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An algorithm for quantitatively estimating non-occupational pesticide exposure intensity for spouses in the Agricultural Health Study

Journal of Exposure Science & Environmental Epidemiology volume 29, pages 344–357 (2019)Cite this article

Abstract

Residents of agricultural areas experience pesticide exposures from sources other than direct agricultural work. We developed a quantitative, active ingredient-specific algorithm for cumulative (adult, married lifetime) non-occupational pesticide exposure intensity for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm addressed three exposure pathways: take-home, agricultural drift, and residential pesticide use. Pathway-specific equations combined (i) weights derived from previous meta-analyses of published pesticide exposure data and (ii) information from the questionnaire on frequency and duration of pesticide use by applicators, home proximity to treated fields, residential pesticide usage (e.g., termite treatments), and spouse’s off-farm employment (proxy for time at home). The residential use equation also incorporated a published probability matrix that documented the likelihood active ingredients were used in home pest treatment products. We illustrate use of these equations by calculating exposure intensities for the insecticide chlorpyrifos and herbicide atrazine for 19,959 spouses. Non-zero estimates for ≥1 pathway were found for 78% and 77% of spouses for chlorpyrifos and atrazine, respectively. Variability in exposed spouses’ intensity estimates was observed for both pesticides, with 75th to 25th percentile ratios ranging from 7.1 to 7.3 for take-home, 6.5 to 8.5 for drift, 2.4 to 2.8 for residential use, and 3.8 to 7.0 for the summed pathways. Take-home and drift estimates were highly correlated (≥0.98), but were not correlated with residential use (0.01‒0.02). This algorithm represents an important advancement in quantifying non-occupational pesticide relative exposure differences and will facilitate improved etiologic analyses in the AHS spouses. The algorithm could be adapted to studies with similar information.

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Acknowledgements

This work was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Z01CP010119) and the National Institute of Environmental Health Sciences (Z01-ES049030). NCD was supported, in part, through contract (HHSN261201400231P) and the American Cancer Society grant 127509-MRSG-15-147-01-CNE.

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Authors and Affiliations

  1. Yale School of Public Health, Yale University, New Haven, CT, USA

    Nicole C. Deziel

  2. Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA

    Nicole C. Deziel, Laura E Beane Freeman, Catherine C. Lerro, Rena R. Jones, Aaron Blair, Barry I. Graubard, Jay H. Lubin, Gabriella Andreotti, Michael C. Alavanja & Melissa C. Friesen

  3. Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA

    Jane A. Hoppin

  4. National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA

    Kent Thomas

  5. Division of Surveillance, Hazard Evaluation and Field Studies, National Institute for Occupational Safety and Health, Cincinnati, OH, USA

    Cynthia J. Hines

  6. Department of Health and Human Services, Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, NC, USA

    Dale P. Sandler & Honglei Chen

  7. Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA

    Honglei Chen

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  1. Nicole C. Deziel
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  2. Laura E Beane Freeman
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Correspondence to Nicole C. Deziel.

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For transparency, NCD discloses that her spouse is an employee of the Dow Chemical Company, a producer of hundreds of chemicals and products, including pesticides. The remaining authors declare that they have no conflict of interest.

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Deziel, N.C., Beane Freeman, L.E., Hoppin, J.A. et al. An algorithm for quantitatively estimating non-occupational pesticide exposure intensity for spouses in the Agricultural Health Study. J Expo Sci Environ Epidemiol 29, 344–357 (2019). https://doi.org/10.1038/s41370-018-0088-z

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