title: The development and validation of two prediction models to identify employees at risk of high sickness absence.
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 (≥ 90 th 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 R(2) 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.
authors.affiliation: Department of Health Sciences, Community and Occupational Medicine, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. corne.roelen@365.nl
authors.forename: Corné A
authors.initials: CA
authors.lastname: Roelen
authors.forename: Willem
authors.initials: W
authors.lastname: van Rhenen
authors.forename: Johan W
authors.initials: JW
authors.lastname: Groothoff
authors.forename: Jac J
authors.initials: JJ
authors.lastname: van der Klink
authors.forename: Ute
authors.initials: U
authors.lastname: Bültmann
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
mesh_terms: D000041:Absenteeism; D000328:Adult; D002138:Calibration; D003718:Denmark; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011594:Psychometrics; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D018582:Sick Leave