Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dut
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RESEARCH ARTICLE
Open Access
Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort Lennart R. A. van der Burg1,2* , Sander M. J. van Kuijk3, Marieke M. ter Wee4, Martijn W. Heymans4, Angelique E. de Rijk5, Goedele A. Geuskens6, Ramon P. G. Ottenheijm1, Geert-Jan Dinant1 and Annelies Boonen2
Abstract Background: Societal expenditures on work-disability benefits is high in most Western countries. As a precursor of long-term work restrictions, long-term sickness absence (LTSA) is under continuous attention of policy makers. Different healthcare professionals can play a role in identification of persons at risk of LTSA but are not well trained. A risk prediction model can support risk stratification to initiate preventative interventions. Unfortunately, current models lack generalizability or do not include a comprehensive set of potential predictors for LTSA. This study is set out to develop and validate a multivariable risk prediction model for LTSA in the coming year in a working population aged 45–64 years. Methods: Data from 11,221 working persons included in the prospective Study on Transitions in Employment, Ability and Motivation (STREAM) conducted in the Netherlands were used to develop a multivariable risk prediction model for LTSA lasting ≥28 accumulated working days in the coming year. Missing data were imputed using multiple imputation. A full statistical model including 27 pre-selected predictors was reduced to a practical model using backward stepwise elimination in a logistic regression analysis across all imputed datasets. Predictive performance of the final model was evaluated using the Area Under the Curve (AUC), calibration plots and the Hosmer-Lemeshow (H&L) test. External validation was performed in a second cohort of 5604 newly recruited working persons. Results: Eleven variables in the final model predicted LTSA: older age, female gender, lower level of education, poor selfrated physical health, low weekly physical activity, high self-rated physical job load, knowledge and skills not matching the job, high number of major life events in the previous year, poor self-rated work ability, high number of sickness absence days in the previous year and being self-employed. The model showed good discrimination (AUC 0.76 (interquartile range 0.75–0.76)) and good calibration in the external validation cohort (H&L test: p = 0.41). (Continued on next page)
* Correspondence: [email protected] 1 Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands 2 Department of Internal Medicine, Division of Rheumatology, Maastricht University Medical Centre and Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 Inte
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