ArticleFrontiers in public health2023
Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners.
Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 7 citations in OpenAlex.
- Prevalence and Multivariate Impact of Musculoskeletal Disorders on General Health, Occupational Fatigue, and Productivity in an Industrial Workforce.La Medicina del lavoro · 2026Article
- Machine learning analysis of the association between psychosocial risks and burnout syndrome in mining workers.Frontiers in public health · 2026Article
- Integrated multimethod analysis of miners' safety behavior and risk interaction for practical applications.Scientific reports · 2025Article
- Unraveling the Psychological Pathways Between Job Strain and Musculoskeletal Disorders: The Mediating Roles of Work-Related Fatigue and Burnout Among Hospital Nurses.Journal of nursing management · 2025Article
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10 authors at 2 institutions in 1 country.
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Abstract
Objective: To understand the prevalence among underground coal miners of musculoskeletal disorders (MSDs), analyze the risk factors affecting MSDs, and develop and validate a risk prediction model for the development of MSDs. Materials and methods: MSD questionnaires were used to investigate the prevalence of work-related musculoskeletal disorders among 860 underground coal miners in Xinjiang. The Chinese versions of the Effort-Reward Imbalance Questionnaire (ERI), the Burnout Scale (MBI), and the Self-Rating Depression Inventory (SDS) were used to investigate the occupational mental health status of underground coal miners. The R4.1.3 software cart installation package was applied to randomly divide the study subjects into a 1:1 training set and validation set, screen independent predictors using single- and multi-factor regression analysis, and draw personalized nomogram graph prediction models based on regression coefficients. Subject work characteristic (ROC) curves, calibration (Calibrate) curves, and decision curves (DCA) were used to analyze the predictive value of each variable on MSDs and the net benefit. Results: (1) The prevalence of MSDs was 55.3%, 51.2%, and 41.9% since joining the workforce, in the past year, and in the past week, respectively; the highest prevalence was in the lower back (45.8% vs. 38.8% vs. 33.7%) and the lowest prevalence was in the hips and buttocks (13.3% vs. 11.4% vs. 9.1%) under different periods. (2) Underground coal miners: the mean total scores of occupational stress, burnout, and depression were 1.55 ± 0.64, 51.52 ± 11.53, and 13.83 ± 14.27, respectively. (3) Univariate regression revealed a higher prevalence of MSDs in those older than 45 years (49.5%), length of service > 15 years (56.4%), annual income <$60,000 (79.1%), and moderate burnout (43.2%). (4) Binary logistic regression showed that the prevalence of MSDs was higher for those with 5-20 years of service (OR = 0.295, 95% CI: 0.169-0.513), >20 years of service (OR = 0.845, 95% CI: 0.529-1.350), annual income ≥$60,000 (OR = 1.742, 95% CI: 1.100-2.759), and severe burnout (OR = 0.284, 95% CI: 0.109-0.739), and that these were independent predictors of the occurrence of MSDs among workers in underground coal mine operations ( Conclusion: The prevalence of MSDs among workers working underground in coal mines was high, and the constructed nomogram showed good discriminatory ability and optimal accuracy.
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