Contributing Risk Factors for Chronic Health Conditions among Oil Drilling Workers: A Predictive Modeling Approach

Chronic health conditions among oil drilling workers represent a significant occupational health challenge in the oil and gas industry. Previous studies have described the prevalence of occupational health outcomes, but there is a need to understand how contributing factors affect chronic health outcomes. This study adopted a cross-sectional design aimed at identifying and quantifying contributing risk factors associated with chronic health conditions among oil drilling workers using logistic regression modelling. A total of 350 oil drilling workers across the Niger Delta region participated in the study. Structured questionnaires captured data on work-related factors (physical job demands, work control/autonomy, exposure frequency), organisational factors (supervisor support, organisational stress support, PPE usage), personal factors (smoking status, exercise frequency), and demographic characteristics (age, years of experience). Logistic regression analysis was employed to examine associations between predictor variables and chronic health outcomes. Results revealed that exposure frequency emerged as the strongest work-related predictor (OR = 2.11, 95% CI: 2.10-2.13, p < 0.001), with each unit increase associated with 111.4% increased odds of chronic health conditions. Organisational stress support demonstrated substantial protective effects (OR = 0.51, 95% CI: 0.50-0.51, p < 0.001), reducing health risk odds by 49.4%. Personal factors showed that smoking increased risk by 29.1% (OR = 1.29, p < 0.001), while exercise frequency decreased risk by 29.4% (OR = 0.71, p < 0.001). Age group emerged as the strongest demographic predictor, with each unit increase associated with 64.9% increased odds (OR = 1.65, 95% CI: 1.63-1.66, p < 0.001). These findings provide evidence that chronic health conditions among oil drilling workers are influenced by multiple interacting factors across work-related, organisational, personal, and demographic domains, supporting the necessity of multifaceted approaches to occupational health management.

Risk factors, protective measures, logistic regression, occupational health surveillance, oil drilling workers, predictive modelling

1. Baker, M. G., Peckham, T. K., & Seixas, N. S. (2020). Estimating the burden of United States workers exposed to infection or disease: A key factor in containing risk of COVID-19 infection. PLoS ONE, 15(4), e0232452. [Google Scholar] [Crossref]

2. Benson, C., Dimopoulos, C., Argyropoulos, C. D., Mikellidou, C. V., & Boustras, G. (2021). Assessing the common occupational health hazards and their health risks among oil and gas workers. Safety Science, 140, 105284. https://doi.org/10.1016/j.ssci.2021.105284 [Google Scholar] [Crossref]

3. Case, A., & Deaton, A. (2005). Broken down by work and sex: How our health declines. In D. A. Wise (Ed.), Analyses in the economics of aging (pp. 185-212). University of Chicago Press. [Google Scholar] [Crossref]

4. Checkoway, H., Pearce, N., & Kriebel, D. (2007). Exposure and dose modelling in occupational epidemiology. Occupational and Environmental Medicine, 64(7), 492-498. [Google Scholar] [Crossref]

5. Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications. [Google Scholar] [Crossref]

6. D'Andrea, M. A., & Reddy, G. K. (2018). Long-term health effects of the Gulf oil spill exposure among oil spill clean-up workers seven years post-disaster. Frontiers in Public Health, 6, 117. https://doi.org/10.3389/fpubh.2018.00117 [Google Scholar] [Crossref]

7. Eyayo, F. (2014). Evaluation of occupational health hazards among oil industry workers: A case study of refinery workers. IOSR Journal of Environmental Science, Toxicology and Food Technology, 8(12), 22-53. [Google Scholar] [Crossref]

8. Gadalla, H. E., Helal, H., & Nofal, A. S. (2024). Impact of the Offshore Oil and Gas Working Environment on the Mental Health and Safety Behaviour of Workers. AIN Journal, (47). [Google Scholar] [Crossref]

9. Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). John Wiley & Sons. [Google Scholar] [Crossref]

10. Ilmarinen, J. (2006). Towards a longer worklife: Ageing and the quality of worklife in the European Union. Finnish Institute of Occupational Health. [Google Scholar] [Crossref]

11. Kale, Ö. A., & Baradan, S. (2020). Identifying factors that contribute to severity of construction injuries using logistic regression model. Teknik Dergi, 31(2), 9919-9940. [Google Scholar] [Crossref]

12. Karasek, R. A., & Theorell, T. (1990). Healthy work: Stress, productivity, and the reconstruction of working life. Basic Books. [Google Scholar] [Crossref]

13. Mearns, K. J., & Reader, T. (2008). Organizational support and safety outcomes: An un-investigated relationship?. Safety science, 46(3), 388-397. [Google Scholar] [Crossref]

14. LaMontagne, A. D., Keegel, T., Louie, A. M., Ostry, A., & Landsbergis, P. A. (2007). A systematic review of the job-stress intervention evaluation literature, 1990-2005. International Journal of Occupational and Environmental Health, 13(3), 268-280. [Google Scholar] [Crossref]

15. Levy, B. S., Wegman, D. H., Baron, S. L., & Sokas, R. K. (2018). Occupational and environmental health: Recognizing and preventing disease and injury (7th ed.). Oxford University Press. [Google Scholar] [Crossref]

16. Mamyrbayev, A., Zhurgenova, A., Komekbay, Z., Kaliev, A., Zhilisbayeva, K., Yerimbetova, G., Satybaldiyeva, U., Bermagambetova, S., & Sakhanova, S. (2025). Assessment of acute and subchronic skin toxicity of drilling fluids. Human and Experimental Toxicology, 44, 1-16. [Google Scholar] [Crossref]

17. https://doi.org/10.1177/09603271251379839 [Google Scholar] [Crossref]

18. McEwen, B. S. (2000). Allostasis and allostatic load: Implications for neuropsychopharmacology. Neuropsychopharmacology, 22(2), 108-124. [Google Scholar] [Crossref]

19. McNamee, R. (2003). Confounding and exposure measurement error. Occupational and Environmental Medicine, 60(3), 227-234. [Google Scholar] [Crossref]

20. Menard, S. (2010). Logistic regression: From introductory to advanced concepts and applications. Sage Publications. [Google Scholar] [Crossref]

21. Nwankwo, C., Ogwu, F., & Ijioma, C. (2021). Environmental and occupational health impacts of oil exploration in Nigeria's Niger Delta. Environmental Health Perspectives, 18(2), 145-162. [Google Scholar] [Crossref]

22. Onder, S. (2013). Evaluation of occupational injuries with lost days among opencast coal mine workers through logistic regression models. Safety Science, 59, 86-92. [Google Scholar] [Crossref]

23. https://doi.org/10.1016/j.ssci.2013.05.002 [Google Scholar] [Crossref]

24. Oyamienlen, O., Nwaogazie, I. L., & Patricks, C. (2023) The Effect of Demographic Factors on Work-related Injuries in the Oil and Gas Industry in the Niger Delta Region in Nigeria. [Google Scholar] [Crossref]

25. Paul, P. S. (2009). Predictors of work injury in underground mines—an application of a logistic regression model. Mining Science and Technology (China), 19(3), 282-289. [Google Scholar] [Crossref]

26. Rajamaki, B., Grosch, J. W., & Burnett, L. R. (2018). Workplace intervention effects on sleep quality: A systematic review. Journal of Occupational Health Psychology, 23(3), 291-304. [Google Scholar] [Crossref]

27. Rappaport, S. M., & Smith, T. J. (1991). Exposure assessment for epidemiology and hazard control. Lewis Publishers. [Google Scholar] [Crossref]

28. Retzer, K. D., Hill, R. D., & Pratt, S. G. (2019). Health risk factors among male workers in mining and other manual labor occupations. American Journal of Men's Health, 13(3), 1557988319850950. [Google Scholar] [Crossref]

29. Robroek, S. J., van Lenthe, F. J., van Empelen, P., & Burdorf, A. (2009). Determinants of participation in worksite health promotion programmes: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 6, 26. [Google Scholar] [Crossref]

30. Setia, M. S. (2016). Methodology series module 3: Cross-sectional studies. Indian Journal of Dermatology, 61(3), 261-264. [Google Scholar] [Crossref]

31. Siegrist, J., & Marmot, M. (2004). Health inequalities and the psychosocial environment. Social Science & Medicine, 58(8), 1463-1473. [Google Scholar] [Crossref]

32. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson. [Google Scholar] [Crossref]

33. Verbeek, J. H., Ijaz, S., Mischke, C., Ruotsalainen, J. H., Mäkelä, E., Neuvonen, K., et al. (2020). Personal protective equipment for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff. Cochrane Database of Systematic Reviews, 4, CD011621. [Google Scholar] [Crossref]

34. Verbeek, J. H., Ijaz, S., Tikka, C., Ruotsalainen, J. H., Mischke, C., & Sauni, R. (2021). Personal protective equipment for preventing highly infectious diseases due to exposure to contaminated body fluids in healthcare staff. Cochrane Database of Systematic Reviews, 5, CD011621. [Google Scholar] [Crossref]

35. World Health Organization. (2017). Protecting workers' health. WHO Fact Sheet. World Health Organization. [Google Scholar] [Crossref]

36. World Health Organization. (2020). Healthy workplaces: A model for action for employers, workers, policy-makers and practitioners. World Health Organization.. [Google Scholar] [Crossref]

• Methane Emissions from Municipal Solid Waste - Case Study in Cai Rang District, Can Tho City, Vietnam
• Youth Activism, Intentional Integration of Policies to Raise Awareness on Climate Change Action among the Youth
• Breathing Spaces: Environmental & User Experience in Dhanmondi and Zigatola Multistoried Apartments, Dhaka, Bangladesh
• Effects of Solid Waste Disposal on Soil Quality in Makurdi Metropolis, Benue State, Nigeria
• Environmental Impact of Artisanal and Small-Scale Gold Mining in Borgu Local Government Area