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Comparative Risk Framework
Methodology and Case Study
National Center for Environmental Assessment
Cincinnati Office
November 9, 1998
SAB Review Draft
INTERNAL DRAFT: DO NOT CITE OR QUOTE
NOTICE
This document is a preliminary draft. It has not been formally released by the U.S.Environmental Protection Agency and should not at this stage be construed to represent Agencypolicy. It is being circulated for comments on its technical accuracy and policy implications.
SAB Review Draft ii 11/09/98
DISCLAIMER
This report is an external draft for review purposes only and does not constitute Agencypolicy. Mention of trade names or commercial products does not constitute endorsement orrecommendation for use.
Portions of this document were authored by the National Risk Management ResearchLaboratory.
SAB Review Draft iii 11/09/98
FOREWORD
This Science Advisory Board Review Draft describes a framework methodology forcomparative risks and provides an application of the methodology for a hypothetical drinkingwater treatment system. The effort was sponsored by the U.S. Environmental ProtectionAgency’s (EPA) National Center for Environmental Assessment-Cincinnati Division (NCEA-Cin). Since its formation in1979, NCEA-Cin has been conducting and supporting risk assessmentresearch in the area of drinking water. Exposure through drinking water to pathogenicmicroorganisms as well as disinfectants and disinfectant byproducts may pose risks to humanhealth such as gastrointestinal illness and related sequellae, cancer, as well as reproductive anddevelopmental impacts. The methodology presented herein offers a way to analyze disparatehealth risks using a common health metric.
The document was strengthened through collaboration with members of U.S. EPA’sNational Risk Management Research Laboratory, Water Supply and Water Resources Divisionand the U.S. Agency of Toxic Substances and Disease Registry. Joshua Cohen, Ph.D. ofGradient Corporation was responsible for the development of much of the case study and manykey concepts in the document. Dr. Cohen wrote the SAS computer codes and executed theanalysis. NCEA-Cin acquired Gradient Corporation’s services through a contract with TN andAssociates, Inc. The authors would also like to acknowledge the efforts of Bette Zwayer andPatricia Daunt (NCEA-Cin) and Patricia Wilder of International Consultants Inc. in thepreparation of the document.
The document has evolved through a series of peer-review efforts. The document wasinitially cleared by NCEA-Cin in 1996 after the reviews of Daniel Guth of NCEA-RTP andMargaret Chu of NCEA-Wash. A Workshop entitled Comparing Risks from DisinfectantByproducts and Microbes in Drinking Water was held in May of 1997 (see list of WorkshopParticipants). This document was internally reviewed again by Randall Bruins, Ph.D. in May of1998. The document was externally peer-reviewed through a contract under the projectofficership of Marilyn Brower (see list of External Peer-Reviewers). James Koopman, MD MPHof the University of Michigan, Department of Epidemiology and Frank A. Sonnenberg, MD of theUniversity of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School alsoprovided external review of the document in June of 1998.
The authors would also like to thank Sam Hayes of U.S. EPA’s National RiskManagement Research Laboratory, Water Supply and Water Resources Division, Donna Tucker(U.S. EPA NCEA-Cin) and Ruth Durham (U.S. EPA NCEA-Cin) for their assistance with thedocument.
The Safe Drinking Water Act Amendment of 1986 required the U.S. EPA to set standardsand establish guidelines for residual disinfectant levels in drinking water and to set maximumcontaminant levels (MCLs) for disinfectant byproducts. Subsequent regulations werepromulgated or proposed by U.S. EPA to provided the needed balance between controlling risksfrom exposure to pathogenic organisms in water and from exposure to byproducts of waterdisinfection. This document was produced in support of the above legislation and U.S. EPAregulations.
SAB Review Draft iv 11/09/98
AUTHORS
U.S. EPA Principal Authors
Brenda BoutinNational Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Mary Beth Brown, D.V.M., M.P.H.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Robert Clark, Ph.D.Water Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
John C. Lipscomb, Ph.D.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Richard MiltnerWater Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Patricia Murphy, M.P.H., Ph.D.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Linda R. Papa, M.S.National Center for Environmental Assessment-Washington DivisionUnited States Environmental Protection Agency Donald Reasoner, Ph.D. Water Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Richard Rheingans, Ph.D.Agency of Toxic Substances and Disease Registry
SAB Review Draft v 11/09/98
AUTHORS (cont.)
U.S. EPA Principal Authors (cont.)
Glenn Rice, M..S.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Linda K. Teuschler, M.S.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Robert Thurnau, M.S.Water Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Contributors
Robert M. Bruce, Ph. DNational Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency
John Bukowski, D.V.M., M.P.H., Ph.D.Exxon Biomedical Sciences, Inc.
David H. ChenOffice of Children's Health ProtectionU.S. Environmental Protection Agency
Chris Cubbison, Ph.D.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Terry Harvey, D.V.M.National Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency
Ben Lykins, M.S.Water Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
SAB Review Draft vi 11/09/98
AUTHORS (cont.)
Contributors (cont.)
Chandrika MoudgalUniversity of Cincinnati
Kathleen SchenckWater Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Susan Schock, M.S.Technology Transfer BranchTechnology Transfer and Support DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Rita Schoeny, Ph.D.Health and Ecological Criteria DivisionOffice of Science and TechnologyOffice of Water
Carolyn L. SmallwoodNational Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency Virendra SethiOak Ridge Postgraduate AppointmentWater Supply and Water Resources DivisionNational Risk Management Research LaboratoriesUnited States Environmental Protection Agency
Doug WilliamsNational Center for Environmental Assessment-Cincinnati DivisionUnited States Environmental Protection Agency
External Peer-Reviewers
Thomas B. AtherholtNew Jersey Department of Environmental Protection
SAB Review Draft vii 11/09/98
AUTHORS (cont.)
External Peer-Reviewers (cont.)
David E. BurmasterAlceon Corporation
Richard E. DanielsonBioVir Laboratories, Inc.
Dale HattisClark University
William A. HuberQuantitative Decisions, Inc.
Moiz MumtazAgency for Toxic Substances and Disease Registry/U.S. Public Health Services
Robert S. RaucherHagler Bailly Services, Inc.
Richard D. RheingansCenters for Disease Control and Prevention/Agency for Toxic Substances and DiseaseRegistry/U.S. Public Health Services
Susan TeefyAlameda County Water District
SAB Review Draft viii 11/09/98
PEER INVOLVEMENT WORKSHOP 1997
Comparing Risks from Disinfectant By-Products and Microbes in Drinking Water
Expert Panel
Gunther CraunThe Professional Building
Joshua CohenGradient Corporation
Thomas CrockerUniversity of Wyoming
Phillippe DanielCamp Dresser & McKee, Inc.
Joseph EisenbergUniversity of California, Berkeley
George GrayHarvard School of Public Health
Arthur GregoryTechto Enterprises
Marty KanarekUniversity of Wisconsin-Madison
Michele MorroneOhio Environmental Protection Agency
Rita SchoenyU.S. Environmental Protection Agency
Paul YarnoldNorthwestern University
SAB Review Draft ix 11/09/98
TABLE OF CONTENTS
Page
1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-1
1.1. BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11.2. PUBLIC DRINKING WATER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-11.3. PROBLEM STATEMENT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-21.4. DOCUMENT GOALS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-31.5. INTENDED AUDIENCE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-4
2. BACKGROUND . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-1
2.1. HISTORY OF DRINKING WATER HEALTH ISSUES . . . . . . . . . . . . . . . . 2-12.2. REGULATORY REQUIREMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2-3
3. DOCUMENT OBJECTIVES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3-1
4. COMPARATIVE RISK FRAMEWORK METHODOLOGY . . . . . . . . . . . . . . . . . . 4-1
4.1. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-1
4.1.1. NAS 1983 Risk Assessment Paradigm . . . . . . . . . . . . . . . . . . . . . . . . . 4-14.1.2. Comparing Risks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-44.1.3. Comparing Public Health Interventions . . . . . . . . . . . . . . . . . . . . . . . . 4-5
4.2. COST EFFECTIVENESS ANALYSIS IN PUBLIC HEALTH . . . . . . . . . . . 4-7
4.2.1. Analytical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-74.2.2. Cost-Effectiveness Analysis (CEA) . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-84.2.3. Cost-Benefit Analysis (CBA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-9
4.3. COMPARATIVE RISK FRAMEWORK METHODOLOGY . . . . . . . . . . . . 4-10
4.3.1. CRFM Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-104.3.2. Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-13
4.4. TRANSLATING RISK CHARACTERIZATION TO HEALTHCONDITIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-17
4.4.1. Issues in Translating Risk to Health Conditions . . . . . . . . . . . . . . . . . 4-184.4.2. Selecting Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-18
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4.5. VALUING HEALTH OUTCOMES USING A COMMON HEALTHMETRIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-19
4.6. ASSESSING THE IMPACT OF ALTERNATIVE INTERVENTIONS . . . . 4-24
4.6.1. Identifying Interventions — Baseline and Alternatives . . . . . . . . . . . . 4-244.6.2. Issues in Assessing Intervention Impact . . . . . . . . . . . . . . . . . . . . . . . 4-254.6.3. Using Tools from Decision Analysis to Estimate Intervention
Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-25
4.7. ASSESSING THE FINANCIAL COST OF ALTERNATIVES . . . . . . . . . . 4-31
4.7.1. Identifying Cost Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-31
4.8. COMPARING RISKS AND COSTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-32
4.8.1. Summary Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-324.8.2. Time Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-334.8.3. Distributional Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-344.8.4. Accounting for Uncertainty and Variability . . . . . . . . . . . . . . . . . . . . 4-36
4.9. CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-38
5. CASE STUDY OVERVIEW AND PARAMETER ASSUMPTIONS . . . . . . . . . . . . 5-1
5.1. CASE STUDY OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-1
5.1.1. Alternative Treatment Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25.1.2. Health Risks Considered . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35.1.3. Quantification of Health Consequences and Financial Costs . . . . . . . . . 5-5
5.2. TAP WATER CONSUMPTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-6
5.2.1. Total Tap Water Consumption (L/kg-day) . . . . . . . . . . . . . . . . . . . . . 5-75.2.2. Consumption of Unheated Tap Water (L/day) . . . . . . . . . . . . . . . . . . . 5-85.2.3. Adjustments for Special Subgroups . . . . . . . . . . . . . . . . . . . . . . . . . . 5-10
5.3. DBP RISKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-12
5.3.1. Response Addition Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-14
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5.3.2. Tap Water Consumption (Y) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-165.3.3. Concentration Data (Ci) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-175.3.4. Toxicologic and Carcinogenic Risk Data . . . . . . . . . . . . . . . . . . . . . . 5-225.3.5. Input Distributions for Simulation Procedures . . . . . . . . . . . . . . . . . . 5-30
5.4. DISTRIBUTION OF CRYPTOSPORIDIUM CONCENTRATIONS INSOURCE WATER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-34
5.4.1. Source Water Oocyst Concentrations . . . . . . . . . . . . . . . . . . . . . . . . 5-345.4.2. Fraction of Oocysts Removed by Water Treatment . . . . . . . . . . . . . . 5-45
5.5. HEALTH RISKS ASSOCIATED WITH EXPOSURE TOCRYPTOSPORIDIUM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-46
5.5.1. Probability of Infection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-475.5.2. Conditional Probability of Mild Illness Given Infection . . . . . . . . . . . 5-485.5.3. Conditional Probability of Moderate to Severe Illness Given
Mild Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-485.5.4. Conditional Probability of Death Given Moderate to Severe
Illness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-49
5.6. TREATMENT TECHNOLOGY COSTS . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-535.7. DISCOUNT RATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-535.8. THE COST OF HEALTH EVENTS IN QUALITY ADJUSTED LIFE
YEARS (QALYs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54
5.8.1. Data Used to Derive QALY Equivalent Costs . . . . . . . . . . . . . . . . . . 5-555.8.2. QALY Equivalent Values Derived . . . . . . . . . . . . . . . . . . . . . . . . . . 5-63
6. CASE STUDY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-1
6.1. UNCERTAINTY AND VARIABILITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26.2. COMPUTATION OF QALY COSTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-8
6.2.1. Computation of a Technology’s QALY Cost: Description . . . . . . . . . 6-126.2.2. Computation of a Technology’s QALY Cost: Equations
and Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-14
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6.3. COMPUTATION OF TECHNOLOGY COSTS . . . . . . . . . . . . . . . . . . . . . 6-296.4. COMPUTATION OF THE COST EFFECTIVENESS RATIO FOR THE
SUPPLEMENTAL DISINFECTION TECHNOLOGY . . . . . . . . . . . . . . . . 6-306.5. RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-31
6.5.1. Ozone Pretreatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-316.5.2. Home Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-456.5.3. Case Study Results Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-50
7. INTERPRETATIONS TAKEN FROM THE CASE STUDY . . . . . . . . . . . . . . . . . . 7-1
7.1. WHAT THE CASE STUDY DOES AND DOES NOT ACCOMPLISH . . . . 7-17.2. INTERPRETATIONS OF THE RESULTS . . . . . . . . . . . . . . . . . . . . . . . . . . 7-5
7.2.1. Results: Baseline Treatment vs. Baseline Plus Ozone Pretreatment . . . 7-87.2.2. Results: Baseline vs. Baseline Plus In-Home Filters . . . . . . . . . . . . . . 7-10
7.3. PERSPECTIVE OF THE LOCAL WATER SYSTEM PURVEYOR . . . . . . 7-137.4. PERSPECTIVE OF THE NATIONAL RISK MANAGER . . . . . . . . . . . . . 7-15
8. RESEARCH NEEDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8-1
9. REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9-1
APPENDIX A: Supporting Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1
APPENDIX B: Computer Code for the Simulation Procedure . . . . . . . . . . . . . . . . . . . B-1
APPENDIX C: Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1
SAB Review Draft xiii 11/09/98
LIST OF TABLES
Table Page
4-1 Summary of Calculated Cost of Illness in QALYs . . . . . . . . . . . . . . . . . . . . . . . . . . 4-23
5-1 Tap Water Consumption in the General Population in mL/kg-day by Age . . . . . . . . . . 5-7
5-2 Tap Water Consumption in the General Population in mL/kg-day by5-Year Age Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-8
5-3 Tap Water Consumption in the General Population in mL/day by Age Group . . . . . . . 5-9
5-4 Fraction of Tap Water Consumed that is Unheated . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-9
5-5 Unheated Tap Water Consumption in mL/day . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-11
5-6 DBP Concentrations Used in the Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-18
5-7 Estimated Bromate Formation in Ohio River Water by Ozonation . . . . . . . . . . . . . . 5-20
5-8 TOX, µg Cl/L, in Simulated Distribution (stored) Pilot Plant Waters . . . . . . . . . . . . 5-20
5-9 Number of TOPKAT® QSAR Predictions by Endpoint for Known DBPsNot in the Miltner 1990 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-21
5-10 Cancer Slope Factor Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-23
5-11 Developmental Slope Factor Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-24
5-12 Reproductive Slope Factor Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-25
5-13 Availability of Developmental and Reproductive Dose-Response Data . . . . . . . . . . . 5-29
5-14 Threshold Model Results using BW2/3 Scaling Factor . . . . . . . . . . . . . . . . . . . . . . . . 5-31
5-15 Cryptosporidium Concentrations in Source Water at the Intake of theTrenton, NJ Water Works Filtration Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-35
5-16 Trenton, NJ Water Works Filtration Plant Data and Proxy Values Used forMeasurements Below the Detection Limit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-40
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LIST OF TABLES
Table Page
5-17 Cryptosporidium Recovery Rates for 20 Quality Control Samples . . . . . . . . . . . . . . 5-42
5-18 Treatment Technology Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-54
5-19 Fraction of Lifespan in Perfect Health Equally Desirable to Full Lifespan withCurrent Health Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-58
5-20 The QALY Value of Life Years at Different Ages . . . . . . . . . . . . . . . . . . . . . . . . . . 5-66
5-21 Probability of Surviving Until the End of Each Five-Year Age Range forIndividuals Alive at the Beginning of that Age Range . . . . . . . . . . . . . . . . . . . . . . . . 5-67
5-22 Conditional Survival Probabilities for the General Population . . . . . . . . . . . . . . . . . . 5-69
5-23 Conditional Survival Probabilities for the AIDS Subpopulation . . . . . . . . . . . . . . . . 5-70
5-24 QALY Cost of GI Illness for the General Population . . . . . . . . . . . . . . . . . . . . . . . . 5-76
5-25 Age-Specific QALY Costs for All Health Endpoints: General PopulationDiscount Rate of 3% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-78
5-26 Age-Specific QALY Costs for All Health Endpoints: AIDS SubpopulationDiscount Rate of 3% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-79
6-1 Illustration of the use of Monte Carlo Techniques to Conduct Sensitivity Analysis . . . 6-9
6-2 Computation of )ri,k for Cryptosporidium-induced Morbidity and Mortality . . . . . . . 6-19
6-3 N_AtRisk for all Health Effects Evaluated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-21
6-4 Age Distribution for the United States General Population and AIDSSubpopulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-22
6-5 Incidence per 100,000 Individuals for Urinary Bladder Cancer, and ColonCancer and Rectum Cancer: By Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-24
6-6 Probability of Cancer Occurring at Age at Left End of Row if the CancerResults from Exposure at Age at Top of Column . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-26
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LIST OF TABLES
Table Page
6-7 Fatality Rate per 100,000 Individuals for Urinary Bladder Cancer, and ColonCancer and Rectum Cancer: By Age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-27
6-8 Probability of Cancer Fatality at Age at Left End of Row if the FatalityResults from Exposure at Age at Top of Column . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-28
6-9 Per Capita Technology Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-31
6-10 Ozone PreTreatment: Per Capita Number of Health Events General Population:Totals over the 20-Year Lifetime of the Treatment Plant . . . . . . . . . . . . . . . . . . . . . 6-32
6-11 Ozone PreTreatment: Cost Effectiveness — Discount Rate of 3% GeneralPopulation: totals over the 20-Year Lifetime of the Treatment Plant . . . . . . . . . . . . . 6-35
6-12 Ozone PreTreatment: General Population Event Counts and QALY Value ofHealth Events—Discount Rate of 3% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-39
6-13 Ozone PreTreatment: AIDS Subpopulation Event Counts and QALY Value ofHealth Events—Discount Rate of 3% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-40
6-14 Ozone PreTreatment: Total Population Event Counts and QALY Value ofHealth Events—Discount Rate of 3% Results over the 20-Year Plant Life . . . . . . . . 6-43
6-15 Ozone Pretreatment: Sensitivity Analysis—CE Ratio for the General Population . . . 6-46
6-16 Ozone Pretreatment: Sensitivity Analysis—CE Ratio for the AIDS Subpopulation . . 6-47
6-17 AIDS Population Event Counts and QALY Value of Health Events—DiscountRate of 3% . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-48
6-18 Home Filters: Sensitivity Analysis—CE Ratio for the AIDS Subpopulation . . . . . . . 6-51
6-19 Results Summary: 3% Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-52
6-20 Results Summary: 5% Discount Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-52
7-1 Cost Evaluations per QALY Unit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7-9
SAB Review Draft xvi 11/09/98
LIST OF FIGURES
Figure Page
1-1 Schematic of Conventional Water Treatment Train . . . . . . . . . . . . . . . . . . . . . . . . . . . 1-5
1-2 Schematic of Conventional Water Treatment Train with Pre-Ozonation . . . . . . . . . . . 1-6
4-1 This Figure Illustrates the Risk Assessment Paradigm as Described in 1983by NAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4-2
4-2 Comparative Risk Assessment Framework Overview . . . . . . . . . . . . . . . . . . . . . . . . 4-11
4-3 Issues Addressed in Comparative Risk Assessment Framework . . . . . . . . . . . . . . . . 4-14
4-4 Decision Tree for Sample Public Health Intervention . . . . . . . . . . . . . . . . . . . . . . . . 4-28
5-1 Log Cryptosporidium Concentrations (Total Count) vs. Normal Distributionz-scores for Measurements Exceeding the Detection Limit of 20 Cysts / 100 L . . . . . 5-38
5-2 Log Cryptosporidium Concentrations (Active Count) vs. Normal Distributionz-scores for Positive Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-38
5-3 Average Simulated CDF for the Average Annual Source WaterCryptosporidium Concentration (Total Organisms / 100 L) . . . . . . . . . . . . . . . . . . . 5-44
5-4 Average Simulated CDF for the Average Annual Source WaterCryptosporidium Concentration (Active Oganisms / 100 L) . . . . . . . . . . . . . . . . . . . 5-44
5-5 Probability Density Function for the Conditional Probability of Death GivenModerate to Severe Illness: General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-52
5-6 Probability Density Function for the Conditional Probability of Death GivenModerate to Severe Illness: AIDS Subpopulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 5-52
6-1 Illustrative Decision Tree: Cancer Health Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-3
6-2 Computation of a Treatment Plant’s QALY Costs . . . . . . . . . . . . . . . . . . . . . . . . . . 6-11
6-3 Cumulative Distribution for the Cost-Effectiveness Ratio for Ozone Pretreatment:General Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-37
SAB Review Draft xvii 11/09/98
LIST OF FIGURES
Figure Page
6-4 Cumulative Distribution for the Cost-Effectiveness Ratio for Ozone Pretreatment:AIDS Subpopulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6-42
7-1 Average Change from Baseline in QALYs By Health Effects . . . . . . . . . . . . . . . . . . . 7-7
SAB Review Draft xviii 11/09/98
LIST OF ABBREVIATIONS
BBDR Biologically based dose response
CBA Cost-benefit analysis
CEA Cost-effectiveness analysis
CUA Cost-utility analysis
DALY Disability adjusted life years
DBP Disinfectant byproduct
DBP RAM Disinfectant byproduct risk assessment model
DCA Dichloroacetic acid
D/DBP Disinfectant / disinfectant byproduct
ESWTR Enhanced Surface Water Treatment Rule
GI Gastrointestinal
GIS Geographic information system
HAA Haloacetic acid
HAN Haloacetonitrile
HAV Hepatitis A virus
IARC International Agency for Research on Cancer
ICR Information Collection Rule
MCL Maximum contaminant level
MCLG Maximum contaminant level goal
NHEERL National Health and Environmental Effects Research Laboratory
QALY Quality Adjusted Life Years
QSAR Quantitative Structure Activity Relationship
RfD Reference dose
SAB Science Advisory Board of the U.S. EPA
SDWAA Safe Drinking Water Act Amendment
SWTR Surface Water Treatment Rule
TCA Trichloroacetic acid
TCE Trichloroethylene
TCR Total Coliform Rule
THM Trihalomethane
TTHM Total trihalomethanes
TOC Total organic carbon
YPLL Years of potential life lost
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EXECUTIVE SUMMARY
The disinfection and treatment of drinking water was instituted as a primary public health
intervention that has greatly decreased the morbidity and mortality from water-borne microbial
diseases such as typhoid and cholera. Although this practice has been highly effective, waterborne
outbreaks of cryptosporidiosis, giardiasis, Norwalk virus, verotoxin-producing Escherichia coli,
and other agents continue to occur when water supplies become contaminated and water
treatment processes are inadequate. Chlorine, in combination with a variety of additional
treatment processes, has been the most widely applied disinfectant and has been used in the
United States since 1908. More recently, other disinfectants have also been used, such as ozone,
chloramine and chlorine dioxide. Trihalomethanes, haloacetic acids, bromate and other chemicals
are now known to be produced during the disinfection process and are considered to be
potentially hazardous disinfection byproducts (DBPs) of this proven intervention.
In response to these health issues, rules and regulations have been established or proposed
under the 1986 Safe Drinking Water Act (SDWA) and the 1996 Safe Drinking Water Act
Amendments (SDWAA) to ensure that disinfection protection be maintained in drinking water
distribution systems (Vasconcelos et al., 1996; Clark et al., 1996). The regulations also require
that a detectable disinfectant residual level be maintained throughout the system in most cases and
that the risks from exposures to disinfectants and DBPs be controlled to the levels mandated
under the SDWAA.
This balancing of microbial efficacy with the control of DBP levels in the drinking water
has emerged as a classic example of a risk trade-off that entails not only concerns for human
SAB Review Draft xx 11/09/98
health across disparate health risks, but also decisions relative to the financial costs of changing or
improving drinking water treatment systems. Research is needed on how aspects of the treatment
process can be selectively changed to minimize the formation of potentially hazardous DBPs
without compromising the efficacy of microbial treatment. The changes in existing drinking water
treatment systems affect the costs of providing water to the customer, both in dollars and in type
and number of health events that potentially could occur in those exposures. The problem is
further complicated by many unknowns, uncertainties, and data gaps surrounding the health
effects information for both DBPs and microbes.
Although both epidemiologic and toxicologic studies of the health effects associated with
exposures to some of the better-studied DBPs have demonstrated concerns for adverse
reproductive and developmental effects as well as for cancer, there are still many unknowns. For
example, there is a need for continued information development characterizing the types and
amounts and mixtures of DBPs formed by different disinfection/treatment train combinations and
source water characteristics. Complementary epidemiologic and animal research is also needed to
characterize any potential health risks, including the nature and likelihood of occurrence of any
DBP-induced adverse health events.
Similar to the DBPs, existing information is sparse regarding the frequency of occurrence
of potential waterborne pathogens. Analytical techniques are unavailable for reliably identifying
and quantifying viable and infectious pathogens, particularly Cryptosporidium. Research is
ongoing to quantify the amounts of pathogens in the source water and to define human dose-
reponse relationships, but better data are needed to define the host susceptibility and immunity,
agent virulence, and the impact of environmental determinants on the spectrum of outcomes
SAB Review Draft xxi 11/09/98
associated with human exposures to pathogens. Data collected during acute waterborne disease
epidemics has contributed to the understanding of the dynamics of many outbreak situations, but
also points to the lack of basic information regarding the endemic occurrence of many waterborne
diseases.
Despite the substantial data gaps and uncertainties which still exist for all the different
aspects of this problem, regulatory schedules dictate decision-making by risk managers in the
absence of complete and often key information regarding selection of drinking water treatment
options best suited to provide the most comprehensive protection to the public.
Because drinking water treatment is a primary public health intervention designed to
accomplish specific infectious disease reduction goals, decisions to change one or more of the
steps in this practice must consider all the consequences of those decisions. Similar to a medical
pharmaceutical treatment, a proposed alternative therapy must demonstrate that it is at least as
effective as the currently accepted standard treatment, and that it poses no unacceptable side-
effects that would outweigh the benefits achieved by the intervention. During the past years
several efforts have been made to develop systematic approaches to assessing a wide range of
public health and medical interventions in this type of interdependent framework. In public health
and medicine, comparisons of alternative therapies and interventions often take the form of cost-
effectiveness analyses (CEA), which can be designed to provide a direct comparison of
qualitatively disparate health outcomes (Haddix et al., 1996; Gold et al., 1996).
The purpose of a CEA is to systematically and quantitatively assess the expected
outcomes and resource costs of alternative interventions, in a way which facilitates decision
making by different stakeholders. Cost-effectiveness analyses of public health interventions
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typically rely on information from a wide range of sources, including epidemiology, clinical
microbiology, clinical trials, and intervention studies, to estimate the expected health outcomes of
a series of alternative programs, policies or practices. The approach can be expanded to include
information from other disciplines (e.g., engineering and risk assessment) regarding the expected
impact of alternative strategies. This is then combined with an economic analysis of the costs of
the intervention to provide decision makers and others with an estimate of the combined impact of
the alternatives.
A perceived impediment to approaching the drinking water chemical and microbial risk
problem this way has been the need to express all the health outcomes of interest with a common
measure, so that valid comparisons can be made across the different health risks. Methods are
available, however, to compare the qualitatively different risks of a case of cancer to a case of
diarrhea. Expression of health risks in directly comparable ways can be accomplished by
calculating the risks in terms of disease mortality and incidence rates, which are then converted
into indicators of life lengthening or life shortening such as lives or life-years gained or lost. This
approach would result in health outcomes that are expressed in natural units such as cases of
cancer caused or averted. In CEA using natural units for health outcomes, the net cost of the
intervention is compared directly to the disease events averted, e.g. costs per cancer, death, or
injury. Alternately, disease mortality and incidence rates can be adjusted (i.e., weighted) by
subjective quality of life estimates to generate measures such as the Quality Adjusted Life Year
(QALYs) (Putnam and Graham, 1993). The weighting factors reflect some social valuation of the
outcome characteristics which includes losses in the length of life from mortality and losses of
quality of life from morbidity associated with a particular condition. This approach would express
SAB Review Draft xxiii 11/09/98
health outcome costs as the number of life-years lost, adjusted for any change in quality
associated with the health outcomes linked to each intervention. In a CEA using these QALY
measures, the net cost of the intervention is compared to the number of QALYs gained or lost.
A CEA designed to compare alternative interventions to a baseline is called an incremental
analysis. An incremental analysis considers the additional cost of an intervention compared to the
baseline, and the additional benefits associated with the intervention compared to the baseline.
The resulting cost-effectiveness ratio (cost per QALY or cost per case averted) reflect this
incremental benefit and cost compared to the baseline, rather than the total costs and benefits
associated with the intervention.
Traditional risk assessment activities have followed the four step paradigm, originally laid
out by NAS (1983): hazard identification, dose-response assessment, exposure assessment, and
risk characterization. This information is translated into an overall conclusion about the likelihood
of an adverse health effect occurring due to the presence of a potential hazard (NAS, 1983; NRC,
1994). Risk assessments have been used to estimate plausible levels of risk that might result from
exposure to environmental pollutants. These assessments are frequently used
to guide regulatory decisions regarding the level of exposure that results in ‘acceptable’ health
risks, with risk numbers that are calculated by design to be conservatively protective. Historically,
risks have been identified, described and managed on a single chemical basis, in isolation of other
exposures and risks that may exist. While this use of risk assessment serves as a starting point,
additional approaches are needed to help decision makers determine how to best address the
identified hazards. This is particularly true when the alternative strategies or interventions for
reducing a health risk may carry costs or health risks of their own. As described above, the
SAB Review Draft xxiv 11/09/98
situation of controlling DBPs while maintaining adequate protection from waterborne pathogens
involves a potential risk tradeoff that requires a ‘comparative risk’ approach wherein the different
health risks are analyzed and managed interdependently. The use of the QALY (and other)
outcome measures in the cost-effectivenss evaluation of public health and medical interventions
suggests their potential usefulness for evaluating environmental health interventions such as
drinking water treatment decisions. This document proposes that these methods be combined
with traditional health risk assessment information and applied to the drinking water comparative
risk problem.
Figures ES-1 and ES-2 present the essence of the proposed framework and methodology,
the integration of CEA as applied to public health interventions with the 1983 NAS Risk
Assessment Paradigm. Figure ES-1 displays an overview of the basic comparative risk
assessment framework. First, traditional risk assessments would be conducted, considering the
microbial and DBP concentrations in finished water, tap water intake rates, and dose-response
assessments for the microbial and chemical agents of concern. Next, to compare these chemical
and microbial risks, the effects or consequences described in the risk characterization must be
translated or expressed in terms of measurable human health conditions, such as cases of cancer
and infection and illness from infectious diseases. The range of potential human health conditions
can then be converted into a common health metric. In this framework, QALYs are used to
capture changes in the length and quality of life associated with the different health conditions. In
addition, the different health conditions can also result in economic costs associated with medical
treatment and lost productivity.
SAB Review Draft xxv 11/09/98
Dose-ResponseAssessment
HazardIdentification
ExposureAssessment
RiskCharacteri-
zation
Dose-ResponseAssessment
HazardIdentification
ExposureAssessment
RiskCharacteri-
zation
Figure ES-1. Comparative Risk Assessment Framework OverviewChemicalRiskAssessment
MicrobialRiskAssessment
HealthCondition
AlternativeInterventionStrategies
HealthCondition
CommonHealthMetric
Comparisonof Costs and
Benefits
Transmission
Inter-individual
InterventionCost
Assessmentof
InterventionFinancial
Cost
EconomicCosts
AssociatedWith HealthConditions
SAB Review Draft xxvi 11/09/98
Figure ES-2. Issues Addressed in Comparative Risk Assessment FrameworkChemicalRiskAssessment
MicrobialRiskAssessment
AlternativeDrinking Water
TreatmentStrategies
1) CL
2) Cl + O3
3) Cl + Point-of-Use Device
For EachTreatment Strategy
Compare Costsand Benefits
($/QALY)
Inter-individual
Dose-ResponseAssessment
HazardIdentification
ExposureAssessment
RiskCharacteri-
zation
HealthCondition
Transmission
InterventionCost
SOURCEWATER
FACTORS
Dose-ResponseAssessment
HazardIdentification
ExposureAssessment
RiskCharacteri-
zation
HealthCondition
CommonHealthMetric
(QALY)
Translation of Human Health Conditions into aCommon Health Metric
1) Value placed on Human Health Condition(severity)
2) Duration of Condition
3) Time of Condition Onset Within Lifespan
Translation of CriticalHealth Effects into
Human HealthConditions
1) Identify Spectrumof Human HealthOutcomes forSubpopulations
2) Duration of Effect
3) Latency:Exposure to Onsetof effect
Assessment ofInitial and Annual
TreatmentFinancial Costs
for FacilityLifetime
Translation of CriticalHealth Effects into
Human HealthConditions
Translation of Human HealthConditions into a CommonHealth Metric
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The comparative risk framework assesses microbial and chemical risks in relation to
specific drinking water treatment alternatives. As a result, an analysis must consider the estimated
impact of baseline and alternative strategies on the different risks under consideration. In this
case, treatments affect contaminant concentrations, exposures, and ultimately changes in expected
health outcomes. In addition to impacting the different health risks, the alternative treatment
strategies also require different resource expenditures for implementation. In the final stage of the
framework, alternative strategies are compared by assessing their expected impact on health
(QALYs) and economic outcomes. Figure ES-2 provides additional detailed information for
specific application of this methodology to the drinking water treatment problem.
This framework provides a systematic approach to assessing the expected impact of
specific treatment alternatives on microbial and chemical risks, and compares these disparate risks
to the costs associated with the alternatives. The cost-effectiveness methodology differs from
traditional risk assessments in that it is constructed as a predictive model and not as a protective
one. The analysis uses central tendency estimates and distributions to describe input parameters
instead of high-end, conservative values that are meant to be protective (and are useful in many
risk management situations). This perspective is taken so that the end product describes the most
complete picture of the results that includes both best estimates and a scientific evaluation of the
extremes; thus the amount of information that is provided for the end user is optimal.
In order to demonstrate the utility of the proposed framework methodology, a case study
is presented for a hypothetical water distribution system. Three treatment options are evaluated in
terms of their impact on microbial risks (GI illnesses and mortality), their impact on DBP-induced
risks (cancer, reproductive toxicity, and developmental toxicity), and their financial costs.
SAB Review Draft xxviii 11/09/98
Through the development of a reasonable set of assumptions regarding the hypothetical drinking
water treatment facility and the population it serves, the case study shows that site-specific and
facility-specific data can be input to the framework to develop a reasonable comparison of
treatment intervention options. The case study highlights critical areas where pertinent research
could potentially change outcomes of the analysis, assuming that the inputs represent, in a
reasonable way, some locations and treatment options under consideration in the U.S. The case
study shows:
• A logical analysis that directly links each treatment technology to the healthoutcomes of interest;
• The major determinants of that outcome and the uncertainties inherent in them;
• That data currently exist in the scientific literature, permitting such an analysis tobe undertaken with appropriate caveats.
While the CEA framework is conceptually a holistic concept, the application of the
framework presented in the case study has limitations. The constraints of the case study include:
• Comparison of only 2 alternative drinking water treatment technologies and nocomparison of changes in the technologic applications (e.g., changes in the levelsof chlorination)
• Limitations in the development of input data distributions for conducting anuncertainty analysis
• Constraints concerning the current scientific measurement and the temporaldistribution of concentrations of D/DBPs in treated drinking water from a singletreatment system. Additionally, there has been no attempt to include the effects ofdistributional effects on estimated D/DBP concentrations.
• Limitations in the understanding of the relationship between health effects andD/DBP exposures in drinking waters inherent in the risk assessments of theseagents both collectively and individually.
• Limitations in the current scientific understanding of the distribution of pathogenicorganisms in source waters, the efficacy of treatment, and the relationship between
SAB Review Draft xxix 11/09/98
exposure concentrations of these organisms and disease in both healthy andimmuno-compromised individuals.
• Limitations in the current scientific understanding of sequelae of more moderatedisease states.
• The Case Study does not evaluate all sensitive subpopulations
• The Case Study does not evaluate outbreak scenarios which may result fromperturbation(s) of drinking water treatment plants or point-of-use devices.Additionally, secondary spread of infection from an infected to a non-infectedmember of the population is not evaluated.
Figures ES-3 and ES-4 depict in detail the different treatment processes being compared.
Specifically, the baseline treatment technology in Figure ES-3 (a standard treatment train of
coagulation, sedimentation, sand filtration and chlorine disinfection) is compared to the baseline
treatment technology augmented by either of two supplemental technologies. The first
supplemental technology, which benefits all tap water consumers, is the addition of ozone
pretreatment to the baseline treatment train (Figure ES-4). This treatment increases the fraction
of pathogens inactivated, and decreases the concentration of many DBPs. The second
supplemental technology is the installation of point-of-use water filters in the homes of individuals
with compromised immune systems.
Health consequences, which are also referred to as the “health costs” associated with a
treatment technology, are the health effects resulting from either the presence of infectious agents
or DBPs in drinking water. Health effect costs depend on three factors: the tap water
consumption rate, the incremental probability of an adverse health effect associated with each liter
of water consumed, and the cost (measured in lost QALYs) associated with each health
SAB Review Draft xxx 11/09/98
event. A response addition model is used to calculate the incremental mixtures risk of DBP-
induced disease for the known components, summing across chemicals, as the product of a slope
factor (representing the potency of each DBP compound), the concentration of each DBP in tap
water, and the tap water consumption rate. These health risk numbers are based on laboratory
animal data.
Pathogens considered in this analysis are limited to Cryptosporidium under the assumption
that the technologies evaluated are not thought to have a differential impact on the concentrations
of other infectious agents. Health risks posed by exposure to Cryptosporidium in drinking water
are modeled by defining four disease states (i.e., infection, mild illness given infection, moderate
to severe illness given mild illness, death given moderate to severe illness). The probability of
becoming infected is assumed to depend on the number of oocysts consumed during a 12 week
period, the assumed minimum duration between infections by this pathogen. The infection dose-
response function and the values of conditional probabilities of being in the different clinical states
depend on one’s immune system status; in the case study, a surrogate group, persons with
Acquired Immune Deficiency Syndrome (AIDS), is used to represent this sensitive subpopulation.
Information from the 1993 outbreak in Wisconsin was used to construct a population-specific
distribution of AIDS cases, based on the estimated number of persons living with AIDS at that
time in the defined geographic area.
DBP concentrations in the finished drinking water for each of the treatment trains in
Figures ES-3 and ES-4 were estimated in large part from data published by Miltner et al. (1990).
Those data, based on a study in which Ohio River water was treated in a pilot plant and then
subjected to a simulated distribution system, are derived from empirical samples of the
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concentrations (ug/L) in water following both chlorination only, and chlorination with ozone
pretreatment. Concentration estimates were made for individual DBPs for each of the treatment
trains and for the amount of unidentified Total Organic Halide (TOX) in the water. Because
bromate is an important by-product of ozonation that has been identified since 1990, estimates
were made of the formation of bromate following ozone pretreatment for use in calculating DBP
risks. These estimates were made using data from more recent results from two additional studies
in which raw Ohio River water was ozonated in the same pilot plant contactor employed in the
Miltner et al. study.
Financial costs considered by the case study are limited to the direct costs of implementing
the technologies evaluated. These costs consist of the capital costs necessary for installing the
technology, and the ongoing operational costs. Other costs, such as medical treatment costs and
the lost productivity costs stemming from morbidity and mortality, could also be included in a
more expanded analysis. In addition to other limitations as noted, the case study addresses only
conditions in which the technologies (treatment plants or in-home filters) are performing as
designed (i.e., it is assumed that no outbreaks of waterborne microbial disease occur within the 20
years of plant operation considered in the case study).
The case study characterizes risks associated with pathogens and DBP exposure by
quantifying discrete health events (e.g., numbers of cases of cancer, mild gastrointestinal illness,
mortality, etc.) and by converting them to a common metric (QALY) useful in comparing and
combining their impacts. By estimating the financial costs (dollars) for each treatment option,
cost-effectiveness (CE) ratios (dollars/QALY) were calculated, and can be compared across
treatment options.
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For this case study, central tendency estimates, distributions or ranges for the model
parameters were generated from data that vary widely in quality and certainty. Predictive models,
expert judgment, or assumptions were used when empirical data were unavailable, and the
associated uncertainty was characterized. The CEA was performed using a simulation procedure
that accounts for the variability and uncertainty that is described by the parameter distributions.
This simulation produces a distribution of CE ratios that represent the possible outcomes for the
CE ratios, given the input parameter distributions. Thus, comparisons can be made across the
treatment options of several CEA outcomes that include: the actual estimated numbers of health
events; the lost QALYs for each of the health outcomes; and the CE ratios.
The case demonstrated the following findings:
• For both the baseline treatment option and the ozone pretreatment supplements,the impact of microbial-mediated illness and death overwhelm the health effectsassociated with DBP chemicals. Overall, the QALY costs of morbidity fromcombined microbial and chemical-related effects were substantially higher than theQALY costs for mortality for both the baseline treatment option and the ozonepretreatment option. Within treatment options, the QALY cost for microbialillness exceeded those for chemically-related effects by more than three orders ofmagnitude. The QALY cost for mortality for microbial illness for either treatmenttrain far exceeded the QALY cost for cancer-related deaths, estimated for both thegeneral population and the AIDs population. For a given treatment train, theQALY costs were also higher for microbe-related health effects than forDBP-related health effects for both populations.
• Capital operational costs incurred by the ozone pretreatment technology aresubstantial in absolute terms. Nonetheless, the case study results that show theincremental gains of this technology over the baseline treatment option yieldsubstantial health benefits per dollar invested. For the total populations (the AIDssubpopulation and the general population together), the CE ratio comparesfavorably with ranges in the literature that are suggested to be cost-effective(Daplan and Bush, 1982). While the establishment of a ozone pretreatment facilitywould require an initial financial outlay, the treatment option would also reducethe total QALY cost associated with anticipated levels of risk for simultaneousexposures to both microbes and DBPs, although nearly all the benefits, asmeasured in terms of QALYs, stem from the former.
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Ozone Pretreatment Option*
Total Population (General & AIDS)
CE Ratio = $1500/QALY
In-Home Filters Option **
AIDS Population
CE Ratio = $150/QALY
* Data from Table 6-14. Total QALYs = 13,056.
** Data from Table 6-17. Total QALYs = 11,636.
All GI
Illness
<1%1%
Death from GI Illness
Death from GI Illness
All DBP Effects
<1%1%
Moderate to Severe
GI Illness
Mild GI
Illness
<1% Effects
Mild GI Illness
Moderate GI Illness
Death
FIGURE ES-5FIGURE ES-5
Average Change from Baseline in Average Change from Baseline in QALYsQALYs By Health Effects By Health Effects
25%25%
11%11%63%63%
99%99%
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• For the comparison of the baseline treatment option and the in-home filtertechnology, the impact of microbial-mediated illness and death are far greater thanthe health effects associated with DBP chemicals. The bulk of the benefits(measured in terms of QALYs) reflect a reduction in mortality from microbialinduced illness, with a much smaller (although not insignificant) value for reducedmorbidity. Differences between the ozone pretreatment technology and the in-home filters in terms of their impact on DBP-induced health effects areinconsequential. On the other hand, it was assumed that home filters completelyeliminate microbial morbidity and mortality, whereas ozone filtration reduces theserisks. The resulting gain in QALY costs comes at a substantial per capitatechnology cost, but the CE ratios for the in-home filtration technology fall wellwithin ranges in the literature that are suggested to be cost-effective (Daplan andBush, 1982).
• It is clear from the sensitivity analyses that most of the uncertainty stems from thelack of data available to accurately estimate parameters related toCryptosporidium: concentrations of viable oocysts in the source water, removalefficacy of the treatments, infectivity rates, and conditional probabilities related tocontracting mild illness, contracting moderate to severe illness, or dying. This is anextremely important point, because microbial morbidity and mortality drive theresults of the case study, while the risks associated with health endpoints related toDBP exposure are essentially inconsequential.
The current case study provides an example of how the framework methodology can
simultaneously assess the cost effectiveness of different drinking water treatment options based on
their impact on several disparate health effects. It is intended to be a learning tool to help develop
improved methodology to identify important health risk factors, recognize data gaps, and choose
directions for investigations of other treatment options. The case study is not intended to provide
definitive answers; discussions of the results are intended to illustrate the type of inferences that
can be drawn from this kind of analysis, while acknowledging that changes to the specific
assumptions used in the study could conceivably change the findings. Because the case study was
performed for a hypothetical, single treatment plant, the results cannot be generalized to represent
results for all similar plants at a regional or a national level. The present investigation is also
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considered a demonstration, and not a definitive assessment, because it does not address all of the
important issues surrounding this problem.
The benefits of this methodology should be apparent from the standpoint of the local
water purveyor who must evaluate treatment options. This methodology may be useful at the
local level for the evaluation of treatment options needed for compliance with new regulations
under the SDWA and its amendments. For most public water supplies serving populations over
10,000, there are new requirements to filter and/or disinfect the water. These options have
different technology costs, but also differ in terms of their efficacy for removal of microbes
(particularly Cryptosporidium) and in terms of DBP formation and resulting concentrations. CEA
methodology could be helpful in the assessment of these financial costs and the wide range of
health impacts.
To reduce the uncertainty and increase the level of confidence in choosing among
treatment options, the local purveyor must carefully evaluate source water quality, specifically
identifying contaminants that may predispose the formation of DBPs and anticipated microbial
concentrations. Additional efforts on the part of local purveyors should include an assessment of
the population to be serviced and the consideration of specifically susceptible subpopulations.
The results of a CEA may aid decisions makers in local governments to more carefully weigh the
expected health outcomes against anticipated capital outlay.
Benefits of this methodology can also be seen from the viewpoint of risk assessors and
managers at the national level who must develop data and draft regulations. From the perspective
of the national risk manager, this methodology can be used to identify assumptions that most
affect analysis results. Stage 1 of the negotiated rule-making by EPA and industry consists of the
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implementation of the Information Collection Rule (ICR) for the collection of occurrence and
treatment data, and the promulgation in 1998 of the Disinfectant/Disinfectant By-Products
(D/DBPs) rule and the Interim Enhanced Surface Water Treatment rule (IESWTR), which would
further reduce exposures to specific D/DBPs and enhance protection from pathogens, especially
Cryptosporidium. The methodology set forth in this document can help provide a sound scientific
basis for determining whether to go beyond the November 1998 Stage 1 DBP rule to lower DBP
limits. This methodology can also be tailored to determine and evaluate the effect of changing
variables within a treatment process such as the amount of disinfectant used, length of contact
time, or changing the order in which disinfectants or filtration procedures are applied. The ICR
will generate empirical data on the microbial and DBP concentrations that occur from specific
treatment trains with detailed information on the source water characteristics; these date will be
useful in future CEA studies.
From the present case study, it is apparent that microbial risks have the most affect on the
overall risk of morbidity and mortality associated with these treatment options under the
conditions assumed for this analysis. All else being equal, risk managers might most logically
consider the option that results in the lowest level of microbial outflow. However, the sensitivity
analysis indicates that microbial risk assumptions, including infectivity and morbidity, are largely
responsible for the uncertainty of the case study results. It follows that further investigation of
microbial parameters should include the effectiveness of disinfection strategies, the infectivity
parameters for microbes to be investigated, and factors that may influence secondary transmission
among the affected population. The lack of an adequate method to identify and quantify viable
Cryptosporidium oocysts is largely responsible for the uncertainties for these parameters. Nation-
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wide variations in geography, rainfall, and land use and variation in the size of treatment facilities,
local economic conditions, and serviced-population size and characteristics indicate that
assumptions made about treatment needs may vary substantially across localities, even among
those that are closely located. When generalized to larger regions, careful assumptions must be
made about parameters such as qualitative and quantitative differences in microbial content and
bromide levels in the source waters. This case study suggests that research into alternative forms
of disinfection should focus on the degree to which alternatives control microbial risks, although
DBP formation may be important in some circumstances.
EPA believes that the proposed CEA approach to this public health intervention will assist
the EPA in determining the balance between adequate water treatment to control and minimize
microbial risk without creating an unacceptably high level of countervailing risks from DBPs. The
framework methodology approach as presented here is intended to support and strengthen
traditional and existing risk assessment and risk management activities.
FIGURE ES-3
Schematic of Conventional Water Treatment Train
FIGURE ES-4
Schematic of Conventional Water Treatment Train with Pre-Ozonation
