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- Title
The development and validation of two prediction models to identify employees at risk of high sickness absence.
- Authors
Roelen, Corné A.; Van Rhenen, Willem; Groothoff, Johan W.; Van Der Klink, Jac J.; Bültmann, Ute; Heymans, Martijn W.
- Abstract
Background: Sickness absence (SA) is a public health risk marker for morbidity and mortality. The aim of this study was to develop and validate prediction models to identify employees at risk of high SA. Methods: Two prediction models were developed using self-rated health (SRH) and prior SA as predictors. SRH was measured by the categories excellent, good, fair and poor in a convenience sample of 535 hospital employees. Prior SA was retrieved from the employer's register. The predictive performance of the models was assessed by logistic regression analysis with high (&geq;90th percentile) vs. non-high (<90th percentile) SA days and SA episodes as outcome variables and by using bootstrapping techniques to validate the models. Results: The overall performance as reflected in the Nagelkerke's pseudo R2 was 11.7% for the model identifying employees with high SA days and 31.8% for the model identifying employees with high SA episodes. The discriminative ability, represented by the area (AUC) under the receiver operating characteristic (ROC), was 0.729 (95% CI 0.667–0.809) for the model identifying employees with high SA days and 0.831 (95% CI 0.784–0.877) for the model identifying employees with high SA episodes. The Hosmer–Lemeshow test showed acceptable calibration for both models. Conclusions: The prediction models identified employees at risk of high SA, but need further external validation in other settings and working populations before applying them in public and occupational health research and care.
- Subjects
NETHERLANDS; RISK assessment; CALIBRATION; CONFIDENCE intervals; STATISTICAL correlation; DISCRIMINANT analysis; FACTOR analysis; HEALTH facility employees; HEALTH status indicators; LONGITUDINAL method; RESEARCH methodology; RESEARCH; RESEARCH evaluation; STATISTICAL sampling; SELF-evaluation; SICK leave; WORK environment; LOGISTIC regression analysis; PREDICTIVE validity; RECEIVER operating characteristic curves; STATISTICAL models; DESCRIPTIVE statistics
- Publication
European Journal of Public Health, 2013, Vol 23, Issue 1, p128
- ISSN
1101-1262
- Publication type
Article
- DOI
10.1093/eurpub/cks036