Pesticide exposure and Parkinson's disease in the AGRICAN study

Pesticide exposure and Parkinson's disease in the AGRICAN study 1006 International Journal of Epidemiology, 2018, Vol. 47, No. 3 therapeutics: conceptual and methodological challenges. PLoS 11. Tennant PWG, Textor J, Gilthorpe MS, Ellison GTH. DAGitty Genet 2017;13:e1006944. and directed acyclic graphs in observational research: a critical 6. Valeri L, Chen J, Garcia-Albeniz X, Krieger N, VanderWeele TJ, review. J Epidemiol Community Health 2017;71:A43. Coull B. The role of stage at diagnosis in colorectal cancer racial/ 12. Bollen KA, Pearl J. Eight myths about causality and structural ethnic survival disparities: a counterfactual causal inference equation models. In: Morgan SL (ed). Handbook of Causal Anal- approach. Cancer Epidemiol Biomarkers Prev 2016;25:83–89. ysis for Social Research. New York, NY: Springer, 2013, pp. 7. Swanson SD. Communicating causality. Eur J Epidemiol 2015;30:1073–75. 301–28. 8. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiolog- 13. Martin AR, Lin M, Granka JM et al. An unexpectedly complex ical epidemiology. Int J Epidemiol 2016;45:1866–86. architecture for skin pigmentation in Africans. Cell 2017;171: 9. Munafo M, Davey Smith G. Robust research needs many lines of 1340–53. evidence. Nature 2018;553:399–401. 14. Rohrer JM. Thinking about correlations and causation: graphi- 10. Pearl J. Theoretical impediments to machine learning with seven cal causal models for observational data. Adv Methods Pract sparks from the causal revolution. arXiv 2018. https://arxiv.org/ abs/1801.04016 (4 March 2018, date last accessed). Psychol Sci 2018;1:27. International Journal of Epidemiology, 2018, 1006–1006 Pesticide exposure and Parkinson’s doi: 10.1093/ije/dyy035 Advance Access Publication Date: 15 March 2018 disease in the AGRICAN study Andrea Wendt Division Work and Health, No ¨ ldnerstr. 40–42, 10317 Berlin, Germany. E-mail: wendt.andrea@baua.bund.de Pouchieu et al. recently published their analysis on the as- participants’ self-reports of exposure to specific active sub- sociation between lifelong pesticide exposure and self- stances is certainly prone to considerable error. reported Parkinson’s disease (PD), using data from the For a gainful use of the PESTIMAT CEM, it is advis- baseline assessment of the AGRICAN cohort study. They able to implement a case-control study nested within the reported odds ratios up to 1.86 for pesticide exposure in AGRICAN study, preferably using incident cases. This relation to specific animals and plants, and odds ratios up should involve: proper case ascertainment, including to 1.57 for exposure with specific active ingredients. Their medical confirmation instead of self-report by partici- dose-response analysis resulted in odds ratios up to 1.62 pants; accurate assessment of age at diagnosis instead of for the longest durations of use of specific active ingredi- the crude assessment done at the baseline examination ents. Definition of exposure involved the time until recruit- (<20, 20–39, 40–60 and >60 years); matching of cases ment into the cohort study, meaning that exposure and a random subgroup of controls according to year of experience after PD onset was part of the exposure meas- birth; and assessment of cumulative exposure until age at ures. Due to this, the conducted analysis is of restricted diagnosis for matched case-control pairs. I would like to use. Unless exposure information is confined to the time ask the authors of the cross-sectional analysis of the before PD diagnosis, results from the AGRICAN study on AGRICAN baseline data whether such an analysis is pesticides and PD cannot be used for any conclusions. planned for the future. It is indeed very much of interest One of the challenges in epidemiologic research is whether it corroborates the associations from the cross- proper exposure assessment. With the application of the sectional analysis. PESTIMAT crop exposure matrix (CEM), the AGRICAN study uses a clever method, in that exposure to certain Reference active substances is allocated based on participants’ re- 1. Pouchieu C, Piel C, Carles C et al. Pesticide use in agriculture and ports of exposure to specific crops in combination with Parkinson’s disease in the AGRICAN cohort study. Int J Epide- pesticide use at work. This approach is of advantage, as miol 2018;47:299–310. V The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association Downloaded from https://academic.oup.com/ije/article-abstract/47/3/1006/4938502 by Ed 'DeepDyve' Gillespie user on 20 June 2018 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Epidemiology Oxford University Press

Pesticide exposure and Parkinson's disease in the AGRICAN study

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Oxford University Press
Copyright
© The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
ISSN
0300-5771
eISSN
1464-3685
D.O.I.
10.1093/ije/dyy035
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Abstract

1006 International Journal of Epidemiology, 2018, Vol. 47, No. 3 therapeutics: conceptual and methodological challenges. PLoS 11. Tennant PWG, Textor J, Gilthorpe MS, Ellison GTH. DAGitty Genet 2017;13:e1006944. and directed acyclic graphs in observational research: a critical 6. Valeri L, Chen J, Garcia-Albeniz X, Krieger N, VanderWeele TJ, review. J Epidemiol Community Health 2017;71:A43. Coull B. The role of stage at diagnosis in colorectal cancer racial/ 12. Bollen KA, Pearl J. Eight myths about causality and structural ethnic survival disparities: a counterfactual causal inference equation models. In: Morgan SL (ed). Handbook of Causal Anal- approach. Cancer Epidemiol Biomarkers Prev 2016;25:83–89. ysis for Social Research. New York, NY: Springer, 2013, pp. 7. Swanson SD. Communicating causality. Eur J Epidemiol 2015;30:1073–75. 301–28. 8. Lawlor DA, Tilling K, Davey Smith G. Triangulation in aetiolog- 13. Martin AR, Lin M, Granka JM et al. An unexpectedly complex ical epidemiology. Int J Epidemiol 2016;45:1866–86. architecture for skin pigmentation in Africans. Cell 2017;171: 9. Munafo M, Davey Smith G. Robust research needs many lines of 1340–53. evidence. Nature 2018;553:399–401. 14. Rohrer JM. Thinking about correlations and causation: graphi- 10. Pearl J. Theoretical impediments to machine learning with seven cal causal models for observational data. Adv Methods Pract sparks from the causal revolution. arXiv 2018. https://arxiv.org/ abs/1801.04016 (4 March 2018, date last accessed). Psychol Sci 2018;1:27. International Journal of Epidemiology, 2018, 1006–1006 Pesticide exposure and Parkinson’s doi: 10.1093/ije/dyy035 Advance Access Publication Date: 15 March 2018 disease in the AGRICAN study Andrea Wendt Division Work and Health, No ¨ ldnerstr. 40–42, 10317 Berlin, Germany. E-mail: wendt.andrea@baua.bund.de Pouchieu et al. recently published their analysis on the as- participants’ self-reports of exposure to specific active sub- sociation between lifelong pesticide exposure and self- stances is certainly prone to considerable error. reported Parkinson’s disease (PD), using data from the For a gainful use of the PESTIMAT CEM, it is advis- baseline assessment of the AGRICAN cohort study. They able to implement a case-control study nested within the reported odds ratios up to 1.86 for pesticide exposure in AGRICAN study, preferably using incident cases. This relation to specific animals and plants, and odds ratios up should involve: proper case ascertainment, including to 1.57 for exposure with specific active ingredients. Their medical confirmation instead of self-report by partici- dose-response analysis resulted in odds ratios up to 1.62 pants; accurate assessment of age at diagnosis instead of for the longest durations of use of specific active ingredi- the crude assessment done at the baseline examination ents. Definition of exposure involved the time until recruit- (<20, 20–39, 40–60 and >60 years); matching of cases ment into the cohort study, meaning that exposure and a random subgroup of controls according to year of experience after PD onset was part of the exposure meas- birth; and assessment of cumulative exposure until age at ures. Due to this, the conducted analysis is of restricted diagnosis for matched case-control pairs. I would like to use. Unless exposure information is confined to the time ask the authors of the cross-sectional analysis of the before PD diagnosis, results from the AGRICAN study on AGRICAN baseline data whether such an analysis is pesticides and PD cannot be used for any conclusions. planned for the future. It is indeed very much of interest One of the challenges in epidemiologic research is whether it corroborates the associations from the cross- proper exposure assessment. With the application of the sectional analysis. PESTIMAT crop exposure matrix (CEM), the AGRICAN study uses a clever method, in that exposure to certain Reference active substances is allocated based on participants’ re- 1. Pouchieu C, Piel C, Carles C et al. Pesticide use in agriculture and ports of exposure to specific crops in combination with Parkinson’s disease in the AGRICAN cohort study. Int J Epide- pesticide use at work. This approach is of advantage, as miol 2018;47:299–310. V The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association Downloaded from https://academic.oup.com/ije/article-abstract/47/3/1006/4938502 by Ed 'DeepDyve' Gillespie user on 20 June 2018

Journal

International Journal of EpidemiologyOxford University Press

Published: Mar 15, 2018

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