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Improving Dietary Exposure Models by Imputing Biomonitoring Data through ABC Methods

Author

Listed:
  • Béchaux Camille

    (Anses – DER, 27 Avenue du général Leclerc, Maisons-Alfort 94700, France)

  • Crépet Amélie

    (Anses – DER, 27 Avenue du général Leclerc, Maisons-Alfort 94700, France)

  • Clémençon Stéphan

    (Telecom ParisTech – LTCI UMR, Paris, France)

Abstract

New data are available in the field of risk assessment: the biomonitoring data which is measurement of the chemical dose in a human tissue (e.g. blood or urine). These data are original because they represent direct measurements of the dose of chemical substances really taken up from the environment, whereas exposure is usually assessed from contamination levels of the different exposure media (e.g. food, air, water, etc.) and statistical models. However, considered alone, these data provide little help from the perspective of Public Health guidance. The objective of this paper is to propose a method to exploit the information provided by human biomonitoring in order to improve the modeling of exposure. This method is based on the Kinetic Dietary Exposure Model which takes into account the pharmacokinetic elimination and the accumulation phenomenon inside the human body. This model is corrected to account for any possible temporal evolution in exposure by adding a scaling function which describes this evolution. Approximate Bayesian Computation is used to fit this exposure model from the biomonitoring data available. Specific summary statistics and appropriate distances between simulated and observed statistical distributions are proposed and discussed in the light of risk assessment. The promoted method is then applied to measurements of blood concentration of dioxins in a group of French fishermen families. The outputs of the model are an estimation of the body burden distribution from observed dietary intakes and the evolution of dietary exposure to dioxins in France between 1930 and today. This model successfully fit to dioxins data can also be used with other biomonitoring data to improve the risk assessment to many other contaminants.

Suggested Citation

  • Béchaux Camille & Crépet Amélie & Clémençon Stéphan, 2014. "Improving Dietary Exposure Models by Imputing Biomonitoring Data through ABC Methods," The International Journal of Biostatistics, De Gruyter, vol. 10(2), pages 277-287, November.
    • Handle: RePEc:bpj:ijbist:v:10:y:2014:i:2:p:11:n:7
    DOI: 10.1515/ijb-2013-0062
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    References listed on IDEAS

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    1. Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
    2. Joyce Paul & Marjoram Paul, 2008. "Approximately Sufficient Statistics and Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-18, August.
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