Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Thompson, Kimberly M.; Burmaster, David E.; Crouch, Edmund A. C.

doi:10.1111/j.1539-6924.1992.tb01307.x

Cited by 205 publications

(103 citation statements)

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“…Most of the literature on MCA (e.g., Thompson, et al 1992;Burmaster and Anderson 1993) suggests that for each MCA an initial sensitivity analysis should be implemented to determine which parameters should be varied and which can safely be held constant without significantly affecting the final risk distribution. The authors agree that in most INEL MCA applications, an initial sensitivity analysis should be implemented.…”

Section: Sensitivity Resultsmentioning

confidence: 99%

“…Less extreme percentiles in the tails of the distribution can also be affected. For example, Thompson et al (1992) note that when they repeated a simulation of 10,000 samples, the 95th percentile was not quite within 1% of the original simulation's estimate of the 95th percentile.…”

Section: Assessmentioning

confidence: 99%

“…Alternatively, certain summary statistics of the resulting distributions could be compare for example, the means, variances, and selected percentiles (e.g., 5Oth, 95th). Thompson et al (1992) use this latter method by computing the relative 37 difference between the results. They felt the distributions to be reproducible if the summary statistics had relative differences of less than 1 %.…”

Section: Assessmentioning

confidence: 99%

“…It is loosely based on an example given in Thompson et al (1992). In this example, a soil is contaminated with benzene, a Group A carcinogen (i.e., a known human carcinogen).…”

Section: Example Monte Carlo) Analysismentioning

confidence: 99%

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Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

Harris¹,

Horn²

1996

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Under DOE Idaho Operations Office Contract DE-AC07-941Di3223. I DISCLAIMERPortions of this document may be illegible in electronic image products. Images are produced from the best available original document. Use of Monte Carlo Methods in Environmental Risk Assessment at the INEL:Applications and Issues EXECUTIVE SUMMARYMonte Carlo Analysis (MCA) is a powerful tool for evaluating risks in the presence of uncertainty. The technique could be useful in future environmental risk assessment work done at the Idaho National Engineering Laboratory (INEL). This document provides the background information useful for risk assessors and risk managers to understand MCA. It presents the general methodology of MCA and identifies issues that will need to be resolved before MCA can be effectively implemented at the INEL.MCA is a conceptually simple method of probabilistic risk assessment. For the input parameters in the risk model, a probability distribution is assigned based on available information about the parameter, these distributions are each randomly sampled n times. The n random realizations from each input parameter are then entered into the model to produce n risk estimates. The input distributions take into account uncertainties in those parameters; the resulting risk values provide an estimate of the risk distribution. Hence, the Monte Carlo analysis essentially propagates the input uncertainties through the risk model to the resulting risk estimates.In this report, the motivation for using MCA as an improvement over the baseline risk assessment (BRA) point estimates is given. Two major shortcomings of the point estimate approach is that it suffers from creeping conservatism so that the user does not know how conservative the estimate is, and it provides limited information to the risk manager and public. The use of MCA helps alleviate both of these problems.The use of MCA helps focus attention on the uncertainty in the risk assessment. Further, it provides a quantitative description of the uncertainty. In this way, the manager and stakeholders are better able to understand the risks and are better able to make informed decisions. A key theme of the document is that uncertainty matters and MCA provides a practical description of the uncertainty.V

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Section: Sensitivity Resultsmentioning

confidence: 99%

Section: Assessmentioning

confidence: 99%

Section: Assessmentioning

confidence: 99%

“…It is loosely based on an example given in Thompson et al (1992). In this example, a soil is contaminated with benzene, a Group A carcinogen (i.e., a known human carcinogen).…”

Section: Example Monte Carlo) Analysismentioning

confidence: 99%

See 2 more Smart Citations

Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

Harris¹,

Horn²

1996

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“…In the 1990s there has been an emphasis on understanding the distribution of susceptibility (29), which was formerly represented by a single default (3), as well as on how to deal with the uncertainties inherent in risk assessment. Formerly dismissed as noise, it is now fashionable to attempt to quantify (6,73) as well as communicate uncertainty (72). Future advances will focus extensively on case-specific models, on use of biomarkers to assess exposure (38a, 48), on risk assessment for mixtures (47), and on quantifying uncertainty.…”

Section: Risk Assessmentmentioning

confidence: 99%

Lessons in Environmental Health in the Twentieth Century

Gochfeld

Goldstein

1999

Annu. Rev. Public Health

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Environmental health has evolved rapidly in recent decades, drawing largely on new analytic technologies, advanced data acquisition and modeling, mechanistic studies in toxicology, and the conceptual framework of risk assessment. The latter combines toxicologic and epidemiologic data with improved techniques for quantifying exposure, producing estimates of risks from environmental hazards or conditions to selected target populations. The public and governments have become increasingly concerned with environmental health and quality. The major lessons have been (a) environmental-health scientists must participate in policy debates; (b) environmental health problems are exceedingly complex and require interdisciplinary research; and (c) environmental health is a global issue. The globalization of commerce, the untested impact of international trade agreements, increased migration, and especially increased population, have profound impact on the quality as well as availability of air, water, land, and food. Global atmospheric transport of pollutants and the effect on atmosphere and climate are two examples of globalization of environmental health.

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Setting Prediction Limits for Mercury Concentrations in Fish Having High Bioaccumulation Potential

1996

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SUMMARYThe potential health risks associated with the consumption of contaminated fish have long been recognized by public health officials as cause for concern, and in the USA many states have developed strategies for issuing fish consumption advisories. The methods and criteria for establishing such advisories vary widely among the states, however, and the advice given to anglers may not be consistent between neighbouring states, even regarding the same body of water. Fish contaminant monitoring data are often used as the basis for adivsories, but few methods are available for the quantification and distributional characterization of contaminant levels in fish. Log-normality of pollutant concentrations (i.e. normality of the logarithm of pollutant concentrations) is a common assumption, yet statistical tests of normality do not always confirm this assumption. An alternative to the log-normal distribution is the S-distribution, which has been shown to approximate many statistical distributions with high accuracy, and often results in improved fit over the log-normal. In this paper we evaluate the performance of the S-distribution in characterizing contaminant concentrations, and compare the results to those obtained using the log-normal distribution. A method based on trends in mercury distribution parameters across length classes is presented and used to obtain 95 per-cent prediction limits for mercury concentrations in king mackerel (Scomberomorus cuvallu). It is shown that this method gives narrower prediction limits compared to those obtained using standard regression techniques.

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Monte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Cited by 205 publications

References 10 publications

Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

Use of Monte Carlo methods in environmental risk assessments at the INEL: Applications and issues

Lessons in Environmental Health in the Twentieth Century

Setting Prediction Limits for Mercury Concentrations in Fish Having High Bioaccumulation Potential

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