Construction and validation of a musculoskeletal disease risk prediction model for underground coal miners - Zhao, Haili; Dou, Hong; Yong, Xianting; Liu, Wei; Yalimaimaiti, Saiyidan; Yang, Ying; Liang, Xiaoqiao; Sun, Lili; Liu, Jiwen; Ning, Li (Frontiers in Public Health, 2023)
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% under different periods; 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%). 2. 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. 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 (P < 0.05). 5. The model has good predictive ability and predictive value . 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.