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Modelling Human Exposure to Chemicals in Food

[Modellering van humane blootstelling aan stoffen via de voeding.]

Synopsis

Exposure to foodborne chemicals is often estimated using the average consumption pattern in the human population. To protect the human population instead of the average individual, however, interindividual variability in consumption behaviour must be taken into account. This report shows how food consumption survey data may be used to derive a statistical exposure model (STEM) that succinctly describes intake of chemicals by the human population as a whole. STEM can serve as a general framework for all foodborne chemicals for which short-term fluctuations in intake may be ignored. It can be used to estimate the percentage of the population exceeding intake criteria (e.g., ADI or TDI). By a recent application (concerning the setting of a dioxin standard for cow's milk) it is illustrated that taking interindividual variability in consumption behaviour into account may have a significant impact on policy formulation. Apart from its direct use in risk assessments, STEM fits well into a larger approach, currently in development at RIVM, which aims at the incorporation of interindividual differences in general into risk analysis methodology. STEM can be easily linked to toxicokinetic models to evaluate the relation of interindividual variability in consumption habits with that in internal doses. This constitutes a first step in the development of a risk analysis methodology in which the percentage of the human population at risk is estimated as accurately as possible.
 

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