ArticleJournal of occupational rehabilitation2020
Development of Prediction Models for Sickness Absence Due to Mental Disorders in the General Working Population.
Article in Journal of occupational rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A Systematic Review and Evaluation of the Tools Measuring Work-Related Psychosocial Factors in Prospective Research on Sickness Absence of Health Care Workers.Journal of occupational rehabilitation · 2026Pooled it
- Work-Related Psychosocial Risk and Protective Factors Influencing Workplace Sickness Absence: A Systematic Literature Review.Journal of occupational rehabilitation · 2026Pooled it
- Improving the Psychosocial Work Environment to Prevent Sickness Absence and Turnover in Nurses: A Systematic Review.Journal of nursing management · 2025Pooled it
- Does a brief work-stress intervention prevent sick-leave during the following 24 months? A randomized controlled trial in Swedish primary care.Work (Reading, Mass.) · 2021Trial
- Machine learning prediction of long-term sickness absence due to mental disorders using Brief Job Stress Questionnaire data.Scientific reports · 2025Article
- Identifying risk factors of long sickness absences: a registry-based study using explainable AI methods.BMJ open · 2025Article
- Social disconnectedness, economic outcomes, and the role of pre-existing mental health conditions: A population-based cohort study.PLOS mental health · 2025Article
- Comparison of Machine Learning Methods in the Study of Cancer Survivors' Return to Work: An Example of Breast Cancer Survivors with Work-Related Factors in the CONSTANCES Cohort.Journal of occupational rehabilitation · 2023Article
- Exposure to work-related violence and/or threats of violence as a predictor of certified sickness absence due to mental disorders: a prospective cohort study of 16,339 Swedish men and women in paid work.International archives of occupational and environmental health · 2023Article
- Does the Number of Reasons for Seeking Care and Self-Rated Health Predict Sick Leave during the Following 12 Months? A Prospective, Longitudinal Study in Swedish Primary Health Care.International journal of environmental research and public health · 2021Article
- Frequent short sickness absence, occupational health service utilisation and long-term sickness absence due to mental disorders among young employees.International archives of occupational and environmental health · 2021Article
- External validation of a prediction model and decision tree for sickness absence due to mental disorders.International archives of occupational and environmental health · 2020Article
- Machine Learning for Work Disability Prevention: Introduction to the Special Series.Journal of occupational rehabilitation · 2020Article
- Identification of risk factors for involuntary psychiatric hospitalization: using environmental socioeconomic data and methods of machine learning to improve prediction.BMC psychiatry · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose This study investigated if and how occupational health survey variables can be used to identify workers at risk of long-term sickness absence (LTSA) due to mental disorders. Methods Cohort study including 53,833 non-sicklisted participants in occupational health surveys between 2010 and 2013. Twenty-seven survey variables were included in a backward stepwise logistic regression analysis with mental LTSA at 1-year follow-up as outcome variable. The same variables were also used for decision tree analysis. Discrimination between participants with and without mental LTSA during follow-up was investigated by using the area under the receiver operating characteristic curve (AUC); the AUC was internally validated in 100 bootstrap samples. Results 30,857 (57%) participants had complete data for analysis; 450 (1.5%) participants had mental LTSA during follow-up. Discrimination by an 11-predictor logistic regression model (gender, marital status, economic sector, years employed at the company, role clarity, cognitive demands, learning opportunities, co-worker support, social support from family/friends, work satisfaction, and distress) was AUC = 0.713 (95% CI 0.692-0.732). A 3-node decision tree (distress, gender, work satisfaction, and work pace) also discriminated between participants with and without mental LTSA at follow-up (AUC = 0.709; 95% CI 0.615-0.804). Conclusions An 11-predictor regression model and a 3-node decision tree equally well identified workers at risk of mental LTSA. The decision tree provides better insight into the mental LTSA risk groups and is easier to use in occupational health care practice.
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