Long-term sickness absence in a working population: development and validation of a risk prediction model in a large Dutch prospective cohort

• Main Authors: Lennart R. A. van der Burg (Author), Sander M. J. van Kuijk (Author), Marieke M. ter Wee (Author), Martijn W. Heymans (Author), Angelique E. de Rijk (Author), Goedele A. Geuskens (Author), Ramon P. G. Ottenheijm (Author), Geert-Jan Dinant (Author), Annelies Boonen (Author)
• Format: Book
• Published: BMC, 2020-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12889-020-08843-x; 1471-2458
• Keywords: Prediction model; Prediction; Long-term sickness absence; Prospective cohort study; Prevention; Calibration; Public aspects of medicine; RA1-1270
• Source: BMC Public Health, Vol 20, Iss 1, Pp 1-9 (2020)

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 self-rated 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). Conclusions This multivariable risk prediction model distinguishes well between older workers with high- and low-risk for LTSA in the coming year. Being easy to administer, it can support healthcare professionals in determining which persons should be targeted for tailored preventative interventions.