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

C.A.M. Roelen, U. Bultmann, W. van Rhenen, J.J.L. van der Klink, J.W.R. Twisk, M.W. Heijmans

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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. © 2013 Roelen et al; licensee BioMed Central Ltd.
Original languageEnglish
Article number105
JournalBMC Public Health
Publication statusPublished - 2013


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