Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.

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.

A nested case-control study of the effects of dust exposure, smoking on COPD in coal workers.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) represents a prevalent ailment, progressively surging within the ranks of coal mine laborers. The current study endeavors to elucidate the effects of dust exposure and smoking on COPD incidence amongst coal mine workers, while concurrently devising preventive strategies for this affliction. METHOD: A nested case-control study was conducted encompassing 1,416 participants aged ≥ 18 years, spanning the duration from (2017-2018) until 2020. A meticulous matching process yielded a cohort of 708 COPD patients, each paired with a control subject, forming a harmonious 1:1 ratio. Multiple logistic regression analysis was employed to scrutinize the associations between smoking, dust exposure with COPD among coal workers. RESULTS: The COPD prevalence within the cohort of coal workers under investigation amounted to 22.66%, with an accompanying incidence density of 0.09/person-year. Following meticulous adjustment for confounding variables, it was discerned that cumulative dust exposure within the range of 47.19 ~ (OR: 1.90, 95% CI: 1.05, 3.44), 101.27 ~ (OR: 1.99, 95% CI: 1.17, 3.39), as well as smoking indices of 72 ~ (OR: 1.85, 95% CI: 1.19, 2.88), 145 ~ (OR: 1.74, 95% CI: 1.17, 2.61), 310 ~ (OR: 1.85, 95% CI: 1.23, 2.77) engender an escalated vulnerability to COPD among coal workers. Furthermore, interaction analysis discerned an absence of both multiplicative and additive interactions between dust exposure, smoking, and COPD occurrence amidst coal workers. CONCLUSION: Dust exposure and smoking were unequivocally identified as precipitating risk factors for COPD incidence within the population of coal workers, albeit devoid of any discernible interaction between these two causal agents.

Analysis of factors affecting nonalcoholic fatty liver disease in Chinese steel workers and risk assessment studies.

BACKGROUND: The global incidence of nonalcoholic fatty liver disease (NAFLD) is rapidly escalating, positioning it as a principal public health challenge with significant implications for population well-being. Given its status as a cornerstone of China's economic structure, the steel industry employs a substantial workforce, consequently bringing associated health issues under increasing scrutiny. Establishing a risk assessment model for NAFLD within steelworkers aids in disease risk stratification among this demographic, thereby facilitating early intervention measures to protect the health of this significant populace. METHODS: Use of cross-sectional studies. A total of 3328 steelworkers who underwent occupational health evaluations between January and September 2017 were included in this study. Hepatic steatosis was uniformly diagnosed via abdominal ultrasound. Influential factors were pinpointed using chi-square (χ2) tests and unconditional logistic regression analysis, with model inclusion variables identified by pertinent literature. Assessment models encompassing logistic regression, random forest, and XGBoost were constructed, and their effectiveness was juxtaposed in terms of accuracy, area under the curve (AUC), and F1 score. Subsequently, a scoring system for NAFLD risk was established, premised on the optimal model. RESULTS: The findings indicated that sex, overweight, obesity, hyperuricemia, dyslipidemia, occupational dust exposure, and ALT serve as risk factors for NAFLD in steelworkers, with corresponding odds ratios (OR, 95% confidence interval (CI)) of 0.672 (0.487-0.928), 4.971 (3.981-6.207), 16.887 (12.99-21.953), 2.124 (1.77-2.548), 2.315 (1.63-3.288), 1.254 (1.014-1.551), and 3.629 (2.705-4.869), respectively. The sensitivity of the three models was reported as 0.607, 0.680 and 0.564, respectively, while the precision was 0.708, 0.643, and 0.701, respectively. The AUC measurements were 0.839, 0.839, and 0.832, and the Brier scores were 0.150, 0.153, and 0.155, respectively. The F1 score results were 0.654, 0.661, and 0.625, with log loss measures at 0.460, 0.661, and 0.564, respectively. R2 values were reported as 0.789, 0.771, and 0.778, respectively. Performance was comparable across all three models, with no significant differences observed. The NAFLD risk score system exhibited exceptional risk detection capabilities with an established cutoff value of 86. CONCLUSIONS: The study identified sex, BMI, dyslipidemia, hyperuricemia, occupational dust exposure, and ALT as significant risk factors for NAFLD among steelworkers. The traditional logistic regression model proved equally effective as the random forest and XGBoost models in assessing NAFLD risk. The optimal cutoff value for risk assessment was determined to be 86. This study provides clinicians with a visually accessible risk stratification approach to gauge the propensity for NAFLD in steelworkers, thereby aiding early identification and intervention among those at risk.

Association of Chinese Visceral Adiposity Index and Carotid Atherosclerosis in Steelworkers: A Cross-Sectional Study.

The Chinese Visceral Adiposity Index (CVAI) is an indicator of visceral adiposity dysfunction used to evaluate the metabolic health of the Chinese population. Steelworkers are more likely to be obese due to their exposure to special occupational factors, and have a higher prevalence of carotid atherosclerosis (CAS). This study aimed to analyze the special relationship between CVAI and CAS among steelworkers. A total of 4075 subjects from a northern steel company were involved in the cross-sectional study. Four logistic regression models were developed to analyze the correlation between CVAI and CAS. In addition, the restricted cubic spline was applied to fit the dose-response association between CVAI and CAS risk. In the study, the prevalence of CAS was approximately 25.94%. After adjustment for potential confounders, we observed a positive correlation between CVAI and CAS risk. Compared to the first CVAI quartile, the effect value odds ratio (OR) and 95% CI in the second, third, and fourth CVAI quartile were 1.523 (1.159-2.000), 2.708 (2.076-3.533), and 4.101 (3.131-5.372), respectively. Additionally, this positive correlation was stable in all subgroups except for female. Furthermore, we also found a non-linear relationship between CVAI and CAS risk (p nonlinear < 0.05). Notably, CVAI could increase the risk of CAS when higher than 106. In conclusion, our study showed that CVAI might be a reliable indicator to identify high-risk populations of CAS among steelworkers.

Development and Internal Validation of Risk Assessment Models for Chronic Obstructive Pulmonary Disease in Coal Workers.

Coal workers are more likely to develop chronic obstructive pulmonary disease due to exposure to occupational hazards such as dust. In this study, a risk scoring system is constructed according to the optimal model to provide feasible suggestions for the prevention of chronic obstructive pulmonary disease in coal workers. Using 3955 coal workers who participated in occupational health check-ups at Gequan mine and Dongpang mine of Hebei Jizhong Energy from July 2018 to August 2018 as the study subjects, random forest, logistic regression, and convolutional neural network models are established, and model performance is evaluated to select the optimal model, and finally a risk scoring system is constructed according to the optimal model to achieve model visualization. The training set results show that the logistic, random forest, and CNN models have sensitivities of 78.55%, 86.89%, and 77.18%; specificities of 85.23%, 92.32%, and 87.61%; accuracies of 81.21%, 85.40%, and 83.02%; Brier scores of 0.14, 0.10, and 0.14; and AUCs of 0.76, 0.88, and 0.78, respectively, and similar results are obtained for the test set and validation set, with the random forest model outperforming the other two models. The risk scoring system constructed according to the importance ranking of random forest predictor variables has an AUC of 0.842; the evaluation results of the risk scoring system shows that its accuracy rate is 83.7% and the AUC is 0.827, and the established risk scoring system has good discriminatory ability. The random forest model outperforms the CNN and logistic regression models. The chronic obstructive pulmonary disease risk scoring system constructed based on the random forest model has good discriminatory power.

A case control study on the relationship between occupational stress and genetic polymorphism and dyslipidemia in coal miners.

Dyslipidemia is one of the known risk factors for cardiovascular disease, and its prevalence is increasing worldwide. At present, the study of dyslipidemia has gradually shifted from simple environmental or genetic factors to environment-gene interactions. In order to further explore the etiology and mechanism of dyslipidemia, we used occupational stress(OS) and LYPLAL1, APOC3 and SOD2 gene as research variables to explore their association with dyslipidemia.Here we used a case-control study to include Han workers from a coal mining enterprise in China to determine the association between study variables and dyslipidemia. Monofactor analysis showed that smoking, drinking, physical activity level, DASH diet score, sleep quality, BMI, hypertension, hyperuricemia, shift work, OS were significantly different between the two groups (P < 0.05). In the APOC3 rs2854116 dominant model, patients with CT/CC genotype had a higher risk of dyslipidemia than those with TT genotype. In SOD2 rs4880 recessive model, patients with GG genotype had a lower risk of dyslipidemia than those with AA/AG genotype, and the difference was statistically significant. We found that rs12137855 and OS, rs2854116 and OS, rs4880 and OS had joint effects, but no interaction based on the multiplication and addition model was found (Pinteraction > 0.05). GMDR model showed that the rs12137855-rs2854116-rs4880-OS four-factor model had the highest cross-validation consistency and training-validation accuracy (P < 0.05), suggesting that there was a high-order interaction between them associated with dyslipidemia. We found that dyslipidemia in coal miners was related to OS and genetic factors. Through this study, we revealed the dual regulation of environmental factors and genetic factors on dyslipidemia. At the same time, this study provides clues for understanding the etiology and mechanism of dyslipidemia.

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset.

OBJECTIVE: Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic. METHODS: We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability. RESULTS: The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models. CONCLUSION: The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.

Influence of occupational exposure on hyperuricemia in steelworkers: a nested case-control study.

BACKGROUND: Occupational exposure may be associated with an increased risk of developing hyperuricemia. This study sheds lights on the association between occupational exposure and hyperuricemia in steelworkers. METHOD: A nested case-control study was conducted within a cohort of workers in steel companies to explore the association between occupational exposure and hyperuricemia. The case group consisted of a total of 641 cases of hyperuricemia identified during the study period, while 641 non-hyperuricemia subjects with the same age and gender distribution were randomly selected from the cohort as the control group. RESULTS: The incidence rate of hyperuricemia among workers in the steel company was 17.30%, with an incidence density of 81.32/1,000 person-years. In comparison to the reference group, the risks of developing hyperuricemia for steelworkers undergoing ever shifts, current shifts, heat exposure, and dust exposure were 2.18 times, 1.81 times, 1.58 times and 1.34 times higher respectively. The odds ratios (ORs) and 95% confidence intervals (CIs) were 1.87(1.12-3.13) and 2.02(1.21-3.37) for the cumulative number of days of night work at 0-1,972.80 and ≥ 1,972.80 (days), respectively. Compared to the group with the cumulative heat exposure of 0 (°C/year), the ORs (95% CI) for the risk of developing hyperuricemia in the groups with the cumulative heat exposure of 0-567.83 and ≥ 567.83 (°C/year) were 1.50(1.02-2.22) and 1.64(1.11-2.43), respectively. The OR (95% CI) for the risk of developing hyperuricemia was 1.56(1.05-2.32) at the cumulative dust exposure of ≥ 30.02 (mg/m3/year) compared to that at the cumulative dust exposure of 0 (mg/m3/year). Furthermore, there was a multiplicative interaction between heat exposure and dust exposure in the development of hyperuricemia. CONCLUSION: Shift work, heat, and dust are independent risk factors for the development of hyperuricemia in steelworkers. Additionally, there is a multiplicative interaction between heat exposure and dust exposure in the development of hyperuricemia. Interventions for shift work, heat and dust may help to reduce the incidence rate of hyperuricemia and improve the health of steelworkers.

A Predictive Model for Abnormal Bone Density in Male Underground Coal Mine Workers.

The dark and humid environment of underground coal mines had a detrimental effect on workers' skeletal health. Optimal risk prediction models can protect the skeletal health of coal miners by identifying those at risk of abnormal bone density as early as possible. A total of 3695 male underground workers who attended occupational health physical examination in a coal mine in Hebei, China, from July to August 2018 were included in this study. The predictor variables were identified through single-factor analysis and literature review. Three prediction models, Logistic Regression, CNN and XG Boost, were developed to evaluate the prediction performance. The training set results showed that the sensitivity of Logistic Regression, XG Boost and CNN models was 74.687, 82.058, 70.620, the specificity was 80.986, 89.448, 91.866, the F1 scores was 0.618, 0.919, 0.740, the Brier scores was 0.153, 0.040, 0.156, and the Calibration-in-the-large was 0.104, 0.020, 0.076, respectively, XG Boost outperformed the other two models. Similar results were obtained for the test set and validation set. A two-by-two comparison of the area under the ROC curve (AUC) of the three models showed that the XG Boost model had the best prediction performance. The XG Boost model had a high application value and outperformed the CNN and Logistic regression models in prediction.