ObjectiveThis study was designed to explore the impact of occupational hazards and occupational stress on job burn-out among factory workers and miners. This study also aimed to provide a scientific basis for the prevention and control of job burn-out among factory workers and miners.DesignA cross-sectional study based on the factory Workers and Miners of Urumqi, Xinjiang. Demographic biases, that is, confounding factors, were eliminated by the propensity score-matched analysis method.ParticipantsAn electronic questionnaire was used to survey 7500 eligible factory workers and miners in Urumqi, the capital of Xinjiang, and 7315 complete questionnaires were returned.Primary outcome measuresA general demographic questionnaire, the Effort–Reward Imbalance (ERI) and the Chinese Maslach Burnout Inventory.ResultsThe total rate of burn-out was 86.5%. Noise (OR 1.34, 95% CI 1.09 to 1.64) and ERI (OR 2.16, 95% CI 1.78 to 2.61) were the risk factors for job burn-out among factory workers and miners (p<0.001).ConclusionThe job burn-out rate of factory workers and miners was high, and the noise and occupational stress factors among occupational hazard factors will affect the likelihood of job burn-out of factory workers and miners. We should control the impact of occupational hazards on factory workers and miners and reduce occupational stress to alleviate workers’ job burn-out.

ObjectiveA nomogram for predicting the risk of mental health problems was established in a population of factory workers and miners, in order to quickly calculate the probability of a worker suffering from mental health problems.MethodsA cross-sectional survey of 7500 factory workers and miners in Urumqi was conducted by means of an electronic questionnaire using cluster sampling method. Participants were randomly assigned to the training group (70%) and the validation group (30%). Questionnaire-based survey was conducted to collect information. A least absolute shrinkage and selection operator (LASSO) regression model was used to screen the predictors related to the risk of mental health problems of the training group. Multivariate logistic regression analysis was applied to construct the prediction model. Calibration plots and receiver operating characteristic-derived area under the curve (AUC) were used for model validation. Decision curve analysis was applied to calculate the net benefit of the screening model.ResultsA total of 7118 participants met the inclusion criteria and the data were randomly divided into a training group (n=4955) and a validation group (n=2163) in a ratio of 3:1. A total of 23 characteristics were included in this study and LASSO regression selected 12 characteristics such as education, professional title, age, Chinese Maslach Burnout Inventory, effort–reward imbalance, asbestos dust, hypertension, diabetes, working hours per day, working years, marital status and work schedule as predictors for the construction of the nomogram. In the validation group, the Brier score was 0.176, the calibration slope was 0.970 and the calibration curve of nomogram showed a good fit. The AUC of training group and verification group were 0.785 and 0.784, respectively.ConclusionThe nomogram combining these 12 characteristics can be used to predict the risk of suffering mental health problems, providing a useful tool for quickly and accurately screening the risk of mental health problems.