Bayesian nonparametric model for clustering individual co-exposure to pesticides found in the French diet.
Résumé
This work introduces a specific application of Bayesian nonparametric statistics to the food risk analysis framework. The goal was to determine the cocktails of pesticide residues to which the French population is simultaneously exposed through its current diet in order to study their possible combined effects on health through toxicological experiments. To do this, the joint distribution of exposures to a large number of pesticides, which we called the co-exposure distribution, was assessed from the available consumption data and food contamination analyses. We proposed modelling the co-exposure using a Dirichlet process mixture based on a multivariate Gaussian kernel so as to determine groups of individuals with similar co-exposure patterns. Posterior distributions and the optimal partition were computed through a Gibbs sampler based on stick-breaking priors. The study of the correlation matrix of the sub-population co-exposures will be used to define the cocktails of pesticides to which they are jointly exposed at high doses. To reduce the computational burden due to the high data dimensionality, a random-block sampling approach was used. In addition, we propose to account for the uncertainty of food contamination through the introduction of an additional level of hierarchy in the model. The results of both specifications are described and compared.
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