Email updates

Keep up to date with the latest news and content from BMC Public Health and BioMed Central.

Open Access Research article

External validation of two prediction models identifying employees at risk of high sickness absence: cohort study with 1-year follow-up

Corné AM Roelen123*, Ute Bültmann3, Willem van Rhenen14, Jac JL van der Klink3, Jos WR Twisk2 and Martijn W Heymans2

Author Affiliations

1 365/Occupational Health Service, PO Box 85091, 3508 AB, Utrecht, the Netherlands

2 Department of Health Sciences section Methodology and Applied Biostatistics, VU University, De Boelelaan 1085-1087, 1081 HV, Amsterdam, the Netherlands

3 Department of Health Sciences section Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, PO Box 196, 9700 AD, Groningen, the Netherlands

4 Center for Human Resource, Organization and Management Effectiveness, Business University Nyenrode, PO Box 130, 3620 AC, Breukelen, the Netherlands

For all author emails, please log on.

BMC Public Health 2013, 13:105  doi:10.1186/1471-2458-13-105

Published: 5 February 2013

Abstract

Background

Two models including age, self-rated health (SRH) and prior sickness absence (SA) were found to predict high SA in health care workers. The present study externally validated these prediction models in a population of office workers and investigated the effect of adding gender as a predictor.

Methods

SRH was assessed at baseline in a convenience sample of office workers. Age, gender and prior SA were retrieved from an occupational health service register. Two pre-defined prediction models were externally validated: a model identifying employees with high (i.e. ≥30) SA days and a model identifying employees with high (i.e. ≥3) SA episodes during 1-year follow-up. Calibration was investigated by plotting the predicted and observed probabilities and calculating the calibration slope. Discrimination was examined by receiver operating characteristic (ROC) analysis and the area under the ROC-curve (AUC).

Results

A total of 593 office workers had complete data and were eligible for analysis. Although the SA days model showed acceptable calibration (slope = 0.89), it poorly discriminated office workers with high SA days from those without high SA days (AUC = 0.65; 95% CI 0.58–0.71). The SA episodes model showed acceptable discrimination (AUC = 0.76, 95% CI 0.70–0.82) and calibration (slope = 0.96). The prognostic performance of the prediction models did not improve in the population of office workers after adding gender.

Conclusion

The SA episodes model accurately predicted the risk of high SA episodes in office workers, but needs further multisite validation and requires a simpler presentation format before it can be used to select high-risk employees for interventions to prevent or reduce SA.

Keywords:
Absenteeism; Forecasting; Generalization; Office workers; Regression prognostics; Sick leave; Transportability