6.1 million for the VSL is appropriate. In
an earlier report, the SAB said: "To the extent that cancer victims suffer greater morbidity, fear,
or dread than the victims of the causes of death involved in VSL studies, it would be appropriate
to attach a "cancer premium" to the value of an avoided death from cancer." It went on to say
that there was little reliable information on what this premium should be. We agree with this
conclusion.
One possibility would be to add to the VSL a number representing the value of avoiding
a nonfatal case of the same type of cancer. We can not endorse that approach here for there is no
17
-------
reason to believe that either the medical costs (cost of illness), the duration of the morbidity, or
its severity would be the same for a nonfatal case and a fatal case of cancer. In fact, we can
think of reasons why they could be quite different. We can endorse adding estimates of the
medical costs of treatment and/or amelioration for fatal cancers to the VSL as a lower bound on
the true value of avoiding fatal cancers.
3.3 Comments on the Computation of Costs
3.3.1 Factors that May Cause Costs to Be Overstated and/or Benefits to Be
Understated
Two features of the existing cost analysis may lead it to overstate the costs of arsenic
regulation, at least to some degree: We recommend that the Agency attempt to take account of
these factors. (1) To the extent that arsenic removal is a joint product of water treatment together
with the removal of other contaminants, the existing cost analysis may overstate the costs (or
understate the benefits) of arsenic regulation. Utilities may already have pre-existing installed
treatment processes for other contaminants that lower the cost of arsenic removal in a manner
not reflected in the current analysis, or utilities may adopt new treatment processes in response
to arsenic regulation that yield other improvements in drinking water quality as a by-product. (2)
In two of three cases, the existing cost analyses for the very large systems affected by the arsenic
regulations note that the costs may be overstated because they do not account for options that
may be available to lower costs associated with ground water entry points. In those two cases it
is stated that: "Depending on the spatial distribution of the wells, it may be possible to
implement centralized treatment, with reduced compliance costs. It may also be possible to
achieve compliance without treatment by blending ground water with surface water. Finally,
depending on the additional capacity available from surface water and unaffected well, the city
could shut down affected wells." Presumably, the same considerations apply to some of the other
systems affected by arsenic regulation and we recommend that the Agency attempt to take them
into account.
3.3.2 Amortization of Costs
In the arsenic benefits analysis capital costs are amortized (expressed as annual
equivalent flows) by using a discount rate of 7%. An alternative calculation based on a 3% rate
is also presented. However, what matters for the impact on utility finances and utility customers
is the actual interest rate at which the affected utilities will finance these investments. We
recommend that the Agency estimate this when calculating the regulatory costs (Freeman 1993,
pp. 213-216; Kolb and Scheraga 1990).
Exhibit 6-7 of the arsenic economic analysis presents data showing recommended cost of
capital estimates for various types of water utility ranging from 4.17% to 5.94%. Having
reviewed the report from which they derive, we do not believe these estimates are adequate.
First, while the analysis allows for the use of different sources of capital by non-small utilities of
different sizes (those serving 10,001 - 50,000 and those serving over 50,000) it assumes that the
costs of various types of capital - long-term debt, short-term debt, equity capital, municipal
bonds - are the same regardless of size for all systems serving over 10,000. We do not believe
this assumption is likely to be accurate. Second, with investor owned utilities the report states
that an after-tax figure is appropriate for the required analysis. We disagree and instead
recommend (1) using a before-tax figure for the cost of capital for investor owned utilities, and
(2) using a separate account to track the revenue gains to the government sector from taxes from
the water system debt.
18
-------
By way of illustration, suppose an investor owned water utility and a public owned water
utility both need to borrow $1 million. Suppose the investor owned utility issues bonds with an
interest rate of 8.5%. The publicly owned utility can borrow at a lower interest rate since the
interest paid on its bonds is tax exempt; it can borrow at 5.19%, to use the figure from page 29 of
the report on Public Water System Cost of Capital. The difference of 3.31% (= 8.5 - 5.1) is the
savings due to the tax exemption on publicly owned system debt. The report recommends using
5.19% as the cost of capital for investor owned utility debt as well as publicly owned utility debt,
because it views the 3.31% interest increment as merely a transfer payment. While this is not
incorrect, it is misleading with respect to the policy implications. Because the investor owned
utility pays a higher interest rate for its debt than the publicly owned utility, its customers will
face a larger cost increase than those of the publicly owned utility. We believe this should be
made explicit in the analysis.
Third, for similar reasons we disagree with the way in which the report treats the
financing of capital costs on a pay-as-you-go basis out of current revenues or accumulated
capital reserves. This type of financing accounts for about 20-30% of cost of capital expenditures
for non-small systems, and 20-60% for small systems. The report imputes an opportunity cost of
capital to funds from this source as though they were amortized over 15 or 30 years. For
example, if a small system needs to fund $1 million of water supply improvement from cash
flow, the report recommends amortizing this as though the funds were being borrowed with
unrated or low rated general obligation bonds at an interest rate of 5.47% amortized over 15
years. Suppose the investment were being made over a 5-year period. If the utility had made no
provision for a sinking fund, it would need to raise the $1 million from higher water rates over
the 5-year period. To the extent there is a sinking fund, the impact on water rates will be less
severe. It is clear, however, that using an imputed cost of capital may not give an accurate
assessment of the short-term impact on water rates when financing water system investments
from cash flow.
3.3.3 Unanticipated Costs
Some comments received by the Committee from the City of Albuquerque question
whether the costs of arsenic regulation may have understated the costs of proper disposal of
residuals from treatment and omitted certain external costs such as the cost of road accidents
caused by the increased transportation of materials used in water treatment. To the extent that
significant external costs or benefits may be incurred as the result of arsenic regulation, these
should be accounted for in the analysis.
In this specific case, from the information currently available to us we do not know
whether there would be a significant external cost of accidents as a result of arsenic regulation.
The analysis of increased truck and car accidents presented by the City of Albuquerque used
estimates of the crash, injury and death rates per hundred million vehicle miles based on data for
1998 statewide interstate commercial truck traffic, Albuquerque truck traffic, and Albuquerque
car traffic. We are not able to assess whether these are reliable estimates of the increase in road
accidents that could be expected to occur as the result of arsenic regulation for at least two
reasons: (1) What is needed is not the average number of accidents per vehicle mile but rather
the marginal increment in accidents per increment in vehicle miles; if the ratio of accidents to
vehicle miles were a constant it would measure what is needed, but we do not know this. (2) We
do not know whether the marginal accident, injury and death rates for an average Albuquerque
driver are the same as the marginal accident rate for drivers employed by the City of
Albuquerque Public Works Department. The data presented by the City of Albuquerque do not
control for this and, as with all observational data, one needs to be wary of potential confounding
factors and omitted variables. If would be useful to know, for example, whether the City of
19
-------
Albuquerque has any corroborating data on its existing experience with road accidents in
connection with the transportation of water treatment materials.
3.3.4 Policy Implications of Regulatory Costs
The Agency should give some attention to policy measures that could be undertaken to
mitigate the financial impacts on smaller systems that lack economies of scale and therefore face
very high compliance costs per account. Implicit in the cost of capital estimates used in the
arsenic benefits analysis are some assumptions about the role of existing government loan and
grant programs in financing costs of compliance. The cost of capital report assumes that these
loan and grant programs account for 5% of capital cost financing for publicly owned systems
serving over 50,000, 8% for publicly owned systems serving 10,001-50,000, 26% for publicly
owned systems serving 501-10,000, 55% for publicly owned systems serving 1-500, 4% for
investor owned systems serving over 10,000, and 55% for private systems serving under 10,000
(pp 28, 41, 47). It would be useful for the Agency to assess whether these existing loan and grant
programs will be adequate to support the volume of demand generated by the arsenic regulations
and whether they need to be supplemented with additional programs of financial assistance.
Other policy measures that could be considered include efforts to promote the
consolidation of very small systems, or the provision of bottled water by very small systems to
meet their customers' needs for potable water. If the latter option is considered, it would, of
course, be necessary to calculate the reduction in all drinking water contaminants that the
provision of bottled water would achieve.
20
-------
REFERENCES
Ahmad SA, Sayed MHSU, Barua S, et al. (2001) Arsenic in drinking water and pregnancy
outcomes. Environ Health Persp: 109:629-631.
Aschengrau A, Zierler S, Cohen A. (1989) Quality of community drinking water and the
occurrence of spontaneous abortion. Arch Environ Healtk44:283-290.
Axelson O, Dahlgren E, Jansson C-D, Rehnlund SO. (1978) Arsenic exposure and mortality: a
case-referent study from a Swedish copper smelter. Brit J Indus Med.35:8-15.
Bekemeier, H, and Hirschelmann, R. (1989) Reactivity of resistance blood vessels ex vivo after
administration of toxic chemicals to laboratory animals: arteriotoxicity. Toxicol
Lett49:49-54.
Borzsonyi M, Bereczky A, Rudnai P, Csanady M, Horvath A. (1992) Epidemiological studies
on human subjects exposed to arsenic in drinking water in southeast Hungary. Arch
Toxico_l;66(l):77-81.
Breslow, NE and Day, NE. (1987) Statistical Methods in Cancer Research Vol n. The Design
and Analysis of Cohort Studies. Lyon: International Agency for Research on Cancer.
Oxford University Press.
Brown CC, Chu KC. Implications of the multistage theory of carcinogenesis applied to
occupational arsenic exposure. J Natl Cancer Inst 1983;70:455-63.
Brown C, and Chu K. 1987. Use of multistage models to infer stage affected by carcinogenic
exposure: example of lung cancer and cigarette smoking. Journal of Chronic Diseases:
40:171-180.
Calderon J, Navarro ME, Jimenez-Capdeville ME, Santos-Diaz MA, Golden A, Rodriguez-
Leyva I, Borja-Aburto V, Diaz-Barriga F. (2001) Exposure to arsenic and lead and
neuropsychological development in Mexican children. Environ Res:85A:69-76.
Carmignani M, Boscolo P, and lannaccone A. (1983) Effects of chronic exposure to arsenate on
the cardiovascular function of rats. Br J Ind Med :40:280-4.
Carmignani M, Boscolo P, and Castellino N. (1985) Metabolic fate and cardiovascular effects of
arsenic in rats and rabbits chronically exposed to trivalent and pentavalent arsenic. Arch
Toxicol Suppl:Suppl8:452-5.
Chen C-J, Chiou H-Y, Chiang M-H, Lin L-J, Tai T-Y. (1996) Dose response relationship
between ischemic heart disease mortality and long-term arsenic exposure. Arterioscler
Thromb Vase BioL16:504-510
Chen C, Hsueh, YM, Lai, MS, Shyu, MP, Chen, SY, Wu, MM, Kuo, TL, Tai, TY. (1995)
Increased prevalence of hypertension and long-term arsenic exposure. Hypertension
25:53-60.
Chen C-J, Wu M-M, Lee S-S, Wang J-D, Cheng S-H,Wu H-Y (1998) Atherogenicity and
carcinogenicity of high arsenic artesian well water. Arteriosclerosis:8:452-460.
R-l
-------
Chiou H, Wei-I, H, Che-Long, S, Shu-Feng, C, Yi-Hsiang, H, Chien-Jen ,C. (1997) Dose-
response relationship between prevalence of cerebrovascular disease and ingested
inorganic arsenic. Stroke 28:1717-23.
Concha G, Vogler G, Lezcano D, Nermell B, Vahter M. (1998) Exposure to inorganic arsenic
metabolites during early human development. Toxicological Sci.:44:185-190.
Engel RR, and Smith AH. (1994) Arsenic in drinking water and mortality from vascular disease:
an ecologic analysis in 30 counties in the United States. Arch Environ Health: 49:418-
427.
Engel RR, Hopenhayn-Rich, Receveur O, Smith AH. (1994) Vascular effects of chronic arsenic
exposure: a review. Epidemiologic Rev. 16:184-209.
Enterline, P. and Henderson, V. (1973) Type of asbestos and respiratory cancer in the asbestos
industry. Archives of Environmental Health27:312-317.
Freeman, AM. (1993) The measurement of environmental and resource values: Theory and
methods. Resources for the Future; Washington, DC .
Gerr F, Letz R, Ryan PB, Green RC.(2000) Neurological effects of environmental exposure to
arsenic in dust and soil among humans. NeuroToxicology:21:475-488.
Gomez-Caminero A. (2001) Cardiovascular Effects of Arsenic During Pregnancy. Doctoral
Dissertation, University of North Carolina, Chapel Hill.
Hattis, D, and Silver, K. (1994) Use of mechanistic data in occupational health risk assessment:
The example of diesel particulates. In Chemical risk Assessment and Occupational
HealthBCurrent Applications. Limitations, and Future Prospects. CM Smith, DC
Christiani, and KT Kelsey, eds., Greenwood Publishing Group, Inc., Westport CT, pp.
167-177.
Hattis, D., Banati, P., and Goble, R. (1999) Distributions of Individual Susceptibility Among
Humans for Toxic Effects—For What Fraction of Which Kinds of Chemicals and Effects
Does the Traditional 10-Fold Factor Provide How Much Protection? Annals of the New
York Academy of Sciences:895:286-316. December.
Hattis, D, Baird, S., and Goble, R. (2001) A Straw Man Proposal for a Quantitative Definition
of the RfD," in Final Technical Report, U.S. Environmental Protection Agency STAR
grant # R825360, "Human Variability in Parameters Potentially Related to Susceptibility
for Noncancer Risks," Paper presented 4/24/01 at the U.S. EPA/DoD symposium on
Issues and Applications in Toxicology And Risk Assessment, Fairborn, Ohio.
Hazelton, WD, Luebeck, EG, Heidenreich, WF, and Moolgavkar, SH. (2000) An analysis of a
historical cohort of Chinese tin miners with arsenic, radon, cigarette smoke, and pipe
smoke exposures using the biologically based two-stage clonal expansion model.
Radiation Research 156:78-94.
Hernandez-Zavala A, Del Razo LM, Aguilar C, et al. (1998) Alteration in bilirubin excretion in
individuals chronically exposed to arsenic in Mexico. Toxicology Letters:99:79-84.
R-2
-------
Hertz-Picciotto I, Smith AH, Holtzman D, Lipsett M, AlexeefFG (1992) Synergism between
occupational arsenic exposure and smoking in lung cancer induction. Epidemiology
3:23-31.
Hertz-Picciotto I, Arrighi HM, Hu S-W (2000). Does arsenic exposure increase the risk for
circulatory disease? Amer J Epidemiol. 151:174-181
Hopenhayn-Rich C, Browning SR, Hertz-Picciotto I, et al. (2000) Chronic arsenic exposure and
risk of infant mortality in two areas of Chile. Environ Health Persp: 108:667-673.
Hsueh Y-M, Wu W-L, Huang Y-L, Chiou H-Y, Tseng C-H, Chen C-J. (1998) Low serum
carotene level and increased risk of ischemic heart disease related to long-term arsenic
exposure. Atherosclerosis: 141:249-257.
Jensen GE, Hansen ML. (1998) Occupational arsenic exposure and glycosylated haemoglobin.
Analyst; 123:77-80.
Kolb, JA and Scheraga, JD. (1990) Discounting the benefits and costs of environmental
regulations. J. of Policy Analysis and Management: 9:381-390.
Lagerkvist BEA, Linderholm H, Nordberg GF. (1988) Arsenic and Raynaud=s phenomenon.
Int Arch Occup Environ Health:60:361-364
Lagerkvist B, Linderholm H, Nordberg GF. (1986) Vasosplastic tendency and Raynaud=s
phenomenon in smelter workers exposed to arsenic. Environ Res:39:465-474.
Lai M-S, Hsueh Y-M, Chen C-J, et al. (1994) Ingested inorganic arsenic and prevalence of
diabetes mellitus. Am J Epidemiol: 139:484-492.
Lewis, DR, Southwick, JW, Ouellet-Hellstrom, R, Rench, J, and Calderon, RL. (1999) Drinking
water arsenic in Utah: A cohort mortality study. Environ. Health Perspect.:107:359-365.
Magat, WA, Viscusi, WK, and Huber, J. (1996) A reference lottery metric for valuing health.
Management Science:42:1118-1130.
Mazumder DNG, Haque R, Ghosh N, et al. (2000) Arsenic in drinking water and the prevalence
of respiratory effects in West Bengal, India. Int J Epidemiol:29:1047-1052.
Morales, KH, Ryan, L, et al. (2000) Risk of internal cancers from arsenic in drinking water.
Envir Health Perspectives: 108:655-661.
NDWAC. (1998) National Drinking Water Advisory Council recommendations resulting from a
meeting held on November 17 and 18, 1998. National Airport Hilton, Arlington,
Virginia. With the attached unpublished report from the NDWAC Benefits Working
Group giving their recommendations to the NDWAC parent Council and dated October
29, 1998.
Nordstrom S, Beckman L, andNordenson I. (1978) Occupational and environmental risks in
and around a smelter in northern Sweden, in. Frequencies of spontaneous abortion.
Hereditas: 88:51-54.
R-3
-------
Nordstrom S, Beckman L, andNordenson I. (1979) Occupational and environmental risks in
and around a smelter in northern Sweden. V. Spontaneous abortion among female
employees and decreased birth weight in their offspring. Hereditas:90291 -296.
Ohnishi K, Yoshida H, Shigeno K, et al. (2000) Prolongation of the QT interval and ventricular
tachycardia in patients treated with arsenic trioxide for acute promyelocytic leukemia.
Ann Intern Med:133:881-885.
Pope, CA, Thun, MJ, Namboodiri, MM, Dockery, DW, Evans, JS, Speizer, FE, and Heath, CW,
JR. (1995) Particulate air pollution as a predictor of mortality in a prospective study of
U.S. adults. Am Journal Respir Crit Care Med: 151:669-674.
Rahman M, Tondel M, Ahmad SK, Axelson O. (1998) Diabetes mellitus associated with arsenic
exposure in Bangladesh. Amer J Epidemiol: 148:198-203.
Rahman M, Tondel M, Chowdhury IA, Axelson O. (1999) Relations between exposure to
arsenic, skin lesions, and glucosuria. Occup Environ Med:56:277-281.
Rahman M, Tondel, M, Ahmad, A, Chowdhury, I, Faruquee, M, Axelson, O. (1999)
Hypertension and arsenic exposure in Bangladesh. Hypertension: 3 3:74-78.
Regas, Diane. (2001) Request for review of the benefits assessment for the arsenic in drinking
water regulation. Memorandum from the Acting Assistant Administrator for Water, US
EPA, June 8.
Reis, LAG, Eisner, MP, Kosary, CL, Hankey, BF, Miller, BA, Clegg, L, and Edwards, BK.
(2001) SEER Cancer Statistics Review, 1973-1998. National Cancer Institute, Bethesda,
MD.
SAB.(2000) An SAB Report on EPA's White Paper Valuing the Benefits of Fatal Cancer Risk
Reductions, EPA Science Advisory Board. EPA-SAB-EEAC-00-013, July.
Smith AH, Hopenhayn-Rich C, Bates MN, Goeden HM, Hertz-Picciotto I, Duggan HM, et al.
(1992) Cancer risks from arsenic in drinking water. Environ Health Persp:97:259-267.
Thomas DC. (1983) Statistical methods for analyzing effects of temporal patterns of exposure
on cancer risks. Scandinavian Journal of Work. Environment, and Health 9:353-366.
Thomas DC. (1987) Pitfalls in the analysis of exposure-time-response relationships. Journal of
Chronic Disease:40:70-78.
Thomas DC. (1988) Models for exposure-time-response relationships with applications to cancer
epidemiology. Annual Review of Public Health: 9:451-482.
Tollerud D, Aminoff M, Goodman S, Herrick R, Hertz-Picciotto I, Hoel D, Olshan A, Orchard
T, Ozer H, Ramos K, Rose N, Woskie S. (1999) Veterans and Agent Orange: Update
1998. National Academy Press, Washington D.C.
Tsai, S-M, Wang, T-N, and Ko, YC. (1998) Cancer mortality trends in a blackfoot disease
endemic community of Taiwan following water source replacement. J Toxicol Environ
Health 55(6):389-404.
R-4
-------
Tsai S-M, Wang T-N, and Ko Y-C. (1999) Mortality for certain diseases in areas with high
levels of arsenic in drinking water. Arch Environ Health:54:186-193.
Tseng C-H, Chong C-K, Chen C-J, Tai T-Y. (1996) Dose-response relationship between
peripheral vascular disease and ingested inorganic arsenic among residents in blackfoot
disease endemic villages in Taiwan. Atherosclerosis: 120:125-33.
Tseng C-H, Tai T-Y, Chong C-K, et al. (2000) Long-term arsenic exposure and incidence of
non-insulin-dependent diabetes mellitus: a cohort study in arseniasis-hyperendemic
villages in Taiwan. Environ Health Persp: 108:846-851.
USEPA. (1997) The Benefits and Costs of the Clean Air Act, 1970-1990. US EPA Office of Air
and Radiation.
USEPA. (1999) The Benefits and Costs of the Clean Air Act, 1990-2010: EPA Report to
Congress. US EPA Office of Air and Radiation.
USEPA. (2000) Arsenic in Drinking Water Rule Economic Analysis. EPA 815-R-00-026,
December.
USEPA. (2000a) Guidelines for Preparing Economic Analyses. EPA 240-R-00-003,
September.
USEPA. (2001) National Primary Drinking Water Regulations; Arsenic and Clarifications to
Compliance and New Source contaminants Monitoring: Final Rule. EPA-815-Z-01,
Federal Register;66(14)6976-7066. January 22.
USEPA. (200 la) Cost of Illness Handbook. See website www.epa.gov/oppt/coi/coi.html.
Viscusi, W. Kip, Wesley A. Magat, and Joel Huber. (1991) Pricing environmental health risks:
survey assessments of risk-risk and risk-dollar tradeoffs for chronic bronchitis, Journal of
Environmental Economics and Management: 21:32-51.
Wu M-M, Kuo T-L, Hwang Y-H, Chen C-J. (1989) Dose-response relation between arsenic
concentration in well water and mortality from cancers and vascular diseases. Amer J
Erjidemio_l;130:1123-1132.
Zeirler S, Theodore M, Cohen A, and Rothman KJ. (1988) Chemical quality of maternal
drinking water and congenital heart disease. Int J Epidemiol: 17:589-594.
R-5
-------
APPENDICES
APPENDIX 1 - BACKGROUND
NDWAC Benefits Workgroup Recommendations, October 1998
The National Drinking Water Advisory Council (NDWAC) was charged with providing EPA
with recommendations on which benefits should be routinely considered in developing its
regulations. They were to address what categories of benefits should be considered, how to
consider qualitative benefits, and how to compare the results of benefits assessments with cost
analyses. NDWAC adopted the following recommendations from the Working Group:
Recommendation 1: EPA should focus its benefits analysis efforts primarily on assessing
effects on human health, defining these effects as clearly as possible and using the best available
data to value them. It is also recommended that EPA consider 1) health risk reductions, 2) taste
and odor improvements, 3) reduction in water system materials damage, 4) commercial water
treatment cost reductions, 5) benefits due to source water protection, and 6) benefits derived
from the provision of information on drinking water quality.
Recommendation 2: EPA should devote substantial efforts to better understanding the health
effects of drinking water contaminants, including the types of effects, their severity and affected
sensitive subpopulations. Better information is also needed on exposures and the effects of
different exposure levels, particularly for contaminants with threshold effects. These efforts
should pay particular attention to obtaining improved information concerning impacts on
children and other sensitive populations.
Recommendation 3: EPA should clearly identify and describe the uncertainties in the benefits
and costs analysis, including descriptions of factors that may lead the analysis to significantly
understate or overstate total benefits and costs. Factors that may have significant but
indeterminate effects on the benefits and costs estimates should also be described.
Recommendation 4: EPA should consider both quantified and non-quantified benefits in
regulatory decision making. The information about quantified and non-quantified (qualitative)
benefits should be presented together in a format, such as a table, to ensure that decision-makers
consider both kinds of information.
Recommendation 5: EPA should consider incremental benefits and costs, total benefits and
costs, the distribution of benefits and costs, and cost-effectiveness in regulatory decision-
making. This information should be presented together in a format, such as a table, to ensure its
consideration by decision-makers.
Recommendation 6: Whenever EPA considers regulation of a drinking water contaminant, it
should evaluate and consider, along with water treatment requirements to remove a contaminant,
source water protection options to prevent such [a] contaminant from occurring. The full range
of benefits of those options should be considered.
A-l
-------
APPENDIX 2
Appendix 2.1 Supplemental Information to Charge Question 1
Estimates of latency can be approached by developing classical Armitage-Doll multi-stage
models of the morbidity and mortality from various cancers in the U.S. population and then
exploring mathematically the expected distributions of times to diagnosis and death from various
cancers, making various plausible assumptions about where arsenic might act in the sequence of
genetic changes leading to the different cancers. Recent (1994-98) U.S. morbidity and mortality
data for different cancers are available from the "SEER" program [Ries, L. A. G., Eisner, M. P.,
Kosary, C. L. Hankey, B. F., Miller, B. A., Clegg, L., and Edwards, B. K. (2001) SEER Cancer
Statistics Review 1973-1998, National Cancer Institute, Bethesda, Md.].
The most straightforward approach to specifying the models is to do a simple set of weighted
regression analyses to these data of the form:
Log(Incidence or Mortality Rate in cases/100,000 population per year) = k*Log(Age - L) + b
In this equation, L is a lag period that represents the typical time between the unobserved birth of
the first cancer cell and either cancer diagnosis or cancer death (for morbidity v. mortality data,
respectively), and k + 1 is the number of "stages" (sequential genetic changes) in the cancer
model. Some fits derived from the data from Taiwan are contained in Attachment 1. The "U.S.
incidence data" worksheet contains SEER incidence and mortality data for lung and bladder
cancer for each sex, but the model fitting has not yet been done. The "5-stage male smoker"
worksheet (see Attachment 2) shows an example of a 5-stage lung cancer model created several
years ago to represent the expected time pattern of development of lung cancer in smokers who
began smoking at age 13. [See Hattis, D., and Silver, K. "Use of Mechanistic Data in
Occupational Health Risk Assessment—The Example of Diesel Particulates," in Chemical Risk
Assessment and Occupational Health—Current Applications. Limitations, and Future Prospects.
C. Mark Smith, David C. Christiani, and Karl T. Kelsey, eds., Greenwood Publishing Group,
Inc., Westport CT 1994, pp. 167-177 for an example of prior use of this approach]
Such a model makes it straightforward to explore the implications of different assumptions about
which stages are affected by arsenic exposures. Additional data available in the literature may
help judge the relative likelihood of different stage-of-action assumptions. In addition to the
Chen et al. (1991) paper cited above, the following by Tsai et al. (1998) might be useful in
estimating the rates at which risks for various health effects might decrease when exposure is
decreased [Tsai, SM, Wang, TN, and Ko, YC. Cancer mortality trends in a blackfoot disease
endemic community of Taiwan following water source replacement. J. Toxicol Environ. Health
55(6):389-404 1998]. It is important that the latent benefits from lowering exposure to
individuals that have had prior arsenic exposure be estimated utilizing the same model utilized to
estimate potency. Mode of action has implications for how rapidly and completely the effects in
the exposed population are reversed as it does when exposure increases to increase the risk of
cancer. Thus, it is important to be consistent in the utilization of mode of action information in
the final treatment of risks.
As indicated above, in the ideal circumstance there needs to be some consideration or at least
acknowledgment of the different ages at the time the rule is put into effect. Benefits will accrue
over a lifetime for children conceived after treatment is instituted. However, at that moment
there will be people of different ages who will gain some benefit. Benefits to these individuals
A-2
-------
could be significantly larger if arsenic were largely a late-stage carcinogen. This appears to be
the basis of the reduction in lifetime risks associated with discontinuation of smoking even after
several years. Arsenic produces a variety of effects at the molecular and cellular level that can
contribute to cancer risk. It is probable that there will be insufficient data to come to hard
conclusions about how different modes of action are contributing to the cancer incidence at
different doses or dose rates. Because the experimental data (i.e. mechanistic data) that is
available today indicate the possibility of several distinctly different modes of action with
different metabolic forms of arsenic at different doses such an exercise will be viewed as being
highly speculative by scientists. Thus, unless more certainty can be brought to the analysis than
was apparent in the Panel's brief review of the literature, it is suggested that such analyses be
confined to the uncertainty analysis as it has the distinct possibility of confusing the more
straightforward derivation of latency information from existing data. It is strongly suggested
that the sophistication of the methodology applied be limited by and consistent with
recommendations of the National Research Council (NRC) panel, which has been charged with
making recommendations on the risk assessment methodology that should be used.
A-3
-------
Attachment 1 to Appendix 2.1
US Incidence and Mortality data for various cancers
All incidence and mortality rates are for 1994-98, obtained from SEER website (Ries, L. A. G., Eisner, M. P.,
Kosary, C. L. Hankey, B. F., Miller, B. A., Clegg, L., and Edwards, B. K. (2001) SEER Cancer Statistics Review
1973-1998, National Cancer Institute, Bethesda, Md.)
U.S.
Age group
all 15+
15-24
25-34
35-44
45-54
55-64
65-74
75+
Population data
Male
98,760,045
18,352,667
20,431,905
21,061,700
15,181,658
10,044,054
8,342,097
4,346,564
for 1995 by age:
Female
106,269,617
17,594,592
20,441,238
21,406,031
15,897,104
11,087,025
10,417,067
9,426,584
Interpolated 5-year age groups beginning at various ages:
Age
midpoint of Age group
range
22.4970298
27.4969777
32.4961483
37.495086
42.4935055
47.491253
52.4873845
57.4814267
62.4719425
67.462545
72.4506775
77.4426083
82.437223
91.7883669
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Male pop Female pop
9,176,334
9,176,334
10,215,953
10,215,953
10,530,850
10,530,850
7,590,829
7,590,829
5,022,027
5,022,027
4,171,049
4,171,049
1,243,504
1,071,621
2,031,439
8,797,296
8,797,296
10,220,619
10,220,619
10,703,016
10,703,016
7,948,552
7,948,552
5,543,513
5,543,513
5,208,534
5,208,534
2,717,212
2,363,956
4,345,416
A-4
-------
Age group
It)
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Male blad
inc per
100K
0.8
1.3
3.1
6.2
13.5
26.8
50.2
83.9
138.7
191.8
237.8
286.8
296.6
Age group
It)
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Male blad
cases
82
133
326
653
1025
2034
2521
4213
5785
8000
2957
3073
6025
Male
kidney inc
1.2
3
6.8
13.2
22.2
35.1
45.1
55.8
71.5
72.7
70.8
71.7
Log(Male
blad
inc/lOOK)
-0.096910
0.113943
0.491362
0.792392
1.130334
1.428135
1.700704
1.923762
2.142076
2.282849
2.376212
2.457579
2.472171
Female
kidney inc
0.6
1.1
1.9
3.4
6.1
10.7
16.2
21.6
29.3
32.9
37.3
38.7
32.9
Female
blad inc
0.9
2
4
9.1
14.4
23.8
32.8
50.3
57.8
67.7
75
Male
kidney
mort
0.1
0.1
0.1
0.2
0.6
1.6
3.6
7
11.4
16.7
22.3
28
34.8
41.1
48.5
Male blad
mort
0.1
0.2
0.5
1.1
2.7
5.5
10.5
19.7
33.3
52.1
82.7
135.1
Female
kidney
mort
0.1
0.1
0.1
0.2
0.3
0.7
1.5
2.8
4.8
7.3
10
13.4
16.6
21.3
24.3
Female
blad mort
0.1
0.2
0.5
0.9
1.8
3.4
5.9
10.1
15.6
25.7
41.7
A-5
-------
Age group
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Male
liver inc
1.3
3.1
6.8
8.8
15.2
21.6
29.1
35.3
39.4
36.5
39.9
Female
liver inc
0.5
0.8
1.6
2.9
4.2
7.1
9.5
13.7
18
20.4
19.7
Male
liver mort
U.I
0.1
0.2
0.3
0.8
2.1
4.4
6.6
10.3
17
23.1
30.9
36.6
43.6
45.4
Female
liver mort
U.I
0.1
0.1
0.2
0.3
0.7
1.3
2.4
3.9
6.5
10.1
14
18.4
22.8
26.7
Age group
20
25
30
35
40
45
50
55
60
65
70
75
80
85
Male lung
inc
0.6
1.8
5.1
13.3
31.9
76
151.7
256
389
508
556.3
553.6
448.3
Female
lung inc
1.5
4.8
10.2
26.1
57.3
104.6
166.4
235
287.3
294.5
268.4
171.9
Male lung
mort
0.1
0.3
1.1
3.6
11
27.4
67.1
133.6
237
357
471.1
525.7
577
521.3
Female
lung mort
0.2
0.9
3
7.4
17.6
40.7
76.5
126.8
180.9
230.6
247.1
243.3
185.5
A-6
-------
Attachment 2 Appendix 2.1
Example of a 5-Stage Multistage Model, Tuned to Represent The Influence of Smoking at Stages 1 and 4
(Observed data quoted by Whittemore for U.S. Veterans study)
Parti
Background
mutation rate 0.000181965
Smoking
increment to
stage 1 mut rate 0.000432
(stage 1 and 4, begin age 13)
(Stage 4 effect is 2X stagel effect)
All smokers
Age
35
45
55
65
75
Lung Cancer
Cases
Observed
6
14
522
527
30
Lung cancer
(Hundreds
of Person- Model
years at predicted
risk) Incidence
per 100,000
1127 19.09
342 63.46
3195 150.09
1977 280.90
72 440.52
Model
Predicted
Cases
Expected
21.52
21.70
479.55
555.33
31.72
ChiA2
1.12E+01
2.73E+00
3.76E+00
1.45E+00
9.30E-02
Total:
19.22050
Average
Cigarettes
Per Day
20.93
21.65
20.57
18.49
16.03
20.02
Smoking/
Average
1.05
1.08
1.03
0.92
0.80
A-7
-------
Attachment 2 Appendix 2.1 (Continued)
Example of a 5-Stage Multistage Model, Tuned to Represent The Influence of Smoking at Stages 1 and 4
(Observed data quoted by Whittemore for U.S. Veterans study) (Table Continued)
PartH
Numbers
Age
(Year)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
9
9.5
10
10.5
11
11.5
12
12.5
13
Stage 0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.OOE+09
.99E+09
ot Susceptible Lung (Jells In Various Stages:
Stage 1
0
1
3
5
7
9
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
4
4
4
4
5
.OOE+00
.82E+05
.64E+05
.46E+05
.28E+05
.09E+05
.09E+06
.27E+06
.45E+06
.64E+06
.82E+06
.OOE+06
.18E+06
.36E+06
.54E+06
.73E+06
.91E+06
.09E+06
.27E+06
.45E+06
.63E+06
.81E+06
.OOE+06
.18E+06
.36E+06
.54E+06
.15E+06
Stage 2
0
0
1
4
9
1
2
3
4
5
7
9
1
1
1
1
1
2
2
2
3
3
3
4
4
4
5
.OOE+00
.OOE+00
.66E+01
.97E+01
.93E+01
.66E+02
.48E+02
.48E+02
.63E+02
.96E+02
.44E+02
.10E+02
.09E+03
.29E+03
.50E+03
.74E+03
.98E+03
.25E+03
.53E+03
.83E+03
.14E+03
.47E+03
.82E+03
.18E+03
.56E+03
.96E+03
.37E+03
Stage 3
0. OOE+00
O.OOE+00
O.OOE+00
1.51E-03
6.02E-03
1.51E-02
3.01E-02
5.27E-02
8.43E-02
1.26E-01
1.81E-01
2.48E-01
3.31E-01
4.30E-01
5.48E-01
6.85E-01
8.43E-01
1.02E+00
1.23E+00
1.46E+00
1.71E+00
2.00E+00
2.32E+00
2.66E+00
3.04E+00
3.46E+00
3.91E+00
Stage 5
Stage 4 (tumor hits)
0.
0.
0.
0.
1
6
2
4
9
1
2
4
6
9
1
1
2
O
4
5
6
8
1
1
1
1
O
OOE+00
OOE+00
OOE+00
OOE+00
.37E-07
.85E-07
.06E-06
.80E-06
.59E-06
.73E-05
.88E-05
.52E-05
.78E-05
.79E-05
.37E-04
.87E-04
.49E-04
.26E-04
.19E-04
.30E-04
.63E-04
.19E-04
.OOE-03
.21E-03
.45E-03
.73E-03
.54E-03
0
0
0
0
0
1.25e-ll
7.48e-ll
2.62e-10
6.98e-10
1.57e-09
3.14e-09
5.76e-09
9.87e-09
0
0
0
0
0
0
0
0
0
0
0
0
0
0
For age+5
Fraction of Incidence
People with Per Year
Tumors Per 100,000
0
0
0
0
0
1.2469E-11
7.4805E-11
2.6179E-10
6.9805E-10
1.5705E-09
3.1407E-09
5.7574E-09
9.8689E-09
1.6035E-08
2.4942E-08
3.7409E-08
5.4409E-08
7.7072E-08
1.0671E-07
1.448E-07
1.9305E-07
2.5336E-07
3.2784E-07
4.1887E-07
5.2905E-07
6.6125E-07
8.1862E-07
A-8
-------
13.5
14
14.5
15
15.5
16
16.5
17
17.5
18
18.5
19
19.5
20
20.5
21
21.5
22
22.5
23
23.5
24
24.5
25
25.5
26
26.5
27
27.5
28
28.5
29
29.5
30
30.5
31
31.5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.99E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.98E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
5
6
6
7
8
8
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
.76E+06
.37E+06
.99E+06
.60E+06
.21E+06
.82E+06
.43E+06
.OOE+07
.06E+07
.13E+07
.19E+07
.25E+07
.31E+07
.37E+07
.43E+07
.49E+07
.55E+07
.61E+07
.67E+07
.73E+07
.80E+07
.86E+07
.92E+07
.98E+07
.04E+07
.10E+07
.16E+07
.22E+07
.28E+07
.34E+07
.40E+07
.46E+07
.52E+07
.58E+07
.64E+07
.70E+07
.76E+07
5
6
6
7
8
9
9
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
3
3
3
3
3
3
4
4
4
4
5
5
5
5
5
.84E+03
.36E+03
.94E+03
.58E+03
.27E+03
.01E+03
.81E+03
.07E+04
.16E+04
.26E+04
.36E+04
.47E+04
.58E+04
.70E+04
.82E+04
.95E+04
.09E+04
.23E+04
.38E+04
.53E+04
.68E+04
.85E+04
.02E+04
.19E+04
.37E+04
.56E+04
.75E+04
.94E+04
.14E+04
.35E+04
.56E+04
.78E+04
.OOE+04
.23E+04
.47E+04
.71E+04
.95E+04
4.39E+00
4.92E+00
5.50E+00
6.13E+00
6.81E+00
7.56E+00
8.38E+00
9.27E+00
1.02E+01
1.13E+01
1.24E+01
1.36E+01
1.50E+01
1.64E+01
1.79E+01
1.96E+01
2.13E+01
2.32E+01
2.53E+01
2.74E+01
2.97E+01
3.21E+01
3.47E+01
3.74E+01
4.03E+01
4.33E+01
4.66E+01
4.99E+01
5.35E+01
5.72E+01
6.12E+01
6.53E+01
6.96E+01
7.41E+01
7.88E+01
8.38E+01
8.89E+01
5
7
1
1
1
2
2
2
O
O
4
5
5
6
7
8
9
1
1
1
1
1
1
1
2
2
2
2
O
O
O
O
4
4
5
5
5
.58E-03
.88E-03
.05E-02
.33E-02
.65E-02
.01E-02
.40E-02
.84E-02
.33E-02
.86E-02
.45E-02
.10E-02
.81E-02
.60E-02
.45E-02
.39E-02
.41E-02
.05E-01
.17E-01
.31E-01
.45E-01
.60E-01
.77E-01
.95E-01
.15E-01
.36E-01
.59E-01
.83E-01
.09E-01
.37E-01
.67E-01
.99E-01
.33E-01
.69E-01
.08E-01
.49E-01
.93E-01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.001
0.001
1.1405E-06
1.6482E-06
2.3648E-06
3.3155E-06
4.5276E-06
6.031E-06
7.8582E-06
1.0045E-05
1.263E-05
1.5655E-05
1.9167E-05
2.3214E-05
2.7852E-05
3.3138E-05
3.9135E-05
4.5912E-05
5.354E-05
6.2099E-05
7.1672E-05
8.2349E-05
9.4225E-05
0.0001074
0.00012199
0.0001381
0.00015586
0.0001754
0.00019684
0.00022035
0.00024607
0.00027415
0.00030478
0.00033812
0.00037436
0.0004137
0.00045634
0.0005025
0.0005524
A-9
-------
32
32.5
33
33.5
34
34.5
35
35.5
36
36.5
37
37.5
38
38.5
39
39.5
40
40.5
41
41.5
42
42.5
43
43.5
44
44.5
45
45.5
46
46.5
47
47.5
48
48.5
49
49.5
50
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.97E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.96E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
2
2
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
.82E+07
.88E+07
.94E+07
.OOE+07
.06E+07
.12E+07
.18E+07
.24E+07
.30E+07
.36E+07
.42E+07
.49E+07
.55E+07
.60E+07
.66E+07
.72E+07
.78E+07
.84E+07
.90E+07
.96E+07
.02E+07
.08E+07
.14E+07
.20E+07
.26E+07
.32E+07
.38E+07
.44E+07
.50E+07
.56E+07
.62E+07
.68E+07
.74E+07
.80E+07
.86E+07
.92E+07
.98E+07
6.20E+04
6.46E+04
6.72E+04
6.99E+04
7.26E+04
7.54E+04
7.82E+04
8.11E+04
8.41E+04
8.71E+04
9.01E+04
9.32E+04
9.64E+04
9.96E+04
1.03E+05
1.06E+05
1.10E+05
1.13E+05
1.17E+05
1.20E+05
1.24E+05
1.27E+05
1.31E+05
1.35E+05
1.39E+05
1.42E+05
1.46E+05
1.50E+05
1.54E+05
1.58E+05
1.63E+05
1.67E+05
1.71E+05
1.75E+05
1.80E+05
1.84E+05
1.89E+05
9
9
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
O
O
O
O
O
O
O
O
4
4
4
4
4
.43E+01
.99E+01
.06E+02
.12E+02
.18E+02
.25E+02
.31E+02
.38E+02
.46E+02
.53E+02
.61E+02
.69E+02
.78E+02
.86E+02
.95E+02
.05E+02
.14E+02
.24E+02
.34E+02
.45E+02
.55E+02
.67E+02
.78E+02
.90E+02
.02E+02
.14E+02
.27E+02
.40E+02
.54E+02
.68E+02
.82E+02
.96E+02
.11E+02
.27E+02
.42E+02
.59E+02
.75E+02
6.39E-01
6.89E-01
7.41E-01
7.96E-01
8.54E-01
9.16E-01
9.81E-01
1.05E+00
1.12E+00
1.20E+00
1.28E+00
1.36E+00
1.45E+00
1.54E+00
1.64E+00
1.74E+00
1.85E+00
1.96E+00
2.08E+00
2.20E+00
2.33E+00
2.46E+00
2.60E+00
2.75E+00
2.90E+00
3.06E+00
3.22E+00
3.39E+00
3.57E+00
3.75E+00
3.94E+00
4.14E+00
4.35E+00
4.57E+00
4.79E+00
5.02E+00
5.26E+00
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.001
0.001
0.001
0.001
0.001
0.001
0.001
.0011
.0012
.0013
.0014
.0015
.0017
.0018
.0019
.0021
.0022
.0024
.0026
.0028
0.003
.0032
.0034
.0037
.0039
.0042
.0044
.0047
0.005
.0054
.0057
.0061
.0064
.0068
.0073
.0077
.0081
0.00060626
0.00066435
0.0007269
0.00079419
0.00086649
0.00094408
0.00102726
0.00111635
0.00121165
0.00131351
0.00142227
0.00153828
0.00166191
0.00179355
0.00193359
0.00208243
0.00224049
0.00240821
0.00258604
0.00277443
0.00297386
0.00318482
0.00340782
0.00364335
0.00389197
0.00415421
0.00443064
0.00472182
0.00502836
0.00535085
0.00568991
0.00604619
0.00642032
0.00681298
0.00722485
0.00765663
0.00810902
18.2678851
58.6853364
A-10
-------
50.5
51
51.5
52
52.5
53
53.5
54
54.5
55
55.5
56
56.5
57
57.5
58
58.5
59
59.5
60
60.5
61
61.5
62
62.5
63
63.5
64
64.5
65
65.5
66
66.5
67
67.5
68
68.5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.95E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.94E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
7
7
7
.04E+07
.10E+07
.16E+07
.22E+07
.27E+07
.33E+07
.39E+07
.45E+07
.51E+07
.57E+07
.63E+07
.69E+07
.75E+07
.81E+07
.87E+07
.93E+07
.98E+07
.04E+07
.10E+07
.16E+07
.22E+07
.28E+07
.34E+07
.40E+07
.46E+07
.51E+07
.57E+07
.63E+07
.69E+07
.75E+07
.81E+07
.87E+07
.93E+07
.98E+07
.04E+07
.10E+07
.16E+07
1.93E+05
1.98E+05
2.02E+05
2.07E+05
2.12E+05
2.16E+05
2.21E+05
2.26E+05
2.31E+05
2.36E+05
2.41E+05
2.46E+05
2.51E+05
2.57E+05
2.62E+05
2.67E+05
2.73E+05
2.78E+05
2.83E+05
2.89E+05
2.95E+05
3.00E+05
3.06E+05
3.12E+05
3.17E+05
3.23E+05
3.29E+05
3.35E+05
3.41E+05
3.47E+05
3.53E+05
3.59E+05
3.66E+05
3.72E+05
3.78E+05
3.85E+05
3.91E+05
4
5
5
5
5
5
6
6
6
6
6
7
7
7
7
7
8
8
8
8
9
9
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.92E+02
.09E+02
.27E+02
.45E+02
.64E+02
.83E+02
.02E+02
.22E+02
.42E+02
.63E+02
.84E+02
.05E+02
.28E+02
.50E+02
.73E+02
.96E+02
.20E+02
.45E+02
.69E+02
.95E+02
.21E+02
.47E+02
.74E+02
.OOE+03
.03E+03
.06E+03
.09E+03
.12E+03
.15E+03
.18E+03
.21E+03
.24E+03
.27E+03
.30E+03
.34E+03
.37E+03
.40E+03
5
5
6
6
6
6
7
7
7
8
8
8
9
9
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
.51E+00
.76E+00
.03E+00
.30E+00
.59E+00
.88E+00
.19E+00
.50E+00
.83E+00
.16E+00
.51E+00
.86E+00
.23E+00
.61E+00
.OOE+01
.04E+01
.08E+01
.13E+01
.17E+01
.21E+01
.26E+01
.31E+01
.36E+01
.41E+01
.46E+01
.52E+01
.57E+01
.63E+01
.69E+01
.75E+01
.81E+01
.87E+01
.93E+01
.OOE+01
.07E+01
.14E+01
.21E+01
0.0086
0.0091
0.0096
0.0102
0.0108
0.0114
0.012
0.0126
0.0133
0.014
0.0148
0.0155
0.0164
0.0172
0.0181
0.019
0.0199
0.0209
0.0219
0.023
0.0241
0.0252
0.0264
0.0277
0.0289
0.0303
0.0316
0.0331
0.0345
0.0361
0.0377
0.0393
0.041
0.0427
0.0446
0.0464
0.0484
0.00858276
0.00907859
0.00959727
0.01013957
0.01070629
0.01129822
0.01191619
0.01256105
0.01323363
0.0139348
0.01466546
0.01542649
0.01621882
0.01704335
0.01790105
0.01879285
0.01971974
0.0206827
0.02168272
0.02272081
0.023798
0.02491532
0.02607383
0.02727457
0.02851864
0.02980709
0.03114104
0.03252158
0.03394983
0.03542691
0.03695394
0.03853207
0.04016245
0.04184622
0.04358453
0.04537857
0.04722948
146.117854
A-ll
-------
69
69.5
70
70.5
71
71.5
72
72.5
73
73.5
74
74.5
75
75.5
76
76.5
77
77.5
78
78.5
79
79.5
80
80.5
81
81.5
82
82.5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.93E+09
.93E+09
.93E+09
.93E+09
.93E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.92E+09
.91E+09
.91E+09
.91E+09
.91E+09
.91E+09
.91E+09
7
7
7
7
7
7
7
7
7
7
7
7
7
7
8
8
8
8
8
8
8
8
8
8
8
8
8
8
.22E+07
.28E+07
.34E+07
.39E+07
.45E+07
.51E+07
.57E+07
.63E+07
.69E+07
.74E+07
.80E+07
.86E+07
.92E+07
.98E+07
.04E+07
.09E+07
.15E+07
.21E+07
.27E+07
.33E+07
.38E+07
.44E+07
.50E+07
.56E+07
.62E+07
.67E+07
.73E+07
.79E+07
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
.97E+05
.04E+05
.11E+05
.17E+05
.24E+05
.31E+05
.37E+05
.44E+05
.51E+05
.58E+05
.65E+05
.72E+05
.79E+05
.86E+05
.94E+05
.01E+05
.08E+05
.16E+05
.23E+05
.31E+05
.38E+05
.46E+05
.53E+05
.61E+05
.69E+05
.76E+05
.84E+05
.92E+05
1.44E+03
1.47E+03
1.51E+03
1.55E+03
1.58E+03
1.62E+03
1.66E+03
1.70E+03
1.74E+03
1.78E+03
1.82E+03
1.86E+03
1.90E+03
1.95E+03
1.99E+03
2.03E+03
2.08E+03
2.12E+03
2.17E+03
2.21E+03
2.26E+03
2.31E+03
2.36E+03
2.41E+03
2.46E+03
2.51E+03
2.56E+03
2.61E+03
2
2
2
2
2
2
2
2
2
O
O
O
O
O
O
O
O
O
O
4
4
4
4
4
4
4
4
5
.28E+01
.36E+01
.43E+01
.51E+01
.59E+01
.68E+01
.76E+01
.85E+01
.94E+01
.03E+01
.12E+01
.21E+01
.31E+01
.41E+01
.51E+01
.62E+01
.72E+01
.83E+01
.94E+01
.05E+01
.17E+01
.29E+01
.41E+01
.53E+01
.66E+01
.78E+01
.91E+01
.05E+01
0.0504
0.0525
0.0546
0.0568
0.0591
0.0614
0.0639
0.0664
0.069
0.0716
0.0744
0.0772
0.0801
0.0831
0.0862
0.0894
0.0927
0.0961
0.0996
0.103
0.107
0.111
0.114
0.118
0.123
0.127
0.131
0.136
0.04913844
0.05110663
0.05313521
0.05522537
0.05737827
0.05959509
0.06187701
0.06422518
0.06664078
0.06912496
0.07167888
0.07430367
0.07700048
0.07977041
0.0826146
0.08553413
0.08853009
0.09160355
0.09475555
0.09798714
0.10129931
0.10469307
0.10816936
0.11172914
0.11537332
0.11910277
0.12291836
0.1268209
304.144013
550.341531
A-12
-------
APPENDIX 2.2
Supplement to Charge Question 2
Studies addressing the major categories of concern at lower exposure levels are listed in the tables (which are not comprehensive, but
rather, representative). These studies demonstrate a broad array of related endpoints and indicate the range and weight of evidence,
qualitatively, as well as the consistency with which these effects are related to arsenic exposure. Such consistency, particularly when
at least some of the studies are of high quality and have adjusted for individual-level confounders, strengthens the evidence for
causality.
I. Human morbidity studies of cardiovascular endpoints
Outcome
Cerebro vascular
disease/cerebral
infarction
ischemic heart
disease
tiectrocaraio-grapnic
abnormalities
Hypertension
Systolic blood
pressure
Vaso spastic
tendency (finger
systolic pressure,
upon cooling)
Authors/year &
location
Chiouetal. 1997
Taiwan
Hsuehetal. 1998
Taiwan
unnisni et ai.zuuu
Japan
Chenetal. 1995
Taiwan
Rahman etal. 1999
Bangladesh
Jensen & Hansen
1998
Denmark
Lagerkvist et al.
1986
Sweden
Design
Retrospective
cohort
Retrospective
cohort
prospective,
patients with
promyelocytic
leukemia
Retrospective
cohort
Retrospective
cohort
Retrospective
cohort
X- sectional
Exposure assessment
Cumulative exposure
Avg concentr'n in H2O
Duration ot exposure via H2O
AS ixior promyelocytic
leukemia
Cumulative exposure
[Avg cone in H2O] *
Cumulative exposure
Avg concentr'n in H2O
Job with arsenic exposure,
urinary As
Urinary As available but not
used-
Estimated exposure at 300
ug/day, or 4 g over 23 years
Dose-response
analysis:
Significant; adjusted for age,
sex, cigarettes, alcohol
Sigmlicant, adjusted tor total
cholesterol, BMI,
hypertension, serum • - and • -
carotene
.prolonged y 1 intervals in all s
patients, serious arrhythmias in
4
Significant; adjusted for age,
sex, diabetes, proteinuria, BMI
Significant; adjusted for age,
sex, BMI
No dose-response analysis
conducted
Measure ot
association
Odds ratio
Odds ratio
Odds ratio
Prevalence ratio
Difference in
means
Difference in
prevalence
Range of exposures
<0.1, 0.1-4.9, >5.0 mg/L-year;
<0.1, 0.1-50, 50.1-2999.9, >300
ug/L
<13, 13-29, >30 years drinking
artesian well water
n mg/Kg iorzu-/y days
0,0.1-6.3,6.4-10.8,10.9-14.7
mg/L-years;
0, .01-.70, >.70 mg/L
0, <1.0, 1.0-5.0, >5.0-10.0 mg/L-
years;
<0.5, 0.5tol.O,>1.0mg/L
Mean of 22.3 nmol/mmol As in
creatinine vs. 12.0 nmol/mmol
for referents
10-340 ug/L (mean=70) in urine
among exposed; 5-20 ug/L among
referents, highest quartile had
mean of 180 ug/L
A-13
-------
I. Human morbidity studies of cardiovascular endpoints (con't)
Outcome
Blackfoot
disease**
Peripheral vascular
disease***
Raynaud
phenomenon,
numbness & other
symptoms
von Willebrand
factor
Authors/year &
location
Chen etal. 1988
Taiwan
Tseng etal. 1996
Taiwan
Lagerkvist et al.
1988 Sweden
Gomez-Caminero
2001
Chile
Design
Retrospective
cohort
Retrospective
cohort
Time trend - start
to end of
vacation
Prospective
cohort of
pregnant women
Exposure assessment
Duration of exposure via
H2O
Cumulative exposure
Duration well water use
Duration living in Bf area
Exposed vs. unexposed
town
Dose- response
analysis:
Significant in highest
exposure group, adjusted for
age, sex, BMI, cigarette
smoking, diabetes
hypertension, serum total
cholesterol, & triglycerides
No dose-response analysis
conducted. Significant
difference in numbness &
other signs,
Significant vs. referents
Measure of
association
Odds ratio
Difference in
prevalence
Difference in
means, odds
ratio for
lowest tertile
Range of exposures
0 (referent) 1-29, >30 years
drinking artesian well water
0 (referent), 0.1-19.9, >20
mg/L-years
0,1-19, 20-29, >30 years
drinking artesian well water
Exposed: mean of 61 ug/L
urine
<2 ug/L (referent), -45 ug/L
(exposed)
* The analysis for this exposure metric did not adjust for all factors in the next column
** Blackfoot disease has been used as an indicator of exposure to arsenic &/or susceptibility to the effects of arsenic, due to its close
association with elevated arsenic exposures.
***Diagnosed by Doppler ultrasound, ABI<0.9 on either side of extremity
A-14
-------
II. Human mortality studies of cardiovascular & renal endpoints
Outcome
Circulatory disease
Cardiovascular
disease
Ischemic heart
Disease
Hypertensive heart
disease
Authors/year &
location
Tsaietal. 1999
Taiwan
Hertz-Picciotto et
al, 2000
US smelter workers
Wuetal. 1989
Taiwan
Axelsonetal. 1978
Sweden, area
around smelter
Hertz-Picciotto et
al, 2000
US smelter workers
Chenetal. 1996
Taiwan
Tsaietal. 1999
Taiwan
Lewis etal. 1999
Utah, USA
Design
Retrospective
cohort 1971 -1994
Retrospective
cohort
Retrospective
cohort 1973- 1986
Case-control
Retrospective
cohort
Two prospective
cohorts 1985-
1993, and 1988-
1995
Retrospective
cohort 1971 -1994
Retrospective
cohort
Exposure assessment
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Cumulative occupational
exposure over the worklife
Villages with arsenic
contaminated water
Employment in exposed
jobs
Cumulative occupational
exposure over the worklife
Avg concentr'n in H2O
Cumulative exposure
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Cumulative exposure.
Means in towns ranged
from 18. 1-164.4 ng/L
Dose-response
analysis:
Significant in both sexes,
adjusted for age, calendar
year
Significant dose response
adjusted for age, year of
hire, and the healthy worker
survivor effect
Significant, adjusted for age,
sex
Significant dose response
Significant dose response
adjusted for age, year of
hire, and the healthy worker
survivor effect
Monotonic dose response,
models adjusted for age, sex,
baseline BMI, cigarette
smoking, serum cholesterol,
triglycerides, diabetes,
hypertension, blackfoot
disease*
Significant in both sexes,
adjusted for age, calendar
year
Significant excess in men
and women
Measure ot
association
Standardized
mortality ratio
Rate ratio
Mortality ratio
Mantel-
Haenszel rate
ratio
Rate ratio
Hazard ratio
from Cox
proportional
hazards model
Standardized
mortality ratio
Standardized
mortality ratio
Range of exposures
0.78 mg/L,
referents: local county, and
national rates
<750 (referent), 750-1999, 2000-
3999,4000-7999, 8000-19,999,
>20,000 ug/m3 -years
<0.3, 0.3-0. 59, >.60 mg/L
Not employed at smelter
(referent), employed at smelter:
'close to' 0.5 mg/m3
<750 (referent), 750-1999, 2000-
3999,4000-7999, 8000-19,999,
>20,000 ug/m3 -years
0 (referent), 0.1-9.9, 10.0-19.9,
20.0+ mg/L years
0.78 mg/L,
referents: local county, and
national rates
<1, 1-4.999, >5.0 mg/L-years,
range
A-15
-------
II. Human mortality studies of cardiovascular & renal endpoints (con't)
Outcome
Cerebro vascular
disease
"
Peripheral
vascular disease
Pulmonary heart
disease
**
Nephritis,
nephrosis
Authors/year &
location
Wuetal. 1989
Taiwan
Tsaietal. 1999
Taiwan
Wuetal. 1989
Taiwan
Tsaietal. 1999
Taiwan
Engel& Smith 1994
USA
Tsaietal. 1999
Taiwan
Engeletal. 1994
Tsaietal. 1999
Taiwan
Lewis etal. 1999
Utah, USA
Design
Retrospective
cohort 1973- 1986
Retrospective
cohort 1971 -1994
Retrospective
cohort 1973- 1986
Retrospective
cohort 1971 -1994
Ecologic study at
the county level
Retrospective
cohort 1971 -1994
Retrospective
cohort 1971 -1994
Retrospective
cohort
Exposure assessment
Villages with arsenic
contaminated water
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Concentr'n in H in
villages with arsenic
contaminated water
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Avg concentr'n in H2O
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Cumulative exposure.
Means in towns ranged
from 18. 1-164.4 ug/L
Dose- response
analysis:
Significant, adjusted for age,
sex
Significant in both sexes,
adjusted for age, calendar
year
Significant, adjusted for age,
sex
No dose measure used,
adjusted for age, sex,
calendar year
No clear monotonic dose
response, but elevated risk
at each level >5 ug/L
No dose measure used,
adjusted for age, sex,
calendar year
No dose measure used,
adjusted for age, sex,
calendar year
Significant excess in men
and women
Measure of
association
Mortality ratio
Standardized
mortality ratio
Mortality ratio
Standardized
mortality ratio
Standardized
mortality ratio
Standardized
mortality ratio
Standardized
mortality ratio
Standardized
mortality ratio
Range of exposures
<0.3, 0.3-0. 59, >.60 mg/L
0.78 mg/L,
referents: local county, and
national rates
<0.3, 0.3-0. 59, >.60 mg/L
0.78 mg/L,
referents: local county, and
national rates
5-10, 10-20, >20 ug/L
0.78 mg/L,
referents: local county, and
national rates
0.78 mg/L,
referents: local county, and
national rates
<1, 1-4.999, >5.0 mg/L-years,
range
* Adjustment for Blackfoot disease attenuated but did not eliminate the association
**For further mortality and morbidity studies of cardiovascular endpoints, see
of arsenic exposure with ISHD
Table 6, Engel et al. 1994.
A-16
-------
///. Animal morbidity studies of cardiovascular endpoints
Outcome
Authors/year
Design
Exposure assessment
Dose-response analysis
adjusted for:
Measure of
association
Exposure level
Animal Studies
Vasoreactivity
Vasoreactivity
Potentiation ot • -
adrenoreceptor
stimulation
Stroke volume,
cardiac output
Vasoreactivity*
Bekemeir &
Hirschelmannl989
Carmignano et al.
1983
Carmignano et al.
1985
Experiment
Experiment
Experiment
Not applicable -
controlled dosing
"
Only one dose group
Only one dose group
Only one dose group
Only one dose group
Only one dose group
15 mg/kg, orally
50 ug/mL drinking water
50 ug/mL drinking water
after administration of isoprenaline, clonidine, tyramine, etc.
A-17
-------
IV. Human mortality and morbidity studies of endocrinologic/metabolic conditions and biomarkers
Outcome
Diabetes mellitus
mortality
Diabetes mellitus
incidence
Glycosylated
hemoglobin
Glucosuria
Hepatic function:
bilirubin excretion,
ALP activity
Authors/year &
location
Tsaietal. 1999
Taiwan
Laietal. 1994
Taiwan
Rahman etal. 1996
Sweden
Tseng et al
2000
Taiwan
Jensen & Hansen
1998
Denmark
Gomez-Caminero
2001
Chile
Rahman etal. 1999
Bangladesh
Hernandez-Zavala
etal. 1998
Mexico
Design
Retrospective
cohort 1971 -1994
Retrospective
cohort
Retrospective
cohort
Prospective
cohort, -2.5 years
follow-up
Retrospective
cohort
Prospective
cohort of
pregnant women
Retrospective
cohort
Retrospective
cohort
Exposure assessment
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Cumulative exposure
Duration well water use*
Job in glassworks with
likely exposure
Cumulative exposure from
H20
Jobs with arsenic
exposure (taxidermists,
construction workers,
wood & electric pylon
impregnators
Exposed vs. unexposed
town
Avg concentr'n in H2O
Cumulative exposure
Mean water concentration
in each of three towns
Dose-response
analysis:
No dose measure used,
adjusted for age, sex,
calendar year
Significant, adjusted for age,
sex, BMI, physical activity
Significant in those with
highest exposure, adjusted
forage
Significant, adjusted for age,
sex, BMI
Significant vs. referents
Significant vs. referents
Significant, adjusted for age
and sex, using cumulative
exposure
Significant differences,
adjusted for age, alcohol,
tobacco, pesticides
Measure ot
association
Standardized
mortality ratio
Odds ratio
Odds ratio
Hazard ratio
from Cox
model
Difference in
medians
Difference in
means, odds
ratio for
>6.5%
Prevalence
ratio
Difference in
means
Range of exposures
0.78 mg/L,
referents: local county, and
national rates
0 (referent), 0.1-15.0, >15.1
mg/L-yrs;
0 (referent, 1-10, 11-20, >21
years drinking artesian well
water
No quantitation available
<17 mg/L years (referent), >17
mg/L years
6-44 nmol/mmol urinary As in
creatinine (referents);
12-295 nmol/mmol (exposed)
<2 ug/L (referent), -45 ug/L
(exposed)
<0.5,0. 5-1. 0,>1.0 mg/L;
<1.0, 1.0-5. 0,>5. 0-10.0, >10.0
mg/L-years
Means: 14.0 ug/L (referent),
1 16 ug/L and 239 ug/L in two
exposed towns
* The analysis for this exposure metric did not adjust for all factors in the next column
A-18
-------
V. Human studies of cancers other than lung and bladder
Outcome
Kidney cancer
Liver cancer
Prostate cancer
Stomach cancer*
Colon cancer*
Rectum cancer*
Liver cancer*
Nasal cancer*
Laryngeal ca*
Skin cancer*
Bone cancer*
Lymphoma*
Authors/year &
location
Smith etal. 1992
Taiwan
Tsaietal. 1999
Taiwan
Lewis etal. 1999
Utah, USA
Tsaietal. 1999
Taiwan
"
"
cc
"
"
"
"
Design
Retrospective
cohort
Retrospective
cohort 1971 -1994
Retrospective
cohort
Retrospective
cohort 1971 -1994
"
"
"
"
"
"
"
Exposure assessment
Cumulative exposure in
H2O
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Cumulative exposure.
Means in towns ranged
from 18. 1-164.4 ug/L
Townships with arsenic
contaminated water from
1900's to mid-1 970's
"
"
"
"
"
"
"
Dose-response
analysis:
Significant, adjusted for age,
sex
Adjusted for age, sex,
calendar year
Significant excess
Adjusted for age, sex,
calendar year
"
"
"
"
"
"
"
Measure of
association
Rate ratio
Standardized
mortality ratio
Standardized
mortality ratio
Standardized
mortality ratio
"
"
"
"
"
"
"
Range of exposures
0.78 mg/L,
referents: local county, and
national rates
<1, l-4.999,>5.0mg/L-years,
range
0.78 mg/L,
referents: local county, and
national rates
"
"
"
"
"
"
"
*Excess observed in both genders. Cancers found in excess in only one gender not included.
A-19
-------
VI. Human morbidity & mortality studies of non-malignant respiratory endpoints
Outcome
Respiratory
effects: cough,
shortness of
breath
Bronchitis
Chronic airways
obstruction
Emphysema
Authors/year &
location
Mazumder et
al.2000
West Bengal, India
Tsaietal. 1999
Taiwan
Engel & Smith
1994
USA
Design
X- sectional
Retrospective
cohort 1971 -1994
Ecologic study at
county level
Exposure assessment
Current concentration
measured in well water
Townships with arsenic
contaminated water from
1900's to mid-1 970's
Avg concentr'n in H2O
Dose-response
analysis:
Significant, adjusted for age
& sex, smokers excluded
Adjusted for age, sex,
calendar year
Adjusted for age, sex, and
calendar year
Measure ot
association
Prevalence
odds ratio
Standardized
mortality ratio
Standardized
mortality ratio
Range of exposures
<50, 50-199, 200-499, 500-799,
>800 ug/L
0.78 mg/L,
referents: local county, and
national rates
5-10, 10-20, >20 ug/L
A-20
-------
VII. Human reproductive studies
Outcome
Spontaneous
abortion
Stillbirth
Preterm birth
Authors/year &
location
Nordstrom et al.
1978
Sweden
Nordstrom et al.
1979
Sweden
Borzsonyi et al
1992
Hungary
Ahmad etal.2001
Bangladesh
Aschengrau et al.
1989
Massachussetts
Borzsonyi et al
1992
Hungary
Hopenhayn-Rich
et al.2000
Chile
Ahmad etal.2001
Bangladesh
Design
Retrospective
cohort of
pregnancies
Retrospective
cohort of
pregnancies
Retrospective
cohort
Retrospective
cohort of
pregnancies
Case-control
Retrospective
cohort
Retrospective
vital statistics
Retrospective
cohort of
pregnancies
Exposure assessment
Residential proximity to a
smelter
Employment in smelter
prior to or during
pregnancy
Concentration in H2O
Concentration in H2O
Duration of residence in
high arsenic area
Concentration in H2O
Concentration in H2O
Concentration in H2O
Comparison of two
communities
Concentration in H2O
Duration of residence in
high arsenic area
Dose- response
analysis:
Trend in frequency by
distance of region to smelter
Highest prevalence among
those living near the smelter
during or after their
employment
Significant difference
comparing high vs. low
arsenic region
Significant difference
comparing high vs. low
arsenic region, and for those
with longer duration
Trend in risk
Significant difference
comparing high vs. low
arsenic region
Significant difference during
period when exposures were
very high
Significant difference
comparing high vs. low
arsenic region, and for those
with longer duration
Measure of
association
Prevalence
ratio
Prevalence
ratio
Prevalence
rate difference
Prevalence
rate
difference
Odds ratio
Prevalence
rate difference
Mortality rate
difference and
ratio
Prevalence
rate
difference
Range of exposures
No quantitation
Low (not quantitated
referent), 170-330 jjg/L
<20 (referent), >100 |ig/L
0.8, 0.8-1.3, 1.4-1. 9 ng/L
Low (not quantitated
referent), 170-330 ng/L
<5 (referent), various levels to
>800 ng/L
<20 (referent), >100 ng/L
A-21
-------
VII. Human reproductive studies (con't)
Outcome
Birthweight
Low birthweight
Congenital
malformations
Coarctation of the
aorta
Neonatal mortality
Postneonatal
mortality
Authors/year &
location
Nordstrom et al.
1978
Sweden
Hopenhayn et
al.2001
Chile
Nordstrom et al.
1979
Sweden
Zierler et al
1988
Massachussetts
Hopenhayn-Rich
et al.2000
Chile
Design
Retrospective
cohort of
pregnancies
Prospective
cohort & review
of vital statistics
Retrospective
cohort of
pregnancies
Case-control
Retrospective
vital statistics
Exposure assessment
Residential proximity to
smelter or employment
Concentration in H2O
Comparison of two
communities
Employment in the smelter
Routine monitoring of
water
Concentration in H2O
Comparison of two
communities
Dose- response
analysis:
Lowest birthweight among
those living nearest the
smelter
Significantly increased risk
of low birth weight
Higher prevalence of
congenital malformations
among employed mothers
Above vs. below the limit of
detection, three-fold
increased risk, adjusted for
seven other contaminants,
source of water, maternal
education
Significant difference during
period when exposures were
very high
Measure of
association
Difference in
birthweight
Odds ratio for
low
birthweight
Prevalence
ratio
Odds ratio
Mortality rate
difference and
ratio
Range of exposures
No quantitation
<2 (referent), 40-50 ng/L
< limit of detection (0.8 ng/L),
>limit of detection
<5 (referent), various levels to
>800 ng/L
A-22
-------
VIII. Human studies of neurologic and neurodevelopmental endpoints
Outcome
Peripheral
neuropathy
Various
neurobehavioral
parameters*
Verbal IQ
Authors/year &
location
Gerr et al.2000
Georgia, USA
Calderonetal.2001
Mexico
Design
Cross-sectional
Cross-sectional
Exposure assessment
Dust & soil arsenic
measurements
Urinary arsenic
Dose-response
analysis:
Significant trend, adjusted
for age, education, sex,
verbal intellectual score,
alcohol
Significant inverse
correlation
Measure ot
association
Odds ratio
Linear
regression
Partial
correlation
coefficient
Range of exposures
House dust: 1-1200 ug/g
Window sill dust: 0.5-1 92
Attic dust 1.2-2635 ug/g
Soil 2.0-1845 ug/g
<50, 50-100, >100 ugAs/g
creatinine;
Range: 27.5-186.2 ug/g
creatinine
*Vibrotactile threshold, standing steadiness, tremor intensity
A-23
-------
A Public Health Based Approach to Calculating the Magnitude of Unquantifted Health Effects
Several of the analyses of the health effects of arsenic in Taiwan use Standardized Mortality Ratios (SMRs) to compare death rates in villages with high
levels of arsenic in drinking water to death rates in unexposed areas. The analysis below compares the number of excess deaths due to lung and bladder cancers
(based on SMRs) with excess deaths due to other cancers and due to vascular disease. The goal is to compare the magnitude of excess deaths for endpoints for
which dose-response has not been quantified to excess deaths for endpoints for which dose-response functions exist. This suggests the possible magnitude of
effects that might be established if dose-response functions were estimated.
The spreadsheet in Attachment 1 to Appendix 2.2, performs this analysis using data reported in Wu et al. (1989) and Tsai et al. (1999). For the Wu et
al. data the basic findings are that (1) cancers other than lung and bladder have similar aggregate excess deaths as the sum of lung plus bladder cancer excess
deaths, and (2) vascular deaths are comparable in number to the sum of lung plus bladder cancer excess deaths. This suggests that the total mortality effect at
the high exposure levels in the Wu et al. study is about three times the effect of the previously quantified lung and bladder cancers. For the Tsai et al. data, the
basic findings are similar for total excess cancer deaths—about double those from lung plus bladder cancer by themselves. However, the vascular excess deaths
for these data are just over half the excess deaths from lung plus bladder cancers. This apparent difference from the Wu et al. results may be related to the fact
that more of the Tsai et al. data are from a somewhat later period relative to the end of exposure than the earlier Wu et al. data. One possible interpretation of this
is that the vascular deaths may tend to have a shorter average lag time relative to exposures than the cancer deaths.
A-24
-------
Attachment 1 to Appendix 2.2
Analysis of Data of Wu et al. for the Population Aggregate Excess Deaths from Various Causes
(Mortality from 1973-1986)
A. Data from Tables 3 and 4 (all data are age-adjusted mortality per 100,000 per year)
Cancers
All sites
Bladder
Kidney
Skin
Lung
Liver
Prostate
Leukemia
Nasopharynx
Esophagus
Stomach
Colon
______________ l^Tolpt ______________
< .3 mg/L
224.56
22.64
8.42
2.03
49.16
47.78
0.95
4.87
3.58
7.62
25.66
7.94
Uterine Cervix
Unidentified sites
43.91
Vascular Diseases
All vascular diseases
Peripheral vascular diseases
Cardiovascular diseases
Cerebrovascular accidents
Unidentified vascular disease
364.1
22.54
125.87
137.8
77.89
.3-.59 mg/L
405.12
61.02
18.9
14.01
100.67
67.62
9
6.52
8.16
9.37
17.82
8.3
83.73
421.47
57.8
153.98
145.36
64.33
? .6 mg/L
534.61
92.71
25.26
32.41
104.08
86.73
9.18
2.69
8.58
6.55
56.42
12.51
97.49
572.68
60.4
259.51
175.72
77.05
Females
< .3 mg/L
162.22
25.6
3.42
1.73
36.71
21.4
3.03
1.59
1.83
6.71
9.05
0.91
50.24
277.5
18.2
91.14
92.42
75.74
.3-.S9 mg/L
277.2
57.02
19.42
14.75
60.82
24.18
4.55
5.81
3.64
18.72
8.16
5.46
54.67
370.79
48.00
153.07
98.11
71.61
? .6 mg/L
487.2
111.3
57.98
18.66
122.16
31.75
0.00
4.89
0.00
5.98
17.21
3.92
113.35
386.41
35.82
144.74
120.68
85.17
A-25
-------
B. Excess Death Rates/100,000 Over < .3 mg/L Group
Cancers
All sites
Bladder
Kidney
Skin
Lung
Liver
Prostate
Leukemia
Nasopharynx
Esophagus
Stomach
Colon
Uterine Cervix
Unidentified sites
Males
.3-.59 mg/L
180.56
38.38
10.48
11.98
51.51
19.84
8.05
1.65
4.58
1.75
-7.84
0.36
0
39.82
Vascular Diseases
All vascular diseases
Peripheral vascular diseases
Cardiovascular diseases
Cerebrovascular accidents
Unidentified vascular disease
57.37
35.26
28.11
7.56
-13.56
? .6 mg/L
310.05
70.07
16.84
30.38
54.92
38.95
8.23
-2.18
5
-1.07
30.76
4.57
0
53.58
208.58
37.86
133.64
37.92
-0.84
Females
.3-.59 mg/L
114.98
31.42
16
13.02
24.11
2.78
0
1.52
4.22
1.81
12.01
-0.89
4.55
4.43
93.29
29.8
61.93
5.69
-4.13
?.6mg/L
324.98
85.7
54.56
16.93
85.45
10.35
0
-3.03
3.3
-1.83
-0.73
8.16
3.01
63.11
108.91
17.62
53.6
28.26
9.43
Mean, Both Sexes
.3-.59 mg/L
147.77
34.9
13.24
12.5
37.81
11.31
4.025
1.585
4.4
1.78
2.085
-0.265
2.275
22.13
75.33
32.53
45.02
6.625
-8.845
? .6 mg/L
317.52
77.89
35.70
23.66
70.19
24.65
4.12
-2.61
4.15
-1.45
15.02
6.37
1.51
58.35
158.75
27.74
93.62
33.09
4.295
Ratio to Lung+Bladder
Ca
.3-.S9 mg/L
2.03
0.48
0.18
0.17
0.52
0.16
0.06
0.02
0.06
0.02
0.03
0.00
0.03
0.30
1.04
0.45
0.62
0.09
-0.12
? .6 mg/L
2.14
0.53
0.24
0.16
0.47
0.17
0.03
-0.02
0.03
-0.01
0.10
0.04
0.01
0.39
1.07
0.19
0.63
0.22
0.03
Wu, M. M., Kuo, T. L., Hwang, Y. H., and Chen, C. J. Dose-response relation between arsenic concentration in well water and mortality from cancers and
vascular diseases. Am J. Epidemiology 130:1123-1132
A-26
-------
Analysis of Population Aggregate Excess Deaths from Various Causes from the Data of Tsai et al. (All mortality data are for 1971-1994—after nearly all phase-
out of the arsenic in drinking water exposure in the mid-1970's. Expected deaths are based on the local comparison group.)
A. Numbers of Deaths for Men
All Causes
Cancers
All sites
Oral
Pharyngeal, except NPC
Nasopharyngeal
Esophagus
Stomach
Intestine
Colon
Rectum
Liver
Gallbladder
Pancreas
Nasal
Laryngeal
Lung
Bone
Skin
Breast
Cervical
Ovary
Prostate
Bladder
Kidney
Brain
Lymphoma
Leukemia
Diabetes mellitus
All listed vascular diseases
Observed
11193
2774
23
24
60
69
195
15
91
46
631
13
30
40
30
699
41
66
48
312
94
19
56
67
188
2563
Numbers of Deaths for Men
Expected
8265. 76
1263.95
20
17.75
50.59
41.2
143.84
7.15
61.05
31.96
345.27
11.68
24.57
13.3
16.81
225.39
16.64
13.65
19.07
34.99
13.91
15.03
34.4
50.07
139.69
2193.62
SMR
1.32
2.19
1.67
1.36
1.83
3
1.78
3.1
2.46
4.83
2.52
8.92
6.76
1.26
1.63
1.34
1.35
1.17
95% LCL SMR
1.29
2.11
1.3
1.17
1.69
2.14
1.2
2.88
1.77
3.74
1.86
7.96
5.46
0.76
1.23
1.04
1.16
95% UCL
SMR
1.35
2.28
2.12
1.46
1.98
4.09
2.55
3.34
3.34
6.15
3.34
9.96
8.27
1.97
2.11
1.7
1.55
Excess
Deaths
2927
1510
3
6
9
28
51
8
30
14
286
1
5
27
13
474
24
52
29
277
80
4
22
17
48
369
Ratio to Lung +
Bladder Ca
3.90
2.01
0.00
0.01
0.01
0.04
0.07
0.01
0.04
0.02
0.38
0.00
0.01
0.04
0.02
0.63
0.03
0.07
0.04
0.37
0.11
0.01
0.03
0.02
0.06
0.49
A-27
-------
Hypertension
Ischemic heart disease
Pulmonary heart disease
Heart disease
Cerebro vascular disease
Vascular disease
Bronchitis
Emphysema
Asthma
Liver cirrhosis
Nephritis, nephrotic syndrome,
nephrosis
Congenital anomalies
158
445
33
534
1286
107
157
31
147
428
206
86
216.83
254.68
65.39
503.37
1123.26
30.09
106.38
38.09
166.13
360.05
176.01
75.68
0.73
1.75
0.5
1.14
3.56
1.48
1.18
1.17
0.62
1.59
0.35
1.08
2.91
1.25
1.08
1.02
0.85
1.92
0.71
1.21
4.3
1.73
1.31
1.34
-59
190
-32
31
163
77
51
-7
-19
68
30
10
-0.08
0.25
-0.04
0.04
0.22
0.10
0.07
-0.01
-0.03
0.09
0.04
0.01
A-28
-------
B. Numbers of Deaths for Women
All Causes
Cancers
All sites
Oral
Pharyngeal, except NPC
Nasopharyngeal
Esophagus
Stomach
Intestine
Colon
Rectum
Liver
Gallbladder
Pancreas
Nasal
Laryngeal
Lung
Bone
Skin
Breast
Cervical
Ovary
Prostate
Bladder
Kidney
Brain
Lymphoma
Leukemia
Diabetes mellitus
All listed vascular diseases
Hypertension
Ischemic heart disease
Pulmonary heart disease
Observed
8875
2029
12
10
29
12
111
8
83
33
224
11
19
29
13
471
34
68
47
122
15
295
128
21
35
40
343
2462
239
283
27
Expected
6329. 72
843.9
7.46
4.24
31.13
7.59
79.46
5.81
58.47
21.98
119.28
12.18
19.75
5.82
2.73
114.02
15.11
11.96
46.48
96.09
13.78
20.96
14.4
11.99
20.57
37.36
221.72
2077.06
198.69
197.02
51.18
SMR
1.4
2.4
2.36
1.4
1.42
1.5
1.88
4.98
4.76
4.13
2.25
5.68
1.27
14.07
8.89
1.75
1.7
1.55
1.19
1.2
1.44
0.53
95% LCL SMR
1.37
2.3
1.13
1.15
1.13
1.03
1.64
3.33
2.53
3.77
1.56
4.41
1.05
12.51
7.42
1.08
1.18
1.39
1.06
1.27
0.35
95% UCL
SMR
1.43
2.51
4.34
1.68
1.76
2.11
2.14
7.15
8.15
4.52
3.14
7.21
1.52
15.78
10.57
2.68
2.37
1.72
1.37
1.61
0.77
Excess
Deaths
2545
1185
5
6
-2
4
32
2
25
11
105
-1
-1
23
10
357
19
56
1
26
1
274
114
9
14
3
121
385
40
86
-24
Ratio to Lung +
Bladder Ca
4.03
1.88
0.01
0.01
0.00
0.01
0.05
0.00
0.04
0.02
0.17
0.00
0.00
0.04
0.02
0.57
0.03
0.09
0.00
0.04
0.00
0.43
0.18
0.01
0.02
0.00
0.19
0.61
0.06
0.14
-0.04
A-29
-------
Heart disease
Cerebro vascular disease
Vascular disease
Bronchitis
Emphysema
Asthma
Liver cirrhosis
Nephritis, nephrotic
syndrome, nephrosis
Congenital anomalies
493
1352
68
148
16
103
164
196
70
511.25
1089.41
29.51
96.55
13.96
123.14
157.71
168.39
59.96
1.24
2.3
1.53
1.16
1.18
1.78
1.3
1.01
1.31
2.93
1.8
1.39
-18
263
38
51
2
-20
6
28
10
-0.03
0.42
0.06
0.08
0.00
-0.03
0.01
0.04
0.02
A-30
-------
C. Men and Women Combined
All Causes
Cancers
All sites
Oral
Pharyngeal, except NPC
Nasopharyngeal
Esophagus
Stomach
Intestine
Colon
Rectum
Liver
Gallbladder
Pancreas
Nasal
Laryngeal
Lung
Bone
Skin
Breast
Cervical
Ovary
Prostate
Bladder
Kidney
Brain
Lymphoma
Leukemia
Diabetes mellitus
All listed vascular
diseases
Hypertension
Excess Deaths
5473
2695
8
12
7
32
83
10
54
25
390
0
5
50
23
831
43
108
1
26
1
29
551
194
13
36
20
170
754
-19
Ratio to Lung + Bladder Ca
3.96
1.95
0.01
0.01
0.01
0.02
0.06
0.01
0.04
0.02
0.28
0.00
0.00
0.04
0.02
0.60
0.03
0.08
0.00
0.02
0.00
0.02
0.40
0.14
0.01
0.03
0.01
0.12
ft 55
-0.01
A-31
-------
Ischemic heart disease
Pulmonary heart disease
Heart disease
Cerebrovascular disease
Vascular disease
Bronchitis
Emphysema
Asthma
Liver cirrhosis
Nephritis, nephrotic
syndrome, nephrosis
Congenital anomalies
276
-57
12
425
115
102
-5
-39
74
58
20
0.20
-0.04
0.01
0.31
0.08
0.07
0.00
-0.03
0.05
0.04
0.01
A-32
------- |