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Bayesian Modeling of Measurement Errors and Pesticide Concentration in Dietary Risk Assessments

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  • Marc Kennedy
  • Andy Hart

Abstract

We propose new models for dealing with various sources of variability and uncertainty that influence risk assessments for dietary exposure. The uncertain or random variables involved can interact in complex ways, and the focus is on methodology for integrating their effects and on assessing the relative importance of including different uncertainty model components in the calculation of dietary exposures to contaminants, such as pesticide residues. The combined effect is reflected in the final inferences about the population of residues and subsequent exposure assessments. In particular, we show how measurement uncertainty can have a significant impact on results and discuss novel statistical options for modeling this uncertainty. The effect of measurement error is often ignored, perhaps due to the laboratory process conforming to the relevant international standards, for example, or is treated in an ad hoc way. These issues are common to many dietary risk analysis problems, and the methods could be applied to any food and chemical of interest. An example is presented using data on carbendazim in apples and consumption surveys of toddlers.

Suggested Citation

  • Marc Kennedy & Andy Hart, 2009. "Bayesian Modeling of Measurement Errors and Pesticide Concentration in Dietary Risk Assessments," Risk Analysis, John Wiley & Sons, vol. 29(10), pages 1427-1442, October.
  • Handle: RePEc:wly:riskan:v:29:y:2009:i:10:p:1427-1442
    DOI: 10.1111/j.1539-6924.2009.01265.x
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    1. Carriquiry, Alicia L. & Fuller, Wayne A., 1996. "A Semiparametric Approach to Estimating Usual Intake Distributions," Staff General Research Papers Archive 1036, Iowa State University, Department of Economics.
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