Forecasting Product Liability Claims: Epidemiology and Modeling in the Manville Asbestos Case / Edition 1

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Overview

This volume presents a rigorous account of statistical forecasting efforts that led to the successful resolution of the Johns-Manville asbestos litigation. This case, taking 12 years to reach settlement, is expected to generate nearly 500,000 claims at a total nominal value of over $34 billion. The forecasting task, to project the number, timing, and nature of claims for asbestos-related injuries from a set of exposed persons of unknown size, is a general problem: the models in this volume can be adapted to forecast industry-wide asbestos liability. More generally, because the models are not overly dependent on the U.S. legal system and the role of asbestos as a dangerous/defective product, this volume will be of interest in other product liability cases, as well as similar forecasting situations for a range of insurable or compensable events. The volume stresses the iterative nature of model building and the uncertainty generated by lack of complete knowledge of the injury process. This uncertainty is balanced against the Court’s need for a definitive settlement, and the volume addresses how these opposing principles can be reconciled.

The volume is written for a broad audience of actuaries, biostatisticians, demographers, economists, epidemiologists, environmental health scientists, financial analysts, industrial-risk analysts, investment analysts, occupational health analysts, product liability analysts, and statisticians. The modest prerequisites include basic concepts of statistics, calculus, and matrix algebra. Care is taken that readers without specialized knowledge in these areas can understand the rationale for specific applications of advanced methods. As a consequence, this volume will be an indispensable reference for all whose work involves these topics.

Eric Stallard, A.S.A., M.A.A.A., is Research Professor and Associate Director of the Center for Demographic Studies at Duke University. He is a Member of the American Academy of Actuaries and an Associate of the Society of Actuaries. He serves on the American Academy of Actuaries Committees on Long Term Care and Social Insurance. He also serves on the Society of Actuaries’ Long Term Care Experience Committee. His research interests include modeling and forecasting for medical demography and health actuarial practice. He was the 1996 winner of the National Institute on Aging’s James A. Shannon Director’s Award.

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.

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Editorial Reviews

From the Publisher
From the reviews:

"This monograph shows then epidemiological prediction in action and is more eloquent than most textbooks as regards to methodology and difficulties encountered with field data. It should interest far outside the circle of asbestos-related epidemiology because of its rigorous exposition, the relevance of its questions, the adequacy of the solutions offered, the discussion of its results, and its legal, financial, and human consequences." Mathematical Population Studies, 12:181-182, 2005

"Over 750,000 claimants have filed suit … for illnesses and deaths related to exposure to asbestos, and at least 65 companies had been driven to bankruptcy; these numbers continue to grow. … This work led to substantial advances in the art of forecasting the number, timing, and nature of new claims. The authors present a lucid explanation of these advances and a description of how the matters in litigation have been settled. The presiding judge, Jack Weinstein, has contributed an informative preface … ." (J.C. Bailar, Short Book Reviews Publication of the International Statistical Institute, Vol. 25 (1), 2005)

"Demographers Eric Stallard, Kenneth Manton and Joel Cohen use a risk assessment framework to estimate the numbers of claims expected during the period between 1990 and 2049 for asbestos-related disease among men exposed to … asbestos products. … Forecasting Product Liability Claims is notable for its illustration of the possibility of using epidemiologic and demographic methods to develop models for broad policy purposes. … It is formula-rich and dense in its description of data sources and the machinery of the models, as it should be." (Jonathan M. Samet, Science-AAAS, May, 2006)

"This book summarizes the statistical models for projecting the number, timing, and nature of future claims, and it discusses how these predictions are used in developing a fair and equitable distribution of insufficient funds. … This book provides a fascinating account of how predication models are used to solve a very-real problem. It makes wonderful reading for statisticians interested in prediction problems, epidemiologists, actuaries, and lawyers involved in product liability suits and having to predict the number of possible litigants." (Johannes Ledolter, Zentralblatt MATH, Vol. 1099 (1), 2007)

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Product Details

  • ISBN-13: 9780387949871
  • Publisher: Springer New York
  • Publication date: 10/25/2004
  • Series: Statistics for Biology and Health Series
  • Edition description: 2004
  • Edition number: 1
  • Pages: 394
  • Product dimensions: 9.21 (w) x 6.14 (h) x 0.94 (d)

Meet the Author

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.

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Table of Contents

Overview
• Epidemiology of Asbestos-Related Diseases
• Forecasts Based on Direct Estimates of Exposure
• Forecasts Based on Indirect Estimates of Exposure
• Uncertainty in Forecasts Based on Indirect Estimates
• Updated Forecasts Based on Indirect Estimates of Exposure
• Uncertainty in Updated Forecasts
• Forecasts Based on a Hybrid Model
• Uncertainty in Forecasts Based on a Hybrid Model
• Conclusions and Implications

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