期刊
BMJ OPEN
卷 12, 期 7, 页码 -出版社
BMJ PUBLISHING GROUP
DOI: 10.1136/bmjopen-2021-057102
关键词
mental health; preventive medicine; public health
资金
- Natural Science Foundation of Xinjiang Uygur Autonomous Region [2020D01A27]
- Postgraduate Innovation Project of Xinjiang Uyghur Autonomous Region [XJ2021G215]
- Outstanding Young Scientist Training Programme of Urumqi Science and Technology Talent Project
- Public Health and Preventive Medicine -Specialties of Higher Education Institutions in Xinjiang Uygur Autonomous Region
A nomogram was established to predict the risk of mental health problems in a population of factory workers and miners, allowing for quick calculation of the probability of a worker suffering from mental health problems.
Objective A 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. Methods A 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. Results A 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. Conclusion The 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.
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