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 Burg,Marieke M Ter Wee,Ramon P G Ottenheijm,Annelies Boonen,Sander M J Van Kuijk,Angelique E De Rijk,Martijn W Heymans,Goedele A Geuskens,Geert-Jan Dinant

doi:10.1186/s12889-020-08843-x

Abstract

BackgroundSocietal 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.MethodsData 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.ResultsEleven 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).ConclusionsThis 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.

Highlights

Societal expenditures on work-disability benefits is high in most Western countries
* Correspondence: l.vanderburg@maastrichtuniversity.nl 1Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, The Netherlands 2Department 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 van der Burg et al BMC Public Health (2020) 20:699 (Continued from previous page). This multivariable risk prediction model distinguishes well between older workers with high- and low-risk for long-term sickness absence (LTSA) in the coming year. It can support healthcare professionals in determining which persons should be targeted for tailored preventative interventions
Subjects were included in the analyses if they were employed at time of inclusion, but were excluded if they received a fulltime disability pension, or had been on LTSA in the previous year

Summary

Introduction

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. Recognition and prevention of long-term restrictions in work participation, e.g. longterm sickness absence (LTSA) (usually defined as more than 4–6 weeks of sickness absence), have become an important target in several countries, including the Netherlands [7]. Most healthcare professionals, such as general practitioners who are usually first consulted when a (medical) problem arises, are not well trained in identifying individuals at risk for restrictions in work participation. Risk prediction models could support early identification by those healthcare professionals, and ensure timely initiation of targeted interventions to prevent long-term work restrictions [6, 8, 9]

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Conclusion