Agricultural activities and the incidence of Parkinson’s disease in the general French population

Abstract

Most studies on pesticides and Parkinson’s disease (PD) focused on occupational exposure in farmers. Whether non-occupational exposure is associated with PD has been little explored. We investigated the association between agricultural characteristics and PD incidence in a French nationwide ecologic study. We hypothesized that persons living in regions with agricultural activities involving more intensive pesticide use would be at higher risk. We identified incident PD cases from French National Health Insurance databases (2010–2012). The proportion of land dedicated to 18 types of agricultural activities was defined at the canton of residence level. We examined the association between agricultural activities and PD age/sex-standardized incidence ratios using multivariable multilevel Poisson regression adjusted for smoking, deprivation index, density of neurologists, and rurality (proportion of agricultural land); we used a false discovery rate approach to correct for multiple comparisons and compute q-values. We also compared incidence in clusters of cantons with similar agricultural characteristics (k-means algorithm). We identified 69,010 incident PD cases. Rurality was associated with higher PD incidence (p < 0.001). Cantons with higher density of vineyards displayed the strongest association (RRtop/bottom quartile = 1.102, 95% CI = 1.049–1.158; q-trend = 0.040). This association was similar in men, women, and non-farmers, stronger in older than younger persons, and present in all French regions. Persons living in the cluster with greatest vineyards density had 8.5% (4.4–12.6%) higher PD incidence (p < 0.001). In France, vineyards rank among the crops that require most intense pesticide use. Regions with greater presence of vineyards are characterized by higher PD risk; non-professional pesticides exposure is a possible explanation.

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References

  1. 1.

    Noyce AJ, Bestwick JP, Silveira-Moriyama L, Hawkes CH, Giovannoni G, Lees AJ, et al. Meta-analysis of early nonmotor features and risk factors for Parkinson disease. Ann Neurol. 2012;72(6):893–901.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    van der Mark M, Brouwer M, Kromhout H, Nijssen P, Huss A, Vermeulen R. Is pesticide use related to Parkinson’s disease? Some clues to heterogeity in study results. Environ Health Perspect. 2012;120(3):340–7.

    Article  PubMed  Google Scholar 

  3. 3.

    Pezzoli G, Cereda E. Exposure to pesticides or solvents and risk of Parkinson disease. Neurology. 2013;80(22):2035–41.

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Baltazar MT, Dinis-Oliveira RJ, de Lourdes BM, Tsatsakis AM, Duarte JA, Carvalho F. Pesticides exposure as etiological factors of Parkinson’s disease and other neurodegenerative diseases-A mechanistic approach. Toxicol Lett. 2014;230(2):85–103.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Costello S, Cockburn M, Bronstein J, Zhang X, Ritz B. Parkinson disease and residential exposure to maneb and paraquat from agricultural applications in the central valley of California. Am J Epidemiol. 2009;169:919–26.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    James KA, Hall DA. Groundwater pesticide levels and the association with Parkinson disease. Int J Toxicol. 2015;34(3):266–73.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Tuppin P, de RL, Weill A, Ricordeau P, Merliere Y. French national health insurance information system and the permanent beneficiaries sample. Rev Epidemiol Sante Publique. 2010;58(4):286–90.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Moisan F, Gourlet V, Mazurie JL, Dupupet JL, Houssinot J, Goldberg M, et al. Prediction model of Parkinson’s disease based on antiparkinsonian drug claims. Am J Epidemiol. 2011;174(3):354–63.

    Article  PubMed  Google Scholar 

  9. 9.

    Moisan F, Kab S, Mohamed F, Canonico M, Le Guern M, Quintin C, et al. Parkinson disease male-to-female ratios increase with age: French nationwide study and meta-analysis. J Neurol Neurosurg Psychiatry. 2016;87(9):952–7.

    Article  PubMed  Google Scholar 

  10. 10.

    Couris CM, Colin C, Rabilloud M, Schott AM, Ecochard R. Method of correction to assess the number of hospitalized incident breast cancer cases based on claims databases. J Clin Epidemiol. 2002;55(4):386–91.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Insee. Institut national de la statistique et des études économiques. Estimation de population. https://www.insee.fr/fr/metadonnees/source/s1169. Accessed 01/12/2017.

  12. 12.

    Chen H, Burton EA, Ross GW, Huang X, Savica R, Abbott RD, et al. Research on the premotor symptoms of Parkinson’s disease: clinical and etiological implications. Environ Health Perspect. 2013;121(11–12):1245–52.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Agreste. Ministère de l’agriculture, de l’agroalimentaire et de la forêt. La statistique, l’évaluation et la prospective agricole. A propos des recensements agricoles et enquêtes structures des exploitations. 1988. http://agreste.agriculture.gouv.fr/enquetes/structure-des-exploitations-964/a-propos-des-recensements/. Accessed 01/12/2017.

  14. 14.

    Inpes. Institut national de prévention et d’éducation pour la santé. Les Baromètres santé, un observatoire des comportements des Français pour orienter les politiques de santé publique. http://www.inpes.sante.fr/Barometres/index.asp. Accessed 01/12/2017.

  15. 15.

    Rey G, Jougla E, Fouillet A, Hemon D. Ecological association between a deprivation index and mortality in France over the period 1997–2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health. 2009;9:33.

    Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Wald L. Elements on the computation of UV maps in the Eurosun database. hal-00788420. 2012. https://hal-mines-paristech.archives-ouvertes.fr/hal-00788420. Accessed 01/12/2017.

  17. 17.

    Qu Z, Gschwind B, Lefevre M, Wald L. Improving HelioClim-3 estimates of surface solar irradiance using the McClear clear-sky model and recent advances in atmosphere composition. Atmos Meas Tech. 2014;7:3927–33.

    Article  Google Scholar 

  18. 18.

    SoDa (Solar Radiation Data) SSfIaR. SoDa Online documents and references. http://www.soda-pro.com/. Accessed 01/12/2017.

  19. 19.

    Kift R, Webb A, Page J, Rimmer J, Janjai S. A Web-based tool for UV irradiance data: predictions for European and Southeast Asian sites. Photochem Photobiol. 2006;82(2):579–86.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Glerup H, Mikkelsen K, Poulsen L, Hass E, Overbeck S, Thomsen J, et al. Commonly recommended daily intake of vitamin D is not sufficient if sunlight exposure is limited. J Intern Med. 2000;247(2):260–8.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Kravietz A, Kab S, Wald L, Dugravot A, Singh-Manoux A, Moisan F, et al. Association of UV radiation with Parkinson disease incidence: a nationwide French ecologic study. Environ Res. 2017;154:50–6.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57(1):289–300.

    Google Scholar 

  23. 23.

    Storey JD. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann Stat. 2003;31(6):2013–35.

    Article  Google Scholar 

  24. 24.

    Lo Siou G, Yasui Y, Csizmadi I, McGregor SE, Robson PJ. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: the Tomorrow Project. Am J Epidemiol. 2011;173(8):956–67.

    Article  PubMed  Google Scholar 

  25. 25.

    Altman DG, Bland JM. Interaction revisited: the difference between two estimates. BMJ. 2003;326(7382):219.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Wang A, Costello S, Cockburn M, Zhang Z, Bronstein J, Ritz B. Parkinson’s disease risk from ambient exposure to pesticides. Eur J Epidemiol. 2011;26(7):547–55.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Gatto NM, Cockburn M, Bronstein J, Manthripragada AD, Ritz B. Well-water consumption and Parkinson’s disease in rural California. Environ Health Perspect. 2009;117(12):1912–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Yitshak Sade M, Zlotnik Y, Kloog I, Novack V, Peretz C, Ifergane G. Parkinson’s disease prevalence and proximity to agricultural cultivated fields. Parkinsons Dis. 2015;2015:576564.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Firestone JA, Smith-Weller T, Franklin G, Swanson P, Longstreth WT Jr, Checkoway H. Pesticides and risk of Parkinson disease: a population-based case-control study. Arch Neurol. 2005;62(1):91–5.

    Article  PubMed  Google Scholar 

  30. 30.

    Dhillon AS, Tarbutton GL, Levin JL, Plotkin GM, Lowry LK, Nalbone JT, et al. Pesticide/environmental exposures and Parkinson’s disease in East Texas. J Agromed. 2008;13(1):37–48.

    Article  Google Scholar 

  31. 31.

    Vlajinac HD, Sipetic SB, Maksimovic JM, Marinkovic JM, Dzoljic ED, Ratkov IS, et al. Environmental factors and Parkinson’s disease: a case-control study in Belgrade, Serbia. Int J Neurosci. 2010;120(5):361–7.

    Article  PubMed  Google Scholar 

  32. 32.

    Kenborg L, Lassen CF, Lander F, Olsen JH. Parkinson’s disease among gardeners exposed to pesticides—a Danish cohort study. Scand J Work Environ Health. 2012;38(1):65–9.

    Article  PubMed  Google Scholar 

  33. 33.

    Elbaz A, Clavel J, Rathouz PJ, Moisan F, Galanaud JP, Delemotte B, et al. Professional exposure to pesticides and Parkinson’s disease. Ann Neurol. 2009;66(4):494–504.

    Article  PubMed  Google Scholar 

  34. 34.

    Nalls MA, Escott-Price V, Williams NM, Lubbe S, Keller MF, Morris HR, et al. Genetic risk and age in Parkinson’s disease: continuum not stratum. Mov Disord. 2015;30(6):850–4.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Tanner CM, Kamel F, Ross GW, Hoppin JA, Goldman SM, Korell M, et al. Rotenone, Paraquat and Parkinson’s Disease. Environ Health Perspect. 2011;119(6):866–72.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Fréry N, Guldner L, Saoudi A, Garnier R, Zeghnoun A, Bidondo ML. Institut de veille sanitaire. Exposure of the French population to environmental chemicals. Volume 2—Polychlorobiphenyls (NDL-PCBs) and pesticides. Institut de veille sanitaire. 2013. http://www.invs.sante.fr/Publications-et-outils/Rapports-et-syntheses/Environnement-et-sante/2013/Exposition-de-la-population-francaise-aux-substances-chimiques-de-l-environnement-Tome-2-Polychlorobiphenyles-PCB-NDL-Pesticides. Accessed 01/12/2017.

  37. 37.

    Ibarluzea J, Alvarez-Pedrerol M, Guxens M, Marina LS, Basterrechea M, Lertxundi A, et al. Sociodemographic, reproductive and dietary predictors of organochlorine compounds levels in pregnant women in Spain. Chemosphere. 2011;82(1):114–20.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    OECD. Organisation for Economic Co-operation and Development. Agri-environmental indicators. http://www.oecd.org/tad/sustainable-agriculture/agri-environmentalindicators.htm. Accessed Accessed 01/12/2017.

  39. 39.

    Aubertot JN, Barbier JM, Carpentier A, Gril JJ, Guichard L, Lucas P et al. Pesticides, agriculture et environnement: réduire l’utilisation des pesticides et en limiter les impacts environnementaux. Institut national de la recherche agronomique (INRA)—Centre national du machinisme agricole du génie rural, des eaux et des forêts (CEMAGREF). 2005. http://institut.inra.fr/Missions/Eclairer-les-decisions/Expertises/Toutes-les-actualites/Pesticides-agriculture-et-environnement#. Accessed 01/12/2017.

  40. 40.

    Moisan F, Spinosi J, Delabre L, Gourlet V, Mazurie JL, Benatru I, et al. Association of Parkinson’s disease and its subtypes with agricultural pesticide exposures in men: a case-control study in France. Environ Health Perspect. 2015;123(11):1123–9.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Moisan F, Spinosi J, Dupupet JL, Delabre L, Mazurie JL, Goldberg M, et al. The relation between type of farming and prevalence of Parkinson’s disease among agricultural workers in five French districts. Mov Disord. 2011;26(2):271–9.

    Article  PubMed  Google Scholar 

  42. 42.

    Deziel NC, Friesen MC, Hoppin JA, Hines CJ, Thomas K, Freeman LE. A review of nonoccupational pathways for pesticide exposure in women living in agricultural areas. Environ Health Perspect. 2015;123(6):515–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Coignard F, Lorente C. Institut de veille sanitaire. Exposition aérienne aux pesticides des populations à proximité de zones agricoles. Institut de veille sanitaire. 2006. http://www.invs.sante.fr/publications/2006/exposition_pesticides/. Accessed 01/12/2017.

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Acknowledgements

SK is the recipient of a doctoral grant from Ministère chargé de l’agriculture et du développement durable, with financial support from Office national de l’eau et des milieux aquatiques, through fees for diffuse pollution attributed to funding of the governmental program ‘Plan Ecophyto’. Funding sources had no role in the design, interpretation, writing of the manuscript, or decision to submit for publication.

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Correspondence to Alexis Elbaz.

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Kab, S., Spinosi, J., Chaperon, L. et al. Agricultural activities and the incidence of Parkinson’s disease in the general French population. Eur J Epidemiol 32, 203–216 (2017). https://doi.org/10.1007/s10654-017-0229-z

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Keywords

  • Parkinson’s disease
  • Pesticides
  • Agriculture
  • Incidence
  • Epidemiology