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Occupational Risk Factors by Sectors: An Observational Study of 20,000 Workers

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  • Luther Dogbla

    (Physiological and Psychosocial Stress, CNRS UMR 6024, LaPSCo, University Clermont Auvergne, CHU Clermont-Ferrand, Occupational Medicine, 63000 Clermont-Ferrand, France
    Occupational Health Service of Cher (APST18), 18000 Bourges, France)

  • Cédric Gouvenelle

    (Occupational Health Service of Cher (APST18), 18000 Bourges, France
    CNRS, Acté, University Clermont Auvergne, 63000 Clermont-Ferrand, France)

  • Florence Thorin

    (Occupational Health Service of Cher (APST18), 18000 Bourges, France)

  • François-Xavier Lesage

    (IDESP, INSERM, CHU Montpellier, University Montpellier, 34000 Montpellier, France)

  • Marek Zak

    (Faculty of Medicine and Health Sciences, Institute of Physiotherapy, The Jan Kochanowski University, 25-369 Kielce, Poland)

  • Ukadike Chris Ugbolue

    (School of Health and Life Sciences, Institute for Clinical Exercise & Health Science, University of the West of Scotland, Glasgow G72 0LH, UK)

  • Barbara Charbotel

    (UMRESTTE, Domaine Rockefeller, University Lyon 1, 69373 Lyon, France)

  • Julien S. Baker

    (Centre for Health and Exercise Science Research, Hong Kong Baptist University, Hong Kong 999077, China)

  • Bruno Pereira

    (Clinical Research and Innovation Direction, CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France)

  • Frédéric Dutheil

    (Physiological and Psychosocial Stress, CNRS UMR 6024, LaPSCo, University Clermont Auvergne, CHU Clermont-Ferrand, Occupational Medicine, WittyFit, 63001 Clermont-Ferrand, France)

Abstract

Objective: We aimed to assess the prevalence of exposure by sector and the sectors of activity most exposed to each exposure, using routine occupational health data, and to quantify the risk of being exposed. Method: Occupational risk factors were assessed by workers followed by the Occupational Health Service of Cher, using self-reported questionnaires. The sectors of activity were grouped into seven sectors, and the risks were grouped into six occupational exposure groups. Comparisons were made using the Chi-squared test and Cramer’s V, and the odds ratios were calculated by using logistic regression. Results: We included 19,891 workers. The construction sector had the highest prevalence ( p < 0.05 vs. all other sectors) of exposure to physical (76%) and biomechanical factors (82%), as well as chemical risks (75%). Human health and social work was the sector with the highest prevalence of exposure to biological factors (69%), psychosocial factors (90%), and atypical working hours (61%). With workers from administrative and support sectors as the reference, construction workers had more chance of declaring exposure to physical factors (OR = 3.28, 95%CI = 2.89 to 3.72), biomechanical factors (1.82, 1.58 to 2.09), and chemical agents (3.83, 3.38 to 4.33). Workers from the human health and social sectors had more chance of being exposed to biological agents (13.4, 11.9 to 15.2), atypical working hours (1.93, 1.75 to 2.14), and psychosocial factors (2.74, 2.38 to 3.16). Conclusion: Psychosocial risk factors were commonly reported in all sectors. Workers in the construction, human health, and social sectors seem to report more exposures than those in other sectors. The analysis of occupational exposures is a necessary basis to build an efficient preventive strategy for occupational health.

Suggested Citation

  • Luther Dogbla & Cédric Gouvenelle & Florence Thorin & François-Xavier Lesage & Marek Zak & Ukadike Chris Ugbolue & Barbara Charbotel & Julien S. Baker & Bruno Pereira & Frédéric Dutheil, 2023. "Occupational Risk Factors by Sectors: An Observational Study of 20,000 Workers," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3632-:d:1072719
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    References listed on IDEAS

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    1. Guillaume Deltreil & Patrick Tardivel & Piotr Graczyk & Mikael Escobar-Bach & Alexis Descatha, 2022. "How to Use Biomechanical Job Exposure Matrices with Job History to Access Work Exposure for Musculoskeletal Disorders? Application of Mathematical Modeling in Severe Knee Pain in the Constances Cohort," IJERPH, MDPI, vol. 19(23), pages 1-11, December.
    2. Nathalie Havet & Alexis Penot & Magali Morelle & Lionel Perrier & Barbara Charbotel & Béatrice Fervers, 2017. "Varied exposure to carcinogenic, mutagenic, and reprotoxic (CMR) chemicals in occupational settings in France," Post-Print halshs-01446700, HAL.
    3. Mariagrazia Squicciarini & Anna-Leena Asikainen, 2011. "A value chain statistical definition of construction and the performance of the sector," Construction Management and Economics, Taylor & Francis Journals, vol. 29(7), pages 671-693, March.
    4. Si Si & Kate Lewkowski & Lin Fritschi & Jane Heyworth & Danny Liew & Ian Li, 2020. "Productivity Burden of Occupational Noise-Induced Hearing Loss in Australia: A Life Table Modelling Study," IJERPH, MDPI, vol. 17(13), pages 1-9, June.
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