The Conception of Variability in Risk Analyses: Developments Since 1980

doi:10.1017/CBO9780511814662.002

2 - The Conception of Variability in Risk Analyses: Developments Since 1980

Published online by Cambridge University Press: 05 June 2012

Dale Hattis

Edited by

Timothy McDaniels and

Mitchell Small

Show author details

Dale Hattis: Affiliation:
Center for Technology, Policy, and Industrial Development, George Perkins Marsh Institute, Clark University, Worcester, MA
Timothy McDaniels: Affiliation:
University of British Columbia, Vancouver
Mitchell Small: Affiliation:
Carnegie Mellon University, Pennsylvania

Book contents

Get access

Summary

INTRODUCTION

This chapter offers a philosophical and historical perspective on the development of the concept of “variability” in the last few decades. The goal is not to provide a treatment of modern mathematical techniques for the analysis of data indicating variability. The recent literature contains excellent works that document analytical tools and are eminently usable by risk analysis practitioners (Cullen and Frey, 1999; Thompson, 1999). Rather, the object is to reflect on the significance and prospects for quantitative assessment of “variability” as an intellectual innovation that has emerged in part from the interdisciplinary fusion of ideas, techniques, and social needs for information for decision making that is the discipline of risk analysis.

Briefly, the nub of the innovation is distinguishing real variation among things from measurement errors, other sources of uncertainties, and stochastic fluctuations (such as the numbers of cosmic rays arriving at a detector in a specified interval). Where uncertainties reflect the imperfections in available information about the world (and are often seen as an annoying fog that obscures investigators' ability to demonstrate differences among groups, but with no real consequence or interest in itself; Hattis, 1996), real quantitative variation among things/people has real implications for differential behavior among the “things” being studied. Such differences can, for example, take the forms of (a) the relative risks to those things/people and (b) the relative desirability of devoting resources to some things rather than to others (if, for example, the things are categories such as industries that could be the subjects of safety inspections or other resource-consuming activities) (Hattis and Goble, 1994).

Information

Type: Chapter
Information: Risk Analysis and Society
An Interdisciplinary Characterization of the Field
, pp. 15 - 45

DOI: https://doi.org/10.1017/CBO9780511814662.002 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

American Conference of Governmental Industrial Hygienists (ACGIH). 1998. Threshold Limit Values for Chemical Substances and Physical Agents and Biological Exposure Indices. American Conference of Governmental Industrial Hygienists, Cincinnati, OH

Albert, R. E. 1985. “U.S. Environmental Protection Agency Revised Interim Guideline for the Health Assessment of Suspect Carcinogens” In D. G. Hoel, R. A. Merrill, and F. P. Perera, eds., Risk Quantitation and Regulatory Policy, Banbury Report No. 19, pp. 307–29. Cold Spring Harbor Laboratory

Alpert, M., and Raiffa, H., 1982. A progress report on the training of probability assessors. In D. Kahneman, P. Slovic, and A. Tversky, eds., Judgment Under Uncertainty, Heuristics and Biases, pp. 294–305. Cambridge University Press, New York

Agency for Toxic Substances and Disease Registry. 1993. The Nature and Extent of Lead Poisoning in Children in the United States: A Report to Congress. U.S. Department of Health and Human Services, Atlanta

Arrow, K. J., and Lind, R. C. 1970. Uncertainty and the evaluation of public investment decisions. American Economic Review, 60: 364–78Google Scholar

Baird, S. J. S., Cohen, J. T., Graham, J. D., Shlyakhter, A. I., and Evans, J. S. 1996. Noncancer risk assessment: A probabilistic alternative to current practice. Health and Ecological Risk Assessment, 2: 79–102CrossRef Google Scholar

Barry, T. M., and Brattin, W. J. 1998. Distribution of radon-222 in community groundwater systems: Analysis of type I left-censored data with single censoring point, Human and Ecological Risk Assessment, 4: 579–603CrossRef Google Scholar

Bier, V. M. 1989. On the treatment of dependence in making decisions about risk. Transactions of the 10th International Conference on Structural Mechanics in Reactor Technology, Volume P (Probabilistic Safety Assessment), pp. 63–8. American Association for Structural Mechanics in Reactor Technology, Anaheim, CA, August 14–18

Bogen, K. T. 1986. Uncertainty in Environmental Health Risk Assessment: A Framework for Analysis and an Application to a Chronic Exposure Situation Involving a Chemical Carcinogen. Doctoral Dissertation, University of California at Berkeley, School of Public Health, Berkeley, CA

Bogen, K. T. 1990. Uncertainty in Environmental Risk Assessment. Garland Publishing, New York

Bogen, K. T. 1995. Methods to approximate joint uncertainty and variability in risk. Risk Analysis, 15: 411–19CrossRef Google Scholar

Bogen, K. T., and Spear, R. C. 1987. Integrating uncertainty and interindividual variability in environmental risk assessment, Risk Analysis, 7: 427–36CrossRef Google Scholar PubMed

Bogen, C. L., Conrado, K. T., and Robison, W. L., 1997. Uncertainty and variability in updated estimates of potential dose and risk at a U.S. nuclear test site – Bikini Atoll, Health Physics, 73: 115–26CrossRef Google Scholar

Bullard, R. D., and Wright, B. H. 1993. Environmental justice for all: Community perspectives on health and research needs. Toxicology and Industrial Health, 9: 821–41CrossRef Google Scholar PubMed

Burmaster, D. E., and Thompson, K. M. 1999. Using animated probability plots to explore the suitability of mixture models with two component distributions, Risk Analysis, 19: 1185–92CrossRef Google Scholar

Burmaster, D. E., and Wilson, A. M. 2000. Fitting second-order finite mixture models to data with many censored values using maximum likelihood estimation, Risk Analysis, 20: 261–71CrossRef Google Scholar PubMed

California Environmental Protection Agency, Department of Pesticide Regulation. 1999. Methyl Bromide – Risk Characterization Document for Inhalation Exposure. Draft, Medical Toxicology, Worker Health and Safety, and Environmental Monitoring and Pest Management Branches, Department of Pesticide Regulation, California Environmental Protection Agency, Sacramento, California, October 15

California Environmental Protection Agency. 1993. CALTOX – A Multimedia Total Exposure Model for Hazardous-Waste Sites Technical Reports. Part 1, Executive Summary, pp. 1–31, and Part 2, The Dynamic Multimedia Transport and Transformation Model, pp. 1–95. Draft Final, December. Office of Scientific Affairs, Department of Toxic Substances Control, Sacramento, California (Later versions available on the web – http://www.cwo.com/̃heroi/caltox.htm.)

Clayton, C. A., Pellizzari, E. D., Whitmore, R. W., Perritt, R. L., and Quackenboss, J. J. 1999. National human exposure assessment survey (NHEXAS): Distributions and associations of lead, arsenic and volatile organic compounds in EPA Region 5. J Exposure Analysis and Environmental Epidemiology, 9: 381–92CrossRef Google Scholar PubMed

Columbia River Inter-Tribal Fish Commission. 1994. A Fish Consumption Survey of the Umatilla, Nez Perce, Yakama, and Warm Springs Tribes of the Columbia River Basin, Technical Report 94-3, Columbia River Inter-Tribal Fish Commission, Portland, Oregon, October

Crump, K. D., Hoel, D., and Peto, R. 1976. Fundamental carcinogenic processes and their implications for low dose risk assessment. Cancer Research, 36: 2973–9Google Scholar PubMed

Cullen, A. C., and Frey, H. C. 1999. Probabilistic Techniques in Exposure Assessment – A Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Plenum Press, New York

Dockery, D., , W., Pope III, C. A., Xu, X., Spengler, J. D., Ware, J. H., Fay, M. E., Ferris, B. G. Jr., and Speizer, F. E. 1993. An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine, 329: 1753–9CrossRef Google Scholar PubMed

Dockery, D. W., Schwartz, J., and Spengler, J. D. 1992. Air pollution and daily mortality: Associations with particulates and acid aerosols. Environmental Research, 59: 362–73CrossRef Google Scholar PubMed

Dourson, M. L., and Stara, J. F. 1983. Regulatory history and experimental support of uncertainty (safety) factors. Regulatory Toxicology and Pharmacology, 3: 224–38CrossRef Google Scholar PubMed

EC/R Incorporated. 2000. A Case Study Residual Risk Assessment forEPA's Science Advisory Board Review – Secondary Lead Smelter Source Category, Volume I: Risk Characterization. Report under EPA Contract No. 68-D6–0065, Prepared for the U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, NC

Finney, D. J. 1971. Probit Analysis. Cambridge University Press, Cambridge

Gale, G. 1984. Science and the philosophers. Nature, 312: 491–5CrossRef Google Scholar

Griffin, S., Marcus, A., Schulz, T., and Walker, S. 1999. Calculating the interindividual geometric standard deviation for use in the integrated exposure uptake biokinetic model for fead in children. Environmental Health Perspectives, 107: 481–7CrossRef Google Scholar

Hamey, P. Y., and Harris, C. A. 1999. The variation of pesticide residues in fruits and vegetables and the associated assessment of risk. Regulatory Toxicology and Pharmacology, 30: S34–S41CrossRef Google Scholar

Hattis, D. 1986. The promise of molecular epidemiology for quantitative risk assessment. Risk Analysis, 6: 181–93CrossRef Google Scholar PubMed

Hattis, D. 1996. Human interindividual variability in susceptibility to toxic effects – From annoying detail to a central determinant of risk. Toxicology, 111: 5–14CrossRef Google Scholar

Hattis, D. 1998. Strategies for assessing human variability in susceptibility, and using variability to infer human risks. In D. A. Neumann and C. A. Kimmel, eds., Human Variability in Response to Chemical Exposure: Measures, Modeling, and Risk Assessment, pp. 27–57. CRC Press, Boca Raton, FL

Hattis, D., and Anderson, E. 1999. What should be the implications of uncertainty, variability, and inherent “biases”/“conservatism” for risk management decision making?”Risk Analysis, 19: 95–107CrossRef Google Scholar

Hattis, D., and Barlow, K. 1996. Human interindividual variability in cancer risks – Technical and management challenges. Health and Ecological Risk Assessment, 2: 194–220CrossRef Google Scholar

Hattis, D., and Burmaster, D. E. 1994. Assessment of variability and uncertainty distributions for practical risk analyses. Risk Analysis, 14: 713–30CrossRef Google Scholar

Hattis, D., and Froines, J. 1992. Uncertainties in risk assessment. In Harvey J. Clewell, III, ed., Conference on Chemical Risk Assessment in the DoD: Science, Policy, and Practice, pp. 69–78. American Conference of Governmental Industrial Hygienists, Cincinnati

Hattis, D., and Goble, R. 1991. Expected values for projected cancer risks from putative genetically-acting agents. Risk Analysis, 11: 359–63CrossRef Google Scholar

Hattis, D., and Goble, R. L. 1994. Current priority-setting methodology: Too little rationality or too much? In A. M. Finkel and D. Golding, eds., Worst Things First? The Debate over Risk-Based National Environmental Priorities, Chapter 7, pp. 107–31. Resources for the Future, Washington, DC

Hattis, D., and Minkowitz, W. W. 1996. Risk evaluation: Criteria arising from legal traditions and experience with quantitative risk assessment in the United States. Environmental Toxicology and Pharmacology, 2: 103–9CrossRef Google Scholar PubMed

Hattis, D., and Minkowitz, B. 1997. Risk evaluation: Legal requirements, conceptual foundations, and practical experiences in the United States. Originally presented at the International Workshop on Risk Evaluation (June 20–21, 1994), Schloss Haegerloch, Germany. Discussion paper No. 93, ISBN 3-932013-16-6, Center of Technology Assessment in Baden-Wuerttemberg

Hattis, D., and Richardson, B. 1980. Noise, General Stress Response, and Cardiovascular Disease Processes: Review and Reassessment of Hypothesized Relationships. MIT Center for Policy Alternatives CPA-80-02, June. Massachusetts Institute of Technology, Cambridge, MA

Hattis, D., and Silver, K. 1994. Human interindividual variability – A major source of uncertainty in assessing risks for non-cancer health effects. Risk Analysis, 14: 421–31CrossRef Google Scholar

Hattis, D., Banati, P., and Goble, R. 1999a. Distributions of individual susceptibility among humans for toxic effects – For what fraction of which kinds of chemicals and effects does the traditional 10-fold factor provide how much protection?Annals of the New York Academy of Sciences, 895: 286–316CrossRef Google Scholar

Hattis, D., Banati, P., Goble, R., and Burmaster, D. 1999b. Human interindividual variability in parameters related to health risks. Risk Analysis, 19: 705–20CrossRef Google Scholar

Hattis, D., Erdreich, L., and Ballew, M. 1987. Human variability in susceptibility to toxic chemicals – A preliminary analysis of pharmacokinetic data from normal volunteers. Risk Analysis, 7: 415–26CrossRef Google Scholar PubMed

Hogue, C. J. R., Buehler, J. W., Strauss, M. A., and Smith, J. C. 1987. Overview of the national infant mortality surveillance (NIMS) project – Design, methods, results. Public Health Reports, 102: 126–38Google Scholar

Huikuri, H. V., Makikallio, T., Airaksinen, J., Mitrani, R., Castellanos, A., and Myerburg, R. J. 1999. Measurement of heart rate variability: A clinical tool or a research toy?Journal of the American College of Cardiology, 34: 1878–83CrossRef Google Scholar PubMed

Huikuri, H. V., Makikallio, T. H., Airaksinen, K. E. J., Seppanen, T., Puukka, P., Haiha, I. J., and Sourander, L. B. 1998. Power-law relationship of heart rate variability as a predictor of mortality in the elderly. Circulation, 97: 2031–6CrossRef Google Scholar PubMed

Industrial Union Department v. American Petroleum Institute, 448 U.S. 607 (1980)

Kaplan, S. 1983. On a two-stage Bayesian procedure for determining failure rates from experimental data. IEEE Transactions on Power Apparatus and Systems, PAS-102: 195–202CrossRef Google Scholar

Kaplan, S., and Garrick, B. J. 1981. On the quantitative definition of risk. Risk Analysis, 1: 11–27CrossRef Google Scholar

Koerner, L. 2000. Linnaeus – Nature and Nation. Harvard University Press, Cambridge, MA. Reviewed by Schiebinger, L. 1999. Dreams of Arctic tea plantations. Science, 287: 1761

Lai, A. A., Fleck, R. J., and Caplan, N. B. 1998. Clinical pharmacokinetics and the pharmacist's role in drug development and evaluation. In A. E. Cato, ed., Clinical Drug Trials and Tribulations, Chapter 5, pp. 79–98. Marcel Dekker, New York and Basel

Lehman, A. J., and Fitzhugh, O. G. 1954. 100-fold margin of safety. Association of Food and Drug Officials of the United States, Quarterly Bulletin, 18: 33–5Google Scholar

Lichtenstein, S., and Fischoff, B. 1977. Do those who know more also know more about how much they know?Organizational Behavior and Human Performance, 20: 159–83CrossRef Google Scholar

National Center for Health Statistics, Data Dissemination Branch, Third National Health and Nutrition Examination Survey (NHANES III), 1988–94. CD-ROM available from the National Technical Information Service, Computer Products Office, Springfield, VA. Also see http://www.cdc.gov/nchs

National Center for Health Statistics. 1990. Linked Birth and Death Records. CD-ROM

National Research Council (NRC). 1993. Pesticides in the Diets of Infants and Children. National Academy Press, Washington, DC

National Research Council (NRC). 1994. Science and Judgment in Risk Assessment. National Academy Press, Washington, DC

Office of Environmental Health Hazard Assessment (OEHHA). 1996. Technical Support Document for Exposure Assessment and Stochastic Analysis. Technical Review Draft, December. California Environmental Protection Agency, Berkeley, CA, Available on the web at www.calepa.cahwnet.gov/oehha

Office of Management and Budget (OMB). 1990. Current regulatory issues in risk assessment and risk management. In Regulatory Program of the United States Government, April 1, 1990–March 31, 1991, pp. 13–26. Office of Management and Budget, Executive Office of the President of the United States, Washington, DC

Ott, W. R. 1995. Environmental Statistics and Data Analysis. Lewis Publishers, Boca Raton, FL

Peters, A., Liu, E., Verrier, R. L., Schwartz, J., Gold, D. R., Mittleman, M., Baliff, J., Oh, J. A., Allen, G., Monahan, K., and Dockery, D. W. 2000. Air pollution and incidence of cardiac arrhythmia. Epidemiology, 11: 11–17CrossRef Google Scholar PubMed

Peterson, D. E., Kanarek, M. S., Kuykendall, M. A., Diedrich, J. M., Anderson, H. A., Remington, P. L., and Sheffy, T. B. 1994. Fish consumption patterns and blood mercury levels in Wisconsin Chippewa Indians. Archives of Environmental Health, 49: 53–8CrossRef Google Scholar PubMed

Pope III, C. A., Thun, M. J., Namboodiri, M. M., Dockery, D. W., Evans, J. S., Speizer, F. E., and Heath, C. W. Jr., 1995. Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. American Journal of Respiratory and Critical Care Medicine, 151: 669–74Google Scholar

Pope, C. A. 3rd, Verrier, R. L., Lovett, E. G., Larson, A. C., Raizenne, M. E., Kanner, R. E., Schwartz, J., Villegas, G. M., Gold, D. R., and Dockery, D. W. 1999. Heart rate variability associated with particulate air pollution. American Heart Journal, 138: 890–9CrossRef Google Scholar PubMed

Public Law No. 79–404, Stat 237 (1946) codified as amended at 5 USC section 551–706 (1982)

Roeder, K. 1994. A graphical technique for detemining the number of components in a mixture of normals. Journal of the American Statistical Association, 89: 487–95CrossRef Google Scholar

Roseberry, A. M., and Burmaster, D. E. 1992. Lognormal distributions for water intake by children and adults. Risk Analysis, 12: 99–104CrossRef Google Scholar PubMed

Rosenthal, A., Gray, G. M., and Graham, J. D. 1992. Legislating acceptable cancer risk from exposure to toxic chemicals. Ecology Law Quarterly, 19: 269–362Google Scholar

Samet, J. M., Zeger, S. L., and Berhane, K. 1995. The association of mortality and particulate air pollution. Particulate Air Pollution and daily Mortality – Replication and Validation of Selected Studies – The Phase I Report of the Particle Epidemiology Evaluation Project, August. Health Effects Institute, Cambridge, MA

Schwartz, J.Air pollution and daily mortality. 1994. A review and meta-analysis. Environmental Research, 64: 36–52CrossRef Google Scholar PubMed

Schwartz, J. 1993. Beyond LOEL's, p values and vote counting: Methods for looking at the shapes and strengths of associations. NeuroToxicology, 14(2–3): 237–46Google Scholar PubMed

Schwartz, J., and Dockery, D. W. 1992. Increased mortality in Philadelphia associated with daily air pollution concentrations. American Review of Respiratory Diseases, 145: 600–4CrossRef Google Scholar PubMed

Simon, T. W. 1999. Two-dimensional Monte Carlo simulation and beyond: A comparison of several probabilistic risk assessment methods applied to a Superfund site. Human and Ecological Risk Assessment, 5: 823–43CrossRef Google Scholar

Spicer, C. W., Buxton, B., Holdren, M. W., Smith, D. L., Kelly, T. J., Rust, S. W., Pate, A. D., Sverdrup, G. M., and Chuang, J. C. 1996. Variability of hazardous air pollutants in an urban area. Atmospheric Environment, 30: 3443–56CrossRef Google Scholar

Suppe, F. 1977. The search for philosophic understanding of scientific theories. In: F. Suppe, ed., The Structure of Scientific Theories, 2nd ed. University of Illinois Press, Champaign

Thompson, K. M. 1999. Developing univariate distributions from data for risk analysis. Human and Ecological Risk Assessment, 5: 755–83CrossRef Google Scholar

Travis, C. C., Richter, S. A., Crouch, A. C., Wilson, R., and Klema, E. D. 1987. Cancer risk management: A review of 132 federal regulatory decisions. Environment Science and Technology, 21: 415–20CrossRef Google Scholar PubMed

U.S. Environmental Protection Agency (EPA). 1986. Guidelines for carcinogen risk assessment. Federal Register, 51: 33992

U.S. Environmental Protection Agency (EPA). 1995. Exposure Factors Handbook. External Review Draft, Exposure Assessment Group, EPA/600/P-96/002B, Washington, DC. More recent versions available on the web at www.epa.gov

U.S. Environmental Protection Agency (EPA). Office of Air Quality Planning and Standards. 1996a. Review of National Ambient Air Quality Standards for Ozone: Assessment of Scientific and Technical Information. OAQPS Staff Paper, U.S. EPA Technology Transfer Network Electronic Bulletin Board, EPA-452\R-96-007, June

U.S. Environmental Protection Agency (EPA). 1996b. Review of the National Ambient Air Quality Standards for Particulate Matter: Policy Assessment of Scientific and Technical Information, Office of Air Quality Planning and Standards. EPA-452-R-96-013. U.S. EPA, Washington, DC

U.S. Environmental Protection Agency (EPA), Risk Assessment Forum. 1997. Guiding Principles for Monte Carlo Analysis. EPA/630/R-97/001, March. This is available in HTML format on the EPA website–http://epa.gov/osp/spc/probpol.htm

U.S. Environmental Protection Agency (EPA). 1999. Radon in drinking water, health risk reduction and cost analysis; Notice. Federal Register, 64: 9560–80

U.S. Environmental Protection Agency (EPA), Science Advisory Board. 1999. Review of Selected Sections of the Cancer Risk Assessment Guidelines by theSAB/SPSubcommittee on Cancer Guidelines. SAB Review Draft of a report to EPA Administrator Carol Browner, 5/17/99

Walden, A. T., and Guttorp, P., Eds. 1992. Statistics in the Environmental & Earth Sciences. Edward Arnold Publishers, London

Accessibility standard: Unknown

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.