1 EPA/600/R-06/066
2 NCEA-C-1598
3 August 2006
4 External Review Draft
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10 Approaches to Estimating the Waterborne
11 Disease Outbreak Burden in the United
12 States: Uses and Limitations of the
13 Waterborne Disease Outbreak
Surveillance System
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38 National Center for Environmental Assessment
39 Office of Research and Development
40 U.S. Environmental Protection Agency
41 Cincinnati, OH
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1 NOTICE
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4 This document is an external review draft. It has not been formally released by
5 the U.S. Environmental Protection Agency and should not at this stage be construed to
6 represent Agency policy. It is being circulated for comments on its technical merit and
7 policy implications.
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43 Preferred citation: [final only]
44 U.S. EPA. 2006. Approaches to Estimating the Waterborne Disease Outbreak Burden in the United
45 States: Uses and Limitations of the Waterborne Disease Outbreak Surveillance System. U.S.
46 Environmental Protection Agency, National Center for Environmental Assessment, Cincinnati, OH.
47 EPA/600/R-06/066.
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1 TABLE OF CONTENTS
2
3
4 Page
5
6 LIST OF TABLES vii
7 LIST OF FIGURES x
8 LIST OF ABBREVIATIONS xiii
9 PREFACE xiv
10 AUTHORS, CONTRIBUTORS AND REVIEWERS xv
11 EXECUTIVE SUMMARY xvii
12
13 1. INTRODUCTION 1-1
14
15 1.1. PURPOSE AND POTENTIAL USEFULNESS OF A BURDEN
16 OF WBDO ANALYSIS 1-2
17
is 1.1.1. Objectives 1-4
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20 1.2. THE WBDO SURVEILLANCE SYSTEM 1-5
21 1.3. MEASURES OF THE BURDEN OF DISEASE 1-8
22 1.4. WILLINGNESS-TO-PAY AND THE VALUE OF A STATISTICAL
23 LIFE 1-9
24 1.5. COST-OF-ILLNESS APPROACH 1-11
25 1.6. COMPONENTS OF THE WBDO BURDEN ANALYSIS 1-12
26
27 2. MEASURES AND METHODS FOR ESTIMATING THE
28 EPIDEMIOLOGIC BURDEN OF INFECTIOUS DISEASE OUTBREAKS
29 ASSOCIATED WITH DRINKING WATER 2-1
30
31 2.1. CASES OF ILLNESS 2-3
32 2.2. DURATION OF ILLNESS 2-8
33 2.3. PHYSICIAN VISITS 2-12
34 2.4. EMERGENCY ROOM VISITS 2-15
35 2.5. HOSPITALIZATIONS 2-16
36 2.6. MORTALITY 2-21
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38 2.6.1. Comparison of WBDOSS and Mead etal.
39 Hospitalization Rates 2-24
40 2.6.2. Fatality per Case Estimations 2-27
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42 2.7. EPIDEMIOLOGIC BURDEN SEVERITY MEASURES 2-35
43
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i TABLE OF CONTENTS cont.
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6 3. RESULTS: PROJECTED EPIDEMIOLOGIC BURDEN ESTIMATE OF
7 REPORTED INFECTIOUS WATERBORNE OUTBREAKS BY SUMMARY
8 CATEGORIES AND IMPACT OF THE MILWAUKEE OUTBREAK 3-1
9
10 3.1. EPIDEMIOLOGIC BURDEN BY ETIOLOGIC AGENT 3-1
11 3.2. EPIDEMIOLOGIC BURDEN BY WATER SYSTEM TYPE 3-6
12 3.3. EPIDEMIOLOGIC BURDEN BY WATER SYSTEM DEFICIENCY 3-11
13 3.4. EPIDEMIOLOGIC BURDEN BYTIME PERIOD 3-18
14 3.5. EPIDEMIOLOGIC BUDEN BY WATER SOURCE TYPE 3-18
15 3.6. OVERALL IMPACT OF MILWAUKEE CRYPTOSPORIDIOSIS
16 OUTBREAK 3-22
17 3.7. FURTHER ANALYSIS OF OUTBREAKS CAUSED BY AGI 3-22
18 3.8. DISCUSSION AND CONCLUSIONS 3-26
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20 4. ECONOMIC METHODS FOR ESTIMATING DISEASE BURDEN
21 ASSOCIATED WITH INFECTIOUS WATERBORNE OUTBREAKS 4-1
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23 4.1. ESTIMATING THE MONETARY BURDEN OF WBDO USING
24 COST-OF-ILLNESS APPROACH 4-2
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26 4.1.1. Severity Classification 4-10
27 4.1.2. Costs of Self Medication 4-11
28 4.1.3. Cost Associated with Physician Visit 4-14
29 4.1.4. Cost Associated with Visiting an Emergency Room 4-14
30 4.1.5. Cost Associated with Hospital Stay 4-16
31 4.1.6. Cost Due to Loss in Productivity 4-19
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33 4.2. USING WILLINGNESS-TO-PAY MEASURE TO VALUE
34 PREMATURE MORTATLITY: VALUE OF STATISTICAL LIFE 4-23
35 4.3. ESTIMATING THE MONETARY BURDEN OF THE
36 WATERBORNE OUTBREAKS 4-24
37
38 5. RESULTS: MONETARY BURDEN ESTIMATE OF REPORTED
39 INFECTIOUS WATERBORNE OUTBREAKS BY SUMMARY
40 CATEGORIES AND IMPACT OF THE MILWAUKEE OUTBREAK 5-1
41
42 5.1. MONETARY BURDEN BY ETIOLOGY 5-1
43 5.2. MONETARY BURDEN BY WATER SYSTEM TYPE 5-3
44 5.3. MONETARY BURDEN BY WATER SYSTEM DEFICIENCY 5-6
45 5.4. MONETARY BURDEN BYTIME PERIOD 5-12
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i TABLE OF CONTENTS cont.
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6 5.5. MONETARY BURDEN BY WATER SOURCE TYPE 5-12
7 5.6. THE OVERALL MONETARY IMPACT OF THE MILWAUKEE
8 CRYPTOSPORIDIOSIS OUTBREAK 5-14
9 5.7. DISCUSSION AND CONCLUSIONS 5-21
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11 6. SENSITIVITY ANALYSES FOR MONETARY BURDEN 6-1
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13 6.1. SENSITIVITY OF THE MONETARY BURDEN TO THE
14 EPIDEMIOLOGIC BURDEN MEASURES 6-1
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16 6.1.1. Method 6-3
17 6.1.2. Results 6-4
is 6.1.3. Discussion 6-6
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20 6.2. MONTE CARLO SENSITIVITY ANALYSIS OF THE MONETARY
21 BURDEN ASSOCIATED WITH WBDO DEATHS 6-8
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23 6.2.1. Methods 6-8
24 6.2.2. Monte Carlo Analysis 6-11
25 6.2.3. Results and Discussion: Preliminary Uncertainty Analysis
26 of the Deaths Associated with the WBDO 6-12
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28 6.3. SENSITIVITY ANALYSIS OF THE MONETARY BURDEN
29 ASSOCIATED WITH THE MILWAUKEE OUTBREAK TO THE
30 REPORTED DURATION OF ILLNESS AND CASE NUMBER 6-16
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32 6.3.1. Alternative Estimates of Duration of Cryptosporidiosis
33 during Milwaukee WBDO 6-17
34 6.3.2. Alternative Estimates of Milwaukee Cryptosporidiosis Cases.... 6-18
35 6.3.3. Affect of Alternative Case Numbers and Duration of Illness
36 on the Burden of the Milwaukee WBDO 6-27
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38 6.4. CONCLUSIONS OF SENSITIVITY ANALYSIS 6-31
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40 7. DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 7-1
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42 7.1. DISCUSSION 7-1
43 7.2. CONCLUSIONS 7-10
44 7.3. RECOMMENDATIONS 7-12
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46 8. REFERENCES 8-1
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i TABLE OF CONTENTS cont.
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6 APPENDIX A: THE WATERBORNE OUTBREAK SURVEILLANCE SYSTEM A-1
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8 APPENDIX B: OUTBREAK INVESTIGATION METHODS ENTERIC
9 WATERBORNE DISEASE OUTBREAKS IN DRINKING
10 WATER 1971-2000 B-1
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12 APPENDIX C: ANNUAL ESTIMATES OF EPIDEMIOLOGIC AND MONETARY
13 DISEASE BURDEN, 1971-2000 C-1
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1 LIST OF TABLES
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4 No. Title
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6 2-1 Availability of Selected Severity Measures in the WBDO Surveillance
7 System 2-2
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9 2-2 Durations of Illness (in Days) by Etiologic Agent, WBDOs, 1971 to 2000 2-4
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11 2-3 Duration of Illness, Milwaukee Cryptosporidium Outbreak 2-11
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13 2-4 Physician Visits by Etiologic Agent, Reported WBDOs, 1971 to 2000 2-13
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15 2-5 Emergency Room Visits by Etiologic Agent, WBDOs, 1971 to 2000 2-17
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17 2-6 Hospitalizations, Reported WBDOs, 1971 to 2000 2-19
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19 2-7 Mortality Reported in the WBDOSS, 1971-2000, by Etiology 2-22
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21 2-8 Hospitalization Rate 2-26
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23 2-9 Case Fatalities per 100,000 Cases According to WBDOSS and
24 Other Sources 2-28
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26 2-10 Comparison of Number of Deaths Reported in WBDOs with Expected
27 Number of Deaths Using Literature-based Fatality-case Ratios 2-34
28
29 2-11 Modifications of the Plausible Predicted Number of WBDO Deaths
30 Estimated from Literature-based Fatality-case Ratios 2-36
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32 2-12 Epidemiological Burden Measures Used in the Analysis Reported
33 Waterborne Outbreaks in Drinking Water for the 30-Year Period,
34 1971 to 2000 2-36
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36 3-1 Projected Epidemiologic Burden of Reported Infectious Waterborne
37 Outbreaks in Drinking Water by Etiologic Agent, 1971 to 2000 3-2
38
39 3-2 Projected Epidemiologic Burden of Reported Infectious Waterborne
40 Outbreaks in Drinking Water by Etiologic Agent, 1971 to 2000 3-3
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42 3-3 Projected Natural Burden of Reported Infectious Waterborne Outbreaks
43 in Drinking Water, 1971 to 2000 3-8
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45 3-4 Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in
46 Drinking Water by Water System Deficiency, 1971 to 2000 3-12
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No.
3-5
3-6
4-1
4-2
4-3
4-4
4-5
4-6
4-7
4-8
4-9
5-1
5-2
5-3
5-4
5-5
5-6
LIST OF TABLES cont.
Title
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in
Drinking Water by Decade, 1971 to 2000
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in
Drinking Water by Water Source Type, 1971 to 2000
Parameter Estimates from Cost-of-lllness Studies
Illness Severity Definitions
Distribution of Cases Using Estimated Severity Measures for
Monetary Burden
Estimated Cost of Self Medication
Estimated Cost of Physician Visits
Estimated Cost of Emergency Room Visits
Estimated Charges per Hospitalization Case
Productivity Losses by Severity for III Persons and Caregivers for
Waterborne Outbreaks
Projected Monetary Burden of Infectious Waterborne Outbreaks in
Drinking Water, 1971 to 2000
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water,
1971 to 2000, by Etiology (Pathogen Group)
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water,
1971 to 2000, by Etiology (Specific Pathogens)
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water,
1971 to 2000, by Water System Classification Type
Monetary Burden by Water System Deficiency, 1971 to 2000
Monetary Burden by Time Period, 1971 to 2000
Monetary Burden by Water Source Type, 1971 to 2000
Page
3-19
3-20
4-7
4-10
4-12
4-13
4-15
4-17
4-18
4-20
4-25
5-2
5-4
5-5
5-7
5-13
5-15
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i LIST OF TABLES cont.
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4 No. Title
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6 5-7 Monetary Burden of Infectious Waterborne Outbreaks in Drinking
7 Water, 1971 to 2000 5-20
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9 6-1 Reported and Projected Epidemiological Burden Measures for U.S.
10 WBDOs which Occurred between 1971 and 2000 6-2
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12 6-2 Percent Change Required in the Epidemiologic Burden to Change
13 Monetary Burden Estimate for U.S. WBDOs by 5% 6-5
14
15 6-3 Sensitivity of the Monetary Burden to Changes in the Epidemiological
16 Burden Excluding the Milwaukee Outbreak 6-7
17
is 6-4 Total Number of Outbreaks and Alternative Estimates of Deaths for
19 Each Etiologic Agent 6-9
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21 6-5 Duration of Illness, Milwaukee Cryptosporidium Outbreak 6-19
22
23 6-6 Distribution of Reported Median Duration of Illness of Cryptosporidium
24 WBDOs, 1971 to 2000 6-20
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26 6-7 Alternative Estimates of Number of Cases Attributable to the
27 Milwaukee WBDO 6-22
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29 6-8 The Alternate Estimated Numbers of Cases and Epidemiologic Burdens
30 of the Milwaukee WBDO 9 Days Median Duration of Illness 6-25
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32 6-9 The Alternate Estimated Numbers of Cases and Epidemiologic Burdens
33 of the Milwaukee WBDO 3 Days Median Duration of Illness 6-26
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35 6-10 Results of Conjectured Alternative Numbers of Cases and Economic
36 Burdens of the Milwaukee WBDO 9 Days Median Duration of Illness 6-28
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38 6-11 Results of Conjectured Alternative Numbers of Cases and Economic
39 Burdens of the Milwaukee WBDO 3 Days Median Duration of Illness 6-29
40
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1 LIST OF FIGURES
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4 No. Title
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6 1-1 Methodology to Determine the Disease Burden of WBDOs 1-6
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8 3-1 Number of Outbreaks for Community System WBDOs by Source Type 3-9
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10 3-2 Number of Person-Days III for Community System WBDOs by Source Type ... 3-9
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12 3-3 Number of Deaths for Community System WBDOs by Source Type 3-10
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14 3-4 Person-Days III for Water System Deficiency in Water Treatment by
15 Etiologic Agent 3-14
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17 3-5 Person-Days III for Deficiency in Water Treatment WBDOs by Etiologic
is Agent (excluding the Milwaukee WBDO) 3-14
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20 3-6 Person-Days III for Distribution System Deficiency 3-15
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22 3-7 Person-Days III for Untreated Groundwater 3-15
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24 3-8 Person-Days III for Water System Deficiency in Untreated Surface Water 3-16
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26 3-9 Pathogens Associated with WBDOs in Surface Water Systems
27 Between 1971 and 2000 3-21
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29 3-10 Pathogens Associated with Person-Days III in Surface Water System
30 Outbreaks Between 1971 and 2000 3-23
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32 3-11 Pathogens Associated with WBDOs in Groundwater Systems Between
33 1971 and 2000 3-23
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35 3-12 Pathogens Associated with Person-Days III in Groundwater Systems
36 Between 1971 and 2000 3-24
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38 3-13 Number of Outbreaks for AGI WBDOs by Source Type 3-24
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40 3-14 Number of Person-Days III for AGI WBDOs by Source Type 3-25
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42 3-15 Number of Outbreaks for AGI WBDOs by Water System Type 3-27
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44 3-16 Number of Person-Days III for AGI WBDOs by Water System Type 3-27
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i LIST OF FIGURES cont.
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4 No. Title
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6 4-1 Illustration of the Components for Monetary Burden Calculations 4-3
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8 5-1 Monetary Burden for WBDOs in Community Water Systems by Source
9 Type Water 5-7
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11 5-2 Monetary Burden for WBDO Caused by Water Treatment Deficiency
12 by Etiologic Agent 5-9
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14 5-3 Monetary Burden for WBDO Caused by Deficiency in Water Treatment
15 by Etiologic Agent (without the Milwaukee WBDO) 5-9
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17 5-4 Monetary Burden for WBDO Caused by Deficiency Distribution System
is by Etiologic Agent 5-10
19
20 5-5 Monetary Burden for WBDO Caused by Untreated Groundwater
21 by Etiologic Agent 5-10
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23 5-6 Monetary Burden for WBDO Caused by Untreated Surface Water
24 by Etiologic Agent 5-11
25
26 5-7 Monetary Burden for WBDO with Unidentified or Miscellaneous Causes
27 by Etiologic Agent 5-11
28
29 5-8 Distribution of Monetary Burden of WBDOs in Surface Water Systems
30 by Etiologic Agent 5-16
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32 5-9 Distribution of Monetary Burden of WBDOs in Surface Water Systems
33 by Etiologic Agent, Excluding the Milwaukee WBDO 5-16
34
35 5-10 Distribution of Monetary Burden of WBDOs in Groundwater Systems
36 by Etiologic Agent 5-17
37
38 5-11 Contribution of the Milwaukee WBDO to the Monetary Burden Estimate
39 from All U.S. WBDOs 5-17
40
41 5-12 Component Distribution for the Monetary Burden Estimates of
42 U.S. WBDOs 5-18
43
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i LIST OF FIGURES cont.
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4 No. Title
5
6 5-13 Component Distribution for the Monetary Burden Estimates Excluding
7 the Milwaukee WBDO 5-18
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9 5-14 Cost-of-lllness Components for Monetary Burden Estimate of
10 U.S. WBDOs 5-19
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12 5-15 Cost-of-lllness Components for Monetary Burden Estimate Excluding
13 Milwaukee WBDO 5-19
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15 6-1 Predicted Distribution of U.S. WBDO Deaths Based on Monte Carlo
16 Simulations with Distributions of the Numbers of Deaths for all
17 Etiologic Agents 6-13
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19 6-2 Predicted Distribution of Monetary Burden of U.S. WBDO Deaths
20 Based on Monte Carlo Simulations with Distributions of the Numbers
21 of Deaths for Each Etiologic Agent and of the VSL 6-14
22
23 6-3 Rank Correlation Coefficients Associated with Mortality Sensitivity Analysis ..6-15
24
25 6-4 COI Estimates Associated with Alternative Impacts of the Milwaukee
26 WBDO 6-30
27
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LIST OF ABBREVIATIONS
AGI Acute gastroenteritis illness of unknown etiology
AIDS Acquired Immunodeficiency Syndrome
CAST Council for Agricultural Science and Technology
CDC Centers for Disease Control and Prevention
COI Cost-of-illness
CPI Consumer Price Index
DALY Disability Adjusted Life Years
ER Emergency room
HCUP Health Care Utilization Project
PCG Productivity losses of caregiver
PI Productivity losses of ill person
PV Physician visit
SDWA Safe Drinking Water Act
SM Self medication
SRSV Small round structured virus
U.S. EPA U.S. Environmental Protection Agency
VSL Value of statistical life
WBDO Waterborne disease outbreak
WBDOSS Waterborne Disease Outbreak Surveillance System
WTP Willingness-to-pay
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1 PREFACE
2
3
4 This report was developed by the U.S. Environmental Protection Agency's (U.S.
5 EPA) Office of Research and Development (ORD), National Center for Environmental
6 Assessment in collaboration with researchers from Craun and Associates, Inc. It
7 contains information concerning a waterborne disease outbreak database that has been
8 jointly maintained by the Centers for Disease Control and Prevention (CDC) and the
9 U.S. EPA since 1971. The document examines waterborne outbreaks from the
10 perspective of disease burden. The term disease burden is a general expression that is
11 used to capture the magnitude of the health impacts that occur; it generally refers to
12 decrements in a population's health, but can include the associated economic burden.
13 This effort supports research mandated by the Safe Drinking Water Act (SDWA)
14 Amendments of 1996. Specifically, section 1458(d) requires the U.S. EPA and CDC to
15 develop a national estimate of waterborne disease occurrence ("the national estimate").
16 This research also addresses the need for improved understanding of the impact of
17 waterborne microbial risks in the U.S.
Draft: Do Not Cite or Quote xiv 9/6/06
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1 AUTHORS, CONTRIBUTORS AND REVIEWERS
2
3
4 This research was sponsored by the U.S. Environmental Protection Agency (U.S.
5 EPA), Office of Research and Development, National Center for Environmental
6 Assessment - Cincinnati Division (NCEA-Cin). NCEA-Cin researchers collaborated with
7 scientists from other organizations to conduct this research and to author this report.
8
9
10 AUTHORS
11
12 Gunther Craun
13 Craun and Associates
14 Staunton, VA 24401
15
16 Michael Craun
17 Craun and Associates
is Staunton, VA 24401
19
20 Matthew T. Heberling
21 National Center for Environmental Assessment
22 U.S. Environmental Protection Agency
23 Cincinnati, OH 45268
24
25 Patricia A. Murphy
26 National Center for Environmental Assessment
27 U.S. Environmental Protection Agency
28 Cincinnati, OH 45268
29
30 Glenn E. Rice (Project Lead)
31 National Center for Environmental Assessment
32 U.S. Environmental Protection Agency
33 Cincinnati, OH 45268
34
35 Mary M. Rothermich
36 National Center for Environmental Assessment
37 U.S. Environmental Protection Agency
38 Cincinnati, OH 45268
39
40 J. Michael Wright
41 National Center for Environmental Assessment
42 U.S. Environmental Protection Agency
43 Cincinnati, OH 45268
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i AUTHORS, CONTRIBUTORS AND REVIEWERS cont.
2
3
4
5 CONTRIBUTOR
6
7 Richard Rheingans
8 School of Public Health
9 Emory University
10 Atlanta, GA 30322
11
12
13 REVIEWERS
14
15 Rebecca Calderon, Ph.D.
16 National Health and Environmental Effects Research Laboratory
17 Office of Research and Development
is U.S. Environmental Protection Agency
19 Research Triangle Park, NC
20
21 Anne Grambsch, Ph.D.
22 National Center for Environmental Assessment
23 Office of Research and Development
24 U.S. Environmental Protection Agency
25 Washington, DC
26
27 Nicole Owens, Ph.D.
28 Office of Policy, Economics and Innovation
29 Office of the Administrator
so U.S. Environmental Protection Agency
31 Washington, DC
32
33 Office of Groundwater and Drinking Water, Office of Water, U.S. Environmental
34 Protection Agency, Washington, DC
35 Pamela Barr
36 Philip Berger
37 Valerie Blank
38 Lisa Christ
39 Yu-Ting Guilaran
40 Jennifer Mclain
41 Stig Regli
42 Susan Shaw
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1 EXECUTIVE SUMMARY
2
3
4 INTRODUCTION
5 The dramatic reduction in the incidence of waterborne infectious diseases
6 brought about by the filtration and chlorination of public drinking water supplies and
7 effective sewage treatment is one of the great public health achievements of the 20th
8 Century. Although water treatment technologies and protection of water sources from
9 sewage contamination are mandated in order to reduce the risk of waterborne disease
10 in the U.S., outbreaks still occur.
11 Information about U.S. waterborne disease outbreaks (WBDOs) is voluntarily
12 reported to the Waterborne Disease Outbreak Surveillance System (WBDOSS), which
13 is maintained by the Centers for Disease Control and Prevention (CDC), the U.S.
14 Environmental Protection Agency (U.S. EPA), and the Council of State and Territorial
15 Epidemiologists. State, territorial and local public health agencies are responsible for
16 detecting and investigating WBDOs and reporting them to this passive surveillance
17 system. CDC and U.S. EPA evaluate the outbreak reports to assess the strength of the
is epidemiologic evidence implicating water and the available information about water
19 quality, sources of contamination and system deficiencies. Information about the
20 occurrence of WBDOs and their causes is published biennially in the Morbidity and
21 Mortality Weekly Report. The illnesses that occur during these WBDOs can range from
22 mild episodes of gastroenteritis to severe outcomes that can result in dehydrating
23 diarrhea, chronic sequelae, hospitalization or death.
24 The purpose of this report is to estimate the burden of disease associated with
25 the 665 WBDOs in the U.S. that were reported to the WBDOSS between 1971 and
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i 2000 and were associated with infectious agents. In health economics, the term burden
2 of disease refers to the composite impact of the number of cases, the cases' severity
3 and, in some instances, the associated economic impacts.
4 LIMITATIONS OF THE WBDOSS FOR ASSESSING DISEASE BURDEN
5 An important limitation of the WBDOSS data set is that not all WBDOs and
6 associated cases of illness are recognized or reported. The reported WBDO events
7 and characteristics do not reflect the true number of outbreaks or incidence of disease,
8 and the extent to which outbreaks are not recognized, not investigated or not reported is
9 unknown. Whether an outbreak is reported depends on many factors including: (a)
10 public awareness, (b) the likelihood that persons who are ill will seek treatment and
11 consult the same health-care providers, (c) availability and extent of laboratory testing,
12 (d) local requirements for reporting cases of particular diseases and (e) the surveillance
13 and investigative activities of state and local public health and environmental agencies.
14 In addition, not all outbreaks are rigorously investigated, and information may be
15 incomplete. Often the primary intent of an outbreak investigation is to determine the
16 cause and to prevent additional illness; such investigations may not focus on identifying
17 epidemiologic information or water quality data that are important in estimating the
is disease burden. Thus, our analyses cannot provide a burden estimate of the true
19 incidence of waterborne outbreak illnesses in the U.S. population. Such an estimate
20 would require additional data and procedures to estimate unreported outbreaks and
21 unrecognized cases including unrecognized endemic cases. Furthermore, the
22 WBDOSS does not include sporadic or endemic cases of waterborne illness. The
23 reader should be mindful of these limitations when comparisons are made between
Draft: Do Not Cite or Quote xviii 9/6/06
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i WBDOs that have occurred during different time periods, in different types of source
2 waters, using different types of treatments attributed to different etiologic agents and as
3 a consequence of various treatment deficiencies. Despite these limitations, the
4 WBDOSS database does constitute the most comprehensive source of information on
5 WBDOs in the U.S. and is useful for demonstrating our surveillance-based approach for
6 analyzing the reported outbreak component of the infectious disease burden posed by
7 contaminated drinking waters.
8 MEASURES OF THE BURDEN OF DISEASE
9 The approach used in this report to determine the burden of waterborne
10 infectious disease outbreaks due to drinking water is illustrated in Figure ES-1. While a
11 variety of measures, such as Disability Adjusted Life Years (DALYs), have been
12 employed to estimate disease burden, we limit this analysis to the benefits assessment
13 measures currently employed in U.S. EPA rulemaking procedures: epidemiologic
14 measures and monetary measures. It is important to note that epidemiologic measures
15 must be obtained or estimated in order to quantify the monetary burden. The monetary
16 burden (expressed in year 2000 U.S. dollars) presented here is consistent with current
17 U.S. EPA economic practices. The U.S. EPA evaluates the monetary burden
is associated with mortality using the "value of a statistical life" (VSL), which is based on
19 willingness to pay approaches for estimating the economic value of reducing the risk of
20 premature death. To estimate the monetary burden associated with the morbidity from
21 waterborne illnesses, U.S. EPA uses cost-of-illness (COI) estimates. For the WBDO
22 analysis, we employed data derived from several peer-reviewed sources that provide
23 COI estimates specifically for waterborne outbreaks.
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1
2
o
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Infectious Disease Outbreaks
Due to Drinking Water
(as Reported in the WBDOSS)
I
Compilation of Reported
Outbreak Severity Measures
and
Estimation of Missing Information
Case Number
Duration-of-lllness
Physician Visits
Emergency Room Visits
Hospitalizations
Deaths
I
Burden in Epidemiologic Units
I
Monetary Valuation
Medical Care
Medications
Productivity Losses at Work and Home
Premature Death
T
Burden in Monetary Units
FIGURE ES-1
Methodology to Determine the Disease Burden of WDBOs
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1 METHODS USED TO ESTIMATE THE EPIDEMIOLOGIC BURDEN
2 Table ES-1 summarizes the information available for the 665 infectious WBDOs
3 reported during 1971-2000. When essential information about illness severity
4 characteristics was inadequately reported for disease burden estimation purposes —
5 either because the information was not requested on CDC 52.12 (i.e., the form
6 investigators use to report outbreaks to the WBDOSS) or the form was incompletely
7 filled out — we estimated values necessary for our analyses. If available, we used
8 information from other WBDOs in the database that were attributed to the same or a
9 similar etiologic agent. If sufficient information was not available from other WBDOs,
10 information was obtained from the scientific and medical peer-reviewed literature.
11 Some 45% of the WBDOs (n=300) were attributed to specific waterborne pathogens
12 that were identified in clinical specimens obtained from the case patients. The other
13 365 outbreaks were identified as "acute gastrointestinal illness of unknown etiology"
14 (AGI) either because laboratory results were not reported or an etiologic agent could not
15 be identified by the tests performed.
16 EPIDEMIOLOGIC BURDEN MEASURES
17 The summary epidemiologic severity measures used for the epidemiologic
is burden analysis are presented in Table ES-2.
19 Duration of Illness
20 By multiplying the average duration of illness and the number of cases, we
21 estimated person-days ill associated with each WBDO. This measure provides a
22 succinct way to compare the population-level health impact of different diseases. For
23 example, the public health impact of a norovirus (2-day typical duration of illness)
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TABLE ES-1
Availability of Severity Measures in the WBDO Surveillance System (Number of
Infectious or Suspected Infectious Drinking Water Outbreaks = 665)
Severity Measure
Cases of Illness
Duration of llness
Hospital admissions
Physician visits
Emergency room visits
Deaths
WBDOs for Which Severity
Measure was Reported
Number
665
282
659
29
15
665
Percent
100
42
99
4
2
100
Does CDC 52. 12
Request this
Measure?
Yes
Yes
Yes
No
No
Yes
TABLE ES-2
Epidemiological Burden Measures Used in the Burden Analysis
Burden Measure
Cases
Person-Days III
Physician Visits
Emergency Room Visits
Hospitalizations
Deaths
Value Used
569,962
4,504,933
41,985
23,575
5,915
66
Reported or Estimated
Reported
Calculated from reported
case numbers and reported
or estimated durations of
illness
Estimated
Estimated
Reported
Reported
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i outbreak of 50 cases could be compared to the public health impact of a Giardia (12-
2 day typical duration of illness) outbreak of eight cases: 100 person-days ill for the
3 norovirus outbreak, 96 person-days ill for the Giardia outbreak.
4 Physician and Emergency Room Visits
5 Form CDC 52.12 does not include information about the number of physician and
6 emergency room visits. When available, we used the physician-visit rate reported in the
7 WBDOSS for the same etiologic agent to estimate unreported rates. For emergency
8 room visits, most estimates were based on the pathogen group rather than a specific
9 pathogen because of sparse information. We estimated visits only for WBDOs in which
10 the number of hospitalizations constituted fewer than 75% of the reported illnesses. For
11 WBDOs where hospitalizations were greater than 75%, we assumed the severity of the
12 illnesses resulted in few cases treated on an outpatient basis. Both estimates are
13 based upon very few reported values and we were unable to locate peer-reviewed
14 literature for alternative estimates. Thus, these components of the burden estimate are
15 highly uncertain.
16 Hospitalizations and Deaths
17 Form CDC 52.12 requests the number of cases hospitalized and deaths
is occurring during an outbreak. All WBDO reports included an entry for deaths and 659
19 of the reports (99%) included hospital admission information. We address the possible
20 under- or over-reporting of these measures by comparison of the WBDOSS data to
21 other infectious disease epidemiologic data available from published literature sources.
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1 EPIDEMIOLOGIC BURDEN ESTIMATES
2 To examine characteristics or circumstances that may be associated with the
3 cause of a WBDO and the magnitude of its burden, we analyzed the epidemiologic data
4 by summarization within five categories of factors potentially relevant to the causation of
5 a WBDO: etiologic agent (i.e., the pathogen), water system type, water system
6 deficiency, time period and water source type. Due to the overwhelming influence of
7 the 1993 Milwaukee cryptosporidiosis WBDO, we also developed comparisons of the
8 impact of the various factors excluding the data from this event. This WBDO occurred
9 in a community water system that used surface waters as a source water and the
10 outbreak was attributed to the protozoan, Cryptosporidium, that occurred in the drinking
11 water due to a treatment deficiency. This WBDO contributed 403,000 (71 %) cases of
12 illness, 3,627,000 (81 %) person-days ill, 20,280 (48%) physician visits, 11,727 (50%)
13 emergency room visits, 4400 (74%) hospitalizations and 50 (76%) deaths to the
14 estimated epidemiologic burden.
15 Epidemiologic Burden by Etiologic Agent
16 Protozoa, primarily Cryptosporidium and Giardia, were associated with the most
17 cases, person-days ill, physician visits, emergency room visits, hospitalizations and
is deaths (Table ES-3). The Milwaukee WBDO accounts for more person-days ill,
19 emergency room visits, hospitalizations and deaths than all other WBDOs combined.
20 Excluding the Milwaukee WBDO, protozoan WBDOs still account for more person-days
21 ill and physician visits than WBDOs caused by viruses or bacteria. However, bacterial
22 WBDOs account for more hospitalizations and almost all of the deaths that were not
23 associated with cryptosporidiosis.
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TABLE ES-3
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Etiologic Agent Type,
1971 to 2000
Etiologic Agent
Type
AGI
Viruses
Bacteria
Outbreaks
365
56
101
Cases
83,493
15,758
20,786
Person-Days III
265,120
53,697
95,615
Physician
Visits
8,822
2,017
1,196
Emergency
Room Visits
9,426
124
931
Hospital-
izations
378
92
928
Deaths
1
0
15
Protozoa
Milwaukee WBDO
All Other WBDO
Total
1
142
665
403,000
46,925
569,962
3,627,000
463,423
4,504,854
20,280
9,669
41,985
11,727
1,366
23,575
4,400
117
5,915
50
0
66
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i Epidemiologic Burden by Water System
2 The most cases (485,844, 85% of total), person-days ill (4,215,965, 93% of total),
3 physician visits (32,400, 77% of total), emergency room visits (16,268, 69% of total),
4 hospitalizations (4,931, 83% of total) and deaths (62, 94% of total) were reported for
5 WBDOs occurring in community water systems. If the Milwaukee WBDO data are
6 excluded from the analysis, WBDOs occurring in community systems had 50% of the
7 total non-Milwaukee cases, 67% of the person-days ill, 55% of the physician visits and
8 75% of the deaths. WBDOs occurring in non-community systems involved 57% of the
9 total non-Milwaukee emergency room visits and 58% of the hospitalizations. The
10 WBDOs that occurred in individual water systems accounted for no more than 3% of
11 any of the measures when Milwaukee data were included and no more than 7% with
12 Milwaukee excluded.
13 Epidemiologic Burden by Source Water
14 WBDOs in surface water systems were reported less frequently than in
15 groundwater systems but resulted in a greater number of cases (457,310), person-days
16 ill (4,058,221), physician visits (29,735), emergency room visits (14,443),
17 hospitalizations (4,644) and deaths (50). Most surface water outbreaks were
is associated with Giardia (48%) or AGI (36%), but most of the person-days ill in surface
19 water outbreaks were associated with Cryptosporidium primarily due to the Milwaukee
20 WBDO. AGI outbreaks were responsible for 62% of groundwater outbreaks and 52% of
21 the person-days ill in these systems.
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i Epidemiologic Burden by Water System Deficiency
2 In comparison to the other water system deficiency issues, WBDOs associated
3 with one or more water treatment deficiencies made the greatest contribution to the
4 epidemiologic burden: 92% of the cases, 83% of the person-days ill, 87% of the
5 physician visits, 86% of the ER visits, 84% of the hospitalizations and 79% of the
6 deaths. Distribution system deficiencies and untreated groundwater accounted for all
7 but about 2% of the remaining burden from each of the severity measures. If the
8 Milwaukee WBDO data are excluded, water treatment deficiencies account for 70-75%
9 of the non-Milwaukee cases, person-days ill, physician visits and emergency room
10 visits, but only 38% of the hospitalizations and 13% of the deaths. Distribution system
11 deficiencies were associated with 75% of the non-Milwaukee deaths and 13% of the
12 hospitalizations. Untreated groundwater was the major contributor to the non-
13 Milwaukee hospitalization burden with 40% of the hospital admissions.
14 Epidemiologic Burden by Time Period
15 The fewest number of WBDOs were reported in the 1990s, however that decade
16 experienced the majority of the disease burden in all measured categories. WBDOs in
17 the 1990s accounted for the most cases (432,195), person-days ill (3,775,241),
is physician visits (23,412), emergency room visits (13,834), hospitalizations (4735) and
19 deaths (59). However, when the Milwaukee WBDO is excluded, the reported number of
20 outbreaks, cases, person-days ill, physician visits, emergency room visits and
21 hospitalizations decreases in each successive decade.
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1 ECONOMIC BURDEN MEASURES AND METHODS
2 Figure ES-2 shows the components quantified to calculate the monetary burden
3 associated with reported WBDOs. The results of the COI and VSL analyses were
4 combined to estimate the monetary burden. Although both measures are expressed in
5 monetary units, it should be noted that the COI measures capture only a subset of the
6 factors that WTP measures capture. The COI estimates do not include averting
7 behavior costs or defensive expenditures (e.g., purchasing a water filter or bottled
8 water), costs of epidemiologic investigation or litigation, nor did they consider anxiety,
9 pain and suffering. COI measures also do not capture costs associated with chronic
10 disease or lost leisure time.
11 The COI measures direct and indirect costs. The direct medical costs include
12 medication, physician visits, emergency room visits and hospital stays. Lost
13 productivity, an indirect cost, is estimated based on a fraction of the duration of illness.
14 The COI of the jth outbreak can be calculated using the mean values of direct and
15 indirect costs reported in other outbreaks (see Equation ES-1).
16 COIj =SMj+PVj +ERj +Hj +Plj +PCGj (Eq. ES-1)
17 where:
is SMj = Total cost of self medication purchased to treat illness associated with
19 the jth outbreak (2000$)1
20 PVj = Total cost of physician visits associated with the jth outbreak (2000$)
1 All cost estimates are adjusted to 2000 U.S. dollars (2000$) using the consumer price index (CPI) for
medical services. The CPI is the average change in prices overtime for a market basket of goods and
services (in this case medical goods and services such as prescription drugs and medical supplies,
physicians' services and hospital services) allowing comparisons using constant monetary units.
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Epidemiologic
Burden
Components
Costs
Considered
Lost Work
Time—
Person III
Lost Work
Time—
Caregiver
Medical Costs:
Medication
Physician Visit
ER Visit Hospital
Visit
Lost
Leisure
Time
Defensive
Expenditures
Investigation
or Litigation
Costs
Valuation
Approach
Chronic
Illness
Costs
Pain and
Suffering
FIGURE ES-2
Components of the Monetary Burden
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= Total cost of emergency room visits associated with the jth outbreak
2 (2000$)
3 HJ = Total cost of hospitalizations associated with the j outbreak (2000$)
4 Plj = Productivity losses of ill persons associated with the jth outbreak (2000$)
5 PCGj = Productivity losses of caregivers associated with the jth outbreak (2000$)
6 By using estimated mean values for the morbidity costs, this equation does not capture
7 important sources of cost variability among cases and across different outbreaks. The
8 definitions and calculations from Equation ES-1 are based largely on an economic
9 analysis of the 1993 Milwaukee Cryptosporidium outbreak by Corso et al. (2003). The
10 majority of COI measures were estimated using illness severity indicators acquired from
11 a telephone survey of Milwaukee residents (Mac Kenzie et al., 1994) and data provided
12 by the medical and financial records of 11 hospitals in Milwaukee (Corso et al., 2003).
13 In the economic burden analysis, we assumed that medical treatment administered and
14 costs for gastrointestinal illnesses have remained constant across years. All cost
15 estimates were updated to 2000 dollars using the Consumer Price Index for various
16 categories of medical care.
17 Because the WBDOs reported in the surveillance system do not identify cases of
is illness by severity categories of mild, moderate and severe (as used in the Corso et al.
19 [2003] Milwaukee WBDO economic analysis), we use surrogate measures (physician
20 visits and emergency room visits comprised moderately ill cases while hospitalizations
21 and deaths comprised severely ill cases). This introduces additional uncertainty into the
22 COI estimates.
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i Cost of Self Medication (SM)
2 For a WBDO, the cost of SM is the total cost of over-the-counter medications for
3 mild, moderate and severe illness (e.g., anti-nausea, anti-diarrheal medications and
4 electrolyte replacement therapy).
5 Cost Associated with Physician Visit (PV)
6 The costs associated with a physician visit include the professional fee and any
7 prescribed medication but not SM cost.
8 Cost Associated with Visiting an Emergency Room (ER)
9 The cost of an ER visit includes the costs of the ER, attending physician,
10 ambulance and prescribed medication. If an ER visit results in a hospital admission,
11 then the visit is also counted as a hospitalization.
12 Cost Associated with Hospital Stay (H)
13 Hospitalization costs are based on the 1997 Nationwide Inpatient Sample data by
14 Health Care Utilization Project (HCUP, 1997). Individual discharges were selected for
15 examination of costs related to particular diseases based on the occurrence of specific
16 International Classification of Diseases, Ninth Revision (ICD-9) codes among the first
17 three diagnoses listed on the hospital discharge report. Observations were analyzed for
is specific pathogens and groups of pathogens, and the Health Care Utilization Project
19 reported the total hospitalization charges for selected pathogens or categories. For the
20 final cost estimates, we multiplied the hospital charges by the national case-weighted
21 cost-to-charge ratio of 0.4.
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i Cost Due to Loss in Productivity
2 Productivity losses potentially have two components: complete days lost and lost
3 productivity while working (i.e., reduced hours or working at less than full capacity). We
4 only calculated the value of a complete day lost. Productivity losses from lost time at
5 work and lost work at home due to illness were considered for
6 •!!! person who recovers
7 • Caregiver(s) for ill person
8 The wage components included salary income, overtime pay, bonus pay and self-
9 employment earnings. Fringe benefits included health insurance and retirement pay.
10 Household production included a number of valued activities, such as cleaning, cooking,
11 home and auto maintenance, child care and child guidance, for which individuals are
12 typically not compensated.
13 Value of Statistical Life
14 The value associated with a premature death due to a WBDO was based on a
15 mean VSL estimate developed by U.S. EPA (2002a).
16 THE MONETARY BURDEN OF WBDOs
17 We estimated the monetary burden (2000$) of premature mortality associated
is with the WBDOs to be valued at approximately $424 million (Table ES-4). The
19 morbidity monetary burden is estimated to be approximately $186 million. The largest
20 morbidity cost is lost productivity of the ill person (66% of the total COI).
21 We combined morbidity and mortality measures into a single metric (i.e., dollars)
22 and make a number of comparisons not easily accomplished with epidemiologic
23 measures. However, the comparisons are greatly influenced by the large monetary
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TABLE ES-4
Monetary Burden of Infectious Waterborne Disease Outbreaks, 1971-2000
Burden Measure
Self Medication
Physician Visits
Emergency Room Visits
Hospitalizations
Productivity Losses of III Persons
Productivity Losses of Caregivers
Total COI (Morbidity)
Value of Statistical Life (Premature Death)
Total
Monetary Burden
$1,272,000
$2,708,000
$9,006,000
$29,936,000
$123,357,000
$19,721,000
$186,000,000
$424,380,000
$610,380,000
Percent
Less than 1
Less than 1
2
5
20
3
30
70
100
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i burden associated with mortality. We present comparisons of the monetary burden by
2 the same five summary categories considered for the epidemiologic analyses.
3 Monetary Burden Estimate by Etiology
4 Protozoan agents account for most of the monetary burden (Table ES-5), and
5 Cryptosporidium is the major contributor to the overall monetary burden (76%). Giardia
6 contributed 2% of the total monetary burden, but if the Milwaukee WBDO data are
7 excluded from the analysis, Giardia would contribute 9%. Non-typhoid Salmonella spp.
8 account for approximately 44% of the monetary burden attributed to bacterial
9 pathogens. If the Milwaukee WBDO is excluded from the analysis, then the monetary
10 burden associated with the bacterial WBDOs ($105 million) and AGI WBDOs ($22
11 million) would rank higher than the protozoan WBDOs ($19 million).
12 Monetary Burden by Water System Type, Water Treatment Deficiency and Time
13 Period
14 Community systems had the largest monetary disease burden, 13 times larger
15 than the burden associated with non-community systems. Water treatment deficiencies
16 were the most important contributors to the monetary burden. The next two most
17 important contributors were distribution system deficiencies and the use of untreated,
is contaminated groundwater. If the Milwaukee WBDO is excluded from the analysis, then
19 distribution system deficiencies become the most important contributor to the monetary
20 burden. Although the fewest number of WBDOs occurred during the 1990s, that
21 decade dominates the monetary burden because the Milwaukee WBDO occurred in
22 1993. The monetary burden associated with WBDOs in the 1990s is more than 10
23 times the monetary burden estimate of either the 1970s or the 1980s. If the Milwaukee
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TABLE ES-5
Monetary Burden, by Etiology (Pathogen Group)
Etiologic Agent Group
AGI
Viruses
Bacteria
Protozoa
Total
Monetary Burden
(2000$)
$21,537,000
$3,252,000
$105,225,000
$480,366,000*
$610,380,000
2 * Monetary Burden of Milwaukee WBDO - $461,148,000 or 96% of total monetary
3 burden for Protozoa.
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i WBDO is excluded, the monetary burden in the 1990s is comparable to the estimates
2 from the 1970s and 1980s.
3 Monetary Impact of the Milwaukee WBDO
4 The Milwaukee WBDO accounted for 76% of the overall monetary burden or
5 approximately $461 million. The relative importance of morbidity measured by COI and
6 mortality measured by VSL is similar whether Milwaukee is included or excluded from
7 the analysis. This WBDO affected morbidity components by decreasing the relative
8 importance of caregiver productivity losses, physician and ER visits and increasing the
9 importance of productivity losses and hospitalizations in the total morbidity monetary
10 estimate.
11 SENSITIVITY ANALYSES
12 We conducted three sensitivity analyses to evaluate key assumptions used to
13 develop the monetary burden estimates and to examine the influence of model input
14 parameters on these predictions. We note that these analyses do not address the
15 under-reporting or over-reporting possibly associated with WBDOs.
16 Sensitivity Analysis 1
17 We estimated the difference in epidemiologic burden measure needed to cause a
is 5% change in the total monetary burden (Table ES-6). The total monetary burden was
19 most sensitive to differences in the number of deaths and person-days ill; a change of
20 only 8% in reported mortality (five deaths) changes the total monetary burden by 5%. A
21 21 % change in the number of person-days ill causes a 5% change in the total monetary
22 burden. When the Milwaukee WBDO is excluded, the total monetary burden also was
23 most sensitive to differences in the number of deaths (6% change required)
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TABLE ES-6
Percent Change Required in the Epidemiologic Burden to Change
Monetary Burden Estimate by 5%
Epidemiological Burden
Measure
Deaths
Person-Days III
Hospitalizations
Emergency Room Visits
Physician Visits
WBDOSS-
Reported
Epidemiologic
Measures
66
4,504,854
5,915
23,575
41,985
Change in the
Projected
Epidemiologic
Measure Required to
Cause a 5% Change
in the Total Monetary
Burden
5
960,962
6,031
79,894
473,193
Percent Change in
Epidemiologic
Measure Required
to Cause a 5%
Change in the Total
Monetary Burden
8%
21%
102%
339%
1,127%
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i and person-days ill (26% change required). The sensitivity of total monetary burden to
2 relatively small changes in the number of deaths is due to the large value associated
3 with reducing the risk of premature death (i.e., VSL) relative to the markedly smaller
4 estimates developed for the morbidity costs.
5 Sensitivity Analysis 2
6 The monetary burden for premature death is based on a central tendency
7 estimate for the number of premature deaths associated with WBDOs and the VSL
8 value. For each pathogen, we developed plausible ranges for the number of deaths
9 linked to WBDOs. We then described an existing distribution for the VSL from previous
10 U.S. EPA analyses and used a Monte Carlo approach to predict a range of monetary
11 burden estimates for these deaths. The purpose of this analysis was to identify the
12 primary sources of uncertainty and to develop a plausible distribution of the monetary
13 burden associated with deaths in the WBDOs. In the analysis, the number of deaths
14 predicted ranges from 63 to 169. The mean of the distribution is 108 deaths and the
15 10th and 90th percentile values are 88 and 129 deaths, respectively. The predicted
16 mean estimate of the monetary disease burden associated with deaths attributed to
17 WBDOs is $684 million; 10th and 90th percentile values are $167 million and $1.3 billion,
is respectively.
19 Based on rank correlation coefficient analysis, nearly all of the model output
20 variability can be explained through the distribution of the VSL. The monetary analysis
21 is affected primarily by the shape of the VSL distribution; however, the right skew of the
22 upper-bound estimates of WBDO deaths also affected the predicted results.
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i Sensitivity Analysis 3
2 Although premature mortality accounts for 70% of the burden associated with the
3 Milwaukee outbreak, the COI estimate for this WBDO accounts for over 75% of the total
4 COI estimate for all 665 WBDOs. The third sensitivity analysis examined the impact of
5 changes in two epidemiologic burden components, case number and illness duration,
6 on the monetary burden estimate. Although not as influential as changes in the number
7 of deaths, case number and illness duration accounted for much of the monetary
8 burden associated with those WBDOs, which had no fatalities reported.
9 We developed several estimates of both the number of cases of illness that
10 occurred during the Milwaukee WBDO and their average duration, and examined the
11 influence of these alternative estimates on the associated monetary disease burden
12 estimated for this WBDO. The Milwaukee WBDO contributed a considerable portion of
13 the total number of person-days ill to this WBDO burden analysis. While the large
14 estimated case number (403,000) is one aspect of the person-days ill burden, the
15 magnitude of this component is also influenced by the duration-of-illness value. The
16 outbreak investigation involved three different surveys, and each group was
17 characterized by different mean and median illness durations (Table ES-7). Because
is information was not available to estimate the number of cases associated with each
19 duration, our analyses compared a 3-day duration for all cases with a 9-day duration for
20 all cases. Nine days is the typical duration of illness reported in the CDC fact sheets for
21 cryptosporidiosis and is also the median of the median durations listed for all 12
22 Cryptosporidium WBDOs reported to the WBDOSS. Among these 12 WBDOs, the
23 median duration ranged from 3 to 74 days.
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TABLE ES-7
Duration of Illness, Milwaukee Cryptosporidium Outbreak Analysis of
Mac Kenzie et al. (1994)
Population Surveyed
Laboratory-Confirmed
Cases
Clinical Infection
Household Survey
Duration (Days)
Median
9
3
3
Mean
12
4.5
-
Range
1-55
1-38
1-45
Survey Information
(number of cases)
285 (Cryptosporidium
positive)
201 watery diarrhea
436 watery diarrhea
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i For the sensitivity analysis, we assumed the average duration of illness in the
2 Milwaukee WBDO was alternatively 3 or 9 days. If a 3-day duration of illness were used
3 instead of a 9-day duration, the monetary burden of morbidity would decrease by
4 approximately one-half.
5 CONCLUSIONS
6 We demonstrate a methodology for assessing the disease burden associated
7 with waterborne outbreaks. Our methodology, which relies on the examination of the
8 WBDO surveillance data, provides additional insight for evaluating the overall burden of
9 waterborne disease in the U.S. The analyses provide a plausible range of estimates of
10 the disease burden of reported waterborne outbreaks from the time period 1971-2000,
11 emphasizing the importance of mortality that may be associated with WBDOs. These
12 analyses include an examination of disease severity and the costs associated with
13 various waterborne pathogens and water system characteristics. This methodology
14 also illustrates the limitations of using this passive surveillance system and reinforces
15 the importance of collecting more detailed epidemiologic data to aid future disease
16 burden efforts. We recommend that additional sensitivity analyses examine the effect
17 that alternative assumptions might have on the disease burden estimates presented
is here. This could help identify the components that have the greatest potential impact
19 on disease burden and could further delineate specific research needs for the future.
20 Although we estimate the burden associated with reported WBDOs, the primary
21 limitation of the analyses was the inability to determine the potential impact of
22 unrecognized and unreported WBDOs. Additional studies should attempt to estimate
23 the number and type of WBDOs that may be unrecognized. We also provide several
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i recommendations in the collection and reporting of WBDO surveillance data for the
2 purpose of improving future burden estimates.
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1 1. INTRODUCTION
2
3 The incidence of devastating waterborne infectious diseases such as cholera and
4 typhoid was dramatically reduced in the United States after filtration and chlorination of
5 drinking water was introduced around 1900. Widespread adoption of these water
6 treatment technologies, as well as improved wastewater management, has been among
7 the great public health achievements of the 20th Century (Cutler and Miller, 2005).
8 However, waterborne disease outbreaks (WBDOs) do still occur in the U.S., with
9 hundreds to thousands of cases of illness attributed to these events every year.
10 Between 1991 and 2002, the average annual number of drinking water outbreaks
11 reported in the U.S. was 17 - only slightly fewer than the annual average of 23 reported
12 throughout 1920-1930 (Craun et al., 2006a).
13 Since 1971, the Centers for Disease Control and Prevention (CDC), the U.S.
14 Environmental Protection Agency (U.S. EPA), and the Council of State and Territorial
15 Epidemiologists have maintained the Waterborne Disease Outbreak Surveillance
16 System (WBDOSS).1 State, territorial and local public health agencies are responsible
17 for detecting and investigating WBDOs and voluntarily reporting them to the CDC, which
18 publishes biennial epidemiologic information on the occurrence and etiology of U.S.
19 WBDOs (e.g., Barwick et al., 2000; Lee et al., 2002). In the WBDOSS, the apparent
20 cause of a reported WBDO is classified into one of five water system categories: (1)
21 water treatment deficiency, (2) distribution system deficiency, (3) untreated
22 groundwater, (4) untreated surface water, or (5) unknown or miscellaneous deficiency.
1 "The unit of analysis for the WBDO surveillance system is an outbreak, not an individual case of a
waterborne disease. Two criteria must be met for an event to be defined as a drinking water-associated
disease outbreak. First, >2 persons must have experienced a similar illness after exposure to water."
(Blackburn etal., 2004)
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1 Since 1981, the lack of or inadequate water treatment as the cause of WBDOs has
2 been reported with decreasing frequency over time, while distribution system
3 deficiencies have been reported more frequently (Craun et al., 2006b).
4 When a WBDO occurs, individuals and communities incur both health and
5 economic impacts. The health impacts can include a broad range of effects from the
6 very mild (such as brief episodes of diarrhea in healthy adults) to severe (such as
7 dehydrating and life-threatening diarrhea in infants or the immunocompromised). The
8 economic impacts can include the costs associated with treatment of the ill as well as
9 lost productivity at work or home. Often in the health policy and health economics
10 literature a composite measure of morbidity and mortality - and in some cases,
11 economic impact - is assessed and expressed in a single metric that captures all the
12 components. Such an assessment is frequently referred to as the burden of disease
13 (Murray and Lopez, 1996; Gold et al., 1996). In general, burden of disease analyses
14 consist of two steps: a thorough evaluation of the epidemiologic data describing the
15 illnesses and an analysis that evaluates the health effects in terms of their impacts on
16 the ill and society as a whole (Murray and Lopez, 1996).
17 1.1. PURPOSE AND POTENTIAL USEFULNESS OF A BURDEN OF WBDO
18 ANALYSIS
19 The purpose of this WBDO analysis is not to provide an estimate of the true
20 incidence and burden of outbreak-related waterborne illnesses in the U.S. (which would
21 require additional data and procedures to estimate unreported outbreaks and
22 unrecognized cases). Rather, the purpose here is to provide a summary of 30 years of
23 WBDOSS information in terms of disease burden measures that are developed from
24 surveillance data. As such, this analysis provides insight only into the public health and
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1 economic impact of the waterborne outbreaks and cases of illness that were reported to
2 the WBDOSS. The methods developed here may provide valuable tools for future,
3 more extensive, U.S. EPA waterborne disease burden analyses, and serve to
4 supplement risk assessment methodology and intervention study approaches to overall
5 burden estimation.
6 Economic analysis has become an integral part of the policy and rule-making
7 process of federal agencies. The Safe Drinking Water Act (SDWA) not only mandates
8 various actions to improve the microbiological quality of water in the U.S., the 1996
9 amendments also require that benefit-cost analysis be publicly available for new federal
10 water quality regulations.2 To date, economic analyses have been conducted for
11 several major rules that target water quality issues that affect endemic levels of
12 waterborne disease. Among these are the Long Term 1 and 2 Enhanced Surface
13 Water Treatment Rules that focus on cryptosporidiosis incidence and the Groundwater
14 Rule that focuses on viral illness incidence.3 Benefit-cost analyses in this context
15 require an estimate of the epidemiologic burden of waterborne disease characteristic of
16 the water source under consideration. The disease burden analyses for these rules
17 used risk assessment methodology (i.e., exposure characterization integrated with a
18 dose-response relationship) to develop estimates of disease incidence in the U.S.
19 population; illness severity distributions and mortality rates for representative illnesses
20 (i.e., cryptosporidiosis and viral diseases) were drawn from a variety of non-waterborne-
21 specific epidemiologic studies, surveillance records, and the medical microbiology
2 SDWA [104/1412(b)(3)(C)] (see http://www.epa.gov/safewater/sdwa/theme.html): Executive Order
12866 (see http://www.whitehouse.gov/omb/inforeg/riaguide.html').
3 For more details on these water treatment rules, see http://www.epa.gov/safewater/standards.html.
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1 literature. In contrast, this burden of WBDO analysis utilizes a surveillance database for
2 the estimates of disease incidence and, in so far as possible, severity and mortality
3 information specifically associated with the cases of illness recorded in the database.
4 We hope this surveillance-based burden estimation methodology for WBDOs will prove
5 to be a valuable addition to risk assessment methodology for future determinations of
6 the total burden of waterborne disease in the U.S.
7 1.1.1. Objectives. The primary objective of this report is to demonstrate an approach
8 for developing a burden of disease estimate that is based on surveillance data. To
9 illustrate our approach, we use the reported information in the WBDOSS to develop a
10 preliminary estimate4 of the infectious disease burden associated with the illnesses
11 recorded in the WBDOSS for outbreaks that occurred over the 30-year period of 1971
12 through 2000. Methods were devised to estimate necessary values for incompletely
13 reported information in the database (see Chapter 2). The secondary objective is to
14 compare WBDO burden estimates across etiologic agents, source water types,
15 treatment deficiencies, and other outbreak characteristics.
16 Epidemiologic and monetary measures are provided here for burden estimation.
17 The epidemiologic measures, which are essential for developing the monetary burden,
18 include the following components:
19 • Cases of illness
20 • Duration of illness
21 • Physician visits
4 The estimate is considered preliminary because it is based solely on outbreaks (and the cases of illness
within those outbreaks) that are reported to the WBDO surveillance system. A comprehensive
assessment would require estimates of both the unrecognized outbreaks and unreported cases as well as
an assessment of possible over-estimates of cases in the surveillance system. These additional levels of
analysis are not provided in this report.
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1 • Emergency room visits
2 • Hospitalizations
3 • Deaths.
4 The monetary measures consider:
5 • Cost of medical care
6 • Cost of prescribed medication and self medication
7 • Productivity losses at work and home
8 • Value of a statistical life.
9 The monetary burden (expressed in U.S. dollars) uses cost-of-illness (COI) and
10 willingness-to-pay (WTP) approaches that are consistent with current U.S. EPA
11 economic practices (U.S. EPA, 2000a). Further discussion of these approaches is
12 presented in Sections 1.4 and 1.5.
13 The burden estimates presented in this report do not include endemic (i.e.,
14 sporadic) cases of waterborne illness unrelated to specific outbreak events nor do they
15 include cases of acute chemical poisonings associated with drinking water. The
16 approach used in this report to determine the burden of waterborne infectious disease
17 outbreaks due to drinking water is illustrated in Figure 1 -1.
18 1.2. THE WBDO SURVEILLANCE SYSTEM
19 The outbreak data considered in this report are obtained from the WBDOSS
20 database and are limited to WBDOs reported from 1971 to 2000. Although reporting of
21 outbreak information to the CDC is voluntary, the CDC does provide a standard form
22 (CDC 52.12) for that purpose. Appendix A includes the various versions of CDC 52.12
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Infectious Disease Outbreaks
Due to Drinking Water
(as Reported in the WBDOSS)
I
Compilation of Reported
Outbreak Severity Measures
and
Estimation of Missing Information
Case Number
Duration-of-lllness
Physician Visits
Emergency Room Visits
Hospitalizations
Deaths
I
Burden in Epidemiologic Units
I
Monetary Valuation
Medical Care
Medications
Productivity Losses at Work and Home
Premature Death
I
Burden in Monetary Units
FIGURE 1-1
Methodology to Determine the Disease Burden of WBDOs
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8/31/06
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1 that have been used from 1971-20005 as well as a detailed description of the
2 surveillance system. The purpose of the WBDOSS is to record the data needed to
3 appraise and periodically report the causes of WBDOs (e.g., etiologic agents, water
4 system deficiencies, and sources of contamination) and the resulting cases of illness.
5 These data can be used to evaluate the adequacy of technologies for providing safe
6 drinking water, and to indicate research priorities that can lead to improved water-quality
7 regulations. This system provides the primary source of data concerning the scope and
8 effects of reported waterborne disease outbreaks in the U.S.
9 A burden of disease analysis would, ideally, be based on an accurate
10 assessment of both the number of cases of illness and the distribution of illness
11 severities associated with those cases. Information on severity characteristics is often
12 limited in the WBDOSS reports because certain kinds of requested information that
13 would be useful for burden estimation are not consistently provided (e.g., duration of
14 illness) or are not even requested on CDC 52.12 (e.g., physician visits). In addition, not
15 all associated cases are recognized or reported (Blackburn et al., 2004). Chapter 2 and
16 Appendix A detail the limitations of the current information in the WBDOSS database.
17 Despite these limitations, the data collected by the WBDOSS constitute the most
18 comprehensive source of information on U.S. outbreaks, and provide a useful basis for
19 demonstrating this surveillance data based approach for developing a burden of
20 disease estimate.
5 The current form can be downloaded from
www.cdc.gov/healthvswimminq/downloads/cdc 5212 waterborne.pdf.
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1 1.3. MEASURES OF THE BURDEN OF DISEASE
2 While traditional public health measures, such as age-standardized death rates,
3 provide a sense of the relative health of one group of people compared to another, in
4 many cases they are inadequate for the public health decision-making needs of
5 contemporary communities and governments (CDC, 2005; Gold et al., 1996; Murray
6 and Lopez, 1996). Advances in public health and sanitation have brought about such
7 great increases in life expectancy that new methods to evaluate public health consider
8 the quality of life as well as the length of life. Quality-of-life issues, from a public health
9 perspective, include the severity and duration of the illness, injury, or disability; pain and
10 suffering; and the physical, psychological and social impacts of poor health.
11 While a variety of measures, such as Disability Adjusted Life Years (DALYs),6
12 have been employed to estimate disease burden in other studies (Murray and Lopez,
13 1996; Havelaaretal., 2000; Pruss et al., 2002), we limit the measures used for this
14 analysis to the benefits assessment measures currently employed in U.S. EPA
15 rulemaking procedures (U.S. EPA, 2000a). The U.S. EPA evaluates the monetary
16 burden associated with mortality using the "value of a statistical life" (VSL), which is an
17 approach for determining the economic value of reducing the risk of premature death.
18 The VSL is an aggregate measure of individuals' WTP to avoid a small change in the
6 DALYs combine information on the burden of premature mortality (in terms of years of life lost) with
preferences for quantitative changes in the quality of life associated with morbid conditions. These
conditions are evaluated based on severity, which is assigned a quantitative weight, and duration. These
weights may be developed through survey techniques. DALYs are the sum of years of life lost and years
lived with disability (Murray and Lopez, 1996). Years lived with disability is measured as the product of
the duration of the disease and a disability weight. DALYs were developed as a systematic method for
estimating morbidity and mortality impacts across different countries and regions of the world (Murray and
Lopez, 1996). DALYs are used in cost-effectiveness analyses, which describe the decrease in DALYs
per dollar allocated for risk reduction.
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1 risk of dying (Hammitt, 2000; U.S. EPA, 2000a).7 To estimate the monetary burden
2 associated with the morbidity from waterborne illnesses, U.S. EPA uses COI estimates.
3 For this WBDO analysis, we have employed data derived from several peer-reviewed
4 sources that provide COI estimates specifically for waterborne outbreaks (e.g., Corso et
5 al., 2003; Harrington et al., 1991).
6 1.4. WILLINGNESS-TO-PAY AND THE VALUE OF A STATISTICAL LIFE
7 Standard U.S. EPA practice for economic analyses to support environmental
8 decision-making is based on the principles of welfare economics8 (U.S. EPA, 2000a).
9 WTP measures, which reflect the monetary value that individuals place on benefits that
10 might be achieved by implementation of an action or program, are consistent with those
11 principles (Freeman, 1993). In the public health realm this could include the WTP for a
12 technology or intervention that reduces the risk of contracting future illnesses. WTP
13 frequently functions as an ex ante9 measure because the value of reducing the risk of
14 contracting an illness is, in many cases, decided before the risk is incurred. WTP would
15 measure the trade-off between health risk and wealth based on an individual's
16 preferences (Freeman, 1993; Hammitt, 2002). WTP can include valuation of medical
17 and non-medical costs (e.g., expenditures for preventative measures, travel time), lost
18 wages due to the disease, pain and suffering, and premature death (U.S. EPA, 1999,
19 2000a, 2002).
7 Essentially, the VSL is used to represent the benefit of avoiding one generic individual's premature
death (rather than that of an identified individual) (see Hammitt [2002] for a theoretical discussion).
8 "Welfare economics" refers to a branch of economic theory that holds that individuals (rather than
elected or appointed decision makers) are the best judges of their own welfare. The basis of welfare
economics lies in the premise that social welfare should be comprised of individuals' welfare and that
these individuals collectively provide the best information on social welfare issues. It is assumed that
resource allocation is appropriately driven by competitive market forces and that income distribution
amongst individuals is appropriate.
9 Ex ante, literally translates from Latin as "beforehand." In economic models the ex ante values (e.g., of
expected gain) are those that are calculated before there is certainty of the outcome.
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1 WTP can be estimated by analyzing revealed preferences from primary
2 "observable" data10 or through surveys of individuals' stated preferences.11 The use of
3 either approach can be controversial due to their inherent limitations. For example, the
4 survey approach is criticized because what people say they would do in a hypothetical
5 situation may be quite different from what they would actually do in a real-life situation
6 (Mitchell and Carson, 1989; U.S. EPA, 2000a). VSL - a WTP measure that is
7 specifically concerned with avoiding the risk of death - can be estimated using revealed
8 preference methods or stated preference methods. For example, VSL could be
9 estimated using labor market data and analyzing differences in wages and risks of
10 workplace mortality or asking individuals if they would be willing to pay some specified
11 amount of money to reduce the risk of a premature death by a specified probability.
12 Among the limitations of the VSL approach is uncertainty about the extent to which
13 survey subjects adequately understand the risk of death from the illness under
14 investigation (e.g., see NOAA, 1993; Viscusi, 1993; Viscusi and Aldy, 2003).
15 An alternative to collecting primary data via observation or survey is to utilize
16 benefit transfer. Benefit transfer applies WTP information from one study to another
17 location or context (Desvousges et al., 1992). The accuracy of benefit transfer depends
18 on the existence and quality of applicable studies. The advantages of benefit transfer
19 includes saving the time and cost of developing new studies. The U.S. EPA typically
10 For example, to estimate the WTP to avoid giardiasis during an outbreak, Harrington et al. (1989)
examined the costs of hauling safe water, boiling water, purchasing bottled water, and expenditures on
water filters and purifiers, sometimes referred to as averting behavior.
11 To determine the benefits of controlling freshwater pollution, Mitchell and Carson (1989) asked
American households to value water quality improvements for the U.S.; Viscusi and Aldy (2003)
summarized the results of a group of studies in which people were asked if they would pay a certain
dollar amount to avoid a specified increased risk of premature death.
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1 transfers VSL estimates related to the number of statistical lives saved by a particular
2 program.
3 In contrast, information regarding the WTP to avoid gastrointestinal disease
4 morbidity is not readily available for benefit transfer (e.g., see Harrington et al., 1989).
5 Generally speaking, WTP is a more comprehensive measure of total value for avoiding
6 a waterborne illness.12 However, estimates based on the COI approach will be
7 substituted as an approximation for the WTP to avoid morbidity in accordance with U.S.
8 EPA practice when few WTP studies exist.
9 1.5. COST-OF-ILLNESS APPROACH
10 The COI is a human capital approach (i.e., quantifiable in terms of market-place
11 productivity) that is based on measured ex post (i.e., known and certain) costs
12 associated with disease (U.S. EPA, 1999, 2000a, 2002; see discussion in Drummond et
13 al., 2000). In this approach, costs are divided into direct costs, which include the market
14 value estimates of treatment costs (e.g., the costs of medication, physician visits,
15 emergency room visits, and hospitalization for infectious diseases), and indirect costs
16 (e.g., lost productivity in the workplace and at home due to morbidity). Although
17 premature death can also be considered an indirect cost when evaluated as lost
18 productivity, a COI approach for mortality valuation is not standard U.S. EPA practice.
19 The COI approach for valuing morbidity provides information on the monetary impact of
20 an outbreak but not necessarily on the severity of the impact (Kuchler and Golan, 1999).
21 COI approaches do not completely capture the impact of an outbreak from a societal
12 U.S. EPA (2000a) states that WTP estimates could underestimate the social costs because they may
not capture health care costs paid by insurance companies, hospitals, or employers (e.g., sick leave).
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1 valuation perspective, because they do not measure individual preferences for avoiding
2 pain and suffering, averting costs, anxiety, or risk attitudes (U.S. EPA, 2000a).
3 1.6. COMPONENTS OF THE WBDO BURDEN ANALYSIS
4 We begin the burden analysis by presenting the epidemiologic data in Chapter 2.
5 If sufficient information is not available directly from the WBDOSS, then data gaps are
6 addressed in two ways:
7 1. Much of the information used to supplement the database gaps is
8 obtained from related data within the WBDOSS itself (e.g., information
9 from a different waterborne outbreak caused by the same or a similar
10 etiologic agent).
11 2. When the information in the database cannot meet that need, information
12 is obtained from the scientific and medical peer-reviewed literature.
13 Chapter 3 compares WBDO disease burden estimates (in epidemiologic units) across
14 etiologic agents, source water types, deficiencies and other outbreak characteristics.
15 Chapter 4 provides the methods used to develop the monetary burden. In Chapter 5,
16 we compare the monetary measures of disease burden estimates across etiologic
17 agents, source water types, deficiencies and other outbreak characteristics. Chapter 6
18 presents three separate sensitivity analyses; these analyses highlight the potential
19 impacts of some of the uncertainties on the monetary burden. The results, conclusions
20 and research needs are discussed in Chapter 7. Appendix A describes the surveillance
21 system and Appendix B categorizes the WBDOs by outbreak investigation method. The
22 annual waterborne outbreak disease burden between 1971 and 2000 is summarized in
23 Appendix C.
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1 2. MEASURES AND METHODS FOR ESTIMATING THE EPIDEMIOLOGIC BURDEN
2 OF INFECTIOUS DISEASE OUTBREAKS ASSOCIATED WITH DRINKING WATER
4 The epidemiologic burden of the infectious disease outbreaks that were reported
5 to the WBDOSS during the 30-year period from 1971 -2000 was evaluated by the
6 following measures of outbreak severity:1
7 • Cases of illness
8 • Duration of illness (used to compute person-days of illness, i.e., duration of
9 illness x number of cases)
10 • Physician visits
11 • Emergency room visits
12 • Hospitalizations
13 • Deaths
14 The measures listed above were not fully reported in the WBDOSS for all of the
15 665 outbreaks on record. Four of the measures are specifically requested on the
16 standard waterborne diseases outbreak reporting form available from the CDC (CDC
17 52.12); these include number of persons ill (both actual and estimated), duration of
18 illness (shortest, longest, and median), number hospitalized, and number of fatalities.
19 Although these four types of information were requested, they were not consistently
20 provided. Number of cases (i.e., persons ill) and number of deaths were available for all
21 665 outbreaks, hospitalization information was included in all but six of the reports and
22 duration of illness was provided for 282 of the outbreaks (Table 2-1). For most of the
23 outbreaks the entries for hospitalizations and deaths were "zero." The number of
1 Here "severity measure" is a generic term that describes how severe the outbreak was in terms of how
many people were affected, how long their illnesses lasted, what medical services they required, and
whether or not the outbreak lead to any deaths.
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TABLE 2-1
Availability of Selected Severity Measures in the WBDO Surveillance System
(Number of Infectious or Suspected Infectious Drinking Water Outbreaks = 665)
Severity Measure
Cases of Illness
Duration of illness
Hospital admissions
Physician visits
Emergency room visits
Deaths
WBDOs for Which Severity Measure
was Reported
Number
665
282
659
29
15
665
Percent
100
42
99
4
2
100
Reports with
Entry of "Zero"
none
none
469
NA
NA
559
Does CDC
52.12 Request
this Measure?
Yes
Yes
Yes
No
No
Yes
2 NA = not applicable because number was not requested on CDC 52.12
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1 physician visits or emergency room visits was available only when local outbreak
2 investigators provided that information in supplemental reports. Twenty-nine (29)
3 reports included physician visit data and 15 included emergency room visit data.
4 In this chapter, the epidemiologic burden components are summarized according
5 to the pathogen identified as the etiologic agent of the outbreak. CDC 52.12 requests
6 laboratory findings for patient specimens (e.g., stool), and, consequently, 300 of the 665
7 outbreaks were attributed to specific waterborne pathogens identified by laboratory
8 analysis. The other 365 outbreaks were identified as "acute gastrointestinal illness of
9 unknown etiology" (AGI) either because laboratory results were not reported or an
10 etiologic agent could not be identified by the tests performed.
11 When data for a severity measure were missing from a WBDO report, a value
12 was estimated for the burden analysis. These estimated values were based on
13 information extracted from the reports of other WBDOs of similar etiology, or, if
14 WBDOSS data were inadequate, from published sources such as CDC fact sheets.
15 2.1. CASES OF ILLNESS
16 The CDC 52.12 form requests information about the number of actual and
17 estimated cases. In the majority of WBDOs (70%), cases of illness were reported as an
18 actual count rather than an estimate. The case numbers presented in this burden
19 analysis are the numbers reported in the WBDOSS regardless of whether the case
20 numbers were actually counted or estimated by local investigators. The number of
21 reported outbreaks attributed to each particular etiologic agent or classed as "AGI" and
22 the total number of reported cases in each category are provided in the second and
23 third columns of Table 2-2.
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TABLE 2-2
Durations of Illness (in Days) by Etiologic Agent, WBDOs, 1971 to 2000
Etiologic Agent
AGI
All WBDOSS
Outbreaks
Out-
breaks
365
Cases
83,493
Outbreaks with Reported Median Durations of Illness
(in days)
Out-
breaks
189
Cases
56,401
Min-
Max
0.1-60
Median of
Reported
Median
Durations
2
Mean of
Reported
Median
Durations
(95% Cl)
4.2
(3.7-4.9)
Estimated Durations for WBDOs
without WBDOSS Duration Records
Mean,
Median, or
Midpoint
(range)
4.2
Source
AGI mean from
WBDOSS
Viruses
Noro virus
SRSV (assumed to be
noro virus)
Rotavirus
Hepatitis A
26
1
1
28
13,100
70
1,761
827
16
1
0
2
5,870
70
0
45
1-4
2.0-2.0
-
26-60
1.75
2.0
-
43
2.0
(1.1-3.2)
-
-
43.0
(5.2-155.2)
2.0
2.0
5.5
(3-8)
21
Norovirus mean,
WBDOSS
Norovirus mean,
WBDOSS
CDC fact sheet3
Ciocca (2000)
Draft: Do Not Cite or Quote
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TABLE 2-2 cont.
Etiologic Agent
All WBDOSS
Outbreaks
Out-
breaks
Cases
Outbreaks with Reported Median Durations of Illness
(in days)
Out-
breaks
Cases
Min-
Max
Median of
Reported
Median
Durations
Mean of
Reported
Median
Durations
(95% Cl)
Estimated Durations for WBDOs
without WBDOSS Duration Records
Mean,
Median, or
Midpoint
(range)
Source
Bacteria
Campylobacterjejuni
Escherichia coli
E. coli & Campylobacter
Plesiomonas
shigelloides
Salmonella, non-
typhoid spp.
Salmonella enterica
serovarTyphi
Shigella
Vibrio cholerae
Yersinia
19
12
1
1
15
5
44
2
2
5,604
1,529
781
60
3,203
282
9,196
28
103
8
7
0
0
5
1
11
0
2
4,285
1,310
0
0
949
60
4,246
0
103
2-6
3-9.3
-
-
2-5
14-14
1.5-7
-
5-10
4.8
4.3
-
-
4
14
3.3
-
7.5
4.4
(1.9-8.6)
5.3
(2.1-11.0)
-
-
3.9
(1.3-9.0)
14.0
(0.4-78.0)
3.8
(1.9-6.7)
-
7.5
(0.9-27.1)
4.4
5.3
4.8
4.8
6
(4-7)
21
3.8
4.8
7.5
C. jejuni mean,
WBDOSS
E. coli mean,
WBDOSS
Bacterial mean,
WBDOSS
Bacterial mean,
WBDOSS
CDC fact sheet"
CDC fact sheef
Shigella mean,
WBDOSS
Bacterial mean,
WBDOSS
Yersinia mean,
WBDOSS
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TABLE 2-2 cont.
Etiologic Agent
All WBDOSS
Outbreaks
Out-
breaks
Cases
Outbreaks with Reported Median Durations of Illness
(in days)
Out-
breaks
Cases
Min-
Max
Median of
Reported
Median
Durations
Mean of
Reported
Median
Durations
(95% Cl)
Estimated Durations for WBDOs
without WBDOSS Duration Records
Mean,
Median, or
Midpoint
(range)
Source
Protozoa
Cryptosporidium
Cyclospora
Entamoeba histolytica
Giardia
Total
15
1
1
126
665
421,473
21
4
28,427
569,962
12
0
0
28
282
408,312
0
0
13,191
494,842
3-74
-
-
0.6-41
8.8
-
-
12
18.6
(9.6-32.5)
-
-
12.7
(8.4-18.4)
8.8
10
(few-30)
15
(several
weeks)
12.7
Cryptopsoridium
median, WBDOSS
Herwaldt (2000)
Stanley (2003)
Giardia mean,
WBDOSS
1
2
3
4
http://www.cdc.gov/ncidod/dvrd/revb/gastro/rotavirus.htm
b http://www.cdc.gov/ncidod/dbmd/diseaseinfo/salmonellosis g.htm
c http://www.cdc.gov/ncidod/dbmd/diseaseinfo/typhoidfever t.htm
SRSV = Small round structured virus
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1 The actual case counts included illnesses reported to the local public health
2 agency or to the local WBDO investigators by physicians, ill persons or clinical
3 laboratories. When local outbreak investigators reported an estimated number of
4 cases, they might have used one of several standard epidemiologic methods to
5 determine the estimate including surveys of selected cohorts, geographic areas, or
6 physicians. The Mac Kenzie et al. (1994) investigation of the Milwaukee
7 Cryptosporidium outbreak that occurred in 1993 provides an example of estimation of
8 case numbers. For this investigation, an extensive search was made to identify
9 symptoms, cases, physician visits, and hospitalizations. Investigators identified 285
10 laboratory-confirmed cases of cryptosporidiosis, and 93% of those cases experienced
11 diarrhea that they characterized as "watery." Another 235 cases of diarrhea
12 experienced during the outbreak time frame (March 1-April 28, 1993) were identified
13 through a telephone survey conducted to identify the clinical symptoms of
14 cryptosporidiosis. Two hundred one (201) of the respondents (86%) reported watery
15 diarrhea symptoms. Subsequently "watery diarrhea" was the case definition used for
16 further case incidence estimation. The number of additional cases attributable to the
17 outbreak was then estimated by means of a second telephone survey of 613
18 households throughout the greater Milwaukee area. Investigators found that 493 (26%)
19 of the 1663 household members surveyed reported experiencing watery diarrhea at
20 some point during the outbreak time frame. By applying the proportion of survey
21 respondents experiencing watery diarrhea (26%) to the total population at risk (1.61
22 million people), investigators estimated that 419,000 persons may have been ill with
23 diarrhea during the Milwaukee WBDO. Subtracting a background rate of 0.5% per
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1 month (16,000 people) for diarrhea due to causes other than cryptosporidiosis, an
2 estimated 403,000 people had watery diarrhea that could be attributed to the
3 Cryptosporidium outbreak.
4 2.2. DURATION OF ILLNESS
5 Duration of illness is a valuable outbreak severity characteristic because, by
6 multiplying the typical duration of a particular illness by the number of persons who
7 experienced that illness, we compute the composite burden measure "person-days ill."
8 The "person-days ill" metric provides a succinct way to compare the population-level
9 health impact of the incidence of different diseases. For example, the public health
10 impact of a norovirus (2-day typical duration of illness) outbreak of 50 cases could be
11 compared to the public health impact of a Giardia (12-day typical duration of illness)
12 outbreak of eight cases: 100 person-days ill for the norovirus outbreak, 96 person-days
13 ill for the Giardia outbreak. The person-days ill measure will be an important
14 component of the burden summaries in Chapter 3.
15 A duration-of-illness characteristic of the outbreak was reported for 282 of the
16 665 WBDOs in the database. We developed estimates for durations of illness for the
17 383 outbreaks in which these data were missing from the reports. Table 2-2 provides
18 reported and estimated duration-of-illness values. The mean of median durations of
19 illness reported for other WBDOs of the same or similar etiology was the primary source
20 of information for missing values. For example, median duration of illness was reported
21 for 28 of the 126 Giardia WBDOs in the database. The mean of these 28 values (12.7
22 days) was used as an estimate for the other 98 Giardia WBDO reports that did not
23 include an entry for duration of illness. The mean of the various median durations of
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1 illness for WBDOs attributed to a particular etiologic agent was usually used for the
2 missing data. For most etiologic agents, the overall mean of median durations of illness
3 and the overall median of median durations of illness of were similar. However, for
4 Cryptosporidium WBDOs, the mean of the characteristic durations of illness reported for
5 11 of the outbreaks was considerably greater than the median due to extremely long
6 durations of illness reported for two of them (i.e., 60 days and 74 days). The median of
7 the 11 outbreak durations of cryptosporidiosis (8.8 days) was used for the burden
8 analysis because this more closely corresponds to the duration of 1-2 weeks reported in
9 the CDC fact sheet for cryptosporidiosis
10 (http://www.dpd.cdc.gov/dpdx/HTML/Crvptosporidiosis.htm).
11 For some of the etiologic agents, there were very few outbreaks with reported
12 durations of illness in the WBDOSS. Our threshold number for estimating missing
13 durations of illness from the WBDO database itself was six or more outbreaks with this
14 information provided. If fewer than six outbreaks were reported for a particular agent,
15 other data sources, or the mean of WBDOSS agent groups, were used to estimate the
16 missing values. Hepatitis A, non-typhoid Salmonella spp., Salmonella enterica serovar
17 Typhi, Entamoeba histolytica, Cyclospora, and rotavirus durations of illness are based
18 on other literature sources (see Table 2-2 footnotes). The estimate for the two Vibrio
19 cholerae outbreaks was derived from the mean of median durations of illness of all
20 bacterial WBDOs (rather than other literature). The illnesses that occurred during the
21 two cholera WBDOs were relatively mild, whereas the typical literature values that are
22 available describe severe cases associated with foreign travel. We considered these
23 inappropriate for the domestic outbreaks in the WBDOSS. No duration of illness was
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1 reported for the single Cyclospora WBDO reported in the surveillance system. We used
2 a duration of illness of 10 days, as reported by Herwaldt and Ackers (1997) for an
3 outbreak in the United States that was associated with imported raspberries. Other
4 data sources were not available for estimating the Plesiomonas shigelloides outbreak
5 so the mean of median durations of all bacterial illnesses from the WBDO database was
6 used for this agent.
7 The Milwaukee outbreak contributes a considerable portion of the total number of
8 person-days ill to this WBDO burden analysis (see Chapter 3). While the large
9 estimated case number (403,000) is one aspect of the person-days ill burden, the
10 magnitude of this component is also influenced by the duration-of-illness value recorded
11 in the WBDOSS (i.e., 9 days). Although Mac Kenzie et al. (1994) report a single
12 duration value of 9 days in the abstract of their published article, their outbreak
13 investigation involved three different surveys of persons in the Milwaukee area during
14 the outbreak. Each group was characterized by different mean and median illness
15 durations: (1) persons with laboratory confirmed cryptosporidiosis (median, 9 days), (2)
16 persons with clinical symptoms consistent with cryptosporidiosis) (median, 3 days), and
17 (3) a household survey of persons with watery diarrhea (median, 3 days) (Table 2-3).
18 The reported duration of illness among these populations ranged from 1 to 55 days. Of
19 the 285 laboratory-confirmed cases, 46% were hospitalized and 48% were immuno-
20 compromised, and these cases may have been among the most severe. A 3-day
21 duration measure in contrast to the 9-day duration measure greatly affects the person-
22 days ill component of the Milwaukee outbreak; this effect will be described in Chapter 3.
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TABLE 2-3
Duration of Illness, Milwaukee Cryptosporidium Outbreak (Mac Kenzie et al., 1994)
Population
Surveyed
Duration (Days)
Median
Mean
Range
Survey Information
Laboratory-
Confirmed Cases
12
1-55
n = 285 lab confirmed cases
Clinical Infection
4.5
1-38
n = 201 respondents with watery
diarrhea (482 total respondents)
Household Survey
1-45
n = 436 interviewees reporting
watery diarrhea (out of 1663 total
household members)
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1 2.3. PHYSICIAN VISITS
2 The number of physician visits is assumed to be underreported in the WBDOSS
3 because this information is not requested on CDC 52.12. Among the 29 WBDO reports
4 that included supplementary physician visit data, only 5.2% of all cases reported for
5 those 29 WBDOs were associated with such visits. When available, we used the
6 physician visit rate reported in the WBDOSS for the same etiologic agent to estimate
7 unreported rates (Table 2-4). For example, for the 118 WBDOs of giardiasis for which
8 no physician visits were reported, we estimated a physician-visit ratio of 307.4 physician
9 visits per 1,000 reported cases based on the physician visit reports provided with 8 of
10 the126 total giardiasis WBDOs. If no WBDO reports for a particular agent included
11 physician visit information, we pooled information from the relevant class of agent as an
12 estimate. For example, the physician visit counts for the one Cryptosporidium and the
13 eight Giardia outbreak reports that included that information were pooled and the sum
14 was divided by the total cases reported for those nine outbreaks to compute a physician
15 visit ratio estimate of 50.6/1000 to apply to the other protozoan outbreaks (Cyclospora
16 and En. histolytica).
17 Information for physician visit rates was extremely limited for the bacterial and
18 viral agents. For bacterial outbreaks, there were data for two C. jejuni WBDOs (51
19 physician visits out of 880 reported cases) and for one S. enterica serovar Typhi
20 outbreak (for which there were only two cases reported, and both cases involved a
21 physician visit). Because the reported typhoid outbreak was so small and because
22 typhoid tends to be a markedly more severe illness than the other bacterial illnesses
23 reported to the WBDOSS, we elected to use only the physician visit rate for C. jejuni as
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TABLE 2-4
Physician Visits (PV) by Etiologic Agent, Reported WBDOs, 1971 to 2000
Etiologic Agent
AGI
All WBDOSS
Outbreaks
Out-
breaks
365
Cases
83,493
WBDOs that Reported Physician Visits
Out-
breaks
14
Cases
7,664
PVs
Reported in
WBDOSS
810
PVper
1000
Cases
105.7
Estimated
(PV/1 000
Cases)
105.7
Source of PV
Value
(all from
WBDOSS data)
AGI
Viruses
Norovirus
SRSV (assumed to be norovirus)
Rota virus
Hepatitis A
26
1
1
28
13,100
70
1,761
827
-
-
1
2
-
-
1,761
103
-
-
146
100
-
-
82.9
970.9
82.9
82.9
82.9
970.9
Rotavirus
Rotavirus
Rotavirus
Hepatitis A
Bacteria
C. jejuni
E. coli
E. coli & Campylobacter
P. shigelloides
19
12
1
1
5,604
1,529
781
60
2
-
-
-
880
-
-
-
51
-
-
-
58.0
-
-
-
58.0
58.0
58.0
58.0
C. jejuni
C. jejuni
C. jejuni
C. jejuni
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TABLE 2-4 cont.
Etiologic Agent
Salmonella, non-typhoid spp.
S. enterica serovar Typhi
Shigella
V. cholerae
Yersinia
All WBDOSS
Outbreaks
Out-
breaks
15
5
44
2
2
Cases
3,203
282
9,196
28
103
WBDOs that Reported Physician Visits
Out-
breaks
-
1
-
-
-
Cases
2
-
-
-
PVs
Reported in
WBDOSS
2
-
-
-
PVper
1,000
Cases
1,000
-
-
-
Estimated
(PV/1 000
Cases)
58.0
1,000
58.0
58.0
58.0
Source of PV
Value
(all from
WBDOSS data)
C. jejuni
S. enterica
serovar Typhi
C. jejuni
C. jejuni
C. jejuni
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
Total
15
1
1
126
665
421 ,473
21
4
28,427
569,962
1
-
-
8
29
403,000
-
-
462
413,872
20,280
-
-
142
21,531
50.3
-
-
307.4
50.3
50.6
50.6
307.4
Cryptosporidium
All protozoa
All protozoa
Giardia
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1 the representative bacterial WBDO physician visit rate (58/1000). For viral outbreaks of
2 gastroenteritis, physician visits were reported for only one WBDO Hepatitis A is not
3 included in this group.2 The physician visit rate derived from the one rotavirus WBDO
4 serves as the estimated rate for norovirus and SRSV.
5 We estimated physician visits only for those WBDOs in which the number of
6 hospitalizations constituted fewer than 75% of the reported cases of illness (n = 629). If
7 the number of hospitalizations was greater than 75%, we assumed the severity of the
8 outbreak illnesses resulted in few cases treated on an outpatient basis.
9 Because the physician visit estimates are based upon very few reported values
10 (recall that this information is not requested on CDC 52.12), and we were unable to
11 locate peer-reviewed literature for alternative estimates, this component of the burden
12 estimate is highly uncertain. The sensitivity of the burden estimate to the uncertainty of
13 the physician visit data is explored in Chapter 6.
14 2.4. EMERGENCY ROOM VISITS
15 As with physician visits, the reporting of emergency room visits during a WBDO
16 is not requested on CDC 52.12. Supplementary information provided with some reports
17 identified only 6% of cases identified in those reports as being associated with
18 emergency room visits. Supplementary information on emergency room visits was
19 provided with a few reports (15) and in these outbreaks only 6% of cases were
20 associated with emergency room visits.
2 Unlike the other viral agents in the WBDO database (i.e., rotavirus, norovirus, and SRSV), Hepatitis A
causes non-gastrointestinal illness. Hepatitis tends to be considerably more severe than the Gl illnesses
caused by the other viruses, so we have elected to present Hepatitis A WBDO data separately from other
viral WBDOs and restrict the physician visit estimate for non-reported norovirus to data from a Gl viral
WBDO.
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1 Since emergency room visits were infrequently reported, most estimates were
2 based on the pathogen group. For example, emergency room visits were reported for
3 only one of the 126 giardiasis outbreaks and none of the other protozoan outbreaks; the
4 rate for that one outbreak (29.1 per 1,000 reported cases) is used for all protozoan
5 WBDOs. The values used to estimate the burden are shown in Table 2-5. Similar to
6 unreported physician visits, unreported emergency room visits were estimated only for
7 WBDOs in which less than 75% of cases were hospitalized.
8 Since the number of WBDOs resulting in reported emergency room visits was
9 small, there is considerable uncertainty in this outbreak severity measure category. To
10 our knowledge, there are no other sources in the peer-reviewed literature that can be
11 used for alternative estimates. The sensitivity of the burden estimates to the uncertainty
12 of the data on emergency room visits is explored in Chapter 6.
13 2.5. HOSPITALIZATIONS
14 The surveillance report form (CDC 52.12) requests the number of
15 hospitalizations occurring during an outbreak, and 659 of the WBDO reports (99%)
16 included this information. An entry of "zero" was provided in 496 of the reports; one or
17 more hospitalizations were recorded in each of the remaining 163 reports, for a total of
18 5915 hospitalizations. Because this information was reported for almost all of the
19 WBDOs, the hospitalization rates for WBDO illnesses were determined by dividing the
20 number of reported hospitalizations for an etiologic agent by the total number of cases
21 reported for that agent (Table 2-6). Because the reporting frequency was 99%, no
22 additional hospitalizations were estimated.
23
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TABLE 2-5
Emergency Room (ER) Visits by Etiologic Agent, WBDOs, 1971 to 2000
Etiologic Agent
AGI
All WBDOSS Outbreaks
Outbreaks
365
Cases
83,493
WBDOs that Reported Emergency Room Visits
Outbreaks
9
Cases
7,839
ER Visits in
WBDOSS
885
ER Visits/
1,000
Cases
112.9
Estimated
(ER/1 ,000
Cases)
112.9
Source
(all from
WBDOSS Data)
AGI
Viruses
Norovirus
SRSV (assumed to be
norovirus)
Rotavirus
Hepatitis A
26
1
1
28
13,100
70
1,761
827
1
0
0
1
1,500
0
0
22
5
0
0
2
3.3
0
0
90.9
3.3
3.3
3.3
90.9
Norovirus
Norovirus
Norovirus
Hepatitis A
Bacteria
C. jejuni
E. coli
E. coli & Campylobacter
P. shigelloides
Salmonella, non-typhoid
spp.
19
12
1
1
15
5,604
1,529
781
60
3,203
2
0
0
0
0
3,871
0
0
0
0
11
0
0
0
0
2.8
0
0
0
0
2.8
4.8a
4.8
4.8
4.8
C. jejuni
All bacteria*
All bacteria
All bacteria
All bacteria
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8/31/06
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TABLE 2-5 cont.
Etiologic Agent
S. enterica serovar
Typhi
Shigella
V. cholerae
Yersinia
All WBDOSS Outbreaks
Outbreaks
5
44
2
2
Cases
282
9,196
28
103
WBDOs that Reported Emergency Room Visits
Outbreaks
0
1
0
0
Cases
0
83
0
0
ER Visits in
WBDOSS
0
8
0
0
ER Visits/
1,000
Cases
0
96.4
0
0
Estimated
(ER/1 ,000
Cases)
4.8
96.4
4.8
4.8
Source
(all from
WBDOSS Data)
All bacteria
Shigella
All bacteria
All bacteria
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
Total
15
1
1
126
665
421,473
21
4
28,427
569,962
0
0
0
1
15
0
0
0
3,500
16,815
0
0
0
102
1,013
0
0
0
29.1
29.1
29.1
29.1
29.1
Giardia
Giardia
Giardia
Giardia
2 * A total of 19 ER visits were reported for the three outbreaks attributed to bacteria that included supplemental ER information (11 for C.jejuni
3 + 8 for Shigella). The total case number of these three outbreaks was 3954. The "all bacteria" ER hospitalization rate was computed as:
4 (3,954/19)* 1000.
Draft: Do Not Cite or Quote
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8/31/06
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TABLE 2-6
Hospitalizations, Reported WBDOs, 1971 to 2000
Etiologic Agent
AGI
All WBDOs Outbreaks
Outbreaks
365
Cases
83,493
WBDOs with Reported Hospitalizations
Outbreaks
61
Cases
41,710
Hospitalizations
378
Hospitalization Rate
(Hospitalized cases/1 ,000
total cases)
4.5
Viruses
Norovirus
SRSV (assumed to be norovirus)
Rota virus
Hepatitis A
26
1
1
28
13,100
70
1,761
827
4
0
0
12
1,154
-
-
348
10
-
-
82
0.8
0
0
99.1
Bacteria
C. jejuni
E. co/;
£. co/; & Campylobacter
P. shigelloides
Salmonella, non-typhoid spp.
S. enterica serovar Typhi
Shigella
19
12
1
1
15
5
44
5,604
1,529
781
60
3,203
282
9,196
8
9
1
1
8
4
22
5,178
520
781
60
1,910
277
5,813
87
122
71
3
82
238
301
15.5
79.8
90.9
50
25.6
844
32.7
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8/31/06
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TABLE 2-6 cont.
Etiologic Agent
V. cholerae
Yersinia
All WBDOs Outbreaks
Outbreaks
2
2
Cases
28
103
WBDOs with Reported Hospitalizations
Outbreaks
1
2
Cases
11
103
Hospitalizations
4
20
Hospitalization Rate
(Hospitalized cases/1 ,000
total cases)
142.9
194.2
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
Total
15
1
1
126
665
421,473
21
4
28,427
569,962
7
0
1
22
163
407,521
-
4
13,423
478,813
4,448
-
1
68
5,915
10.6
0
250.0
2.4
Draft: Do Not Cite or Quote
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1 Although we did not employ any estimation procedures to supplement the
2 hospitalization data from the WBDOSS, in Section 2.6.1 we offer the interested reader a
3 comparison of the WBDO rates of hospitalization to those estimated by Mead et al.
4 (1999). The Mead et al. study was designed to evaluate the impact of foodborne
5 illnesses on the disease burden in the U.S. due to infectious agents that primarily cause
6 gastrointestinal illnesses.
7 2.6. MORTALITY
8 CDC 52.12 requests the number of fatalities associated with a WBDO, and all
9 WBDO reports included an entry for deaths. For the vast majority, this entry was "zero,"
10 but for six of the WBDOs one or more deaths were reported (Table 2-7). Because this
11 information was reported for all of the WBDOs, the fatality-case ratios for WBDO
12 illnesses were determined by dividing the number of reported deaths for an etiologic
13 agent by the total number of cases from all outbreaks reported for that agent and
14 normalizing these ratios to 100,000 cases.
15 It is unclear to what extent local investigators conducted specific analyses of
16 mortality or searched death certificates for possible WBDO-related deaths. For the
17 Milwaukee outbreak, Hoxie et al. (1997) assessed cryptosporidiosis-associated
18 mortality incidence before, during, and after the 1993 WBDO period. They reported that
19 an excess of 50 deaths occurred as a result of the WBDO; the underlying cause of most
20 of these deaths was Acquired Immunodeficiency Syndrome (AIDS) with
21 cryptosporidiosis listed as a contributing cause. However, investigators who reported
22 deaths for other WBDOs did not specify the source of information about the deaths nor
23
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TABLE 2-7
Mortality Reported in the WBDOSS, 1971-2000, by Etiology
Etiologic Agent
AGI
Reported Outbreaks
Outbreaks
365
Cases
83,493
Outbreaks with One or More Reported
Deaths
Outbreaks
1
Cases
38
Reported
Deaths
1
Case Fatality Ratio per
100,000 cases
(Reported Deaths divided by
Reported Cases x 100,000)
1.2
Viruses
Norovirus
SRSV (assumed to be norovirus)
Rota virus
Hepatitis A
26
1
1
28
13,100
70
1,761
827
0
0
0
0
-
-
-
-
-
-
-
-
-
-
-
-
Bacteria
C. jejuni
E. co/;
£. co/; & Campylobacter
P. shigelloides
Salmonella, non-typhoid spp.
S. enterica serovar Typhi
19
12
1
1
15
5
5,604
1,529
781
60
3,203
282
0
1
1
0
1
0
-
243
781
-
625
-
-
4
2
-
7
-
-
261.6
256.1
-
218.5
-
Draft: Do Not Cite or Quote
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TABLE 2-7 cont.
Etiologic Agent
Shigella
V. cholerae
Yersinia
Reported Outbreaks
Outbreaks
44
2
2
Cases
9,196
28
103
Outbreaks with One or More Reported
Deaths
Outbreaks
1
0
0
Cases
94
-
-
Reported
Deaths
2
-
-
Case Fatality Ratio per
100,000 cases
(Reported Deaths divided by
Reported Cases x 100,000)
21.7
-
-
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
Total
15
1
1
126
665
421,473
21
4
28,427
569,962
1
0
0
0
6
403,000
-
-
-
404,781
50
-
-
-
66
11.9
-
-
-
Draft: Do Not Cite or Quote
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1 did they note whether the infectious disease of the outbreak was the underlying or a
2 contributing cause of death.
3 Issues associated with the possible under- or over-reporting of mortality are
4 discussed in Section 2.6.2.
5 2.6.1. Comparison of WBDOSS and Mead etal. (1999) Hospitalization Rates. To
6 explore possible under- or over-reporting of hospitalizations in the WBDOSS, we
7 compared the pathogen-specific and AGI hospitalization rates for the reported WBDOs
8 with pathogen-specific and AGI hospitalization rates reported in Mead et al. (1999). The
9 objective of the Mead et al. report was to estimate the burden of foodborne infectious
10 disease in the U.S.; the paper, however, also reports estimates of total numbers of
11 cases, hospitalizations, and deaths associated with microbial pathogens that, though
12 potentially foodborne, can also be transmitted by water or person-to-person contact.
13 Mead et al. used information from a number of surveillance sources including the
14 Foodborne Diseases Active Surveillance Network (FoodNet) (CDC, 1999a), the
15 National Notifiable Diseases Surveillance System (CDC, 1998a), the Public Health
16 Laboratory Information System (Bean et al., 1992), the Gulf Coast States Vibrio
17 Surveillance System (Levine and Griffin, 1993), the Foodborne Disease Outbreak
18 Surveillance System (Bean et al., 1990), the National Hospital Ambulatory Medical Care
19 Survey (Woodwell, 1997), the National Hospital Discharge Survey (Graves and Gillium,
20 1997), the National Vital Statistics System (McCaig and McLemore, 1994; McCaig,
21 1997; McCaig and Stussman, 1997), CDC reports, and selected published studies. The
22 Mead et al. report included pathogen-specific hospitalization rates for cases that were
23 culture-confirmed or actually reported (to FoodNet, CDC, published outbreak reports),
Draft: Do Not Cite or Quote 2-24 8/31/06
-------
1 and estimated numbers of hospitalizations for estimated total case numbers (Table 2-8).
2 We also provide WBDOSS hospitalization rates in Table 2-8 for comparison.
3 The values for the confirmed/reported cases from Mead et al. (Table 2-8, fourth
4 column) reflect higher hospitalization rates while the rates for estimated total case
5 numbers (Table 2-8, fifth column) are typically lower. Consider that patients
6 hospitalized for gastrointestinal illness would be routinely tested for pathogens; this
7 routine would inherently demonstrate a high hospitalization rate among the cases
8 confirmed by hospital laboratories. In contrast, the estimated-cases category would
9 include many mild and non-medically-attended cases - so a lower hospitalization rate
10 would be expected. The WBDO hospitalization rates generally fall between the
11 confirmed/reported and estimated rates of Mead et al., or near the estimated rate. The
12 exceptions were WBDOs of Cyclospora, V. cholerae, S. enterica serovar Typhi, and
13 rotavirus. For Cyclospora, the case number sample size (n=21) in the WBDO database
14 was too small to expect representative information regarding this agent. The V.
15 cholerae hospitalization rate from Mead et al. was based almost exclusively on foreign-
16 acquired infection and may not be appropriate for the two WBDOs in the U.S. that were
17 characterized by relatively mild illness for this pathogen. The hospitalization rate for
18 WBDOs of S. enterica serovar Typhi is somewhat higher than the Mead et al. rates, but
19 all the presented rates (844, 750, and 750 hospitalizations per 1,000 reported cases)
20 are markedly higher than that for any other pathogen and the relative difference
21 between them is small. There were no reported hospitalizations associated with the
22 single reported WBDO of rotavirus that occurred primarily among adult tourists (n=1761)
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TABLE 2-8
Hospitalization Rate (Hospitalized cases per 1 ,000 cases)
Etiologic Agent
AGI
Total
WBDO
Cases
83,493
WBDOSS
(Based on reported
hospitalizations
relative to total
WBDO Cases)
4.5
Mead etal. (1999)
(Appendix); Culture-
Confirmed/Reported
(Based on reported
cases)
-
Mead etal. (1999);
Estimated
(Based on
estimated total
cases)3
4.5
Viruses
Norovirus
SRSV (assumed to be
norovirus)
Rota virus
Hepatitis A
13,100
70
1,761
827
0.8
0
0
99.1
-
-
-
130
2.1
-
12.8
130
Bacteria
C. jejuni
E. coli
E. coli & Campylobacter
P. shigelloides
Salmonella, non-typhoid
spp.
S. enterica serovar Typhi
Shigella
V. cholerae
Yersinia
5,604
1,529
781
60
3,203
282
9,196
28
103
15.5
79.8
90.9
50
25.6
844
32.7
143
194
102
295
-
-
221
750
139
340C
242
5.4
29.5
-
-
11.6
750
13.9
333b
12.7
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
421,473
21
4
28,427
10.6
0
250
2.4
150
20
-
-
6.6
1.0
-
2.5C
2
O
4
5
6
7
The estimated rate for hospitalizations amongst total estimated cases was determined by dividing the
total estimated hospitalizations by the total estimated illnesses for each pathogen. These case and
hospitalization numbers for specific pathogens are provided by Mead et al. (1999) in their Table 3, and for
AGI in their Figure 1.
b 96% of cases reported to CDC were acquired abroad
c Estimated hospitalization rate by Mead et al. (1999)
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1 in a resort area. The hospitalization rate estimated by Mead et al. for rotavirus
2 (12.8/1,000) probably reflects the hospitalization rate for young children who typically
3 experience much more severe illness from rotavirus infections than do adults.
4 2.6.2. Fatality per Case Estimations. Although all the WBDO reports included entries
5 for deaths due to the outbreak, under- or over-reporting of the number of deaths is
6 possible. Deaths that occur as a result of a WBDO-acquired illness may not get
7 attributed to that incident on the WBDOSS report or on the patient's death certificate.
8 Unless an outbreak investigation includes an evaluation of death certificates or a
9 mortality study that considers deaths before, during, and after the WBDO, reported
10 deaths might not represent the actual mortality attributable to the incident. Even though
11 a death may occur during the outbreak period or shortly thereafter, an attending
12 physician may not certify that the WBDO pathogen was a contributing or underlying
13 cause of death, or an outbreak investigator may not conclude that a death is WBDO-
14 related, even if the illness or infectious agent etiology is listed on the death certificate.
15 For example, no deaths were indicated on the CDC 52.12 filed to report a
16 cryptosporidiosis outbreak that occurred in Clark County, Nevada over the first 3
17 months of 1994. However, there were at least 20 cryptosporidiosis-associated deaths
18 among HIV-positive persons that occurred in Clark County by the end of June that year
19 (Goldstein, 1996). Although these deaths may have been attributable to the waterborne
20 outbreak, they are not recorded in the WBDOSS.
21 To investigate possible under- or over-reporting of mortality resulting from
22 WBDOs, we considered four other estimates of mortality due to infectious diseases that
23 can be food or waterborne (Table 2-9). Three of the other compilations address the
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TABLE 2-9
Case Fatalities per 100,000 Cases According to WBDOSS and Other Sources
Etiologic Agent
AGI
WBDOSS
(1971 to
2000)
1.2
Food borne
Outbreaks
Reported to CDC:
1983-1987;
CAST (1994)
-
Mead etal. (1999)
Based on Culture-
Confirmed or Reported
to Food Net/CDC
-
Based on
Estimated
Cases3
2d
Bennett etal. (1987)
from Closing the Gap
Based on "Est. True
Annual Incidence"
CDC Survey Datab
-
Todd (1989) for
Foodborne Disease0
Based on
Reported
Cases
40
Based on
Estimated
Cases
0.4
Viruses
Noro virus
SRSV (assumed to be
norovirus)
Rota virus
Hepatitis A
0
-
0
0
0
-
0
94
-
-
-
300s
1e
-
Of
100
0.1
-
10
300
0.1
-
-
300
0
-
-
3
Bacteria
C. jejuni
E. co/;O157:H7and£
co/;O157:H7 from mixed
outbreak
P. shigelloides
Salmonella, non-typhoid
spp.
S. enterica serovar Typhi
Shigella
V. cholerae
0
260
0
219
0
21.7
0
138
625
-
125
-
30
0
100h
830'
-
780j
400k
160j
600m
5.1
83
-
41
364
15.6
0
100
200
-
100
6,000'
200
1,000'
50
2,000
-
100
-
125
1,000
0.5
20
-
1.1
60
1.25
10
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TABLE 2-9 cont.
Etiologic Agent
Yersinia
WBDOSS
(1971 to
2000)
0
Food borne
Outbreaks
Reported to CDC:
1983-1987;
CAST (1994)
-
Meadetal. (1999)
Based on Culture-
Confirmed or Reported
to Food Net/CDC
50n
Based on
Estimated
Cases3
3.1
Bennett et al. (1987)
from Closing the Gap
Based on "Est. True
Annual Incidence"
CDC Survey Datab
50
Todd (1989) for
Foodborne Disease0
Based on
Reported
Cases
25
Based on
Estimated
Cases
0.25
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
11.9
0
0
0
-
-
-
0
500°
50P
-
-
22
0
-
0.5q
50,000'
-
300
0.1
-
-
-
1
-
-
-
0
3 Table 3, Mead et al. (1999), Estimated total deaths/Estimated total cases.
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
a
b
c
d
e
f
g
h
i
j
k
1
m
n
0
P
q
Estimates acquired from CDC experts and based on 1985 case incidence and infection-attributable death records.
Fatality:case ratios (as %) presented in Table 2, Todd (1989). Note: Fatality:case ratios for estimated cases assumed to be 100X lower than for
reported cases.
5,000 deaths/173,000,000 cases AGI (Figure, Mead et al., 1999)
Assumed to account for 11 % of 2,800 fatal cases of viral AGI each year. Mead appendix reference to Mounts et al. (1999).
"Very low." Mead appendix reference to Kilgore et al. (1995).
Based on hepatitis surveillance. Mead appendix references to Hepatitis surveillance report no. 56 (1996) and Hoofnagle et al. (1995).
Culture-confirmed cases reported to FoodNet, 1996/97. Mead appendix reference to FoodNet (CDC, 1998b,c).
Mortality associated with sporadic cases reported to FoodNet, 1996/97. Mead appendix reference to FoodNet (CDC, 1998b,c).
Average case-fatality rate reported to FoodNet, 1996/97. Mead appendix reference to FoodNet (CDC, 1998b,c).
Based on outcomes of 2254 cultured-confirmed cases. Mead appendix reference to Mermin et al. (1998).
Based on small numbers: Typhoid 36 deaths/600cases; Cholera 3 deaths/25 cases; Crypto 25 deaths/50 cases.
Based on cases reported to CDC, 1992-94. Mead appendix reference to Mahon et al (1996).
Case-fatality rate assumed to be low (0.5%) based on 1996 FoodNet surveillance. Mead appendix reference to FoodNet (CDC, 1998b).
Average case-fatality rate among cases reported to FoodNet, 1997/98. Mead appendix reference to FoodNet (CDC, 1999).
Case-fatality rate assumed low (0.5%). Mead appendix reference to Herwaldt and Ackers (1997) and Herwald and Beach (1999).
Case-fatality rate assumed to be "exceedingly low" (Mead et al., 1999 [appendix]).
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1 burden of foodborne illnesses: Mead et al. (1999), Todd (1989) and the Council for
2 Agricultural Science and Technology (CAST, 1994) and the fourth, Bennett et al. (1987),
3 addresses the burden of all infectious diseases in the U.S.
4 Drawing from the information in the resources listed in the hospitalization-rate
5 discussion above, Mead et al. reported pathogen-specific fatality-case ratios for
6 confirmed/reported cases, and estimated the number of deaths occurring amongst the
7 estimated total cases. Todd's fatality-case ratios were based upon the Bennett et al.
8 (1987) report and other sources including CDC annual summary data, CDC
9 correspondence, and published reports. The CAST task force compiled case number
10 and mortality data reported for foodborne outbreaks that occurred in the period from
11 1983 through 1987. The fatality-case ratios reported by Bennett et al. were obtained
12 from survey data collected from experts in the various divisions of the CDC regarding
13 infectious disease incidence in 1985.
14 Note that the Mead et al., CAST, and Todd fatality-case ratios for "reported"
15 cases in Table 2-9 are consistently greater than those for "estimated" cases. This
16 phenomenon occurs because estimated case numbers include unreported cases and,
17 frequently, unreported cases include the milder episodes of illness, many of which do
18 not require medical attention. Far fewer fatalities per incident number of cases can be
19 expected when large numbers of mild cases are included in the total. Furthermore,
20 culture-confirmation of a case would much more likely be sought for patients who
21 present to their physicians with severe symptoms; consequently, a higher fatality-case
22 ratio can be expected for culture-confirmed cases. To estimate the number of deaths
23 occurring among the estimated cases, Mead et al. calculated the number of reported
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1 pathogen-specific deaths available from FoodNet, reported outbreaks, and other
2 published sources (see footnotes, Table 2-9) and assumed that twice that many deaths
3 might have occurred among the estimated cases (two deaths/estimated total). For
4 those viral and protozoan agents with no reported deaths, the fatality-case ratio was
5 estimated from literature review. Todd assumed that the fatality-case ratio for estimated
6 case incidence was 100-fold less than that computed for reported cases. The approach
7 for determining fatality-case ratios in Bennett et al. is unclear and appears to represent
8 estimated cases for some etiologic agents and reported cases for others. The fatality-
9 case ratios for some of the etiologic agents in the Bennett et al. report appear to be
10 based on very low case numbers, such as those for Cryptosporidium, V. cholerae, and
11 S. enterica serovar Typhi. The reporting of very few cases of cryptosporidiosis by
12 Bennett et al. and the extremely high fatality-case ratio associated with them were likely
13 affected by the fact that these data are from 1985, which was very early in the course of
14 the U.S. HIV-AIDS epidemic. Prior to the AIDS epidemic, cryptosporidiosis was rarely
15 recognized or reported. In 1985 it would likely have been the severe and often fatal
16 cases of cryptosporidiosis that occurred in AIDS patients that were noted and reported.
17 Fatality-case ratios for the reported WBDOs were zero except for E. coli
18 0157:H7 (and one WBDO attributed to E. coli 0157:H7 and Campylobacterbui in which
19 the deaths were specifically associated with E. coli 0157:H7), non-typhoid Salmonella
20 spp., Shigella, Cryptosporidium, and AGI. Fatality-case ratios of zero can be expected
21 among many of the reported WBDO etiologies, in part, because so few cases of any of
22 the types of infectious diseases included in the WBDOSS are reported, and, in general,
23 overall fatality-case ratios for these diseases are low when the total case incidence from
Draft: Do Not Cite or Quote 2-31 8/31/06
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1 all causes is estimated. For example, using the fatality-case ratio developed by the
2 most recent literature source considered here - Mead et al. (1999) - one death per
3 20,000 estimated cases of campylobacteriosis could be expected (fatality-case ratio,
4 0.00005). Since the WBDOSS includes only 5604 cases attributable to Campylobacter
5 spp., it is not surprising that there was no report of deaths.
6 Because case number totals for all etiologic agents reported to the WBDOSS
7 included not only symptom- and culture-confirmed cases, but also, for some outbreaks,
8 estimated case numbers, it is reasonable to expect that for some agents, the fatality-
9 case ratios would be closer to the reported/confirmed case ratios provided by CAST,
10 Mead et al., and Todd, while for others they would be closer to the estimated case
11 ratios, depending on the proportion of estimated cases in the WBDO case total for a
12 particular agent. And, except for Cryptosporidium, all WBDO agent categories that
13 included a non-zero fatality-case ratio (AGI, E. coli 0157:H7, non-typhoid Salmonella
14 spp., and Shigella) fall between the confirmed/reported and estimated values of the
15 literature based compilations. The WBDOSS fatality-case ratio for Cryptosporidium is
16 less than the lowest literature-source value of 22 deaths/100,000 cases proposed by
17 Mead et al. for estimated cases (Table 3, Mead et al., 1999), but at 11.9 deaths/100,000
18 cases, not markedly so. We considered the range for the number of deaths that might
19 have occurred during the 30-year WBDO reporting period if the fatality-case ratios
20 acquired from the aforementioned literature sources were used for estimation of the
21 expected (rather than WBDOSS-reported) number of deaths. We applied the lowest
22 and the highest values offered by the four sources (except for the Bennett
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1 Cryptosporidium3 and S. enterica serovar Typhi4 values) to the reported case numbers
2 in the WBDO database to estimate the lowest and highest number of deaths that could
3 plausibly be expected (Table 2-10). All of the lowest values for predicted numbers of
4 deaths from WBDOs are based on fatality-case ratios developed for estimated case
5 totals. Many (9 of 15) of the lowest values are based on the fatality-case ratios provided
6 by Todd for estimated cases (who assumed that the fatality-case ratio for estimated
7 cases is 1/100 of that computed for reported/confirmed cases). All the highest predicted
8 death numbers were calculated from fatality-case ratios that were based on
9 reported/confirmed cases, and these are all greater than the reported WBDO number of
10 deaths.
11 For three of the pathogen classifications, AGI, E. coli 0157:H7, and
12 Cryptosporidium, the high estimates were markedly greater than the reported WBDO
13 deaths. Todd selected a 40/100,000 fatality-case ratio for 6309 reported cases of AGI
14 and cites CDC annual summary data as his source (CDC, 1981a,b, 1983a,b;
15 MacDonald and Griffin, 1986). Todd also provided the highest E. coli 0157:H7 fatality-
16 case ratio (2000 deaths/100,000 reported cases) for 30 reported cases as ascertained
17 from the same CDC annual summaries cited above. The highest fatality-case ratio for
18 cryptosporidiosis was provided by Bennett et al.; however, their 50,000 deaths/100,000
19 cases value indicates that there would have been over 200,000 deaths due to the
3 The Bennett et al., 50% fatality ratio is unrealistically large having been based on only the 50 cases that
were estimated to be the "current incidence" in 1987 as determined by CDC experts from data collected
in 1985. Furthermore, these may have been particularly severe considering that effective a nti retro viral
therapy for AIDS patients was not generally available at that time.
4 The Bennett et al., fatality-case ratio for typhoid was based on the expectation of 36 deaths among 600
cases (6% of cases). This appears to be an exceptionally high value considering that Mermin et al.
(1998), of the Foodborne and Diarrheal Diseases Branch of the CDC examined 2445 reports of culture-
confirmed typhoid received by the CDC between 1985 and 1994 and found only 10 deaths reported from
these cases (0.4%).
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TABLE 2-10
Comparison of Number of Deaths Reported in WBDOs with Expected Number of Deaths Using
Literature-based Fatality-case Ratios
(Rounded to nearest whole number; if values are < 0.5 but > 0, the entry is "< 1")
AGI
WBDO
Reported
Deaths
1
Low Estimate from
Literature Sources
<1a
High Estimate
from Literature
Sources
33b
Viruses
Norovirus
SRSV (assumed to be norovirus)
Rota virus
Hepatitis A
0
0
0
0
<1b
-
<1C
<1a
<1C
-
<1d
2e
Bacteria
C. jejuni
E. co/;O157:H7 and mixed E. coli
01 57 :H7S & C. jejuni
P. shigelloides
Salmonella, non-typhoid spp.
S. enterica serovar Typhi
Shigella
V. cholerae
Yersinia
0
6
0
7
0
2
0
0
<1a
<1a
-
<1a
<1a
<1a
Oc
Oa
8f
46b
-
25e
1e
18d
<1d
<1e
Protozoa
Cryptosporidium
Cyclospora
En. histolytica
Giardia
Totals
50
0
0
0
66
93C
Oc
-
Oa
94
2,107e
<1e
<1d
<1b
2,243
2
3
4
5
6
7
Based on Todd, fatality-case ratio for estimated case numbers.
b Based on Todd, fatality-case ratio for confirmed/reported case numbers.
c Based on Mead et al., fatality-case ratio for estimated case numbers.
d Based on Bennett et al., fatality-case ratios.
e Based on Mead et al., fatality-case ratio for confirmed/reported case numbers. See Footnotes 3 and 4
in text regarding Bennett et al.'s higher estimates for S. enterica serovar Typhi and Cryptosporidium.
f based on CAST, fatality-case ratios
9 deaths and majority of infections in this outbreak due to E. coli O157:H7
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1 Milwaukee outbreak. Because that estimation is implausibly excessive, we used the
2 fatality-case ratio acquired from Mead et al. (based on 450 cases of cryptosporidiosis
3 reported to FoodNet in 1997-1998) for our upper-end estimate of Cryptosporidium-
4 associated WBDO deaths in Table 2-10.
5 Over the 30-year surveillance period, 66 deaths were reported to the WBDOSS.
6 If the lowest and highest literature-based fatality-case ratios are used, without
7 modification, to predict the number of expected deaths among the cases in the
8 WBDOSS, the range would be 94-2243 (Table 2-10). Obviously, these values are
9 driven by the cryptosporidiosis case incidence due to the Milwaukee outbreak. Because
10 the Milwaukee case incidence was estimated (only 285 cases were culture-confirmed)
11 we contend that the Mead et al. fatality-case ratio based on estimated cases
12 (22/100,000) is the more appropriate choice for establishing a plausible range for
13 deaths due to the WBDOs. This reduces the literature-based estimate for the
14 Cryptosporidium associated death toll to 93, and the range for predicted deaths
15 becomes 94-228 (Table 2-11). And finally, because the Cryptosporidium-assoaaied
16 deaths attributed to the Milwaukee outbreak were extensively investigated by Hoxie et
17 al. (1997), we suggest further modification of the plausible range for total deaths by
18 limiting the Cryptosporidium-assoc\ated deaths to the 50 reported to the WBDOSS.
19 This yields a range of 51 to 185 predicted deaths due to reported WBDOs over 30 years
20 (which contains the WBDOSS reported value of 66).
21 2.7. EPIDEMIOLOGIC BURDEN SEVERITY MEASURES
22 The summary epidemiologic severity measures used for our burden analysis are
23 presented in Table 2-12. The number of cases, hospitalizations, and deaths are used
Draft: Do Not Cite or Quote 2-35 8/31/06
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TABLE 2-1 1
Modifications of the Plausible Predicted Number of WBDO Deaths Estimated from
Literature-based Fatality-case Ratios
Totals from Table 2-10
Using only Mead et al., fatality-case ratio for estimated case
numbers for Cryptosporidium (because the 403,000 cases of
cryptosporidiosis were estimated for Milwaukee) yielding
estimate of 93 WBDO Cryptosporidium deaths
Using only the 50 Cryptosporidium deaths attributed to the
Milwaukee outbreak data in the WBDOSS
Low
Estimate
from
Literature
Sources
94
94
51
High
Estimate
from
Literature
Sources
2,243
228
185
TABLE 2-1 2
Epidemiological Burden Measures Used in the Analysis
Reported Waterborne Outbreaks in Drinking Water for the 30-Year Period, 1971 to 2000
Epidemiological
Burden Measure
Cases
Person-Days of Illness
Physician Visits
Emergency Room Visits
Hospitalizations
Deaths
Value Used in the Burden Analysis
569,962
4,504,933*
41,985
23,575
5,915
66
Reported or
Estimated
Reported
Estimated
Estimated
Estimated
Reported
Reported
3 * If 3 days duration of illness is assumed for cryptosporidiosis occurring during the
4 Milwaukee outbreak (i.e., the median duration ascertained from survey respondents),
5 the Person-Days of Illness value changes to 2,086,933.
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1 as reported. Person-days ill, physician visit, and emergency room visit numbers were
2 derived with the estimation methods described earlier in this chapter. Inaccurate
3 reporting and paucity of data create uncertainty in the burden measures. The sensitivity
4 of the burden estimate to uncertainty in the various burden components is examined in
5 Chapter 6.
Draft: Do Not Cite or Quote 2-37 8/31/06
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1 3. RESULTS: PROJECTED EPIDEMIOLOGIC BURDEN ESTIMATE OF REPORTED
2 INFECTIOUS WATERBORNE OUTBREAKS BY SUMMARY CATEGORIES
3 AND IMPACT OF THE MILWAUKEE OUTBREAK
4
5 The epidemiologic burden estimate is presented in this chapter by five summary
6 categories: etiology, water system type, water system deficiency, time period and water
7 source type. We conduct these analyses to identify the specific divisions within the
8 summary categories that have been associated with the largest epidemiologic burden.
9 Due to the magnitude of illness associated with the Milwaukee WBDO, we develop
10 additional comparisons within the summary categories by excluding the Milwaukee
11 WBDO. This allows trends that may be evidenced by data from the other 664 reported
12 WBDOs to be examined.
13 3.1. EPIDEMIOLOGIC BURDEN BY ETIOLOGIC AGENT
14 Etiologic agents were identified in only 45% of reported WBDOs. Over the
15 30-year period, protozoans caused the most outbreaks when the etiologic agent was
16 identified. Protozoan agents were associated with the most cases (449,925), person-
17 days ill (4,090,423), physician visits (29,949), emergency room visits (13,093),
18 hospitalizations (4,517) and deaths (50) (Table 3-1). The major contributors to the
19 burden of protozoan WBDOs are Cryptosporidium and Giardia (Table 3-2). Other
20 protozoan agents (i.e., Cyclospora and En. histolytica) were reported in only one
21 outbreak each and contribute little to the epidemiologic burden estimate.
22 AGI WBDOs (i.e., outbreaks with no identified etiologic agent) were associated
23 with the second highest burden for person-days ill, physician visits and emergency room
24 visits; however, bacterial WBDOs were associated with more hospitalizations and
25 deaths than AGI WBDOs (Table 3-1).
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TABLE 3-1
Projected Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Etiologic Agent,
1971 to 2000*
Etiologic Agent
AGI
Viruses
Bacteria
Outbreaks
365
56
101
Cases
83,493
15,758
20,786
Person-Days III
265,120
53,697
95,615
Physician
Visits
8,822
2,017
1,196
Emergency
Room Visits
9,426
124
931
Hospital-
izations
378
92
928
Deaths
1
0
15
Protozoa
Milwaukee WBDO
All Other WBDO
Total
1
142
665
403,000
46,925
569,962
3,627,000
463,423
4,504,854
20,280
9,669
41,985
11,727
1,366
23,575
4,400
117
5,915
50
0
66
Column totals for physician visits, emergency room visits, and hospitalizations do not sum due to rounding.
Draft: Do Not Cite or Quote
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TABLE 3-2
Projected Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Etiologic Agent,
1971 to 2000
Etiologic Agent
AGI
Outbreaks
365
Cases
83,493
Person-Days
III
265,120
Physician
Visits
8,822
Emergency
Room Visits
9,426
Hospital-
izations
378
Deaths
1
Viruses
Norovirus
SRSV (assumed to be
norovirus)
Rotavirus
Hepatitis A
Bacteria
C. jejuni
E. coll
E. coll & Campylobacter
P. shigelloides
26
1
1
28
19
12
1
1
13,100
70
1,761
827
5,604
1,529
781
60
25,139
9,686
91
18,782
26,082
10,537
60
210
1,086
6
146
780
325
89
45
3
43
0
6
75
16
7
4
0
10
0
0
82
87
122
71
3
0
0
0
0
0
4
2
0
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TABLE 3-2 cont.
Etiologic Agent
Salmonella non-typhoid
spp.
S. enterica serovar Typhi
Shigella
V. cholerae
Yersinia
Outbreaks
15
5
44
2
2
Cases
3,203
282
9,196
28
103
Person-Days
III
17,328
5,502
31,104
950
134
Physician
Visits
186
7
533
2
6
Emergency
Room Visits
15
1
886
0
0
Hospital-
izations
82
238
301
4
10
Deaths
7
0
2
0
0
Protozoa
Cryptosporidium
Milwaukee WBDO
All Other WB DO
Cyclospora
En. histolytica
Giardia
Total
1
14
1
1
126
665
403,000
18,473
21
4
28,427
569,962
3,627,000
170,834
228
3,749
292,319
4,504,854
20,280
929
1
0
8,738
41,985
11,727
538
1
0
827
23,575
4,400
48
0
1
68
5,915
50
0
0
0
0
66
1 AGI = Acute gastrointestinal illness of unknown etiology
2 SRSV = Small round structured virus
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1 Bacterial WBDOs resulted in about 25% more reported cases of illnesses than
2 viral WBDOs (20,786 cases versus 15,758 cases). The major contributors to the
3 burden of bacterial WBDOs were Shigella, Campylobacter, E. coli and non-typhoid
4 Salmonella spp. (Table 3-2). When compared to viral WBDOs, bacterial WBDOs also
5 resulted in larger estimates of person-days ill, emergency room visits, hospitalizations
6 and deaths (Table 3-1). However, viral WBDOs resulted in almost twice as many
7 physician visits than bacterial WBDOs. Fifty-four percent of the physician visits
8 associated viral WBDOs are due to norovirus (Table 3-2). In viral WBDOs, over half of
9 the person-days ill were due to Hepatitis A which accounted for only 5% of the cases
10 attributed to viral WBDOs.
11 Tables 3-1 and 3-2 show that the Milwaukee WBDO is, by far, the largest WBDO
12 reported between 1971 and 2000. Table 3-1 shows that, for each epidemiologic burden
13 measure, the Milwaukee WBDO is greater than the corresponding burden measure,
14 reported for all other protozoan WBDOs, all AGI WBDOs, all bacterial WBDOs and viral
15 WBDOs. In fact, this single outbreak accounts for more cases, person-days ill,
16 emergency room visits, hospitalizations and deaths than all other WBDOs combined.
17 Excluding the Milwaukee WBDO, the types of pathogens that contribute the most
18 to individual burden measures differs from those identified when Milwaukee is included.
19 Table 3-1 shows that protozoan WBDOs still account for more person-days ill and
20 physician visits than any other type of pathogen. Bacterial WBDOs account for more
21 hospitalizations and 15 of the 16 reported deaths. The AGI WBDOs account for more
22 cases and emergency room visits than any of the specific pathogens (we note that
23 these outbreaks are likely caused by various pathogens). Excluding the AGI and the
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1 Milwaukee WBDOs, Table 3-2 shows that Giardia, Cryptosporidium and norovirus
2 accounted for the most cases of reported WBDOs; Giardia, Cryptosporidium and
3 Shigella accounted for the most person-days ill. If AGI and the Milwaukee WBDOs are
4 excluded, Giardia, norovirus, and Cryptosporidium accounted for the most physician
5 visits; Shigella, Giardia and Cryptosporidium accounted for most of the emergency room
6 visits. If AGI and the Milwaukee WBDOs are excluded, three bacterial WBDOs are
7 associated with the most hospitalizations: Shigella, S. enterica serovar Typhi and
8 E. coll. Finally, we note that, when the Milwaukee WBDO is excluded, bacterial WBDOs
9 accounted for most of the remaining deaths; the primary agents that caused these
10 deaths were non-typhoid Salmonella spp. and E. coll 0157:H7.1'2
11 3.2. EPIDEMIOLOGIC BURDEN BY WATER SYSTEM TYPE
12 In the WBDOSS, water systems are classified as community, non-community or
13 individual (Appendix A).3 For our projected burden estimates, all burden measures
14 except number of outbreaks are greatest for community systems; community systems
15 accounted for the most cases (485,844), person-days ill (4,215,965), physician visits
1 Although most strains of E. coli are not pathogenic, there are a number of diarrheagenic strains. Of
particular concern are the enterohemorrhagic strains such as O157:H7. The WBDOSS specifically
identifies the nine E. coli outbreaks that have occurred since 1989 as strain O157:H7.
2 We note that the WBDOSS does not track cases of hemolytic uremic syndrome (HUS), which has been
linked to E. coli O157 infections. However, HUS cases have been noted in external reports describing
some of the £. coli O157:H7 outbreaks included in the WBDOSS (Swerdlow et al., 1992; CDC, 1999c;
Olsenetal.,2002).
3 Community and noncommunity water systems are public water systems that serve >15 service
connections or an average of >25 residents for >60 days/year. A community water system serves year-
round residents of a community, subdivision, or mobile home park with >15 service connections or an
average of >25 residents. A noncommunity water system can be nontransient or transient. Nontransient
systems serve >25 of the same persons for >6 months of the year, but not year-round (e.g., factories or
schools), whereas transient systems provide water to places in which persons do not remain for long
periods of time (e.g., restaurants, highway rest stations or parks). Individual water systems are small
systems not owned or operated by a water utility that serve <15 connections or <25 persons. Outbreaks
associated with water not intended for drinking (e.g., lakes, springs and creeks used by campers and
boaters, irrigation water and other nonpotable sources with or without taps) are also classified as
individual systems.
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1 (32,400), emergency room visits (16,268), hospitalizations (4,931) and deaths (62).
2 Although non-community systems reported 75 more WBDOs than community systems
3 (Table 3-3), all other summary measures were substantially less than those reported by
4 community systems. Summary burden measures were the lowest for individual
5 systems reflecting the low number of individual system outbreaks reported.
6 If the Milwaukee WBDO is excluded, Table 3-3 shows that the remaining
7 community system WBDOs and the non-community WBDOs report comparable
8 numbers of cases. While for the remaining community system WBDOs (i.e., excluding
9 Milwaukee) we estimate more than twice as many person-days ill and nearly 40% more
10 physician visits than non-community system WBDOs, for non-community system
11 WBDOs we estimate nearly 50% more emergency room visits and nearly 70% more
12 physician visits than community system WBDOs. The 253 remaining community
13 system WBDOs report 12 deaths and the non-community system WBDOs report 4
14 deaths.
15 Communities receive their drinking water from surface waters, groundwaters or a
16 mix of the two. Figure 3-1 shows the number of community system outbreaks that were
17 associated with each type of water source. The figure shows that surface water
18 sources and groundwater sources have accounted for roughly the same number of
19 community system WBDOs. Figures 3-2 and 3-3 show that community system WBDOs
20 that occurred in communities served by surface water systems have resulted in the
21 largest number of person-days ill and deaths. When the Milwaukee WBDO is excluded
22 from the analysis, WBDOs in community systems served by groundwater accounted for
23 the remaining 12 deaths that occurred in community systems; however, groundwater
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TABLE 3-3
Projected Natural Burden of Reported Infectious Waterborne Outbreaks in Drinking Water, 1971 to 2000
Water System
Classification
Outbreaks
Cases
Person-Days III
Physician
Visits
Emergency
Room Visits
Hospital-
izations
Deaths
Community
Milwaukee WBDO
All Other WBDO
Non-Community
Individual
Total
1
253
329
82
665
403,000
82,844
78,703
5,415
569,962
3,627,000
588,965
262,157
26,732
4,504,854
20,280
12,120
8,812
773
41,985
11,727
4,541
6,744
563
23,575
4,400
531
885
99
5,915
50
12
4
0
66
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1
2
3
4
5
6
1
Unknown
23 -
Mixed (9%)
4
(2%)
Surface Water
117
(46%)
Ground Water
110
(43%)
FIGURE 3-1
Number of Outbreaks for Community System WBDOs by Source Type
Mixed
15,000^
Unknown
20,000
Surface Water
4,034,000
(97%)
Ground Water
146,000
(3%)
FIGURE 3-2
Number of Person-Days III for Community System WBDOs by Source Type
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1
2
O
4
5
Ground Water
12
(19%)
Surface Water
50
(81%)
FIGURE 3-3
Number of Deaths for Community System WBDOs by Source Type*
Mixed contamination and unknown contaminant accounted for no deaths.
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8/31/06
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1 sources account for only 25% of the person-days ill in community system WBDOs
2 because the remaining surface water WBDOs account for nearly 70% of the
3 person-days ill.
4 3.3. EPIDEMIOLOGIC BURDEN BY WATER SYSTEM DEFICIENCY
5 WBDOs are categorized in the surveillance system according to the deficiency
6 that may have caused or contributed to the outbreak (Appendix A). The five major
7 categories are water treatment deficiencies; distribution system deficiencies; untreated,
8 contaminated groundwater; untreated, contaminated surface water; miscellaneous and
9 unknown deficiencies. The most important contributor to the projected epidemiologic
10 burden for all measures was one or more water treatment deficiencies (Table 3-4).
11 WBDOs caused by one or more water treatment deficiencies accounted for the most
12 outbreaks (269), cases (525,733), person-days ill (4,281,583), physician visits (36,348),
13 emergency room visits (20,068), hospitalizations (4,980) and deaths (52). The next two
14 most important contributors to the epidemiologic burden were distribution system
15 deficiencies and the use of untreated, contaminated groundwater. Although more
16 WBDOs were reported in untreated groundwater systems, the other epidemiologic
17 burden severity measures were roughly equivalent (i.e., same order of magnitude). The
18 lowest epidemiologic burden was associated with WBDOs caused by miscellaneous or
19 unknown deficiencies or untreated surface water. U.S. EPA regulations now prohibit
20 the use of untreated surface water for community and non-community water systems
21 (U.S. EPA, 2003). Regulations pertaining to groundwater are currently under
22 development.
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TABLE 3-4
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Water System Deficiency,
1971 to 2000
Deficiency
Outbreaks
Cases
Person-Days
III
Physician
Visits
Emergency
Room Visits
Hospital-
izations
Deaths
Deficiency in Water Treatment
Milwaukee WBDO
All Other WBDO
Distribution System
Deficiency
Untreated Groundwater
Miscellaneous
Unknown Deficiency
Untreated Surface Water
Total
1
268
83
211
41
23
38
665
403,000
122,733
15,305
22,285
2,053
3,372
1,214
569,962
3,627,000
654,583
98,314
83,803
14,873
16,570
9,711
4,504,854
20,280
16,068
2,311
2,605
223
291
208
41,985
11,727
8,341
824
2,217
193
173
100
23,375
4,400
580
201
602
43
84
5
5,915
50
2
12
2
0
0
0
66
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1 If the Milwaukee WBDO is excluded, Table 3-4 shows that the remaining
2 WBDOs, caused by one or more water treatment deficiencies, account for more
3 outbreaks, cases, person-days ill, physician visits, emergency room visits and
4 hospitalizations than all other types of deficiencies. However, distribution system
5 deficiencies have reported more deaths (12) than the remaining WBDOs caused by one
6 or more water treatment deficiencies (2), untreated groundwater (2), untreated
7 contaminated surface water (0), miscellaneous (0) and unknown deficiencies (0). While
8 the second highest number of outbreaks, cases, physician visits, emergency room visits
9 and hospitalizations are reported for WBDOs caused by untreated groundwater,
10 distribution system deficiencies account for the second highest person-days ill and
11 deaths.
12 The three types of deficiencies causing the fewest number of outbreaks are
13 miscellaneous (41), untreated contaminated surface water (38) and unknown
14 deficiencies (23); no deaths were reported for any WBDOs attributed to these
15 deficiencies. Of these three causes of WBDOs, untreated contaminated surface waters
16 reported the fewest numbers of cases, person-days ill, physician visits, emergency
17 room visits and hospitalizations. Despite causing the smallest number of outbreaks,
18 WBDOs caused by unknown deficiencies reported the most cases and hospitalizations.
19 They also had the highest estimates of person-days ill and physician visits. We
20 estimated more emergency room visits for WBDOs associated with miscellaneous
21 causes than for those caused by unknown deficiencies.
22 Figures 3-4 through 3-8 illustrate the person-days ill associated with each
23 etiologic agent for each type of deficiency. Figure 3-4 reveals that Cryptosporidium
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9
10
11
12
13
AGI
175,000^
All Other
314,000^
(3%)
Giardia
232,000
Cryptosporidium
3,784,000
(88%)
FIGURE 3-4
2
3
4 Person-Days III for Water System Deficiency in Water Treatment by Etiologic Agent*
6
8 * Percentages differ slightly from those listed in text due to rounding.
Salmonella, non-
_ . . typhoid spp.
Rotavirus Jr rr
P. shigelloides
<1000 -
(2%)
Norovirus \
17,000 — _ _
(3%)
S. enterica semvar
Typhi
4,000
<1%) Shlgella
^- 23,000
^^ (4%)
Hepatitis A
5,000
(1%)
Giardia /
232,000^
(34%)
AGI
175,000
(26%)
C. jejuni
^- 18,000
(3%)
Cryptosporidium
157,000
(24%)
FIGURE 3-5
Person-Days III for Deficiency in Water Treatment WBDOs by Etiologic Agent (excluding
the Milwaukee WBDO)
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Norovirus
4,000
(4%)
r
Hepatitis A
1,000
(1%)
Giardia
53,000
(54%)
S. enterica serovar
Typhi
1,000
(1%)
Salmonella non-
typhoid spp.
12,000
(12%)
Shigella
1,000
(1%) _
V. cholerae
<1,000
1
2
3
4
5
C. jejuni
3,000
(3%)
Cyclospora
- <1,000
Cryptosporidium
<1,000
E. coli
1,000
(1%)
FIGURE 3-6
Person-Days III for Distribution System Deficiency
Salmonella non-
typhoid spp.
<1,000
S. enterica serovar
Typhi Shigella
<1,000 /- 5,000
(6%)
Yersinia
1,000
(1%)
Hepatitis A
13,000 -
(15%)
Norovirus
2,000 —
(2%)
En. histolytica
<1,000 -
Giardia
2,000
(2%)
E. coli &
Campylobacter
4,000
(5%)
E. coli
1,000-
(1%)
Cryptosporidium
1,000
6
1
AGI
54,000
(64%)
FIGURE 3-7
Person-Days III for Untreated Groundwater
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Salmonella non
typhoid spp.
<1,000
Hepatitis A
<1,000
(3%)
Shi gel I a
1,000
(10%
Giardia
5,000 —
(48%)
C. jejuni
<1,000
(1%)
1
2
3
4
5
6
7
8
9
10
FIGURE 3-8
Person-Days III for Water System Deficiency in Untreated Surface Water
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8/31/06
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1 accounts for most (88%) of the person-days ill associated with water treatment
2 deficiencies; over 95% of these person-days associated with Cryptosporidium occurred
3 during the Milwaukee WBDO. We note that this single outbreak also was associated
4 with most of the deaths reported in the WBDOSS. Figure 3-5 reveals that, if the
5 Milwaukee WBDO is excluded from the analysis, Giardia (36%), AGI (27%) and
6 Cryptosporidium (24%) account for nearly 86% of the person-days ill that occurred due
7 to water treatment deficiency. Figure 3-6 reveals that Giardia (54%) accounts for over
8 half of the person-days ill for WBDOs attributed to distribution system deficiencies.
9 Outbreaks attributed to AGI (22%) and Salmonella (12%) combined account for 34% of
10 the person-days ill associated with distribution system deficiencies. Previously, we
11 reported that outbreaks attributed to distribution system deficiencies were associated
12 with 12 (18%) of the deaths reported in the WBDOSS. Non-typhoid Salmonella spp. (7)
13 and E. coli (4) accounted for most of these deaths. Outbreaks associated with AGI
14 accounted for 65% of the person-days ill when the cause of the outbreak was attributed
15 to untreated groundwater (Figure 3-7). Outbreaks associated with Hepatitis A, the most
16 frequently identified etiologic agent, accounted for 15% of all person-days ill. The two
17 deaths caused by untreated groundwater were associated with an E. coli and
18 Campylobacter outbreak.
19 The epidemiologic burden associated with the remaining outbreak causes
20 reported in the WBDOSS is substantially smaller than the burden associated with
21 treatment deficiencies, distribution system deficiencies and untreated groundwater.
22 When the cause of the outbreak was attributed to untreated surface water, Giardia
23 (46%) and AGI (38%) accounted for 84% of all person-days ill (Figure 3-8).
Draft: Do Not Cite or Quote 3-17 8/31/06
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1 3.4. EPIDEMIOLOGIC BURDEN BY TIME PERIOD
2 The fewest number of outbreaks occurred in the 1990s, however, that decade
3 experienced the majority of burden in all measured categories (Table 3-5) due to the
4 Milwaukee WBDO. WBDOs that occurred in the 1990s accounted for the most cases of
5 illness (432,195), person-days ill (3,775,241), physician visits (23,412), emergency
6 room visits (13,834), hospitalizations (4735) and deaths (59). The majority of the cases
7 was reported in 1993, the year of the Milwaukee WBDO (Appendix C). In 24 of the 30
8 years in our surveillance period, fewer than 10,000 cases were reported annually, and
9 in 13 years, 2000 or fewer cases were reported. Since 1993, the largest number of
10 cases reported annually in WBDOs was 2492. The annual reported and projected
11 burden information for WBDOs is presented in Appendix C.
12 When the Milwaukee WBDO is excluded, the number of outbreaks, cases,
13 person-days ill, physician visits, emergency room visits and hospitalizations decreases
14 in each successive decade (Table 3-5). In general, across each of these measures, the
15 largest percent change occurs between the decade of the 1980s and 1990s. Only
16 deaths attributed to WBDOs increase in successive decades.
17 3.5. EPIDEMIOLOGIC BURDEN BY WATER SOURCE TYPE
18 Reported WBDOs in surface water systems occurred less frequently than in
19 groundwater systems (183 versus 425), but WBDOs in surface water systems
20 experienced a greater number of cases (457,310), person-days ill (4,058,221),
21 physician visits (29,735), emergency room visits (14,443), hospitalizations (4,644), and
22 deaths (50) (Table 3-6). Most of the surface water outbreaks were associated with
23 Giardia (48%) or AGI (36%) (Figure 3-9). However, most of the person-days ill in
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TABLE 3-5
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Decade, 1971 to 2000
Decade
Outbreaks
Cases
Person-Days
III
Physician
Visits
Emergency
Room Visits
Hospitalizations
Deaths
1991 to 2000
Milwaukee WBDO
All Other WBDO
1981 to 1990
1971 to 1980
Total
1
144
235
285
665
403,000
29,195
63,236
74,531
569,962
3,627,000
148,211
342,920
386,772
4,504,854
20,280
3,132
6,941
1 1 ,632
41,985
11,727
2,107
4,467
5,274
23,575
4,400
335
391
789
5,915
50
9
4
3
66
Draft: Do Not Cite or Quote
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TABLE 3-6
Epidemiologic Burden of Reported Infectious Waterborne Outbreaks in Drinking Water by Water Source Type,
1971 to 2000
Water Source Type
Outbreaks
Cases
Person-Days III
Physician
Visits
Emergency
Room Visits
Hospitalizations
Deaths
Surface Water
Milwaukee WBDO
All Other WBDO
Groundwater
Unknown
Mixed
Total
1
182
425
51
6
665
403,000
54,310
105,750
3,997
2,905
569,962
3,627,000
431,221
407,068
23,653
15,913
4,504,933
20,280
9,455
1 1 ,460
460
330
41,985
11,727
2,716
8,387
518
227
23,575
4,400
244
1,208
43
20
5,915
50
0
16
0
0
66
Draft: Do Not Cite or Quote
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Cryptosporidium
3%
Giardia
48%
Shigella
3%
C. jejuni
r 2%
Norovirus
f~ 2%
£. co/;
•"" 1%
Hepatitis A
1%
Salmonella, non-
typhoid spp.
\ 1%
\ Cyclospora
\ 1%
S. enterica serovar
Typhi
1%
Rotavirus
1%
1
2
FIGURE 3-9
3 Pathogens Associated with WBDOs in Surface Water Systems Between 1971 and 2000
Draft: Do Not Cite or Quote
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1 surface water outbreaks were associated with Cryptosporidium (92%), primarily due to
2 the Milwaukee WBDO, which accounted for over 89% of all person-days ill associated
3 with Cryptosporidium (Figure 3-10). Groundwater outbreaks were primarily associated
4 with AGI (62%) (Figure 3-11). AGI outbreaks were responsible for the greatest number
5 of person-days ill in groundwater systems (52%) (Figure 3-12). Unknown and mixed
6 water sources were negligible contributors to the epidemiologic burden estimate.
7 3.6. OVERALL IMPACT OF MILWAUKEE CRYPTOSPORIDIOSIS OUTBREAK
8 The Milwaukee WBDO contributes a significant portion of the projected
9 epidemiologic burden for reported WBDOs, and therefore, the epidemiologic burden
10 estimates are highly sensitive to the severity measures reported in Milwaukee. This
11 WBDO contributed 403,000 (71 %) cases of illness, 3,627,000 (81 %) person-days ill,
12 20,280 (48%) physician visits, 11,727 (50%) emergency room visits, 4,400 (74%)
13 hospitalizations, and 50 (76%) deaths to the projected burden. Consequently, the
14 summary burden categories associated with this WBDO (community water systems,
15 protozoan agents, Cryptosporidium, water treatment deficiencies, outbreaks from 1991
16 to 2000 and surface water outbreaks) have the highest burden. This demonstrates the
17 impact that a very large WBDO can have on the epidemiologic burden.
18 3.7. FURTHER ANALYSIS OF OUTBREAKS CAUSED BY AGI
19 WBDOs attributed to AGI contribute significantly to the epidemiologic burdens for
20 the reported WBDO. Because these outbreaks could be caused by different organisms,
21 we stratified the AGI WBDOs across source water and system type. Figure 3-13 shows
22 that 72% of the outbreaks attributed to AGI have occurred in systems served by
23 groundwater sources. Figure 3-14 shows that these groundwater WBDOs accounted
Draft: Do Not Cite or Quote 3-22 8/31/06
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Cryptosporidium
92%
All other
Organisms
8%
2
3
4
FIGURE 3-10
Pathogens Associated with Person-Days III in Surface Water System Outbreaks
Between 1971 and 2000
Giardia
7% Hepatitis A
6%
5
6
1
Norovirus
5%
C. Jejuni
3%
Salmonella, non-
typhoid spp.
/ 3%
__^ E. co//
2%
\_ Cryptosporidium
2%
FIGURE 3-11
Pathogens Associated with WBDOs in Groundwater Systems Between 1971 and 2000
Draft: Do Not Cite or Quote
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1
2
4
5
6
7
Giardia
13%
Cryptosporidium
10%
Norovirus
6%
Hepatitis A
^~ 4%
Salmonella, non-
typhoid spp.
4%
^\^_ Shigella
4%
FIGURE 3-12
Pathogens Associated with Person-Days III in Groundwater Systems Between 1971 and
2000
Unknown
33 ——_
Mixed (9%)
Surface Water
68
(19%)
Ground Water
263
(72%)
FIGURE 3-13
Number of Outbreaks for AGI WBDOs by Source Type
Draft: Do Not Cite or Quote
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Unknown
12,380
(5%)
Surface Water
38,944
(15%)
Ground Water
- 213,785
(80%)
1
2
3
4
5
6
7
FIGURE 3-14
Number of Person-Days III for AGI WBDOs by Water Source Type
Draft: Do Not Cite or Quote
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1 for 81 % of the person-days ill attributed to the AGI. This suggests that WBDOs
2 occurring in groundwater sources may be caused by etiologic agents that are difficult to
3 detect (e.g., viruses). Figures 3-15 and 3-16 show that non-community systems
4 account for over 60% of the outbreaks and the person-days ill attributed to AGI. This
5 suggests that it is more difficult to identify an etiologic agent in WBDOs that occur in
6 non-community systems than those WBDOs that occur in other systems.
7 3.8. DISCUSSION AND CONCLUSIONS
8 When comparing multiple epidemiologic burden measures for the various water
9 system categories, it is not always clear which category makes the most important
10 contribution to the overall burden. In some analyses, one category may be an important
11 contributor to most but not all burden measures. For example, when analyzing the
12 projected epidemiologic burden by etiologic agent group we found that AGI WBDOs
13 caused more outbreaks, cases, person-days illness and physician visits than bacterial
14 WBDOs, but bacterial WBDOs caused more hospitalizations and deaths. In order to
15 rank the various summary measures by their relative importance, a weighting approach
16 of the burden severity measures should be considered. In Chapters 4 and 5, we
17 present an economic weighting to the burden measures. Because the economic
18 measures are developed using the same unit (dollars), they can be summed, allowing
19 the various severity measures to be combined into a single severity expression—the
20 monetary burden. The methodology for determining the monetary burden is described
21 in Chapter 4, and a summary of the monetary burden measures for the WBDOs is
22 provided in Chapter 5.
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Non-community
228
(62%)
Community
- 98
(27%)
Individual
x- 39
(11%)
2
3
4
5
6
1
8
9
10
FIGURE 3-15
Number of Outbreaks for AGI WBDOs by Water System Type
Non-community
164,000
(62%)
Community
82,000
(31%)
Individual
19,000
(7%)
FIGURE 3-16
Number of Person-Days III for AGI WBDOs by Water System Type
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1 4. ECONOMIC METHODS FOR ESTIMATING DISEASE BURDEN ASSOCIATED
2 WITH INFECTIOUS WATERBORNE OUTBREAKS
4 As stated in Chapter 1, disease burden can be estimated by epidemiologic
5 measures, summary population health measures (e.g., Disability Adjusted Life Years
6 [DALYs]), cost-of-illness (COI) and willingness-to-pay (WTP). Disease burden
7 measures can capture different dimensions of the impact of microbial illness, such as
8 premature mortality, pain and suffering, economic losses to society and individuals and
9 any other intangibles that society values. Some measures allow for comparisons of
10 outbreaks and illnesses that impact these dimensions in different ways. Corso et al.
11 (2003), for example, estimate the medical costs and lost productivity associated with an
12 outbreak of cryptosporidiosis using COI. Harrington et al. (1989) and Kocagil et al.
13 (1998) estimate lower-bound WTP1 because they include medical costs, lost
14 productivity, defensive or averting expenditures and, in the case of Kocagil et al.,
15 premature mortality.
16 In this chapter, we discuss the methods used in this report to estimate the
17 monetary burden associated with infectious WBDOs. The approach presented is
is applied only to the number of reported cases for each WBDO. In Section 4.1, we
19 describe the COI approach, including the basis of costs for self-medication, emergency
20 room visits, hospitalizations and lost productivity (i.e., morbidity costs). In Section 4.2,
21 we present the concept of the value of a statistical life (VSL) based on WTP values that
22 estimates individuals' collective preferences for trade-offs between avoiding premature
1 The results from Harrington et al. (1989) and Kocagil et al. (1998) are considered lower-bound
estimates of WTP because they do not capture dimensions such as pain and suffering.
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i mortality and wealth. Using the COI and VSL approaches is standard practice for
2 benefit-cost analyses in the U.S. EPA (U.S. EPA, 2000b, 2006a).
3 Figure 4-1 outlines the components we used to calculate the monetary burden; it
4 also illustrates the components that we did not quantify. Additional categories of burden
5 that are considered beyond the scope of this analysis include health effects to children
6 and chronic illness associated with both bacterial and viral illness. The results of the
7 COI and VSL analyses are combined to estimate the monetary burden (Chapter 5); we
8 note that, although both measures are expressed in monetary units, human capital
9 measures, such as COI measures, capture only a subset of the factors that WTP
10 measures capture. COI measures are limited because they do not capture all aspects
11 of disease burden such as pain and suffering, anxiety or lost leisure time. Expressing
12 the burden in terms of epidemiologic units (Chapters 2 and 3) and monetary units
13 through the COI and VSL approaches (Chapters 4 and 5) allows us to estimate the
14 enteric disease burden associated with reported WBDOs from two different
15 perspectives.2 This provides an opportunity to compare the burden over time and
16 among the various etiologic agents, water system types and system deficiencies.
17 4.1. ESTIMATING THE MONETARY BURDEN OF WBDO USING COST-OF-
18 ILLNESS APPROACH
19
20 An outbreak can have a substantial economic impact on a community. Using
21 cost estimates, such as those from Corso et al. (2003), we compare monetary burden
22 associated with WBDOs. We then compare the monetary burden associated with
2 Epidemiologic units are the basis of the COI estimates developed for each WBDO. Uncertainties in the
estimation of the aggregated epidemiologic units will be propagated through the subsequent analysis.
Draft: Do Not Cite or Quote 4-2 8/31/06
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Epidemiologic
Burden
Components
Effects
Considered
Lost Work
Time—
Person III
Lost Work
Time—
Caregiver
Medical Costs:
Medication
Physician Visit
ER Visit Hospital
Visit
Lost
Leisure
Time
Defensive
Expenditures
Investigation
or Litigation
Costs
Valuation
Approach
Chronic
Illness
Costs
Pain and
Suffering
FIGURE 4-1
3
4
Illustration of the Components for Monetary Burden Calculations
(Adapted from U.S. EPA, 2000c)
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8/31/06
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i different pathogens or different outbreak causes, such as treatment failure or
2 contaminated source water. Other applications using monetary measures, such as
3 examining the efficiency of regulations or management alternatives, typically require
4 additional information and assumptions; these are not evaluated in this report.
5 The COI approach measures direct medical costs and indirect costs such as
6 productivity losses due to temporary ailments (Rice et al., 1967). The direct medical
7 costs include medication (Section 4.1.2), physician visits (Section 4.1.3), emergency
8 room visits (Section 4.1.4) and hospital stays (Section 4.1.5). The loss of productivity of
9 the average person is assumed to be days lost based on a fraction of the duration of
10 illness (Section 4.1.6). Traditionally, in COI studies, the primary cost associated with
11 premature mortality is based on an individual's expected future earnings had they
12 remained alive until some average age of death. This estimate is consistent with other
13 components of the COI, in that it represents the monetary costs incurred by society;
14 however, it is not consistent with Agency protocol (Whitman, 2003). Therefore, the
15 value of a premature mortality is based on the VSL (see Section 4.2).
16 The COI of the jth outbreak could be calculated by summing the costs of each
17 case, dependent on cost related to self-medication (e.g., over-the-counter medications),
is physician visits, emergency room visits, hospitalizations and productivity losses of the ill
19 person and their caregiver(s) (e.g., family members). However, because this type of
20 data is not recorded in the database, calculating COI at the individual level is not
21 feasible. Alternatively, the COI of the jth outbreak can be estimated by using mean
22 values reported for other outbreaks (Equation 4-1).
Draft: Do Not Cite or Quote 4-4 8/31/06
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20
21
22
CO/j = (A/iNxCSM) + (A/pvxCPV) + (A/ERxCER) + (A/HxCHP) +
S [(^PIS^S^D) + (^PCGSXDS^D)] ,- 4_r
S=1 \ T" i
= SMj + PV.S + ER.S +H.S+ P/j +
2 where:
3 NHI = Number of ill persons
4 CSM = Mean cost of self medication (2000$)
5 Npy = Number of physician visits
6 CPV = Mean cost of physician visit (2000$)
7 NER = Number of emergency room visits
8 CER = Mean cost of emergency room visit (2000$)
9 NH = Number of hospitalizations
o CHP = Mean cost of hospitalizations for specific pathogens (2000$)
PPI = Percent days lost for each severity category (based on fraction of
duration) for ill persons multiplied by number of persons in each severity
cateqorv
11
12
13 category
*^ j
PPCG = Percent days lost for each severity category (based on fraction of
duration) for caregivers multiplied by number of persons in each severity
category
14
15
16 category
17 D = Duration (Days)
is LD = Value of a lost day (2000$)
19 s = Severity categories: mild, moderate and severe
SMj = Total cost of self medication purchased to treat illness associated with
the jth outbreak (2000$)
PVj = Total cost of physician visits associated with the jth outbreak (2000$)
23 ERj = Total cost of emergency room visits associated with the jth outbreak
24 (2000$)
25 HJ = Total cost of hospitalizations associated with the jth outbreak (2000$)
Draft: Do Not Cite or Quote 4-5 8/31/06
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i Plj = Productivity losses of ill persons associated with the jth outbreak (2000$)
2 PCGj = Productivity losses of caregivers associated with the jth outbreak (2000$)
3 By using estimated mean values for the morbidity costs,3 this equation does not capture
4 important sources of cost variability between cases and across different outbreaks (see
5 Table 4-1).
6 The definitions and calculations from Equation 4-1 are based largely on the
7 economic analysis of the 1993 Milwaukee Cryptosporidium outbreak (Mac Kenzie et al.,
8 1994; Corso et al., 2003). The majority of COI measures (SM, PV, ER, PI and PCG)
9 were estimated using the Corso et al. approach. Corso et al. (2003) based their
10 measures of COI on a telephone survey of Milwaukee residents by Mac Kenzie et al.
11 (1994), which allowed for the categorization of cases based on severity. Corso et al.
12 (2003) also collected primary data from the medical and financial records of 11 hospitals
13 in Milwaukee. They did not include averting behavior costs or defensive expenditures
14 (e.g., purchasing a water filter or bottled water), costs of epidemiologic investigation or
15 litigation nor did they consider pain and suffering. Therefore, the COI estimates for this
16 analysis do not either. Not including these costs or considerations is warranted because
17 • the Milwaukee outbreak represents almost 71 % of all cases of illness reported in
is WBDOs during 1971-2000
19 • the economic analysis is fairly recent
20 • the analysis is presented in sufficient detail for our use.4
21
3AII cost estimates are adjusted to 2000 U.S. dollars (2000$) using the consumer price index (CPI) for
medical services. The CPI is the average change in prices overtime for a market basket of goods and
services (in this case medical goods and services such as prescription drugs and medical supplies,
physicians' services, and hospital services). It is typically used to measure inflation, but can also be used
to develop comparisons using constant monetary units (U.S. Department of Labor, 2000).
4 For analyses of specific outbreaks, values which are specific to the area of the outbreak should be used
if available. Analyses do not exist for these WBDOs, so we note a potential bias in the burden estimate.
Draft: Do Not Cite or Quote 4-6 8/31/06
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TABLE 4-1
Parameter Estimates from Cost-of-lllness Studies (cost estimates adjusted to 2000$)
Components
Pathogen
Physician visits
Hospital visits
ER visits
Medication
Lost work time
Loss of work productivity
Length of illness (days)
Work loss days
Corso et al.
(2003)
Crypto-
sporidium
$58
$8,142
$289
$12, $91f
$206'
-
i
1.3,3.8, 13.5"
U.S. EPA's
LT2ESWTR
(2006a)
Crypto-
sporidium
$58
$7,937b
$289
$91
$88j
$27j
4.7, 9.4, 34m
1.3,3.8, 13.5"
Kocagil et al.
(1998)
Crypto-
sporidium
-
$12,419C
$197e
$2S
-
-
-
-
Harrington
etal. (1991)
Giardia
$88
$244
$66
$68h
$876k
$905k
42 (mean)
6.3, 12.7°
Zimmerman et
al. (2001)
Rotavirus
$62a
$2,487d
-
—
-
-
-
-
1 Median cost of rotavirus-associated outpatient visit
2 b Based on Corso et al. (2003), 71% of severe illness patients that visited the ER were hospitalized. U.S.
3 EPA (2006a) removed these ER costs from their hospitalization cost estimate.
4 c Medical expenditures for severe illness (i.e., hospitalization)
5 d Median cost of rotavirus-associated hospitalization
6 e Medical expenditures for physician visit or ER visit
7 f Cost of medication prescribed after seeking healthcare—moderate illness and severe illness,
8 respectively (Self-medication prior to seeking healthcare can be found in Table 4-4.)
9 9 Over-the-counter medications
10 h Medication costs associated with medical treatment
11 ' Average cost of productivity losses across illness severity (mild, moderate and severe) where average
12 productivity losses were $113, $413 and $1409 in 1993$, respectively This value also includes the value
13 of those who are not employed.
14 j Per day value includes both lost work time and lost unpaid work time and is calculated from U.S. EPA's
15 enhanced COI analysis. Loss of work productivity is calculated as a portion (30%) of lost work time.
16 k Average per confirmed case evaluated at the implicit after-tax wage rate of the unemployed,
17 homemakers and retirees equal to $6.39 per hour (average after-tax wage rate of employed) (Harrington
18 etal., 1989, 1991).
19 ' Corso et al. (2003) does not estimate a mean duration of illness for moderate or severe illness. The
20 duration of illness for mild cases was estimated as 4.7 days.
21 mThe U.S. EPA (2006a), using Monte Carlo analysis, calculated the mean duration of illness for
22 moderate and severe illness. Corso et al. (2003) only has an estimate for mild cases.
23 " Mild, moderate and severe illness, respectively
24 ° Employed and homemakers, respectively
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TABLE 4-1 cont.
Components
Pathogen
Physician visits
Hospital visits
ER visits
Medication
Lost work time
Loss of work productivity
Length of illness (days)
Work loss days
Cohen et al.
(1978)
Foodborne
Salmonella
$699P
$8,785q
-
-
$1,421*
-
-
12, 3V
ERS
Calculator
(2006)
Foodborne
Salmonella
$93
$11,966
$262
0
$191,$186,
$185U
-
-
4.5, 1.6, 0.5W
AGA (2001)
Foodborne
All
$114
$5,848r
$350
-
-
-
-
-
AGA (2001)
Chronic
diarrhea
All
$123
$2,453r
$255
-
-
-
-
-
Ezzati-Rice et
al. (2004)
All expenses
-
$5,195,
$10, 917s
$315, $594S
-
-
-
-
-
2 p Study states that approximately 68% of $222 for outpatient visits (ER or office) is for medical care and
3 the remainder is accounted for by estimates of lost productivity (based on assumption). Therefore,
4 medical portion is $151 in 1976$.
5 q Includes physician fees, operations and medication
6 r Comprised of two parts: (1) facility costs and (2) physician visits and procedures
7 s Median, mean, respectively, per person with expense
8 ' Study determined each worker's daily salary and multiplied it by days of work lost (average of both
9 employed and caregivers).
10 u Average daily wage rate depending on severity Severity categories, hospitalized, sought medical care,
11 and did not seek medical care, respectively, were assumed to have different age distributions leading to
12 different average daily wage rates.
13 v Average lost work days for employed patients (102 of 117 employed patients) and caregivers (39 of
14 102), respectively
15 w Hospitalized, sought medical care and did not seek medical care, respectively.
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i Specific assumptions are highlighted in each section where the Corso et al. analysis
2 was used. This COI analysis is limited because we estimate disease burden using the
3 same process regardless of year; we assume that medical treatment administered and
4 costs for gastrointestinal illnesses have remained constant across years.
5 For comparison purposes, general economic analyses are reported in Table 4-1.
6 Besides Corso et al. (2003), we present nine other COI studies. U.S. EPA (2006a),
7 expanding on Corso et al., analyzed the effects of the Long Term 2 Enhanced Surface
8 Water Rule. Kocagil et al. (1998) focused on Lancaster County, PA to estimate the
9 value of preventing a Cryptosporidium contamination event. Harrington et al. (1991)
10 examined the economic losses caused by waterborne giardiasis in Luzerne County, PA.
11 Zimmerman et al. (2001) calculated costs for rotavirus-associated hospitalizations and
12 outpatient visits for privately insured children during the period of 1993 to 1996. Cohen
13 et al. (1976) analyzed the economic costs of a foodborne outbreak of salmonellosis
14 (due to non-typhoid Salmonella spp.) in Colorado. The Economic Research Service
15 (ERS, 2006) of the U.S. Department of Agriculture calculated the costs of different
16 foodborne illnesses. We present their cost estimates for salmonellosis. The last three
17 studies are not specific to any particular pathogen. The American Gastrointestinal
is Association (AGA) calculated the economic costs for common disorders. We included
19 only two of the gastrointestinal disorders: foodborne and chronic diarrhea. Ezzati-Rice
20 et al. (2004) presented the costs of health care based on the Medical Expenditure Panel
21 Survey; we included their per person expenditures for hospital visits and ER visits. All
22 cost estimates are adjusted to 2000$ using the consumer price index (CPI) for medical
23 services. Our analysis could have utilized U.S. EPA's expanded analysis of Corso et al.
Draft: Do Not Cite or Quote 4-9 8/31/06
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i (2003); however, for simplification purposes and to utilize the duration-of-illness
2 estimates from the WBDOSS, we decided to proceed with the approach in Corso et al.
3 4.1.1. Severity Classification. In this analysis, physician visits, emergency room
4 visits, hospitalizations and deaths are surrogate measures for the severity of illness in
5 reported WBDOs (Table 4-2). We use the same measures of severity that Corso et al.
6 (2003) used in their Milwaukee WBDO analysis. Because the WBDOs reported in the
7 surveillance system do not identify cases of illness by severity categories of mild,
8 moderate and severe, this introduces additional uncertainty into the COI estimates.
TABLE 4-2
Illness Severity Definitions
Category
Severe Illness
Moderate Illness
Mild Illness
Definition
Hospitalizations + Deaths
Physician Visits + ER Visits
All reported cases that are not moderate or severe
10 The unit of reporting in the WBDOSS is an outbreak; therefore, it is not possible
11 to match severity measures at the individual case level or distinguish whether there is
12 an overlap in reported physician visits, emergency room visits, hospitalizations and
13 deaths. For example, some individuals who visit a physician or emergency room may
14 also require hospitalization. Thus, in some outbreaks, using the severity definitions in
15 Table 4-2, there is a slight overestimation of severe illnesses. Since the numbers of
16 physician visits, emergency room visits, hospitalizations and deaths are relatively small
17 compared to the total number of cases, this slight overestimation likely has minimal
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i impact on the COI analysis (see Chapter 6). In addition, the number of mild, moderate
2 or severe cases does not exceed the total number of cases reported for any outbreak.
3 Table 4-3 shows the distribution of reported cases in reported WBDOs by the
4 three severity categories. The distribution of protozoan illnesses in WBDOs by severity
5 categories was similar to the distribution reported by Corso et al. in the Milwaukee
6 Cryptosporidium outbreak. The distribution of mild, moderate and severe cases of viral
7 WBDOs and all WBDOs in reported outbreaks was fairly similar to the cases of
8 protozoan WBDOs. This provides some support to using the Milwaukee data for the
9 COI analysis. The distribution of AGI shows a greater percentage of moderate cases
10 than the other groups. The reported bacterial WBDOs have a greater percentage of
11 severe cases than the other etiologic groups (Table 4-3). Thus, we probably
12 underestimated the burden for bacterial and AGI WBDOs based on this COI approach.
13 4.1.2. Costs of Self Medication (SM). For an outbreak, the cost of SM is the total cost
14 of over-the-counter medications for mild, moderate and severe illness (e.g., anti-
15 nausea, anti-diarrheal medications and electrolyte replacement therapy). Corso et al.
16 (2003) obtained information from medical charts about the percentage of moderately
17 and severely ill individuals who self medicated prior to seeking healthcare during the
is Milwaukee outbreak. Corso et al. assumed that the percentage of mild cases (30%)
19 that self medicated was similar to that for moderate cases of illness. The SM cost for
20 mild illness prior to seeking healthcare was an assumption made by Corso et al.
21 In the COI analysis, we use the percentage of cases that self medicate and the
22 estimated SM costs reported in Corso et al. (Table 4-4). We calculate the SM cost by
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TABLE 4-3
Distribution of Cases Using Estimated Severity Measures for Monetary Burden
Severity
Classification
Mild
Moderate
Severe
Total
AGI
Cases
65,048
18,066
379
83,493
Percent
78
22
0
100
Viruses
Cases
13,634
2,032
92
15,758
Percent
87
13
1
100*
Bacteria
Cases
17,718
2,125
943
20,786
Percent
85
10
5
100
Protozoa
Cases
402,318
43,040
4,567
449,925
Percent
89
10
1
100
All WBDOs
Cases
498,718
65,263
5,981
569,962
Percent
88
11
1
100
2 * Rounding error, column does not total to 100
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TABLE 4-4
Estimated Cost of Self Medication*
Item
% Self Medication
Cost of Self Medication
(1993$)
Cost of Self Medication
(2000$)
Mild
30%
$5.73
$7.40
Moderate
30%
$5.92
$7.65
Severe
29%
$6.74
$8.79
Notes
Corso et al. (2003)
Corso et al. (2003)
1 * SM = NMiid x $7.40 x 0.3 + NMod x $7.65 x 0.3 + NSev x
2 where:
3 NMiid = Number of mild cases
4 NMod = Number of moderate cases
5 Nsev = Number of severe cases
5.79x0.29
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i multiplying the number of illnesses in each severity category by the corresponding SM
2 cost and the percent that self medicated. The total SM cost for a WBDO is the sum of
3 self medication costs for mild, moderate and severe cases. These calculations are
4 based on an assumption that the distribution of persons who self medicate and the SM
5 costs incurred during the Milwaukee Cryptosporidium outbreak are similar to the
6 distribution of persons who self medicate and the SM costs incurred during WBDOs
7 caused by other etiologies.
8 4.1.3. Cost Associated with Physician Visit (PV). The costs associated with a
9 physician visit include the professional fee and any prescribed medication (not SM
10 cost). Our PV analysis is based on the Corso et al. (2003) economic analysis of the
11 1993 Milwaukee Cryptosporidium WBDO. We assumed that the cost of a PV is similar
12 for cases in WBDOs of Cryptosporidium and other etiologies. Cost estimates of PV are
13 updated to 2000 dollars using the CPI for medical care (Table 4-5). Information about
14 physician visits is not requested on the WBDO report form (CDC 52.12) but is reported
15 for 4% of the reported WBDOs.
16 4.1.4. Cost Associated with Visiting an Emergency Room (ER). The cost of an ER
17 visit includes the costs of the ER, attending physician, ambulance and prescribed
is medication. An ER visit is not considered a hospitalization. If an ER visit results in a
19 hospital admission, then the visit is also counted as a hospitalization. Information on
20 ER visits is not requested on the WBDO report form (CDC 52.12) and is only reported in
21 2% of the outbreaks. Thus, the number of ER visits is likely under reported in the
22 WBDOSS, and the corresponding costs associated with these cases as reported would
23 also be underestimated. ER visit costs are based on Corso et al. (2003). We assumed
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TABLE 4-5
Estimated Cost of Physician Visits*
Item
Cost of Physician Visit (1993$)
% Prescribed Medication
Cost of Prescribed Medication
Estimated Cost of Prescribed
Medication per Physician Visit
Estimated Cost of Physician Visit
(1993$)
Cost of Physician Visit (2000$)
Cost
$45.00
54%
$8.91
$4.81
$49.81
$64.50
Notes
Corso et al. (2003)
Corso et al. (2003)
Moderate Illness
Corso et al. (2003)
Moderate Illness
(0.54 x$ 8.91)
$45.00 + $4.81
PV = Number of Physician Visits x $64.50
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i that the costs of a visit, ambulance and prescribed medicine and the percentage of
2 cases requiring an ambulance (16%) and medication (48%) are similar for WBDOs of
3 Cryptosporidium and other etiologies. The ER cost estimate is updated to 2000 dollars
4 using the CPI for medical care (Table 4-6).
5 4.1.5. Cost Associated with Hospital Stay (H). Hospitalization costs are based on
6 the 1997 Nationwide Inpatient Sample data by Health Care Utilization Project (HCUP,
7 1997). The Nationwide Inpatient Sample is a statistically valid sample of hospital
8 discharges, diagnoses and charges for over 7 million hospital stays in the United States
9 in 1997. Individual discharges were selected based on the occurrence of specific ICD-9
10 codes among the first three diagnoses listed on the hospital discharge report.
11 Observations were analyzed for specific pathogens and groups of pathogens, and the
12 HCUP reported the total hospitalization charges for selected pathogens or categories.
13 Since total hospital charges were developed for specific etiologies and included the
14 natural range of symptom severities for selected pathogens, all stages of disease
15 severity should be captured.
16 For the COI analysis, we considered the number of reported and estimated
17 hospitalizations for each WBDO and the average charge per hospitalization (Table 4-7).
is When estimates were not available or not reported for a specific pathogen, appropriate
19 pathogens were grouped. For AGI outbreaks, we used hospitalization charges from
20 "Diarrhea and Gastroenteritis, Undetermined Agent," ICD codes 001-009 (excluding 3.2
21 and 6.2), 558.9, 787.91.
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TABLE 4-6
Estimated Cost of Emergency Room Visits*
Item
Cost of Emergency Room Visit (1993$)
Percent Requiring Ambulance
Cost of Ambulance (1993$)
Estimated Cost of Ambulance per
Emergency Room Visit (1993$)
Percent Requiring Prescription
Medication
Cost of Prescription Medication (1993$)
Estimated Cost of Prescription
Medication per Emergency Room Visit
(1993$)
Total Estimated Emergency Room Visit
Cost per Emergency Room Visit
(1993$)
Total Estimated Emergency Room Visit
Cost per Emergency Room Visit
(2000$)
Cost
$224.00
16%
$228.00
$37.16
48%
$70.52
$33.85
$295.01
$382.02
Notes
Corso et al. (2003)
Corso et al. (2003)
Severe Illness
Corso et al. (2003)
Severe Illness
(0.16 x $228.00)
Corso et al. (2003)
Severe Illness
Corso et al. (2003)
Severe Illness
(0.48 x $ 70.52)
$224.00 + $37.16 + $33.85
2 *ER = Number of ER Visits x $382.02
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TABLE 4-7
Estimated Charges per Hospitalized Case*
Disease or Etiologic Agent
Bacterial infections
Yersinia
Typhoid
Shigellosis
Other Salmonella infections
E. coll
Cholera
Campylobacter
Other virus unspecified
Norovirus
Rotavirus
Calicivirus
Adenovirus
Protozoan infections
Cryptosporldlum
Glardla
Diarrhea and Gastroenteritis,
undetermined agent
ICD Codes
Calculated
8.44
002
004
003 (excluding 3.2)
8.0
001
8.43
088
8.63
8.61
8.65
8.62
Calculated
7.4
7.1
001-009
(excluding 3.2 and
6.2), 558.9, 787.91
Mean Charge (2000$)
$7,836.34
$9,677.97
$16,172.96
$6,781.94
$9,825.80
$8,605.38
$5,752.38
$8,027.91
$4,351.20
$4,518.06
$3,919.09
$1,885.95
$11,538.71
$9,093.80
$13,886.10
$7,257.03
$7,603.87
* H = Number of Hospitalizations x Hospitalization Charge for Specific Pathogen or
Pathogen Group x 0.4
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i Using the CPI for medical care, we updated HCUP information for hospitalization
2 charges in 1997 dollars to 2000 dollars. Next, we multiplied the hospital charges by the
3 national case-weighted cost-to-charge ratio of 0.4 (CMS, 2004).
4 4.1.6. Cost Due to Loss in Productivity. Productivity losses can arise from
5 decreased production at work and decreased household production due to illness, and
6 we considered productivity losses for two groups:
7 • III person who recovers (PI)
8 • Caregiver(s) for ill person (PCG).
9 Productivity losses can potentially have two components: complete days lost and lost
10 productivity while working (i.e., reduced hours or working at less than full capacity). We
11 only calculate the value of a complete day lost (see Figure 4-1). Therefore, we assume
12 that individuals, once they return to work, do not have reduced hours and are working at
13 full capacity even though the illness is still occurring (i.e., Table 4-8 shows the
14 difference between days lost from work by severity). This differs from U.S. EPA
15 (2006a), which based results on Harrington et al. (1991), who found that employees
16 worked at approximately a 30% capacity once they returned to work. We decided not to
17 estimate the lost productivity while working because our calculation for complete days
is lost does not easily provide an estimate of lost productivity days by severity
19 classification. This suggests that we are underestimating productivity losses.
20 Grosse (2003) estimated average earnings for each age and gender group in
21 which earnings were comprised of two broad components: wages/fringe benefits and
22 household production. The wage components included salary income, overtime pay,
23 bonus pay and self-employment earnings based on the Current Population Survey
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TABLE 4-8
Productivity Losses by Severity for III Persons and Caregivers for
Waterborne Outbreaks
Category
Mean Days Lost for Work, III Persons
(Corsoetal., 2003)
Mean Days Lost for Work, Caregivers
(Corsoetal., 2003)
Mean Days Lost for Work, III Persons /
Median Duration of Outbreak*
Mean Days Lost for Works, Caregivers /
Median Duration of Outbreak*
Mild
1.3
0.1
14.4%
1.1%
Moderate
3.8
1.3
42.2%
14.4%
Severe
13.5
3.9
150.0%
43.3%
2 * The rates of productivity loss shown are for a WBDO with a median duration of 9 days.
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i (CPS, 2001). Fringe benefits included health insurance and retirement pay. Household
2 production included a number of valued activities, such as cleaning, cooking, home and
3 auto maintenance, child care and child guidance, for which individuals are typically not
4 compensated. Grosse assumed that the average person works 250 days per year and
5 that household services need to be performed every day. Combining the data for men
6 and women, Grosse (2003) estimated the value of a lost day of primary activity to be
7 $144/day5 (2000$) using the following formula:
8 Value of a lost day = (Annual Earnings/250) + (Annual Household Services/365) (Eq. 4-2)
9 We used this estimate in all calculations of PI and PCG.6
10 4.1.6.1. Productivity Losses for III and Caregiver (PI, PCG) — For persons
11 who are ill and recover, we estimated time lost from work for both ill persons and their
12 caregivers (Table 4-8). We based the distribution of productivity losses on the analyses
13 by Corso et al. (2003). Corso et al. categorized cryptosporidiosis cases into three
14 groups based on information gathered during a random phone survey done by the City
15 of Milwaukee Health Department. Categorization into mild, moderate or severe
16 depended on the type of medical care received and days of productivity lost for the ill
17 and their caregivers. Due to limited reported data, Corso et al. estimated the days of
is productivity lost for caregivers with severe illness cases assuming that caregivers were
5Harrington et al. (1991) estimate productivity losses at $42.82/day (2000$), which is more than $100
lower than our estimate. We attribute this partially to their average duration. They estimated a mean
productivity loss of $730 (1984$), with an average duration of 41.6 days. They suggest that their duration
appears extraordinarily long compared to other Giardia outbreaks. Mean productivity loss was calculated
by adding value of workdays lost and loss of productivity. This mean loss is $17.55/day (1984$) of
illness.
6The difference between U.S. EPA's traditional and enhanced COI for this particular calculation is the
value of lost unpaid work time for the traditional COI, which is half the value of the enhanced COI. Other
approaches to estimate the value of a day lost are available (e.g., see U.S. EPA, 2006a), which
calculates the value of a lost work day as a fraction of a full day, 3.5 hours). When combining both lost
work time and lost unpaid work time, the estimate of $144 is still $67 and $55 higher than U.S. EPA
(2006a) traditional and enhanced COI, respectively.
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i needed for 50% of the duration of hospitalization for the ill person. Productivity losses
2 for the ill and their caregivers were determined for the other WBDOs by multiplying the
3 rates for each illness severity by the reported or estimated median duration for each
4 WBDO (Table 4-8). For these other non-Milwaukee WBDOs, we used information from
5 the WBDOSS to obtain actual or estimated values for the median duration for the
6 various etiologic agents.
7 For each outbreak, we calculated cost due to complete days lost of productivity
8 for both the ill person and caregiver by the following equations:
9 PI = [(Nmiid x RmHd) + (Nmod x Rmod) + (Nsev x Rsev)] x D x LD (Eq. 4-3)
10 PCG = [(Nmi,d x RmNd) + (Nmod x Rmod) + (Nsev x Rsev)] x D x LD (Eq. 4-4)
11 where:
12 N = Number of cases
13 D = Median duration of illness
14 R = Rate of days lost for work based on illness duration (Table 4-8)
15
16 LD = Value of a lost day = $144/day (2000$).
17 To compute the lost productivity costs from Table 4-8, we assume
is • productivity losses are always some constant fraction of the duration of
19 illness based upon severity grouping
20 • other waterborne pathogens have a similar rate of productivity loss to
21 median duration of illness as Cryptosporidium.
22 We are uncertain how representative these ratios are for assessing the severity of other
23 pathogens. Additional studies are needed to test the validity of these assumptions.
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1 4.2. USING WILLINGNESS-TO-PAY MEASURE TO VALUE PREMATURE
2 MORTALITY: VALUE OF STATISTICAL LIFE
o
J
4 The VSL approach is used to estimate the value of a WBDO fatality in a separate
5 calculation (although we combine the COI estimates with the value of a WBDO fatality
6 for the total monetary burden estimate). VSL measures an individual's WTP for a
7 change in the risk of dying (Freeman, 1993). For example, suppose 10,000 individuals
8 are willing to pay $5 each for an intervention that would reduce the risk of dying by one
9 in 1,000,000. The VSL for this group would equal $5,000,000 for one less death per
10 year. If 1,000,000 individuals were willing to pay $5 for an intervention that would
11 reduce the risk of dying by two in 1,000,000, then the VSL would be $2.5 million (i.e., $5
12 million divided by two). VSL is not a component of the traditional COI approaches,
13 which are usually limited to the costs incurred in caring for the ill and production lost to
14 morbidity and premature mortality. Due to a paucity of data and empirical studies, the
15 VSL is assumed to be independent of age and weights all deaths the same. Thus, the
16 VSL is rooted in the economic tradition of "consumer sovereignty" (i.e., individuals are
17 the best judges for their own well-being) representing the trade-off between changes in
is wealth and the probability of survival in a period of time (Hammitt, 2000). In the U.S.
19 EPA, societal WTP is the standard approach to estimating a dollar value on mortality
20 benefits of environmental regulations (U.S. EPA, 2000a).
21 The U.S. EPA (2000a) recommends a mean VSL estimate of $4.8 million (1990
22 dollars), and the Office of Water used this value after an adjustment for real income
23 growth and inflation for the disinfectants and disinfection by-products rule, the proposed
24 groundwater rule and the interim enhanced surface water rule. The benefit transfer of
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i VSL studies, updated to 2000 dollars, results in an estimate of $6.43 million (see
2 Chapter 1).
3 VSL = Number of Deaths x $6.43 Million (Eq. 4-5)
4 4.3. ESTIMATING THE MONETARY BURDEN OF THE WATERBORNE
5 OUTBREAKS
6
7 The monetary burden (2000$) presented in Table 4-9 is based on the
8 methodology described in Sections 4.1 and 4.2 and the epidemiologic burden measures
9 developed in Chapters 2 and 3 for the WBDOs that occurred from 1971 to 2000. Using
10 a COI approach, we calculate the burden of the morbidities associated with the WBDOs
11 to be approximately $186 million. Based on the VSL approach, we estimate the burden
12 of the premature mortalities associated with the WBDOs to be valued at approximately
13 $424 million (70% of the total burden). The largest cost of morbidity is lost productivity
14 of the ill person (66% of total COI) while hospitalization costs and lost productivity of the
15 caregiver follow in impact (16% and 11 % of total COI, respectively). Following the
16 approach described in this chapter, Chapter 5 presents comparisons of the monetary
17 burden by different summary categories.
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TABLE 4-9
Projected Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water,
1971 to 2000
Burden Measure
Self Medication
Physician Visits
Emergency Room Visits
Hospitalizations
III Productivity Losses
Caregiver Productivity Losses
Total CO I
Value of Statistical Life
Total
Monetary Burden*
(2000$)
$1,272,000
$2,708,000
$9,006,000
$29,936,000
$123,357,000
$19,721,000
$186,000,000
$424,380,000
$610,380,000
Percent of Total Monetary
Burden
<1
<1
2
5
20
3
30
70
100
2 * The estimate of monetary burden does not include loss of work productivity, lost
3 leisure time, pain and suffering, defensive expenditures, investigation or litigation costs,
4 or chronic illness costs (see Figure 4-1). In addition, the burden estimate does not
5 include the specific health effects to children.
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1 5. RESULTS: MONETARY BURDEN ESTIMATE OF REPORTED INFECTIOUS
2 WATERBORNE OUTBREAKS BY SUMMARY CATEGORIES AND
3 IMPACT OF THE MILWAUKEE OUTBREAK
4
5 In this chapter, we evaluate differences in monetary burden by etiology, water
6 system type, water system deficiency, and water source type. We identify the specific
7 categories that have been associated with the greatest burden. Stratifying by water
8 source type and treatment deficiency, we compare the monetary burden among
9 different pathogens. Because of the effect of the Milwaukee WBDO on the
10 epidemiologic burden measures, the overall summary and category specific monetary
11 burden associated with Milwaukee will always be the most dominant in the following
12 comparisons. We also consider how the Milwaukee WBDO affects the overall monetary
13 burden by comparing the results with and without it. Our analyses demonstrate how
14 this large outbreak of waterborne cryptosporidiosis can affect the overall and category-
15 specific monetary burden (Section 5.6). All monetary values are adjusted to 2000$.
16 As noted in previous chapters, WBDO reporting is voluntary and the surveillance
17 data may reflect the available resources for the detection and investigation of outbreaks
18 and laboratory capabilities for identifying the etiologies. Readers should consider that
19 mortality is more heavily weighted than morbidity measures in our monetary burden
20 estimates and that burden differences for a specific etiology or water system type may
21 reflect reporting differences (see section on WBDO surveillance system limitations in
22 Appendix A).
23 5.1. MONETARY BURDEN BY ETIOLOGY
24 Protozoan agents account for most of the monetary burden (Table 5-1) and the
25 most cases, person-days ill, physician visits, emergency room visits, hospitalizations,
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TABLE 5-1
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water, 1971 to
2000, by Etiology (Pathogen Group)
Etiologic Agent Type
AGI
Viruses
Bacteria
Protozoa
Total
Monetary Burden3
$21,537,000
$3,252,000
$105,225,000
$480,366,000b
$610,380,000
2 a All estimates in 2000$.
3 b Monetary Burden of Milwaukee WBDO - $461,148,000 or 96% of total monetary
burden for Protozoa.
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1 and deaths (Table 3-2). Cryptosporidium is the major contributor to the monetary
2 burden of protozoan WBDOs (Table 5-2). Although other protozoan agents (i.e.,
3 Cyclospora and En. histolytica) contribute relatively little to the monetary burden
4 estimate, Giardia contributes 29% of the monetary burden for protozoan WBDOs;
5 however, if the Milwaukee WBDO is excluded, Giardia contributes 71 %.
6 The monetary burden associated with WBDOs attributed to bacterial agents is
7 approximately 80% smaller than the WBDOs attributed to protozoan agents (Table 5-1).
8 Non-typhoid Salmonella spp. account for approximately 44% of the monetary burden
9 attributed to bacterial pathogens (Table 5-2). AGI WBDOs were generally associated
10 with the second highest epidemiologic burden for several measures including person-
11 days ill, physician visits, and emergency room visits, but bacterial WBDOs were
12 associated with more hospitalizations and, more importantly from the monetary burden
13 perspective, 14 more deaths than AGI WBDOs (Table 3-2). This large number of
14 deaths associated with bacterial pathogens explains the change in ranking between the
15 monetary and epidemiologic burden estimates for AGI and bacterial WBDOs. If the
16 Milwaukee WBDO is excluded from the analysis, then the monetary burden associated
17 with the bacterial WBDOs ($105 million) and AGI WBDOs ($22 million) would rank
18 higher than the protozoan WBDOs ($19 million).
19 5.2. MONETARY BURDEN BY WATER SYSTEM TYPE
20 Water systems are classified as community, non-community, or individual as
21 defined in Appendix A. Community systems had the largest monetary disease burden
22 between 1971 and 2000 (Table 5-3), 13 times larger than the burden associated with
23 non-community systems and nearly 300 times larger than the burden associated with
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TABLE 5-2
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water, 1971 to 2000,
by Etiology (Specific Pathogens)
Etiologic Agent
Monetary Burden
AGI
AGI
$21,537,000
Viruses
Hepatitis A
Norovirus
Rotavirus
SRSV (assumed to be norovirus)
$2,137,000
$830,000
$282,000
$3,000
Bacteria
Salmonella non-typhoid spp.
E. coll
Shigella
E. coll & Campylobacter
S. enterica serovar Typhi
C. jejuni
Yersinia
P. shigelloides
V. cholerae
$45,931,000
$26,591,000
$15,254,000
$13,298,000
$2,866,000
$1,098,000
$150,000
$19,000
$18,000
Protozoa
Cryptosporidium
Giardia
En. histolytica
Cyclospora
Total
$466,659,000*
$13,692,000
$9,000
$6,000
$610,380,000
2 * Monetary Burden of Milwaukee WBDO - $461,148,000 or 99% of total monetary
3 burden for Cryptosporidium.
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TABLE 5-3
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water, 1971 to 2000,
by Water System Classification Type
Water System Classification
Community
Non-Community
Individual
Total
Monetary Burden
$565,047,000a
$43,422,000
$1,910,000
$610,380,000b
2
3
4
a Monetary Burden of Milwaukee WBDO - $461,148,000 or 82% of total monetary
burden for community systems.
b Burden estimates do not sum to total due to rounding.
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1 individual systems. The monetary burden for the Milwaukee WBDO, which is a
2 community system, is estimated at $461 million. Figure 5-1 shows that, for WBDOs
3 occurring in community water systems, the monetary burden is largest for those
4 systems using surface water sources. If the Milwaukee outbreak is excluded from the
5 analysis, community system WBDOs still have the highest monetary burden estimate,
6 but the contribution of non-community systems to the total remaining monetary burden
7 increases dramatically. Excluding the Milwaukee WBDO, non-community system
8 WBDOs resulted in more emergency room visits and more hospitalizations than
9 community systems. Differences in premature mortality (12 deaths in community
10 systems versus four deaths in non-community systems), explain why the monetary
11 burden for the community systems without the Milwaukee WBDO is still significantly
12 larger than the estimate for the non-community systems. If the Milwaukee WBDO is
13 excluded, the monetary burden in WBDOs occurring in community water systems using
14 groundwater ($84 million; see Figure 5-1) is greater than the burden in community water
15 systems using surface water sources ($18 million).
16 5.3. MONETARY BURDEN BY WATER SYSTEM DEFICIENCY
17 From the perspective of water system deficiency, the most important contributor
18 to the monetary burden was one or more water treatment deficiencies (Table 5-4). The
19 Milwaukee WBDO was attributed to a water treatment deficiency. The next two most
20 important contributors were distribution system deficiencies and the use of untreated,
21 contaminated groundwater. If the Milwaukee WBDO is excluded from the analysis, then
22 distribution system deficiencies become the most important contributor to the monetary
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1
2
3
4
5
6
Mixed
$691,000
Unknown
$956,000
Surface Water
$479,064,000
(85%)
Ground Water
- $84,337,000
(15%)
FIGURE 5-1
Monetary Burden for WBDOs in Community Water Systems by Type of Source Water
TABLE 5-4
Monetary Burden by Water System Deficiency, 1971 to 2000
Deficiency
Deficiency in Water Treatment
Distribution System Deficiency
Untreated Groundwater
Miscellaneous
Unknown Deficiency
Untreated Surface Water
Total
Monetary Burden
$505,341 ,000a
$82,595,000
$19,991,000
$764,000
$1,220,000
$468,000
$610,380,000b
7
8
9
a Monetary Burden of Milwaukee WBDO - $461,148,000 or 91 % of total monetary
burden for water treatment deficiencies.
b Burden estimates do not sum to total due to rounding.
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1 burden. The smallest burden was associated with WBDOs caused by miscellaneous,
2 unknown deficiencies, and untreated surface water.
3 Figures 5-2 through 5-7 show the monetary burden associated with each
4 etiologic agent for each type of deficiency. In Chapter 3, we developed similar
5 comparisons for person-days ill and deaths. Figure 5-2 shows that Cryptosporidium
6 accounts for most (92%) of the monetary burden associated with water treatment
7 deficiencies; 99% of this burden is associated with Milwaukee Cryptosporidium WBDO,
8 in which 50 deaths occurred. Water treatment deficiencies that resulted in WBDOs
9 caused by Shigella (3%), Giardia (2%), and AGI (2%) account for 7% of the remaining
10 monetary burden. If the Milwaukee WBDO is excluded, then water treatment
11 deficiencies that resulted in WBDOs caused by Shigella, Giardia, and AGI account for
12 most of this monetary burden (Figure 5-3). Figure 5-4 shows that non-typhoid
13 Salmonella (55%) and E. coli (31 %) account for 86% of the monetary disease burden
14 attributed to distribution system deficiencies. Although Giardia accounted for most of
15 the person-days ill associated with WBDOs caused by distribution system deficiencies,
16 non-typhoid Salmonella (55%) and E. coli outbreaks were associated with 7 and 4
17 deaths, respectively. The outbreak associated with both E Coli and Campylobacter
18 accounted for 67% of the monetary disease burden when the cause of the outbreak was
19 attributed to untreated groundwater (Figure 5-5). AGI outbreaks are associated with
20 only 16% of this monetary burden. Recall that AGI and Hepatitis A outbreaks were
21 associated with the most person-days ill associated with WBDOs occurring in untreated
22 groundwater, but that two deaths were associated with the E coli and Campylobacter
23 outbreak.
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2
3
4
5
6
1
8
9
10
P. shigelloides
$19,000
Hepatitis A
$421,000—__
(<1%)
Rotavirus
$282,000-
Giardia £. co//
$10,814,000 $288,000
(2%) (
-------
1
2
3
4
5
6
7
8
9
10
V. cholerae
$2,000
Shigella
$72,000
AGI C. ye/'u
$7,639,000 $117,000
(9%) (<1%)
S. enterica serovar
Typhi
$502,000
(1%)
Salmonella, non-
typhoid spp.
$45,507,000
(55%)
Cryptospohdium
$1,000
Cyclospora
__ $6,000
£. co//
r- $25,961,000
/ (31%)
Giardia
$2,535,000
(3%) Norovirus
J ^~ $153,000
Hepatitis A
_— $102,000
FIGURE 5-4
Monetary Burden for WBDO Caused by Deficiency Distribution System by Etiologic
Agent
S. enterica serovar
Salmonella, non-
typhoid spp.
$27,000
Typhi
$95 goo
(<1'o/0)
Shigella
$1,029,000
(5%)
Yersinia
$150,000
(1%)
Norovirus
$62,000-
Giardia
$91,000-
Hepatitis A
$1,583,000—
(8%) ^
£. histolytica
$9,000
£. co// &
Campylobacter
$13,298,000
(67%)
AGI
$3,266,000
(16%)
Cryptosporidium
$59,000
C. j'e/un/
— $18,000
£. co//
$302,000
(2%)
FIGURE 5-5
Monetary Burden for WBDO Caused by Untreated Groundwater by Etiologic Agent
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Shigella
Salmonella, non- $45,000
typhoid spp. (10%
<$1,000
(<
Hepatitis A
$31,000 -
(7%)
Giardia
$218,000
(46%)
AGI
-$171,000
(36%)
C. jejuni
^ $3,000
(1%)
2
3
4
5
FIGURE 5-6
Monetary Burden for WBDO Caused by Untreated Surface Water by Etiologic Agent
6
1
Salmonella, non
typhoid spp.
$118,000
Norovirus (go/0)
$50,000-\^
(3%) ^-
Giardia
$33,000
(2%)
E. coli
$41,000
(2%)
r
Cryptospohdium
$707,000
(36%)
Shigella
$39,000
(2%)
V. cholerae
$16,000
(1%)
AGI
$589,000
(30%)
C. jejuni
^— $393,000
(20%)
FIGURE 5-7
9
10
Monetary Burden for WBDO with Unidentified or Miscellaneous Causes by
Etiologic Agent
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1 The monetary burden associated with the remaining outbreak causes reported in
2 the WBDOSS is substantially smaller than the burden associated with treatment
3 deficiencies, distribution system deficiencies and untreated groundwater. Figure 5-6
4 reveals that, when the cause of the outbreak was attributed to untreated surface water,
5 Giardia (47%) and AGI (36%) accounted for 83% of the monetary burden; the same
6 etiologic agents also accounted for most of the person-days ill associated with untreated
7 surface waters. Figure 5-7 suggests that, if the deficiency was not identified or
8 categorized as miscellaneous, then Cryptosporidium (36%), AGI (30%) and
9 Campylobacter (20%) account for 85% of this monetary burden.
10 5.4. MONETARY BURDEN BY TIME PERIOD
11 Differences in the detection and reporting of WBDOs during the 30-year period
12 are not considered in the analysis. The WBDO surveillance system is voluntary and
13 any trends may reflect differences in reporting and investigation of WBDO.
14 Consequently, the following data should be interpreted cautiously.
15 Although the fewest number of outbreaks occurred during the 1990's, that
16 decade dominates the monetary burden (Table 5-5) because the Milwaukee WBDO
17 occurred in 1993. The monetary burden associated with WBDOs in the 1990's is more
18 than ten times the monetary burden estimate of either the 1970's or the 1980's. If the
19 Milwaukee WBDO is excluded, the monetary burden in the 1990's is comparable to the
20 estimates from the 1970's and 1980's.
21 5.5. MONETARY BURDEN BY WATER SOURCE TYPE
22 Although there were fewer WBDOs in surface water systems than in groundwater
23 systems, the surface water system-based Milwaukee WBDO accounted for 79% of the
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TABLE 5-5
Monetary Burden by Time Period, 1971 to 2000
Decade
1971 to 1980
1981 to 1990
1991 to 2000
Total
Monetary Burden
$41,644,000
$41,824,000
$526,912,000*
$610,380,000
2
3
4
* Monetary Burden of Milwaukee WBDO - $461,148,000 or 87% of total monetary
burden for 1991 to 2000.
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1 total monetary burden (Table 5-6). If the Milwaukee outbreak is excluded, monetary
2 burden attributed to groundwater systems is nearly seven times greater than the burden
3 associated with surface water systems. Unknown and mixed water sources were
4 negligible contributors ($45 million) to the overall burden.
5 Figures 5-8 and 5-9 show that the monetary burden in surface water systems is
6 primarily associated with protozoan WBDOs. Cryptosporidium WBDOs dominate
7 monetary burden associated with the surface water outbreaks (Figure 5-8). If the
8 Milwaukee WBDO is excluded, Giardia outbreaks comprise 56% of the monetary
9 burden associated with surface water systems (Figure 5-9). WBDOs attributed to
10 bacterial agents dominate the monetary burden associated with groundwater outbreaks
11 (Figure 5-10).
12 5.6. THE OVERALL MONETARY IMPACT OF THE MILWAUKEE
13 CRYPTOSPORIDIOSIS OUTBREAK
14 The Milwaukee outbreak accounted for 76% of the overall monetary burden
15 (Figure 5-11). Most of the deaths and person-days ill occurred during this WBDO as
16 previously noted; therefore, we conducted additional analyses to explore the influence
17 of the Milwaukee WBDO on specific aspects of the monetary disease burden estimate.
18 We computed and compared the monetary burden with and without the Milwaukee
19 outbreak statistics. The total burden from the Milwaukee outbreak is approximately
20 $461 million; total burden excluding the Milwaukee outbreak is $149 million. However,
21 the relative importance of morbidity measured by COI and mortality measured by VSL is
22 similar whether Milwaukee is included or excluded from the analysis (Figures 5-12 and
23 5-13). We also examined the morbidity components of the monetary burden estimate
24 and their effect (Figures 5-14 and 5-15). Table 5-7 summarizes the relative importance
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TABLE 5-6
Monetary Burden by Water Source Type, 1971 to 2000
Etiologic Agent
Groundwater
Surface Water
Unknown
Mixed
Total
Monetary Burden
128,093,000
480,225,000*
1,253,000
809,000
610,380,000
2 * Monetary Burden of Milwaukee WBDO - $461,148,000 or 87% of total monetary
3 burden for surface water.
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1
2
3
4
5
6
Cryptosporidium
97%
I
Giardia
r 2%
Other Agents
1%
FIGURE 5-8
Distribution of Monetary Burden of WBDOs in Surface Water Systems
by Etiologic Agent
Cryptosporidium
21%
Giardia
56%
4%
S. enterica
serovar Typhi
3%
Shigella
2%
Other Agents
2%
9
10
11
FIGURE 5-9
Distribution of Monetary Burden of WBDOs in Surface Water Systems by Etiologic
Agent, Excluding the Milwaukee WBDO
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E. co//'
21% "
Salmonella, non-
typhoid spp.
36%
Shigella
11%
Other Agents
2%
E. co// &
Campylobacter
10%
Giardia
/~ 2%
S. enterica
serovar Typhi
2%
Hepatitis A
2%
2
3
4
5
6
FIGURE 5-10
Distribution of Monetary Burden of WBDOs in Groundwater Systems
by Etiologic Agent
Milwaukee
$461,148,000
(76%)
All Other
WBDOs
$149,271,000
(24%)
9
10
FIGURE 5-11
Contribution of the Milwaukee WBDO to the Monetary Burden Estimate from All
U.S. WBDOs
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VSL,
$424,380,000—
(70%)
COI,
r $186,000,000
(30%)
COI = Cost-of-lllness
VSL = Value of Statistical Life
FIGURE 5-12
Component Distribution for the Monetary Burden Estimates of U.S. WBDOs
4
5
6
1
VSL,
$102,880,000
(69%)
COI,
$46,352,000
(31%)
COI = Cost-of-lllness
VSL = Value of Statistical Life
FIGURE 5-13
Component Distribution for the Monetary Burden Estimates Excluding
the Milwaukee WBDO
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Caregiver
Productivity
Losses,
$19,721,000
(11%)
III Productivity
Losses,
$123,357,000
(66%)
Physician Visit
Costs,
$2,708,000(1%)
ER Vis it Costs,
$9,006,000 (5%)
Hospital Costs,
— $29,936,000
(16%)
Self Medication
Costs,
$1,272,000(1%)
4
5
6
FIGURE 5-14
Cost-of-lllness Components for Monetary Burden Estimate of U.S. WBDOs
Caregiver
Productivity,
$5,959,000(13%)
III Productivity
Losses,
$28,597,000
(61%)
Physician Visit
Costs,
$1,400,000 (3%)
ER Visit Costs,
$4,526,000(10%)
Hospital Costs,
$5,496,000(12%)
Self Medication
Costs, $374,000
(1%)
FIGURE 5-15
7 Cost-of-lllness Components for Monetary Burden Estimate Excluding Milwaukee WBDO
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TABLE 5-7
Monetary Burden of Infectious Waterborne Outbreaks in Drinking Water, 1971 to 2000
Burden Measure
Self Medication
Physician Visits
Emergency Room
Visits
Hospitalizations
III Productivity Losses
Caregiver Productivity
Losses
Total Cost-of-lllness
Value of Statistical
Life
Total
Monetary Burden
$1,272,000
$2,708,000
$9,006,000
$29,936,000
$123,357,000
$19,721,000
$186,000,000
$424,380,000
$610,380,000
Monetary Burden
Excluding Milwaukee
$374,000
$1,400,000
$4,526,000
$5,496,000
$28,597,000
$5,959,000
$46,352,000
$102,880,000
$149,232,000
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1 of the components of the monetary burden estimate. The total monetary burden based
2 on the morbidity measures is $186 million when Milwaukee is included and $46 million
3 when Milwaukee is excluded. The effect of the Milwaukee WBDO was to decrease the
4 importance of the contributions of caregiver productivity losses, physician and ER visits
5 and increase the importance of productivity losses and hospitalizations in the total
6 morbidity monetary estimate.
7 5.7. DISCUSSION AND CONCLUSIONS
8 Monetary burden combines morbidity and mortality measures into a single
9 metric. It allows a number of comparisons not easily accomplished with epidemiologic
10 measures. However, the comparisons are greatly influenced by the large monetary
11 burden associated with mortality, determined by the VSL estimate. The VSL is
12 substantially greater than the monetary values placed on all other epidemiologic
13 measures. WBDOs caused by pathogens that are associated with a high mortality rate
14 will likely be identified as the most important in the monetary burden measures. The
15 monetary values used for these morbidities associated with infection disease likely
16 underestimate individuals' willingness-to-pay to reduce the risk of incurring the
17 morbidity. These monetary values are based on COI approaches. As discussed in
18 Chapter 4, such approaches likely capture a subset of disease attributes that individuals
19 value.1 For both of these reasons, the values used to estimate the monetary burden of
20 the morbidity measures are low compared to the VSL.
21 As expected, we found that the largest burden is associated with the Milwaukee
22 Cryptosporidium WBDO, in which a large number (50) of deaths were reported. The
1 COI approaches capture the costs from a societal perspective rather than an individual perspective,
which is reflected in WTP measures.
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1 monetary burden associated with this WBDO is evident when comparing the relative
2 importance of the burden among various categories (i.e., community water systems,
3 protozoan agents, Cryptosporidium, water treatment deficiencies, outbreaks reported
4 from 1991 to 2000, and surface water outbreaks). A very large WBDO of
5 cryptosporidiosis or another etiology with severe illness would also have a significant
6 impact on the overall monetary burden and on specific categories such as water source
7 and treatment.
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1 6. SENSITIVITY ANALYSES FOR MONETARY BURDEN
2
3 Sensitivity analyses examine the influence of model input parameters on
4 predictions. Allowing the values of the input parameters to vary over a range (e.g., a
5 distribution of uncertainty in the model parameters), we can observe the relative change
6 in model response. We conduct three such analyses to evaluate key assumptions used
7 to develop the monetary burden estimates. In the first sensitivity analysis (Section 6.1),
8 we identify the epidemiologic variables that have the greatest impact on the total
9 monetary burden estimate.
10 In the second analysis (Section 6.2), we evaluate uncertainties associated with
11 both the number of deaths attributed to WBDOs and their valuation. Approximately
12 70% ($424 million) of the total monetary burden estimate is associated with deaths. For
13 each pathogen, we develop plausible ranges of deaths linked to WBDOs. We describe
14 an existing distribution for the VSL and use a Monte Carlo approach to predict a
15 plausible range of monetary burden estimates for these deaths.
16 The final analysis examines the impact of alternative illness durations and case
17 estimates on the monetary burden estimated for the Milwaukee WBDO. About 76%
18 ($461 million) of the total monetary burden estimate is associated with the Milwaukee
19 WBDO. Although premature mortality ($322 million) accounts for 70% of the burden
20 associated with this outbreak, the COI estimate for the Milwaukee WBDO accounts for
21 over 75% of the total COI estimate for all WBDO.
22 6.1. SENSITIVITY OF THE MONETARY BURDEN TO THE EPIDEMIOLOGIC
23 BURDEN MEASURES
24 Table 6-1 shows the epidemiologic burden measures reported for the WBDOs
25 and their projected occurrence that were estimated in Chapter 2. It also shows the
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TABLE 6-1
Reported and Projected Epidemiological Burden Measures for U.S. WBDOs
which Occurred between 1971 and 2000
Epidemiological Burden
Measure
Deaths0
Person-Days IIId
Hospitalizationsc
Emergency Room Visits
Physician Visits
Reported
Occurrence3
66
3,992,923
5,915
1,013
21,531
Projected
Occurrence13
66
4,504,854
5,915
23,575
41,985
Additional Occurrence
Estimates
0
511,931
0
22,562
20,454
2 Reported occurrence refers to the totals actually reported in the WBDOSS. Critical
3 data are missing for some WBDO (Chapter 2).
4 b Projected occurrence refers to the totals used in the main analysis (Chapters 2 and 3).
5 These totals include estimates for data not reported to the WBDOSS (e.g., some
6 outbreak reports show no estimate for duration of illness).
7 c Requested on CDC 52.12.
8 d Derived from the number of cases and illness duration which are requested on CDC
9 52.12.
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1 Additional Occurrence Estimates, which are the differences between the Projected and
2 the Reported Occurrences for each measure.
3 We briefly review the five projected epidemiologic measures. Because the
4 computed rates for mortalities and for hospitalizations were comparable to the rates of
5 occurrence reported in the literature, we assumed that this passive surveillance system
6 does not underestimate or miss such severe events. Consequently, we did not develop
7 approaches to adjust the estimates for hospitalizations and deaths; Table 6-1 shows the
8 reported and projected estimates for mortalities and hospitalizations are the same.1
9 Using only the WBDOs with duration estimates would underestimate the total person-
10 days ill associated with all reported WBDOs. Therefore, we estimated durations for the
11 remaining 42% of the WBDOs that did not report illness duration based primarily on the
12 duration of illness caused by similar waterborne pathogens. We projected that there
13 were approximately 4.5 million person-days ill associated with all of the WBDOs that
14 were reported between 1971 and 2000; the projected estimate is roughly 500,000
15 person-days larger (13%) than if it had been based solely on the reported measures.
16 Since emergency room visits and physician visits were not requested on the
17 surveillance form, information for these visits was reported for few WBDOs; we
18 projected additional occurrence of these measures, based primarily on reported rates
19 for similar pathogens (Table 6-1).
20 6.1.1. Method. We estimate the change in the projected occurrence of the
21 epidemiologic burden measure needed to cause a 5% change in the total monetary
1 The Milwaukee WBDO accounted for 50 of the 66 deaths attributed to the U.S. WBDOs that occurred
between 1971 and 2000. The study by Hoxie et al. (1997), which examined the excess mortality
attributable to the Milwaukee WBDO based on the causes of death reported before, during, and after the
WBDO, thoroughly analyzes this WBDO.
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1 burden (Eq. 6-1). U.S. EPA (1997) and Breed et al. (2004) use similar approaches in a
2 watershed delivery model and an ecosystem productivity analysis, respectively (see
3 also discussion of approaches to sensitivity analyses in Morgan and Henrion, 1990).
4 The quantity of the projected occurrence for each epidemiologic burden measure (Table
5 6-1 ) forms the denominator of the equation and the change in the projected occurrence
6 forms the numerator. We note that the monetary value weights the required change in
7 occurrence. Rearranging Eq. 6-1 to yield Eq. 6-2, we solve for the change required for
8 each epidemiologic burden measure (converted to percentages) to change the total
9 monetary burden estimate by 5% (Table 6-2).
10 TM6*1.05
"
V (Eq. 6-1)
11 where:
12 TMB= Total monetary burden
13 POi = Projected occurrence for given epidemiologic burden measure
14 used in Main Study
15 POc = Projected occurrence for given epidemiologic burden measure
16 needed to change TMB by 5%
17 V = Economic value of given epidemiologic burden measure
7MB*1.05*PO,
18 POC = - v (Eq-6-2)
19 6.1.2. Results. Table 6-2 shows that the total monetary burden was most sensitive to
20 differences in the number of deaths and person-days ill; a change in projected mortality
21 by only 8% (5 deaths) changes the total monetary burden by 5%. A 21 % change in the
22 projected number of person-days ill causes a 5% change in the total monetary burden.
23 For hospitalizations, emergency room visits, and physician visits a larger change (102%
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TABLE 6-2
Percent Change Required in the Epidemiologic Burden to Change
Monetary Burden Estimate for U.S. WBDOs by 5%
Epidemiological Burden
Measure
Deaths
Person-Days III
Hospitalizations
Emergency Room Visits
Physician Visits
Projected
Occurrence
66
4,504,854
5,915
23,575
41,985
Change in the
Projected
Epidemiologic Burden
Measure Required to
Cause a 5% Change
in the Total Monetary
Burden
5
960,962
6,031
79,894
473,193
Percent Change in
Epidemiologic
Burden Measure
Required to Cause
a 5% Change in the
Total Monetary
Burden
8%
21%
102%
339%
1,127%
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1 to 1127%) in the projected measure is required to cause a 5% change in the total
2 monetary burden. When the Milwaukee WBDO is excluded, the total monetary burden
3 also was most sensitive to differences in the number of deaths and person-days ill
4 (Table 6-3). For hospitalizations, emergency room visits, and physician visits a larger
5 change (94% to 517%) in the measure is required to cause a 5% change in the total
6 monetary burden.
7 6.1.3. Discussion. The sensitivity of total monetary burden to relatively small changes
8 in the number of deaths is due to the large value associated with reducing the risk of
9 premature death and the relatively small monetary estimates developed for the
10 morbidities. While the VSL is based on WTP, the monetary estimates for the
11 morbidities are based on COI approaches. As noted in Chapter 4, these monetary
12 estimates based on COI approaches (i.e., the approach used for all of the monetary
13 burden estimates for the morbidity endpoints) likely underestimate values developed
14 using WTP approaches. Thus, even if relevant WTP studies were conducted, a small
15 change in the projected number of deaths will still have a large effect on the monetary
16 burden. Although the projections of emergency room visits and physician visits are
17 likely the most uncertain since no comparable epidemiologic data were identified in the
18 published literature (Chapter 2) and the projections of these measures are based upon
19 few WBDOs, this sensitivity analysis suggests that the total monetary burden is
20 considerably less sensitive to these two epidemiologic measures than to the deaths and
21 person-days ill (Table 6-2).
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TABLE 6-3
Sensitivity of the Monetary Burden to Changes in the Epidemiological Burden
Excluding the Milwaukee Outbreak
Epidemiological Burden
Measure
Deaths
Person-Days III
Hospitalizations
Emergency Room Visits
Physician Visits
Projected
Occurrence
16
877,854
1,515
11,848
21,705
Change in the
Projected
Epidemiologic Burden
Measure Required to
Cause a 5% Change
in the Total Monetary
Burden
1
227,840
1,430
18,943
112,193
Percent Change in
Epidemiologic
Burden Measure
Required to Cause
a 5% Change in the
Total Monetary
Burden
6%
26%
94%
160%
517%
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1 6.2. MONTE CARLO SENSITIVITY ANALYSIS OF THE MONETARY BURDEN
2 ASSOCIATED WITH WBDO DEATHS
O
4 The monetary burden for premature death is based on a central tendency
5 estimate for the number of premature deaths associated with WBDOs and the VSL. In
6 this Monte Carlo analysis, we develop a plausible distribution of the monetary burden of
7 disease associated with WBDO deaths. We use a reported distribution of the VSL from
8 previous U.S. EPA analyses and distributions of the plausible number of deaths that
9 could be associated with WBDOs for each pathogenic agent, as ascertained by case-
10 fatality estimates from several literature sources. We use Monte Carlo2 methods to
11 predict an overall distribution of the burden estimate in monetary units. The purpose is
12 to identify the primary sources of uncertainty in the estimate and to develop a plausible
13 distribution of the monetary burden associated with deaths in the WBDOs.
14 6.2.1. Methods.
15 6.2.1.1. Distributions of Deaths — For each etiologic agent category (except
16 Cryptosporidium), we developed distributions of the plausible number of deaths that
17 could be expected if the lowest and highest case-fatality ratios from the literature
18 sources discussed in Chapter 2 (Section 2.6.2) are applied to the cases reported to the
19 WBDOSS (Table 6-4).
20 The 50 reported deaths in the WBDOSS that are attributed to Cryptosporidium in
21 Table 6-4 are based on the death certificate analysis of Hoxie et al. (1997) that
22 identified cryptosporidiosis as the underlying or a contributing cause of death among
2 Monte Carlo simulation is a mathematical technique that randomly chooses a value for each variable
(within a specified probability distribution) used in a model. Based on the chosen values, this technique
calculates an output value. The selection and calculation steps are repeated multiple times. The
outcomes are compiled forming a probability distribution for the model. This distribution is used to
estimate the likelihood of a specific outcome (e.g., what is the median or 95th percentile value). Such a
simulation can also be used to examine which variables have the largest influence on model output.
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TABLE 6-4
Total Number of Outbreaks and Alternative Estimates of Deaths for Each Etiologic Agent
Etiological Agent
(General)
AGI
Outbreaks
365
Cases
83,493
Low
Expected
Deaths
0
Reported
Deaths
(WBDOSS)
1
High
Expected
Deaths
33
Viruses
Norovirus
SRSV (assumed to be
norovirus)*
Rotavirus*
Hepatitis A
26
1
1
28
13,100
70
1,761
827
0
0
0
0
0
0
0
0
0
0
0
2
Bacteria
C. jejuni
E. coli/E. coli &
Campylobacter
P. shigelloides*
Salmonella, non-typhoid
spp.
S. enterica serovar Typhi
Shigella
V. choleras
Yersinia
19
12
1
15
5
44
2
2
5,604
1,529
60
3,203
282
9,196
28
103
0
2
0
0
0
0
0
0
0
6
0
7
0
2
0
0
8
48
0
25
1
18
0
0
Protozoa
Cryptosporidium
Cyclospora*
En. histolytica*
Giardia
Total
15
1
1
126
665
421,473
21
4
28,427
569,962
50
0
0
0
52
50
0
0
0
66
71
0
0
0
206
2 AGI = acute gastrointestinal illness of unknown etiology
3 SRSV = small round structured virus
4 * Only a single outbreak for each etiologic agent; relatively confident in enumeration of deaths.
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1 residents of the Milwaukee vicinity who died during the 2-year period following the
2 Milwaukee outbreak. The analysis revealed 54 cryptosporidiosis-associated deaths that
3 occurred during that time interval, whereas, based on pre-outbreak trends, only four
4 would have been expected. Hoxie and colleagues also demonstrate that the total
5 number of AIDS deaths, excluding cryptosporidiosis-associated AIDS deaths, was
6 significantly greater than predicted during the 6 months after the outbreak (19 more
7 deaths than expected [95% Cl = 12, 26]), and that non-cryptosporidiosis-associated
8 AIDS deaths were lower than expected during the subsequent two 6-month intervals.
9 These changes in the pattern of AIDS deaths suggest that premature mortality among
10 persons with AIDS could have been associated with the outbreak, and that
11 cryptosporidiosis as a contributing cause of death may have been under-reported on
12 their death certificates.3 Should that have been the case, the 19 excess AIDS deaths
13 that occurred within 6 months after the outbreak may have been cryptosporidiosis-
14 associated, and as such, will be considered in our analysis of the distribution of
15 plausible number of deaths. Conversely, the 50 cryptosporidiosis-associated deaths
16 attributed to the Milwaukee WBDO may be an overestimate due to increased
17 cryptosporidiosis awareness following the outbreak, but there are no available data to
18 determine a possible lower bound for cryptosporidiosis mortality.
19 Application of the very high case-fatality ratios reported for Cryptosporidium in
20 the literature sources reviewed in Chapter 2 (Section 2.6.2) yielded mortality estimates
21 that we deemed outside the plausible range expected in the WBDOSS. Because the
3 Hoxie et al. (1997) reported that 85% of the cryptosporidiosis-associated deaths that occurred in the
Milwaukee vicinity between March 1993 and March 1995 occurred in individuals with AIDS listed as the
underlying cause of death. Ideally, we would develop two case-fatality rates: one for the AIDS population
and one for the general population. For this component of the upper-bound estimate, we would apply the
rates separately to WBDO cases that have AIDS and the general population; however, in the absence of
such data for each Cryptosporidium WBDO, we apply the rate to all Cryptosporidium WBDO cases.
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1 vast majority of WBDO cryptosporidiosis cases are accounted for by the Milwaukee
2 outbreak and the case-fatality ratio for these cases is thoroughly developed in the Hoxie
3 et al. analysis, we use the Milwaukee outbreak case-fatality ratio as the basis for
4 developing the high estimate presented in Table 6-4: total cryptosporidiosis deaths from
5 all 15 Cryptosporidium WBDOs include the possible 19 additional deaths suggested by
6 Hoxie et al. plus two more projected by applying the Milwaukee case-fatality ratio (50
7 deaths/403,000 cases) to the remaining 18,473 cases associated with the other
8 Cryptosporidium WBDOs.4 For each category of pathogen, triangular distributions were
9 developed. The values for low expected deaths, reported deaths and high expected
10 deaths correspond to the minimum, mode and maximum values of the distribution,
11 respectively.
12 6.2.1.2. Distribution of Value of Statistical Life (VSL) Measures — The
13 Economic Analysis of Long Term 2 Enhanced Surface Water Treatment Rule used a
14 Weibull distribution for the value of a statistical life to estimate the uncertainty
15 surrounding the VSL (U.S. EPA, 2006). This distribution included updating the previous
16 value of the VSL to 2000$. We use their distribution which has a mean of $6.3 million,5
17 median of $5.5 million, a 5th percentile value of $1.0 million and a 95th percentile value
18 of $14.5 million. We note that the U.S. EPA and other groups are actively re-evaluating
19 the VSL and its distribution (e.g., U.S. EPA, 2006).
20 6.2.2. Monte Carlo Analysis. The Monte Carlo analysis was conducted using Crystal
21 Ball 2000 (Decisioneering, Inc., Denver, CO) and consisted of 50,000 iterations. Rank
4 Craun et al. (2001), Craun and Frost (2002), and Hunter and Syed (2001) suggest that it is possible for
the Milwaukee case estimate (Mac Kenzie et al., 1994) to be subject to recall bias. If the 403,000 cases
estimated to have occurred during the Milwaukee WBDO is an overestimate, then the case-fatality rate
could be higher than this rate.
5 In the main analysis, the VSL value is $6.43 million.
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1 correlation coefficients were calculated to analyze the impact of model parameters on
2 the simulation results.
3 6.2.3. Results and Discussion: Preliminary Uncertainty Analysis of the Deaths
4 Associated with the WBDO. Figure 6-1 shows that the number of deaths predicted
5 ranges from 63 to 169 in this analysis. The mean of the distribution is 108 deaths and
6 the 10th and 90th percentile values are 88 and 129 deaths, respectively.
7 Figure 6-2 shows the predicted mean estimate of the monetary disease burden
8 associated with deaths attributed to WBDOs to be $684 million. The minimum and
9 maximum values of the distribution are $3.5 million and $4.4 billion and the 10th and 90th
10 percentile values are $167 million and $1.3 billion, respectively.
11 Based on our main analysis, the monetary burden associated with WBDO deaths
12 was $424 million (Figure 5-12); the mean value in this sensitivity analysis was $260
13 million larger ($684 million). Figure 6-3 shows that, based on rank correlation
14 coefficient analysis, nearly all of the model output variability can be explained through
15 the distribution of the VSL. The distribution of the output is due primarily to the shape of
16 the VSL distribution. It is also due to right skew of the upper-bound estimates of deaths
17 associated with WBDOs. Comparing the reported totals (Table 6-4, column 6) to upper-
18 bound totals shows that at the upper end of the distribution there are over 3 times more
19 deaths than are listed in the reported data (column 5). The lower-bound values were
20 only 23% less than the reported values, which is expected because we used the same
21 estimate for the low and reported mortality values (n = 50).
22 We considered conducting an additional Monte Carlo analysis that evaluated
23 each epidemiologic measure and each monetary measure, but doing this was not
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50,000 Trials
80 90 100 110 120 130 140 150 160 170
0.00
2
3
5
6
1
8
FIGURE 6-1
Predicted Distribution of U.S. WBDO Deaths Based on Monte Carlo Simulations with
Distributions of the Numbers of Deaths for all Etiologic Agents
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1
2
3
4
50,000 Trials
Distribution of Burden of Premature Mortality
0.02
1,100
1,000
900
100
$300,000,000 $600,000,000 $900,000,000 $1,200,000,000 $1,500,000,000 $1,800,000,000 $2,100,000,000
FIGURE 6-2
Predicted Distribution of Monetary Burden of U.S. WBDO Deaths Based on Monte Carlo Simulations with Distributions of
the Numbers of Deaths for Each Etiologic Agent and of the VSL
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
50,000 Trials 0.00 0.20 0.40 0.60 0.80 1.00
VSL
E. coliE. coli and Campylo...
AGI I p.10
Salmonella non-typhoid 0.06
Cryptosporidium
Shigella 0.04
Campylobacterjejuni 0.02
f
Hepatitus A
Salmonell typhoid Iu
1
FIGURE 6-3
Rank Correlation Coefficients Associated with Mortality Sensitivity Analysis
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1 possible because we identified no studies on a national scale that systematically
2 evaluated the uncertainty and variability in distributions of the COI measures for the
3 morbidities associated with U.S. waterborne diseases. Although the data listed in Table
4 4-1 could have served as a primary source of information for the development of the
5 COI distributions, we determined that there were insufficient data on which to develop
6 meaningful distributions. In general, the studies described in Table 4-1 present only
7 "central tendency" values for each COI measure as reported from different studies.
8 While we were confident in the estimates of the central tendencies, we had little
9 confidence in the information describing the spread of the data. If we developed an
10 analysis based only on the distribution of these central tendency measures but did not
11 capture appropriately the spread of these data, then the analysis would underestimate
12 the potential impacts of the uncertainty in these data.6 Therefore, we limited our
13 analysis to uncertainty in the monetary burden associated with WBDO deaths.
14 6.3. SENSITIVITY ANALYSIS OF THE MONETARY BURDEN ASSOCIATED WITH
15 THE MILWAUKEE OUTBREAK TO THE REPORTED DURATION OF ILLNESS
16 AND CASE NUMBER
17 This sensitivity analysis examines the impact of changes in two epidemiologic
18 burden components, case number and illness duration, on the monetary burden
19 estimate. Although not as influential as changes in the number of deaths (Section 6.1),
20 these two components account for much of the monetary burden associated with the
21 659 WBDOs which report no fatalities (i.e., no deaths are associated with over 99% of
22 665 total WBDOs reported in the WBDOSS between 1971 and 2000). Both the duration
23 of illness and the number of cases of illness are needed to compute the person-days ill,
24 which is then used to estimate the monetary burden associated with lost productivity.
6 A comprehensive uncertainty analysis, while outside the scope of this effort, is clearly needed.
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1 Chapter 6 shows that these two components require a magnitude change of 21-25% to
2 change the total monetary burden estimate by 5%.
3 To illustrate the impact on monetary burden, we develop several estimates of
4 both the number of cases of illness that occurred during the Milwaukee WBDO and their
5 average duration. We then examine the influence of these alternative estimates on the
6 associated monetary disease burden estimated for this WBDO. The Milwaukee WBDO
7 is well studied, making it a convenient source of published estimates for this illustrative
8 analysis. Although most of the monetary burden is associated with the 50 deaths
9 attributed to the the Milwaukee outbreak, in the main analyses this WBDO contributes
10 significantly to the number of person-days ill and monetary burden due to the large
11 number of estimated cases (403,000) and illness duration (i.e., 9 days) (Chapters 3 and
12 5). We did not examine alternative estimates of the number of premature mortalities
13 because of the large impact of small changes on monetary burden (Sections 6.1 and
14 6.2) and the focus of this section. Most of the case number and duration estimates
15 reported for the other WBDOs are subject to the same uncertainties described in
16 subsequent sections for the Milwaukee WBDO (e.g., recall bias, uncertain background
17 illness rates) and, as noted in Chapter 2, the methods we used to estimate the
18 unreported measures are also uncertain.
19 6.3.1. Alternative Estimates of Duration of Cryptosporidiosis During Milwaukee
20 WBDO. Although Mac Kenzie et al. (1994) report only a median illness duration of 9
21 days in the abstract of their published article, they surveyed three populations with
22 different mean and median illness durations: (1) persons with laboratory confirmed
23 cryptosporidiosis, (2) persons with clinically-defined cryptosporidiosis (i.e., symptoms
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1 consistent with cryptosporidiosis) and (3) a household survey of persons with watery
2 diarrhea (the case-definition used to identify cryptosporidiosis in Mac Kenzie et al.).
3 The reported duration of illness among these populations ranged from 1 to 55 days
4 (Table 6-5). Median values of 3 days duration for watery diarrhea were reported in the
5 clinical infection and household surveys, which contrast sharply with the median
6 duration of 9 days for laboratory-confirmed cases. Of the 285 laboratory-confirmed
7 patients 46% were hospitalized and 48% were immuno-compromised, and these cases
8 may have been among the most severe and long lasting. For our main epidemiologic
9 and monetary burden analyses, we used the reported median duration of illness of 9
10 days. Nine days is the typical duration of illness reported in the CDC fact sheets for
11 cryptosporidiosis and is also the midpoint of the median durations listed for all 12
12 Cryptosporidium WBDOs (Table 6-6). In these WBDOs, the median duration reported
13 during a Cryptosporidium WBDO ranged from 3 to 74 days. For this sensitivity analysis,
14 we assumed that the average duration of cryptosporidiosis in the Milwaukee WBDO
15 was alternatively 3 or 9 days.
16 6.3.2. Alternative Estimates of Milwaukee Cryptosporidiosis Cases. The
17 WBDOSS attributes 403,000 cases of cryptosporidiosis to the Milwaukee outbreak.
18 This is the central estimate of the number of cases estimated by Mac Kenzie et al.
19 (1994) in their outbreak investigation (details provided in Chapter 2). They estimated
20 the number of people that had symptoms consistent with cryptosporidiosis during the
21 outbreak by means of a telephone survey in which 26% of the respondents reported
22 watery diarrhea during the period of the outbreak (defined as March 1-April 28, 1993).
23 By applying the proportion of persons experiencing the symptom compatible with
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TABLE 6-5
Duration of Illness, Milwaukee Cryptosporidium Outbreak (Mac Kenzie et al., 1994)
Population Surveyed
Duration (Days)
Median
Mean
Range
Survey Information
Laboratory-Confirmed
Cases
12
1 to 55
n = 285 lab confirmed cases
Clinical Infection
4.5
1 to 38
n = 201 respondents with watery
diarrhea (482 total respondents)
Household Survey
1 to 45
n = 436 interviewed with watery
diarrhea (1663 total household
members)
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TABLE 6-6
Distribution of Reported Median Duration of Illness of Cryptosporidium WBDOs,
1971 to 2000
Median Reported Duration of Illness
3.0
4.0
5.0
6.0
7.0
8.6
9.0*
11.0
24.0
60.0
74.0
Number of WBDOs Reporting Median
Duration Value
1
1
1
1
1
1
1*
2
1
1
1
* Milwaukee WBDO
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1 cryptosporidiosis to the total population at risk (1.61 million people), they estimated that
2 419,000 persons (95% confidence interval = 386,000-451,000) may have been ill during
3 the Milwaukee WBDO (Table 6-7). After subtracting a background rate of 0.5% per
4 month for diarrhea due to all causes (16,000 people/2-month outbreak period), it was
5 determined that 403,000 people experienced watery diarrhea due to the
6 cryptosporidiosis outbreak.
7 To develop a high-end case number estimate for burden analysis, we subtract
8 the background cases from the value of the upper 95% confidence interval and project
9 435,000 cases. Although not used here, other approaches could be considered for
10 development of a high-end estimate. For example, a study of Cryptosporidium-spec\i\c
11 antibody responses in children by McDonald et al. (2001) suggests that infection may
12 have been more widespread,7 and Naumova et al. (2003) also emphasize the
13 importance of secondary transmission especially among children and the elderly, which
14 could have led to additional unreported cases. The estimated 403,000 cases include
15 only the symptomatic cases that occurred between March 1 and April 28, 1993. Given
16 the 2-month duration of the study, we assume that this estimate consists of primary and
17 secondary cases; however, secondary cases that occurred after this survey time period
18 would not be included in the case estimate of Mac Kenzie et al. (1994). This estimate
19 also would not include asymptomatic cases; while such cases could contribute to
20 secondary spread in the population, they would not contribute to either the
21 epidemiologic or monetary burden estimates since they would not be described by the
22 epidemiologic measures used in our analysis.
7 We note that infection does not imply that the individual was ill.
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TABLE 6-7
Alternative Estimates of Number of Cases Attributable to the Milwaukee WBDO
Source of Background
Incidence Estimate
Mac Kenzie et al. (1994)
Upper 95% Cl
WBDOSS
Mac Kenzie et al. (1994)
Lower 95% Cl
Meadetal. (1999)
Roy et al. (in press)
Hunter and Syed (2001)
Background
Incidence
(Episodes
[cases] per
person per
year)
0.06b
0.06b
0.06b
0.61C
0.65d
1 .404e
Background Rate
(% of Milwaukee
area residents3
experiencing
background
[i.e., non-outbreak-
related] cases of
diarrhea per
month)
0.5%b
0.5%b
0.5%b
5.1%c
5.4%d
11.7%e
Cases of
Diarrheal Illness
(computed from
Mac Kenzie's survey-
based estimate of
41 9,000 [95% Cl,
386,000-451,000]
cases of watery
diarrhea)
435,000
403,000
370,000
255,317
244,583
42,260
2 greater Milwaukee area population of 1,610,000
3 b restricted to cases of "watery diarrhea"
4 c mean of age-adjusted incidence of episodes or cases of "any diarrhea, with or without
5 vomiting" presented in Mead et al. as derived from 1996/97 FoodNet data (CDC,
6 1998b), the Cleveland study (Dingle et al., 1964), and the Tecumseh study (Monto and
7 Koopman, 1980)
8 d episodes or cases of AGI defined as "3 or more loose stools in a 24-hour period
9 resulting in an impairment of daily activities or diarrhea duration greater than one day"
10 e episodes or cases of AGI of any symptom profile ascertained from FoodNet 1997 data
11 (CDC, 1998c)
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1 To develop a low-end estimate, we subtract the background rate used by
2 Mac Kenzie et al. (16,000) from their lower-bound 95% confidence interval (386,000)
3 and estimate that the outbreak consisted of 370,000 cases. Although not used for this
4 burden analysis of WBDOSS reported cases, several other evidentiary lines could be
5 considered for development of alternative low-end estimates of the number of
6 Milwaukee cases. To estimate the number of cases that occurred during a WBDO,
7 epidemiologic investigations rely on subjects' recollection of experiencing specific
8 symptoms during a specific period of time and the identification of an appropriate
9 background illness rate to compare with the increased disease incidence. Even though
10 the 1993 Milwaukee cryptosporidiosis outbreak investigation (Mac Kenzie et al., 1994;
11 Hoxie et al., 1997; Proctor et al., 1998) was quite extensive, Hunter and Syed (2001)
12 suggest that outbreak-related cases may have been overestimated due to recall bias
13 and the use of a background incidence rate that was too low.
14 The background rate assumed in the Mac Kenzie study was 0.5% per month (or
15 16,000 cases during the 2-month period per 1,610,000 people in greater Milwaukee -
16 the equivalent of an annual diarrheal risk of about 0.06 cases per person per year); the
17 source was cited as "unpublished data." Roy et al. (in press) estimate general
18 background incidence rates of AGI in the United States to be 0.65 episodes per person-
19 year (this would indicate 174,417 background AGI cases during the 2-month Milwaukee
20 WBDO, a 5.0% per month rate). The Roy et al. background incidence rate for AGI is
21 comparable to that that we computed (0.61 episodes per person-year) for AGI
22 characterized by diarrhea of any type (with or without vomiting) based on the rates
23 provided in Table 4 of Mead et al. (1999). Mead et al. evaluated retrospective
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1 community-based studies in the United States (Dingle et al., 1964 [the Cleveland study];
2 Monto and Koopman, 1980 [the Tecumseh study]) and 1996/97 FoodNet data, and
3 developed age-adjusted rates of AGI with several symptom profiles. Age-adjustment
4 was conducted because the Cleveland and Tecumseh studies over-sample children.
5 By considering the age-adjusted incidence of diarrheal illness provided by Mead et al.,
6 we compute an average background diarrhea incidence of rate of 0.61 cases per
7 person-year (5.0% per month;8163,682 cases per 1,610,000 people per 2-month
8 period). Hunter and Syed, in considering the same data sets as Mead et al., suggest a
9 background incidence rate of 11.7% per month,9 or 376,740 cases per 1,610,000 per
10 2-month period - the equivalent of an annual diarrheal illness incidence of about 1.4
11 cases per person per year (presumably for all AGI symptom profiles and without age-
12 adjustment). If such a background rate was representative of Milwaukee at that time,
13 the outbreak cryptosporidiosis cases would number only 42,260 after accounting for the
14 higher background rate of diarrheal illness. Alternative estimates are summarized in
15 Table 6-7.
16 Furthermore, recall bias may result in the reporting of more illnesses than
17 actually occurred (Craun and Frost, 2002; Craun et al., 2001; Hunter and Syed, 2001).
18 These researchers reason that the Mac Kenzie et al. estimate could be subject to recall
19 bias, given the increased publicity and the primary investigators' reliance on self-
20 reporting of non-specific diarrheal illness. Hunter and Syed point out that, according to
An incidence rate of 0.61 cases per person-year/12 = 0.051 cases per person-month, i.e., a background
rate of 5.0% per month.
9 An incidence rate of 1.4 cases per person-year/12 = 0.117 cases per person-month, i.e., a background
rate of 11.% per month.
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TABLE 6-8
The Alternative Estimated Numbers of Cases and Epidemiologic Burdens of the Milwaukee WBDO
9 Days Median Duration of Illness
Alternative
19
119
1119
Cases
435,000
403,000
370,000
Physician
Visits
21,890
20,280
18,620
Emergency
Room Visits
12,658
11,727
10,770
Hospitalizations
4,749
4,400
4,040
Deaths
50
50
50
Person-
Days III
3,915,000
3,627,000
3,330,000
Number
Cases Self
Medicating
130,453
120,856
110,960
III
Productivity
Days Lost
710,308
658,055
604,170
Caregiver
Productivity
Days Lost
103,157
95,568
87,740
1 19 = case number reported for upper bound of 95 percentile confidence interval in Mac Kenzie et al. and 9-day duration.
2 119 = case number as reported in waterborne outbreak database and 9-day duration.
3 1119 = case number reported for lower bound of 95 percentile confidence interval in Mac Kenzie et al. and 9-day duration.
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TABLE 6-9
The Alternative Estimated Numbers of Cases and Epidemiologic Burdens of the Milwaukee WBDO
3 Days Median Duration of Illness
Alternative
13
113
1113
Cases
435,000
403,000
370,000
Physician
Visits
21,890
20,280
18,619
Emergency
Room Visits
12,658
11,727
10,767
Hospitalizations
4,749
4,400
4,040
Deaths
50
50
50
Person-
Days III
1,305,000
1,209,000
1,110,000
Number
Cases Self
Medicating
130,452
120,856
110,960
III
Productivity
Days Lost
236,769
219,352
201,390
Caregiver
Productivity
Days Lost
34,385
31,856
29,247
2 13 = case number reported for upper bound of 95 percentile confidence interval in Mac Kenzie et al. and 3-day duration.
3 IIS = case number as reported in waterborne outbreak database and 3-day duration.
4 I IIS = case number reported for lower bound of 95 percentile confidence interval in Mac Kenzie et al. and 3-day duration.
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1 Wheeler et al. (1999), in comparison to prospective studies, retrospective studies
2 overestimate diarrheal illness in a community by a factor of 2.8.
3 6.3.3. Effect of Alternative Case Numbers and Duration of Illness on the Burden
4 of the Milwaukee WBDO. Tables 6-8 and 6-9 present the conjectured epidemiologic
5 burden possibilities under six alternative combinations of case number and duration-of-
6 illness estimates for the Milwaukee outbreak: three different case number estimates
7 evaluated at 3 and 9 days duration of illness. Because this analysis focuses on
8 alternative case and illness duration estimates, the number of deaths attributed to this
9 WBDO was not changed in any of the alternatives. The number of physician visits,
10 emergency room visits, hospitalizations and number of cases that self-medicated are
11 affected by changes in conjectured case number (i.e., 435,000 vs. 403,000 vs.
12 370,000). As the number of cases declines in the conjectured estimates, there will be a
13 proportional decrease in these estimates. Person-days ill varies with both case number
14 and duration of illness. For example, the number of person-days ill reported in Table
15 6-8 (median duration of illness is assumed to be 9 days) is three times greater than the
16 corresponding number of person-days ill listed in Table 6-9 (median duration of illness is
17 assumed to be 3 days).
18 Tables 6-10 and 6-11 show that the COI associated with these conjectured
19 estimates for the Milwaukee outbreak could range from approximately $61 million to
20 $151 million. The total monetary burden could range from $383 million to $472 million;
21 thus, most (approximately $322 million) of the monetary burden is associated with the
22 50 deaths attributed to this outbreak. The COI estimated for the median duration of
23 three days is roughly one-half the value estimated for nine days (Figure 6-4). Tables
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TABLE 6-10
Results of Conjectured Alternative Numbers of Cases and Economic Burdens of the Milwaukee WBDO
9 Days Median Duration of Illness
Alternative
19
II9
III9
Physician
Visit Cost
($)
1,411,926
1,308,060
1,200,948
ER Visit
Costs
($)
4,835,800
4,480,063
4,113,209
Hospital
Costs
($)
26,380,144
24,439,536
22,438,284
Self
Medication
Costs
($)
969,872
898,525
824,949
Cost of I II
Productivity
Losses
($)
102,284,317
94,759,953
87,000,453
Cost of
Caregiver
Productivity
Losses
($)
14,854,535
13,761,787
12,634,891
Cost of Illness
Total
($)
150,736,594
139,647,925
128,212,735
Estimated
Burden of
Death
($)
321,500,000
321,500,000
321,500,000
Total
Monetary
Burden
($)
472,236,594
461,147,925
449,712,735
2 19 = case number reported for upper bound of 95 percentile confidence interval in Mac Kenzie et al. and 9-day duration.
3 II9 = case number as reported in waterborne outbreak database and 9-day duration.
4 III9 = case number reported for lower bound of 95 percentile confidence interval in Mac Kenzie et al. and 9-day duration.
5 $ = all dollar estimates in 2000$
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TABLE 6-1 1
Results of Conjectured Alternative Numbers of Cases and Economic Burdens of the Milwaukee WBDO
3 Days Median Duration of Illness
Alternative
13
IIS
III3
Physician
Visit Cost
($)
1,411,926
1,308,060
1,200,948
ER Visit
Costs
($)
4,835,800
4,480,063
4,113,209
Hospital
Costs
($)
26,380,144
24,439,536
22,438,284
Self
Medication
Costs
($)
969,872
898,525
824,949
Cost of I II
Productivity
Losses
($)
34,094,772
31,586,651
29,000,151
Cost of
Caregiver
Productivity
Losses
($)
4,951,512
4,587,262
4,211,630
Cost of
Illness
Total
($)
72,644,026
67,300,098
61,789,172
Estimated
Burden of
Death
($)
321,500,000
321,500,000
321,500,000
Total
Monetary
Burden
($)
394,144,026
388,800,098
383,289,172
2
O
4
5
6
13 = case number reported for upper bound of 95 percentile confidence interval in Mac Kenzie et al. and 3-day duration.
IIS = case number as reported in waterborne outbreak database and 3-day duration.
III3 = case number reported for lower bound of 95 percentile confidence interval in Mac Kenzie et al. and 3-day duration.
$ = all dollar estimates in 2000$
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2
3
4
5
6
160,000,000 -,
140,000,000 -
120,000,000 -
100,000,000 -
o
Q
80,000,000 -
60,000,000 -
40,000,000 -
20,000,000 -
II9
III9
113
1113
FIGURE 6-4
COI Estimates Associated with Alternative Impacts of the Milwaukee WBDO
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1 6-10 and 6-11, which list the results of each economic measure for each alternative
2 outbreak, show that lost productivity of both the ill person and the caregiver account for
3 most of the differences across the alternative COI estimates. For example, assuming
4 that there were 403,000 cases resulting from the Milwaukee WBDO, the lost productivity
5 for the ill is valued at $95 million if duration of illness is 9 days but only $32 million if it is
6 3 days.
7 6.4. CONCLUSIONS OF SENSITIVITY ANALYSIS
8 This chapter describes three separate examinations of the uncertainty associated
9 with the monetary burden estimate. The first analysis demonstrates how changes in the
10 various epidemiologic measures (e.g., total hospitalizations, total person-days ill) would
11 alter the total monetary burden estimate. Relatively small changes in the number of
12 deaths and person-days ill will bring about a 5% difference in the total burden,
13 illustrating that deaths, case numbers and duration of illness are the most influential
14 factors in these burden estimates. In contrast, the overall magnitude of the medical
15 treatment components (i.e., numbers of hospitalizations, physician visits and emergency
16 room visits) would have to be markedly different from the estimated values to affect the
17 total burden to a significant degree. These results suggest that uncertainty in the
18 numbers of deaths and cases and in the duration of illness is of much greater concern
19 than the uncertainty in the medical treatment factors.
20 The second and third analyses were conducted because the information needed
21 to develop a comprehensive uncertainty analysis was not available. As noted
22 previously, while we are confident in the central tendency measures, we were unable to
23 develop distributions that we deemed adequate for this analysis. The development and
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1 publication of data sets for the costs associated with the various morbidities that result
2 from a WBDO is a clear research need. Also needed are valid methods used to
3 quantify plausible distributions of the illness durations, physician visits, emergency room
4 visits and hospitalizations associated with WBDOs. Because we could not conduct a
5 comprehensive uncertainty analysis, we focused the following two analyses on the two
6 components of the WBDO surveillance system that had the greatest impact on the total
7 monetary burden: the total number of deaths attributable to WBDOs and the 1993
8 Milwaukee cryptosporidiosis outbreak. Chapters 4 and 5 document these impacts.
9 Deaths and the Milwaukee outbreak account for roughly 70% and 76% of the monetary
10 disease burden, respectively (consider that 50 deaths were reported for the Milwaukee
11 WBDO alone and only 16 deaths occurred in the remaining 665 WBDOs).
12 In the second analysis, we developed a distribution of the number of deaths
13 associated with each pathogenic agent and for AGI and used a distribution for the VSL.
14 This analysis showed that the distribution of the VSL was the most important contributor
15 to the monetary disease burden associated with premature mortalities. The distribution
16 of deaths associated with each agent was relatively small when compared to the
17 distribution used to represent the VSL.
18 The third analysis focused on the impact of alternative case and duration
19 estimates during the 1993 Milwaukee cryptosporidiosis outbreak, which was responsible
20 for the majority of the burden in all of the burden estimates. The analysis showed that,
21 if a 3-day average duration of illness was used instead of a 9-day duration, then the
22 monetary burden would decrease by approximately one-half. For the 9-day duration,
23 decreasing case estimates by 8% resulted in total monetary burden estimates that were
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1 2.5% lower than those based on the reported values. The same case reductions for the
2 3-day duration showed 1.6% lower monetary burden estimates for the Milwaukee
3 WBDO. This further highlights the importance of the contribution of the total number of
4 deaths that occurred during the outbreak.
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1 7. DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS
2
3 We examined the epidemiologic and monetary burden from WBDOs reported in
4 the U.S. from 1971 to 2000. Monetary burden estimates were based on epidemiologic
5 measures recorded in the WBDOSS including the number of cases of illness, illness
6 duration, hospital admissions, physician visits, emergency room visits and deaths. We
7 estimated unreported severity measures such as illness duration and the number of
8 physician and emergency room visits based on data available from published literature
9 or, preferably, from other outbreak data in the WBDOSS. We also examined the
10 sensitivity of the total disease burden estimate to various assumptions (e.g., illness
11 duration in the Milwaukee outbreak, the magnitude of the value of statistical life (VSL))
12 in order to address some of the uncertainty in the results.
13 7.1. DISCUSSION
14 The total estimated monetary burden from the 665 outbreaks reported in the
15 30-year WBDOSS was $610 million. This was based on 66 deaths, approximately
16 570,000 cases of illness and over 4.5 million person-days ill. The VSL analysis, which
17 estimates the monetary burden from premature mortality, accounted for $424 million of
18 the total burden. The COI analysis, which estimates costs related to morbidity including
19 medical expenses and productivity loss (i.e., days lost for work valued by lost wages
20 and household production for the sick individual and their caregivers), accounted for the
21 remaining $186 million. Similar to the Corso et al. (2003) analysis of the Milwaukee
22 cryptosporidiosis outbreak, productivity losses accounted for the majority of the COI
23 disease burden estimate for WBDOs during 1971-2000. The proportion of the COI
24 burden due to productivity losses in our analysis was 77%.
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1 The number of cases ill and the duration of illness were used to calculate person-
2 days ill attributable to WBDOs. The majority of WBDO cases and estimated person-
3 days ill occurred in surface water systems. This was mostly due to the Milwaukee
4 cryptosporidiosis outbreak, which contributed 403,000 of the 570,000 cases recorded
5 from 1971 to 2000. Given the magnitude of the Milwaukee outbreak and its impact on
6 the overall disease burden, we examined the epidemiologic burden associated with and
7 without the Milwaukee outbreak. Without the Milwaukee outbreak cases, the reported
8 number of cases of illness in groundwater systems was twice as high as the number in
9 surface water systems while person-days ill estimates were slightly higher in surface
10 water systems.
11 Community systems serve over 272 million persons in the U.S., of which 181
12 million are served by surface water (U.S. EPA, 2005). Groundwater serves over 111
13 million people in the U.S. and is the primary source for most non-community water
14 systems. Although they serve fewer than 25 million people in the U.S., non-community
15 systems accounted for the majority (n = 329) of the reported WBDOs. In spite of the
16 greater frequency of WBDOs in non-community systems, most of the epidemiologic
17 burden occurred in community water systems irrespective of whether Milwaukee was
18 considered. After excluding Milwaukee, reported cases in non-community and
19 community system outbreaks were fairly comparable, but the person-days ill estimate
20 remained more than twice as high in community systems. This is likely due in part to
21 longer average duration of protozoan infections, which largely occur in surface water-
22 supplied community water systems. In contrast, the shorter duration of illness reported
23 for outbreaks from non-community systems is consistent with a viral etiology more
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1 commonly found in groundwater outbreaks (Borchardt et al., 2003). Overall, the total
2 monetary burden associated with community outbreaks was 13 times larger than non-
3 community systems with the Milwaukee outbreak included and 2.5 times larger without
4 Milwaukee.
5 Among the 300 outbreaks of known etiology, 143 were attributed to protozoa,
6 101 to bacteria and 56 to viruses. After excluding Milwaukee, protozoan outbreaks
7 accounted for nearly 47,000 cases of illness. This was more than two times and more
8 than three times the reported cases from bacterial and viral outbreaks, respectively.
9 The person-days ill estimate for protozoan outbreaks was 463,000, more than 3 times
10 higher than the combined estimate for both viral and bacterial outbreaks. The 365 AGI
11 outbreaks accounted for over 83,000 reported cases of illness and an estimated
12 265,000 person-days ill.
13 The ability for passive WBDO surveillance systems to accurately estimate the
14 different epidemiologic measures is critical for the burden estimates that were
15 developed. This is not only important at the individual outbreak level, but incomplete
16 reporting of epidemiologic data could distort some of the comparisons that were made
17 by etiologic agent grouping. For example, only one rotavirus outbreak was reported to
18 the WBDOSS during the 30-year period. Since rotavirus was the only viral outbreak
19 other than Hepatitis A with reported physician visits, the rotavirus data was used to
20 estimate physician visits for other viruses such as norovirus and small, round structured
21 viruses (assumed to be norovirus). If the epidemiologic measures for the rotavirus
22 outbreak are inaccurate or not representative of typical outbreaks, the impact of these
23 errors would be compounded by their use in estimating measures for other viral
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1 outbreaks. Since data limitations resulted in the estimation of unreported measures
2 based on other outbreaks with similar etiology (or etiologic group), we urge caution in
3 the interpretation of the findings based on limited data.
4 The disease burden estimates presented in this report are dependent on the
5 extent to which outbreaks were investigated, detected, reported and recorded in the
6 WBDOSS. The likelihood that an outbreak is detected and recorded is dependent on
7 local and state disease surveillance capabilities as well as a variety of factors including
8 water service system and source water type. For small non-community water systems
9 that serve part-time or transient populations and non-residential areas, there is an
10 increased likelihood for outbreaks to go undetected due to insufficient clustering of
11 cases (Lee et al., 2002). Outbreaks may also go undetected in larger communities due
12 to factors such as decentralized health care systems and numerous, non-integrated
13 laboratory facilities (Board on Life Sciences, 2004). Outbreaks that result in mild
14 symptoms, have low attack rates or are not caused by an easily identifiable etiologic
15 agent are also more likely to go unrecognized. Because we do not consider unreported
16 outbreaks that may have occurred during 1971-2000 when estimating disease burden,
17 they likely are underestimates of the actual burden attributable to all possible WBDOs.
18 In our burden analyses, we did not attempt to identify likely etiologic agents for
19 outbreaks categorized as AGI; however, we did examine the frequency of AGI outbreak
20 by water system type. Since most of the AGI outbreaks occurred in groundwater
21 systems, a viral origin is suspected for most of these outbreaks (Barwick et al., 2000;
22 Lee et al., 2002). Recent advances in molecular methods have increased the likelihood
23 that viruses will be detected, but linking WBDOs to viruses remains a challenge since
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1 clinical specimens and water samples are still not routinely examined for viruses
2 (Blackburn et al., 2004; Yoder et al., 2004). We, therefore, expect considerable
3 uncertainty in the disease burden estimates for viruses due to the likelihood that many
4 of the AGI outbreaks are of viral etiology and the possibility that viral illnesses are less
5 effectively captured by surveillance systems than protozoan or bacterial illness cases
6 (Wheeler etal., 1999).
7 The ability of the passive WBDOSS to capture the true magnitude of the WBDO
8 disease burden in the U.S. is limited given the presumed under-reporting of outbreaks
9 and variability in thoroughness and rigor in reporting of epidemiologic data for different
10 outbreaks. Case number reports for outbreaks are dependent on the capacity of local
11 public health agencies and laboratories to identify cases and link these in a timely
12 manner to a common source of exposure to an etiologic agent. Case enumeration is
13 also impacted by the nature of the illness occurring during an outbreak. Since
14 waterborne infectious disease often manifests as gastroenteritis or another self4imiting
15 illness with mild symptoms, only a small proportion of cases may seek medical
16 attention, thereby limiting the number of ill persons that are reported to a disease
17 surveillance system. For example, the FoodNet survey of 14,647 U.S. residents
18 conducted during 2000-2001 indicated that 5% of those surveyed reported acute
19 diarrheal illness during the previous 4 weeks (Imhoff et al., 2004). Only 23% of those
20 who were ill visited a health care provider, and 17% of those seeking medical care
21 reported submitting a stool specimen for culture. This indicates that only 4% of those
22 who were ill were asked to submit a stool sample, greatly limiting the likelihood of
23 identifying an etiologic agent for most cases for acute gastrointestinal illnesses.
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1 Although mild cases of disease may frequently go unreported, they could
2 represent a large portion of the disease burden from WBDOs. Mild cases accounted for
3 nearly 43% of the total disease burden (based on COI analyses) from the Milwaukee
4 outbreak. This may not be representative of other outbreaks that are less thoroughly
5 investigated, since an estimated 88% of the mild cases did not seek medical care
6 (Corso et al., 2003). Garthright et al. (1988) estimated the total costs from medical
7 expenses and lost productivity associated with mild gastrointestinal illness in the U.S.
8 during 1985 at $44.9 billion for cases with no physician consultation, $6.3 billion for
9 cases with physician consultation and $1.7 billion for cases requiring hospitalization
10 (cost estimates were adjusted to 2000 U.S. dollars using the consumer price index for
11 medical services noted in Chapter 4). Cases of disease are not reported as mild,
12 moderate or severe in the WBDOSS, but we designated a proportion of cases in each
13 category based on the limited medical treatment data available in the WBDOSS. For
14 the COI analysis, we defined severe cases as individuals who died or were hospitalized
15 due to an infection related to a WBDO (see Chapter 4 for further information). Moderate
16 cases included individuals who visited emergency rooms or physicians and mild cases
17 were the remaining reported cases of illness. Our disease burden approach adjusted
18 for under-reported emergency room and physician visits but did not consider under-
19 reporting of mild cases. The degree of under-reporting among mild cases could not be
20 estimated since most of these cases do not seek medical attention, which limited our
21 ability to stratify the disease burden analyses by severity of illness categories.
22 The cases of illness reported to the WBDOSS most likely include acute cases of
23 gastrointestinal disease and, therefore, our analyses likely underestimate the burden
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1 associated with complications of infections (e.g., hemolytic uremic syndrome following
2 E. coll 0157). In addition, the lack of data on immune status and infrequent reporting of
3 age limited our ability to quantify effects of chronic waterborne disease that may have
4 occurred in susceptible populations such as the elderly or patients with HIV/AIDS.
5 Another limitation of the analyses was that the direct costs did not include certain
6 categories of expenditures. Specifically, the estimates do not include the other costs of
7 seeking care such as transportation and costs of hiring caregivers. Nor do they include
8 the costs of protective or averting behaviors such as bottled water or filters.
9 Accurate case enumeration is contingent on a thorough epidemiologic
10 investigation and quantification of the total population exposed during an outbreak. In
11 addition to actual reported case counts in the WBDOSS, local investigators may provide
12 an estimated count based on the reported attack rate and information on the population
13 exposed to the suspected contamination source. Since this information is not always
14 known for each outbreak, this results in variability in the case estimation approach
15 across outbreaks. We used the number of cases of illness per outbreak as reported in
16 the WBDOSS, including the actual counts reported for 70% of WBDOs. Using the
17 actual reported case numbers may lead to under-reporting in some of the outbreaks.
18 Identification of cases of illness can also be affected by the magnitude of and publicity
19 surrounding an outbreak as over-reporting of infectious disease symptoms has been
20 previously noted in retrospective epidemiologic studies (Wheeler et al., 1999).
21 We examined the potential for under- and over-reporting of gastroenteritis cases
22 associated with the Milwaukee cryptosporidiosis outbreak and also assessed the impact
23 of variable disease severity estimates for average duration of illness. This outbreak
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1 accounted for $461 million of the $610 million total burden for all reported outbreaks
2 during 1971-2000 and was based on 403,000 reported cases, 9 days average duration
3 of illness and a monthly background diarrheal incidence of 0.5% among residents of the
4 greater Milwaukee area. Given the magnitude of burden attributable to the Milwaukee
5 WBDO, we examined the extent that alternative values would impact the overall burden.
6 If a case estimate of 370,000 and disease duration of 3 days is assumed, the alternative
7 disease burden was $383 million. If a case estimate of 435,000 and disease duration of
8 9 days is assumed, the alternative disease burden was $472 million. Based on these
9 alternative estimates, the Milwaukee outbreak would still account for most of the
10 monetary burden estimated from reported WBDOs. This is largely due to the impact of
11 mortality on disease burden, since the number of deaths was held constant in this
12 sensitivity analysis.
13 Most of the cases of illness reported to the WBDOSS were assumed to be
14 primary cases, but we could not distinguish the extent to which secondary cases due to
15 person-to-person transmission impacted the number of reported cases. The likelihood
16 that secondary cases were detected and reported in epidemiologic outbreak
17 investigations is dependent on the latency and incubation periods of the etiologic agent
18 and the time frame of the outbreak investigation. WBDO investigations of outbreaks of
19 longer duration including those based on retrospective community surveys are more
20 likely to detect secondary cases unless specifically restricted in time to target primary
21 cases. For example, secondary transmission in the Milwaukee outbreak has been
22 estimated at 10% for the general population (Eisenberg et al., 2005) and was likely
23 more prevalent among the elderly (Naumova et al., 2003). While extensive
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1 epidemiologic investigations may better reflect the true magnitude of an outbreak,
2 including secondary cases may limit comparisons of the disease burden across etiologic
3 agent groups and may limit the potential to generalize reported epidemiologic measures
4 based on limited outbreak data.
5 The magnitude of under- or over-reporting of epidemiologic measures in the
6 WBDOSS is unknown; therefore, we used sensitivity analyses to examine the extent
7 that under- or over-reporting may influence our monetary estimates. We demonstrated
8 that the total monetary burden was most sensitive to estimates of person-days ill and
9 mortality. The influence of person-days ill, largely due to its use in productivity loss
10 calculations for both caregiver and the ill person, accounted for most of the COI
11 contribution to disease burden. These data further emphasize the need for accurate
12 estimation of the number of cases and the duration of illness for WBDOs since they
13 determine the contribution of person-days ill to disease burden estimates.
14 Disease burden is sensitive to the large monetary value ascribed to saving one
15 generic life (e.g., $6.43 million/death). This value is based on a review of VSL studies
16 that served as the basis for the monetary burden approach (U.S. EPA, 2000a). A
17 limitation of this approach was that it did not consider the variation across studies.
18 Although transferring VSL estimates is standard practice for U.S. EPA analyses, our
19 approach does not address the differences in the risk and population characteristics
20 (U.S. EPA, 2000a). For example, individuals may value occupational mortality risks
21 differently from environmental risks. It is also important to note that we are using VSL
22 estimates to describe the monetary burden of WBDO deaths, rather than to estimate the
23 value of a risk reduction. The use of sensitivity analyses have been recommended to
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1 address the uncertainty in VSL estimates (U.S. EPA, 2000a); therefore, we examined
2 the impact on the WBDO disease burden by using the distribution of the VSL described
3 in Chapter 6 and mortality estimate distributions predicted for different etiologic agents.
4 This analysis, based on the Weibull distribution and a mean of 108 deaths associated
5 with WBDOs, resulted in an additional $260 million attributable to premature mortality
6 compared to the disease burden based on the VSL central tendency approach using a
7 mean of 66 deaths presented in Chapters 4. This analysis showed that the variability
8 and uncertainty in VSL values is a significant source of the overall uncertainty in the
9 estimated burden associated with premature mortality.
10 7.2. CONCLUSIONS
11 In addition to mandating actions to improve the microbiological quality of water,
12 the 1996 amendments to the SDWA also mandated benefit-cost analyses for newly
13 proposed regulations. Estimates of the incidence and severity of diseases attributable
14 to drinking water as well as an assessment of the social and economic costs of the
15 occurrence of these diseases are essential for the conduct of benefit-cost analyses.
16 Three approaches are typically used to develop a waterborne disease incidence
17 estimate: (1) using risk assessment methods that utilize pathogen exposure information
18 and dose-response algorithms (2) generalizing epidemiologic study results to the
19 general population and (3) analyzing public health surveillance data. These
20 approaches, along with examples of estimates of endemic waterborne risks, are
21 discussed in detail in a special issue of the Journal of Water and Health to be published
22 in 2006.
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1 Economic analyses of new water regulations in the U.S. primarily focus on
2 evaluating endemic disease incidence that occurs when treatment and distribution
3 systems are functioning according to established practices (i.e., not under treatment
4 failure or deficiency situations). The U.S. EPA has largely relied on risk assessment
5 methods to develop the endemic disease incidence estimates needed for benefit-cost
6 analyses of proposed drinking water regulations. In the future, these risk assessment
7 estimates of burden will be complemented and strengthened by the SDWA-mandated
8 "national estimate" of waterborne disease. This mandate requires the U.S. EPA and the
9 CDC to jointly conduct pilot waterborne disease occurrence studies in at least five major
10 public water supply systems (U.S. EPA, 1998); one study already conducted has used
11 an epidemiologic intervention study design approach (e.g., Colford et al., 2005).
12 In contrast to those Agency efforts focused on examining the endemic disease
13 burden, we demonstrate a methodology for assessing the burden associated with
14 waterborne outbreaks. Our methodology relies on the third method described above for
15 estimating disease burden: analyzing surveillance data. Although this approach, like
16 the others, is affected by the accuracy of available data and the limitations of the
17 methodology that was developed, it provides additional insight for evaluating the overall
18 burden of waterborne disease in the U.S. This analysis provides a range of estimates
19 of the burden of reported waterborne outbreaks from 1971-2000, and this information
20 contributes to the body of knowledge that regulators need for informed decision-making.
21 The disease burden approach presented here allows for comparison of disparate public
22 health concerns through metrics that incorporate indicators of disease severity, costs
23 and societal values. The analysis presented here also examined the potential utility of
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1 using passive surveillance systems to develop disease burden estimates; the outcome
2 of this examination reinforces the importance of collecting more detailed epidemiologic
3 data, including disease severity measures to aid future disease burden efforts.
4 Although we were able to quantify the burden associated with reported WBDOs,
5 a main limitation of the analyses was the inability to determine the potential impact of
6 unrecognized and unreported WBDOs. Additional analyses could help identify the
7 important characteristics of unrecognized WBDOs that may aid in the estimation of the
8 potential impact of unrecognized and unreported WBDOs on waterborne disease
9 burden. Developing categorization approaches for determining the likely etiologic agent
10 or group associated with AGI outbreaks would also help to further refine the disease
11 burden estimates that are presented here. These efforts could help address some of
12 the uncertainty in the waterborne disease burden developed here.
13 7.3. RECOMMENDATIONS
14 This waterborne disease burden analysis was effective at determining the utility
15 of the WBDOSS for estimating disease burden. To address some of the uncertainty in
16 the disease burden estimates, additional data are needed including specific
17 improvements in the WBDO surveillance system. The following recommendations are
18 suggested to improve waterborne disease burden estimates in the future:
19 • Information needed to determine disease burden should be specifically
20 requested on CDC 52.12. This includes physician visits, emergency room visits
21 and the age distribution of the identified cases.
22 • Efforts are needed to standardize outbreak reporting to allow for comparisons of
23 disease burden between reported WBDOs. Information should be requested
24 about the method used to determine the number of actual and estimated cases
25 for each outbreak.
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1 • Information should also be requested about the method used to ascertain the
2 number of deaths, hospitalizations and illness duration for each reported
3 outbreak. Suggested questions include: Were hospitalizations based on
4 admission or discharge diagnosis? Was infection from the waterborne source a
5 contributing cause or the underlying cause of death? What time period was
6 considered for the WBDO? How many patients were interviewed to obtain the
7 illness duration information?
8 • Additional focused studies in selected outbreaks could improve the estimates of
9 the number of mild cases not seeking formal care and the costs (self-medication
10 and productivity losses) associated with them.
11 • Additional efforts, such as linking disease surveillance systems with water quality
12 monitoring systems, are needed to examine the effectiveness of current water
13 quality surveillance activities.
14 • Studies should be designed and conducted to assess the effectiveness of the
15 current WBDO surveillance system in detecting waterborne disease outbreaks.
16 • Studies should also be conducted to help estimate the number and type of
17 WBDOs that may be unrecognized.
18 • Death certificate analyses should be conducted among sensitive populations for
19 severe outbreaks to determine increases in mortality that may be attributable to
20 waterborne disease outbreaks.
21 In addition to the aforementioned recommendations, additional sensitivity
22 analyses are needed to examine the effect that alternative assumptions might have on
23 the disease burden estimates presented here. This could help identify the components
24 that have the greatest potential impact on disease burden and could further delineate
25 specific research needs for the future.
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APPENDIX A
THE WATERBORNE OUTBREAK SURVEILLANCE SYSTEM
A.1. INTRODUCTION
National statistics on waterborne outbreaks have been compiled and reported in
the United States since 1920. In 1971, the CDC, the U.S. EPA, and the Council of
State and Territorial Epidemiologists began a collaborative, passive surveillance
program for the collection of data on the occurrence and causes of waterborne. State,
territorial, and local public health agencies have the primary responsibility for detecting
and investigating waterborne outbreaks, and they voluntarily report them to the CDC on
Standard Form 52.12.1 Occasionally, the CDC and U.S. EPA are invited to participate
in the investigation.
The standard reporting form, which has been used since 1974, solicits data on
the characteristics of the outbreak (including the number of ill persons, dates of illness
onset, and location that define the outbreak), results from epidemiologic studies, testing
of water and patient samples, and contributory issues, such as water distribution,
disinfection, and environmental factors. CDC annually requests reports from state and
territorial public health agencies, and from the Freely Associated States (including
Republic of Marshall Islands, Federated States of Micronesia, and Republic of Palau).
Additional information regarding the water quality, water system and treatment is
obtained from the state's drinking water agency as needed.
1 Appendix B shows various forms used during 1971-2002. The current form can be found at
www.cdc.gov/healthyswimming/downloads/cdc 5212 waterborne.pdf.
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Surveillance summaries of reported waterborne outbreaks have been published
biennially or annually since 1973 (CDC, 1973, 1974, 1976a,b, 1977, 1979, 1980, 1981,
1982a,b, 1983, 1984, 1985; St. Louis, 1988; Levine and Craun, 1990; Herwaldt et al.,
1991; Moore etal., 1993; Kramer etal., 1996; Levyetal., 1998; Barwick et al., 2000;
Lee et al., 2002; Blackburn et al., 2004). The surveillance system includes outbreaks
associated with drinking water, recreational water, and other types of water exposures.
Numerical and text data are abstracted from the outbreak form and supporting
documents and entered into a database maintained by CDC and U.S. EPA. For the
analyses in this report, we used information from drinking water outbreaks reported
during the 30-year period 1971-2000. Although surveillance information was recently
made available for 2001-2002, the detailed information was not readily available for our
analyses.
A.2. USES OF THE WATERBORNE OUTBREAK SURVEILLANCE DATA
WBDO surveillance efforts have the following objectives: (1) characterize the
epidemiology of waterborne outbreaks; (2) identify the etiologic agents that caused
waterborne outbreaks and determine why the outbreaks occurred; (3) encourage public
health personnel to detect and investigate waterborne outbreaks; and (4) collaborate
with local, state, federal, and international agencies on initiatives to prevent waterborne
disease. The surveillance data have been helpful in identifying the important
waterborne pathogens and evaluating the relative degrees of risk associated with
different types of source water and systems, the adequacy of current technologies and
regulations (Lee et al., 2002; Blackburn et al., 2004).
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A.2.1. Classification of Waterborne Outbreaks and Water Systems. Two criteria
must be met for an event to be defined as a waterborne outbreak (Lee et al., 2002;
Blackburn et al., 2004). First, two or more persons must have experienced a similar
illness after exposure to water. This criterion is waived for single cases of laboratory-
confirmed primary amebic meningoencephalitis and for single cases of chemical
poisoning if water-quality data indicate contamination by the chemical. Second,
epidemiologic evidence must implicate water as the probable source of the illness.
Epidemiologic evidence is important because waterborne pathogens of concern in the
United States may have multiple transmission routes, including person-to-person
contact, contact with fomites, and ingestion of contaminated food as well as
contaminated water. The evidence must associate water with illnesses before it can be
considered as a waterborne outbreak.
The CDC and U.S. EPA classify reported waterborne outbreaks according to the
strength of the evidence implicating water as the vehicle of transmission (Lee et al.,
2002; Blackburn et al., 2004). The classification scheme is based on the epidemiologic
and water-quality data provided by the investigators. Epidemiologic data are weighted
more than water-quality data. Although outbreaks without water-quality data might be
included, reports that lack epidemiologic data are not. Single cases of primary amebic
meningoencephalitis or chemical poisoning are not classified according to this scheme.
The classification system was developed in 1989 (Herwaldt et al., 1991). Before 1989,
an informal, but similar, approach was used to evaluate the evidence. A classification of
I indicates that adequate epidemiologic and water-quality data were reported (Table
A-1); however, "the classification does not necessarily imply whether an investigation
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TABLE A-1
Classification of Investigations of Waterborne Disease Outbreaks in the United States
Class
I
II
III
IV
Epidemiologic Data
Adequate
Data were provided about exposed and
unexposed persons, and the relative risk or
odds ratio was >2, or the p-value was <0.05
Adequate
Provided, but limited
Epidemiologic data were provided that did not
meet the criteria for Class I, or the claim was
made that ill persons had no exposures in
common besides water, but no data were
provided.
Provided, but limited
Water-quality Data
Provided and adequate
Historical information or laboratory data
(e.g., the history that a chlorinator
malfunctioned or a water main broke, no
detectable free-chlorine residual, or the
presence of coliforms in the water)
Not provided or inadequate (e.g., laboratory
of water not done)
testing
Provided and adequate
Not provided or inadequate
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was optimally conducted" (Lee et al., 2002) or that all information requested on the
report form was provided. Although anecdotal reports of possible waterborne illness are
not included, outbreaks with limited epidemiologic evidence may be included (Craun et
al., 2001). During 1992-1996, 29% of the reported WBDOs had limited epidemiologic
evidence (classification III); in none of the WBDOs were both the epidemiologic and
water quality evidence limited (classification IV) (Craun et al., 2001). A classification of
II or III should not be interpreted to mean that investigations were inadequate or
incomplete (Lee et al., 2002; Blackburn et al., 2004). Outbreaks and the resulting
investigations occur under various circumstances, and not all outbreaks can or should
be rigorously investigated (Lee et al., 2002; Blackburn et al., 2004). In addition,
outbreaks that affect few persons are more likely to receive a classification of III, rather
than I, on the basis of the relatively limited sample size available for analysis (Lee et al.,
2002; Blackburn et al., 2004). By establishing guidelines to include WBDOs with limited
evidence, investigators are encouraged to report outbreaks which may have been
difficult to investigate or where some of the findings may not be conclusive (Craun et al.,
2001).
The CDC and U.S. EPA also classify each water system associated with a
waterborne outbreak as having one of the following deficiencies: untreated surface
water; untreated groundwater; treatment deficiency (e.g., temporary interruption of
disinfection, inadequate disinfection, and inadequate or no filtration); distribution system
deficiency (e.g., cross-connection, contamination of water mains during construction or
repair, and contamination of a storage facility); and unknown or miscellaneous
deficiency (e.g., contaminated ice, faucets, containers, or bottled water).
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Water sources are identified as either surface water, groundwater, or mixed (both
surface water and groundwater sources). Public drinking water systems that may be
associated with outbreaks are classified as either community or noncommunity based
on definitions of the SDWA; drinking water-associated outbreaks involving private,
individual water systems are also tabulated (Figure A-1). Individual water systems
serve families that do not have access to a public system. Drinking water outbreaks are
also associated with the ingestion of water not intended for consumption, contaminated
bottled water, and contamination of water or ice contaminated at its point of use (e.g., a
contaminated water faucet or serving container). Waterborne outbreaks associated with
cruise ships are not included in the waterborne outbreak surveillance system.
A.3. CASES OF ILLNESS AND SEVERITY OF ILLNESS
In the surveillance system, the primary unit of analysis is an outbreak, not an
individual case of a waterborne disease. However, information is requested on the
report form about the actual and estimated numbers of cases of illness, cases
hospitalized, and fatalities. The report form also requests information about the actual
and estimated numbers of persons exposed (at risk), incubation period, duration of
illness, the number of patient specimens (e.g., stool, vomitus, serum) examined and
laboratory findings.
The case definition will vary among the outbreaks depending upon the suspected
etiology and the signs and symptoms that are considered important by each
investigator. The report form requests information about patient histories and the
number of persons with various symptoms. The symptoms highlighted on the report
form include diarrhea, vomiting, cramps, fever, nausea, rash, and conjunctivitis.
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Drinking Water Systems
Public Water Systems
Public or private ownership
(Subject to EPA Regulations)
Individual Water Systems
(If regulated, state or local regulations)
Non-Community
1
Community
Use of non-public sources
Privately owned home or farm wells,
springs, or surface water sources
I Transient (e.g., gas stations, parks, resorts,
campgrounds, restaurants, and motels
with their own water systems)
Non-transient (e.g., schools, factories,
office buildings, and hospitals
with their own water systems)
Streams, ponds, or shallow wells
not intended for drinking
Bottled water (commercial bottled water is
regulated by FDA; individuals may also
fill their own containers)*
* Footnote: In some instances, bottled water is used
in lieu of a community supply or by non-community systems
FIGURE A-1
Types of Drinking Water Systems Used for Outbreak Classification
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Information about the number of stools per day may also be used to define a case, and
stools may be further described as watery, loose, or containing mucus or blood (CDC
52.12; Benenson, 1995). If a separate investigative report is enclosed, the specific case
definition is usually provided. Otherwise, the case definition must be assumed from
information provided on the report form. The report form specifically requests
information about the number of persons with diarrhea at a frequency of three stools per
day or diarrhea with an alternative definition to be provided by the investigator. The
report form also requests information about a confirmed or suspected etiology.
The information requested on the standard report form can help describe the
cases and impact associated with a specific outbreak, but investigators may not provide
complete information about all of the measures that are considered important for
estimating the outbreak's impact. The primary purpose of an investigation is to identify
the cause of the outbreak so that steps can be taken to stop the outbreak, and this
presumes that the recognition of an WBDO is timely. If water is implicated in an
outbreak investigation where cases are continuing to occur, the focus will be on
understanding the circumstances that led to the outbreak and developing corrective
measures to ensure that the water is safe. In addition, WBDOs may be retrospectively
investigated to identify the etiologic agent and water system deficiencies. In this case,
limited information may be available to the investigator. Thus, identification of the full
impact of the WBDO may be of secondary importance, depending on the suspected
etiology, population at risk, and available resources. Illnesses among travelers and
tourists may be geographically dispersed making it difficult to recognize all cases. Also,
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there has been controversy surrounding reported WBDOs and the possible over
estimation of cases (Craun etal., 2001).
As previously noted, the cases reported in the surveillance system may be based
on limited information. In addition, cases may be reported in several ways. Reported
cases may be either an actual or estimated number, and the reported cases may be
based on signs and symptoms or may be confirmed by laboratory analysis of
specimens. If both actual and estimated case counts are included on the outbreak
report form, the CDC tabulates the estimated case count if the study population was
sampled randomly or the estimated count was calculated by using the attack rate (Lee
etal., 2002).
Recurring methodological problems may also limit the information about
waterborne transmission. For example, an outbreak may impact relatively few persons
making it difficult to identify a waterborne association, or there may be a large number
of asymptomatic infections or mild illnesses that are not able to be identified because of
the lack of resources. In addition, not all WBDO investigations identify both primary and
secondary cases to assess the full impact of the outbreak. Primary cases are persons
who are exposed to and infected by contaminated water; secondary cases are persons
who are infected by and became ill after contact with primary case-patients. Primary
cases can be a source of secondary infection, since some waterborne pathogens are
easily spread by person-to-person transmission (Craun et al., 2001). The standard
report form does not distinguish between primary and secondary cases; this information
is available only from comments that may be noted on the remarks section of the report
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form or separate reports attached to the form. If primary cases and secondary cases
are reported, only primary cases are included in the database.
A.4. LIMITATIONS OF THE SURVEILLANCE DATA
The key limitation of the data collected as part of the surveillance system is that
the information pertains to outbreaks of waterborne disease. The reported statistics do
not include endemic or sporadic cases of waterborne disease that are not recognized as
an outbreak, and the epidemiologic trends and water-quality concerns observed in
outbreaks might not necessarily reflect or correspond with trends associated with
endemic waterborne illness. Endemic disease is the usual ongoing prevalence of a
disease in a population or geographic area, and specifically-designed epidemiologic
studies are needed to provide a quantitative estimate of the risk attributable to drinking
water. The CDC and U.S. EPA are currently conducting epidemiologic studies of
endemic waterborne disease risks, and these risks are not considered in our analyses.
Since the surveillance is passive and outbreak reporting is voluntary, the
surveillance statistics represent only a portion of the waterborne outbreaks that occur in
the United States. The thoroughness of reporting varies, and the epidemiologic
information (e.g., population exposed, attack rates, cases and severity of illness) may
be inconsistent or sparse. Thus, not all of the cases that occurred may be included in
the outbreak reports. As previously noted, cases of Illness may also be overestimated
due to recall or other epidemiologic biases or inadequate information about the size of
the exposed population (Craun and Frost, 2002; Craun et al., 2001; Cooper et al.,
1995). For example, in the Milwaukee cryptosporidiosis outbreak, the largest
waterborne outbreak reported in the U.S., an extensive investigation was conducted
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and considerable efforts went into estimating the cases of illness and their severity
(Mac Kenzie et al., 1994; Hoxie et al., 1996; Naumova et al., 2003; Proctor et al., 1998;
McDonald et al., 2001). There are few outbreaks where similar efforts were expended
to estimate the number of cases and their severity. However, even with these efforts,
there is still uncertainty about the outbreak's impact on Milwaukee residents. Hunter
and Syed (2001) suggest that cases attributed to the waterborne outbreak were greatly
overestimated, and a study of Cryptosporidium-spec\i\c antibody responses in children
by McDonald et al. (2001) suggest that infection was much more widespread than
previously appreciated. Unfortunately, McDonald et al. provided no information about
symptoms or severity of cryptosporidiosis in the infected children.
In addition, not all waterborne outbreaks are recognized and investigated and not
all investigated outbreaks are reported to CDC or U.S. EPA. For example, outbreaks
occurring in national parks, tribal lands, or military bases may not be reported to state or
local authorities (Blackburn et al., 2004). There are few estimates of the number of
waterborne outbreaks that may go unrecognized and unreported (Craun, 1986; Hopkins
et al., 1985), and studies have not been performed that assess the sensitivity of the
surveillance system regarding unrecognized and unreported outbreaks (Blackburn et
al., 2004). Thus, any estimates of underreporting of outbreaks should be viewed with
caution.
Blackburn et al. (2004) suggest that data in the surveillance system markedly
underestimate the true incidence of waterborne outbreaks. In part, this is because
multiple factors influence whether waterborne outbreaks are recognized and
investigated by local or state public health agencies. These include public awareness of
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the outbreak, availability of laboratory testing, requirements for reporting diseases, and
resources available to the local health departments for surveillance and investigation of
probable outbreaks. In addition, changes in the capacity of local and state public health
agencies and laboratories to detect an outbreak might influence the numbers of
outbreaks reported in each state relative to others. Thus, the states with the majority of
outbreaks reported during this period might not be the states where the majority of
outbreaks actually occurred. An increase in the number of outbreaks reported could
either reflect an actual increase in outbreaks or a change in sensitivity of surveillance
practices. As with any passive surveillance system, accuracy of the data depends
greatly on the reporting agencies (state, local and territorial health departments in this
case). Thus, independent of the recognition or investigation of a given outbreak,
reporting bias can influence the final data.
Most likely to be recognized and investigated are outbreaks of acute illness
characterized by a short incubation period, outbreaks that result in serious illness or
symptoms requiring medical treatment, and outbreaks of recently recognized etiologies
for which laboratory methods have become more sensitive or widely available
(Blackburn et al., 2004). Increased reporting often occurs as etiologies become better
recognized, water system deficiencies identified, and state surveillance activities and
laboratory capabilities increase (Frost et al., 1995, 1996; Hopkins et al., 1985).
Recommendations for improving waterborne disease outbreak investigations include
increased laboratory support for clinical and water analyses, enhanced surveillance
activities, and assessment of sources of potential bias (Craun et al., 2001; Frost et al.,
2003; Hunter etal., 2003).
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During the 30-year surveillance period (1971-2000) included in our analysis, an
etiologic agent was not identified in 55% of the reported waterborne outbreaks of
infectious disease. The identification of the etiologic agent of a waterborne outbreak
depends on the timely recognition of the outbreak so that appropriate clinical and
environmental samples can be collected. Additionally, the laboratory involved must
have the capability to test for a particular organism in order to detect it. For example,
routine testing of stool specimens at laboratories will include tests for the presence of
enteric bacterial pathogens and might also include an ova and parasite examination.
However, Cryptosporidium spp., among the most commonly reported waterborne
pathogens, is often not included in standard ova and parasite examinations, and thus
must be specifically requested (Jones et al., 2004). Additionally, though norovirus
testing is being performed more commonly, testing for other viral agents is rarely done
(Blackburn etal., 2004).
Outbreaks classified as AGI are likely caused by a variety of etiologic agents.
The symptoms and severity of illness associated with these outbreaks can vary based
on the etiologic agent. Testing, when conducted, may not identify an agent. For
example during 1999-2000, laboratory testing for enteric pathogens was conducted in
five of the 17 AGI outbreaks; stool specimens were negative for parasitic and bacterial
pathogens in four outbreaks. In the fifth AGI outbreak affecting only two persons, stool
specimens tested negative for Giardia intestinalis but positive for Blastocystis hominis.
Whether B. hominis was the cause of the reported illness was unclear because its
pathogenicity has been debated in the scientific community (Lee et al., 2002).
Suspected pathogens were noted by investigators of the following four additional AGI
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outbreaks on the basis of symptoms of illness: norovirius was suspected in one
outbreak and G. intestinalis in one outbreak; a bacterial pathogen and an unknown
chemical were each suspected in the two remaining outbreaks.
Finally, collection of water-quality data which can help determine contamination
sources or identify the waterborne pathogen depends primarily on local and state
statutory requirements, the availability of investigative personnel, and the technical
capacity of the laboratories that test the water. Not all reported waterborne outbreaks
have adequate information about waterborne pathogens, indicators of fecal
contamination, or likely sources of the contamination.
A.5. REFERENCES
Barwick, R.S., D.A. Levy, G.F. Craun, M.J. Beach and R.L. Calderon. 2000.
Surveillance for waterborne disease outbreaks—United States, 1997-1998. Morb.
Mort. Weekly Report 49(SS-4):1-35.
Blackburn, B.C., G.F. Craun, J.S. Yoderetal. 2004. Surveillance for waterborne-
disease outbreaks associated with drinking water—United States, 2001-2002. Morb.
Mort. Weekly Report 53(SS-8):23-45.
CDC (Centers for Disease Control and Prevention). 1973. Foodborne Outbreaks,
Annual Summary 1972. U.S. Dept. of Health and Human Services, Atlanta, GA.
Publication 76-8185. November.
CDC (Centers for Disease Control and Prevention). 1974. Foodborne and Waterborne
Disease Outbreaks, Annual Summary 1973. U.S. Dept. of Health and Human Services,
Atlanta, GA. Publication 75-8185. August.
CDC (Centers for Disease Control and Prevention). 1976a. Foodborne and
Waterborne Disease Outbreaks, Annual Summary 1974. U.S. Dept. of Health and
Human Services, Atlanta, GA. Publication 76-8185. January.
CDC (Centers for Disease Control and Prevention). 1976b. Foodborne and
Waterborne Disease Outbreaks, Annual Summary 1975. U.S. Dept. of Health and
Human Services, Atlanta, GA. Publication 76-8185. August.
Draft: Do Not Cite or Quote A-14 8/31/06
-------
CDC (Centers for Disease Control and Prevention). 1977. Foodborne and Waterborne
Disease Outbreaks, Annual Summary 1976. U.S. Dept. of Health and Human Services,
Atlanta, GA. Publication 78-8185. October.
CDC (Centers for Disease Control and Prevention). 1979. Foodborne and Waterborne
Disease Outbreaks, Annual Summary 1977. U.S. Dept. of Health and Human Services,
Atlanta, GA. Publication 79-81385. August.
CDC (Centers for Disease Control and Prevention). 1980. Water-related Disease
Outbreaks, Annual Summary 1978. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 80-8385. May.
CDC (Centers for Disease Control and Prevention). 1981. Water-related Disease
Outbreaks, Annual Summary 1979. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 81-8385. September.
CDC (Centers for Disease Control and Prevention). 1982a. Water-related Disease
Outbreaks, Annual Summary 1980. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 82-8385. February.
CDC (Centers for Disease Control and Prevention). 1982b. Water-related Disease
Outbreaks, Annual Summary 1981. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 82-8385. September.
CDC (Centers for Disease Control and Prevention). 1983. Water-related Disease
Outbreaks, Annual Summary 1982. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 83-8385. August.
CDC (Centers for Disease Control and Prevention). 1984. Water-related Disease
Outbreaks, Annual Summary 1983. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 84-8385. September.
CDC (Centers for Disease Control and Prevention). 1985. Water-related Disease
Outbreaks, Annual Summary 1984. U.S. Dept. of Health and Human Services, Atlanta,
GA. HHS Publication 99-2510. November.
Craun, G.F., Ed. 1990. Methods for the Investigation and Prevention of Waterborne
Disease Outbreaks. U.S. Environmental Protection Agency, Cincinnati, OH.
EP A/600/1-90/005a.
Craun, G.F., F.J. Frost, R.L. Calderon et al. 2001. Improving waterborne disease
outbreak investigations. Int. J. Environ. Health Res. 11:229-243.
Frost, F.J., R.L. Calderon and G.F. Craun. 1995. Waterborne disease surveillance:
findings of a survey of state and territorial epidemiology programs. J. Environ. Health
58:6-11.
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Frost, F.J., G.F. Craun and R.L. Calderon. 1996. Waterborne disease surveillance:
What is it and do we need it? J. Am. Water Works Assoc. 88(9):66-75.
Frost, F.J., R.L. Calderon and G.F. Craun. 2003. Improving waterborne disease
surveillance. In: Drinking Water Regulation and Health, F.W. Pontious, Ed. John Wiley
& Sons, New York, NY. pp. 25-44.
Herwaldt, B.L., G.F. Craun, S.L. Stokes and D.D. Juranek. 1991. Waterborne-disease
outbreaks, 1989-1990. Morb. Mort. Weekly Report 40(SS-3):1-21.
Hopkins, R.S., P. Shillam, B. Gaspard, L. Eisnackand R.J. Karlin. 1985. Waterborne
disease in Colorado: Three years' surveillance and 18 outbreaks. Am. J. Pub. Health.
75:254.
Hunter, P.R., M. Waite and E. Ronchi, Ed. 2003. Drinking Water and Infectious
Disease - Establishing the Links. CRC Press (Boca Raton, FL) and IWA Publishing
(London), pp. 221.
Kramer, M.H., B.L. Herwaldt, G.F. Craun, R.L. Calderon and D.D. Jurane. 1996.
Surveillance for waterborne-disease outbreaks-United States, 1993-1994. Morb. Mort.
Weekly Report 45(SS-1):1-33.
Lee, S.H., D.A. Levy, G.F. Craun, M.J. Beach and R.L. Calderon. 2002. Surveillance
for waterborne-disease outbreaks- United States, 1999-2000. Morb. Mort. Weekly
Report 51(SS-8):1-48.
Levine, W.C. and G.F. Craun. 1990. Waterborne disease outbreaks, 1986-1988.
Morb. Mort. Weekly Report 39(SS-1):1-13.
Levy, D.A., M.S. Bens, G.F. Craun, R.L. Calderon and B.L. Herwaldt. 1998.
Surveillance for waterborne-disease outbreaks-United States, 1995-1996, Morb. Mort.
Weekly Report 47(SS-5):1-33.
McDonald, A.C., W.R. Mac Kenzie, D.G. Addiss et al. 2001. Crytosporidium parvum-
specific antibody responses among children residing in Milwaukee during the 1993
waterborne outbreak. J. Inf. Dis. 183:1373-1379.
Moore, A.C., B.L. Herwaldt, G.F. Craun, R.L. Calderon, A.K. Highsmith and D.D.
Juranek. 1993. Surveillance for waterborne disease outbreaks-United States,
1991-1992. Morb. Mort. Weekly Report 42(SS-5):1-22.
St. Louis, M.E. 1988. Water-related disease outbreaks, 1985-1996. Morb. Mort.
Weekly Report 37(SS-2): 15-24.
Draft: Do Not Cite or Quote A-16 8/31/06
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APPENDIX B
OUTBREAK INVESTIGATION METHODS
ENTERIC WATERBORNE DISEASE OUTBREAKS IN DRINKING WATER 1971-2000
Draft: Do Not Cite or Quote B-1 8/31/06
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TABLE B-1
Case Counts Reported in Enteric Waterborne Disease Outbreaks in Drinking Water by Time Period, 1971-2000
How Cases Were
Reported
Cases, Actual
Cases, Estimated
Unknown
Total
1971 to 1980
Number of
Reported
Outbreaks
192
49
44
285
Number of
Reported
Cases
16,817
52,162
5,552
74,531
1981 to 1990
Number of
Reported
Outbreaks
171
56
8
235
Number of
Reported
Cases
13,467
49,587
182
63,236
1991 to 2000
Number of
Reported
Outbreaks
100
43
2
145
Number of
Reported
Cases
5,959
426,181
55
432,195
Draft: Do Not Cite or Quote
B-2
8/31/06
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TABLE B-2
Case Counts Reported in Enteric Waterborne Disease Outbreaks in Drinking Water by Type of System, 1971-2000
How Cases Were
Reported
Cases, Actual
Cases, Estimated
Unknown
Total
Community
Number of
Reported
Outbreaks
170
72
12
254
Number of
Reported
Cases
18,421
491,786
4,063
514,270
Individual
Number of
Reported
Outbreaks
64
6
12
82
Number of
Reported
Cases
944
409
155
1,508
Non-community
Number of
Reported
Outbreaks
229
70
30
329
Number of
Reported
Cases
16,878
35,735
1,571
54,184
Draft: Do Not Cite or Quote
B-3
8/31/06
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TABLE B-3
How Reported Cases Were Estimated in Enteric Waterborne Disease Outbreaks in Drinking Water by Time Period,
1971-2000
How Cases Were Estimated
Cohort survey
Unknown
Guess
Random survey
Cohort and physician survey
Physician Survey
Total
1971 to 1980
Number of
Reported
Outbreaks
26
8
9
5
1
0
49
Number of
Reported
Cases
21,419
14,797
2,051
12,695
1,200
0
52,162
1981 to 1990
Number of
Reported
Outbreaks
23
15
11
6
1
0
56
Number of
Reported
Cases
20,661
7,445
4,053
17,343
85
0
49,587
1991 to 2000
Number of
Reported
Outbreaks
15
6
13
8
0
1
43
Number of
Reported
Cases
2,191
1,885
1,847
420,188
0
70
426,181
Draft: Do Not Cite or Quote
B-4
8/31/06
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TABLE B-4
How Reported Cases Were Estimated in Enteric Waterborne Disease Outbreaks in Drinking Water by Type of System,
1971-2000
How Cases Were Estimated
Cohort survey
Unknown
Guess
Random survey
Cohort and physician survey
Physician Survey
Total
Community
Number of
Reported
Outbreaks
33
15
6
17
1
0
72
Number of
Reported
Cases
24,800
17,038
457
448,291
1,200
0
491,786
Individual
Number of
Reported
Outbreaks
0
1
4
0
1
0
6
Number of
Reported
Cases
0
150
174
0
85
0
409
Non-community
Number of
Reported
Outbreaks
31
13
23
2
0
1
70
Number of
Reported
Cases
19,471
6,939
7,320
1,935
0
70
35,735
Draft: Do Not Cite or Quote
B-5
8/31/06
-------
TABLE B-5
How Case Counts Were Obtained in Enteric Waterborne Disease Outbreaks in Drinking Water by Time Period,
1971-2000
How Actual Cases Were
Obtained
Cohort survey
Unknown
All population at risk surveyed
Cohort and physician survey
Laboratory positive cases
Physician, hospital survey
Random survey
Total
1971 to 1980
Number of
Reported
Outbreaks
96
41
38
12
3
2
0
192
Number of
Reported
Cases
7,310
5,867
2,008
1,457
39
136
0
16,817
1981 to 1990
Number of
Reported
Outbreaks
88
41
22
8
6
2
4
171
Number of
Reported
Cases
4,062
5,046
617
1,912
759
15
1,056
13,467
1991 to 2000
Number of
Reported
Outbreaks
59
6
30
2
2
1
0
100
Number of
Reported
Cases
4,328
338
814
203
153
123
0
5,959
Draft: Do Not Cite or Quote
B-6
8/31/06
-------
TABLE B-6
How Case Counts Were Obtained in Enteric Waterborne Disease Outbreaks in Drinking Water by Type of System,
1971-2000
How Actual Cases Were
Obtained
Cohort survey
Unknown
All population at risk surveyed
Cohort and physician survey
Laboratory positive cases
Physician, hospital survey
Random survey
Total
Community
Number of
Reported
Outbreaks
95
36
13
13
7
2
4
170
Number of
Reported
Cases
6,196
7,148
770
2,324
912
15
1,056
18,421
Individual
Number of
Reported
Outbreaks
23
6
33
1
1
0
0
64
Number of
Reported
Cases
541
35
364
2
2
0
0
944
Non-community
Number of
Reported
Outbreaks
125
46
44
8
3
3
0
229
Number of
Reported
Cases
8,963
4,068
2,305
1,246
37
259
0
16,878
Draft: Do Not Cite or Quote
B-7
8/31/06
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APPENDIX C
ANNUAL ESTIMATES OF EPIDEMIOLOGIC AND MONETARY DISEASE BURDEN,
1971-2000
Draft: Do Not Cite or Quote C-1 8/31/06
-------
TABLE C-1
Reported and Projected Epidemiological Burden by Year
CO
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Outbreaks
18
27
25
20
21
32
28
30
38
46
32
41
42
24
20
19
14
(/5
CD
CO
0
5,179
1,448
1,762
8,087
10,842
5,033
3,227
11,389
9,817
17,747
4,726
3,569
21,033
1,770
1,914
1,505
22,122
"S §
o <= =
0. 0
CD (/5
rv *—
LL CD
0_
14,854
645
1,482
26,613
36,580
7,891
1,426
53,690
29,955
7,437
870
6,787
43,663
7,022
1,768
3,311
6,388
Projected
Person-Days
III
19,770
8,180
12,151
34,282
40,512
24,373
21,518
57,919
89,775
78,291
25,212
21,684
84,951
13,776
13,395
8,856
145,004
Physician
Visits
Reported
96
15
2
627
123
6
3
160
9
47
4
'o £ "cj
'(/) CD
>^> '5*
jz -^ !r
D_ 0_
665
241
239
1,761
1,063
623
575
2,070
2,213
2,183
479
557
2,712
357
335
194
1,546
Emergency
Room Visits
Reported
91
79
179
102
5
1
4
540
Emergency
Room Visits
Projected
575
146
163
466
530
464
255
547
571
1,557
290
267
1,963
85
129
97
1,159
Hospital Visits
Reported
63
20
193
214
61
105
21
24
15
73
19
57
60
12
102
18
49
Hospital Visits
Projected
63
20
193
214
61
105
21
24
15
73
19
57
60
12
102
18
49
Deaths
Reported
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Deaths
Projected
1
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Draft: Do Not Cite or Quote
C-2
8/31/06
-------
TABLE C-1 cont.
CO
CD
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Total
Outbreaks
15
13
15
16
24
12
14
15
7
7
10
13
27
665
CO
CD
CO
CO
0
2,160
2,670
1,767
12,981
4,840
404,114
1,310
2,492
843
1,752
1,703
1,163
997
569,962
Reported
Person-Days
III
3,114
3,364
840
34,255
37,137
3,635,960
3,383
7,102
1,477
2,669
7,475
2,716
3,052
3,992,923
Projected
Person-Days
III
8,722
12,641
8,679
34,572
39,626
3,637,297
10,161
27,099
2,928
3,325
8,727
6,649
4,779
4,504,854
Physician
Visits
Reported
97
4
48
20,283
2
5
21,531
Physician
Visits
Projected
238
280
243
1,347
441
20,355
189
560
77
182
91
78
92
41,985
Emergency
Room Visits
Reported
4
8
1,013
Emergency
Room Visits
Projected
170
130
177
1,417
283
11,749
99
120
24
26
53
23
40
23,575
Hospital Visits
Reported
15
49
10
30
16
4,432
10
21
5
3
87
97
34
5,915
Hospital Visits
Projected
15
49
10
30
16
4,432
10
21
5
3
87
97
34
5,915
Deaths
Reported
0
4
0
0
0
57
0
0
0
0
0
2
0
66
Deaths
Projected
0
4
0
0
0
57
0
0
0
0
0
2
0
66
Draft: Do Not Cite or Quote
C-3
8/31/06
-------
TABLE C-2
Reported and Projected Economic Burden by Year
Year
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Total
Physician
Visit Costs
Reported
($)
6,192
968
129
-
40,442
7,934
-
-
387
194
10,320
581
3,032
258
-
-
-
-
6,257
258
-
3,096
1,308,254
-
129
323
-
-
-
-
1,388,750
Physician
Visit Costs
Adjusted
($)
42,906
15,551
15,383
113,577
68,542
40,164
37,082
133,490
142,758
140,814
30,893
35,936
174,947
23,007
21,603
12,541
99,711
15,336
18,030
15,690
86,895
28,470
1,312,887
12,202
36,117
4,957
11,751
5,863
5,000
5,921
2,708,025
Emergency
Room Visit
Costs
Reported ($)
-
-
-
-
34,764
30,180
-
68,382
38,966
1,910
-
-
382
-
1,528
-
206,291
-
-
1,528
-
-
-
-
3,056
-
-
-
-
-
386,986
Emergency
Room Costs
Projected
($)
219,687
55,730
62,128
177,973
202,382
177,370
97,485
208,985
218,017
594,967
110,848
101,959
749,956
32,451
49,419
36,873
442,902
64,762
49,589
67,576
541,336
108,151
4,488,469
37,756
45,967
9,010
9,886
20,189
8,884
15,369
9,006,075
Hospitalization
Costs Reported
($)
120,045
46,162
1,217,719
604,941
183,891
319,034
69,626
73,902
41,686
231,208
72,233
153,667
193,656
39,607
474,410
57,728
139,034
41,737
158,624
29,114
90,969
49,609
24,596,165
29,879
66,697
15,487
9,525
344,192
341,787
123,576
29,935,910
Hospitalization
Costs Projected
($)
120,045
46,162
1,217,719
604,941
183,891
319,034
69,626
73,902
41,686
231,208
72,233
153,667
193,656
39,607
474,410
57,728
139,034
41,737
158,624
29,114
90,969
49,609
24,596,165
29,879
66,697
15,487
9,525
344,192
341,787
123,576
29,935,910
Self
Medication
Costs
Reported ($)
1 1 ,524
3,222
3,976
18,024
24,143
11,210
7,171
25,305
21,807
39,423
10,510
7,943
46,717
3,934
4,283
3,347
49,167
4,800
5,952
3,927
28,828
10,754
900,132
2,911
5,540
1,873
3,890
3,809
2,614
2,225
1,268,959
Self
Medication
Costs
Projected ($)
11,608
3,249
4,005
18,191
24,209
1 1 ,276
7,233
25,488
22,007
39,703
10,556
8,002
47,063
3,966
4,317
3,369
49,330
4,831
5,976
3,957
29,035
10,804
901,018
2,933
5,590
1,881
3,906
3,820
2,622
2,234
1,272,179
Cost-of-lllness
Prod Losses
Reported
($)
311,200
14,612
33,734
632,235
878,856
185,436
32,096
1,150,298
635,379
157,190
51,276
240,720
940,858
153,320
170,618
76,467
230,936
67,181
100,059
18,888
733,679
781,588
90,942,357
72,835
156,270
34,051
56,669
183,347
73,458
81,367
99,196,978
Draft: Do Not Cite or Quote
C-4
8/31/06
-------
TABLE C-2 cont.
Year
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Total
Cost-of-
Illness Prod
Losses
Projected
($)
881,203
342,964
1,148,163
1,217,468
1,108,598
736,060
738,542
1,823,893
3,042,217
2,462,206
724,984
865,483
2,765,017
461,139
593,730
286,026
3,711,700
292,519
424,019
290,913
1,034,351
1,072,592
95,226,904
320,789
872,837
82,233
92,241
302,907
261,737
173,515
123,356,953
Cost of
Caregiver
Productivity
Losses
Reported ($)
24,471
1,607
3,280
67,099
110,874
19,329
3,040
99,143
51,845
12,752
1 1 ,726
41,867
81,670
13,500
44,570
7,664
56,056
5,737
14,857
1,893
61,417
62,279
11,819,198
6,185
14,067
3,874
4,631
20,642
9,631
10,454
12,685,357
Cost of
Caregiver
Productivity
Losses
Projected ($)
218,996
88,410
319,561
279,470
187,738
145,298
170,778
386,138
702,675
513,923
130,744
216,290
600,623
103,840
144,354
59,495
559,355
63,266
89,944
64,940
203,039
175,815
13,862,845
67,199
188,777
14,461
16,027
56,915
54,109
35,900
19,720,927
Cost-of-
Illness
Reported
($)
473,432
66,571
1,258,838
1,322,300
1,272,968
573,122
111,933
1,417,030
790,070
442,676
156,064
444,777
1,266,315
210,618
695,409
145,206
681,484
119,454
285,748
55,608
914,893
907,325
129,566,104
111,810
245,760
55,608
74,716
551,990
427,490
217,621
144,862,940
Cost-of-lllness
Projected
($)
1,494,445
552,066
2,766,960
2,411,620
1,775,359
1,429,201
1,120,746
2,651,896
4,169,361
3,982,821
1,080,258
1,381,337
4,531,263
664,011
1,287,835
456,031
5,002,033
482,449
746,183
472,191
1,985,626
1,445,441
140,388,287
470,758
1,215,986
128,029
143,337
733,886
674,138
356,516
186,000,069
VSL Cost
Reported
($)
6,430,000
-
12,860,000
-
-
-
-
-
-
-
-
-
-
-
-
~
-
-
25,720,000
-
-
-
366,510,000
-
-
-
-
-
12,860,000
-
424,380,000
VSL Cost
Projected
($)
6,430,000
-
12,860,000
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
25,720,000
-
-
-
366,510,000
-
-
-
-
-
12,860,000
-
424,380,000
Total
Economic
Burden
Reported
($)
6,903,432
66,571
14,118,838
1,322,300
1,272,968
573,122
111,933
1,417,030
790,070
442,676
156,064
444,777
1,266,315
210,618
695,409
145,206
681,484
119,454
26,005,748
55,608
914,893
907,325
496,076,104
111,810
245,760
55,608
74,716
551,990
13,287,490
217,621
569,242,940
Total
Economic
Burden
Projected
($)
7,924,445
552,066
15,626,960
2,411,620
1,775,359
1,429,201
1,120,746
2,651,896
4,169,361
3,982,821
1,080,258
1,381,337
4,531,263
664,011
1,287,835
456,031
5,002,033
482,449
26,466,183
472,191
1,985,626
1,445,441
506,898,287
470,758
1,215,986
128,029
143,337
733,886
13,534,138
356,516
610,380,069
Draft: Do Not Cite or Quote
C-5
8/31/06
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