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First‐Order Reliability Analysis of Public Health Risk Assessment

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  • Maged M. Hamed

Abstract

This paper demonstrates a new methodology for probabilistic public health risk assessment using the first‐order reliability method. The method provides the probability that incremental lifetime cancer risk exceeds a threshold level, and the probabilistic sensitivity quantifying the relative impact of considering the uncertainty of each random variable on the exceedance probability. The approach is applied to a case study given by Thompson et al.(1) on cancer risk caused by ingestion of benzene‐contaminated soil, and the results are compared to that of the Monte Carlo method. Parametric sensitivity analyses are conducted to assess the sensitivity of the probabilistic event with respect to the distribution parameters of the basic random variables, such as the mean and standard deviation. The technique is a novel approach to probabilistic risk assessment, and can be used in situations when Monte Carlo analysis is computationally expensive, such as when the simulated risk is at the tail of the risk probability distribution.

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  • Maged M. Hamed, 1997. "First‐Order Reliability Analysis of Public Health Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 17(2), pages 177-185, April.
    • Handle: RePEc:wly:riskan:v:17:y:1997:i:2:p:177-185
    DOI: 10.1111/j.1539-6924.1997.tb00857.x
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    1. 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.
    2. 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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