ESTIMATION OF DIETARY EXPOSURE TO CHEMICALS - A CASE-STUDY ILLUSTRATING METHODS OF DISTRIBUTIONAL ANALYSES FOR FOOD-CONSUMPTION DATA

There are a number of sources of variability in food consumption patte rns and residue levels of a particular chemical (e.g., pesticide, food additive) in commodities that lead to an expected high level of varia bility in dietary exposures across a population. This paper focuses on examples of consumption pattern survey data for specific commodities, namely that for wine and grape juice, and demonstrates how such data might be analyzed in preparation for performing stochastic analyses of dietary exposure. Data from the NIAAA/NHIS wine consumption survey we re subset for gender and age group and, with matched body weight data from the survey database, were used to define empirically-based percen tile estimates for wine intake (mu l wine/kg body weight) for the stra ta of interest. The data for these two subpopulations were analyzed to estimate 14-day consumption distributional statistics and distributio ns for only those days on which wine was consumed. Data subsets for al l wine-consuming adults and wine-consuming females ages 18 through 45, were determined to fit a lognormal distribution (R(2) = 0.99 for both datasets). Market share data were incorporated into estimation of chr onic exposures to hypothetical chemical residues in imported table win e. As a separate example, treatment of grape juice consumption data fo r females, ages 18-40, as a simple lognormal distribution resulted in a significant underestimation of intake, and thus exposure, because th e actual distribution is a mixture (i.e., multiple subpopulations of g rape juice consumers exist in the parent distribution). Thus, deriving dietary intake statistics from food consumption survey data requires careful analysis of the underlying empirical distributions.