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An observational field study was conducted to assess the feasibility of a community duplicate diet collection method; a dietary monitoring tool that is population-based. The purpose was to establish an alternative procedure to duplicate diet sampling that would be more efficient for a large, defined population, e.g., in the National Children's Study (NCS). Questionnaire data and food samples were collected in a residence so as not to lose the important component of storage, preparation, and handling in a contaminated microenvironment. The participants included nine Hispanic women of child bearing age living in Apopka, FL, USA. Foods highly consumed by Hispanic women were identified based on national food frequency questionnaires and prioritized by permethrin residue concentrations as measured for the Pesticide Data Program. Participants filled out questionnaires to determine if highly consumed foods were commonly eaten by them and to assess the collection protocol for the food samples. Measureable levels of permethrin were found in 54% of the samples. Questionnaire responses indicated that the collection of the community duplicate diet was feasible for a defined population.

The physical and chemical environment influences children's exposures to pesticides in and around the home. Children's activities, which increase their potential for exposure especially during eating, have been captured in the Children's Dietary Intake Model (CDIM). In addition to the chemical exposure associated with the food itself, this model incorporates excess dietary exposures due to handling of food during consumption. To stochastically evaluate CDIM, distributions of measured, and in some cases estimated, model factors were determined from measurements of permethrin, chlorpyrifos, and diazinon derived from assembled databases and laboratory experiments. Using the distributions of these factors, Monte Carlo simulations were performed to obtain distributions of total dietary intake of pesticides. To target the sources of pesticide contamination that were influencing total dietary intake, each factor was evaluated. We found pesticide surface concentration to be highly influential. By excluding surface concentration, we were also able to determine the influence of the other factors based on the F-statistic. Transfer efficiencies, followed by pesticide residue in consumed foods and amount of food consumed, were the next most influential factors within the model. With these distributions for model inputs, CDIM has the potential to more accurately predict total dietary intake of a contaminant by a child.

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