Unknown

Dataset Information

0

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.

SUBMITTER: van der Burg LRA 

PROVIDER: S-EPMC7227258 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

van der Burg Lennart R A LRA   van Kuijk Sander M J SMJ   Ter Wee Marieke M MM   Heymans Martijn W MW   de Rijk Angelique E AE   Geuskens Goedele A GA   Ottenheijm Ramon P G RPG   Dinant Geert-Jan GJ   Boonen Annelies A  

BMC public health 20200515 1


<h4>Background</h4>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 generalizabili  ...[more]

Similar Datasets

| S-EPMC6329504 | biostudies-literature
| S-EPMC7354686 | biostudies-literature
| S-EPMC8812422 | biostudies-literature
| S-EPMC7004170 | biostudies-literature
| S-EPMC3343027 | biostudies-other
| S-EPMC9902268 | biostudies-literature
| S-EPMC2668565 | biostudies-other
| S-EPMC4828479 | biostudies-literature
| S-EPMC8238732 | biostudies-literature
| S-EPMC8043853 | biostudies-literature