Quantitative risk assessment of environmental hazards

Our plain-English summary of Quantitative risk assessment of environmental hazards, by S.L. Brown (Annual Review of Public Health, 1985).

The study

• Author: S.L. Brown
• Journal: Annual Review of Public Health
• Published: 1985;6:247-67
• Cited by: 7 articles, as counted by NIH iCite
• Read it: PubMed · Publisher
• In J-M's library: Johns-Manville's company medical library held a copy (J-M no. 004395).

Abstract

Quantitative risk assessment may never become a rigorous scientific discipline because of the inherent difficulties in working with highly uncertain and often controversial data and methods, and because the predictions of risk assessment may not be subject to validation in their most important areas of application. However, the potential benefits of having quantitative estimates of risk may make quantitative risk assessment a valuable adjunct to traditional methods for making individual and social decisions about health hazards in the home, workplace, and general environment. Risk assessment, which is the process of estimating risks to populations exposed to hazardous agents or activities, must be distinguished from risk management, which is the process of forming and implementing a strategy for accepting or abating the risks. To the extent possible, these two processes should be kept separate. Quantitative risk assessment is in principle capable of estimating individual or lifetime excesses of specific health effects from exposures to a specified hazard. These excesses may be estimated on an absolute basis or expressed as a relative risk in comparison with the baseline risk that would exist without exposure. An individual risk is usually expressed in terms of the probability of developing the health effect in some time period following a specified exposure, whereas a population risk is the overall number of effects expected in a defined population with a defined distribution of exposure levels and patterns. The variation of risk with time after exposure may imply a constant absolute risk, a constant relative risk, or some other dependence on time, usually after a minimal latent period has elapsed. Risk estimates can rarely be made directly from observed human data, and models for extrapolating or projecting risk estimates from the conditions of observation to the actual conditions of exposure must be used. Dose-response relationships are used for extrapolating from high laboratory or occupational exposures to low exposures encountered more frequently in human populations. Thresholds of dose or nonlinear dose-response relationships may be related to nonlinear pharmacokinetics prior to the ultimate exposure of the critical organ to the proximate carcinogen or other hazardous agent. Time-response models estimate risks for periods after exposure longer than have been observed in epidemiologic studies. Extrapolations from experiments with laboratory animals to humans are made difficult because of great differences in size, lifespan, physiology, and metabolism between human and animal.(ABSTRACT TRUNCATED AT 400 WORDS)

In plain English

This article was a review, by one author at the National Academy of Sciences, of quantitative risk assessment of environmental hazards; it reported no new data. The author defined key terms, argued for separating risk assessment from risk management, and described models for extrapolating from high to low doses, animals to humans, and over time, stressing uncertainty. The author also described a National Research Council committee's assessment of asbestos in ambient air. That committee assumed risk rose linearly with dose and treated chrysotile as equal in carcinogenic potential per unit dose to crocidolite or other amphiboles. For lifelong exposure at 0.0004 fibers per cm3, it predicted 64 excess lifetime lung cancers per million male smokers, 23 in female smokers, 6 in male nonsmokers and 3 in female nonsmokers, plus 9 mesotheliomas per million people. Based on statistical uncertainty in the epidemiologic data, its upper bounds were 290 lung cancers per million male smokers and 350 mesotheliomas per million people. Its lower limits were zero excess. The author concluded quantitative risk assessment, despite drawbacks, forces decision makers to be more explicit about assumptions and uncertainties.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Narrative review of quantitative risk assessment methods, with worked examples drawn from published assessments. It is not an original study.
• Who was studied: No study population. The review drew on published assessments, mainly from cancer risk assessment. These included saccharin, radiation, formaldehyde, arsenic and a National Research Council committee's assessment of nonoccupational exposure to asbestiform fibers.
• Exposure: For the asbestos example: asbestos in ambient indoor and outdoor air. The committee assumed that ambient fibers act like workplace fibers apart from concentration, that response is linear with dose, and that chrysotile equals crocidolite or any other amphibole in carcinogenic potential per unit dose. It used 0.0004 fibers per cm3 as a typical person's year-round average exposure.
• Main results:
  • The National Research Council committee's assessment, as described in the review, predicted lifetime lung cancer cases from asbestos exposure, above baseline risk, per million people exposed at 0.0004 fibers per cm3: 64 in male smokers, 23 in female smokers, 6 in male nonsmokers and 3 in female nonsmokers. The differences reflected different baseline risks, a multiplicative effect between smoking and asbestos.
  • The committee estimated 9 lifetime mesothelioma cases per million people, regardless of sex and smoking status.
  • Based on uncertainties in the statistical analysis of the available epidemiologic data, the committee's upper bounds were 290 lifetime lung cancers per million male smokers and 350 lifetime mesotheliomas per million people. Its lower limits for both diseases were zero excess.
  • The committee assessed only lung cancer and mesothelioma. It assumed that few if any cases of asbestosis would arise from low ambient concentrations, and it used only human occupational data for the dose-response analysis.
• The authors' conclusion: The author concluded that quantitative risk assessment may never become a rigorous scientific discipline but can be a valuable adjunct to traditional decision-making. At the least, it forces decision makers to be more systematic and explicit about their assumptions and uncertainties.
• Limitations the authors state:
  • The lower limit of the asbestos risk estimates was set at zero because it is unclear whether low-level exposure to small fibers is equivalent to high-level exposure to much longer fibers. A threshold in the dose-response relationship may also be involved.
  • The asbestos estimates assumed lifelong constant exposure.
  • The author stated that the predictions of quantitative risk assessment can rarely be checked by direct observation.

Topics

Medical terms in this study, explained in our Lexicon: Relative Risk, Dose-response relationship, Occupational Exposure.

Related studies

• The contribution of environmental monitoring in the epidemiological assessment of exogenous risk. The experience of ARPA in the Emilia-Romagna Region of Italy — A. Zavatti, P. Lauriola (1999)
• Pleural mesothelioma and occupational and non-occupational asbestos exposure: a case-control study with quantitative risk assessment — D. Ferrante, D. Mirabelli, et al. (2016)
• Asbestos spraying, an occupational and environmental hazard — R. Barnes (1976)
• Asbestos exposure--quantitative assessment of risk — J.M. Hughes, H. Weill (1986)
• Time-related factors in quantitative risk assessment — K.S. Crump, B.C. Allen, et al. (1987)
• Guadalupe corridor transportation project asbestos health risk assessment, San Jose, California — W.E. Steiner, J.L. Koehler, et al. (1990)

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