title: Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.
abstract: BACKGROUND
  Prognostic models including age, self-rated health and prior sickness absence (SA) have been found to predict high (≥ 30) SA days and high (≥ 3) SA episodes during 1-year follow-up. More predictors of high SA are needed to improve these SA prognostic models. The purpose of this study was to investigate fatigue as new predictor in SA prognostic models by using risk reclassification methods and measures.
  
  
  METHODS
  This was a prospective cohort study with 1-year follow-up of 1,137 office workers. Fatigue was measured at baseline with the 20-item checklist individual strength and added to the existing SA prognostic models. SA days and episodes during 1-year follow-up were retrieved from an occupational health service register. The added value of fatigue was investigated with Net Reclassification Index (NRI) and integrated discrimination improvement (IDI) measures.
  
  
  RESULTS
  In total, 579 (51 %) office workers had complete data for analysis. Fatigue was prospectively associated with both high SA days and episodes. The NRI revealed that adding fatigue to the SA days model correctly reclassified workers with high SA days, but incorrectly reclassified workers without high SA days. The IDI indicated no improvement in risk discrimination by the SA days model. Both NRI and IDI showed that the prognostic model predicting high SA episodes did not improve when fatigue was added as predictor variable.
  
  
  CONCLUSIONS
  In the present study, fatigue increased false-positive rates which may reduce the cost-effectiveness of interventions for preventing SA.
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, VU University, Amsterdam, The Netherlands. corne.roelen@arboned.nl.|Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. corne.roelen@arboned.nl.|ArboNed, PO Box 158, 8000 AD, Zwolle, The Netherlands. corne.roelen@arboned.nl.
authors.forename: Corné A M
authors.initials: CA
authors.lastname: Roelen
authors.affiliation: Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
authors.forename: Ute
authors.initials: U
authors.lastname: Bültmann
authors.affiliation: Department of Health Sciences, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
authors.forename: Johan W
authors.initials: JW
authors.lastname: Groothoff
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, VU University, Amsterdam, The Netherlands.
authors.forename: Jos W R
authors.initials: JW
authors.lastname: Twisk
authors.affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, VU University, Amsterdam, The Netherlands.
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
mesh_terms: D000041:Absenteeism; D000328:Adult; D057189:Checklist; D005221:Fatigue / Q000453:epidemiology* / Q000209:etiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009784:Occupational Diseases / Q000453:epidemiology* / Q000209:etiology; D009786:Occupational Health Services / Q000706:statistics & numerical data; D011446:Prospective Studies; D018570:Risk Assessment / Q000145:classification; D012307:Risk Factors; D018582:Sick Leave / Q000145:classification* / Q000706:statistics & numerical data
keywords: Absenteeism; Prediction models; Reclassification table; Risk distribution; Risk stratification; Sick leave