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 patterns and residue levels of a particular chemical (e.g., pesticide, food additive) in commodities that lead to an expected high level of variability 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 were subset for gender and age group and, with matched body weight data from the survey database, were used to define empirically-based percentile estimates for wine intake (microliter wine/kg body weight) for the strata of interest. The data for these two subpopulations were analyzed to estimate 14-day consumption distributional statistics and distributions for only those days on which wine was consumed. Data subsets for all wine-consuming adults and wine-consuming females ages 18 through 45, were determined to fit a lognormal distribution (R2 = 0.99 for both datasets). Market share data were incorporated into estimation of chronic exposures to hypothetical chemical residues in imported table wine. As a separate example, treatment of grape juice consumption data for females, ages 18-40, as a simple lognormal distribution resulted in a significant underestimation of intake, and thus exposure, because the actual distribution is a mixture (i.e., multiple subpopulations of grape 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.
Driver
J H
JH
Technology Sciences Group Inc., Washington, D.C., USA.
Ginevan
M E
ME
Whitmyre
G K
GK
D000293:Adolescent; D000328:Adult; D001628:Beverages / Q000032:analysis; D001835:Body Weight; D004032:Diet; D004350:Drug Residues / Q000032:analysis; D004435:Eating; D004781:Environmental Exposure; D005260:Female; D005503:Food Additives / Q000032:analysis; D005506:Food Contamination; D005638:Fruit; D006801:Humans; D007256:Information Systems; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009316:National Institutes of Health (U.S.); D010573:Pesticide Residues / Q000032:analysis; D013269:Stochastic Processes; D014481:United States; D014920:Wine / Q000032:analysis
D005503:Food Additives; D010573:Pesticide Residues