Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners

Our plain-English summary of Development and validation of a nomogram for predicting the risk of mental health problems of factory workers and miners, by Y. Lu, Q. Liu, H. Yan, et al (BMJ Open, 2022).

The study

• Authors: Y. Lu, Q. Liu, H. Yan, T. Liu
• Journal: BMJ Open
• Published: 2022;12(7):e057102
• Type of article: Randomized trial
• Cited by: 4 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

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.

© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

In plain English

This cross-sectional survey studied factory workers and miners in 202 enterprises in Urumqi, China, in January to May 2019. Of 7500 questionnaires, 7118 were valid. Mental health problems were defined by SCL-90 symptom checklist scores, not by clinical diagnosis. Workers answered yes or no about asbestos dust exposure (no fiber type named), and 981 (14%) said yes. The authors built a scoring chart (a nomogram) that estimates the chance of mental health problems from 12 factors, including burnout, work stress and asbestos dust. For asbestos dust, which the table labels as 'no versus yes', the odds ratio was 1.55 (95% CI 1.28 to 1.87). The article does not state which group was the reference, and this is an association, not proof of cause. Severe burnout, compared with no burnout, had the largest odds ratio, 19.84 (95% CI 13.88 to 28.34). The AUC measures how well the model separated workers with and without checklist-defined problems. It was 0.785 in the group used to build the model and 0.784 in a test group from the same survey. The authors concluded that the nomogram can predict risk. They noted it was untested in other populations and may miss some influences.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Cross-sectional questionnaire survey used to develop and internally validate a prediction model (nomogram). Participants were randomly split into a training group and a validation group. Mental health problems were defined by SCL-90 symptom checklist scores.
• Who was studied: Factory workers and miners from 202 enterprises in the Urumqi region, China, surveyed January to May 2019. 7500 questionnaires were distributed, 7315 were returned and 7118 were valid: 4955 in the training group and 2163 in the validation group.
• Exposure: Workers answered yes or no to exposure to coal dust, silica dust, asbestos dust, benzene, lead, noise and brucellosis. Exposure was self-reported on the questionnaire, and no asbestos fiber type was specified. The study also measured job burnout (CMBI), effort-reward imbalance (ERI), demographics, hypertension and diabetes.
• Main results:
  • Among all 7118 participants, 981 (14%) reported exposure to asbestos dust. Table 1 gives this as 13.8%.
  • In the multivariate logistic regression (training group), asbestos dust had an odds ratio of 1.55 (95% CI 1.28 to 1.87), p less than 0.001. The table labels this comparison as 'No versus yes' and does not state the reference group explicitly.
  • Severe job burnout (CMBI), compared with no burnout, had the largest odds ratio in the model: 19.84 (95% CI 13.88 to 28.34), p less than 0.001.
  • Effort-reward imbalance had an odds ratio of 2.43 (95% CI 2.12 to 2.79).
  • The model's area under the curve, a measure of discrimination, was 0.785 in the training group and 0.784 in the validation group.
  • In the validation group, the Brier score was 0.176 and the calibration slope was 0.970.
  • The authors reported that 37.08% of the factory workers and miners surveyed had mental health problems.
• The authors' conclusion: The authors concluded that the nomogram combining the 12 characteristics can be used to predict the risk of mental health problems. They described it as a tool for quickly screening that risk.
• Limitations the authors state:
  • The authors were not certain that all possible influences on mental health were covered.
  • The nomogram was validated only internally, in the same population. It lacks external validation in other regions and countries.

Related studies

• Effects of Occupational Hazards on Job Stress and Mental Health of Factory Workers and Miners: A Propensity Score Analysis — Y. Lu, Z. Zhang, et al. (2020)
• The Status of Occupational Burnout and Its Influence on the Psychological Health of Factory Workers and Miners in Wulumuqi, China — Y. Lu, Z. Zhang, et al. (2020)
• Association of occupational burnout and occupational exposure factors on psychological health among factory workers and miners: a propensity score analysis — Y. Lu, Z. Zhang, et al. (2021)
• The mental health of asbestos-exposed subjects with pleural abnormalities — P. Franklin, M. Robinson, et al. (2015)
• Living with mesothelioma: a systematic review of mental health and well-being impacts and interventions for patients and their informal carers — V. Sherborne, S. Ejegi-Memeh, et al. (2024)
• A novel nomogram for peritoneal mesothelioma predicts survival — N.P. Schaub, M. Alimchandani, et al. (2013)

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