Background: Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. Methods: This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models—XG Boost, Support Vector Machine (SVM), and Random Forest (RF)—were employed. Results: Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. Conclusions: The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

Occupational Hearing Loss (OHL) is a significant health concern among oil workers, influenced by environmental and occupational factors. This study investigates the independent and synergistic effects of air pollution and noise exposure on OHL risk in this high-risk population. A prospective cohort study included 1,179 oil workers from Hebei Province, China, with baseline data (2017–2019) and follow-up until 2023. Air pollution exposure (PM2.5, PM10, SO2, NO2, CO, and the Air Quality Comprehensive Index, AQCI) was assessed using monitoring station data, while cumulative noise exposure (dB(A)-year) was calculated from workplace measurements. Multivariable-adjusted Modified Poisson regression and Restricted Cubic Splines (RCS) analyzed associations and dose-response relationships. The OHL incidence was 34.5% (n = 407). Air pollutants exhibited significant nonlinear associations with OHL: PM2.5 showed a U-shaped curve, SO2 an inverted U-shape, and NO2 an S-shaped curve. Noise exposure ≥ 85 dB(A)-year tripled OHL risk (Relative Risk, RR = 2.361, Confidence Interval, 95%CI: 1.861–2.994). Synergistic effects were robust for AQCI (RR = 5.776, 95%CI:3.225–10.345) and NO2 (RR = 4.297, 95%CI:1.879–9.825) with high noise exposure, while PM10 demonstrated antagonistic effects. Air pollution and noise exposure independently and synergistically increase OHL risk in oil workers. AQCI and NO2, combined with noise, are critical risk factors. These findings underscore the need for integrated exposure monitoring, optimized ventilation, and enhanced personal protective measures in high-risk occupational settings.

Steelworkers are exposed to complex occupational hazards; however, evidence regarding the joint effects of these hazards on hypertension-diabetes comorbidi

Renal dysfunction poses a significant global health burden, with occupational hazards in the petroleum industry potentially contributing to its development