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On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment

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  • Maged M. Hamed
  • Philip B. Bedient

Abstract

A central part of probabilistic public health risk assessment is the selection of probability distributions for the uncertain input variables. In this paper, we apply the first‐order reliability method (FORM)(1–3) as a probabilistic tool to assess the effect of probability distributions of the input random variables on the probability that risk exceeds a threshold level (termed the probability of failure) and on the relevant probabilistic sensitivities. The analysis was applied to a case study given by Thompson et al.(4) on cancer risk caused by the ingestion of benzene contaminated soil. Normal, lognormal, and uniform distributions were used in the analysis. The results show that the selection of a probability distribution function for the uncertain variables in this case study had a moderate impact on the probability that values would fall above a given threshold risk when the threshold risk is at the 50th percentile of the original distribution given by Thompson et al.(4) The impact was much greater when the threshold risk level was at the 95th percentile. The impact on uncertainty sensitivity, however, showed a reversed trend, where the impact was more appreciable for the 50th percentile of the original distribution of risk given by Thompson et al.4 than for the 95th percentile. Nevertheless, the choice of distribution shape did not alter the order of probabilistic sensitivity of the basic uncertain variables.

Suggested Citation

  • Maged M. Hamed & Philip B. Bedient, 1997. "On the Effect of Probability Distributions of Input Variables in Public Health Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 17(1), pages 97-105, February.
  • Handle: RePEc:wly:riskan:v:17:y:1997:i:1:p:97-105
    DOI: 10.1111/j.1539-6924.1997.tb00848.x
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    References listed on IDEAS

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    1. Dale Hattis & David E. Burmaster, 1994. "Assessment of Variability and Uncertainty Distributions for Practical Risk Analyses," Risk Analysis, John Wiley & Sons, vol. 14(5), pages 713-730, October.
    2. Kimberly M. Thompson & David E. Burmaster, 1991. "Parametric Distributions for Soil Ingestion by Children," Risk Analysis, John Wiley & Sons, vol. 11(2), pages 339-342, June.
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    4. Betsy Ruffle & David E. Burmaster & Paul D. Anderson & Henry D. Gordon, 1994. "Lognormal Distributions for Fish Consumption by the General U.S. Population," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 395-404, August.
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    6. Ann M. Roseberry & David E. Burmaster, 1992. "Lognormal Distributions for Water Intake by Children and Adults," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 99-104, March.
    7. Roy L. Smith, 1994. "Use of Monte Carlo Simulation for Human Exposure Assessment at a Superfund Site," Risk Analysis, John Wiley & Sons, vol. 14(4), pages 433-439, August.
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    9. Brent Finley & Dennis Paustenbach, 1994. "The Benefits of Probabilistic Exposure Assessment: Three Case Studies Involving Contaminated Air, Water, and Soil," Risk Analysis, John Wiley & Sons, vol. 14(1), pages 53-73, February.
    10. Kimberly M. Thompson & David E. Burmaster & Edmund A.C. Crouch3, 1992. "Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 12(1), pages 53-63, March.
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