**BIBLIO:** preliminary guideline for reporting bibliometric reviews of the biomedical literature

**COPD:** chronic obstructive pulmonary disease

**EHR:** electronic health record

**EMR:** electronic medical record

**FAIR:** findable, accessible, interoperable, and reusable

**FERMA:** risk factors of the rural environment and the allergic and respiratory disease

**GDP:** gross domestic product

**JEM:** job-exposure matrix

**MIAI:** Multidisciplinary Institute in Artificial Intelligence

**MSA:** Mutualité Sociale Agricole

**NOCCA:** Nordic Occupational Cancer Study

**PRISMA-ScR:** Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

**TRACTOR:** Tracking and Monitoring Occupational Risks in Agriculture

Edited by A Mavragani; submitted 05.06.24; peer-reviewed by G Wu, F Clerc; comments to author 30.09.24; revised version received 08.10.24; accepted 07.11.24; published 09.01.25

### Please cite as:

Petit P, Vuillerme N

Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024)

JMIR Public Health Surveill 2025;11:e62939

URL: https://publichealth.jmir.org/2025/1/e62939

doi: 10.2196/62939

PMID:

©Pascal Petit, Nicolas Vuillerme. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 09.01.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
# Multimedia Appendices

Title: Leveraging administrative health databases to address health challenges in farming populations: a scoping review and bibliometric analysis (1975-2024)

## Authors

Pascal Petit¹*; Nicolas Vuillerme¹,²

¹ Univ. Grenoble Alpes, AGEIS, 38000 Grenoble, France

² Institut Universitaire de France, Paris, France

## Correspondence (present address)

* Corresponding author at: Pascal Petit. Laboratoire AGEIS – Université Grenoble Alpes. Bureau 315. Bâtiment Jean Roget. UFR de Médecine. Domaine de La Merci. 38706 La Tronche Cedex, France.

E-mail address: pascal.petit@univ-grenoble-alpes.fr (P. Petit). Telephone: + 33 4 76 63 71 04.

## Table of content

Table S1. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. 3

Table S2. Criteria used for the publication selection. 4

Table S3. Bibliometric index metrics. 5

Table S4. The BIBLIO checklist for reporting the bibliometric reviews of the biomedical literature. 6

Figure S1. Publication type. 7

Figure S2. Scientific productivity in terms of publications per period of time. 8

Figure S3. Historical direct citation network. 9

Figure S4. Most prolific authors' country. 11

Table S5. Top 25 of the most cited publications. 12

Table S6. Other bibliometric analysis information. 13

Multimedia appendix 2. Publications included and analyzed. 20

Figure S5. Number of publications per country. 21

Figure S6. Main goal addressed by study. 22

Figure S7. Number of publications per cohort. 23

Figure S8. Type of digital data used. 24

Figure S9. Active data used. 25

Figure S10. Most frequent farming exposure proxy. 26

Figure S11. Most frequent health events studied. 27

Figure S12. Most frequent cancer studied. 28

Figure S13. Most frequent neurodegenerative disease studied. 29

Figure S14. Most frequent mental health issue studied. 30

Table S7. Top 50 of the most frequent keywords. 31

Figure S15. Top 5 of the most frequent keywords – growth trends. 33

Figure S16. Top ten of the most popular keywords by total number of years of appearance. 34

Figure S17. Top 10 of the most frequent keywords by time period. 35

Multimedia appendix 3. Co-occurrence between exposome-related keywords and health event-related keywords. 36

Figure S18. Keyword co-occurrence network between potential risk factor and mental health disorder keywords. 37

Figure S19. Chord diagram of keyword co-occurrence between potential risk factor and neurodegenerative disease keywords. 38

Figure S20. Keyword co-occurrence network between potential risk factor and neurodegenerative disease keywords. 39

Figure S21. Chord diagram of keyword co-occurrence between potential risk factor and autoimmune disease keywords. 40

Figure S22. Keyword co-occurrence network between potential risk factor and autoimmune disease keywords. 41

Figure S23. Chord diagram of keyword co-occurrence between potential risk factor and breast and genital organ cancer keywords. 42
<table><tr><td>60</td><td>Figure S24. Keyword co-occurrence network between potential risk factor and breast and genital organ cancer keywords.</td><td>43</td></tr><tr><td>61</td><td>Figure S25. Chord diagram of keyword co-occurrence between potential risk factor and digestive organ, lip, oral cavity and pharynx cancer keywords.</td><td>44</td></tr><tr><td>62</td><td>Figure S26. Keyword co-occurrence network between potential risk factor and digestive organ, lip, oral cavity and pharynx cancer keywords.</td><td>45</td></tr><tr><td>63</td><td>Figure S27. Chord diagram of keyword co-occurrence between potential risk factor and lymphohematopoietic and mesenchymal cancer keywords.</td><td>46</td></tr><tr><td>64</td><td>Figure S28. Keyword co-occurrence network between potential risk factor and lymphohematopoietic and mesenchymal cancer keywords.</td><td>47</td></tr><tr><td>65</td><td>Figure S29. Chord diagram of keyword co-occurrence between potential risk factor and brain, skin, ocular and endocrine gland cancer keywords.</td><td>48</td></tr><tr><td>66</td><td>Figure S30. Keyword co-occurrence network between potential risk factor and brain, skin, ocular and endocrine gland cancer keywords.</td><td>49</td></tr><tr><td>67</td><td>Figure S31. Chord diagram of keyword co-occurrence between potential risk factor and respiratory and urinary tract cancer keywords.</td><td>50</td></tr><tr><td>68</td><td>Figure S32. Keyword co-occurrence network between potential risk factor and respiratory and urinary tract cancer keywords.</td><td>51</td></tr><tr><td>69</td><td>Figure S33. Chord diagram of keyword co-occurrence between potential risk factor and infectious disease keywords.</td><td>52</td></tr><tr><td>70</td><td>Figure S34. Keyword co-occurrence network between potential risk factor and infectious disease keywords.</td><td>53</td></tr><tr><td>71</td><td>Figure S35. Chord diagram of keyword co-occurrence between potential risk factor and respiratory disorder keywords.</td><td>54</td></tr><tr><td>72</td><td>Figure S36. Keyword co-occurrence network between potential risk factor and respiratory disorder keywords.</td><td>55</td></tr><tr><td>73</td><td>Figure S37. Chord diagram of keyword co-occurrence between potential risk factor and reproductive disorder keywords.</td><td>56</td></tr><tr><td>74</td><td>Figure S38. Keyword co-occurrence network between potential risk factor and reproductive disorder keywords.</td><td>57</td></tr><tr><td>75</td><td>Figure S39. Chord diagram of keyword co-occurrence between potential risk factor and injury and work-related disease keywords.</td><td>58</td></tr><tr><td>76</td><td>Figure S40. Keyword co-occurrence network between potential risk factor and injury and work-related disease keywords.</td><td>59</td></tr><tr><td>77</td><td>Figure S41. Chord diagram of keyword co-occurrence between potential risk factor and other health event keywords.</td><td>60</td></tr><tr><td>78</td><td>Figure S42. Keyword co-occurrence network between potential risk factor and other health event keywords.</td><td>61</td></tr><tr><td>79</td><td>Figure S43. Keyword co-occurrence network between potential risk factor and mortality keywords.</td><td>62</td></tr><tr><td>80</td><td>Abbreviations</td><td>63</td></tr><tr><td>81</td><td>References</td><td>64</td></tr></table>

98

99
100 Table S1. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews

101 (PRISMA-ScR) Checklist.

102

<table><thead><tr><th>SECTION</th><th>ITEM</th><th>PRISMA-ScR CHECKLIST ITEM</th><th>REPORTED ON PAGE #</th></tr></thead><tbody><tr><td><strong>TITLE</strong></td><td></td><td></td><td></td></tr><tr><td>Title</td><td>1</td><td>Identify the report as a scoping review.</td><td>1</td></tr><tr><td><strong>ABSTRACT</strong></td><td></td><td></td><td></td></tr><tr><td>Structured summary</td><td>2</td><td>Provide a structured summary that includes (as applicable): background, objectives, eligibility criteria, sources of evidence, charting methods, results, and conclusions that relate to the review questions and objectives.</td><td>2, 3</td></tr><tr><td><strong>INTRODUCTION</strong></td><td></td><td></td><td></td></tr><tr><td>Rationale</td><td>3</td><td>Describe the rationale for the review in the context of what is already known. Explain why the review questions/objectives lend themselves to a scoping review approach.</td><td>4-7</td></tr><tr><td>Objectives</td><td>4</td><td>Provide an explicit statement of the questions and objectives being addressed with reference to their key elements (e.g., population or participants, concepts, and context) or other relevant key elements used to conceptualize the review questions and/or objectives.</td><td>7</td></tr><tr><td><strong>METHODS</strong></td><td></td><td></td><td></td></tr><tr><td>Protocol and registration</td><td>5</td><td>Indicate whether a review protocol exists; state if and where it can be accessed (e.g., a Web address); and if available, provide registration information, including the registration number.</td><td>8</td></tr><tr><td>Eligibility criteria</td><td>6</td><td>Specify characteristics of the sources of evidence used as eligibility criteria (e.g., years considered, language, and publication status), and provide a rationale.</td><td>8-10</td></tr><tr><td>Information sources*</td><td>7</td><td>Describe all information sources in the search (e.g., databases with dates of coverage and contact with authors to identify additional sources), as well as the date the most recent search was executed.</td><td>8-10</td></tr><tr><td>Search</td><td>8</td><td>Present the full electronic search strategy for at least 1 database, including any limits used, such that it could be repeated.</td><td>9</td></tr><tr><td>Selection of sources of evidence†</td><td>9</td><td>State the process for selecting sources of evidence (i.e., screening and eligibility) included in the scoping review.</td><td>8-10</td></tr><tr><td>Data charting process‡</td><td>10</td><td>Describe the methods of charting data from the included sources of evidence (e.g., calibrated forms or forms that have been tested by the team before their use, and whether data charting was done independently or in duplicate) and any processes for obtaining and confirming data from investigators.</td><td>10-14</td></tr><tr><td>Data items</td><td>11</td><td>List and define all variables for which data were sought and any assumptions and simplifications made.</td><td>10-14</td></tr><tr><td>Critical appraisal of individual sources of evidence§</td><td>12</td><td>If done, provide a rationale for conducting a critical appraisal of included sources of evidence; describe the methods used and how this information was used in any data synthesis (if appropriate).</td><td>NA</td></tr><tr><td>Synthesis of results</td><td>13</td><td>Describe the methods of handling and summarizing the data that were charted.</td><td>10-14</td></tr><tr><td><strong>RESULTS</strong></td><td></td><td></td><td></td></tr><tr><td>Selection of sources of evidence</td><td>14</td><td>Give numbers of sources of evidence screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally using a flow diagram.</td><td>14-17, supplementary materials</td></tr><tr><td>Characteristics of sources of evidence</td><td>15</td><td>For each source of evidence, present characteristics for which data were charted and provide the citations.</td><td>14-26, supplementary materials</td></tr><tr><td>Critical appraisal within sources of evidence</td><td>16</td><td>If done, present data on critical appraisal of included sources of evidence (see item 12).</td><td>NA</td></tr><tr><td>Results of individual sources of evidence</td><td>17</td><td>For each included source of evidence, present the relevant data that were charted that relate to the review questions and objectives.</td><td>14-26, supplementary materials</td></tr><tr><td>Synthesis of results</td><td>18</td><td>Summarize and/or present the charting results as they relate to the review questions and objectives.</td><td>14-39, supplementary materials</td></tr><tr><td><strong>DISCUSSION</strong></td><td></td><td></td><td></td></tr><tr><td>Summary of evidence</td><td>19</td><td>Summarize the main results (including an overview of concepts, themes, and types of evidence available), link to the review questions and objectives, and consider the relevance to key groups.</td><td>40-47</td></tr><tr><td>Limitations</td><td>20</td><td>Discuss the limitations of the scoping review process.</td><td>48, 49</td></tr><tr><td>Conclusions</td><td>21</td><td>Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications and/or next steps.</td><td>49-51</td></tr><tr><td><strong>FUNDING</strong></td><td></td><td></td><td></td></tr><tr><td>Funding</td><td>22</td><td>Describe sources of funding for the included sources of evidence, as well as sources of funding for the scoping review. Describe the role of the funders of the scoping review.</td><td>52</td></tr></tbody></table>

103 JBI = Joanna Briggs Institute; PRISMA-ScR = Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews.

104 Reviews.

105 * Where *sources of evidence* (see second footnote) are compiled from, such as bibliographic databases, social media platforms, and Web sites.

106 † A more inclusive/heterogeneous term used to account for the different types of evidence or data sources (e.g., quantitative and/or qualitative

107 research, expert opinion, and policy documents) that may be eligible in a scoping review as opposed to only studies. This is not to be confused

108 with *information sources* (see first footnote).

109 ‡ The frameworks by Arksey and O'Malley (6) and Levac and colleagues (7) and the JBI guidance (4, 5) refer to the process of data extraction

110 in a scoping review as data charting.

111 § The process of systematically examining research evidence to assess its validity, results, and relevance before using it to inform a decision.

112 This term is used for items 12 and 19 instead of "risk of bias" (which is more applicable to systematic reviews of interventions) to include and

113 acknowledge the various sources of evidence that may be used in a scoping review (e.g., quantitative and/or qualitative research, expert opinion,

114 and policy document).

115 Peters MDJ, Godfrey C, McInerney P, BaldiniSoares C, Khalil H, Parker D. Scoping reviews. In: Aromataris E, Munn Z, eds. Joanna Briggs Institute Reviewer's Manual. Adelaide, Australia: Joanna Briggs Inst; 2017.

116 Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8:19-32. doi: 10.1080/1364557032000119616

117 Peters MD, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid Based Healthc. 2015;13(3):141-146. doi:10.1097/XEB.0000000000000050

118 Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5:69. Published 2010 Sep 20. doi:10.1186/1748-5908-5-69.

119 From: Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA Extension for Scoping Reviews (PRISMA ScR):

123 Checklist and Explanation. Ann Intern Med. 2018;169:467-473. doi: 10.7326/M18-0850.
<table><caption>**Table S2.** Criteria used for the publication selection.</caption><thead><tr><th rowspan="2">Question</th><th rowspan="2">Description</th><th colspan="2">Answer</th></tr><tr><th>no</th><th>yes/can't tell</th></tr></thead><tbody><tr><td colspan="4"><strong>Stage 1: Record language</strong></td></tr><tr><td>Q<sub>11</sub></td><td>Is the record written in English or French?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>1</sub> = Q<sub>11</sub>; Record eligible for stage 2 if S<sub>1</sub> > 0</td></tr><tr><td colspan="4"><strong>Stage 2: Screening record title</strong></td></tr><tr><td>Q<sub>21</sub></td><td>Does the title mention terms related to administrative health data?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>22</sub></td><td>Does the title mention terms related to humans?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>23</sub></td><td>Does the title mention terms related to farming?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>24</sub></td><td>Does the title mention terms related to public health or epidemiological research?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>2</sub> = Q<sub>21</sub> × Q<sub>22</sub> × Q<sub>23</sub> × Q<sub>24</sub>; Record eligible for stage 3 if S<sub>2</sub> = 1</td></tr><tr><td colspan="4"><strong>Stage 3: Screening record abstract</strong></td></tr><tr><td>Q<sub>31</sub></td><td>Does the abstract describe an analysis using administrative health database?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>32</sub></td><td>Does the abstract describe an analysis conducted/related to humans?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>33</sub></td><td>Does the abstract describe an analysis related to farming?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>34</sub></td><td>Does the abstract describe an analysis related to public health or epidemiological research?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>3</sub> = Q<sub>31</sub> × Q<sub>32</sub> × Q<sub>33</sub> × Q<sub>34</sub>; Record eligible for stage 4 if S<sub>3</sub> = 1</td></tr><tr><td colspan="4"><strong>Stage 4: Screening record content</strong></td></tr><tr><td>Q<sub>41</sub></td><td>Does the record describe an analysis using administrative health database?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>42</sub></td><td>Does the record describe an analysis conducted/related to humans?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>43</sub></td><td>Does the record describe an analysis conducted/related to farming population?</td><td>0</td><td>1</td></tr><tr><td>Q<sub>44</sub></td><td>Does the record describe an analysis related to public health or epidemiological research?</td><td>0</td><td>1</td></tr><tr><td colspan="4">S<sub>4</sub> = Q<sub>41</sub> × Q<sub>42</sub> × Q<sub>43</sub> × Q<sub>44</sub>; Record eligible for stage 4 if S<sub>4</sub> = 1</td></tr><tr><td colspan="4"><strong>Score = S<sub>1</sub> × S<sub>2</sub> × S<sub>3</sub> × S<sub>4</sub>; Record included and analyzed if score ≥ 1</strong></td></tr></tbody></table>

Note: Q: question, S: score.
<caption>Table S3. Bibliometric index metrics.</caption>

• h index

The h index attempts to measure both the productivity and citation impact of the published body of work of an entity (e.g., author, institution, journal) [1,2]. It refers to the total number of publications by a particular entity with at least the same number of citations.

• m index

The m index is calculated by dividing the h index by the number of years of an entity's productive life (e.g., researcher) [1].

• g index

The g index of an entity corresponds to the largest number g such that the top g publications have at least g² or more citations together [2].

• Y index

The Y index refers to the sum of both the total number of first-authored publications and the total number of corresponding-author publications [3].

• Dominance factor

The dominance factor (DF) refers, for a particular researcher, to the proportion of multi-authored publications as specific author's rank to the total number of multi-authored publications [4].

The dominance factor for being a first/primary author (DF<sub>first</sub>) refers to the proportion of multi-authored publications as a first author to the total number of multi-authored publications. It is calculated as follows:

$$DF_{first} = \frac{\text{number of multi - authored papers as first author}}{\text{total number of multi - authored papers}} \times 100$$

The dominance factor for being a last/senior author (DF<sub>last</sub>) refers to the proportion of multi-authored publications as a last author to the total number of multi-authored publications. It is calculated as follows:

$$DF_{last} = \frac{\text{number of multi - authored papers as last author}}{\text{total number of multi - authored papers}} \times 100$$

• Fractionalized frequency

The fractional frequency (FF) intends to reflect/measure an author's contribution. For one publication, it is calculated as follows:

$$FF_i = \frac{1}{\text{total number of authors in the paper}_i}$$

For a given researcher, the FF corresponds to:

$FF_{author} = \sum_{i=1}^{n} FF_i$, where n refers to the total number of publications for which a researcher was an author.

• Annual growth rate

The annual growth rate refers to the variable's change in percentage as a year-over-year statistic [5], and is calculated as follows:

$$Annual Growth Rate = \frac{(end value - first value)}{first value} \times 100$$
<table><caption>182 Table S4. The BIBLIO checklist for reporting the bibliometric reviews of the biomedical literature.</caption><thead><tr><th>Section/Topic</th><th>Item No.</th><th>Checklist item</th><th>Reported on page No.</th></tr></thead><tbody><tr><td><strong>Title</strong></td><td></td><td></td><td></td></tr><tr><td>Identification</td><td>1</td><td>Identify the report as a bibliometric review in the title.</td><td>1</td></tr><tr><td>Issues/topics</td><td>2</td><td>Indicate the key issues/topics under investigation and coverage of time period.</td><td>1</td></tr><tr><td><strong>Abstract</strong></td><td></td><td></td><td></td></tr><tr><td>Structured summary</td><td>3</td><td>Structured summary including (as applicable): background, methods, results (key findings) and conclusions.</td><td>2, 3</td></tr><tr><td><strong>Introduction/Background</strong></td><td></td><td></td><td></td></tr><tr><td>Justification/ Rationale/ Explanation</td><td>4</td><td>Present review of existing knowledge and epidemiological information.</td><td>4-7</td></tr><tr><td>Objectives</td><td>5</td><td>Statement of the objective (s) or question (s).</td><td>7</td></tr><tr><td><strong>Methods</strong></td><td></td><td></td><td></td></tr><tr><td>Search engines (data sources)</td><td>6</td><td>Describe all information sources (such as electronic databases, contact with study authors, trial registers or other grey literature sources).</td><td>8-10</td></tr><tr><td>Search strategy</td><td>7</td><td>Keywords and systematization criteria (date of search, language, type of document) for the search.</td><td>8-11</td></tr><tr><td>Time period</td><td>8</td><td>The period that the review covers and the justification.</td><td>9</td></tr><tr><td>Eligibility criteria</td><td>9</td><td>Describe all inclusion and exclusion criteria; languages; study design, type of publication and time period.</td><td>9, 10</td></tr><tr><td>Data refinement (data selection procedure)</td><td>10</td><td>Remove the irrelevant articles; inspection to eliminate duplicate and unrelated articles (after evaluation of the title, abstract and content).</td><td>9, 10</td></tr><tr><td>Quality assessment (optional)</td><td>11</td><td>Assessment of papers by three authors and the use of assessing checklists.</td><td>NA</td></tr><tr><td>Data synthesis</td><td>12</td><td>Describe the methods used for summarizing, handling, synthesis, tabulations or schematic displays. Describe how the data were analyzed.</td><td>10-14</td></tr><tr><td><strong>Results</strong></td><td></td><td></td><td></td></tr><tr><td>Descriptive findings (statistics)</td><td>13</td><td>- Provide details of the search and selection process in a flow diagram.<br/>- Number of citations retrieved (number of publication, year of publication, type of documents, country of publication, articles with the highest impact, most impactful authors, most impactful articles, authors with the highest production, top journals, top institutions, ...)</td><td>14-17, supplementary materials</td></tr><tr><td>Schematic map and trend</td><td>14</td><td>Summarize and/or present the schematic maps and trends using an appropriate software to present citations, journals, authors, top journals, time trends, emerging literature, and any relevant indicators (as applicable) [1-5].</td><td>27-39, supplementary materials</td></tr><tr><td>Tabulation and summarizing the findings</td><td>15</td><td>General recommendation: Studies under consideration could be summarized and organized by different subtitles and different scenarios. Regardless, results need to be presented in separate tables covering each subtitle. The followings are some options that could help to summarize the findings.<br/>Option 1:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to the study designs and main study types.<br/>Option 2:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, indicate the review type (scoping review, narrative review, systematic review, and meta-analysis) should be specified.<br/>- Summarize the findings according to outcome measures or populations. For example, see [6].<br/>Option 3:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table. Also, specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to concept [7].<br/>Option 4:<br/>- Start the presentation with a historical view [when and who first published on the topic].<br/>- Report on review papers. The result should be listed in a separate table, and also specify the review type (scoping review, narrative review, systematic review, and meta-analysis).<br/>- Summarize the findings according to different subtitles relevant to the main topic [8].</td><td>14-39, supplementary materials</td></tr><tr><td>Synthesis of findings</td><td>16</td><td>Synthesize the findings as much as possible, find the gap, and propose a model, hypothesis, etc. (if applicable).</td><td>27-39, supplementary materials</td></tr><tr><td><strong>Discussion</strong></td><td></td><td></td><td></td></tr><tr><td>Summary of evidence</td><td>17</td><td>Summarize the main findings. The findings should be presented in more "general" or "accessible" terms.</td><td>40-48</td></tr><tr><td>Interpretation</td><td>18</td><td>Include interpretation consistent with results. Explanations for observed outcomes, similarities, and differences reported would be essential.</td><td>40-48</td></tr><tr><td>Strengths and limitations</td><td>19</td><td>Discuss the strengths and limitations.</td><td>48-49</td></tr><tr><td>Conclusion(s)</td><td>20</td><td>Provide a general interpretation of the results with respect to the review questions and objectives, as well as potential implications.</td><td>49-51</td></tr></tbody></table>

<footnote n="1">1. McDougal L, Dehingia N, Cheung WW, Dixit A, Raj A. COVID-19 burden, author affiliation and women's well-being: A bibliometric analysis of COVID-19 related publications including focus on low-and middle-income countries. eClinicalMedicine 2022; 52: 101606.</footnote>

<footnote n="2">2. Henstock L, Wong R, Tsuchiya A, Spencer A. Behavioral theories that have influenced the way health state preferences are elicited and interpreted: A bibliometric mapping analysis of the time trade-off method with VOSviewer visualization. Front Health Serv 2022; 2: 848087.</footnote>

<footnote n="3">3. Bodea F, Bungau SG, Negru AP, Radu A, Tarce AG, Tit DM, et al. Exploring new therapeutic avenues for ophthalmic disorders: Glaucoma-related molecular docking evaluation and bibliometric analysis for improved management of ocular diseases. Bioengineering 2023; 10(8): 983.</footnote>

<footnote n="4">4. Sang XZ, Wang CQ, Chen W, Rong H, Hou LJ. An exhaustive analysis of post-traumatic brain injury dementia using bibliometric methodologies. Front Neurol 2023; 14: 1165059.</footnote>

<footnote n="5">5. Ramli MI, Hamzaid NA, Engkasan JP, Usman J. Respiratory muscle training: a bibliometric analysis of 60 years' multidisciplinary journey. Biomed Eng Online 2023; 22(1): 50.</footnote>

<footnote n="6">6. Akosman I, Kumar N, Mortenson R, Lans A, De La Garza Ramos R, Eleswarapu A, et al. Racial differences in perioperative complications, readmissions, and mortalities after elective spine surgery in the United States: A systematic review using AI-assisted bibliometric analysis. Glob Spine J 2023; 21925682231186759.</footnote>

<footnote n="7">7. Tavousi M, Mohammadi S, Sadighi J, Zarei F, Kermani RM, Rostami R, Montazeri A. Measuring health literacy: A systematic review and bibliometric analysis of instruments from 1993 to 2021. Plos One 2022; 17(7): e0271524.</footnote>

<footnote n="8">8. Montazeri A. Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007. J Exp Clin Cancer Res 2008; 27: 32.</footnote>

<footnote n="184">184 **Rights and permissions:** The original source of the checklist is: Montazeri A, Mohammadi S, M.Hesari P, Ghaemi M, Riazi H, Sheikhi-Mobarakeh Z. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Systematic Reviews 2023; 12: 239. doi: 10.1186/s13643-023-02410-2. The article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). A changes was made to the original checklist to add in full references to the cited sources.</footnote>

<footnote n="185">185 Sheikhi-Mobarakeh Z. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Systematic Reviews 2023; 12: 239. doi: 10.1186/s13643-023-02410-2. The article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). A changes was made to the original checklist to add in full references to the cited sources.</footnote>

<footnote n="186">186 Sheikhi-Mobarakeh Z. Preliminary guideline for reporting bibliometric reviews of the biomedical literature (BIBLIO): a minimum requirements. Systematic Reviews 2023; 12: 239. doi: 10.1186/s13643-023-02410-2. The article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). A changes was made to the original checklist to add in full references to the cited sources.</footnote>

<footnote n="187">187 Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). A changes was made to the original checklist to add in full references to the cited sources.</footnote>

<footnote>188</footnote>
<figure><img src="image_8.png" /><figcaption>189 **Figure S1.** Publication type.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>191 **Figure S2.** Scientific productivity in terms of publications per period of time.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>193 **Figure S3.** Historical direct citation network.</figcaption></figure>

194

195

196 Jouneau S, Marette S, Robert AM, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in dairy farmers: AIRBAg study. Environ Res. 2019;169:1-6.

197 doi:10.1016/j.envres.2018.10.026

198 Roux P, Guillien A, Soumagne T, et al. Smoking habits in French farmers: a cross-sectional study. BMC Public Health. 2017;17(1):166. doi:10.1186/s12889-017-4030-4

199 Alavanja MC, Dosemeci M, Samanic C, et al. Pesticides and lung cancer risk in the agricultural health study cohort. Am J Epidemiol. 2004;160(9):876-885.

200 doi:10.1093/aje/kwh290
201 Reif J, Pearce N, Fraser J. Cancer risks in New Zealand farmers. Int J Epidemiol. 1989;18(4):768-774. doi:10.1093/ije/18.4.768

202 Marescaux A, Degano B, Soumagne T, Thaon I, Laplante JJ, Dalphin JC. Impact of farm modernity on the prevalence of chronic obstructive pulmonary disease in dairy farmers.

203 Occup Environ Med. 2016;73(2):127-133. doi:10.1136/oemed-2014-102697

204 Tual S, Lemarchand C, Boulanger M, et al. Exposure to Farm Animals and Risk of Lung Cancer in the AGRICAN Cohort. Am J Epidemiol. 2017;186(4):463-472.

205 doi:10.1093/aje/kwx125.

206 Petit P, Gandon G, Chabardès S, Bonneterre V. Agricultural activities and risk of central nervous system tumors among French farm managers: Results from the TRACTOR

207 project. Int J Cancer. 2022;151(10):1737-1749. doi:10.1002/ijc.34197.

208 Piel C, Pouchieu C, Tual S, et al. Central nervous system tumors and agricultural exposures in the prospective cohort AGRICAN. Int J Cancer. 2017;141(9):1771-1782.

209 doi:10.1002/ijc.30879

210 Lemarchand C, Tual S, Levêque-Morlais N, et al. Cancer incidence in the AGRICAN cohort study (2005-2011). Cancer Epidemiol. 2017;49:175-185.

211 doi:10.1016/j.canep.2017.06.003.

212 Morrison H, Savitz D, Semenciw R, et al. Farming and prostate cancer mortality. Am J Epidemiol. 1993;137(3):270-280. doi:10.1093/oxfordjournals.aje.a116674.

213 Alavanja MC, Samanic C, Dosemeci M, et al. Use of agricultural pesticides and prostate cancer risk in the Agricultural Health Study cohort. Am J Epidemiol. 2003;157(9):800-

214 814. doi:10.1093/aje/kwg040

215 Mills PK, Yang R. Prostate cancer risk in California farm workers. J Occup Environ Med. 2003;45(3):249-258. doi:10.1097/01.jom.0000058339.05741.0c.

216 Kachuri L, Harris MA, MacLeod JS, Tjepkema M, Peters PA, Demers PA. Cancer risks in a population-based study of 70,570 agricultural workers: results from the Canadian

217 census health and Environment cohort (CanCHEC). BMC Cancer. 2017;17(1):343. doi:10.1186/s12885-017-3346-x

218 Togawa K, Leon ME, Lebailly P, et al. Cancer incidence in agricultural workers: Findings from an international consortium of agricultural cohort studies (AGRICOH). Environ

219 Int. 2021;157:106825. doi:10.1016/j.envint.2021.106825.

220 Pukkala E, Martinsen JI, Lynge E, et al. Occupation and cancer - follow-up of 15 million people in five Nordic countries. Acta Oncol. 2009;48(5):646-790.

221 doi:10.1080/02841860902913546

222 Leon ME, Schinasi LH, Lebailly P, et al. Pesticide use and risk of non-Hodgkin lymphoid malignancies in agricultural cohorts from France, Norway and the USA: a pooled

223 analysis from the AGRICOH consortium. Int J Epidemiol. 2019;48(5):1519-1535. doi:10.1093/ije/dyz017

224 Bucchi L, Nanni O, Ravaioli A, et al. Cancer mortality in a cohort of male agricultural workers from northern Italy. J Occup Environ Med. 2004;46(3):249-256.

225 doi:10.1097/01.jom.0000116804.10496.fc

226 Brownson RC, Chang JC, Davis JR, Bagby JR Jr. Occupational risk of prostate cancer: a cancer registry-based study. J Occup Med. 1988;30(6):523-526.
227 **Figure S4.** Most prolific authors’ country.

228 MPC: multi-country publication, SCP: single-country publication.

229

230

<figure><img src="image_11.png" /></figure>

231
<table><caption>232 Table S5. Top 25 of the most cited publications.</caption><thead><tr><th>Publication</th><th>Publication year</th><th>Journal</th><th>DOI</th><th>Total citation</th><th>Local citation (%)</th><th>Average citation per year</th></tr></thead><tbody><tr><td>Pukkala et al. 2009 [6]</td><td>2009</td><td>Acta Oncol</td><td>10.1080/02841860902913546</td><td>485</td><td>21 (4.33%)</td><td>30.3</td></tr><tr><td>Alavanja et al. 2003 [7]</td><td>2003</td><td>Am J Epidemiol</td><td>10.1093/aje/kwg040</td><td>283</td><td>6 (2.12%)</td><td>12.9</td></tr><tr><td>Elbaz et al. 2009 [8]</td><td>2009</td><td>Ann Neurol</td><td>10.1002/ana.21717</td><td>206</td><td>7 (3.40%)</td><td>12.9</td></tr><tr><td>Andersen et al. 1999 [9]</td><td>1999</td><td>Scand J Work Envion Health</td><td>none</td><td>206</td><td>0 (0.00%)</td><td>7.92</td></tr><tr><td>Karjalainen et al. 2001 [10]</td><td>2001</td><td>Am J Respir Crit Care Med</td><td>10.1164/ajrccm.164.4.2012146</td><td>162</td><td>0 (0.00%)</td><td>6.75</td></tr><tr><td>Vingård et al. 1991 [11]</td><td>1991</td><td>Int J Epidemiol</td><td>10.1093/ije/20.4.1025</td><td>157</td><td>1 (0.64%)</td><td>4.62</td></tr><tr><td>Alavanja et al. 2004 [12]</td><td>2004</td><td>Am J Epidemiol</td><td>10.1093/aje/kwh290</td><td>155</td><td>7 (4.52%)</td><td>7.38</td></tr><tr><td>Andreotti et al. 2018 [13]</td><td>2018</td><td>J Natl Cancer Inst</td><td>10.1093/jnci/djx233</td><td>143</td><td>0 (0.00%)</td><td>20.4</td></tr><tr><td>Wigle et al. 1990 [14]</td><td>1990</td><td>J Natl Cancer Inst</td><td>10.1093/jnci/82.7.575</td><td>136</td><td>0 (0.00%)</td><td>3.89</td></tr><tr><td>Engel et al. 2005 [15]</td><td>2005</td><td>Am J Epidemiol</td><td>10.1093/aje/kwi022</td><td>116</td><td>2 (1.72%)</td><td>5.8</td></tr><tr><td>Hemminki et al. 1981 [16]</td><td>1981</td><td>J Epidemiol Community Health</td><td>10.1136/jech.35.1.11</td><td>114</td><td>0 (0.00%)</td><td>2.59</td></tr><tr><td>Weichenthal et al. 2014 [17]</td><td>2014</td><td>Environ Health Perspect</td><td>10.1289/ehp.1307277</td><td>113</td><td>0 (0.00%)</td><td>10.3</td></tr><tr><td>Levecque et al. 2003 [18]</td><td>2003</td><td>Hum Mol Genet</td><td>10.1093/hmg/ddg009</td><td>111</td><td>0 (0.00%)</td><td>5.05</td></tr><tr><td>Morrison et al. 1993 [19]</td><td>1993</td><td>Am J Epidemiol</td><td>10.1093/oxfordjournals.aje.a116674</td><td>106</td><td>5 (4.72%)</td><td>3.31</td></tr><tr><td>Kristensen et al. 1996 [20]</td><td>1996</td><td>Scand J Work Envion Health</td><td>10.5271/sjweh.104</td><td>98</td><td>0 (0.00%)</td><td>3.38</td></tr><tr><td>Orton et al. 2011 [21]</td><td>2011</td><td>Neurology</td><td>10.1212/WNL.0b013e31820a0a9f</td><td>94</td><td>1 (1.06%)</td><td>6.71</td></tr><tr><td>Stokes et al. 1995 [22]</td><td>1995</td><td>Occup Environ Med</td><td>10.1136/oem.52.10.648</td><td>92</td><td>0 (0.00%)</td><td>3.07</td></tr><tr><td>Vukusic et al. 2007 [23]</td><td>2007</td><td>J Neurol Neurosurg Psychiatry</td><td>10.1136/jnnp.2006.101196</td><td>90</td><td>0 (0.00%)</td><td>5.00</td></tr><tr><td>Mills and Yang, 2003 [24]</td><td>2003</td><td>J Occup Environ Med</td><td>10.1097/01.jom.0000058339.05741.0c</td><td>88</td><td>5 (5.68%)</td><td>4.00</td></tr><tr><td>Eriksson and Karlsson, 1992 [25]</td><td>1992</td><td>Br J Ind Med</td><td>none</td><td>87</td><td>0 (0.00%)</td><td>2.64</td></tr><tr><td>Wiklund et al. 1989 [26]</td><td>1989</td><td>Br J Ind Med</td><td>none</td><td>87</td><td>0 (0.00%)</td><td>2.42</td></tr><tr><td>Pouchieu et al. 2018 [27]</td><td>2018</td><td>Int J Epidemiol</td><td>10.1093/ije/dyx225</td><td>86</td><td>1 (1.16%)</td><td>12.3</td></tr><tr><td>Karjalainen et al. 2003 [28]</td><td>2003</td><td>Chest</td><td>10.1378/chest.123.1.283</td><td>85</td><td>0 (0.00%)</td><td>3.86</td></tr><tr><td>Leon et al. 2019 [29]</td><td>2019</td><td>Int J Epidemiol</td><td>10.1093/ije/dyz017</td><td>85</td><td>5 (5.88%)</td><td>14.2</td></tr><tr><td>Lee et al. 2007 [30]</td><td>2007</td><td>Int J Cancer</td><td>10.1002/ijc.22635</td><td>83</td><td>2 (2.41%)</td><td>4.61</td></tr><tr><td>Reif et al. 1989 [31]</td><td>1989</td><td>Int J Epidemiol</td><td>10.1093/ije/18.4.768</td><td>83</td><td>4 (4.82%)</td><td>2.31</td></tr></tbody></table>

233 Note: DOI: digital object identifier. Please refer to the beginning of the supplemental materials for the definition of the bibliometric indices.
<caption>Table S6. Other bibliometric analysis information.</caption>

• *The most prolific countries*

Studies were led by authors from 34 countries, predominantly high-income nations, with 24.3% of studies (72/296) involving multi-country collaborations. US-based authors contributed the most publications (91/296, 30.7%), followed by France (71/296, 24.0%) and Finland (35/296, 11.8%). US authors also had the most citations (3495/9379, 37.2%), with France and Finland ranking second and third. The highest average AGRs were seen in authors from India (25%), France (15%), and the US (10.1%) (Table MA1).

Positive associations were found between the number of publications and GDP (ρ=0.46, p=5.9e-3), R&D researchers (ρ=0.62, p=1.1e-4), and cereal yield (ρ=0.41, p=0.02). In contrast, negative associations were observed with agricultural employment (ρ=-0.60, p=1.6e-4), sex-specific employment in agriculture, agricultural value added to GDP (ρ=-0.51, p=2.1e-3), as well as fertilizer consumption (ρ=-0.37, p=0.04) (Table MA2).

Countries with the highest scientific production per GDP were Iceland, Finland, and Malawi, while Iceland, Finland, and Norway led per capita and cereal production (Table MA3). The US, Norway, and Canada exhibited the highest scientific production per fertilizer consumption, and Norway, Finland, and Denmark excelled in scientific output per hectare of agricultural land. The US, France, and Finland were leaders in scientific output related to crop, food, livestock production indices, and cereal yield. Notably, the US, Sweden, and France led in output per agricultural employment, while the US, Canada, and the UK had the highest scientific production relative to agriculture, forestry, and fishing value added to GDP.

<table><caption>Table MA1. The most productive author's countries.</caption><thead><tr><th>Country</th><th>Continent</th><th>Publication period</th><th>Number of publications (%)</th><th>Total citation</th><th>Local citation (%)</th><th>Average citation per year</th><th>Mean AGR</th><th>Fractionalized frequency (%)</th><th>h-index</th><th>g-index</th><th>m-index</th></tr></thead><tbody><tr><td>USA</td><td>North America</td><td>1981-2024</td><td>91 (30.7%)</td><td>3495</td><td>81 (2.32%)</td><td>81.3</td><td>10.1</td><td>72.6</td><td>37</td><td>57</td><td>0.841</td></tr><tr><td>France</td><td>Europe</td><td>1998-2024</td><td>71 (24.0%)</td><td>1762</td><td>69 (3.92%)</td><td>65.3</td><td>15.0</td><td>60.3</td><td>22</td><td>40</td><td>0.815</td></tr><tr><td>Finland</td><td>Europe</td><td>1980-2024</td><td>35 (11.8%)</td><td>1429</td><td>30 (2.10%)</td><td>37.2</td><td>-16.7</td><td>21.6</td><td>17</td><td>35</td><td>0.378</td></tr><tr><td>Sweden</td><td>Europe</td><td>1986-2024</td><td>31 (10.5%)</td><td>1290</td><td>33 (2.56%)</td><td>43.9</td><td>-17.5</td><td>18.2</td><td>19</td><td>31</td><td>0.487</td></tr><tr><td>Denmark</td><td>Europe</td><td>1988-2024</td><td>28 (9.46%)</td><td>828</td><td>31 (3.74%)</td><td>31.9</td><td>-9.46</td><td>15.1</td><td>15</td><td>28</td><td>0.405</td></tr><tr><td>Norway</td><td>Europe</td><td>1996-2024</td><td>27 (9.12%)</td><td>965</td><td>37 (3.83%)</td><td>41.8</td><td>-9.20</td><td>12.0</td><td>13</td><td>27</td><td>0.448</td></tr><tr><td>Canada</td><td>North America</td><td>1975-2022</td><td>22 (7.43%)</td><td>799</td><td>13 (1.63%)</td><td>18.0</td><td>-13.9</td><td>16.3</td><td>13</td><td>22</td><td>0.260</td></tr><tr><td>Australia</td><td>Oceania</td><td>1986-2024</td><td>15 (5.07%)</td><td>177</td><td>9 (5.08%)</td><td>4.85</td><td>-6.41</td><td>10.6</td><td>7</td><td>13</td><td>0.179</td></tr><tr><td>UK</td><td>Europe</td><td>1984-2021</td><td>12 (4.05%)</td><td>294</td><td>9 (3.06%)</td><td>8.50</td><td>-15.8</td><td>6.53</td><td>9</td><td>12</td><td>0.220</td></tr><tr><td>Italy</td><td>Europe</td><td>1988-2024</td><td>11 (3.72%)</td><td>170</td><td>7 (4.12%)</td><td>4.59</td><td>-9.01</td><td>6.58</td><td>7</td><td>11</td><td>0.189</td></tr><tr><td>China</td><td>Asia</td><td>1995-2024</td><td>10 (3.38%)</td><td>85</td><td>0 (0.00%)</td><td>3.73</td><td>-16.4</td><td>9.00</td><td>4</td><td>10</td><td>0.114</td></tr><tr><td>Iceland</td><td>Europe</td><td>2006-2024</td><td>10 (3.38%)</td><td>570</td><td>24 (4.21%)</td><td>30.0</td><td>-21.1</td><td>2.74</td><td>7</td><td>10</td><td>0.368</td></tr><tr><td>Netherlands</td><td>Europe</td><td>2004-2023</td><td>10 (3.38%)</td><td>217</td><td>14 (6.45%)</td><td>10.9</td><td>-18.3</td><td>4.64</td><td>6</td><td>10</td><td>0.286</td></tr><tr><td>New Zealand</td><td>Oceania</td><td>1986-2021</td><td>8 (2.70%)</td><td>364</td><td>11 (3.02%)</td><td>10.1</td><td>-5.56</td><td>3.86</td><td>7</td><td>8</td><td>0.179</td></tr><tr><td>Spain</td><td>Europe</td><td>1998-2023</td><td>8 (2.70%)</td><td>186</td><td>4 (2.15%)</td><td>7.31</td><td>-23.1</td><td>4.32</td><td>6</td><td>8</td><td>0.222</td></tr><tr><td>Germany</td><td>Europe</td><td>2001-2024</td><td>7 (2.36%)</td><td>166</td><td>2 (1.20%)</td><td>6.92</td><td>-16.7</td><td>3.79</td><td>4</td><td>7</td><td>0.167</td></tr><tr><td>South Korea</td><td>Asia</td><td>2002-2021</td><td>7 (2.36%)</td><td>136</td><td>7 (5.15%)</td><td>6.80</td><td>-20.0</td><td>4.11</td><td>5</td><td>7</td><td>0.217</td></tr><tr><td>India</td><td>Asia</td><td>2020-2023</td><td>5 (1.69%)</td><td>22</td><td>1 (4.55%)</td><td>5.50</td><td>-25.0</td><td>4.00</td><td>2</td><td>4</td><td>0.400</td></tr><tr><td>Japan</td><td>Asia</td><td>1999-2023</td><td>4 (1.35%)</td><td>59</td><td>0 (0.00%)</td><td>2.36</td><td>-8.00</td><td>3.00</td><td>3</td><td>4</td><td>0.115</td></tr><tr><td>Switzerland</td><td>Europe</td><td>1988-2014</td><td>4 (1.35%)</td><td>98</td><td>5 (5.10%)</td><td>7.26</td><td>-11.1</td><td>2.62</td><td>4</td><td>4</td><td>0.108</td></tr><tr><td>Belgium</td><td>Europe</td><td>2016-2017</td><td>2 (0.68%)</td><td>15</td><td>0 (0.00%)</td><td>7.50</td><td>0.00</td><td>1.33</td><td>2</td><td>2</td><td>0.222</td></tr><tr><td>Brazil</td><td>South America</td><td>2017-2024</td><td>2 (0.68%)</td><td>5</td><td>0 (0.00%)</td><td>0.63</td><td>-12.5</td><td>1.50</td><td>1</td><td>2</td><td>0.125</td></tr><tr><td>Ethiopia</td><td>Africa</td><td>2022-2024</td><td>2 (0.68%)</td><td>1</td><td>0 (0.00%)</td><td>0.33</td><td>-33.3</td><td>1.50</td><td>1</td><td>1</td><td>0.333</td></tr><tr><td>Austria</td><td>Europe</td><td>2019</td><td>1 (0.34%)</td><td>1</td><td>0 (0.00%)</td><td>1.00</td><td>0.00</td><td>0.33</td><td>1</td><td>1</td><td>0.167</td></tr><tr><td>Colombia</td><td>South America</td><td>2020</td><td>1 (0.34%)</td><td>15</td><td>0 (0.00%)</td><td>15.0</td><td>0.00</td><td>0.25</td><td>1</td><td>1</td><td>0.200</td></tr><tr><td>Costa Rica</td><td>Central America</td><td>2006</td><td>1 (0.34%)</td><td>12</td><td>0 (0.00%)</td><td>12.0</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.053</td></tr><tr><td>Greece</td><td>Europe</td><td>2016</td><td>1 (0.34%)</td><td>7</td><td>0 (0.00%)</td><td>7.00</td><td>0.00</td><td>0.33</td><td>1</td><td>1</td><td>0.111</td></tr><tr><td>Laos</td><td>Asia</td><td>2019</td><td>1 (0.34%)</td><td>6</td><td>0 (0.00%)</td><td>6.00</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.167</td></tr><tr><td>Malawi</td><td>Africa</td><td>2015</td><td>1 (0.34%)</td><td>15</td><td>0 (0.00%)</td><td>15.0</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.100</td></tr><tr><td>Malaysia</td><td>Asia</td><td>2001</td><td>1 (0.34%)</td><td>24</td><td>0 (0.00%)</td><td>24.0</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.042</td></tr><tr><td>Mexico</td><td>Central America</td><td>2020</td><td>1 (0.34%)</td><td>15</td><td>0 (0.00%)</td><td>15.0</td><td>0.00</td><td>0.25</td><td>1</td><td>1</td><td>0.200</td></tr><tr><td>Morocco</td><td>Africa</td><td>2018</td><td>1 (0.34%)</td><td>7</td><td>0 (0.00%)</td><td>7.00</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.143</td></tr><tr><td>Nigeria</td><td>Africa</td><td>2018</td><td>1 (0.34%)</td><td>1</td><td>0 (0.00%)</td><td>1.00</td><td>0.00</td><td>1.00</td><td>1</td><td>1</td><td>0.143</td></tr><tr><td>Turkey</td><td>Europe</td><td>2021</td><td>1 (0.34%)</td><td>0</td><td>0 (0.00%)</td><td>0.00</td><td>0.00</td><td>0.50</td><td>1</td><td>1</td><td>0.250</td></tr></tbody></table>

Note: AGR: annual growth rate. Please refer to the beginning of the supplemental materials for the definition of the bibliometric indices.
<table><caption>Table MA2. Spearman correlation between scientific production and country characteristics</caption><thead><tr><th>Country characteristics</th><th>Spearman's p</th><th>p</th></tr></thead><tbody><tr><td>Scientific production per researchers in R&D (per million people)</td><td>0.62</td><td>1.1e-04</td></tr><tr><td>Scientific production per GDP</td><td>0.46</td><td>5.9e-03</td></tr><tr><td>Scientific production per cereal yield (kg per hectare)</td><td>0.41</td><td>0.016</td></tr><tr><td>Scientific production per female employment in agriculture (% of female employment)</td><td>-0.69</td><td>6.6e-06</td></tr><tr><td>Scientific production per employment in agriculture (% of total employment)</td><td>-0.66</td><td>2.1e-05</td></tr><tr><td>Scientific production per male employment in agriculture (% of male employment)</td><td>-0.60</td><td>1.6e-04</td></tr><tr><td>Scientific production per permanent cropland (% of land area)</td><td>-0.53</td><td>1.5e-03</td></tr><tr><td>Scientific production per agriculture, forestry, and fishing, value added (% of GDP)</td><td>-0.51</td><td>2.1e-03</td></tr><tr><td>Scientific production per fertilizer consumption (% of fertilizer production)</td><td>-0.37</td><td>0.036</td></tr><tr><td>Scientific production per fertilizer consumption (kilograms per hectare of arable land)</td><td>-0.37</td><td>0.036</td></tr><tr><td>Scientific production per livestock production index</td><td>-0.26</td><td>0.14</td></tr><tr><td>Scientific production per food production index</td><td>-0.24</td><td>0.17</td></tr><tr><td>Scientific production per cereal production (metric tons)</td><td>0.20</td><td>0.25</td></tr><tr><td>Scientific production per crop production index</td><td>-0.14</td><td>0.44</td></tr><tr><td>Scientific production per land under cereal production (hectares)</td><td>0.12</td><td>0.48</td></tr><tr><td>Scientific production per population</td><td>-0.07</td><td>0.69</td></tr><tr><td>Scientific production per agricultural land (% of land area)</td><td>-0.05</td><td>0.77</td></tr><tr><td>Scientific production per total labor force</td><td>-0.03</td><td>0.85</td></tr><tr><td>Scientific production per agricultural land (km²)</td><td>0.01</td><td>0.96</td></tr></tbody></table>

*Note:* GDP: gross domestic product, R&D: research and development.
<table><caption>263 Table MA3. Spearman correlation between scientific production and country characteristics</caption><thead><tr><th>Country</th><th>SciProd per GDP</th><th>SciProd per pop.</th><th>SciProd per res. in R&D (per million people)</th><th>SciProd per fertilizer cons. (% of fertilizer prod.)</th><th>SciProd per fertilizer cons. (kg per hectare of arable land)</th><th>SciProd per agricult. land (km²)</th><th>SciProd per agricult. land (% of Land area)</th><th>SciProd per land under cereal prod. (hectares)</th><th>SciProd per permanent cropland (% of land area)</th><th>SciProd per cereal prod. (metric tons)</th><th>SciProd per crop prod. index</th><th>SciProd per food prod. index</th><th>SciProd per livestock prod. index</th><th>SciProd per cereal yield (kg per hectare)</th><th>SciProd per female employ. in agri. (% of female employ.)</th><th>SciProd per male employ. in agri. (% of male employ.)</th><th>SciProd per employ. in agri. (% of total employ.)</th><th>SciProd per agri., forestry, and fishing, value added (% of GDP)</th><th>SciProd per total labor force</th></tr></thead><tbody><tr><td>USA</td><td>3.6e-12</td><td>2.7e-07</td><td>0.02</td><td>0.67</td><td>0.67</td><td>2.0e-05</td><td>1.86</td><td>1.2e-06</td><td>3.1e+02</td><td>1.8e-07</td><td>0.85</td><td>0.87</td><td>0.82</td><td>0.01</td><td>59.3</td><td>22.8</td><td>32.8</td><td>68.1</td><td>5.3e-07</td></tr><tr><td>France</td><td>2.6e-11</td><td>1.1e-06</td><td>0.01</td><td>0.05</td><td>0.21</td><td>2.1e-04</td><td>1.13</td><td>7.2e-06</td><td>21.6</td><td>9.7e-07</td><td>0.67</td><td>0.66</td><td>0.63</td><td>9.4e-03</td><td>15.1</td><td>10.9</td><td>12.4</td><td>6.74</td><td>2.2e-06</td></tr><tr><td>Finland</td><td>1.3e-10</td><td>6.3e-06</td><td>4.5e-03</td><td>0.17</td><td>0.15</td><td>1.2e-03</td><td>3.69</td><td>2.6e-05</td><td>2.1e+03</td><td>8.2e-06</td><td>0.30</td><td>0.31</td><td>0.31</td><td>8.2e-03</td><td>5.85</td><td>3.32</td><td>4.19</td><td>3.57</td><td>1.2e-05</td></tr><tr><td>Sweden</td><td>5.3e-11</td><td>3.0e-06</td><td>3.9e-03</td><td>0.10</td><td>0.17</td><td>7.3e-04</td><td>2.98</td><td>1.9e-05</td><td>2.8e+03</td><td>4.5e-06</td><td>0.26</td><td>0.27</td><td>0.27</td><td>4.9e-03</td><td>16.6</td><td>5.99</td><td>8.83</td><td>6.17</td><td>5.3e-06</td></tr><tr><td>Denmark</td><td>7.1e-11</td><td>4.7e-06</td><td>3.6e-03</td><td>0.06</td><td>0.10</td><td>8.9e-04</td><td>0.35</td><td>1.5e-05</td><td>36.1</td><td>2.8e-06</td><td>0.26</td><td>0.27</td><td>0.26</td><td>3.9e-03</td><td>9.98</td><td>3.64</td><td>5.15</td><td>4.19</td><td>8.8e-06</td></tr><tr><td>Norway</td><td>4.7e-11</td><td>5.0e-06</td><td>4.0e-03</td><td>0.52</td><td>0.08</td><td>2.4e-03</td><td>8.71</td><td>7.3e-05</td><td>2.0e+03</td><td>1.7e-05</td><td>0.21</td><td>0.26</td><td>0.26</td><td>5.6e-03</td><td>7.54</td><td>3.54</td><td>4.67</td><td>5.29</td><td>9.0e-06</td></tr><tr><td>Canada</td><td>1.0e-11</td><td>5.7e-07</td><td>4.6e-03</td><td>0.30</td><td>0.17</td><td>3.5e-05</td><td>3.10</td><td>9.9e-07</td><td>1.1e+03</td><td>3.3e-07</td><td>0.20</td><td>0.20</td><td>0.19</td><td>5.2e-03</td><td>9.21</td><td>4.99</td><td>6.30</td><td>10.0</td><td>1.0e-06</td></tr><tr><td>Australia</td><td>9.0e-12</td><td>5.8e-07</td><td>3.3e-03</td><td>0.04</td><td>0.14</td><td>2.9e-06</td><td>0.22</td><td>7.3e-07</td><td>2.9e+02</td><td>2.7e-07</td><td>0.10</td><td>0.13</td><td>0.15</td><td>5.2e-03</td><td>3.91</td><td>2.33</td><td>2.80</td><td>3.58</td><td>1.1e-06</td></tr><tr><td>UK</td><td>3.9e-12</td><td>1.8e-07</td><td>2.6e-03</td><td>0.03</td><td>0.03</td><td>6.1e-05</td><td>0.15</td><td>3.0e-06</td><td>23.4</td><td>4.5e-07</td><td>0.11</td><td>0.12</td><td>0.11</td><td>1.5e-03</td><td>9.75</td><td>4.07</td><td>5.54</td><td>9.41</td><td>3.5e-07</td></tr><tr><td>Italy</td><td>5.5e-12</td><td>1.9e-07</td><td>4.1e-03</td><td>0.01</td><td>0.04</td><td>5.3e-05</td><td>0.16</td><td>1.7e-06</td><td>1.03</td><td>4.7e-07</td><td>0.08</td><td>0.09</td><td>0.10</td><td>1.9e-03</td><td>1.25</td><td>1.33</td><td>1.30</td><td>3.40</td><td>4.2e-07</td></tr><tr><td>China</td><td>5.6e-13</td><td>7.1e-09</td><td>6.3e-03</td><td>0.05</td><td>0.02</td><td>1.9e-06</td><td>0.18</td><td>9.7e-08</td><td>4.94</td><td>1.6e-08</td><td>0.09</td><td>0.09</td><td>0.09</td><td>1.6e-03</td><td>0.19</td><td>0.16</td><td>0.17</td><td>0.24</td><td>1.3e-08</td></tr><tr><td>Iceland</td><td>3.6e-10</td><td>2.6e-05</td><td>1.3e-03</td><td>4.8e-04</td><td>0.04</td><td>4.7e-04</td><td>0.47</td><td>3.3e-03</td><td>no data</td><td>1.1e-03</td><td>0.08</td><td>0.09</td><td>0.09</td><td>2.1e-03</td><td>2.02</td><td>0.68</td><td>0.98</td><td>1.14</td><td>4.3e-05</td></tr><tr><td>Netherlands</td><td>1.0e-11</td><td>5.7e-07</td><td>1.7e-03</td><td>0.12</td><td>0.01</td><td>4.4e-04</td><td>0.15</td><td>1.9e-05</td><td>6.89</td><td>4.6e-06</td><td>0.09</td><td>0.10</td><td>0.10</td><td>1.1e-03</td><td>3.54</td><td>1.92</td><td>2.33</td><td>1.78</td><td>1.0e-06</td></tr><tr><td>New Zealand</td><td>3.2e-11</td><td>1.6e-06</td><td>1.4e-03</td><td>0.02</td><td>3.6e-03</td><td>5.3e-05</td><td>0.14</td><td>3.0e-05</td><td>28.5</td><td>6.7e-06</td><td>0.07</td><td>0.08</td><td>0.08</td><td>8.9e-04</td><td>1.04</td><td>0.60</td><td>0.74</td><td>0.61</td><td>2.6e-06</td></tr><tr><td>Spain</td><td>5.7e-12</td><td>1.7e-07</td><td>2.6e-03</td><td>0.06</td><td>0.05</td><td>2.4e-05</td><td>0.12</td><td>1.0e-06</td><td>0.79</td><td>2.9e-07</td><td>0.07</td><td>0.07</td><td>0.07</td><td>1.8e-03</td><td>0.90</td><td>0.70</td><td>0.75</td><td>1.82</td><td>3.4e-07</td></tr><tr><td>Germany</td><td>1.7e-12</td><td>8.3e-08</td><td>1.3e-03</td><td>0.10</td><td>0.02</td><td>3.6e-05</td><td>0.13</td><td>8.9e-07</td><td>4.83</td><td>1.4e-07</td><td>0.07</td><td>0.07</td><td>0.06</td><td>8.7e-04</td><td>1.93</td><td>1.87</td><td>1.89</td><td>6.35</td><td>1.6e-07</td></tr><tr><td>South Korea</td><td>3.9e-12</td><td>1.4e-07</td><td>8.0e-04</td><td>7.7e-03</td><td>0.01</td><td>3.0e-04</td><td>0.29</td><td>3.0e-06</td><td>3.05</td><td>6.6e-07</td><td>0.06</td><td>0.07</td><td>0.06</td><td>1.0e-03</td><td>0.38</td><td>0.47</td><td>0.43</td><td>0.15</td><td>2.4e-07</td></tr><tr><td>India</td><td>1.5e-12</td><td>3.5e-09</td><td>0.02</td><td>0.02</td><td>0.02</td><td>2.8e-06</td><td>0.08</td><td>4.7e-08</td><td>1.09</td><td>1.4e-08</td><td>0.04</td><td>0.04</td><td>0.04</td><td>1.4e-03</td><td>0.06</td><td>0.09</td><td>0.08</td><td>0.12</td><td>8.4e-09</td></tr><tr><td>Japan</td><td>9.5e-13</td><td>3.2e-08</td><td>7.3e-04</td><td>0.03</td><td>8.3e-03</td><td>5.6e-05</td><td>0.21</td><td>8.2e-07</td><td>2.30</td><td>1.9e-07</td><td>0.03</td><td>0.04</td><td>0.04</td><td>5.9e-04</td><td>0.51</td><td>0.67</td><td>0.59</td><td>2.11</td><td>5.8e-08</td></tr><tr><td>Switzerland</td><td>5.0e-12</td><td>4.6e-07</td><td>7.2e-04</td><td>3.9e-03</td><td>8.2e-03</td><td>2.3e-04</td><td>0.09</td><td>1.9e-05</td><td>6.19</td><td>2.8e-06</td><td>0.03</td><td>0.04</td><td>0.04</td><td>5.7e-04</td><td>0.98</td><td>0.66</td><td>0.79</td><td>1.90</td><td>8.0e-07</td></tr><tr><td>Belgium</td><td>3.5e-12</td><td>1.7e-07</td><td>3.5e-04</td><td>0.03</td><td>3.4e-03</td><td>1.1e-04</td><td>0.04</td><td>5.7e-06</td><td>2.52</td><td>6.1e-07</td><td>0.02</td><td>0.02</td><td>0.02</td><td>2.0e-04</td><td>0.92</td><td>0.59</td><td>0.69</td><td>1.49</td><td>3.7e-07</td></tr><tr><td>Brazil</td><td>1.0e-12</td><td>9.3e-09</td><td>2.3e-03</td><td>2.5e-03</td><td>5.4e-03</td><td>8.3e-07</td><td>0.07</td><td>7.2e-08</td><td>1.43</td><td>1.5e-08</td><td>0.02</td><td>0.02</td><td>0.02</td><td>3.8e-04</td><td>0.18</td><td>0.08</td><td>0.11</td><td>0.12</td><td>1.8e-08</td></tr><tr><td>Ethiopia</td><td>1.6e-11</td><td>1.6e-08</td><td>0.02</td><td>no data</td><td>0.05</td><td>3.4e-06</td><td>0.04</td><td>1.8e-07</td><td>0.99</td><td>6.3e-08</td><td>0.02</td><td>0.02</td><td>0.02</td><td>6.9e-04</td><td>0.03</td><td>0.02</td><td>0.03</td><td>0.03</td><td>3.2e-08</td></tr><tr><td>Austria</td><td>2.1e-12</td><td>1.1e-07</td><td>1.7e-04</td><td>7.0e-03</td><td>3.6e-03</td><td>2.8e-05</td><td>0.02</td><td>9.4e-07</td><td>0.83</td><td>1.7e-07</td><td>8.3e-03</td><td>9.7e-03</td><td>9.9e-03</td><td>1.4e-04</td><td>0.12</td><td>0.15</td><td>0.14</td><td>0.20</td><td>2.1e-07</td></tr><tr><td>Colombia</td><td>2.9e-12</td><td>1.9e-08</td><td>0.01</td><td>7.0e-04</td><td>1.4e-03</td><td>2.2e-06</td><td>0.02</td><td>5.7e-07</td><td>0.44</td><td>2.2e-07</td><td>9.0e-03</td><td>9.0e-03</td><td>9.1e-03</td><td>2.3e-04</td><td>0.13</td><td>0.03</td><td>0.05</td><td>0.04</td><td>3.9e-08</td></tr><tr><td>Costa Rica</td><td>1.5e-11</td><td>1.9e-07</td><td>1.8e-03</td><td>4.3e-05</td><td>1.1e-03</td><td>3.6e-05</td><td>0.02</td><td>6.1e-06</td><td>0.14</td><td>2.6e-06</td><td>9.5e-03</td><td>9.5e-03</td><td>8.8e-03</td><td>2.1e-04</td><td>0.10</td><td>0.04</td><td>0.05</td><td>0.04</td><td>3.9e-07</td></tr><tr><td>Greece</td><td>4.6e-12</td><td>9.5e-08</td><td>2.5e-04</td><td>1.6e-03</td><td>4.1e-03</td><td>1.1e-05</td><td>0.01</td><td>5.6e-07</td><td>0.11</td><td>1.6e-07</td><td>8.7e-03</td><td>9.2e-03</td><td>8.6e-03</td><td>2.2e-04</td><td>0.04</td><td>0.06</td><td>0.05</td><td>0.14</td><td>2.0e-07</td></tr><tr><td>Laos</td><td>6.5e-11</td><td>1.3e-07</td><td>0.06</td><td>no data</td><td>0.02</td><td>4.3e-05</td><td>0.10</td><td>8.1e-07</td><td>1.37</td><td>1.8e-07</td><td>8.8e-03</td><td>8.7e-03</td><td>5.5e-03</td><td>2.2e-04</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.02</td><td>3.2e-07</td></tr><tr><td>Malawi</td><td>7.6e-11</td><td>4.9e-08</td><td>0.02</td><td>1.0e-05</td><td>0.01</td><td>1.7e-05</td><td>0.02</td><td>4.7e-07</td><td>0.47</td><td>2.0e-07</td><td>7.4e-03</td><td>6.8e-03</td><td>5.3e-03</td><td>4.1e-04</td><td>0.01</td><td>0.01</td><td>0.01</td><td>0.03</td><td>1.2e-07</td></tr><tr><td>Malaysia</td><td>2.5e-12</td><td>3.0e-08</td><td>4.2e-04</td><td>5.0e-04</td><td>4.4e-04</td><td>1.2e-05</td><td>0.04</td><td>1.3e-06</td><td>0.04</td><td>3.4e-07</td><td>9.4e-03</td><td>9.7e-03</td><td>9.5e-03</td><td>2.2e-04</td><td>0.07</td><td>0.05</td><td>0.05</td><td>0.02</td><td>5.8e-08</td></tr><tr><td>Mexico</td><td>7.1e-13</td><td>7.8e-09</td><td>2.4e-03</td><td>1.9e-03</td><td>9.2e-03</td><td>9.4e-07</td><td>0.02</td><td>8.7e-08</td><td>0.69</td><td>2.6e-08</td><td>8.7e-03</td><td>8.5e-03</td><td>8.3e-03</td><td>2.5e-04</td><td>0.09</td><td>0.03</td><td>0.04</td><td>0.08</td><td>1.7e-08</td></tr><tr><td>Morocco</td><td>7.5e-12</td><td>2.7e-08</td><td>9.3e-04</td><td>3.5e-03</td><td>0.01</td><td>3.2e-06</td><td>0.01</td><td>1.6e-07</td><td>0.25</td><td>8.6e-08</td><td>8.4e-03</td><td>8.6e-03</td><td>8.6e-03</td><td>4.3e-04</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.04</td><td>8.1e-08</td></tr><tr><td>Nigeria</td><td>2.1e-12</td><td>4.6e-09</td><td>0.03</td><td>1.6e-04</td><td>0.05</td><td>1.5e-06</td><td>0.01</td><td>5.2e-08</td><td>0.14</td><td>3.3e-08</td><td>8.3e-03</td><td>8.3e-03</td><td>8.9e-03</td><td>5.8e-04</td><td>0.02</td><td>0.02</td><td>0.02</td><td>0.03</td><td>1.3e-08</td></tr><tr><td>Turkey</td><td>1.1e-12</td><td>1.2e-08</td><td>5.6e-04</td><td>2.1e-03</td><td>6.7e-03</td><td>2.4e-06</td><td>0.02</td><td>7.1e-08</td><td>0.21</td><td>2.6e-08</td><td>8.0e-03</td><td>7.8e-03</td><td>7.3e-03</td><td>2.9e-04</td><td>0.01</td><td>0.03</td><td>0.02</td><td>0.02</td><td>2.9e-08</td></tr></tbody></table>

264 Note: agri.: agriculture, agricult.: agricultural, cons.: consumption, employ.: employment, GDP: gross domestic product, pop.: population, prod.: production, R&D: research and

265 development, res.: researcher, SciProd: scientific production.
• The most active journals

266

267

268 Articles were published in 118 journals (Table 4). Most journals published less than five studies and received on average 3.86 citations. The most active journals were the American Journal Of Industrial Medicine (18/296, 6.1%), the Journal Of Occupational And Environmental Medicine (14/296, 4.7%), as well as the International Journal Of Epidemiology, the Occupational And Environmental Medicine, and the Scandinavian Journal Of Work Environment & Health, which all had 13 publications (Table MA4).

269

270

271

Notably, 11 of the top 20 journals are ranked in the first quartile (Q1) of their respective category, primarily in public health and occupational health, with 2022 impact factors ranging from 2.3 to 12.7.

272

273

<caption>Table MA4. Top 20 of the most active journals.</caption>

274

<table><thead><tr><th>Journal</th><th>Publication start</th><th>Number of publications (%)</th><th>Total citation</th><th>h-index</th><th>Average citation per year</th><th>Mean AGR</th><th>g-index</th><th>m-index</th><th>Impact factor (2022)</th><th>Rank</th></tr></thead><tbody><tr><td>American Journal Of Industrial Medicine</td><td>1989</td><td>18 (6.08%)</td><td>515</td><td>15</td><td>14.3</td><td>-27.8</td><td>18</td><td>0.42</td><td>3.5</td><td>Q2</td></tr><tr><td>Journal Of Occupational And Environmental Medicine</td><td>1985</td><td>14 (4.73%)</td><td>423</td><td>12</td><td>10.6</td><td>-20.8</td><td>14</td><td>0.30</td><td>3.2</td><td>Q3</td></tr><tr><td>International Journal Of Epidemiology</td><td>1980</td><td>13 (4.39%)</td><td>813</td><td>13</td><td>18.1</td><td>-14.2</td><td>13</td><td>0.29</td><td>7.7</td><td>Q1</td></tr><tr><td>Occupational And Environmental Medicine</td><td>1995</td><td>13 (4.39%)</td><td>351</td><td>9</td><td>11.7</td><td>-20.0</td><td>13</td><td>0.30</td><td>4.9</td><td>Q1</td></tr><tr><td>Scandinavian Journal Of Work Environment & Health</td><td>1990</td><td>13 (4.39%)</td><td>649</td><td>12</td><td>18.5</td><td>-20.6</td><td>13</td><td>0.34</td><td>6.3</td><td>Q1</td></tr><tr><td>Cancer Causes & Control</td><td>1997</td><td>9 (3.04%)</td><td>223</td><td>8</td><td>7.96</td><td>-14.8</td><td>9</td><td>0.29</td><td>2.3</td><td>Q4</td></tr><tr><td>Environmental Research</td><td>1993</td><td>9 (3.04%)</td><td>76</td><td>4</td><td>2.38</td><td>-6.25</td><td>8</td><td>0.13</td><td>8.3</td><td>Q1</td></tr><tr><td>Environmental Health Perspectives</td><td>2006</td><td>7 (2.36%)</td><td>357</td><td>7</td><td>18.8</td><td>-26.7</td><td>7</td><td>0.37</td><td>10.5</td><td>Q1</td></tr><tr><td>International Journal Of Environmental Research And Public Health</td><td>2011</td><td>7 (2.36%)</td><td>56</td><td>3</td><td>4.00</td><td>-11.5</td><td>7</td><td>0.21</td><td>4.6</td><td>Q2</td></tr><tr><td>International Archives Of Occupational And Environmental Health</td><td>2001</td><td>6 (2.03%)</td><td>118</td><td>6</td><td>4.92</td><td>-23.5</td><td>6</td><td>0.25</td><td>3.0</td><td>Q3</td></tr><tr><td>International Journal Of Cancer</td><td>1989</td><td>6 (2.03%)</td><td>153</td><td>4</td><td>4.25</td><td>-11.8</td><td>6</td><td>0.11</td><td>6.4</td><td>Q1</td></tr><tr><td>Journal Of Agromedicine</td><td>2009</td><td>6 (2.03%)</td><td>98</td><td>5</td><td>6.13</td><td>12.5</td><td>6</td><td>0.31</td><td>2.4</td><td>Q3</td></tr><tr><td>American Journal Of Epidemiology</td><td>1993</td><td>5 (1.69%)</td><td>669</td><td>5</td><td>20.9</td><td>-8.00</td><td>5</td><td>0.16</td><td>5.0</td><td>Q1</td></tr><tr><td>Journal Of Epidemiology And Community Health</td><td>1981</td><td>5 (1.69%)</td><td>264</td><td>5</td><td>6.00</td><td>-31.2</td><td>5</td><td>0.11</td><td>6.3</td><td>Q1</td></tr><tr><td>British Journal Of Industrial Medicine</td><td>1988</td><td>5 (1.69%)</td><td>256</td><td>5</td><td>6.92</td><td>-30</td><td>5</td><td>0.14</td><td>none</td><td>none</td></tr><tr><td>BMJ Open</td><td>2013</td><td>4 (1.35%)</td><td>35</td><td>3</td><td>2.92</td><td>-28.6</td><td>4</td><td>0.25</td><td>2.9</td><td>Q2</td></tr><tr><td>Frontiers In Public Health</td><td>2022</td><td>4 (1.35%)</td><td>8</td><td>2</td><td>2.67</td><td>0.00</td><td>2</td><td>0.67</td><td>5.2</td><td>Q1</td></tr><tr><td>American Journal Of Public Health</td><td>1990</td><td>3 (1.01%)</td><td>144</td><td>3</td><td>4.11</td><td>-5.00</td><td>3</td><td>0.09</td><td>12.7</td><td>Q1</td></tr><tr><td>Annals Of Epidemiology</td><td>2010</td><td>3 (1.01%)</td><td>133</td><td>3</td><td>8.87</td><td>-22.2</td><td>3</td><td>0.20</td><td>5.6</td><td>Q1</td></tr><tr><td>Cancer Epidemiology</td><td>2017</td><td>3 (1.01%)</td><td>40</td><td>2</td><td>5.00</td><td>-14.3</td><td>3</td><td>0.25</td><td>2.6</td><td>Q2</td></tr></tbody></table>

275

Note: AGR: annual growth rate. Please refer to the beginning of the supplemental materials for the definition of the bibliometric indices.
• *The most prolific authors*

The top 25 of the most prolific authors included 28 individuals who contributed to at least 7 (7/296, 2.4%) publications (Table MA5). Among these authors, only nine were females (9/28, 32%), and twelve (12/28, 43%) published their last paper more than three years ago. I. Baldi led in total publications (18/296, 6.1%), followed closely by P Lebailly and E.I. Pukkala, each with 16 publications (16/296, 5.4%). In terms of first authorship, P.K. Mills had a dominant presence, achieving a first author contribution of 100%, followed by P. Petit (85.7%), S. Jouneau (42.9%), and C. Piel (42.9%). D.P. Sandler emerged as the most cited author in this group, with 943 citations, followed by E.I. Pukkala (932) and A. Blair (905). The majority of the articles published by these authors focused on cancer risk (23/28, 82%), with additional research on neurodegenerative disorders (8/28, 29%), particularly Parkinson's disease (7/28, 25%), depression (6/28, 21%), and respiratory conditions (5/28, 18%).

<table><caption>Table MA5. Top 25 of the most prolific authors.</caption><thead><tr><th>Author</th><th>Sex</th><th>Publication period</th><th>Number of publications</th><th>FF (%)</th><th>DF1 (%)</th><th>DF last (%)</th><th>Total citation</th><th>Local citation</th><th>Average citation per year</th><th>h-index</th><th>Mean AGR (%)</th><th>g-index</th><th>m-index</th><th>Y-index</th><th>Thematic</th></tr></thead><tbody><tr><td>BALDI I</td><td>F</td><td>2011-2024</td><td>18 (6.1%)</td><td>1.61</td><td>5.56</td><td>27.8</td><td>494</td><td>33 (6.7%)</td><td>35.3</td><td>10</td><td>3.57</td><td>18</td><td>0.71</td><td>5.10</td><td>Cancer, neurobehavior effect, aging, mortality, PD</td></tr><tr><td>LEBAILLY P</td><td>M</td><td>2011-2024</td><td>16 (5.4%)</td><td>1.45</td><td>0.00</td><td>37.5</td><td>361</td><td>32 (8.9%)</td><td>25.8</td><td>10</td><td>12.5</td><td>16</td><td>0.71</td><td>6.00</td><td>Cancer, neurobehavior effect, mortality, PD</td></tr><tr><td>PUKKALA EI</td><td>M</td><td>1997-2023</td><td>16 (5.4%)</td><td>2.66</td><td>12.5</td><td>62.5</td><td>932</td><td>25 (2.7%)</td><td>34.5</td><td>10</td><td>-20.4</td><td>16</td><td>0.36</td><td>10.2</td><td>Cancer</td></tr><tr><td>LYNGE E</td><td>F</td><td>1988-2024</td><td>15 (5.1%)</td><td>3.27</td><td>20.0</td><td>13.3</td><td>418</td><td>5 (1.2%)</td><td>11.3</td><td>9</td><td>-17.6</td><td>15</td><td>0.24</td><td>3.61</td><td>Cancer</td></tr><tr><td>TUAL S</td><td>F</td><td>2017-2022</td><td>13 (4.4%)</td><td>1.10</td><td>15.4</td><td>0.00</td><td>358</td><td>27 (7.5%)</td><td>59.7</td><td>9</td><td>0.00</td><td>13</td><td>1.12</td><td>2.00</td><td>Cancer, PD</td></tr><tr><td>BLAIR A</td><td>M</td><td>1981-2017</td><td>11 (3.7%)</td><td>2.02</td><td>18.2</td><td>27.3</td><td>905</td><td>22 (2.4%)</td><td>24.5</td><td>10</td><td>-16.2</td><td>11</td><td>0.23</td><td>3.61</td><td>Cancer</td></tr><tr><td>SANDLER DP</td><td>M</td><td>2003-2020</td><td>11 (3.7%)</td><td>1.27</td><td>0.00</td><td>0.00</td><td>943</td><td>17 (1.8%)</td><td>52.4</td><td>11</td><td>-22.2</td><td>11</td><td>0.50</td><td>0.00</td><td>Cancer, CVD, injury</td></tr><tr><td>BOULANGER M</td><td>F</td><td>2017-2022</td><td>10 (3.4%)</td><td>0.93</td><td>20.0</td><td>0.00</td><td>151</td><td>11 (7.3%)</td><td>25.2</td><td>7</td><td>5.56</td><td>10</td><td>0.88</td><td>2.00</td><td>Cancer, mortality</td></tr><tr><td>KJAERHEIM K</td><td>F</td><td>1999-2023</td><td>10 (3.4%)</td><td>1.21</td><td>20.0</td><td>10.0</td><td>839</td><td>29 (3.5%)</td><td>33.6</td><td>8</td><td>-12.0</td><td>10</td><td>0.31</td><td>2.24</td><td>Cancer</td></tr><tr><td>DEGANO B</td><td>M</td><td>2016-2020</td><td>9 (3.0%)</td><td>1.00</td><td>0.00</td><td>44.4</td><td>123</td><td>12 (9.8%)</td><td>24.6</td><td>5</td><td>16.7</td><td>9</td><td>0.56</td><td>4.00</td><td>COPD, depression</td></tr><tr><td>ELBAZ A</td><td>M</td><td>2003-2021</td><td>9 (3.0%)</td><td>1.45</td><td>22.2</td><td>55.6</td><td>549</td><td>8 (1.5%)</td><td>28.9</td><td>8</td><td>-26.3</td><td>9</td><td>0.36</td><td>5.39</td><td>PD, motor neuron disease</td></tr><tr><td>LAPLANTE JJ</td><td>M</td><td>1998-2019</td><td>9 (3.0%)</td><td>0.85</td><td>0.00</td><td>0.00</td><td>210</td><td>16 (7.6%)</td><td>9.55</td><td>7</td><td>11.4</td><td>9</td><td>0.26</td><td>0.00</td><td>Asthma, cancer, COPD, depression</td></tr><tr><td>MARCOTULLIO E</td><td>F</td><td>2017-2022</td><td>9 (3.0%)</td><td>0.72</td><td>0.00</td><td>0.00</td><td>181</td><td>20 (11.0%)</td><td>30.2</td><td>8</td><td>4.17</td><td>9</td><td>1.00</td><td>0.00</td><td>Cancer, PD</td></tr><tr><td>MARTINSEN JI</td><td>M</td><td>2009-2023</td><td>9 (3.0%)</td><td>1.37</td><td>0.00</td><td>0.00</td><td>573</td><td>24 (4.2%)</td><td>38.2</td><td>8</td><td>-20.0</td><td>9</td><td>0.50</td><td>0.00</td><td>Cancer</td></tr><tr><td>BONNETERRE V</td><td>M</td><td>2020-2024</td><td>8 (2.7%)</td><td>1.79</td><td>12.5</td><td>87.5</td><td>28</td><td>16 (57.1%)</td><td>5.60</td><td>3</td><td>30.0</td><td>5</td><td>1.00</td><td>7.07</td><td>AD, cancer, depression, IBD, thyroid disorders</td></tr><tr><td>DALPHIN JC</td><td>M</td><td>1998-2020</td><td>8 (2.7%)</td><td>0.86</td><td>12.5</td><td>50.0</td><td>195</td><td>11 (5.6%)</td><td>8.48</td><td>6</td><td>-4.35</td><td>8</td><td>0.22</td><td>4.12</td><td>Asthma, cancer, COPD, depression</td></tr><tr><td>FREEMAN LEB</td><td>F</td><td>2013-2023</td><td>8 (2.7%)</td><td>0.75</td><td>0.00</td><td>37.5</td><td>380</td><td>10 (2.6%)</td><td>34.6</td><td>7</td><td>-22.7</td><td>8</td><td>0.58</td><td>3.00</td><td>Cancer, injury</td></tr><tr><td>HOPPIN JA</td><td>F</td><td>2003-2017</td><td>8 (2.7%)</td><td>0.88</td><td>0.00</td><td>25.0</td><td>803</td><td>15 (1.9%)</td><td>53.5</td><td>8</td><td>-20.0</td><td>8</td><td>0.36</td><td>2.00</td><td>Cancer, injury</td></tr><tr><td>SOU MAGNE TE</td><td>M</td><td>2016-2020</td><td>8 (2.7%)</td><td>0.88</td><td>12.5</td><td>0.00</td><td>120</td><td>12 (10.0%)</td><td>24.0</td><td>5</td><td>0.00</td><td>8</td><td>0.56</td><td>1.00</td><td>COPD, depression</td></tr><tr><td>PETIT P</td><td>M</td><td>2022-2024</td><td>7 (2.4%)</td><td>1.63</td><td>85.7</td><td>14.3</td><td>21</td><td>14 (66.7%)</td><td>7.00</td><td>3</td><td>83.3</td><td>4</td><td>1.00</td><td>6.08</td><td>AD, cancer, depression, IBD, thyroid disorders</td></tr><tr><td>ALAVANJA MCR</td><td>M</td><td>2003-2017</td><td>7 (2.4%)</td><td>0.77</td><td>28.6</td><td>57.1</td><td>752</td><td>17 (2.3%)</td><td>50.1</td><td>7</td><td>-13.3</td><td>7</td><td>0.32</td><td>4.47</td><td>Cancer, injury</td></tr><tr><td>JOUNEAU S</td><td>M</td><td>2015-2022</td><td>7 (2.4%)</td><td>0.55</td><td>42.9</td><td>0.00</td><td>69</td><td>6 (8.7%)</td><td>8.63</td><td>4</td><td>-25.0</td><td>7</td><td>0.40</td><td>3.00</td><td>Asthma, COPD</td></tr><tr><td>KOUTROS S</td><td>F</td><td>2010-2020</td><td>7 (2.4%)</td><td>0.67</td><td>0.00</td><td>0.00</td><td>369</td><td>6 (1.6%)</td><td>33.6</td><td>6</td><td>-9.09</td><td>7</td><td>0.40</td><td>0.00</td><td>Cancer</td></tr><tr><td>LYNCH CF</td><td>M</td><td>2003-2023</td><td>7 (2.4%)</td><td>0.75</td><td>0.00</td><td>0.00</td><td>744</td><td>15 (2.0%)</td><td>35.4</td><td>6</td><td>-19.0</td><td>7</td><td>0.27</td><td>0.00</td><td>Cancer</td></tr><tr><td>MILLS PK</td><td>M</td><td>2001-2009</td><td>7 (2.4%)</td><td>3.17</td><td>100</td><td>0.00</td><td>327</td><td>6 (1.8%)</td><td>36.3</td><td>7</td><td>-44.4</td><td>7</td><td>0.29</td><td>7.00</td><td>Cancer</td></tr><tr><td>NORDBY KC</td><td>M</td><td>2004-2023</td><td>7 (2.4%)</td><td>0.90</td><td>28.6</td><td>0.00</td><td>183</td><td>11 (6.0%)</td><td>9.15</td><td>5</td><td>-20.0</td><td>7</td><td>0.24</td><td>2.00</td><td>Cancer</td></tr><tr><td>PIEL C</td><td>M</td><td>2017-2022</td><td>7 (2.4%)</td><td>0.51</td><td>42.9</td><td>0.00</td><td>200</td><td>9 (4.5%)</td><td>33.3</td><td>6</td><td>8.33</td><td>7</td><td>0.75</td><td>3.00</td><td>Cancer, PD</td></tr><tr><td>TZOURIO C</td><td>M</td><td>2003-2015</td><td>7 (2.4%)</td><td>0.95</td><td>0.00</td><td>42.9</td><td>538</td><td>7 (1.3%)</td><td>41.4</td><td>7</td><td>-23.1</td><td>7</td><td>0.32</td><td>3.00</td><td>PD</td></tr></tbody></table>

Note: AD: Alzheimer's disease, AGR: annual growth rate, COPD: chronic obstructive pulmonary disease, CVD: cardiovascular disorders, DF: dominance factor, DF1: dominance factor for being first author, DF last: dominance factor for being last author, F: female, FF: fractionalized frequency, IBD: inflammatory bowel disease, M: male, PD: Parkinson's disease.
• The most active institutions

289

290

There were 338 institutions reported. The top 20 most active institutions comprised 24 organizations that contributed eleven or more publications (11/296, 3.7%) (Table MA6). These institutions were spread across six countries, with 13 from France (13/24, 54%), four from the US (4/24, 17%), three from Finland (3/24, 13%), two from Denmark (2/24, 8%), and one each from Sweden and Norway (1/24, 4%). INSERM (France) led with the highest number of publications (47/296, 15.9%), followed by Karolinska Institutet (Sweden) (24/296, 8.1%), and the University of Oslo (Norway) (23/296, 7.8%). INSERM was the most cited (1390/9379, 14.8%), followed closely by NIH (US) and NCI (US), each with 1336 citations (1336/9379, 14.2%). Among the top 20, CHU Bordeaux (35.3%), Université Grenoble Alpes (25.0%), and CHU Grenoble Alpes (25.0%) recorded the highest mean AGR.

297

298

<table><caption>Table MA6. Top 20 of the most active institutions.</caption><thead><tr><th>Institution</th><th>Country</th><th>Publication period</th><th>Number of publications (%)</th><th>Total citation</th><th>Local citation (%)</th><th>Average citation per year</th><th>Mean AGR</th><th>Fractionalized frequency (%)</th><th>h-index</th><th>g-index</th><th>m-index</th></tr></thead><tbody><tr><td>INSERM</td><td>France</td><td>2003-2024</td><td>47 (15.9%)</td><td>1390</td><td>47 (3.38%)</td><td>63.2</td><td>6.19</td><td>7.73</td><td>20</td><td>37</td><td>0.91</td></tr><tr><td>Karolinska Institutet</td><td>Sweden</td><td>1987-2024</td><td>24 (8.11%)</td><td>1214</td><td>29 (2.67%)</td><td>31.9</td><td>-23.2</td><td>7.53</td><td>15</td><td>24</td><td>0.40</td></tr><tr><td>University of Oslo</td><td>Norway</td><td>1996-2024</td><td>23 (7.77%)</td><td>1137</td><td>35 (3.93%)</td><td>39.2</td><td>-17.2</td><td>5.71</td><td>12</td><td>23</td><td>0.41</td></tr><tr><td>Université de Bordeaux</td><td>France</td><td>2011-2024</td><td>22 (7.43%)</td><td>566</td><td>34 (6.01%)</td><td>40.4</td><td>6.43</td><td>2.96</td><td>13</td><td>22</td><td>0.93</td></tr><tr><td>National Institutes of Health (NIH)</td><td>US</td><td>1998-2023</td><td>21 (7.09%)</td><td>1336</td><td>29 (2.17%)</td><td>51.4</td><td>-19.2</td><td>4.24</td><td>17</td><td>21</td><td>0.63</td></tr><tr><td>National Cancer Institute (NCI)</td><td>US</td><td>1998-2023</td><td>21 (7.09%)</td><td>1336</td><td>29 (2.17%)</td><td>51.4</td><td>-19.2</td><td>4.24</td><td>17</td><td>21</td><td>0.63</td></tr><tr><td>CHU Bordeaux</td><td>France</td><td>2012-2023</td><td>19 (6.42%)</td><td>500</td><td>33 (6.60%)</td><td>41.7</td><td>35.3</td><td>2.38</td><td>11</td><td>19</td><td>0.85</td></tr><tr><td>Unicancer</td><td>France</td><td>2011-2023</td><td>19 (6.42%)</td><td>479</td><td>32 (6.68%)</td><td>36.8</td><td>-12.8</td><td>2.28</td><td>11</td><td>19</td><td>0.79</td></tr><tr><td>Université de Caen-Normandie</td><td>France</td><td>2011-2024</td><td>18 (6.08%)</td><td>450</td><td>33 (7.33%)</td><td>32.1</td><td>2.38</td><td>2.35</td><td>10</td><td>18</td><td>0.71</td></tr><tr><td>Centre François Baclesse</td><td>France</td><td>2011-2023</td><td>16 (5.41%)</td><td>429</td><td>28 (6.53%)</td><td>33.0</td><td>-12.8</td><td>1.95</td><td>10</td><td>16</td><td>0.71</td></tr><tr><td>Finnish Cancer Registry</td><td>Finland</td><td>2005-2024</td><td>16 (5.41%)</td><td>864</td><td>25 (3.80%)</td><td>43.2</td><td>-17.3</td><td>2.95</td><td>10</td><td>16</td><td>0.39</td></tr><tr><td>Assistance Publique - Hôpitaux de Paris (AP-HP)</td><td>France</td><td>2003-2023</td><td>15 (5.07%)</td><td>571</td><td>7 (1.23%)</td><td>27.2</td><td>-14.3</td><td>2.21</td><td>9</td><td>15</td><td>0.41</td></tr><tr><td>University of Copenhagen</td><td>Denmark</td><td>1997-2024</td><td>15 (5.07%)</td><td>621</td><td>24 (3.86%)</td><td>22.2</td><td>-14.3</td><td>2.57</td><td>8</td><td>15</td><td>0.29</td></tr><tr><td>CNRS</td><td>France</td><td>2011-2024</td><td>14 (4.73%)</td><td>152</td><td>5 (3.29%)</td><td>10.9</td><td>-7.14</td><td>2.32</td><td>4</td><td>12</td><td>0.29</td></tr><tr><td>Tampere university</td><td>Finland</td><td>1980-2024</td><td>14 (4.73%)</td><td>886</td><td>24 (2.71%)</td><td>19.7</td><td>-11.1</td><td>2.92</td><td>9</td><td>14</td><td>0.20</td></tr><tr><td>Université de Franche-Comté</td><td>France</td><td>1998-2020</td><td>13 (4.39%)</td><td>257</td><td>15 (5.84%)</td><td>11.2</td><td>-7.97</td><td>2.74</td><td>9</td><td>13</td><td>0.33</td></tr><tr><td>Finnish Institute of Occupational Health</td><td>Finland</td><td>1997-2020</td><td>12 (4.05%)</td><td>518</td><td>3 (0.58%)</td><td>21.6</td><td>-16.7</td><td>6.83</td><td>10</td><td>12</td><td>0.36</td></tr><tr><td>NIEHS</td><td>US</td><td>2003-2020</td><td>12 (4.05%)</td><td>1086</td><td>17 (1.57%)</td><td>60.3</td><td>-25.0</td><td>2.34</td><td>12</td><td>12</td><td>0.56</td></tr><tr><td>Sorbonne Université</td><td>France</td><td>2003-2019</td><td>12 (4.05%)</td><td>595</td><td>9 (1.51%)</td><td>35.0</td><td>-20.6</td><td>1.68</td><td>10</td><td>12</td><td>0.46</td></tr><tr><td>Université Grenoble Alpes (UGA)</td><td>France</td><td>2019-2024</td><td>11 (3.72%)</td><td>35</td><td>14 (40.0%)</td><td>5.83</td><td>25.0</td><td>1.56</td><td>4</td><td>5</td><td>1.33</td></tr><tr><td>CHU Grenoble Alpes</td><td>France</td><td>2019-2024</td><td>11 (3.72%)</td><td>35</td><td>14 (40.0%)</td><td>5.83</td><td>25.0</td><td>1.56</td><td>4</td><td>5</td><td>1.33</td></tr><tr><td>CHU Besançon</td><td>France</td><td>1998-2019</td><td>11 (3.72%)</td><td>247</td><td>15 (6.07%)</td><td>11.2</td><td>-9.85</td><td>2.58</td><td>9</td><td>11</td><td>0.33</td></tr><tr><td>Danish Cancer Society</td><td>Denmark</td><td>1988-2024</td><td>11 (3.72%)</td><td>323</td><td>0 (0.00%)</td><td>8.73</td><td>-13.5</td><td>4.83</td><td>7</td><td>11</td><td>0.19</td></tr><tr><td>University of Iowa</td><td>US</td><td>2002-2023</td><td>11 (3.72%)</td><td>829</td><td>19 (2.29%)</td><td>37.7</td><td>-18.2</td><td>3.78</td><td>8</td><td>11</td><td>0.35</td></tr></tbody></table>

299

300

301

302

Note: AGR: annual growth rate, CNRS: Centre national de la recherche scientifique (French National Centre for Scientific Research), INSERM: Institut national de la santé et de la recherche médicale (French National Institute of Health and Medical Research), NIEHS: National Institute of Environmental Health Sciences.
Please refer to the beginning of the supplemental materials for the definition of the bibliometric indices.
• The most active funding bodies

Funding sources were not reported for 156 (156/296, 52.7%) publications. Among the top 20 of the most active funding bodies, which contributed to 18 or more publications (18/296, 6.1%), the NIH (US) led with 22 publications (22/296, 7.4%), followed by Mutualité Sociale Agricole (MSA) (France) (19/296, 6.4%), and Medline (US) (18/296, 6.1%). MIAI@Grenoble Alpes (France) (83.3%), the French national cancer institute (INCa) (42.5%), and Ligue Contre Le Cancer (France) (37.5%) demonstrated the highest mean AGR among this top 20 (Table MA7).

<table><caption>**Table MA7.** Top 20 of the most active funding bodies.</caption><thead><tr><th>Funding bodies</th><th>Country</th><th>Publication period</th><th>Number of publications (%)</th><th>Total citation</th><th>Local citation (%)</th><th>Average citation per year</th><th>Mean AGR</th><th>Fractionalized frequency (%)</th><th>h-index</th><th>g-index</th><th>m-index</th></tr></thead><tbody><tr><td>National Institutes Of Health (NIH)</td><td>US</td><td>1985-2022</td><td>22 (7.43%)</td><td>840</td><td>16 (2.02%)</td><td>22.1</td><td>-11.0</td><td>10.0</td><td>18</td><td>22</td><td>0.45</td></tr><tr><td>Mutualité Sociale Agricole (MSA)</td><td>France</td><td>2009-2023</td><td>19 (6.42%)</td><td>632</td><td>27 (4.27%)</td><td>42.1</td><td>8.33</td><td>4.51</td><td>11</td><td>19</td><td>0.69</td></tr><tr><td>Medline</td><td>US</td><td>1985-2020</td><td>18 (6.08%)</td><td>664</td><td>14 (2.27%)</td><td>18.4</td><td>-19.9</td><td>9.17</td><td>15</td><td>18</td><td>0.38</td></tr><tr><td>French National Agency for Research (ANR)</td><td>France</td><td>2009-2024</td><td>15 (5.07%)</td><td>423</td><td>15 (3.55%)</td><td>26.4</td><td>-1.56</td><td>3.87</td><td>6</td><td>15</td><td>0.38</td></tr><tr><td>French national cancer institute (INCa)</td><td>France</td><td>2015-2022</td><td>15 (5.07%)</td><td>333</td><td>21 (6.31%)</td><td>41.6</td><td>42.5</td><td>3.71</td><td>10</td><td>15</td><td>1.00</td></tr><tr><td>National Institute for Occupational Safety & Health (NIOSH)</td><td>US</td><td>2005-2022</td><td>15 (5.07%)</td><td>295</td><td>13 (4.41%)</td><td>16.4</td><td>-13.9</td><td>8.21</td><td>8</td><td>15</td><td>0.40</td></tr><tr><td>ANSES</td><td>France</td><td>2010-2022</td><td>14 (4.73%)</td><td>278</td><td>25 (8.99%)</td><td>21.4</td><td>0.00</td><td>3.26</td><td>9</td><td>14</td><td>0.60</td></tr><tr><td>Centre François Baclesse</td><td>France</td><td>2017-2022</td><td>12 (4.05%)</td><td>291</td><td>26 (8.93%)</td><td>48.5</td><td>0.00</td><td>1.31</td><td>9</td><td>12</td><td>1.12</td></tr><tr><td>Ligue Contre Le Cancer</td><td>France</td><td>2017-2022</td><td>12 (4.05%)</td><td>272</td><td>25 (9.19%)</td><td>45.3</td><td>37.5</td><td>1.61</td><td>9</td><td>12</td><td>1.12</td></tr><tr><td>Association Pour La Recherche Sur Le Cancer (ARC)</td><td>France</td><td>2017-2022</td><td>11 (3.72%)</td><td>268</td><td>20 (7.46%)</td><td>44.7</td><td>3.33</td><td>1.17</td><td>9</td><td>11</td><td>1.12</td></tr><tr><td>Fondation De France</td><td>France</td><td>2017-2022</td><td>11 (3.72%)</td><td>268</td><td>20 (7.46%)</td><td>44.7</td><td>3.33</td><td>1.17</td><td>9</td><td>11</td><td>1.12</td></tr><tr><td>French National Institute of Agricultural Medicine (INMA)</td><td>France</td><td>2017-2022</td><td>11 (3.72%)</td><td>270</td><td>26 (9.63%)</td><td>45.0</td><td>2.78</td><td>1.18</td><td>9</td><td>11</td><td>1.12</td></tr><tr><td>French Ministry of Higher education and scientific research</td><td>France</td><td>2009-2024</td><td>10 (3.38%)</td><td>433</td><td>16 (3.70%)</td><td>27.1</td><td>-25.0</td><td>1.47</td><td>8</td><td>10</td><td>0.50</td></tr><tr><td>French National Office for Water and Aquatic Environments (Onema)</td><td>France</td><td>2017-2022</td><td>9 (3.04%)</td><td>189</td><td>25 (13.2%)</td><td>31.5</td><td>5.56</td><td>1.69</td><td>6</td><td>9</td><td>0.75</td></tr><tr><td>Miai@Grenoble Alpes</td><td>France</td><td>2022-2024</td><td>7 (2.36%)</td><td>21</td><td>14 (66.7%)</td><td>7.00</td><td>83.3</td><td>2.83</td><td>3</td><td>4</td><td>1.00</td></tr><tr><td>National Institute Of Environmental Health Sciences (NIEHS)</td><td>US</td><td>2010-2020</td><td>7 (2.36%)</td><td>397</td><td>0 (0.00%)</td><td>36.1</td><td>-22.7</td><td>2.62</td><td>6</td><td>7</td><td>0.40</td></tr><tr><td>Novartis Pharma</td><td>Switzerland</td><td>2018-2022</td><td>7 (2.36%)</td><td>33</td><td>4 (12.1%)</td><td>6.60</td><td>13.3</td><td>2.62</td><td>4</td><td>5</td><td>0.57</td></tr><tr><td>National Cancer Institute (NCI)</td><td>US</td><td>2014-2023</td><td>6 (2.03%)</td><td>316</td><td>1 (0.32%)</td><td>31.6</td><td>-20.0</td><td>1.70</td><td>4</td><td>6</td><td>0.36</td></tr><tr><td>Nordic Cancer Union</td><td>Nordic countries</td><td>2009-2023</td><td>6 (2.03%)</td><td>514</td><td>21 (4.09%)</td><td>34.3</td><td>-20.0</td><td>3.27</td><td>4</td><td>6</td><td>0.25</td></tr><tr><td>Centers for Disease Control and Prevention (CDC)</td><td>US</td><td>2009-2022</td><td>5 (1.69%)</td><td>32</td><td>0 (0.00%)</td><td>2.29</td><td>-21.4</td><td>1.54</td><td>3</td><td>5</td><td>0.19</td></tr></tbody></table>

Note: AGR: annual growth rate, ANSES: French Agency for Food, Environmental and Occupational Health & Safety.

Please refer to the beginning of the supplemental materials for the definition of the bibliometric indices.
314 Multimedia appendix 2. Publications included and analyzed.

315

316 Please refer to the MS Excel file entitled "Multimedia appendix 2".
<figure><img src="image_12.png" /><figcaption>317 **Figure S5.** Number of publications per country.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>319 Figure S6. Main goal addressed by study.</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>321 Figure S7. Number of publications per cohort.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>323 Figure S8. Type of digital data used.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>325 **Figure S9.** Active data used.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>327 **Figure S10.** Most frequent farming exposure proxy.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>329 Figure S11. Most frequent health events studied.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>331 Figure S12. Most frequent cancer studied.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>333 Figure S13. Most frequent neurodegenerative disease studied.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Figure S14. Most frequent mental health issue studied.</figcaption></figure>
<table><caption>337 Table S7. Top 50 of the most frequent keywords.</caption><thead><tr><th>Keyword</th><th>First appearance</th><th>Last appearance</th><th>Number of publications</th><th>Total citation</th><th>Local citation</th><th>Average citation per year</th><th>Mean AGR</th><th>h-index</th><th>g-index</th><th>m-index</th></tr></thead><tbody><tr><td>Cancer</td><td>1975</td><td>2024</td><td>150 (50.7%)</td><td>5766</td><td>137 (2.74%)</td><td>115</td><td>13.5</td><td>43</td><td>70</td><td>0.860</td></tr><tr><td>Mortality</td><td>1981</td><td>2024</td><td>96 (32.4%)</td><td>3097</td><td>75 (2.81%)</td><td>70.4</td><td>8.15</td><td>34</td><td>53</td><td>0.773</td></tr><tr><td>Pesticide</td><td>1981</td><td>2024</td><td>88 (29.7%)</td><td>3569</td><td>81 (2.44%)</td><td>81.1</td><td>-7.46</td><td>36</td><td>58</td><td>0.818</td></tr><tr><td>Occupation</td><td>1980</td><td>2024</td><td>82 (27.7%)</td><td>2959</td><td>55 (2.33%)</td><td>65.8</td><td>14.2</td><td>31</td><td>53</td><td>0.689</td></tr><tr><td>Farmer</td><td>1981</td><td>2024</td><td>77 (26.0%)</td><td>2856</td><td>91 (3.25%)</td><td>64.9</td><td>6.36</td><td>34</td><td>52</td><td>0.773</td></tr><tr><td>Agriculture</td><td>1980</td><td>2024</td><td>74 (25.0%)</td><td>1701</td><td>55 (3.85%)</td><td>37.8</td><td>-9.96</td><td>26</td><td>39</td><td>0.578</td></tr><tr><td>Epidemiology</td><td>1991</td><td>2024</td><td>57 (19.3%)</td><td>1396</td><td>17 (1.47%)</td><td>41.1</td><td>23.1</td><td>21</td><td>37</td><td>0.618</td></tr><tr><td>Exposure</td><td>1992</td><td>2024</td><td>57 (19.3%)</td><td>1713</td><td>27 (1.76%)</td><td>61.2</td><td>-2.73</td><td>21</td><td>41</td><td>0.636</td></tr><tr><td>Lymphohematopoietic cancer</td><td>1981</td><td>2023</td><td>54 (18.2%)</td><td>2906</td><td>66 (2.69%)</td><td>67.6</td><td>-13.8</td><td>31</td><td>53</td><td>0.705</td></tr><tr><td>Respiratory cancer</td><td>1987</td><td>2024</td><td>51 (17.2%)</td><td>2566</td><td>67 (3.29%)</td><td>67.5</td><td>-16.1</td><td>28</td><td>50</td><td>0.737</td></tr><tr><td>Risk</td><td>1992</td><td>2024</td><td>51 (17.2%)</td><td>1909</td><td>27 (1.53%)</td><td>57.8</td><td>6.01</td><td>25</td><td>43</td><td>0.758</td></tr><tr><td>Male genital organ cancer</td><td>1987</td><td>2024</td><td>47 (15.9%)</td><td>2865</td><td>75 (3.12%)</td><td>75.4</td><td>-12.5</td><td>29</td><td>47</td><td>0.763</td></tr><tr><td>Risk factor</td><td>1997</td><td>2024</td><td>44 (14.9%)</td><td>818</td><td>17 (2.08%)</td><td>29.2</td><td>-19.0</td><td>17</td><td>27</td><td>0.607</td></tr><tr><td>Digestive organ cancer</td><td>1987</td><td>2023</td><td>41 (13.9%)</td><td>2331</td><td>58 (3.09%)</td><td>63.0</td><td>-11.3</td><td>26</td><td>41</td><td>0.684</td></tr><tr><td>Occupational exposure</td><td>1992</td><td>2024</td><td>41 (13.9%)</td><td>789</td><td>39 (4.94%)</td><td>23.9</td><td>2.01</td><td>17</td><td>27</td><td>0.515</td></tr><tr><td>Worker</td><td>1992</td><td>2024</td><td>41 (13.9%)</td><td>1243</td><td>32 (2.81%)</td><td>37.7</td><td>-4.85</td><td>19</td><td>35</td><td>0.576</td></tr><tr><td>Injury</td><td>1991</td><td>2024</td><td>40 (13.5%)</td><td>734</td><td>18 (2.69%)</td><td>21.6</td><td>-5.44</td><td>15</td><td>26</td><td>0.441</td></tr><tr><td>Cohort</td><td>1992</td><td>2024</td><td>39 (13.2%)</td><td>879</td><td>40 (4.55%)</td><td>26.6</td><td>1.41</td><td>16</td><td>29</td><td>0.485</td></tr><tr><td>Incidence</td><td>1985</td><td>2023</td><td>34 (11.5%)</td><td>1187</td><td>29 (3.21%)</td><td>30.4</td><td>-13.7</td><td>20</td><td>34</td><td>0.500</td></tr><tr><td>Neurodegenerative disease</td><td>2000</td><td>2024</td><td>33 (11.1%)</td><td>1216</td><td>13 (1.12%)</td><td>55.3</td><td>-10.0</td><td>17</td><td>33</td><td>0.680</td></tr><tr><td>Urinary tract cancer</td><td>1987</td><td>2023</td><td>33 (11.1%)</td><td>1975</td><td>47 (3.03%)</td><td>53.4</td><td>-9.59</td><td>21</td><td>33</td><td>0.553</td></tr><tr><td>Workplace accident</td><td>1987</td><td>2024</td><td>32 (10.8%)</td><td>727</td><td>17 (2.76%)</td><td>20.8</td><td>-6.58</td><td>15</td><td>26</td><td>0.395</td></tr><tr><td>Respiratory disorder</td><td>1986</td><td>2024</td><td>32 (10.8%)</td><td>901</td><td>21 (2.46%)</td><td>23.1</td><td>-15.2</td><td>15</td><td>30</td><td>0.385</td></tr><tr><td>US</td><td>1994</td><td>2024</td><td>32 (10.8%)</td><td>1167</td><td>14 (1.26%)</td><td>40.2</td><td>-25.0</td><td>18</td><td>32</td><td>0.581</td></tr><tr><td>Brain cancer</td><td>1981</td><td>2024</td><td>31 (10.5%)</td><td>1858</td><td>61 (3.58%)</td><td>42.2</td><td>-10.6</td><td>23</td><td>31</td><td>0.523</td></tr><tr><td>Health</td><td>1997</td><td>2023</td><td>31 (10.5%)</td><td>463</td><td>25 (5.45%)</td><td>17.1</td><td>-12.0</td><td>12</td><td>21</td><td>0.429</td></tr><tr><td>Lip, oral cavity, and pharynx cancer</td><td>1975</td><td>2024</td><td>30 (10.1%)</td><td>1827</td><td>55 (3.93%)</td><td>36.5</td><td>-14.8</td><td>21</td><td>30</td><td>0.420</td></tr><tr><td>Male</td><td>1985</td><td>2023</td><td>30 (10.1%)</td><td>1150</td><td>29 (2.86%)</td><td>29.5</td><td>-15.4</td><td>19</td><td>30</td><td>0.475</td></tr><tr><td>Skin cancer</td><td>1975</td><td>2023</td><td>30 (10.1%)</td><td>1999</td><td>44 (2.80%)</td><td>40.8</td><td>-22.8</td><td>22</td><td>30</td><td>0.440</td></tr><tr><td>Prevalence</td><td>1998</td><td>2024</td><td>28 (9.46%)</td><td>438</td><td>17 (3.88%)</td><td>16.2</td><td>-14.8</td><td>12</td><td>20</td><td>0.444</td></tr><tr><td>Socio-economic status</td><td>1980</td><td>2024</td><td>27 (9.12%)</td><td>685</td><td>7 (1.33%)</td><td>15.2</td><td>-24.4</td><td>16</td><td>26</td><td>0.356</td></tr><tr><td>Animal farming</td><td>1975</td><td>2023</td><td>26 (8.78%)</td><td>883</td><td>29 (3.28%)</td><td>18.0</td><td>-20.1</td><td>15</td><td>26</td><td>0.300</td></tr><tr><td>Breast cancer</td><td>1987</td><td>2024</td><td>26 (8.78%)</td><td>1628</td><td>48 (3.63%)</td><td>42.8</td><td>-18.7</td><td>20</td><td>26</td><td>0.526</td></tr><tr><td>Farming activity</td><td>1985</td><td>2023</td><td>25 (8.45%)</td><td>925</td><td>35 (3.82%)</td><td>23.7</td><td>-10.7</td><td>14</td><td>25</td><td>0.350</td></tr><tr><td>Mental health</td><td>1991</td><td>2024</td><td>25 (8.45%)</td><td>512</td><td>6 (1.22%)</td><td>15.1</td><td>-28.4</td><td>12</td><td>22</td><td>0.353</td></tr><tr><td>Asthma</td><td>1986</td><td>2024</td><td>24 (8.11%)</td><td>743</td><td>5 (0.72%)</td><td>19.1</td><td>-12.4</td><td>13</td><td>24</td><td>0.333</td></tr><tr><td>Farm</td><td>1992</td><td>2024</td><td>24 (8.11%)</td><td>516</td><td>8 (1.75%)</td><td>15.6</td><td>-6.57</td><td>10</td><td>22</td><td>0.303</td></tr><tr><td>Herbicide</td><td>1984</td><td>2024</td><td>22 (7.43%)</td><td>1349</td><td>26 (1.93%)</td><td>32.9</td><td>-12.6</td><td>15</td><td>22</td><td>0.366</td></tr><tr><td>Mesenchymal cancer</td><td>1984</td><td>2023</td><td>20 (6.76%)</td><td>1466</td><td>35 (3.07%)</td><td>36.6</td><td>-18.1</td><td>14</td><td>20</td><td>0.341</td></tr><tr><td>COPD</td><td>1987</td><td>2024</td><td>19 (6.42%)</td><td>298</td><td>14 (5.60%)</td><td>11.0</td><td>-1.32</td><td>8</td><td>17</td><td>0.211</td></tr><tr><td>Environmental exposure</td><td>1999</td><td>2024</td><td>19 (6.42%)</td><td>688</td><td>12 (3.20%)</td><td>31.3</td><td>-17.9</td><td>11</td><td>19</td><td>0.423</td></tr><tr><td>Female</td><td>1980</td><td>2024</td><td>19 (6.42%)</td><td>454</td><td>7 (1.54%)</td><td>10.1</td><td>-25.6</td><td>11</td><td>19</td><td>0.244</td></tr><tr><td>Female genital organ cancer</td><td>1987</td><td>2024</td><td>19 (6.42%)</td><td>1083</td><td>47 (4.34%)</td><td>28.5</td><td>-21.1</td><td>14</td><td>19</td><td>0.368</td></tr><tr><td>Insecticide</td><td>1992</td><td>2023</td><td>19 (6.42%)</td><td>1218</td><td>33 (2.71%)</td><td>38.1</td><td>-21.9</td><td>14</td><td>19</td><td>0.424</td></tr><tr><td>Parkinson's disease</td><td>2000</td><td>2021</td><td>19 (6.42%)</td><td>972</td><td>12 (1.32%)</td><td>51.2</td><td>-19.7</td><td>16</td><td>19</td><td>0.640</td></tr><tr><td>Pesticide applicator</td><td>1987</td><td>2024</td><td>19 (6.42%)</td><td>984</td><td>32 (3.57%)</td><td>25.9</td><td>-30.7</td><td>14</td><td>19</td><td>0.368</td></tr><tr><td>Smoking</td><td>1989</td><td>2022</td><td>19 (6.42%)</td><td>586</td><td>14 (2.66%)</td><td>17.2</td><td>-18.6</td><td>12</td><td>19</td><td>0.333</td></tr><tr><td>Assessment</td><td>2003</td><td>2024</td><td>18 (6.08%)</td><td>293</td><td>0 (0.00%)</td><td>13.3</td><td>-18.9</td><td>10</td><td>17</td><td>0.455</td></tr><tr><td>CVD</td><td>1987</td><td>2023</td><td>17 (5.74%)</td><td>623</td><td>3 (0.52%)</td><td>18.3</td><td>-18.9</td><td>11</td><td>17</td><td>0.289</td></tr></tbody></table>
<table><tr><td>Disease</td><td>1997</td><td>2023</td><td>17 (5.74%)</td><td>385</td><td>2 (0.58%)</td><td>14.3</td><td>-11.1</td><td>8</td><td>17</td><td>0.286</td></tr><tr><td>Follow-up</td><td>1997</td><td>2024</td><td>17 (5.74%)</td><td>562</td><td>5 (0.96%)</td><td>20.1</td><td>-23.8</td><td>10</td><td>17</td><td>0.357</td></tr><tr><td>Surveillance</td><td>1988</td><td>2024</td><td>17 (5.74%)</td><td>291</td><td>13 (5.04%)</td><td>7.86</td><td>-17.6</td><td>8</td><td>17</td><td>0.216</td></tr></table>

338 Note: AGR: annual growth rate, COPD: chronic obstructive pulmonary disease, CVD: cardiovascular disorder. Please refer to the beginning of the supplemental materials for

339 the definition of the bibliometric indices.
340

<figure><img src="image_22.png" /><figcaption>Figure S15. Top 5 of the most frequent keywords – growth trends.</figcaption></figure>

341
342 **Figure S16.** Top ten of the most popular keywords by total number of years of appearance.

343 AAW: workplace accident; ALS: amyotrophic lateral sclerosis, BMI: body mass index, BP: blood pressure, COPD: chronic obstructive pulmonary disease, CVD: cardiovascular

344 disorder, EMR: electronic medical record, IgE: immunoglobulin E, MSD: musculoskeletal disorder, SES: socio-economic status, US: United States of America.

<figure><img src="image_23.png" /></figure>
<figure><img src="image_24.png" /></figure>
353 **Figure S18.** Keyword co-occurrence network between potential risk factor and mental health disorder keywords.

354 BMI: body mass index, SES: socio-economic status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to

355 potential risk factors.

<figure><img src="image_25.png" /></figure>
357 **Figure S19.** Chord diagram of keyword co-occurrence between potential risk factor and neurodegenerative disease keywords.

358 ALS: amyotrophic lateral sclerosis, SES: socio-economic status, VOC: volatile organic compound.

<figure><img src="image_26.png" /></figure>
360 **Figure S20.** Keyword co-occurrence network between potential risk factor and neurodegenerative disease keywords.

361 ALS: amyotrophic lateral sclerosis, SES: socio-economic status, VOC: volatile organic compound. Larger the node size, higher the number of co-occurrence. Red nodes refer

362 to health events while blue nodes refer to potential risk factors.

<figure><img src="image_27.png" /></figure>
<caption>365 **Figure S21.** Chord diagram of keyword co-occurrence between potential risk factor and autoimmune disease keywords.</caption>

<figure><img src="image_28.png" /><figcaption>366 BMI: body mass index, BP: blood pressure, IBD: inflammatory bowel disease, SES: socio-economic status, VOC: volatile organic compound.</figcaption></figure>
368 **Figure S22.** Keyword co-occurrence network between potential risk factor and autoimmune disease keywords.

369 BMI: body mass index, BP: blood pressure, IBD: inflammatory bowel disease, SES: socio-economic status, VOC: volatile organic compound. Larger the node size, higher the

370 number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_29.png" /></figure>
373 **Figure S23.** Chord diagram of keyword co-occurrence between potential risk factor and breast and genital organ cancer keywords.

374 2,4-D: 2,4-dichlorophenoxyacetic acid, BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, PAH: polycyclic aromatic hydrocarbon,

375 PCB: polychlorinated biphenyls, SES: socio-economic status.

<figure><img src="image_30.png" /></figure>
377 **Figure S24.** Keyword co-occurrence network between potential risk factor and breast and genital organ cancer keywords.

378 2,4-D: 2,4-dichlorophenoxyacetic acid, BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, PAH: polycyclic aromatic hydrocarbon,

379 PCB: polychlorinated biphenyls, SES: socio-economic status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes

380 refer to potential risk factors.

<figure><img src="image_31.png" /></figure>
382 **Figure S25.** Chord diagram of keyword co-occurrence between potential risk factor and digestive organ, lip, oral cavity and pharynx cancer keywords.

383 BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, ETS: environmental tobacco smoking, PAH: polycyclic aromatic hydrocarbon,

384 SES: socio-economic status, VOC: volatile organic compound, UV: ultraviolet.

<figure><img src="image_32.png" /></figure>
386 **Figure S26.** Keyword co-occurrence network between potential risk factor and digestive organ, lip, oral cavity and pharynx cancer keywords.

387 BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, ETS: environmental tobacco smoking, PAH: polycyclic aromatic hydrocarbon,

388 SES: socio-economic status, VOC: volatile organic compound, UV: ultraviolet. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events

389 while blue nodes refer to potential risk factors.

<figure><img src="image_33.png" /></figure>
<caption>391 **Figure S27.** Chord diagram of keyword co-occurrence between potential risk factor and lymphohematopoietic and mesenchymal cancer keywords.</caption>

<figure><img src="image_34.png" /><figcaption>392 2,4-D: 2,4-dichlorophenoxyacetic acid, BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, SES: socio-economic status, VOC: volatile organic compound.</figcaption></figure>
394

**Figure S28.** Keyword co-occurrence network between potential risk factor and lymphohematopoietic and mesenchymal cancer keywords.

395

2,4-D: 2,4-dichlorophenoxyacetic acid, BMI: body mass index, DDT: dichlorodiphenyltrichloroethane, SES: socio-economic status, VOC: volatile organic compound.

396

Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_35.png" /></figure>
398

<caption>**Figure S29.** Chord diagram of keyword co-occurrence between potential risk factor and brain, skin, ocular and endocrine gland cancer keywords.</caption>

399

DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, PAH: polycyclic aromatic hydrocarbon, SES: socio-economic status, UV: ultraviolet.

<figure><img src="image_36.png" /></figure>
402 **Figure S30.** Keyword co-occurrence network between potential risk factor and brain, skin, ocular and endocrine gland cancer keywords.

403 DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, PAH: polycyclic aromatic hydrocarbon, SES: socio-economic status, UV: ultraviolet.

404 health events while blue nodes refer to potential risk factors.

<figure><img src="image_37.png" /></figure>
407 **Figure S31.** Chord diagram of keyword co-occurrence between potential risk factor and respiratory and urinary tract cancer keywords.

408 DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, ETS: environmental tobacco smoking, PAH: polycyclic aromatic hydrocarbon, SES: socio-economic status.

409

<figure><img src="image_38.png" /></figure>
411 **Figure S32.** Keyword co-occurrence network between potential risk factor and respiratory and urinary tract cancer keywords.

412 DDT: dichlorodiphenyltrichloroethane, EMF: electromagnetic field, ETS: environmental tobacco smoking, PAH: polycyclic aromatic hydrocarbon, SES: socio-economic

413 status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_39.png" /></figure>
416 **Figure S33.** Chord diagram of keyword co-occurrence between potential risk factor and infectious disease keywords.

417 SES: socio-economic status.

<figure><img src="image_40.png" /></figure>
420
421

<caption>**Figure S34.** Keyword co-occurrence network between potential risk factor and infectious disease keywords.</caption>

SES: socio-economic status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_41.png" /></figure>
424 **Figure S35.** Chord diagram of keyword co-occurrence between potential risk factor and respiratory disorder keywords.

425 BMI: body mass index, BP: blood pressure, COPD: chronic obstructive pulmonary disease, IgE: immunoglobulin E, IgG: immunoglobulin G, psy: psychosocial, SES: socio-

426 economic status.

<figure><img src="image_42.png" /></figure>
428 **Figure S36.** Keyword co-occurrence network between potential risk factor and respiratory disorder keywords.

429 BMI: body mass index, BP: blood pressure, COPD: chronic obstructive pulmonary disease, IgE: immunoglobulin E, IgG: immunoglobulin G, psy: psychosocial, SES: socio-

430 economic status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_43.png" /></figure>
433 **Figure S37.** Chord diagram of keyword co-occurrence between potential risk factor and reproductive disorder keywords.

434 BMI: body mass index, SES: socio-economic status.

<figure><img src="image_44.png" /></figure>
436 **Figure S38.** Keyword co-occurrence network between potential risk factor and reproductive disorder keywords.

437 BMI: body mass index, SES: socio-economic status. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to

438 potential risk factors.

<figure><img src="image_45.png" /></figure>
441 **Figure S39.** Chord diagram of keyword co-occurrence between potential risk factor and injury and work-related disease keywords.

442 AAW: workplace accident, BMI: body mass index, MSD: musculoskeletal disorder, psy: psychological, SES: socio-economic status, WRD: work-related disease.

<figure><img src="image_46.png" /></figure>
445 **Figure S40.** Keyword co-occurrence network between potential risk factor and injury and work-related disease keywords.

446 AAW: workplace accident, BMI: body mass index, MSD: musculoskeletal disorder, psy: psychological, SES: socio-economic status, WRD: work-related disease. Larger the

447 node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_47.png" /></figure>
450 **Figure S41.** Chord diagram of keyword co-occurrence between potential risk factor and other health event keywords.

451 BMI: body mass index, BP: blood pressure, CKD: chronic kidney disease, CVD: cardiovascular disease, psy: psychological, SES: socio-economic status.

<figure><img src="image_48.png" /></figure>
454

**Figure S42.** Keyword co-occurrence network between potential risk factor and other health event keywords.

455

BMI: body mass index, BP: blood pressure, CKD: chronic kidney disease, CVD: cardiovascular disease, psy: psychological, SES: socio-economic status. Larger the node

456

size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_49.png" /></figure>

457

458
459

**Figure S43.** Keyword co-occurrence network between potential risk factor and mortality keywords.

460

DBCP: 1,2-dibromo-3-chloropropane, EMF: electromagnetic field, ETS: environmental tobacco smoking, PAH: polycyclic aromatic hydrocarbon, SES: socio-economic status,

461

VOC: volatile organic compound. Larger the node size, higher the number of co-occurrence. Red nodes refer to health events while blue nodes refer to potential risk factors.

<figure><img src="image_50.png" /></figure>
463 **Abbreviations**

464
465 90%CI: 90% confidence interval
466 AAW: workplace accident
467 AGEIS: Autonomy, Gerontology, E-health, Imaging & Society (Autonomie, Gérontologie, E-santé, Imagerie et Société in French)
468
469 AGR: annual growth rate
470 AGRICAN: Agriculture and Cancer
471 AHD: administrative health database
472 AHS: Agricultural Health Study
473 AI: artificial intelligence
474 AMI: Aging Multidisciplinary Investigation
475 ANR: French National Research Agency (Agence Nationale de la Recherche in French)
476 AS: association strength
477 BIBLIO: preliminary guideline for reporting bibliometric reviews of the biomedical literature
478 BMI: body mass index
479 CHU: university hospital (centre hospitalier universitaire in French)
480 COPD: chronic obstructive pulmonary disease
481 CNAP: Cancer in the Norwegian Agricultural population cohort
482 CVD: cardiovascular disorder
483 DDT: dichlorodiphenyltrichloroethane
484 EHR: electronic health record
485 EMR: electronic medical record
486 FAIR: findable, accessible, interoperable and reusable
487 FERMA: risk factors of the rural environment and the allergic and respiratory disease
488 FF: fractional frequency
489 FINJEM: Finnish information system on occupational exposure
490 GDP: gross domestic product
491 IARC: International Agency for Research on Cancer
492 IBD: inflammatory bowel disease
493 ICMJE: International Committee of Medical Journal Editors
494 INCa: French national cancer institute (Institut National du Cancer in French)
495 INSERM: French National Institute of Health and Medical Research (Institut national de la santé et de la recherche médicale in French)
497 IQR: interquartile range
498 JEM: job-exposure matrix
499 mHealth: mobile health
500 MIAI: Multidisciplinary Institute in Artificial Intelligence
501 ML: machine learning
502 MSA: National Health Insurance Fund for Agricultural Workers and Farmers (Mutualité Sociale Agricole in French)
503
504 MSD: musculoskeletal disorder
505 NAWS: National Agricultural Workers Survey
506 NCI: National Cancer Institute
507 NIH: National Institutes of Health
508 NOCCA: Nordic Occupational Cancer Study
509 ORCID: Open Researcher and Contributor ID
510 PFAS: per- and polyfluoroalkyl substances
511 PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews and evidence maps
513 Q1: first quartile
514 R&D: research and development
515 SES: socio-economic status
516 TEDI: toxicological and exposure database inventory
517 TRACTOR: Tracking and monitoring occupational risks in agriculture
518 UFW: United Farm Workers of America
519 UGA: Université Grenoble Alpes
520 UK: United Kingdom
521 US: United States
522 UV: ultraviolet
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