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Impact of Pesticide Exposure Misclassification on Estimates of Related Risks in the Agricultural Health Study
Citation:
Blair, A., K. W. THOMAS, J. Coble, D. SANDLER, C. Hines, L. B. Freeman, C. Lynch, C. Knott, M. P. Purdue, S. H. Zham, M. R. Alavanja, M. Dosemeci, F. Kamel, J. HOPPIN, AND J. Lubin. Impact of Pesticide Exposure Misclassification on Estimates of Related Risks in the Agricultural Health Study. OCCUPATIONAL AND ENVIRONMENTAL MEDICINE. BMJ / British Medical Journal Publishing Group, London, Uk, 68(7):537-541, (2011).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA′s mission to protect human health and the environment. HEASD′s research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA′s strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Background: The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators (largely fanners) and their spouses in Iowa and North Carolina. We evaluate the impact of occupational pesticide exposure misclassification on relative risks using data from the cohort and the AHS Pesticide Exposure Study (AHS/PES). Methods: We assessed the impact of exposure misclassification on relative risks using the range of correlation coefficients observed between measured post-application urinary levels of 2,4-dichlorophenoxyacetic acid (2,4-D) and chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications. Results: Correlations between urinary levels of 2,4-0 and chlorpyrifos metabolite and estimated exposure intensity scores from the expert-derived algorithm were about 0.4 for 2,4-D (n=64, 0.8 for liquid chlorpyrifos (n=4), and 0.6 far granular chlorpyrifos (n=12). Correlations of urinary levels with individual exposure determinants (e.g., kilograms of active ingredient used, duration of application, or number of acres treated) were lower and ranged from -0.36 to 0.19.These findings indicate that scores from an a priori expert-derived algorithm developed for the AHS were more closely related to measured urinary levels than the several individual exposure determinants evaluated here. Estimates of potential bias in relative risks observed in the AHS based on the correlations from the AHS/PES and the proportion of the AHS cohort exposed to various pesticides indicate that nondifferential misclassification of exposure using the algorithm would bias some estimates toward the null, but less than the misclassification associated with individual exposure determinants. Conclusions: Although correlations between algorithm scores and urinary levels were quite good (ie, correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power. when exposure estimates are based on an expert algorithm compared to estimates based on separate individual exposure determinants often used in epidemiologic studies. Although correlations between algorithm scores and urinary levels were quite good (i.e., correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power.
URLs/Downloads:
Occupational and Environmental Medicine
Impact of Pesticide Exposure Misclassification on Estimates of Related Risks in the Agricultural Health Study (PDF, NA pp, 4663 KB, about PDF)