# Investigating Parkinson's disease risk across farming activities using data mining and large-scale administrative health data

Pascal Petit, François Berger, Vincent Bonneterre, Nicolas Vuillerme

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Pascal Petit, François Berger, Vincent Bonneterre, Nicolas Vuillerme. Investigating Parkinson's disease risk across farming activities using data mining and large-scale administrative health data. npj Parkinson's Disease, 2025, 11, pp.13. 10.1038/s41531-024-00864-2. hal-04874259

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Submitted on 8 Jan 2025

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# Investigating Parkinson's disease risk across farming activities using data mining and large-scale administrative health data

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Pascal Petit<sup>1</sup>✉, François Berger<sup>2</sup>, Vincent Bonneterre<sup>3,4</sup> & Nicolas Vuillerme<sup>1,5</sup>

The risk of Parkinson's disease (PD) associated with farming has received considerable attention, in particular for pesticide exposure. However, data on PD risk associated with specific farming activities is lacking. We aimed to explore whether specific farming activities exhibited a higher risk of PD than others among the entire French farm manager (FM) population. A secondary analysis of real-world administrative insurance claim data and electronic health/medical records (TRACTOR project) was conducted to estimate PD risk for 26 farming activities using data mining. PD cases were identified through chronic disease declarations and antiparkinsonian drug claims. There were 8845 PD cases among 1,088,561 FMs. The highest-risk group included FMs engaged in pig farming, cattle farming, truck farming, fruit arboriculture, and crop farming, with mean hazard ratios (HRs) ranging from 1.22 to 1.67. The lowest-risk group included all activities involving horses and small animals, as well as gardening, landscaping and reforestation companies (mean HRs: 0.48–0.81). Our findings represent a preliminary work that suggests the potential involvement of occupational risk factors related to farming in PD onset and development. Future research focusing on farmers engaged in high-risk farming activities will allow to uncover potential occupational factors by better characterizing the farming exposome, which could improve PD surveillance among farmers.

Parkinson's disease (PD) is the most common muscular functioning disorder and the second most common neurodegenerative disease after Alzheimer's disease (AD), which affects millions of adults worldwide<fnref n="1" />⁻⁸. PD has a long prodromal phase (up to several decades), during which many possible protective and risk factors can contribute to the onset, development, and/or progression of PD⁴,⁹. PD is a multifactorial disorder characterized by complex interactions between genetic, behavioral, and environmental factors<fnref n="1" />,⁴,⁶,⁸,¹²,¹³. Most individuals diagnosed with PD have no family history of the disease, since genetics only accounts for 5–10% of the PD case<fnref n="1" />,³,⁴. Some protective (e.g., cigarette smoking)⁴,⁶,⁸,¹²,¹³ and risk factors (e.g., alcohol consumption)⁴ are modifiable, while others are not (e.g., aging and being a male)⁶,¹⁴. Identifying and understanding these factors is crucial to developing effective prevention strategies and interventions⁹,¹¹.

The PD risk associated with farming activities has received considerable attention, in particular for pesticide exposure²,⁷,¹⁵, which involves low-dose cocktail effects¹⁶. The exposure to pesticides and other stressors (agricultural exposome¹⁷) strongly depends on the type of crops and livestock. Because of the broad range of farming activities, it is essential to study specific farming types that could act as a proxy for distinct agricultural exposome, as highlighted by several recent works¹⁵,¹⁷⁻¹⁹. However, to our knowledge, studies examining the association between PD risk and specific farming activities are limited, with investigations typically covering only 4–18 different types of crops and animal farming¹⁸⁻²⁶. Most of these studies are not population-based and rely on a case-control design, which is prone to recall bias. They also focus on limited geographical areas, with few sex-specific analyses<fnref n="1" />,⁷,¹⁹. Furthermore, the existing research has primarily been conducted in France (n = 5)¹⁸⁻²², the US (n = 2)²⁵,²⁶, and Canada (n = 2)²³,²⁴.

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<footnote n="1">¹Univ. Grenoble Alpes, AGEIS, 38000 Grenoble, France. ²Univ. Grenoble Alpes, INSERM, Unit 1205, Braintech Lab, 38000 Grenoble, France. ³Univ. Grenoble</footnote>

<footnote>Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000 Grenoble, France. ⁴CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000 Grenoble, France. ⁵Institut Universitaire de France, 75000</footnote>

<footnote>Paris, France.</footnote>

<footnote>e-mail: pascal.petit@univ-grenoble-alpes.fr</footnote>

<footnote>Parkinson's
Foundation</footnote>
French studies include two nationwide ecological studies¹⁸,²⁰ as well as one case-control study²², one cross-sectional study²¹, and one prospective cohort¹⁹, which were limited geographically. To complement these studies, large-scale administrative health data can be used. Administrative health data does not require additional time or effort for data collection and is population-based, with large sample sizes spanning multiple years²⁷⁻²⁹.

This study aimed, for the first time, to explore, using data mining and nationwide administrative health data, whether and to what extent specific farming activities exhibited a higher risk of PD than others, both overall and by sex category, among the entire French farm manager (FM) population. A FM refers to an individual who owns and/or oversees a farm (farm/company managers, owners, and self-employed persons) while performing a broad range of activities directly in the field (e.g., pesticide application, tractor driving, harvesting, or milking)³⁰.

# Results

## Population characteristics

There were 1,088,561 FMs, among which 8845 FMs were identified as PD cases in the main analysis (Supplementary Table 1), resulting in an incidence rate of 0.28 [95% CI: 0.27–0.29] PD cases per 1000 persons-years, with 0.40 [0.38–0.41] cases per 1000 persons-years for males and 0.24 [0.23–0.25] cases per 1000 persons-years for females (Supplementary Table 2). All PD cases had a drug reimbursement, while only 3034 (34%) had a LTI (long-term illness) declaration and 317 (4%) had an ODC declaration (occupational disease covered under workers’ compensation statutes) for PD induced by pesticide exposure (Supplementary Table 3). Overall, FMs with PD were older than FMs without PD (mean age of 55 years old vs. 47 years old) (Table 1).

## PD risk associated with farming activities

Associations varied by types of crops and animal farming (Table 2, and Supplementary Table 4). Elevated HRs were observed for fruit arboriculture (HR = 1.35 [1.08–1.68]), pig farming (HR = 1.39 [1.16–1.68]), dairy farming (HR = 1.48 [1.40–1.57]), mixed cattle farming (HR = 1.58 [1.41–1.46]), and crop farming (HR = 1.67 [1.53–1.82]) (Table 2). Modestly elevated HR were found for cow farming (HR = 1.22 [1.14–1.30]), truck farming (HR = 1.22 [1.09–1.36]), and unspecified and mixed farming (HR = 1.09 [1.02–1.16]). A positive trend was observed in viticulture (HR = 1.07 [0.94–1.21]) and ovine and caprine farming (HR = 1.07 [0.96–1.20]) (Table 2).

By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies (HR = 0.81 [0.69–0.95]), small animal farming (HR = 0.51 [0.39–0.67]), training, dressage and riding clubs (HR = 0.49 [0.35–0.71]), stud farming (HR = 0.48 [0.37–0.63]), and rural craftsperson (HR = 0.14 [0.06–0.34]) (Table 2). Most activities did not exhibit any sex difference, with the exception of viticulture and stud farming (Supplementary Table 5). In viticulture, male FMs (HR = 1.23 [1.05–1.43]) had a more elevated HR than females (HR = 0.89 [0.72–1.11]) (Supplementary Table 5).

All sensitivity analyses yielded similar results to the main analysis (Fig. 1, Supplementary Table 4). There were a few exceptions. When excluding PD cases diagnosed in 2012 (SA1), the positive association with fruit arboriculture disappeared (Supplementary Table 4). Regarding the sensitivity analysis for which PD cases were solely identified using ODC declaration for PD induced by pesticide exposure (SA2), positive associations were found only for pig farming (HR = 2.47 [1.31–4.64]), mixed cattle farming (HR = 2.14 [1.37–3.33]), viticulture (HR = 1.65 [1.15–2.38]), unspecified and mixed farming (HR = 1.42 [1.05–1.93]), and dairy farming (HR = 1.32 [0.99–1.77]) (Supplementary Table 4).

The bar plot consists of two panels. The left panel shows the main analysis, with each bar representing the negative log-transformed adjusted *p*-value (x-axis) for each farming activity (y-axis). Red bars indicate a higher PD risk (hazard ratio > 1), green bars a lower PD risk (hazard ratio < 1), and white bars denote activities where the hazard ratio 95% confidence interval includes 1. Vertical dashed lines mark the −log10 (0.05) threshold. The right panel presents a stacked bar plot showing the percentage of sensitivity

analyses out of 17 (x-axis) in which each farming activity (y-axis) was associated with higher (red), lower (green), or unchanged PD risk (white). Please refer to the Supplementary materials for more details about sensitivity analyses.

All analyses adjusted for sex (for “both sexes” only), age, farm establishment year, median farm surface, number of associates, unemployment status, farm count, family status, partner employment status, farm location, number of comorbidities, and performing a secondary activity.

# Discussion

For the first time, the association between PD risk and multiple farming activities was examined, overall and by sex category, in the entire French FM population using data mining and large-scale real-world administrative health data. As suspected, the association between the risk of PD and farming highly varied depending on the activity considered, with a 3.4-fold range observed between the lowest and highest HRs. The highest-risk group included FMs engaged in pig farming, cattle farming, fruit arboriculture, viticulture, truck farming, and crop farming, while the lowest-risk group included all FMs engaged in activities involving horses and unspecified small animal farming, but also gardening, landscaping and reforestation companies. This PD risk heterogeneity suggests that part of the risk may possibly be attributable to specific farming practices, with a few sex-specific PD risks that could denote differences in occupational exposures and tasks between males and females, as previously suggested¹⁵. Potential hormonal disparities may also have played a role because several studies suggest a possible protective effect of female sex hormones (especially estrogen)³¹⁻³⁴. However, this study is only a preliminary work, which highlights the need for further research.

FMs with PD were older than FMs without PD, which is consistent with the literature as PD affects people late in life, usually starting after 50 years old²,⁵,⁷,³⁵. In line with previous works, the PD incidence rate increased with age, was higher for males than females⁴,⁵,⁷,¹⁹,²¹, and was consistent with those of the entire French population³⁶ and other studies⁴,⁶,⁷,²³.

During farming activities, FMs can be exposed to numerous environmental co-occurring contaminants (agricultural exposomes)²⁹, some of which could be implicated in PD. Of particular interest are pesticides, which are used to protect crops and livestock from pest infestations and diseases¹⁶,³⁷. Numerous studies have reported an association between pesticide exposure and PD, but results are often inconsistent and limited, in particular for specific pesticide compounds, probably because of the large number of different pesticides each FM used throughout his/her career¹,²,⁸,¹²,¹⁵,¹⁸,³⁵. The paraquat herbicide¹⁹,³⁸⁻⁴⁰, 2,4-D herbicide⁷,⁴⁴,⁴⁷, triazine (atrazine) herbicides²,⁴⁴,⁴⁸,⁴⁹, rotenone insecticide¹⁹,⁴⁰,⁴¹,⁴⁵,⁵⁰, fipronil insecticide²,⁵,⁴⁵¹, organophosphates (OPs)⁵⁰,⁵²⁻⁵⁵, organochlorines⁴⁴,⁵³,⁵⁴,⁵⁶,⁵⁷, pyrethroids⁴⁰,⁴⁴,⁴⁹, as well as the fungicides maneb¹⁹,³⁹,⁴⁶, mancozeb¹²,¹⁹, and ziram⁷,¹²,¹⁹,⁴⁶ are among the most frequently incriminated pesticides.

While the pesticides used may sometimes be common between farming types, their amount, frequency, intensity, and mode of application also strongly vary and change over time¹⁸,¹⁹,²¹. For instance, there is a higher use of pesticides in truck farming, crop farming, fruit arboriculture, and viticulture than in other farming activities¹⁸. Farms specialized in orchards rank first in terms of insecticide use, whereas viticulture uses mostly fungicides, and cereal crop farming employs insecticides, herbicides, and fungicides, but with lower frequency and intensity¹⁸,²². Regarding the treatment of ectoparasites in animal husbandry, OP insecticides have preferentially been used on animals since the 1970s¹⁹, replacing the organochlorine insecticide lindane, before being substituted by pyrethroids and sometimes oral medications only (ivermectine). Hence, the farming type is a proxy of agricultural exposure that is easier to assess than specific pesticides²¹ and may better reflect the agricultural exposome²⁹, for which pesticide association plays a pivotal role, with a low-dose cocktail effect¹⁶.

Several French studies reported an association between PD and pesticide use and pesticide expenditures in vineyards¹⁸,²² or in regions with a greater presence of vineyards, cereal farming, fruit crops, fresh vegetable farming, and pig farming²⁰,²¹. Another French study (prospective cohort
<table><caption>Table 1 | Baseline characteristics of the study population, TRACTOR project, France, 2012–2016</caption><thead><tr><th></th><th>FM without PD (n = 1,079,716)</th><th>FM with PD (n = 8845)</th></tr></thead><tbody><tr><td>Main characteristics</td><td>n (%)</td><td>n (%) [overall %]</td></tr><tr><td>Sex</td><td></td><td></td></tr><tr><td>Male</td><td>745,798 (69.1)</td><td>5500 (61.2) [0.73]</td></tr><tr><td>Female</td><td>333,918 (30.9)</td><td>3345 (38.8) [0.99]</td></tr><tr><td>Age at baseline: 2012/01/01 (years)</td><td></td><td></td></tr><tr><td>Mean (SD) [min-max]</td><td>46.5 (14.1) [18-96]</td><td>55.8 (12.6) [34-93]</td></tr><tr><td>Family status</td><td></td><td></td></tr><tr><td>Single</td><td>465,923 (43.2)</td><td>3087 (34.9) [0.66]</td></tr><tr><td>As a couple</td><td>613,793 (56.8)</td><td>5758 (65.1) [0.93]</td></tr><tr><td>First year of the farm's establishment</td><td></td><td></td></tr><tr><td>Median (IQR)</td><td>1994 (39)</td><td>1990 (39.9)</td></tr><tr><td>Farm surface (expressed in hectares)</td><td></td><td></td></tr><tr><td>Median (IQR)</td><td>1.64 (14.0)</td><td>1.93 (14.1)</td></tr><tr><td>Farm location (region)</td><td></td><td></td></tr><tr><td>Auvergne-Rhône-Alpes</td><td>116,588 (10.8)</td><td>840 (9.5) [0.72]</td></tr><tr><td>Bourgogne-Franche-Comté</td><td>65,323 (6.05)</td><td>541 (6.12) [0.82]</td></tr><tr><td>Bretagne</td><td>80,791 (7.48)</td><td>511 (5.78) [0.63]</td></tr><tr><td>Centre - Val de Loire</td><td>49,424 (4.58)</td><td>410 (4.64) [0.82]</td></tr><tr><td>Corse</td><td>5296 (0.49)</td><td>30 (0.34) [0.56]</td></tr><tr><td>Grand Est</td><td>81,022 (7.5)</td><td>732 (8.28) [0.90]</td></tr><tr><td>Hauts-de-France</td><td>47,908 (4.44)</td><td>490 (5.54) [1.01]</td></tr><tr><td>Île-de-France</td><td>13,978 (1.29)</td><td>106 (1.2) [0.75]</td></tr><tr><td>Normandie</td><td>79,917 (7.4)</td><td>721 (8.15) [0.89]</td></tr><tr><td>Nouvelle-Aquitaine</td><td>178,771 (16.6)</td><td>1612 (18.2) [0.89]</td></tr><tr><td>Occitanie</td><td>165,387 (15.3)</td><td>1438 (16.3) [0.86]</td></tr><tr><td>Provence-Alpes-Côte d'Azur</td><td>113,201 (10.5)</td><td>852 (9.63) [0.75]</td></tr><tr><td>Pays de la Loire</td><td>82,110 (7.6)</td><td>562 (6.35) [0.68]</td></tr><tr><td>Number of farms</td><td></td><td></td></tr><tr><td>1 farm</td><td>1,053,455 (97.6)</td><td>8655 (97.9) [0.81]</td></tr><tr><td>>1 farm</td><td>26,261 (2.43)</td><td>190 (2.15) [0.72]</td></tr><tr><td>Farm type (farm clustering)</td><td></td><td></td></tr><tr><td>Individual farm</td><td>750,001 (69.5)</td><td>6587 (74.5) [0.87]</td></tr><tr><td>Farm with work partners</td><td>329,715 (30.5)</td><td>2258 (25.5) [0.68]</td></tr><tr><td>Partner work status</td><td></td><td></td></tr><tr><td>Perform task to help farm manager</td><td>126,923 (11.8)</td><td>1328 (15) [1.04]</td></tr><tr><td>Do not perform task to help farm manager</td><td>952,793 (88.2)</td><td>7517 (85) [0.78]</td></tr><tr><td>Number of associates</td><td></td><td></td></tr><tr><td>0</td><td>829,228 (76.8)</td><td>7004 (79.2) [0.84]</td></tr><tr><td>≥ 1</td><td>250,488 (23.2)</td><td>1841 (20.8) [0.73]</td></tr><tr><td>Secondary farming activity<sup>1</sup></td><td></td><td></td></tr><tr><td>No secondary farming activity performed in parallel to a main activity</td><td>678,780 (62.9)</td><td>7810 (88.3) [1.14]</td></tr><tr><td>At least one farming activity performed in parallel to a main activity</td><td>400,936 (37.1)</td><td>1035 (11.7) [0.26]</td></tr><tr><td>Number of different yearly activities performed</td><td></td><td></td></tr><tr><td>Only one activity during the observation period</td><td>971,334 (90.0)</td><td>8116 (91.8) [0.83]</td></tr></tbody></table>

<table><caption>Table 1 (continued) | Baseline characteristics of the study population, TRACTOR project, France, 2012–2016</caption><thead><tr><th></th><th>FM without PD (n = 1,079,716)</th><th>FM with PD (n = 8845)</th></tr></thead><tbody><tr><td>Two activities during the observation period</td><td>101,735 (9.42)</td><td>704 (7.96) [0.69]</td></tr><tr><td>At least three activities during the observation period</td><td>6647 (0.62)</td><td>25 (0.28) [0.37]</td></tr><tr><td>Lack of job security</td><td></td><td></td></tr><tr><td>Has never been unemployed during the observation period</td><td>1,077,605 (99.8)</td><td>8838 (99.9) [0.81]</td></tr><tr><td>Has been unemployed during the observation period</td><td>2111 (0.2)</td><td>7 (0.08) [0.33]</td></tr><tr><td>Median yearly insurance premium (euros)</td><td></td><td></td></tr><tr><td>Median (IQR)</td><td>5166 (40818)</td><td>4592 (40877)</td></tr><tr><td>Employees</td><td></td><td></td></tr><tr><td>No employee</td><td>783,007 (72.5)</td><td>7137 (80.7) [0.90]</td></tr><tr><td>At least one employee</td><td>296,709 (27.5)</td><td>1708 (19.3) [0.57]</td></tr><tr><td>Work status</td><td></td><td></td></tr><tr><td>Working as a farm manager</td><td>814,203 (75.4)</td><td>6544 (74) [0.80]</td></tr><tr><td>Working as a solidarity contributor*</td><td>265,513 (24.6)</td><td>2301 (26) [0.86]</td></tr><tr><td>Retirement status</td><td></td><td></td></tr><tr><td>Did not retired before the disease diagnosis or end of follow-up</td><td>761,653 (70.5)</td><td>2808 (31.7) [0.37]</td></tr><tr><td>Retired before the disease diagnosis or end of follow-up</td><td>318,063 (29.5)</td><td>6037 (68.3) [1.86]</td></tr><tr><td>Pre-existing disability</td><td></td><td></td></tr><tr><td>Did not become disabled before the disease diagnosis or end of follow-up</td><td>1,075,485 (99.6)</td><td>8657 (97.9) [0.80]</td></tr><tr><td>Became disabled before the disease diagnosis or end of follow-up</td><td>4231 (0.39)</td><td>188 (2.13) [4.25]</td></tr><tr><td>Number of pre-existing comorbidities (long term illness)</td><td></td><td></td></tr><tr><td>0 comorbidity before the disease diagnosis or end of follow-up</td><td>704,914 (65.3)</td><td>4602 (52) [0.65]</td></tr><tr><td>1 comorbidity before the disease diagnosis or end of follow-up</td><td>214,432 (19.9)</td><td>2464 (27.9) [1.14]</td></tr><tr><td>>1 comorbidity before the disease diagnosis or end of follow-up</td><td>160,370 (14.9)</td><td>1779 (20.1) [1.10]</td></tr></tbody></table>

Note: FM farm manager, IQR interquartile range, PD Parkinson's disease, SD arithmetic standard deviation.

aPercentage of PD cases among all FMs with PD.

bPercentage of PD cases among all FMs with the given characteristics.

cA secondary farming activity is defined as a farming activity (e.g., grassland farming) that a FM can perform in addition to its main activity (e.g., ovine farming). The nature of the secondary activity is however unknown.

dFMs who farm on a small surface (<12.5 ha) or who works less than 1200 h/year.

AGRICAN), including both FMs and farm workers from 13 French departments using self-administered questionnaires, reported that lifelong pesticide use was associated with an increased risk of PD in all types of activities (e.g., cattle, crop farming)¹⁹. This study, which included 1732 PD cases, also found positive associations between PD and farmers exposed to insecticides on animals, which were particularly strong for pig farming¹⁹. A Canadian case-control study with 403 PD cases reported a higher PD risk for ever-occupational contact with cattle and found a positive trend for swine²³.
<table><caption>Table 2 | Risks of Parkinson's disease by agricultural activity, TRACTOR project, France, 2012–2016</caption><thead><tr><th>Farming activity</th><th>Sex</th><th>Study population no. (%)</th><th>PD no. (%)</th><th>Unexposed PD no.</th><th>HR [95% CI]<sup>†</sup></th><th>p</th><th>adj. p</th></tr></thead><tbody><tr><td rowspan="3">Crop farming (e.g., wheat, corn, and industrial grower)</td><td>Both sexes</td><td>317,458 (29.2)</td><td>2998 (33.9)</td><td>5847</td><td>1.67 [1.53–1.82]</td><td>5.0e-30</td><td>2.7e-29</td></tr><tr><td>Female</td><td>106,016 (31.4)</td><td>1238 (37.0)</td><td>2107</td><td>1.74 [1.50–2.02]</td><td>3.4e-13</td><td>4.3e-13</td></tr><tr><td>Male</td><td>211,442 (28.1)</td><td>1760 (32.0)</td><td>3740</td><td>1.67 [1.50–1.87]</td><td>3.5e-20</td><td>9.6e-20</td></tr><tr><td rowspan="3">Mixed cattle farming</td><td>Both sexes</td><td>31,309 (2.88)</td><td>324 (3.66)</td><td>8521</td><td>1.58 [1.41–1.76]</td><td>1.3e-15</td><td>2.1e-14</td></tr><tr><td>Female</td><td>8206 (2.43)</td><td>116 (3.47)</td><td>3229</td><td>1.72 [1.43–2.07]</td><td>1.1e-08</td><td>3.0e-08</td></tr><tr><td>Male</td><td>23,103 (3.08)</td><td>208 (3.78)</td><td>5292</td><td>1.47 [1.28–1.69]</td><td>7.0e-8</td><td>1.5e-07</td></tr><tr><td rowspan="3">Dairy farming</td><td>Both sexes</td><td>162,906 (15.0)</td><td>1412 (16.0)</td><td>7433</td><td>1.48 [1.40–1.57]</td><td>5.8e-40</td><td>3.2e-39</td></tr><tr><td>Female</td><td>50,385 (14.9)</td><td>532 (15.9)</td><td>2813</td><td>1.45 [1.32–1.59]</td><td>2.4e-14</td><td>3.6e-14</td></tr><tr><td>Male</td><td>112,521 (15.0)</td><td>880 (16.0)</td><td>4620</td><td>1.45 [1.35–1.56]</td><td>6.5e-23</td><td>2.4e-22</td></tr><tr><td rowspan="3">Pig farming</td><td>Both sexes</td><td>13,760 (1.26)</td><td>111 (1.25)</td><td>8734</td><td>1.39 [1.16–1.68]</td><td>5.2e-04</td><td>1.5e-03</td></tr><tr><td>Female</td><td>3948 (1.17)</td><td>42 (1.26)</td><td>3303</td><td>1.62 [1.19–2.20]</td><td>1.9e-03</td><td>4.4e-03</td></tr><tr><td>Male</td><td>9812 (1.31)</td><td>69 (1.25)</td><td>5431</td><td>1.25 [0.99–1.59]</td><td>0.06</td><td>0.07</td></tr><tr><td rowspan="3">Cow farming</td><td>Both sexes</td><td>113,244 (10.4)</td><td>1015 (11.5)</td><td>7830</td><td>1.22 [1.14–1.30]</td><td>6.0e-9</td><td>2.0e-7</td></tr><tr><td>Female</td><td>33,603 (9.96)</td><td>371 (11.1)</td><td>2974</td><td>1.23 [1.11–1.37]</td><td>1.6e-04</td><td>4.8e-04</td></tr><tr><td>Male</td><td>79,641 (10.6)</td><td>644 (11.7)</td><td>4856</td><td>1.20 [1.10–1.30]</td><td>2.0e-5</td><td>1.1e-04</td></tr><tr><td rowspan="3">Truck farming, floriculture/flower-growing</td><td>Both sexes</td><td>43,684 (4.01)</td><td>312 (3.53)</td><td>8533</td><td>1.22 [1.09–1.36]</td><td>6.5e-04</td><td>4.2e-03</td></tr><tr><td>Female</td><td>13,350 (3.96)</td><td>122 (3.65)</td><td>3223</td><td>1.32 [1.10–1.58]</td><td>3.0e-3</td><td>9.6e-03</td></tr><tr><td>Male</td><td>30,334 (4.04)</td><td>190 (3.45)</td><td>5310</td><td>1.16 [1.00–1.34]</td><td>0.05</td><td>0.06</td></tr><tr><td rowspan="3">Fruit arboriculture</td><td>Both sexes</td><td>25,090 (2.30)</td><td>201 (2.27)</td><td>8644</td><td>1.35 [1.08–1.68]</td><td>8.9e-03</td><td>0.03</td></tr><tr><td>Female</td><td>7981 (2.37)</td><td>67 (2.00)</td><td>3278</td><td>1.34 [0.91–2.02]</td><td>0.12</td><td>0.15</td></tr><tr><td>Male</td><td>17,109 (2.28)</td><td>134 (2.44)</td><td>5366</td><td>1.36 [1.04–1.79]</td><td>0.02</td><td>0.05</td></tr><tr><td rowspan="3">Unspecified and mixed farming (e.g., polyculture, mixed farming, diversified farming)</td><td>Both sexes</td><td>129,013 (11.9)</td><td>1013 (11.5)</td><td>7832</td><td>1.09 [1.02–1.17]</td><td>9.2e-03</td><td>0.04</td></tr><tr><td>Female</td><td>39,914 (11.8)</td><td>396 (11.8)</td><td>2949</td><td>1.11 [1.00–1.23]</td><td>0.06</td><td>0.09</td></tr><tr><td>Male</td><td>89,099 (11.9)</td><td>617 (11.2)</td><td>4883</td><td>1.07 [0.98–1.16]</td><td>0.13</td><td>0.17</td></tr><tr><td rowspan="3">Viticulture</td><td>Both sexes</td><td>122,713 (11.3)</td><td>1169 (13.2)</td><td>7676</td><td>1.07 [0.94–1.21]</td><td>0.3</td><td>0.31</td></tr><tr><td>Female</td><td>43,370 (12.9)</td><td>492 (14.7)</td><td>2853</td><td>0.89 [0.72–1.11]</td><td>0.24</td><td>0.30</td></tr><tr><td>Male</td><td>79,343 (10.6)</td><td>677 (12.3)</td><td>4823</td><td>1.23 [1.05–1.43]</td><td>9.2e-03</td><td>0.03</td></tr><tr><td rowspan="3">Ovine and caprine farming</td><td>Both sexes</td><td>49,061 (4.51)</td><td>330 (3.73)</td><td>8515</td><td>1.07 [0.96–1.20]</td><td>0.2</td><td>0.65</td></tr><tr><td>Female</td><td>17,529 (5.20)</td><td>118 (3.53)</td><td>3227</td><td>0.96 [0.80–1.15]</td><td>0.64</td><td>0.82</td></tr><tr><td>Male</td><td>31,532 (4.20)</td><td>212 (3.85)</td><td>5288</td><td>1.15 [1.00–1.32]</td><td>0.05</td><td>0.65</td></tr><tr><td rowspan="3">Wood production (e.g., lopping)</td><td>Both sexes</td><td>11,431 (1.05)</td><td>55 (0.62)</td><td>8790</td><td>1.11 [0.85–1.44]</td><td>0.46</td><td>0.79</td></tr><tr><td>Female</td><td>303 (0.09)</td><td>2 (0.06)</td><td>3343</td><td>Not calculated</td><td></td><td></td></tr><tr><td>Male</td><td>11,128 (1.48)</td><td>53 (0.96)</td><td>5447</td><td>1.18 [0.90–1.55]</td><td>0.23</td><td>0.52</td></tr><tr><td rowspan="3">Garden center/tree nursery</td><td>Both sexes</td><td>5418 (0.50)</td><td>33 (0.37)</td><td>8812</td><td>1.09 [0.78–1.54]</td><td>0.61</td><td>0.79</td></tr><tr><td>Female</td><td>1438 (0.43)</td><td>6 (0.18)</td><td>3339</td><td>0.66 [0.30–1.48]</td><td>0.31</td><td>0.79</td></tr><tr><td>Male</td><td>3980 (0.53)</td><td>27 (0.49)</td><td>5473</td><td>1.28 [0.88–1.87]</td><td>0.2</td><td>0.79</td></tr><tr><td rowspan="3">Poultry and rabbit farming</td><td>Both sexes</td><td>25,863 (2.38)</td><td>124 (1.40)</td><td>8721</td><td>0.90 [0.75–1.07]</td><td>0.24</td><td>0.92</td></tr><tr><td>Female</td><td>10,196 (3.02)</td><td>53 (1.58)</td><td>3292</td><td>0.87 [0.66–1.14]</td><td>0.3</td><td>0.92</td></tr><tr><td>Male</td><td>15,667 (2.09)</td><td>71 (1.29)</td><td>5429</td><td>0.91 [0.72–1.15]</td><td>0.43</td><td>0.92</td></tr><tr><td rowspan="3">Unspecified large animal farming (e.g., ostrich, llama)</td><td>Both sexes</td><td>3020 (0.28)</td><td>16 (0.18)</td><td>8829</td><td>1.15 [0.70–1.87]</td><td>0.58</td><td>0.75</td></tr><tr><td>Female</td><td>1458 (0.43)</td><td>6 (0.18)</td><td>3339</td><td>0.82 [0.37–1.83]</td><td>0.63</td><td>0.75</td></tr><tr><td>Male</td><td>1562 (0.21)</td><td>10 (0.18)</td><td>5490</td><td>1.45 [0.78–2.70]</td><td>0.24</td><td>0.75</td></tr><tr><td rowspan="3">Agricultural work companies (e.g., pesticide applications, harvest reaping)</td><td>Both sexes</td><td>15,635 (1.44)</td><td>60 (0.68)</td><td>8785</td><td>0.90 [0.70–1.16]</td><td>0.4</td><td>1.0</td></tr><tr><td>Female</td><td>1887 (0.56)</td><td>7 (0.21)</td><td>3338</td><td>0.77 [0.37–1.61]</td><td>0.49</td><td>1.0</td></tr><tr><td>Male</td><td>13,748 (1.83)</td><td>53 (0.96)</td><td>5447</td><td>0.94 [0.72–1.23]</td><td>0.65</td><td>1.0</td></tr></tbody></table>
<table><caption>Table 2 (continued) | Risks of Parkinson's disease by agricultural activity, TRACTOR project, France, 2012–2016</caption><thead><tr><th>Farming activity</th><th>Sex</th><th>Study population no. (%)</th><th>PD no. (%)</th><th>Unexposed PD no.</th><th>HR [95% CI]<sup>†</sup></th><th>p</th><th>adj. p</th></tr></thead><tbody><tr><td rowspan="3">Sylviculture/forestry (e.g., thinning, pruning)</td><td>Both sexes</td><td>2166 (0.20)</td><td>16 (0.18)</td><td>8829</td><td>1.10 [0.68–1.80]</td><td>0.7</td><td>0.96</td></tr><tr><td>Female</td><td>361 (0.11)</td><td>6 (0.18)</td><td>3339</td><td>1.57 [0.71–3.50]</td><td>0.27</td><td>0.68</td></tr><tr><td>Male</td><td>1805 (0.24)</td><td>10 (0.18)</td><td>5490</td><td>1.00 [0.54–1.85]</td><td>0.99</td><td>1.0</td></tr><tr><td rowspan="3">Company representative/authorized representative</td><td>Both sexes</td><td>1935 (0.18)</td><td>9 (0.10)</td><td>8836</td><td>1.22 [0.64–2.35]</td><td>0.55</td><td>0.64</td></tr><tr><td>Female</td><td>1500 (0.45)</td><td>7 (0.21)</td><td>3338</td><td>1.20 [0.57–2.52]</td><td>0.63</td><td>0.69</td></tr><tr><td>Male</td><td>435 (0.06)</td><td>2 (0.04)</td><td>5498</td><td>Not calculated</td><td></td><td></td></tr><tr><td rowspan="3">Unspecified specialized farming (e.g., herbs, mushrooms)</td><td>Both sexes</td><td>6671 (0.61)</td><td>29 (0.33)</td><td>8816</td><td>0.82 [0.57–1.18]</td><td>0.28</td><td>0.6</td></tr><tr><td>Female</td><td>2438 (0.72)</td><td>9 (0.27)</td><td>3336</td><td>0.63 [0.33–1.22]</td><td>0.17</td><td>0.5</td></tr><tr><td>Male</td><td>4233 (0.56)</td><td>20 (0.36)</td><td>5480</td><td>0.93 [0.60–1.45]</td><td>0.75</td><td>0.89</td></tr><tr><td rowspan="3">Stationary sawmill (e.g., edging, trimming, decking, debarking)</td><td>Both sexes</td><td>791 (0.07)</td><td>6 (0.07)</td><td>8839</td><td>1.24 [0.56–2.76]</td><td>0.6</td><td>0.72</td></tr><tr><td>Female</td><td>55 (0.02)</td><td>0 (0)</td><td>3345</td><td>Not calculated</td><td></td><td></td></tr><tr><td>Male</td><td>736 (0.10)</td><td>6 (0.11)</td><td>5494</td><td>1.41 [0.63–3.14]</td><td>0.4</td><td>0.6</td></tr><tr><td rowspan="3">Shellfish farming (e.g., oyster farming, scallop aquaculture)</td><td>Both sexes</td><td>3825 (0.35)</td><td>12 (0.14)</td><td>8833</td><td>0.65 [0.37–1.15]</td><td>0.14</td><td>0.59</td></tr><tr><td>Female</td><td>736 (0.22)</td><td>3 (0.09)</td><td>3342</td><td>0.71 [0.23–2.21]</td><td>0.56</td><td>0.65</td></tr><tr><td>Male</td><td>3089 (0.41)</td><td>9 (0.16)</td><td>5491</td><td>0.67 [0.35–1.29]</td><td>0.23</td><td>0.59</td></tr><tr><td rowspan="3">Salt works/salt evaporation pond</td><td>Both sexes</td><td>980 (0.09)</td><td>3 (0.03)</td><td>8842</td><td>0.59 [0.19–1.83]</td><td>0.36</td><td>0.48</td></tr><tr><td>Female</td><td>220 (0.07)</td><td>1 (0.03)</td><td>3344</td><td>Not calculated</td><td></td><td></td></tr><tr><td>Male</td><td>760 (0.10)</td><td>2 (0.04)</td><td>5498</td><td>Not calculated</td><td></td><td></td></tr><tr><td rowspan="3">Gardening, landscaping and reforestation companies</td><td>Both sexes</td><td>49,177 (4.52)</td><td>150 (1.70)</td><td>8695</td><td><strong>0.81 [0.69–0.95]</strong></td><td><strong>0.01</strong></td><td><strong>0.05</strong></td></tr><tr><td>Female</td><td>2562 (0.76)</td><td>10 (0.30)</td><td>3335</td><td>0.91 [0.49–1.70]</td><td>0.77</td><td>0.88</td></tr><tr><td>Male</td><td>46,615 (6.20)</td><td>140 (2.55)</td><td>5360</td><td>0.88 [0.74–1.04]</td><td>0.13</td><td>0.27</td></tr><tr><td rowspan="3">Unspecified small animal farming (e.g., frogs, snails, bees)</td><td>Both sexes</td><td>19,984 (1.84)</td><td>54 (0.61)</td><td>8791</td><td><strong>0.51 [0.39–0.67]</strong></td><td><strong>7.7e–07</strong></td><td><strong>1.9e–05</strong></td></tr><tr><td>Female</td><td>8632 (2.56)</td><td>18 (0.54)</td><td>3327</td><td><strong>0.39 [0.25–0.62]</strong></td><td><strong>7.3e–05</strong></td><td><strong>3.3e–04</strong></td></tr><tr><td>Male</td><td>11,352 (1.51)</td><td>36 (0.65)</td><td>5464</td><td><strong>0.59 [0.43–0.82]</strong></td><td><strong>1.7e–03</strong></td><td><strong>2.7e–03</strong></td></tr><tr><td rowspan="3">Stud farming</td><td>Both sexes</td><td>17,269 (1.59)</td><td>53 (0.60)</td><td>8792</td><td><strong>0.48 [0.37–0.63]</strong></td><td><strong>1.3e–07</strong></td><td><strong>7.6e–07</strong></td></tr><tr><td>Female</td><td>7486 (2.22)</td><td>13 (0.39)</td><td>3332</td><td><strong>0.28 [0.16–0.48]</strong></td><td><strong>4.8e–06</strong></td><td><strong>1.4e–05</strong></td></tr><tr><td>Male</td><td>9783 (1.30)</td><td>40 (0.73)</td><td>5460</td><td><strong>0.64 [0.47–0.87]</strong></td><td><strong>4.5e–03</strong></td><td><strong>7.9e–03</strong></td></tr><tr><td rowspan="3">Training, dressage, riding clubs</td><td>Both sexes</td><td>14,979 (1.38)</td><td>30 (0.34)</td><td>8815</td><td><strong>0.49 [0.35–0.71]</strong></td><td><strong>1.2e–04</strong></td><td><strong>2.5e–03</strong></td></tr><tr><td>Female</td><td>6687 (1.98)</td><td>7 (0.21)</td><td>3338</td><td><strong>0.29 [0.14–0.60]</strong></td><td><strong>9.6e–04</strong></td><td><strong>4.8e–03</strong></td></tr><tr><td>Male</td><td>8292 (1.10)</td><td>23 (0.42)</td><td>5477</td><td><strong>0.63 [0.42–0.95]</strong></td><td><strong>0.03</strong></td><td><strong>0.05</strong></td></tr><tr><td rowspan="3">Rural craftsperson (e.g., mason, mechanics)</td><td>Both sexes</td><td>7696 (0.71)</td><td>5 (0.06)</td><td>8840</td><td><strong>0.14 [0.06–0.34]</strong></td><td><strong>1.3e–05</strong></td><td><strong>2.0e–5</strong></td></tr><tr><td>Female</td><td>283 (0.08)</td><td>0 (0)</td><td>3345</td><td>Not calculated</td><td></td><td></td></tr><tr><td>Male</td><td>7413 (0.99)</td><td>5 (0.09)</td><td>5495</td><td><strong>0.15 [0.06–0.36]</strong></td><td><strong>2.1e–05</strong></td><td><strong>3.0e–5</strong></td></tr></tbody></table>

Abbreviations: PD Parkinson's disease, HR hazard ratio, *m* number of exposed PD cases, *p* *p*-value, adj. *p* *p*-value adjusted using the Benjamini-Hochberg approach.

†The percentages in brackets refer to the ratio of exposed PD cases in the study population to the total number of PD cases in the overall population.

‡Main analysis: adjusted for sex (for "both sexes" only), age, the first year of the farm's establishment, farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and having a secondary farming activity.

Regarding animal farming, FMs can also be exposed to pesticides from animal manure and feeds, in particular for cattle and pigs<sup>14,23</sup>, such as diethyltoluamide (35%), a repellent that has been implicated with PD<sup>14</sup>.

Interestingly, our findings for PD are different than those from a previous study conducted in the same population examining AD risk, in which only three farming activities were found to have a higher risk of AD (crop farming, fruit arboriculture, and viticulture), while all animal farming activities exhibited a lower risk of AD<sup>30</sup>. This could suggest that different exposome and potentially pesticides with different mechanisms could be involved. One possibility could be that the site of initial misfolding events differs between both neurodegenerative diseases. The Braak and dual-hit hypotheses posit that PD may first initiate in the olfactory structures (olfactory bulb) and the gut enteric nerves (gut-to-brain pathway)<sup>9,58,59</sup>. Hence, pesticides could gain access to the brain via the olfactory pathway or the vagus nerve, which could eventually trigger the spread of PD by a

templating mechanism in a prion-like manner<sup>9,59</sup>. In contrast to PD, AD does not seem to be affected by the olfactory pathways<sup>59,60</sup>.

The cocktail effect, involving a low-dose mixture of pesticides, is now considered a pivotal physiopathological issue<sup>16</sup>. It makes classical epidemiological study very challenging. Recently, large-scale screening on dopaminergic cells demonstrated the involvement of 53 pesticides and the impact of specific mixtures on PD<sup>16</sup>. These in vitro studies are not fully relevant for the in vivo occupational situation, as it is not known if these compounds can reach the dopaminergic cells in the brain and also what the relevant dose is associated with neurodegeneration<sup>61</sup>. Our finding of specific farming activities associated with higher PD risk probably reflects specific mixtures of pesticides and environmental factors (agricultural exposomes). It suggests that "real-life screening" should be synergistically associated with this new field of diseases<sup>61,62</sup>. The chemical exposome<sup>62,63</sup> needs to be better objectified in the future, using biological exposure monitoring (e.g., urine,
<figure><img src="image_6.png" /><figcaption>Fig. 1 | Farming activities and risks of PD, TRACTOR project, France, 2012–2016. Comparison of primary and sensitivity analyses.</figcaption></figure>

blood, hair, or fat tissues) to help in the identification of specific mixtures of pesticides or environmental cocktails involved in PD neurodegeneration⁶¹,⁶².

The microbiota is today hypothesized to play a key role in PD⁶⁴. Changes in gut microbiota can generate pro-inflammatory mediators and alpha-synuclein aggregation, which can be transported from the enteric nervous system to the brain via the vagus nerve (“Gut-Brain axis”). Some pesticides can disrupt the nasal and gut microbiome, cause mitochondrial dysfunction, affect lysosomal function, or exert neurotoxic effects, in particular pesticides composed of heavy metals such as manganese, zinc, aluminum, copper, and mercury⁵,¹⁴,¹⁸,⁴⁹. For instance, several fungicides contain metals, such as maneb (containing manganese), ziram (containing zinc), mancozeb (containing both zinc and manganese), or the Bordeaux mixture (containing copper), that were/are used in viticulture, crop, and fruit farming¹⁸,⁶⁵. Exposure to some pathogenic bacteria can cause epigenetic changes in several PD risk factor genes⁶⁴. The microbiome of farmers, and in particular livestock breeders, is completely altered by their specific occupational exposure, as shown by numerous studies conducted mainly in pig breeders⁶⁶⁻⁷⁰. Contact with animals during breeding activities is associated with changes in the dermal, nasal, and gut microbiota, as the microbiome of the upper respiratory track is then swallowed. In particular, it has been shown that the microbiome and resistance of farmers are very similar to those of their animals⁶⁶⁻⁷⁰.

Contact with animals can also expose FMs to zoonotic agents and endotoxins²³,⁷¹,⁷², in particular Mycobacterium avium ss. paratuberculosis (MAP), which could start as an enteric infection via the vagus nerve, as hypothesized by one study⁷³. Cattle farmers were also found to have a higher risk of both hypothyroidism and hyperthyroidism¹⁷ but also inflammatory bowel disease²⁴, which have been associated with PD risk⁶⁴,⁷⁵.

Previous studies found evidence that alpha-synuclein's activities can be affected by polystyrene and other particles found, for instance, in micro-plastics (MPs) and nanoplastics (NPs)⁷⁶,⁷⁷. MPs/NPs represent emerging environmental pollutants, with up to 430,000 tons of MPs potentially entering agricultural fields annually in Europe⁷⁸,⁷⁹. Pesticides and other toxic agents (e.g., infectious agents) can bind NPs that can act as carriers to facilitate blood-brain barrier crossing based on the “Trojan Horse” effect⁸⁰,⁸¹. There are many possible sources of MPs/NPs contamination in agriculture. Direct contamination sources include plastic mulching, the cover of plastic greenhouses, or polymer-based fertilizers⁷⁹. Indirect sources of contamination include the application of biosolids, the application of compost, or irrigation with waste-treated water⁷⁸,⁷⁹.

Our study is the first to focus on the entire FM population. Strengths of our study include the largest sample of FMs ever studied, its population-based and nationwide design, the large number of exposed PD cases, sex-specific analyses, adjustments to several potential confounders (sex, age, geographical area, farm surface), as well as the wide range of agricultural exposures with detailed information on 26 farming activities. In addition, contrary to case control studies or studies using declarative data, the administrative nature of our data (farming type as a means of assessing exposure) was not prone to recall bias, which could have led to exposure misclassification¹,¹⁸. We chose to use a time-on-study approach instead of using age as timescale because several studies suggest that time-on-study models may be preferable, as they perform at least as well as left-truncated age scale models⁸²⁻⁸⁴. Additionally, they tend to be more robust against misspecification of the underlying time scale and generally offer better predictive ability⁸²⁻⁸⁴.

Our findings need to be considered in light of some limitations. First, the administrative nature of available data, which is characterized by a lack of accurate information on confounders and precise dates of disease onset⁴,²⁹. Indeed, the date of diagnosis or first treatment does not equate to the date of disease, in particular because there are no specific tests to detect PD and because individuals are most commonly diagnosed years after symptom onset once the motor symptoms (e.g., freezing of gait) set in¹,⁴⁻⁶. Potential PD case misidentification cannot be excluded because PD cases were identified with either the date of LTI declaration, ODC declaration, or first treatment reimbursement. However, diagnostic misclassifications are unlikely to depend on the farming type¹⁸, and several sensitivity analyses were conducted to address this bias, which yielded consistent results (Supplementary material). Clinical information on PD FMs (e.g., severity scales, non-motor symptoms, disease phenotypes) as well as genetic information (from both causal and at-risk genetic variants) were not available. This prevented us from accounting for these factors in the analysis, which is a limitation. Indeed, given the complex nature of PD, which is characterized by a dynamic interplay between genes and the environment, some genetic variants (e.g., ABCB1) may increase susceptibility to pesticide exposures associated with PD¹⁰,¹¹,¹⁵,¹⁴,³⁴,³⁵,⁵³,⁵⁶,⁸⁵.

Because of the administrative nature of our data, some potential confounding factors of interest (e.g., cigarette smoking, genetic information, use of protective equipment) were not available¹⁷,³⁰,⁷⁴. This could represent a bias if their absence confounds or masks the genuine relationship between
farming activities and PD. This is particularly true for cigarette smoking, which is a protective factor in PD<sup>4,6,8,12,13,35</sup>. Even though FMs have the lowest smoking prevalence in the French population, some of the differences observed may be associated with smoking because the prevalence of active smoking varies from one farming activity to another<sup>86,87</sup>. To address this bias, we conducted a sensitivity analysis, which yielded consistent results (Supplementary material). The possibility of residual confounding cannot be excluded because some farming activities are highly heterogeneous in nature, with, for instance, crop and fruit farming that involve a broad range of practices/tasks and pesticide usage depending on the type of crop (e.g., cereal, potatoes, barley, wheat) or fruit (e.g., walnut, apple). Residual confounding due to environmental exposure (e.g., air pollution in regions with a high density of industrial plants) should be limited because we adjusted for farm locations. While specific chemical, physical, or biological exposures were not available, farming activity serves as a relevant proxy for agricultural exposure that is easier to assess than specific pesticides or stressors<sup>15,18-21,29</sup>. To address this limitation and complement this study, job and crop exposure matrices could refine exposure estimates by enabling cumulative exposure indexing for pesticides, heavy metals, and organizational factors<sup>88-92</sup>. Undertaking such a task, which is beyond the scope of this study, would present major challenges, as it entails navigating various potential biases and limitations that must be carefully considered and addressed. The farming activity coding system used by Mutualité Sociale Agricole (MSA) is not an international classification, which may hinder the transcoding process. Some farming activities (e.g., crop farming) may be too broad (not descriptive enough) to allow for an accurate exposure assessment using crop or job exposure matrices. Finding relevant crop or job exposure matrices is also challenging due to the lack of a gold standard<sup>93</sup>. In addition, crop or job exposure matrices have also some limitations that may bias risk estimates, such as an heterogeneity in performance depending on the exposure and outcome of interest as well as the assumption of homogeneity within jobs/categories<sup>88-90,93-95</sup>. Ideally, the crop or job exposure matrix should define each farming activity as specifically as possible, with exposure estimates given for a minimal combination of the year, country, region, sex, work status (e.g., FMs or farm workers), and farming activity. However, to the best of our knowledge, such matrices do not exist, at least not in France. Because farming practices can differ from a country to another, as well as within countries, it is important to use crop and job exposure matrices that are comparable to the population studied. To our knowledge, no French job exposure matrix has been specifically designed for farming. Consequently, several crop or job exposure matrices would have to be used. However, each crop or job exposure matrix is created by potentially different experts, aims, and methodologies, which could make them not easily comparable and compatible with one another<sup>88-90,93-95</sup>. In France, there are several crop and job exposure matrices, in particular Matgène<sup>96</sup> and Pestimat<sup>88</sup>. For Matgène<sup>96</sup>, exposure to pesticides is not provided, exposure estimates are not available for each sex and for each region, and exposure probabilities are provided as ranges. While French activity nomenclature (NAF) codes are available in both Matgène<sup>96</sup> and MSA data, NAF codes from MSA are, however, not reliable due to their declarative nature and non-evolving nature (i.e., rarely updated). Regarding Pestimat<sup>88</sup>, only exposure to pesticides is available, but not for the entire France and not for all types of crops<sup>37</sup>. Exposure estimates are not available for each sex and for each region. In addition, exposure estimates are only known for crop, vegetable, and fruit farming activities. However, pesticides, and in particular insecticides, are used in animal farming as well<sup>19</sup>. Moreover, the farming activities (e.g., crop farming) in MSA data are too broad compared to the information from Pestimat<sup>88</sup> (e.g., potato farming), which would force us to consider that each crop farmer, regardless of the crops, is exposed to the same pesticides, which would introduce bias and exposure misclassification. Linking crop and job exposure matrices with MSA data is an issue that deserves to be explored in the future, as highlighted by a previous work<sup>97</sup>. Standardizing or developing new matrices tailored to French agriculture is therefore essential. Another perspective would be to investigate whether FMs engaged in various farming activities have a higher PD risk compared to a non-farming population.

Because occupational data was only available between 2002–2016 and because the average age at baseline was 47 years old, the exposure characterization only took into account the most recent half of the individuals’ careers. However, FMs have a relatively stable career because most FMs (90%) never changed their main activity between 2002-2016. Hence, the impact of this bias should be limited. The generalizability of our findings may be limited as farming practices and risk factors can differ between countries and populations (e.g., farm workers, FMs).

Our findings represent a preliminary work that suggests the potential involvement of occupational risk factors related to farming in PD onset and development. Our study could guide future research aiming to examine such factors by identifying vulnerable populations and potential research avenues. Future work should focus on farmers engaged in high-risk farming activities (i.e., pig farming, crop farming, cattle farming, mixed farming, viticulture, truck farming, and fruit arboriculture) to better characterize their exposome and potential association with PD because the combined exposure to multiple stressors (e.g., cocktail effect) may result in a synergistic adverse effect on PD risk, as alluded to by several studies in the literature. Confirmation of our findings in longitudinal studies and in other countries would also be valuable.

## Methods

### Data source

A secondary use of routinely collected data from Mutualité Sociale Agricole (MSA), the unique social security scheme of all French farmers, available to the TRACTOR (Tracking and monitoring occupational risks in agriculture) project was conducted<sup>17,30,74,98-100</sup>.

Insurance claim data routinely collected through the completion of mandatory forms by FMs on an annual basis from 2002 to 2016 was available. These claims provide information on sociodemographic (e.g., age, sex, family status), farm characteristics (e.g., farm surface), and farming activities. Farming activities are coded into 26 categories (e.g., pig farming, viticulture) by MSA following a national thesaurus defined by French laws<sup>30</sup>.

Electronic health/medical records from 2012–2016 were also available. These records provide information on declared chronic illnesses (LTIs), such as PD (LTI n°16), that grant full coverage of health care expenditures to FMs who hold them. Electronic health/medical records also pertain to ODCs, such as “*PD induced by pesticide exposures*” (ODC n°RA 58 in the agricultural scheme table). Since 2012, in France, PD can be considered a work-related disease in farmers under specific conditions, including a diagnosis confirmed by a neurologist and occupational exposure to pesticides for 10 years or more<sup>7</sup>. Data on drug reimbursements was also available. Each LTI and ODC is assigned an ICD-10 code (10th revision of the International Statistical Classification of Diseases and Related Health Problems), while each drug is assigned an ATC code (Anatomical Therapeutic Chemical classification system). January 1st, 2012, was defined as the baseline time point (i.e., time zero), and December 31st, 2016, as the follow-up end. The Kaplan-Meier reverse method was used to determine the median follow-up.

The data was analyzed from September 2023 to September 2024. The variables used for this study were complete. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) was used as reporting guidelines (Supplementary Table 6).

### Ethics approval

This study was conducted in accordance with the Declaration of Helsinki, and ethics approval was obtained from the French independent administrative authority responsible for safeguarding privacy and personal data (CNIL) (authorization number: MMS/SBM/AE171001). The need for informed consent was waived by CNIL (i.e., ethics committee) for the TRACTOR project because data analyses were only descriptive and results were reported at a large collective scale (i.e., farming activity level), because data were pseudomyzed, and because measures were undertaken to prevent the risk of reidentification of individuals.
## Study population and outcome

All FMs who performed at least one of the 26 activities once (1 yearly declaration to MSA) between 2002 and 2016 were included. The farming activity was considered a proxy for occupational exposure (agricultural activity exposomes), as previously done in other works<sup>17-21</sup>. The FMs' degree of involvement in the daily tasks was unknown (not recorded by MSA). The duration of exposure for each activity was determined by calculating the number of years in which a FM engaged in the activity based on the yearly declarations made to MSA during the period from 2002 to 2016.

PD cases were identified using ICD-10 codes for FMs declared with PD through the LTI and ODC insurance declaration schemes, as well as with ATC codes for PD drugs given to FMs (with or without LTI or ODC)<sup>20-22,101</sup>. FMs were considered to have PD if they had at least one LTI declaration for PD (ICD-10 code G20 or F02), one ODC declaration for PD, or one reimbursement of any drugs solely used to treat PD (i.e., all antiparkinsonian agents, with the exception of pramipexole, rotigotine, amantadine, and lisuride) (Supplementary Table 7). In addition, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases.

## Statistical analysis

All statistical analyses were performed with R software 4.3.1® (R Core Team, Vienna, Austria) for Windows 10©. When the number of exposed PD cases was ≥3, Cox proportional hazards models and associated hazard ratios (HRs) were used to investigate whether specific farming activities exhibited a higher risk of PD than others, both overall and by sex category (i.e., one separate model for each sex). Because we did not have access to the general population nor to other occupational sectors not related to agriculture, a separate model was created for each of the 26 farming activities, comparing FMs who had not previously engaged in a given farming activity from 2002 to 2016 with those who had prior experience (leave-one-group-out approach).

For each model, the dependent variables were the timescale (continuous) and the PD diagnosis (two categories: yes or no). The time to the oldest PD insurance declaration (LTI or ODC) or PD drug reimbursement was used as the underlying timescale. In addition, the farming activity was parameterized as a time-dependent variable to account for potential immortal time bias.

Overall analyses were adjusted for sex, with interaction tests conducted to assess potential sex differences. All analyses were adjusted for age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity (Supplementary Table 8). We ensured that the included variables were non-collinear (variance inflation factor ≤ 2.5). A covariate\*time interaction was added to the model when the assumption of a proportional hazard rate, assessed by the independence of scaled Schoenfeld's residuals and time, was not met. Multiple testing was accounted for using the Benjamini-Hochberg approach.

Seventeen sensitivity analyses (SAs) were undertaken to test hypotheses and address potential sources of bias (Supplementary Table 8). For example, PD cases diagnosed in 2012 were excluded to increase the likelihood that identified PD cases were incident cases (SA1). In another SA (SA2), the PD case identification was based solely on ODC declarations. Please refer to the Supplementary material for more details.

## Data availability

The data that supports the findings of this study is not publicly available. A reasonable request to the Mutualité Sociale Agricole (MSA) can be made, but restrictions apply to the availability of these data due to both the individual and medical nature of the data, which requires approval from both the MSA and the French independent administrative authority protecting privacy and personal data (CNIL). Further information is available from the corresponding author upon request.

## Code availability

The underlying code for this study is not publicly available for proprietary reasons but can be made available upon reasonable request.

Received: 2 July 2024; Accepted: 2 December 2024;

Published online: 08 January 2025

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## Acknowledgements

This work was partially supported by MIAI@Grenoble Alpes [ANR-19-P3IA-0003, 2019], and the French National Research Agency within the framework of the "Investissements d'avenir" program [ANR-10-AIRT-05, 2012; ANR-15-IDEX-02, 2016]. The funding sources had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The authors declare no conflict of interest relating to the material presented in this article. Its contents, including any opinions and/or conclusions expressed, are solely those of the authors. The authors thank the Mutualité Sociale Agricole (MSA), their representatives, and their chosen intermediaries within the framework of the TRACTOR project for their help in understanding and accessing MSA data. In particular, the authors would like to thank Nadia Joubert, Damien Ozenfant, Véronique Danguy, Delphine Burguet, Marc Parmentier, Patrick Le Bourhis, Ileana Radoi, Gaëtan Deffontaines, Jean-Marc Soulat, and Bernard Salles, head of MSA's scientific committee. The authors also thank the ANSES (French Agency for Food, Environmental and Occupational Health & Safety), in particular Jean-Luc Volatier, Fabrizio Botta, Maître Brugioni, Ohri Yamada, Alexandra Papadopoulos, and Henri Bastos for their
support regarding the TRACTOR project. The authors thank Pr Pierrick
Bedouch, and Dr Sébastien Chanoine for giving the authors access to the
Thériaque database.

Author contributions

Pascal Petit: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing—Original Draft, Writing— Review & Editing, Visualization, Supervision, Funding acquisition, Project administration. François Berger: Conceptualization, Methodology, Validation, Writing—Review & Editing. Vincent Bonneterre: Writing—Review & Editing, Supervision, Project administration. Nicolas Vuillerme: Writing— Review & Editing, Funding acquisition, Resource.

Competing interests

The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains
supplementary material available at
https://doi.org/10.1038/s41531-024-00864-2.

**Correspondence** and requests for materials should be addressed to
Pascal Petit.

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© The Author(s) 2025
## Main analysis

<figure><img src="image_7.png" /></figure>

## Sensitivity analyses (SAs)

<figure><img src="image_8.png" /></figure>
# Supplementary material

## Title

Investigating Parkinson's disease risk across farming activities using data mining and large-scale administrative health data

## Authors

Pascal Petit, François Berger, Vincent Bonneterre, Nicolas Vuillerme

## Table of contents

<table><tr><td>Supplementary Table 1: Number of farm managers and identified PD cases for each analysis conducted</td><td>3</td></tr><tr><td>Supplementary Table 2: Incidence rate per analysis</td><td>4</td></tr><tr><td>Supplementary Table 3: Number of farm managers with a LTI, ODC or ATC code of interest</td><td>5</td></tr><tr><td>Supplementary Table 4: Farming activities and risks of PD, TRACTOR project, France, 2012-2016. Comparison of the main analysis with all sensitivity analyses for all sex categories.</td><td>6</td></tr><tr><td>Supplementary Table 5: Comparison of results from the interaction tests regarding the sex between the main analysis and all sensitivity analyses.</td><td>6</td></tr><tr><td>Supplementary Table 6: STROBE Statement Checklist</td><td>7</td></tr><tr><td>Supplementary Table 7: ICD-10 and ATC codes used for identifying PD cases among farm managers depending on the analysis</td><td>8</td></tr><tr><td>Supplementary Table 8: List of covariates considered in the statistical analyses</td><td>9</td></tr><tr><td>Sensitivity analysis</td><td>10</td></tr><tr><td>Sensitivity analysis 1 - excluding PD cases diagnosed in 2012</td><td>10</td></tr><tr><td>Supplementary Figure 1: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 1</td><td>11</td></tr><tr><td>Sensitivity analysis 2 – PD identification using only ODC declaration data</td><td>12</td></tr><tr><td>Supplementary Figure 2: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 2</td><td>13</td></tr><tr><td>Sensitivity analyses 3 to 5 – age restrictions</td><td>14</td></tr><tr><td>Supplementary Figure 3: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 3</td><td>15</td></tr><tr><td>Supplementary Figure 4: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 4</td><td>16</td></tr><tr><td>Supplementary Figure 5: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 5</td><td>17</td></tr><tr><td>Sensitivity analyses 6 to 14 – addressing potential case misclassification</td><td>18</td></tr><tr><td>Supplementary Figure 6: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 6</td><td>21</td></tr><tr><td>Supplementary Figure 7: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 7</td><td>22</td></tr><tr><td>Supplementary Figure 8: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 8</td><td>23</td></tr><tr><td>Supplementary Figure 9: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 9</td><td>24</td></tr><tr><td>Supplementary Figure 10: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 10</td><td>25</td></tr><tr><td>Supplementary Figure 11: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 11</td><td>26</td></tr><tr><td>Supplementary Figure 12: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 12</td><td>27</td></tr><tr><td>Supplementary Figure 13: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 13</td><td>28</td></tr><tr><td>Supplementary Figure 14: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 14</td><td>29</td></tr><tr><td>Sensitivity analysis 15 – using only one farming activity as reference</td><td>30</td></tr><tr><td>Supplementary Figure 15: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 15</td><td>31</td></tr></table>
<table>
  <tbody>
    <tr>
      <td><i>Sensitivity analysis 16 – adjusting for smoking</i></td>
      <td>32</td>
    </tr>
    <tr>
      <td><b>Supplementary Figure 16:</b> Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 16</td>
      <td>33</td>
    </tr>
    <tr>
      <td><i>Sensitivity analysis 17 – control of other diseases not related to farming exposure</i></td>
      <td>34</td>
    </tr>
    <tr>
      <td><b>Supplementary Figure 17:</b> Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 17</td>
      <td>36</td>
    </tr>
    <tr>
      <td><b>References</b></td>
      <td>37</td>
    </tr>
  </tbody>
</table>
<table><caption>1 Supplementary Table 1: Number of farm managers and identified PD cases for each analysis conducted</caption><thead><tr><th>Analysis</th><th>FMs without PD<br>No. (%)</th><th>FMs with PD<br>No. (%)</th><th>Total number<br>of FMs</th></tr></thead><tbody><tr><td>Main analysis</td><td>1079716 (99.2%)</td><td>8845 (0.81%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 1 - excluding PD cases diagnosed in 2012</td><td>1079716 (99.4%)</td><td>5910 (0.54%)</td><td>1085626</td></tr><tr><td>Sensitivity analysis 2 - PD identification only using ODC declaration data</td><td>1088244 (99.9%)</td><td>317 (0.03%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 3 - only including FMs ≥ 40 years</td><td>715050 (98.9%)</td><td>7893 (1.09%)</td><td>722943</td></tr><tr><td>Sensitivity analysis 4 - only including FMs ≥ 50 years</td><td>446334 (98.6%)</td><td>6388 (1.41%)</td><td>452722</td></tr><tr><td>Sensitivity analysis 5 - only including FMs ≥ 60 years</td><td>165468 (98.1%)</td><td>3206 (1.90%)</td><td>168674</td></tr><tr><td>Sensitivity analysis 6 - PD identification only using ODC and LTI declaration data</td><td>1085439 (99.7%)</td><td>3122 (0.29%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 7 - PD identification only using drug reimbursement data</td><td>1080343 (99.2%)</td><td>8218 (0.76%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 8 - excluding FMs with ADRD (LTI declaration)</td><td>1080113 (99.2%)</td><td>8448 (0.78%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 9 - PD identification only using LTI declaration data (G20, G21)</td><td>1085449 (99.7%)</td><td>3112 (0.29%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 10 - PD identification only using LTI declaration data (F02, G20, G21)</td><td>1085534 (99.7%)</td><td>3027 (0.28%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 11 - considering drug reimbursement not solely used for PD</td><td>1078438 (99.1%)</td><td>10123 (0.93%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 12 - PD identification using LTI, ODC or ≥ 2 drug reimbursements</td><td>1081419 (99.3%)</td><td>7142 (0.66%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 13 - PD identification using LTI, ODC or ≥ 3 drug reimbursements</td><td>1081951 (99.4%)</td><td>6610 (0.61%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 14 - PD identification using LTI, ODC or ≥ 6 drug reimbursements</td><td>1082990 (99.5%)</td><td>5571 (0.51%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 15 - using only one farming activity as reference</td><td>1079716 (99.2%)</td><td>8845 (0.81%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 16 - adjusting on smoking</td><td>1079716 (99.2%)</td><td>8845 (0.81%)</td><td>1088561</td></tr><tr><td>Sensitivity analysis 17 - control of other diseases not related to farming exposure</td><td>530961 (98.4%)</td><td>8845 (1.64%)</td><td>539806</td></tr></tbody></table>

2

3 Abbreviations: ADRD: Alzheimer's disease and other related dementias, F02: ICD-10 code for dementia in other diseases classified elsewhere, FM: farm manager, G20: ICD-10 code for

4 Parkinson's disease, G21: ICD-10 code for secondary parkinsonism, ICD-10: 10th revision of the International Statistical Classification of Diseases and Related Health Problems, LTI: long-term

5 illness, No: number of, ODCs: occupational diseases covered under workers' compensation statutes, PD: Parkinson's disease.
6 Supplementary Table 2: Incidence rate per analysis

<table><thead><tr><th rowspan="2">Analysis</th><th colspan="2">Both sexes</th><th colspan="2">Male</th><th colspan="2">Female</th></tr><tr><th>n</th><th>IR [95%CI]<sup>a</sup></th><th>n</th><th>IR [95%CI]<sup>a</sup></th><th>n</th><th>IR [95%CI]<sup>a</sup></th></tr></thead><tbody><tr><td>Main analysis</td><td>8845</td><td>0.28 [0.27-0.29]</td><td>5500</td><td>0.40 [0.38-0.41]</td><td>3345</td><td>0.24 [0.23-0.25]</td></tr><tr><td>Sensitivity analysis 1 - excluding PD cases diagnosed in 2012</td><td>5910</td><td>0.19 [0.18-0.20]</td><td>3713</td><td>0.26 [0.25-0.27]</td><td>2197</td><td>0.16 [0.15-0.17]</td></tr><tr><td>Sensitivity analysis 2 - PD identification only using ODC declaration data</td><td>317</td><td>0.01 [0.009-0.02]</td><td>285</td><td>0.01 [0.009-0.02]</td><td>32</td><td>0.004 [0.003-0.005]</td></tr><tr><td>Sensitivity analysis 3 - only including FMs ≥ 40 years</td><td>7893</td><td>0.49 [0.48-0.50]</td><td>4718</td><td>0.59 [0.57-0.61]</td><td>3175</td><td>0.44 [0.43-0.46]</td></tr><tr><td>Sensitivity analysis 4 - only including FMs ≥ 50 years</td><td>6388</td><td>1.04 [1.02-1.07]</td><td>3575</td><td>1.09 [1.06-1.13]</td><td>2813</td><td>0.98 [0.95-1.02]</td></tr><tr><td>Sensitivity analysis 5 - only including FMs ≥ 60 years</td><td>3206</td><td>1.72 [1.66-1.78]</td><td>1544</td><td>1.62 [1.54-1.71]</td><td>1662</td><td>1.82 [1.73-1.91]</td></tr><tr><td>Sensitivity analysis 6 - PD identification only using ODC and LTI declaration data</td><td>3122</td><td>0.10 [0.08-0.11]</td><td>2083</td><td>0.12 [0.11-0.13]</td><td>1039</td><td>0.09 [0.08-0.10]</td></tr><tr><td>Sensitivity analysis 7 - PD identification only using drug reimbursement data</td><td>8218</td><td>0.26 [0.25-0.27]</td><td>5104</td><td>0.37 [0.36-0.38]</td><td>3114</td><td>0.22 [0.21-0.23]</td></tr><tr><td>Sensitivity analysis 8 - excluding FMs with ADRD (LTI declaration)</td><td>8448</td><td>0.27 [0.26-0.28]</td><td>5302</td><td>0.37 [0.36-0.39]</td><td>3146</td><td>0.23 [0.22-0.24]</td></tr><tr><td>Sensitivity analysis 9 - PD identification only using LTI declaration data (G20, G21)</td><td>3112</td><td>0.11 [0.08-0.12]</td><td>2077</td><td>0.12 [0.11-0.13]</td><td>1035</td><td>0.09 [0.08-0.10]</td></tr><tr><td>Sensitivity analysis 10 - PD identification only using LTI declaration data (F02, G20, G21)</td><td>3027</td><td>0.10 [0.09-0.12]</td><td>1994</td><td>0.12 [0.11-0.13]</td><td>1033</td><td>0.09 [0.08-0.10]</td></tr><tr><td>Sensitivity analysis 11 - considering drug reimbursement not solely used for PD</td><td>10123</td><td>0.32 [0.31-0.33]</td><td>6206</td><td>0.47 [0.45-0.48]</td><td>3917</td><td>0.27 [0.26-0.28]</td></tr><tr><td>Sensitivity analysis 12 - PD identification using LTI, ODC or ≥ 2 drug reimbursements</td><td>7142</td><td>0.23 [0.22-0.24]</td><td>4481</td><td>0.32 [0.30-0.33]</td><td>2661</td><td>0.20 [0.19-0.21]</td></tr><tr><td>Sensitivity analysis 13 - PD identification using LTI, ODC or ≥ 3 drug reimbursements</td><td>6610</td><td>0.21 [0.20-0.22]</td><td>4163</td><td>0.29 [0.28-0.30]</td><td>2447</td><td>0.18 [0.17-0.19]</td></tr><tr><td>Sensitivity analysis 14 - PD identification using LTI, ODC or ≥ 6 drug reimbursements</td><td>5571</td><td>0.18 [0.17-0.19]</td><td>3641</td><td>0.25 [0.24-0.26]</td><td>2130</td><td>0.16 [0.15-0.17]</td></tr><tr><td>Sensitivity analysis 15 - using only one farming activity as reference</td><td>8845</td><td>0.28 [0.27-0.29]</td><td>5500</td><td>0.40 [0.38-0.41]</td><td>3345</td><td>0.24 [0.23-0.25]</td></tr><tr><td>Sensitivity analysis 16 - adjusting on smoking</td><td>8845</td><td>0.28 [0.27-0.29]</td><td>5500</td><td>0.40 [0.38-0.41]</td><td>3345</td><td>0.24 [0.23-0.25]</td></tr><tr><td>Sensitivity analysis 17 - control of other diseases not related to farming exposure</td><td>8845</td><td>0.81 [0.79-0.83]</td><td>5500</td><td>1.00 [0.96-1.03]</td><td>3345</td><td>0.73 [0.71-0.75]</td></tr></tbody></table>

7

8 Abbreviations: ADRD: Alzheimer's disease and other related dementias, F02: ICD-10 code for dementia in other diseases classified elsewhere, FM: farm manager, G20: ICD-10 code for Parkinson's disease, G21: ICD-10 code for secondary parkinsonism, ICD-10: 10th revision of the International Statistical Classification of Diseases and Related Health Problems, IR: incidence rate, LTI: long-term illness, ODCs: occupational diseases covered under workers' compensation statutes, PD: Parkinson's disease, PR: prevalence.

9

10

11 a incidence rates (IRs) are expressed as cases per 1000 persons-years.
<table><caption>**Supplementary Table 3:** Number of farm managers with a LTI, ODC or ATC code of interest</caption><thead><tr><th>ATC code</th><th>Drug name</th><th>Indication*</th><th>Number of FMs<br>No. (%)</th></tr></thead><tbody><tr><td>N04</td><td>Anti-parkinson drugs</td><td>PD and other disorders (cf. below)</td><td>10123 (0.93)</td></tr><tr><td>N04</td><td>Anti-parkinson drugs solely used for PD</td><td>PD only (cf. below)</td><td>8845 (0.81)</td></tr><tr><td>N04BA02</td><td>Levodopa and decarboxylase inhibitor</td><td>PD only</td><td>2698 (0.25)</td></tr><tr><td>N04BC08</td><td>Piribedil</td><td>PD only</td><td>2286 (0.21)</td></tr><tr><td>N04BC05</td><td>Pramipexole</td><td>PD, restless legs syndrome</td><td>2056 (0.19)</td></tr><tr><td>N04BC04</td><td>Ropinirole</td><td>PD only</td><td>1468 (0.13)</td></tr><tr><td>N04BD02</td><td>Rasagiline</td><td>PD only</td><td>1300 (0.12)</td></tr><tr><td>N04AA01</td><td>Trihexyphenidyl</td><td>PD, parkinsonism</td><td>1125 (0.10)</td></tr><tr><td>N04AA12</td><td>Tropatepine</td><td>PD, parkinsonism</td><td>1009 (0.09)</td></tr><tr><td>N04BA03</td><td>Levodopa, decarboxylase inhibitor and COMT inhibitor</td><td>PD only</td><td>873 (0.08)</td></tr><tr><td>N04BC09</td><td>Rotigotine</td><td>PD, restless legs syndrome</td><td>416 (0.04)</td></tr><tr><td>N04BB01</td><td>Amantadine</td><td>PD, influenza A, postherpetic neuralgia</td><td>253 (0.02)</td></tr><tr><td>N04AA02</td><td>Biperiden</td><td>PD, parkinsonism</td><td>168 (0.02)</td></tr><tr><td>N04BX02</td><td>Entacapone</td><td>PD only</td><td>110 (0.01)</td></tr><tr><td>N04BC07</td><td>Apomorphine</td><td>PD only</td><td>102 (0.01)</td></tr><tr><td>N04BD01</td><td>Selegiline</td><td>PD only</td><td>32 (2.9e-03)</td></tr><tr><td>N04BC01</td><td>Bromocriptine</td><td>PD only</td><td>19 (1.7e-03)</td></tr><tr><td>N04BX01</td><td>Tolcapone</td><td>PD only</td><td>9 (8.3e-04)</td></tr><tr><td>N02CA07</td><td>Lisuride</td><td>PD, hyperprolactinaemia disorders</td><td>0</td></tr><tr><td>N04BA01</td><td>Levodopa</td><td>PD only</td><td>0</td></tr><tr><td>N04BC02</td><td>Pergolide</td><td>PD only</td><td>0</td></tr></tbody></table>

Abbreviations: COMT: catechol-O-methyltransferase, FM: farm manager, No: number of, PD: Parkinson's disease.

*According to the Vidal (https://www.vidal.fr/), Thesorimed (https://theso.prod-un.thesorimed.org/monographie) and BCB databases (https://www.bcb.fr/v2/app/recherche.jsp).

<table><caption>**B. Number of PD farm managers with a LTI or ODC declaration based on the ICD-10 code**</caption><thead><tr><th>ICD-10 code</th><th>Definition</th><th>Number of FMs<br>No. (%)</th></tr></thead><tbody><tr><td>G20</td><td>Parkinson's disease</td><td>3034 (0.28)</td></tr><tr><td>G21</td><td>Secondary parkinsonism</td><td>98 (9.0e-03)</td></tr><tr><td>F02</td><td>Dementia in other diseases classified elsewhere</td><td>12 (1.1e-03)</td></tr></tbody></table>

Abbreviations: ICD-10: 10th revision of the International Statistical Classification of Diseases and Related Health Problems, FM: farm manager, No: number of, ODCs: occupational diseases covered under workers' compensation statutes, PD: Parkinson's disease.
26

**Supplementary Table 4:** Farming activities and risks of PD, TRACTOR project, France, 2012-2016. Comparison of the main analysis with all sensitivity analyses for all sex categories.

27

28

Please refer to the MS Excel file entitled *Supplementary Tables 4 and 5*, in the tab named "*Supplementary Table 4*".

29

30

**Supplementary Table 5:** Comparison of results from the interaction tests regarding the sex between the main analysis and all sensitivity analyses.

31

Please refer to the MS Excel file entitled *Supplementary Tables 4 and 5*, in the tab named "*Supplementary Table 5*".
<table><caption>32 Supplementary Table 6: STROBE Statement Checklist</caption><thead><tr><th></th><th>Item No</th><th>Recommendation</th><th>Page</th></tr></thead><tbody><tr><td>Title and abstract</td><td>1</td><td>(a) Indicate the study's design with a commonly used term in the title or the abstract<br>(b) Provide in the abstract an informative and balanced summary of what was done and what was found</td><td>1, 2<br>2</td></tr><tr><td>Introduction</td><td></td><td></td><td></td></tr><tr><td>Background/rationale</td><td>2</td><td>Explain the scientific background and rationale for the investigation being reported</td><td>3</td></tr><tr><td>Objectives</td><td>3</td><td>State specific objectives, including any prespecified hypotheses</td><td>3, 4</td></tr><tr><td>Methods</td><td></td><td></td><td></td></tr><tr><td>Study design</td><td>4</td><td>Present key elements of study design early in the paper</td><td>14, 15</td></tr><tr><td>Setting</td><td>5</td><td>Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection</td><td>14, 15</td></tr><tr><td>Participants</td><td>6</td><td>(a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up<br>(b) For matched studies, give matching criteria and number of exposed and unexposed</td><td>14, 15<br>DNA</td></tr><tr><td>Variables</td><td>7</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable</td><td>15-17, suppl</td></tr><tr><td>Data sources/ measurement</td><td>8*</td><td>For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group</td><td>15-17, suppl</td></tr><tr><td>Bias</td><td>9</td><td>Describe any efforts to address potential sources of bias</td><td>16,17, suppl</td></tr><tr><td>Study size</td><td>10</td><td>Explain how the study size was arrived at</td><td>14-16</td></tr><tr><td>Quantitative variables</td><td>11</td><td>Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why</td><td>15-17, suppl</td></tr><tr><td>Statistical methods</td><td>12</td><td>(a) Describe all statistical methods, including those used to control for confounding<br>(b) Describe any methods used to examine subgroups and interactions<br>(c) Explain how missing data were addressed<br>(d) If applicable, explain how loss to follow-up was addressed<br>(e) Describe any sensitivity analyses</td><td>15-17, suppl<br>15-17, suppl<br>15-17, suppl<br>DNA<br>16, 17, suppl</td></tr><tr><td>Results</td><td></td><td></td><td></td></tr><tr><td>Participants</td><td>13*</td><td>(a) Report numbers of individuals at each stage of study-e.g., numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, and analyzed<br>(b) Give reasons for non-participation at each stage<br>(c) Consider use of a flow diagram</td><td>4<br>DNA<br>DNA</td></tr><tr><td>Descriptive data</td><td>14*</td><td>(a) Give characteristics of study participants (e.g., demographic, clinical, social) and information on exposures and potential confounders<br>(b) Indicate number of participants with missing data for each variable of interest<br>(c) Summarize follow-up time (e.g., average and total amount)</td><td>4, Table 1<br>DNA<br>4, suppl</td></tr><tr><td>Outcome data</td><td>15*</td><td>Report numbers of outcome events or summary measures over time</td><td>4, suppl</td></tr><tr><td>Main results</td><td>16</td><td>(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g., 95% confidence interval). Make clear which confounders were adjusted for and why they were included<br>(b) Report category boundaries when continuous variables were categorized<br>(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period</td><td>4-7, suppl<br>DNA<br>DNA</td></tr><tr><td>Other analyses</td><td>17</td><td>Report other analyses done-e.g., analyses of subgroups and interactions, and sensitivity analyses</td><td>5-7, suppl</td></tr><tr><td>Discussion</td><td></td><td></td><td></td></tr><tr><td>Key results</td><td>18</td><td>Summarize key results with reference to study objectives</td><td>8</td></tr><tr><td>Limitations</td><td>19</td><td>Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias</td><td>8, 12-14</td></tr><tr><td>Interpretation</td><td>20</td><td>Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence</td><td>8, 12-14</td></tr><tr><td>Generalizability</td><td>21</td><td>Discuss the generalizability (external validity) of the study results</td><td>14</td></tr><tr><td>Other information</td><td></td><td></td><td></td></tr><tr><td>Funding</td><td>22</td><td>Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based</td><td>18</td></tr></tbody></table>

<footnote n="34">34 *Give information separately for exposed and unexposed groups.</footnote>

<footnote n="35">35 DNA: does not applied, suppl: supplementary material.</footnote>
<table><caption>36 Supplementary Table 7: ICD-10 and ATC codes used for identifying PD cases among farm managers depending on the analysis</caption><thead><tr><th>Data origin</th><th>Classification system</th><th>Code</th><th>Definition</th><th>Drug Indication§</th><th>Analysis</th></tr></thead><tbody><tr><td rowspan="3">Long-term illness scheme/declaration (requirement to be considered as a PD case: at least one declaration of any of these three ICD-10 codes)</td><td>ICD-10</td><td>F02</td><td>Dementia in other diseases classified elsewhere</td><td>/</td><td>MA, SA1, SA3-6, SA8, SA10-17</td></tr><tr><td>ICD-10</td><td>G20</td><td>Parkinson's disease</td><td>/</td><td>MA, SA1, SA3-6, SA8-17</td></tr><tr><td>ICD-10</td><td>G21</td><td>Secondary parkinsonism</td><td>/</td><td>SA6, SA9-11</td></tr><tr><td>Occupational disease scheme (requirement to be considered as a PD case: at least one declaration for PD induced by pesticides)</td><td>ODC</td><td>RA 58</td><td>PD induced by pesticide exposure</td><td>/</td><td>MA, SA1-6, SA8, SA11-17</td></tr><tr><td rowspan="20">Drug reimbursement for PD (requirement to be considered as a PD case: at least one reimbursement of any of these ATC codes)</td><td>ATC</td><td>N04BA01</td><td>Levodopa</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BA02</td><td>Levodopa and decarboxylase inhibitor</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BA03</td><td>Levodopa, decarboxylase inhibitor and COMT inhibitor</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BD01</td><td>Selegiline</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BD02</td><td>Rasagiline</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BX01</td><td>Tolcapone</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BX02</td><td>Entacapone</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BC01</td><td>Bromocriptine</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BC04</td><td>Ropinirole</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BC07</td><td>Apomorphine</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04BC08</td><td>Piribedil</td><td>PD only</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04AA01</td><td>Trihexyphenidyl*</td><td>PD, parkinsonism</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04AA02</td><td>Biperiden*</td><td>PD, parkinsonism</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N04AA12</td><td>Tropatepine*</td><td>PD, parkinsonism</td><td>MA, SA1, SA3-5, SA7-8, SA11-17</td></tr><tr><td>ATC</td><td>N02CA07</td><td>Lisuride</td><td>PD, hyperprolactinemia disorders</td><td>SA11</td></tr><tr><td>ATC</td><td>N04BB01</td><td>Amantadine</td><td>PD, influenza A, postherpetic neuralgia</td><td>SA11</td></tr><tr><td>ATC</td><td>N04BC05</td><td>Pramipexole</td><td>PD, Restless Legs Syndrome</td><td>SA11</td></tr><tr><td>ATC</td><td>N04BC09</td><td>Rotigotine</td><td>PD, Restless Legs Syndrome</td><td>SA11</td></tr></tbody></table>

37 Abbreviations: ATC: Anatomical Therapeutic Chemical classification system, COMT: catechol-O-methyltransferase, ICD-10: 10th revision of the International Statistical Classification of Diseases and Related Health Problems, MA: main analysis, ODCs: occupational diseases covered under workers' compensation statutes, PD: Parkinson's disease, SA: sensitivity analysis.

38

39 * Farm managers only on anticholinergics and neuroleptics (drug-induced parkinsonism) were not considered.

40 § According to the Vidal (https://www.vidal.fr/), Thesorimed (https://theso.prod-un.thesorimed.org/monographie) and BCB databases (https://www.bcb.fr/v2/app/recherche.jsp).
41 Supplementary Table 8: List of covariates considered in the statistical analyses

<table><thead><tr><th>Dependent variable</th><th>Modality</th></tr></thead><tbody><tr><td>PD diagnosis (LTI declaration or drug reimbursement)</td><td>2 categories: yes or no</td></tr><tr><td>Time to first PD insurance declaration, or drug reimbursement</td><td>continuous</td></tr><tr><td><strong>Independent variables</strong></td><td></td></tr><tr><td>Activity</td><td>2 categories: yes or no</td></tr><tr><td>Sex<sup>†</sup></td><td>2 categories: female or male</td></tr><tr><td>Age</td><td>continuous</td></tr><tr><td>First year of the farm's establishment, years</td><td>continuous</td></tr><tr><td>Median yearly farm surface, hectares</td><td>continuous</td></tr><tr><td>Median yearly insurance premium, euros</td><td>continuous</td></tr><tr><td>Number of associates</td><td>continuous</td></tr><tr><td>Unemployment status</td><td>2 categories: never unemployed or had been unemployed at least once over the period 2002-2016</td></tr><tr><td>Number of farms</td><td>2 categories: 1 or &gt; 1</td></tr><tr><td>Family status</td><td>2 categories: single or as a couple</td></tr><tr><td>Partner work status</td><td>2 categories: perform or do not perform task to help farm manager</td></tr><tr><td>Having a secondary farming activity<sup>†</sup></td><td>2 categories: yes or no</td></tr><tr><td>Number of pre-existing medical comorbidities</td><td>continuous</td></tr><tr><td>Farm location</td><td>96 categories: 96 metropolitan French administrative geographical areas (departments)</td></tr></tbody></table>

42 *Abbreviations*: LTI: long-term illness, PD: Parkinson's disease.

43 Categorical variables with more than two categories were converted to binary variables by applying the one-hot encoding approach, with the exclusion of one category to prevent multicollinearity.

44 For each binary variable (e.g., activity), the reference was set as the ‘no’ or ‘0’ modality.

45 *The analysis was adjusted on sex only for “both sexes”; otherwise, sex was used for subgroup analyses.

46 † A secondary farming activity is defined as a farming activity (e.g., grassland farming) that a FM can perform in addition to its main activity (e.g., ovine farming). The nature of the secondary

47 activity is, however, unknown.
48 Sensitivity analysis

49 Thirteen sensitivity analyses (SAs) were undertaken to test hypotheses and address potential sources of bias.

50

51 Sensitivity analysis 1 - excluding PD cases diagnosed in 2012

52

53 To increase the likelihood that identified PD cases were incident cases, a sensitivity analysis was conducted by
54 excluding PD cases diagnosed in 2012. Similarly to the main analysis, for this sensitivity analysis, FMs were
55 considered to have PD if they had at least one LTI declaration for PD (ICD-10 code G20 or F02), one ODC
56 declaration for PD, or one reimbursement of any drugs solely used to treat PD (i.e., all antiparkinsonian agents,
57 with the exception of pramipexole, rotigotine, amantadine, and lisuride) (Supplementary Table 7). However, FMs
58 only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced
59 parkinsonism) were not considered PD cases.

60 The highest-risk group included FMs engaged in crop farming, mixed cattle farming, dairy farming, truck farming, pig farming, viticulture, unspecified and mixed farming (Supplementary Table 4, Supplementary Figure 1). A positive trend was also observed for ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, and stud farming. No farming activities exhibit a sex difference.
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<figure><img src="image_9.png" /><figcaption>Supplementary Figure 1: Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 1</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.

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In this sensitivity analysis, the PD case identification was based solely on ODC declarations for PD induced by pesticide exposures, knowing that compensation for PD as an occupational disease among long-term pesticide users, with causality presumption, only started in May 2012 in France¹. The highest-risk group included FMs engaged in mixed cattle farming, pig farming, viticulture, and unspecified and mixed farming (Supplementary Table 4, Supplementary Figure 2). By contrast, no activity had reduced HRs, probably because of the small number of PD cases (317 in total) identified for this analysis. Viticulture was the only farming activity that exhibited a sex difference, with a PD risk around two times higher in females than males.
<figure><img src="image_10.png" /><figcaption>**Supplementary Figure 2:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 2</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
Because PD is not common before age 40¹⁻³, one SA was restricted to FMs who were 40 years and older (SA3), another one to FMs who were 50 years and older (SA4), and a third to FMs who were 60 years and older (SA5), respectively. Similarly to the main analysis, for these sensitivity analyses, FMs were considered to have PD if they had at least one LTI declaration for PD (ICD-10 code G20 or F02), one ODC declaration for PD, or one reimbursement of any drugs solely used to treat PD (i.e., all antiparkinsonian agents, with the exception of pramipexole, rotigotine, amantadine, and lisuride) (Supplementary Table 7). However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered PD cases.

Regarding the sensitivity analysis restricted to FMs ≥ 40 years (SA3), the highest-risk group included FMs engaged in mixed cattle farming, pig farming, dairy farming, cow farming, crop farming, unspecified and mixed farming, truck farming, and fruit arboriculture (Supplementary Table 4, Supplementary Figure 3). A positive trend was also observed for viticulture and ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, shellfish farming and rural craftsperson. Dairy farming and stud farming were the only activities exhibiting a sex difference, with male FMs having a higher risk than females.

Regarding the sensitivity analysis restricted to FMs ≥ 50 years (SA4), the highest-risk group included FMs engaged in mixed cattle farming, dairy farming, crop farming, pig farming, cow farming, and truck farming (Supplementary Table 4, Supplementary Figure 4). A positive trend was also observed for viticulture and unspecified and mixed farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, shellfish farming and rural craftsperson. Four activities exhibited a sex difference, with male FMs having a higher risk than females for dairy farming, pig farming, and stud farming; while female FMs engaged in crop farming had a higher risk than males.

Regarding the sensitivity analysis restricted to FMs ≥ 60 years (SA5), the highest-risk group included FMs engaged in dairy farming, mixed cattle farming, pig farming, fruit arboriculture, cow farming, and crop farming (Supplementary Table 4, Supplementary Figure 5). A positive trend was also observed for viticulture and truck farming. By contrast, the lowest-risk group included FMs engaged in stud farming and small animal farming. Three activities exhibited a sex difference, with male FMs having a higher risk than females for fruit arboriculture, pig farming, and stud farming.
<figure><img src="image_11.png" /><figcaption>**Supplementary Figure 3:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 3</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.

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<figure><img src="image_12.png" /><figcaption>**Supplementary Figure 4:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 4</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_13.png" /><figcaption>**Supplementary Figure 5:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 5</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
To address potential case misclassification due to the potential lack of specificity of health data, ten sensitivity analyses with varying stringency were conducted (Supplementary Table 7). For instance, the LTI declaration for G21 (secondary Parkinsonism) was considered as PD cases in several sensitivity analyses. The rationale behind this decision is that, sometimes, differentiating primary Parkinson's disease (G20) from secondary Parkinsonism (G21) can be challenging, especially in the early stages. If only G20 is used to identify Parkinson's disease cases, there may be a risk of under-capturing relevant cases that are initially coded as G21 but may later transition to G20. This inclusion could avoid missing cases where secondary parkinsonism was initially misdiagnosed but later identified as primary PD, or where a person exhibits features of both.

The first one, SA2, was described previously.

For SA6, FMs were considered PD cases if they had either at least one LTI (F02, G20, G21) or ODC declaration. The highest-risk group included FMs engaged in crop farming, mixed cattle farming, dairy farming, pig farming, fruit arboriculture, truck farming, unspecified and mixed farming, and cow farming (Supplementary Table 4, Supplementary Figure 6). By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, and stud farming. Viticulture was the only farming activity exhibiting a sex difference, with male FMs having a higher risk than females.

For SA7, FMs were considered PD cases if they had at least one drug reimbursement of any anti-Parkinson drugs solely used to treat PD. However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases. The highest-risk group included FMs engaged in mixed cattle farming, dairy farming, crop farming, cow farming, pig farming, truck farming, unspecified and mixed farming, and viticulture (Supplementary Table 4, Supplementary Figure 7). A positive trend was also observed for ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, shellfish farming and rural craftsperson. Pig farming was the only farming activity exhibiting a sex difference, with female FMs having a higher risk than males.

For SA8, FMs who had at least one drug reimbursement of any anti-Parkinson drugs solely used to treat PD and also a LTI declaration for Alzheimer's disease and other related dementias (ADRD) that corresponded to ICD-10 codes F00 ("dementia in Alzheimer's disease"), F01 ("vascular dementia"), F02 ("dementia in other diseases classified elsewhere"), F03 ("unspecified dementia"), and G30 ("Alzheimer's disease") were not considered PD cases. In addition, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases. The highest-risk group included FMs engaged in mixed cattle farming, crop farming, dairy farming, pig farming, cow farming, unspecified and mixed farming, and truck farming (Supplementary Table 4, Supplementary Figure 8). A positive trend was also observed for viticulture and ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping
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and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, shellfish farming and rural craftsperson. No activity exhibited a sex difference.

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For SA9, FMs were considered PD cases if they had one LTI declaration with either the ICD-10 code G20 ("Parkinson's disease") or G21 ("secondary parkinsonism"). The highest-risk group included FMs engaged in crop farming, mixed cattle farming, dairy farming, pig farming, fruit arboriculture, cow farming, unspecified and mixed farming, and truck farming (Supplementary Table 4, Supplementary Figure 9). A positive trend was also observed for viticulture and sylviculture. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, and stud farming. Only viticulture exhibited a sex difference, with male FMs having a higher risk than females.

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For SA10, FMs were considered PD cases if they had one LTI declaration with either the ICD-10 code F02, G20, or G21. The highest-risk group included FMs engaged in crop farming, mixed cattle farming, dairy farming, fruit arboriculture, viticulture, pig farming, cow farming, unspecified and mixed farming, and truck farming (Supplementary Table 4, Supplementary Figure 10). A positive trend was also observed for sylviculture. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, and stud farming. Only viticulture exhibited a sex difference, with male FMs having a higher risk than females.

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For SA11, FMs were considered PD cases if they had either at least one LTI declaration (F02, G20, G21), one ODC declaration, or one drug reimbursement of any anti-Parkinson drugs even, if the drug is not solely used for PD. The highest-risk group included FMs engaged in crop farming, dairy farming, mixed cattle farming, pig farming, fruit arboriculture, cow farming, viticulture, unspecified and mixed farming, and truck farming (Supplementary Table 4, Supplementary Figure 11). A positive trend was also observed for ovine and caprine farming and wood production. By contrast, the lowest-risk group included FMs engaged in small animal farming, training, dressage and riding clubs, stud farming, and rural craftsperson. Three activities exhibited a sex difference, with male FMs having a higher risk than females for viticulture and stud farming; while female FMs engaged in mixed cattle farming had a higher risk than males.

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For SA12, FMs were considered PD cases if they had either at least one LTI (F02, G20), one ODC declaration, or two drug reimbursements of any anti-Parkinson drugs solely used to treat PD. However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases. The highest-risk group included FMs engaged in crop farming, dairy farming, mixed cattle farming, cow farming, pig farming, truck farming, and fruit arboriculture (Supplementary Table 4, Supplementary Figure 12). A positive trend was also observed for unspecified and mixed farming, viticulture, and ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, and rural craftsperson. Viticulture and stud farming were the only two farming activities exhibiting a sex difference, with male FMs having a higher risk than females.

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For SA13, FMs were considered PD cases if they had either at least one LTI (F02, G20), one ODC declaration, or three drug reimbursements of any anti-Parkinson drugs solely used to treat PD. However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases. The highest-risk group included FMs engaged in crop farming, dairy farming, mixed cattle farming, pig farming, cow farming, truck farming, and fruit arboriculture (Supplementary Table 4, Supplementary Figure 13). A positive trend was also observed for unspecified and mixed farming, viticulture, and ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, and stud farming. A negative trend was also observed for shellfish farming. Viticulture, stud farming and gardening, landscaping and reforestation companies were the only farming activities exhibiting a sex difference, with male FMs having a higher risk than females, with the exception of gardening, landscaping and reforestation companies.

For SA14, FMs were considered PD cases if they had either at least one LTI (F02, G20), one ODC declaration, or six drug reimbursements of any anti-Parkinson drugs solely used to treat PD. However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered as PD cases. The highest-risk group included FMs engaged in dairy farming, crop farming, mixed cattle farming, pig farming, cow farming, fruit arboriculture, and unspecified and mixed farming (Supplementary Table 4, Supplementary Figure 14). A positive trend was also observed for truck farming, and viticulture. By contrast, the lowest-risk group included FMs engaged in poultry and rabbit farming, gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, and stud farming. Viticulture and training, dressage, riding clubs were the only farming activities exhibiting a sex difference, with male FMs having a higher risk than females. A trend was also observed for unspecified specialized farming and stud farming, with risk higher for males than females. In contrast, a trend with higher risk among females than males was observed for both truck farming and shellfish farming.
<figure><img src="image_14.png" /><figcaption>**Supplementary Figure 6:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 6</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.

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<figure><img src="image_15.png" /><figcaption>**Supplementary Figure 7:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 7</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_16.png" /><figcaption>**Supplementary Figure 8:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 8</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<table><caption>**Supplementary Figure 9:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 9</caption><thead><tr><th></th><th>Both sexes</th><th>Female</th><th>Male</th></tr></thead><tbody><tr><td>Crop farming (e.g., wheat, corn, and industrial grower)</td><td>n = 319341, m = 1115</td><td>n = 106825, m = 429</td><td>n = 212516, m = 686</td></tr><tr><td>Mixed cattle farming</td><td>n = 31514, m = 119</td><td>n = 8288, m = 34</td><td>n = 23226, m = 85</td></tr><tr><td>Dairy farming</td><td>n = 163823, m = 495</td><td>n = 50749, m = 168</td><td>n = 113074, m = 327</td></tr><tr><td>Pig farming</td><td>n = 13828, m = 43</td><td>n = 3977, m = 13</td><td>n = 9851, m = 30</td></tr><tr><td>Fruit arboriculture</td><td>n = 25209, m = 82</td><td>n = 8022, m = 26</td><td>n = 17187, m = 56</td></tr><tr><td>Truck farming, floriculture/flower-growing</td><td>n = 43881, m = 115</td><td>n = 13430, m = 42</td><td>n = 30451, m = 73</td></tr><tr><td>Unspecified and mixed farming (e.g., polyculture, mixed farming, diversified farming)</td><td>n = 129633, m = 393</td><td>n = 40172, m = 138</td><td>n = 89461, m = 255</td></tr><tr><td>Cow farming</td><td>n = 113914, m = 345</td><td>n = 33859, m = 115</td><td>n = 80055, m = 230</td></tr><tr><td>Sylviculture/forestry (e.g., thinning, pruning)</td><td>n = 2172, m = 10</td><td>n = 363, m = 4</td><td>n = 1809, m = 6</td></tr><tr><td>Viticulture</td><td>n = 123514, m = 368</td><td>n = 43746, m = 116</td><td>n = 79768, m = 252</td></tr><tr><td>Ovine and caprine farming</td><td>n = 49281, m = 110</td><td>n = 17617, m = 30</td><td>n = 31664, m = 80</td></tr><tr><td>Agricultural work companies (e.g., pesticide applications, harvest reaping)</td><td>n = 15672, m = 23</td><td>n = 1890, m = 4</td><td>n = 13782, m = 19</td></tr><tr><td>Company representative/authorized representative</td><td>n = 1940, m = 4</td><td>n = 1504, m = 3</td><td>n = 436, m = 1</td></tr><tr><td>Wood production (e.g., lopping)</td><td>n = 11470, m = 16</td><td>n = 305, m = 0</td><td>n = 11165, m = 16</td></tr><tr><td>Unspecified large animal farming (e.g., ostrich, llama)</td><td>n = 3030, m = 6</td><td>n = 1462, m = 2</td><td>n = 1568, m = 4</td></tr><tr><td>Poultry and rabbit farming</td><td>n = 25956, m = 31</td><td>n = 10240, m = 9</td><td>n = 15716, m = 22</td></tr><tr><td>Garden center/tree nursery</td><td>n = 5443, m = 8</td><td>n = 1443, m = 1</td><td>n = 4000, m = 7</td></tr><tr><td>Training, dressage, riding clubs</td><td>n = 14998, m = 11</td><td>n = 6694, m = 0</td><td>n = 8304, m = 11</td></tr><tr><td>Unspecified specialized farming (e.g., herbs, mushrooms)</td><td>n = 6693, m = 7</td><td>n = 2447, m = 0</td><td>n = 4246, m = 7</td></tr><tr><td>Shellfish farming (e.g., oyster farming, scallop aquaculture)</td><td>n = 3833, m = 4</td><td>n = 737, m = 2</td><td>n = 3096, m = 2</td></tr><tr><td>Gardening, landscaping and reforestation companies</td><td>n = 49291, m = 36</td><td>n = 2571, m = 1</td><td>n = 46720, m = 35</td></tr><tr><td>Stud farming</td><td>n = 17297, m = 25</td><td>n = 7494, m = 5</td><td>n = 9803, m = 20</td></tr><tr><td>Unspecified small animal farming (e.g., frogs, snails, bees)</td><td>n = 20025, m = 13</td><td>n = 8649, m = 1</td><td>n = 11376, m = 12</td></tr><tr><td>Salt works/salt evaporation pond</td><td>n = 983, m = 0</td><td>n = 221, m = 0</td><td>n = 762, m = 0</td></tr><tr><td>Stationary sawmill (e.g., edging, trimming, decking, debarking)</td><td>n = 796, m = 1</td><td>n = 55, m = 0</td><td>n = 741, m = 1</td></tr><tr><td>Rural craftsperson (e.g., mason, mechanics)</td><td>n = 7700, m = 1</td><td>n = 283, m = 0</td><td>n = 7417, m = 1</td></tr></tbody></table>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_17.png" /><figcaption>**Supplementary Figure 10:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 10</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis) while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease, while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_18.png" /><figcaption>**Supplementary Figure 11:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 11</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_19.png" /><figcaption>**Supplementary Figure 12:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 12</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_20.png" /><figcaption>**Supplementary Figure 13:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 13</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
<figure><img src="image_21.png" /><figcaption>**Supplementary Figure 14:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 14</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
In the main analysis and other sensitivity analyses, we considered farm managers to be exposed to a given farming activity if they had been engaged in this activity at least once between 2002 and 2016 (1 yearly declaration to MSA). Instead, in this sensitivity analysis, each farm manager was considered to be engaged solely in their longest farming activity ever practiced in terms of number of years. In case the longest exposure corresponded to several farming activities (e.g., 4 years as a dairy farmer, then 4 years as crop farmer), only the oldest farming activity (e.g., dairy farming in the previous example) was considered.

Similarly to the main analysis, for this sensitivity analysis, FMs were considered to have PD if they had at least one LTI declaration for PD (ICD-10 code G20 or F02), one ODC declaration for PD, or one reimbursement of any drugs solely used to treat PD (i.e., all antiparkinsonian agents, with the exception of pramipexole, rotigotine, amantadine, and lisuride) (Supplementary Table 7). However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered PD cases.

The highest-risk group included FMs engaged in mixed cattle farming, dairy farming, crop farming, fruit arboriculture, cow farming, sylviculture, truck farming, pig farming, and viticulture (Supplementary Table 4, Supplementary Figure 15). By contrast, the lowest-risk group included FMs engaged in gardening, landscaping and reforestation companies, small animal farming, training, dressage and riding clubs, stud farming, and rural craftsperson. Two activities exhibited a sex difference, with male FMs having a higher risk than females for viticulture and stud farming.
<figure><img src="image_22.png" /><figcaption>**Supplementary Figure 15:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 15</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
Because we did not have smoking data, we performed a sensitivity analysis adjusting for smoking. To that end, we randomly generated the smoking status (current smokers/non-smokers) by assuming that there was a prevalence of current smokers of 19% for crop farming, 9% for cattle farming, 6% for pig farming, 15% for poultry farming, and 18% for all other farming activities. These prevalences were based on a cross-sectional study conducted in 3787 French farmers affiliated with MSA in 2013⁴.

Similarly to the main analysis, for this sensitivity analysis, FMs were considered to have PD if they had at least one LTI declaration for PD (ICD-10 code G20 or F02), one ODC declaration for PD, or one reimbursement of any drugs solely used to treat PD (i.e., all antiparkinsonian agents, with the exception of pramipexole, rotigotine, amantadine, and lisuride) (Supplementary Table 7). However, FMs only on anticholinergics (trihexyphenidyl, biperiden, and tropatepine) and neuroleptics (drug-induced parkinsonism) were not considered PD cases.

The highest-risk group included FMs engaged in crop farming, mixed cattle farming, dairy farming, pig farming, cow farming, truck farming, fruit arboriculture, unspecified and mixed farming, and viticulture (Supplementary Table 4, Supplementary Figure 16). A positive trend was also observed for ovine and caprine farming. By contrast, the lowest-risk group included FMs engaged in small animal farming, training, dressage and riding clubs, stud farming, and rural craftsperson. Stud farming was the only farming activity exhibiting a sex difference, with male FMs having a higher risk than females.
<figure><img src="image_23.png" /><figcaption>**Supplementary Figure 16:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 16</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, smoking, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
To ensure that any observed effect may be specific to the exposure being studied (i.e., farming-related factors potentially associated with Parkinson's disease) and not confounded by general health or morbidity, SA17 was restricted to farm managers (non-PD cases) with no known disease or only those with at least one of the following health conditions:

* Metabolic disorders (ICD-10 codes: E70-E88; LTI #17)

* Diabetes mellitus (ICD-10 codes: E08-E13; LTI: #8)

* Overweight, obesity, and other hyperalimentation (ICD-10 codes: E65-E68)

* Schizophrenia (ICD-10 codes: F20-F29)

* Diseases of the eye and adnexa (ICD-10 codes: H00-H59)

* Diseases of the ear and mastoid process (ICD-10 codes: H60-H95)

* Hypertension (ICD-10 code: I10; LTI #12)

* Coronary and ischemic heart diseases (ICD-10 codes: I20-I25; LTI: #3, 5 and 13)

* Cerebrovascular disease (ICD-10 codes: I60-I69; LTI: #1)

* Arthrosclerosis (ICD-10 code: I70)

* Anemia (ICD-10 codes: D63, D64)

* Gout (ICD-10 code: M10)

* Psoriasis (ICD-10 code: L40)

* Hepatitis (ICD-10 codes: B15-B19)

* HIV (LTI #7)

* Hypercholesterolemia (≥ 3 drug reimbursements; ATC codes: C10AA, C10AB, C10B)

* Hemoglobinopathies, chronic constitutional, or severe acquired hemolysis (LTI #10)

* Hemophilias or severe constitutional disorders of hemostasis (LTI #11)

* Cystic fibrosis (LTI #18)

* Severe chronic kidney disease or primary nephrotic syndrome (LTI #19)

* Medullary insufficiency or other chronic cytopenias (LTI #2)

* Progressive idiopathic structural scoliosis (LTI #26)

* Organ transplant consequences (LTI #28)

* Complicated bilharziasis (LTI #4)

* Chronic active liver disease or cirrhosis (LTI #6)

* No known disease

In summary, SA17 aimed to ensure that the farm managers without Parkinson's disease might exhibit other health conditions that are unrelated to the exposure under investigation. This approach may help clarify whether the exposure has a specific influence on Parkinson's risk, as opposed to a general health effect that might predispose individuals to various unrelated diseases. Nonetheless, while these unrelated diseases were carefully selected based on expert judgment, their inclusion may have introduced other biases or confounding factors into the model that
413 are challenging to evaluate. The criteria for identifying Parkinson's disease cases remained the same as in the
414 primary analysis.

415
416 Results from SA17 showed that the highest-risk group included FMs engaged in dairy farming, mixed cattle
417 farming, pig farming, crop farming, fruit arboriculture, cow farming, truck farming, unspecified and mixed
418 farming, wood production, and tree nursery (Supplementary Table 4, Supplementary Figure 17). A positive trend
419 was also observed for ovine and caprine farming, viticulture, poultry and rabbit farming, and gardening,
420 landscaping and reforestation companies. By contrast, the lowest-risk group included FMs engaged in small animal
421 farming, training, dressage and riding clubs, stud farming, and rural craftsperson. Tree nursery, stud farming and
422 gardening, landscaping and reforestation companies were the only farming activities exhibiting a sex difference,
423 with male FMs having a higher risk than females.
<figure><img src="image_24.png" /><figcaption>**Supplementary Figure 17:** Farming activities and risks of Parkinson's disease, TRACTOR project, 2012-2016 – sensitivity analysis 17</figcaption></figure>

Multivariable Cox regression models for Parkinson's disease according to each agricultural activity (y-axis) are displayed when the number of exposed cases was sufficient (m ≥ 3). The hazard ratio is represented by a point (x-axis), while error bars represent the 95% confidence interval. The red error bars refer to a higher risk of Parkinson's disease while the green error bars represent a lower risk of Parkinson's disease. The black error bars indicate situations where there is no difference in risk of Parkinson's disease among the farm managers engaged in the considered activity compared to the population of farm managers not performing the considered activity. All analyses were adjusted for sex (for "both sexes" only), age, first year of the farm's establishment, median farm surface, number of associates, unemployment status, total number of farms, family status, partner work status, farm location, number of comorbidities, and performing a secondary farming activity. n, number of exposed farm managers; m, number of exposed Parkinson's disease cases.
432 References

433

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437 2. Aloizou, A. M. et al. Pesticides, cognitive functions and dementia: A review. *Toxicol. Lett.* **326**, 31-51 (2020).

439 3. Bjørklund, G., Dadar, M., Chirumbolo, S., & Aaseth, J. The Role of Xenobiotics and Trace Metals in Parkinson's Disease. *Mol. Neurobiol.* **57** (3), 1405-1417 (2020).

441 4. Guillien, A. et al. Prevalence and risk factors for COPD in farmers: a cross-sectional controlled study. *Eur. Respir. J.* **47** (1), 95-103 (2016).

442