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

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.

journal_name

BMC Public Health

journal_title

BMC public health

authors

van der Burg LRA,van Kuijk SMJ,Ter Wee MM,Heymans MW,de Rijk AE,Geuskens GA,Ottenheijm RPG,Dinant GJ,Boonen A

doi

10.1186/s12889-020-08843-x

subject

Has Abstract

pub_date

2020-05-15 00:00:00

pages

699

issue

issn

1471-2458

pii

10.1186/s12889-020-08843-x

journal_volume

pub_type

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