Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study. Issue 7 (21st January 2011) - Blair, Aaron; Thomas, Kent; Coble, Joseph; Sandler, Dale P; Hines, Cynthia J; Lynch, Charles F; Knott, Charles; Purdue, Mark P; Zahm, Shelia Hoar; Alavanja, Michael C R; Dosemeci, Mustafa; Kamel, Freya; et al.
Abstract : Background: The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators 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 a chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications. Results: Correlations between urinary levels of 2, 4-D and a chlorpyrifos metabolite and algorithm estimated intensity scores were about 0.4 for 2, 4-D (n=64), 0.8 for liquid chlorpyrifos (n=4) and 0.6 for granular chlorpyrifos (n=12). Correlations of urinary levels with 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 a priori expert-derived algorithm scores were more closely related to measured urinary levels than individual exposure determinants evaluated here. Estimates of potential bias in relative risks based on the correlations from the AHS/PES indicate that non-differential misclassification of exposure using the algorithm would bias estimates towards the null, but less than that from 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.