title: Cross-national validation of prognostic models predicting sickness absence and the added value of work environment variables.
abstract: OBJECTIVE
  To validate Dutch prognostic models including age, self-rated health and prior sickness absence (SA) for ability to predict high SA in Danish eldercare. The added value of work environment variables to the models' risk discrimination was also investigated.
  
  
  METHODS
  2,562 municipal eldercare workers (95% women) participated in the Working in Eldercare Survey. Predictor variables were measured by questionnaire at baseline in 2005. Prognostic models were validated for predictions of high (≥30) SA days and high (≥3) SA episodes retrieved from employer records during 1-year follow-up. The accuracy of predictions was assessed by calibration graphs and the ability of the models to discriminate between high- and low-risk workers was investigated by ROC-analysis. The added value of work environment variables was measured with Integrated Discrimination Improvement (IDI).
  
  
  RESULTS
  1,930 workers had complete data for analysis. The models underestimated the risk of high SA in eldercare workers and the SA episodes model had to be re-calibrated to the Danish data. Discrimination was practically useful for the re-calibrated SA episodes model, but not the SA days model. Physical workload improved the SA days model (IDI = 0.40; 95% CI 0.19-0.60) and psychosocial work factors, particularly the quality of leadership (IDI = 0.70; 95% CI 053-0.86) improved the SA episodes model.
  
  
  CONCLUSIONS
  The prognostic model predicting high SA days showed poor performance even after physical workload was added. The prognostic model predicting high SA episodes could be used to identify high-risk workers, especially when psychosocial work factors are added as predictor variables.
authors.affiliation: ArboNed Occupational Health Service, PO Box 85091, 3508 AB, Utrecht, The Netherlands, corne.roelen@arboned.nl.
authors.forename: Corné A M
authors.initials: CA
authors.lastname: Roelen
authors.forename: Christina M
authors.initials: CM
authors.lastname: Stapelfeldt
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
authors.forename: Willem
authors.initials: W
authors.lastname: van Rhenen
authors.forename: Merete
authors.initials: M
authors.lastname: Labriola
authors.forename: Claus V
authors.initials: CV
authors.lastname: Nielsen
authors.forename: Ute
authors.initials: U
authors.lastname: Bültmann
authors.forename: Chris
authors.initials: C
authors.lastname: Jensen
mesh_terms: D000041:Absenteeism; D000328:Adult; D003430:Cross-Sectional Studies; D016208:Databases, Factual; D003718:Denmark; D004777:Environment; D005260:Female; D006282:Health Personnel / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011237:Predictive Value of Tests; D012372:ROC Curve; D012307:Risk Factors; D018582:Sick Leave / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires; D016526:Workload; D017132:Workplace