Probabilistic dietary exposure assessment taking into account variability in both amount and frequency of consumption.
Probabilistic dietary exposure assessments that are fully based on Monte Carlo sampling from the raw intake data may not be appropriate. This paper shows that the data should first be analysed by using a statistical model that is able to take the various dimensions of food consumption patterns into account. A (parametric) model is discussed that takes into account the interindividual variation in (daily) consumption frequencies, as well as in amounts consumed. Further, the model can be used to include covariates, such as age, sex, or other individual attributes. Some illustrative examples show how this model may be used to estimate the probability of exceeding an (acute or chronic) exposure limit. These results are compared with the results based on directly counting the fraction of observed intakes exceeding the limit value. This comparison shows that the latter method is not adequate, in particular for the acute exposure situation. A two-step approach for probabilistic (acute) exposure assessment is proposed: first analyse the consumption data by a (parametric) statistical model as discussed in this paper, and then use Monte Carlo techniques for combining the variation in concentrations with the variation in consumption (by sampling from the statistical model). This approach results in an estimate of the fraction of the population as a function of the fraction of days at which the exposure limit is exceeded by the individual.
Slob
Wout
W
National Institute of Public Health and the Environment (RIVM), 3720 BA Bilthoven, The Netherlands. wout.slob@rivm.nl
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D004032:Diet; D004435:Eating / Q000502:physiology*; D005247:Feeding Behavior; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D011153:Population; D012044:Regression Analysis