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Using meta-regression models to systematically evaluate data in the published literature: relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations
Citation:
Deziel, N., L. Beane Freeman, B. Graubard, R. Jones, J. Hoppin, K. Thomas, C. Hines, A. Blair, D. Sandler, H. Chen, J. Lubin, G. Andreotti, AND M. Friesen. Using meta-regression models to systematically evaluate data in the published literature: relative contributions of agricultural drift, para-occupational, and residential use exposure pathways to house dust pesticide concentrations. X2016, Barcelona, SPAIN, September 06 - 08, 2016.
Impact/Purpose:
EPA/ORD collaboration with the Agricultural Health Study led by NCI and NIEHS.
Description:
Background: Data reported in the published literature have been used qualitatively to aid exposure assessment activities in epidemiologic studies. Analyzing these data in computational models presents statistical challenges because these data are often reported as summary statistics. Many previous analyses using published data have weighted the summary statistics by the number of measurements, but this does not account for the measurements’ variability. We describe the application of mixed-effects meta-regression models to evaluate the relative contributions of three exposure pathways (agricultural drift, para-occupational, residential use) on published pesticide concentrations in the house dust of homes in agricultural areas. Methods: We abstracted pesticide house dust concentrations reported as summary statistics (e.g., geometric means (GM)) from studies in North American agricultural areas published from 1995-2015. We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its variance. The dependent variable was either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). Results: For the drift pathway, the predicted GM decreased 35% (95% Confidence Interval [CI]: 19-48; based on 52 statistics from 7 studies) for each natural log(ft) between homes and fields. For the para-occupational pathway, GMs were 2.3 times higher (95%CI: 1.5-3.3; 15 statistics, 5 studies) in homes of farmers who applied pesticides more versus less recently or frequently. For the residential use pathway, GMs were 1.0 (95%CI: 0.8-1.2), 1.3 (95%CI: 1.1-1.4), and 1.5 (95%CI: 1.2-1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 0%, 1-19%, and ≥20%, respectively (88 statistics, 5 studies). Conclusion: These findings quantify relative contributions of three pathways in agricultural populations that may be useful for developing pesticide exposure metrics for epidemiologic studies. The meta-regression models can be updated as additional data become available.  
