title: The history of registered sickness absence predicts future sickness absence.
abstract: BACKGROUND
  The history of sickness absence has been found to predict future sickness absence.
  
  
  OBJECTIVE
  To establish the review period of historical sickness absence data that is needed to predict future sickness absence.
  
  
  METHODS
  The individual number of days and episodes of sickness absence were ascertained for 762 hospital employees from 2004 to 2008 inclusive. Past sickness absence was included stepwise in ordinal regression models. The explained variance of the ordinal regression models reflected the extent to which future sickness absence could be predicted and was expressed in percentages calculated as Nagelkerke's pseudo R(2) × 100%.
  
  
  RESULTS
  A total of 551 employees (72%) had complete data and were eligible for regression analysis. Days of sickness absence in the past year predicted up to 15% of future days of sickness absence. Adding the sickness absence data of the past 2 or 3 years did not further increase the predictability of days of sickness absence. Episodes of sickness absence in the past year predicted up to 25% of future episodes of sickness absence. The predictability of episodes of sickness absence increased to 30% when the past 2 years of sickness absence were included in the regression model, but did not further increase when sickness absence of the past 3 years was included.
  
  
  CONCLUSIONS
  Employees who are more likely to have an above average sickness absence can be identified from their history of sickness absence in the past 2 years.
authors.affiliation: Department of Public and Occupational Health, EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands. corne.roelen@arboned.nl
authors.forename: C A M
authors.initials: CA
authors.lastname: Roelen
authors.forename: P C
authors.initials: PC
authors.lastname: Koopmans
authors.forename: J A H
authors.initials: JA
authors.lastname: Schreuder
authors.forename: J R
authors.initials: JR
authors.lastname: Anema
authors.forename: A J
authors.initials: AJ
authors.lastname: van der Beek
mesh_terms: D000041:Absenteeism; D000328:Adult; D005544:Forecasting; D006801:Humans; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D010564:Personnel, Hospital / Q000706:statistics & numerical data*; D012044:Regression Analysis; D018582:Sick Leave / Q000639:trends*; D013997:Time Factors