title: Determinants of sick-leave duration: a tool for managers?
abstract: OBJECTIVE
  To provide managers with tools to manage episodes of sick-leave of their employees, the influence of factors such as age, gender, duration of tenure, working full-time or part-time, cause and history of sick-leave, salary and education on sick-leave duration was studied.
  
  
  METHODS
  In a cross-sectional study, data derived from the 2005 sick-leave files of a Dutch university were examined. Odds ratios of the single risk factors were calculated for short spells (<or=7 days), medium spells (8- 42 days), long spells (43-91 days) or extended spells (>or=91 days) of sick-leave. Next, these factors were studied in multiple regression models.
  
  
  RESULTS
  Age, gender, duration of employment, cause and history of sick-leave, salary and membership of scientific staff, studied as single factors, have a significant influence on sick-leave duration. In multiple models, this influence remains for gender, salary, age, and history and cause of sick-leave. Only in medium or long spells and regarding the risk for a long or an extended spell do the predictive values of models consisting of psychological factors, work-related factors, salary and gender become reasonable.
  
  
  CONCLUSIONS
  The predictive value of the risk factors used in this study is limited, and varies with the duration of the sick-leave spell. Only the risk for an extended spell of sick-leave as compared to a medium or long spell is reasonably predicted. Factors contributing to this risk may be used as tools in decision-making.
authors.affiliation: University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands. p.a.flach@rug.nl
authors.forename: Peter A
authors.initials: PA
authors.lastname: Flach
authors.forename: Boudien
authors.initials: B
authors.lastname: Krol
authors.forename: Johan W
authors.initials: JW
authors.lastname: Groothoff
mesh_terms: D000328:Adult; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D004651:Employment; D005260:Female; D006801:Humans; D008297:Male; D009426:Netherlands; D016272:Occupational Health; D011237:Predictive Value of Tests; D012307:Risk Factors; D012456:Salaries and Fringe Benefits; D012737:Sex Factors; D018582:Sick Leave; D011795:Surveys and Questionnaires; D013997:Time Factors; D014495:Universities; D017132:Workplace