title: Health measures in prediction models for high sickness absence: single-item self-rated health versus multi-item SF-12.
abstract: BACKGROUND
  Self-rated health (SRH) has been found to predict sickness absence (SA). The present study investigated the effect of replacing single-item SRH by a multi-item health measure on SA predictions.
  
  
  METHODS
  Longitudinal study of 2059 Norwegian nurses with assessments in three waves each separated by 1 year. Health was measured by single-item SRH and multi-item SF-12 in waves 1 and 2. SA was self-reported in all three waves and high SA was defined as more than or equal to 31 SA days within the last 12 months. Predictions of high SA by a model including age, prior SA and single-item SRH were compared with predictions by a model including age, prior SA and multi-item SF-12. Both models were bootstrapped to correct for over-optimism and prospectively validated for their predictions in a new time frame.
  
  
  RESULTS
  1253 nurses (61%) had complete data for analysis. The SF-12 model predicted the risk of high SA more accurately (χ(2) = 4.294; df = 8) and was more stable over time than the SRH model (model χ(2) = 14.495; df = 8). Both prediction models correctly discriminated between high-risk and low-risk individuals in 73% of the cases at wave 2 and in 71% of the cases at wave 3.
  
  
  CONCLUSIONS
  The accuracy of predictions increased when single-item SRH was replaced by multi-item SF-12, but the discriminative ability did not improve. Single-item SRH suffices to identify employees at increased risk of high SA.
authors.affiliation: 1 Department of Health Sciences, VU University, The Netherlands corne.roelen@arboned.nl.
authors.forename: Corne A M
authors.initials: CA
authors.lastname: Roelen
authors.affiliation: 1 Department of Health Sciences, VU University, The Netherlands.
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
authors.affiliation: 1 Department of Health Sciences, VU University, The Netherlands.
authors.forename: Jos W R
authors.initials: JW
authors.lastname: Twisk
authors.affiliation: 2 Finnish Centre for Pensions, Finland.
authors.forename: Mikko
authors.initials: M
authors.lastname: Laaksonen
authors.affiliation: 3 Department of Psychosocial Science, University of Bergen, Norway.
authors.forename: Ståle
authors.initials: S
authors.lastname: Pallesen
authors.affiliation: 4 Department of Occupational Medicine, Haukeland University Hospital, Norway.
authors.forename: Nils
authors.initials: N
authors.lastname: Magerøy
authors.affiliation: 5 Department of Global Public Health and Primary Care, University of Bergen, Norway.
authors.forename: Bente E
authors.initials: BE
authors.lastname: Moen
authors.affiliation: 5 Department of Global Public Health and Primary Care, University of Bergen, Norway.
authors.forename: Bjørn
authors.initials: B
authors.lastname: Bjorvatn
mesh_terms: D000328:Adult; D000367:Age Factors; D005260:Female; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008603:Mental Health; D009726:Nurses; D012307:Risk Factors; D057566:Self Report / Q000592:standards*; D012737:Sex Factors; D018582:Sick Leave / Q000706:statistics & numerical data*; D012941:Social Responsibility; D011795:Surveys and Questionnaires / Q000592:standards*