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Summary
Summary
I write this foreword for two reasons: first, to acknowledge the gratitude of our court system to scientists willing to lend their talents to forensic tasks, and of myself, in particular, for the pathbreaking work of Eric Stallard, Kenneth G. Manton, and Joel E. Cohen in the Manville Asbestos Case; and second, because their work suggests both great strength and utility in their statisti cally based design and its limitations in predicting events strongly affected by political and social choices that are difficult to foretell as well as by de mographic and epidemiologic factors that can be prophesied with somewhat more confidence - at least in the short term. It is by now almost axiomatic that almost every important litigation in the United States requires experts to help judges and juries arrive at an under standing of the case sufficient to permit a sensible resolution within the flexible scope of our rules of law. The Supreme Court has laid down useful rough cri teria for the courts in assessing the capability of proffered experts beginning 1 with the Daubert line of cases. It has also allowed the courts to appoint ex 2 perts to supplement those designated by the parties. Dr. Joel E. Cohen and Professor Margaret E. Berger were appointed by me in the Manville asbestos cases pursuant to Rule 706 of the Federal Rules of Evidence to help project future claims. Discovery provisions have improved utilization of experts by 3 requiring advance reports and depositions.
Author Notes
Kenneth G. Manton, Ph.D. is Research Professor, Research Director, and Director of the Center for Demographic Studies at Duke University, and Medical Research Professor at Duke University Medical Center's Department of Community and Family Medicine. Dr. Manton is also a Senior Fellow of the Duke University Medical Center's Center for the Study of Aging and Human Development. His research interests include mathematical models of human aging, mortality, and chronic disease. He was the 1990 recipient of the Mindel C. Sheps Award in Mathematical Demography presented by the Population Association of America; and in 1991 he received the Allied-Signal Inc. Achievement Award in Aging administered by the Johns Hopkins Center on Aging.
Joel E. Cohen, Ph.D., Dr. P.H., is Professor of Population, and Head of the Laboratory of Populations, Rockefeller University. He also is Professor of Populations at Columbia University. His research interests include the demography, ecology, epidemiology, and social organization of human and non-human populations, and related mathematical concepts. In 1981, he was elected Fellow of the MacArthur and Guggeneheim Foundations. He was the 1992 recipient of the Mindel C. Sheps Award in Mathematical Demography presented by the Population Association of America; and in 1994, he received the Distinguished Statistical Ecologist Award at the Sixth International Congress of Ecology.
Table of Contents
1 Overview | p. 1 |
1.1 Introduction | p. 1 |
1.2 Asbestos and Health | p. 1 |
1.3 History of Asbestos | p. 4 |
1.4 Epidemiological Discovery | p. 5 |
1.5 Johns-Manville Corporation | p. 6 |
1.6 Manville Trust | p. 6 |
1.7 Manville Trust Litigation | p. 7 |
1.8 Project History | p. 9 |
1.9 Results | p. 11 |
1.10 Organization of Monograph | p. 14 |
2 Epidemiology of Asbestos-Related Diseases | p. 17 |
2.1 Introduction | p. 17 |
2.2 Design Issues in Studying Occupational Exposure | p. 18 |
2.2.1 Measures of Risk | p. 19 |
2.2.2 Design Issues | p. 22 |
2.3 Studies of Health Risks of Occupational Exposures | p. 24 |
2.3.1 Health Risks of a Cohort of Insulation Workers Occupationally Exposed to Asbestos | p. 25 |
2.3.2 A Case-Control Study of Asbestos Risks in the United States and Canada | p. 35 |
2.3.3 Short-Term Amosite Exposure Among Factory Workers in New Jersey | p. 37 |
2.3.4 Effects of Chrysotile Exposure Among Miners and Millers in Quebec | p. 38 |
2.3.5 Mesothelioma Risks Among World War II Shipyard Workers | p. 40 |
2.3.6 Effects of Asbestos Exposure Among a Cohort of Retired Factory Workers | p. 42 |
2.4 Increases in Disease Risk Associated with Exposure to Asbestos | p. 44 |
2.5 Effects of Fiber Type on Disease Risks | p. 52 |
2.6 Simian Virus 40 and Mesothelioma | p. 57 |
3 Forecasts Based on Direct Estimates of Exposure | p. 61 |
3.1 Introduction | p. 61 |
3.2 Selikoff's Study: General Description | p. 61 |
3.2.1 Data | p. 61 |
3.2.2 Model and Methods | p. 62 |
3.3 Selikoff's Six Tasks | p. 62 |
3.3.1 Task 1: Identify the Industries and Occupations Where Asbestos Exposure Took Place | p. 63 |
3.3.2 Task 2: Estimate the Number, Timing, and Duration of Employment of Exposed Workers | p. 67 |
3.3.3 Task 3: Estimate Risk Differentials Among Occupations and Industries | p. 71 |
3.3.4 Task 4: Estimate Dose-Response Models for Cancer Risks | p. 74 |
3.3.5 Task 5: Project Future Asbestos-Related Cancer Mortality | p. 76 |
3.3.6 Task 6: Estimate and Project Deaths Due to Asbestosis | p. 76 |
3.4 Sensitivity of Selikoff's Projections | p. 79 |
3.5 Alternative Projections of Health Implications | p. 81 |
4 Forecasts Based on Indirect Estimates of Exposure | p. 89 |
4.1 Introduction | p. 89 |
4.2 Background | p. 89 |
4.3 Walker's Study: General Description | p. 93 |
4.3.1 Data | p. 93 |
4.3.2 Model and Methods | p. 94 |
4.4 Walker's Five Tasks | p. 94 |
4.4.1 Task 1: Determine the Effective Number of Past Asbestos Workers | p. 95 |
4.4.2 Task 2: Project Mesothelioma Incidence | p. 112 |
4.4.3 Task 3: Project Lung Cancer Incidence | p. 115 |
4.4.4 Task 4: Estimate Current and Future Asbestosis Prevalence | p. 119 |
4.4.5 Task 5: Estimate the Amount of Asbestos-Related Disease Likely to Occur in Women | p. 124 |
4.5 Asbestos-Related Disease Projections by Other Authors | p. 125 |
4.6 Conclusions | p. 127 |
5 Uncertainty in Forecasts Based on Indirect Estimates | p. 129 |
5.1 Introduction | p. 129 |
5.2 Qualitative Sources of Uncertainty in Walker's Projections | p. 129 |
5.2.1 Uncertainties in Either Direction | p. 130 |
5.2.2 Why Walker's Projections May Be Too Low | p. 132 |
5.2.3 Why Walker's Projections May Be Too High | p. 133 |
5.3 Sensitivity Analysis of Walker's Projections | p. 134 |
5.3.1 Results for Single Parameters | p. 138 |
5.3.2 Results for All Variables Jointly | p. 139 |
5.3.3 Summary of Uncertainty Results | p. 142 |
5.4 Further Sensitivity Analysis of Walker's Mesothelioma Projections | p. 143 |
5.4.1 Projection Methodology | p. 145 |
5.4.2 Alternative Scenarios | p. 147 |
5.4.3 Results | p. 149 |
5.5 Conclusions | p. 152 |
6 Updated Forecasts Based on Indirect Estimates of Exposure | p. 155 |
6.1 Introduction | p. 155 |
6.2 Factors Considered | p. 155 |
6.3 Assumptions | p. 160 |
6.4 First-Stage Calibration: Overview | p. 165 |
6.5 Data Preparation | p. 169 |
6.5.1 Step 1: Nonmesothelioma Mortality Rates | p. 169 |
6.5.2 Step 2: National Estimates of Mesothelioma Incidence Counts | p. 172 |
6.5.3 Step 3: Distribution of Age and Date at Start of Asbestos Exposure for Mesothelioma Incidence Among Manville Trust Claimants | p. 174 |
6.5.4 Step 4: Normalization of Exposure | p. 189 |
6.5.5 Step 5: Intensity of Exposure | p. 190 |
6.6 Model Estimation | p. 191 |
6.6.1 Step 6: Stratification of National Estimates of Mesothelioma Incidence Counts, by Level of Asbestos Exposure | p. 191 |
6.6.2 Step 7: Estimation of the IWE Population Exposed to Asbestos Prior to 1975 by Level of Asbestos Exposure | p. 192 |
6.6.3 Step 8: Adjustments to Exposure During 1955-1974, by Level of Asbestos Exposure | p. 198 |
6.6.4 Step 9: Adjustments to Reflect Improvements in the Workplace During 1960-1974, by Level of Asbestos Exposure | p. 198 |
6.6.5 Step 10: Renormalization by Level of Asbestos Exposure | p. 199 |
6.7 Model Projection | p. 200 |
6.7.1 Step 11: Forward Projection of the At-Risk IWE Population by Level of Asbestos Exposure | p. 202 |
6.7.2 Step 12: Forward Projection of Mesothelioma Incidence by Level of Asbestos Exposure | p. 202 |
6.8 Nonparametric Hazard Modeling of Claim Filing Rates: CHR Model | p. 208 |
6.8.1 Step 1: Distribution of 1990-1994 Claims by Attained Age, TSFE, and Disease/Injury | p. 208 |
6.8.2 Step 2: Estimation of Claim Hazard Rates by Attained Age, TSFE, and Disease/Injury | p. 209 |
6.8.3 Step 3: Claim Projections | p. 213 |
7 Uncertainty in Updated Forecasts | p. 217 |
7.1 Introduction | p. 217 |
7.2 Analysis S1: Constant Age-Specific Claim Runoff | p. 222 |
7.3 Analysis S2: Ratio Estimation of Nine Asbestos-Related Diseases - PTS Model | p. 223 |
7.4 Analysis S3: Parametric Claim Hazard Rate Model | p. 224 |
7.5 Analysis S4: Mesothelioma Incidence Function | p. 229 |
7.5.1 Sensitivity to the b Parameter | p. 232 |
7.5.2 Sensitivity to the k Parameter | p. 233 |
7.6 Analysis S5: Adjustments to the IWE Exposed Population | p. 235 |
7.7 Analysis S6: National Mesothelioma Incidence Counts | p. 236 |
7.8 Analysis S7: Nonmesothelioma Mortality Rates | p. 237 |
7.9 Analysis S8: Excess Mortality Among Insulation Workers | p. 239 |
7.10 Analysis S9: Decline in Claim Filing Rates | p. 240 |
7.11 Overall Sensitivity: Analyses S1-S9 | p. 241 |
7.12 Analysis S10: Manville Trust Calibrations | p. 247 |
7.13 Conclusions | p. 249 |
8 Forecasts Based on a Hybrid Model | p. 251 |
8.1 Introduction | p. 251 |
8.2 Model Overview | p. 252 |
8.2.1 First Stage | p. 252 |
8.2.2 Second Stage | p. 254 |
8.3 Data Preparation | p. 255 |
8.3.1 Step 1: Nonmesothelioma Mortality Rates | p. 255 |
8.3.2 Step 2: Occupation Groups with Significant Asbestos Exposure | p. 256 |
8.3.3 Step 3: Distribution of Mesothelioma Claim Counts 1990-1994 by Attained Age at the Time of Claim and TSFE | p. 257 |
8.3.4 Step 4: Distribution of Mesothelioma Claim Counts by Age at Start of Exposure and Date of First Exposure | p. 270 |
8.3.5 Step 5: Normalization of Exposure | p. 273 |
8.4 Model Estimation | p. 273 |
8.4.1 Step 6: Estimation of the OSHA Model for Mesothelioma | p. 273 |
8.4.2 Step 7: Estimation of the Population Exposed to Asbestos Prior to 1975 | p. 284 |
8.5 Model Projection | p. 288 |
8.5.1 Step 8: First-Stage Calibration | p. 288 |
8.5.2 Step 9: Forward Projection of Mesothelioma Mortality | p. 288 |
8.6 Second Stage: CHR Forecasting Model | p. 290 |
8.6.1 Step 1: Distribution of Disease-Specific Claim Counts for 1990-1994 by Attained Age and TSFE | p. 290 |
8.6.2 Step 2: Second-Stage Calibration | p. 290 |
8.6.3 Step 3: At-Risk Population Projections | p. 294 |
8.6.4 Step 4: Claim Projections | p. 298 |
8.7 Conclusions | p. 308 |
9 Uncertainty in Forecasts Based on a Hybrid Model | p. 311 |
9.1 Introduction | p. 311 |
9.2 Impact of Claim Filing Rules | p. 313 |
9.3 Baseline Model: SDIS Criterion | p. 314 |
9.4 Analysis S1: Validated Disease | p. 315 |
9.5 Analysis S2: Multiple Diseases | p. 320 |
9.6 Analysis S3: CHR Smoothing | p. 325 |
9.7 Analysis S4: Exposure Smoothing | p. 327 |
9.8 Analysis S5: Weibull k Parameter | p. 328 |
9.9 Analysis S6: Relative Risks of Mesothelioma | p. 330 |
9.10 Analysis S7: Duration of Exposure | p. 332 |
9.11 Overall Sensitivity: Analyses S1-S7 | p. 334 |
9.12 Conclusions | p. 336 |
10 Conclusions and Implications | p. 345 |
10.1 Introduction | p. 345 |
10.2 Data | p. 347 |
10.3 Comparisons of Original and Updated Data | p. 350 |
10.4 Comparisons of Actual and Projected Numbers of Claims | p. 354 |
10.5 Health and Vital Statistics Data, 1990-1999 | p. 359 |
10.6 Conclusions | p. 374 |
References | p. 377 |
Index | p. 389 |