Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners
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...
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BMJ Publishing Group
2022-07-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/7/e057102.full |
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| author | Qi Liu Tao Liu Yaoqin Lu Huan Yan |
| author_facet | Qi Liu Tao Liu Yaoqin Lu Huan Yan |
| author_sort | Qi Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-71d4e085df22424a81b1a4713f49805b |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-71d4e085df22424a81b1a4713f49805b2025-01-30T19:40:09ZengBMJ Publishing GroupBMJ Open2044-60552022-07-0112710.1136/bmjopen-2021-057102Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and minersQi Liu0Tao Liu1Yaoqin Lu2Huan Yan3Department of International and Humanistic Nursing, School of Nursing, University of South China, Hengyang, Hunan, China5 Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, School of Biological Science and Medical Engineering International Research Institute for Multidisciplinary Science, Beihang University, Beijing, ChinaSchool of Public Health, Xinjiang Medical University, Urumqi, ChinaDepartment of Nutrition and Food Hygiene, Xinjiang Medical University, Urumqi, ChinaObjective 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.https://bmjopen.bmj.com/content/12/7/e057102.full |
| spellingShingle | Qi Liu Tao Liu Yaoqin Lu Huan Yan Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners BMJ Open |
| title | Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| title_full | Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| title_fullStr | Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| title_full_unstemmed | Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| title_short | Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| title_sort | development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners |
| url | https://bmjopen.bmj.com/content/12/7/e057102.full |
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