Cover image for Forecasting product liability claims : epidemiology and modeling in the Manville asbestos case
Title:
Forecasting product liability claims : epidemiology and modeling in the Manville asbestos case
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Series:
Statistics for biology and health
Publication Information:
New York, NY : Springer, 2005
ISBN:
9780387949871

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30000010122368 KF1297.A73 S72 2005 Open Access Book Book
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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 Overviewp. 1
1.1 Introductionp. 1
1.2 Asbestos and Healthp. 1
1.3 History of Asbestosp. 4
1.4 Epidemiological Discoveryp. 5
1.5 Johns-Manville Corporationp. 6
1.6 Manville Trustp. 6
1.7 Manville Trust Litigationp. 7
1.8 Project Historyp. 9
1.9 Resultsp. 11
1.10 Organization of Monographp. 14
2 Epidemiology of Asbestos-Related Diseasesp. 17
2.1 Introductionp. 17
2.2 Design Issues in Studying Occupational Exposurep. 18
2.2.1 Measures of Riskp. 19
2.2.2 Design Issuesp. 22
2.3 Studies of Health Risks of Occupational Exposuresp. 24
2.3.1 Health Risks of a Cohort of Insulation Workers Occupationally Exposed to Asbestosp. 25
2.3.2 A Case-Control Study of Asbestos Risks in the United States and Canadap. 35
2.3.3 Short-Term Amosite Exposure Among Factory Workers in New Jerseyp. 37
2.3.4 Effects of Chrysotile Exposure Among Miners and Millers in Quebecp. 38
2.3.5 Mesothelioma Risks Among World War II Shipyard Workersp. 40
2.3.6 Effects of Asbestos Exposure Among a Cohort of Retired Factory Workersp. 42
2.4 Increases in Disease Risk Associated with Exposure to Asbestosp. 44
2.5 Effects of Fiber Type on Disease Risksp. 52
2.6 Simian Virus 40 and Mesotheliomap. 57
3 Forecasts Based on Direct Estimates of Exposurep. 61
3.1 Introductionp. 61
3.2 Selikoff's Study: General Descriptionp. 61
3.2.1 Datap. 61
3.2.2 Model and Methodsp. 62
3.3 Selikoff's Six Tasksp. 62
3.3.1 Task 1: Identify the Industries and Occupations Where Asbestos Exposure Took Placep. 63
3.3.2 Task 2: Estimate the Number, Timing, and Duration of Employment of Exposed Workersp. 67
3.3.3 Task 3: Estimate Risk Differentials Among Occupations and Industriesp. 71
3.3.4 Task 4: Estimate Dose-Response Models for Cancer Risksp. 74
3.3.5 Task 5: Project Future Asbestos-Related Cancer Mortalityp. 76
3.3.6 Task 6: Estimate and Project Deaths Due to Asbestosisp. 76
3.4 Sensitivity of Selikoff's Projectionsp. 79
3.5 Alternative Projections of Health Implicationsp. 81
4 Forecasts Based on Indirect Estimates of Exposurep. 89
4.1 Introductionp. 89
4.2 Backgroundp. 89
4.3 Walker's Study: General Descriptionp. 93
4.3.1 Datap. 93
4.3.2 Model and Methodsp. 94
4.4 Walker's Five Tasksp. 94
4.4.1 Task 1: Determine the Effective Number of Past Asbestos Workersp. 95
4.4.2 Task 2: Project Mesothelioma Incidencep. 112
4.4.3 Task 3: Project Lung Cancer Incidencep. 115
4.4.4 Task 4: Estimate Current and Future Asbestosis Prevalencep. 119
4.4.5 Task 5: Estimate the Amount of Asbestos-Related Disease Likely to Occur in Womenp. 124
4.5 Asbestos-Related Disease Projections by Other Authorsp. 125
4.6 Conclusionsp. 127
5 Uncertainty in Forecasts Based on Indirect Estimatesp. 129
5.1 Introductionp. 129
5.2 Qualitative Sources of Uncertainty in Walker's Projectionsp. 129
5.2.1 Uncertainties in Either Directionp. 130
5.2.2 Why Walker's Projections May Be Too Lowp. 132
5.2.3 Why Walker's Projections May Be Too Highp. 133
5.3 Sensitivity Analysis of Walker's Projectionsp. 134
5.3.1 Results for Single Parametersp. 138
5.3.2 Results for All Variables Jointlyp. 139
5.3.3 Summary of Uncertainty Resultsp. 142
5.4 Further Sensitivity Analysis of Walker's Mesothelioma Projectionsp. 143
5.4.1 Projection Methodologyp. 145
5.4.2 Alternative Scenariosp. 147
5.4.3 Resultsp. 149
5.5 Conclusionsp. 152
6 Updated Forecasts Based on Indirect Estimates of Exposurep. 155
6.1 Introductionp. 155
6.2 Factors Consideredp. 155
6.3 Assumptionsp. 160
6.4 First-Stage Calibration: Overviewp. 165
6.5 Data Preparationp. 169
6.5.1 Step 1: Nonmesothelioma Mortality Ratesp. 169
6.5.2 Step 2: National Estimates of Mesothelioma Incidence Countsp. 172
6.5.3 Step 3: Distribution of Age and Date at Start of Asbestos Exposure for Mesothelioma Incidence Among Manville Trust Claimantsp. 174
6.5.4 Step 4: Normalization of Exposurep. 189
6.5.5 Step 5: Intensity of Exposurep. 190
6.6 Model Estimationp. 191
6.6.1 Step 6: Stratification of National Estimates of Mesothelioma Incidence Counts, by Level of Asbestos Exposurep. 191
6.6.2 Step 7: Estimation of the IWE Population Exposed to Asbestos Prior to 1975 by Level of Asbestos Exposurep. 192
6.6.3 Step 8: Adjustments to Exposure During 1955-1974, by Level of Asbestos Exposurep. 198
6.6.4 Step 9: Adjustments to Reflect Improvements in the Workplace During 1960-1974, by Level of Asbestos Exposurep. 198
6.6.5 Step 10: Renormalization by Level of Asbestos Exposurep. 199
6.7 Model Projectionp. 200
6.7.1 Step 11: Forward Projection of the At-Risk IWE Population by Level of Asbestos Exposurep. 202
6.7.2 Step 12: Forward Projection of Mesothelioma Incidence by Level of Asbestos Exposurep. 202
6.8 Nonparametric Hazard Modeling of Claim Filing Rates: CHR Modelp. 208
6.8.1 Step 1: Distribution of 1990-1994 Claims by Attained Age, TSFE, and Disease/Injuryp. 208
6.8.2 Step 2: Estimation of Claim Hazard Rates by Attained Age, TSFE, and Disease/Injuryp. 209
6.8.3 Step 3: Claim Projectionsp. 213
7 Uncertainty in Updated Forecastsp. 217
7.1 Introductionp. 217
7.2 Analysis S1: Constant Age-Specific Claim Runoffp. 222
7.3 Analysis S2: Ratio Estimation of Nine Asbestos-Related Diseases - PTS Modelp. 223
7.4 Analysis S3: Parametric Claim Hazard Rate Modelp. 224
7.5 Analysis S4: Mesothelioma Incidence Functionp. 229
7.5.1 Sensitivity to the b Parameterp. 232
7.5.2 Sensitivity to the k Parameterp. 233
7.6 Analysis S5: Adjustments to the IWE Exposed Populationp. 235
7.7 Analysis S6: National Mesothelioma Incidence Countsp. 236
7.8 Analysis S7: Nonmesothelioma Mortality Ratesp. 237
7.9 Analysis S8: Excess Mortality Among Insulation Workersp. 239
7.10 Analysis S9: Decline in Claim Filing Ratesp. 240
7.11 Overall Sensitivity: Analyses S1-S9p. 241
7.12 Analysis S10: Manville Trust Calibrationsp. 247
7.13 Conclusionsp. 249
8 Forecasts Based on a Hybrid Modelp. 251
8.1 Introductionp. 251
8.2 Model Overviewp. 252
8.2.1 First Stagep. 252
8.2.2 Second Stagep. 254
8.3 Data Preparationp. 255
8.3.1 Step 1: Nonmesothelioma Mortality Ratesp. 255
8.3.2 Step 2: Occupation Groups with Significant Asbestos Exposurep. 256
8.3.3 Step 3: Distribution of Mesothelioma Claim Counts 1990-1994 by Attained Age at the Time of Claim and TSFEp. 257
8.3.4 Step 4: Distribution of Mesothelioma Claim Counts by Age at Start of Exposure and Date of First Exposurep. 270
8.3.5 Step 5: Normalization of Exposurep. 273
8.4 Model Estimationp. 273
8.4.1 Step 6: Estimation of the OSHA Model for Mesotheliomap. 273
8.4.2 Step 7: Estimation of the Population Exposed to Asbestos Prior to 1975p. 284
8.5 Model Projectionp. 288
8.5.1 Step 8: First-Stage Calibrationp. 288
8.5.2 Step 9: Forward Projection of Mesothelioma Mortalityp. 288
8.6 Second Stage: CHR Forecasting Modelp. 290
8.6.1 Step 1: Distribution of Disease-Specific Claim Counts for 1990-1994 by Attained Age and TSFEp. 290
8.6.2 Step 2: Second-Stage Calibrationp. 290
8.6.3 Step 3: At-Risk Population Projectionsp. 294
8.6.4 Step 4: Claim Projectionsp. 298
8.7 Conclusionsp. 308
9 Uncertainty in Forecasts Based on a Hybrid Modelp. 311
9.1 Introductionp. 311
9.2 Impact of Claim Filing Rulesp. 313
9.3 Baseline Model: SDIS Criterionp. 314
9.4 Analysis S1: Validated Diseasep. 315
9.5 Analysis S2: Multiple Diseasesp. 320
9.6 Analysis S3: CHR Smoothingp. 325
9.7 Analysis S4: Exposure Smoothingp. 327
9.8 Analysis S5: Weibull k Parameterp. 328
9.9 Analysis S6: Relative Risks of Mesotheliomap. 330
9.10 Analysis S7: Duration of Exposurep. 332
9.11 Overall Sensitivity: Analyses S1-S7p. 334
9.12 Conclusionsp. 336
10 Conclusions and Implicationsp. 345
10.1 Introductionp. 345
10.2 Datap. 347
10.3 Comparisons of Original and Updated Datap. 350
10.4 Comparisons of Actual and Projected Numbers of Claimsp. 354
10.5 Health and Vital Statistics Data, 1990-1999p. 359
10.6 Conclusionsp. 374
Referencesp. 377
Indexp. 389