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BMC Public Health. 2013 Feb 5;13:105. doi: 10.1186/1471-2458-13-105.

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

Author information

1
365/Occupational Health Service, PO Box 85091, 3508 AB, Utrecht, the Netherlands. corne.roelen@365.nl

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.

PMID:
23379546
PMCID:
PMC3599809
DOI:
10.1186/1471-2458-13-105
[Indexed for MEDLINE]
Free PMC Article
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