Iterative use of a priori exposure matrices to improve the characterization of chemical exposures in agricultural work studies.
研究通过迭代使用基于农药使用间接决定因素的先验暴露矩阵，将农业工人问卷中特定化学品的缺失值从44%降至9%，提高了病例对照研究的效率。
To assess pesticide exposures of agricultural workers, a priori exposure matrices based on "circumstantial determinants" of pesticide use were incorporated into the questionnaire of a case-referent study. Circumstantial determinants (crops cultivated, their surface areas and crop infestations) were recalled more frequently than specific chemicals. After the matrices were applied, the proportion of missing values fell from 44 to 9% for specific chemicals, from 97 to 17% for the dose for each treatment, and from 80 to 16% for number of treatments per year in a random sample of 40 questionnaires. The number of workers changed from 19 to 30 for parathion use, from 4 to 10 for mancozeb use, and from 4 to 12 for DDT (dichlorodiphenyltrichloroethane) use when the matrix was applied. The matrix enabled exposure levels to be assigned in each case. Provided that the matrix used is valid, this approach could increase the efficiency of case-referent studies on agricultural exposure to chemicals.