Risk of future sickness absence in frequent and long-term absentees

Occup Med (Lond). 2008 Jun;58(4):268-74. doi: 10.1093/occmed/kqn040. Epub 2008 Apr 4.

Abstract

Background: Prior absence is an important predictor for sickness absence, but little is known about the recurrence among frequent and/or long-term absentees, over a longer period of time.

Aim: To monitor sickness absence among frequent and long-term absentees in order to investigate their risk of recurrent absence.

Methods: Longitudinal cohort study in employees working in three large Dutch postal and telecommunications companies. In the first year of study, we distinguished employees who were absent four times or more (frequent absence), employees who were absent for >or=6 weeks (long-term absence), combined frequent and long-term absence and a reference population. The absence rates in these groups were followed-up for 4 years.

Results: The study population (n = 53,990) comprised 4126 frequent absentees, 3585 long-term absentees, 979 combined frequent and long-term absentees and a reference population (n = 45,300). Frequent absentees had a higher risk of recurrent frequent absence when compared to the reference population, with rate ratios (RR) amounting to 4.9 [95% confidence interval (CI) 4.7-5.1] in men and 3.2 (95% CI 3.0-3.4) in women. They also had a higher risk of developing long-term absence: RR = 1.9 (95% CI 1.8-2.0) in men and 1.5 (95% CI 1.4-1.6) in women. Long-term absentees had high risk of recurrence: RR = 1.9 (95% CI 1.8-2.0) in men and RR = 1.4 (95% CI 1.3-1.5) in women.

Conclusions: Employees with prior frequent and/or long-term absence were at risk of recurrent absence. Frequent absence was a prognostic factor predicting future long-term absence.

Publication types

  • Multicenter Study

MeSH terms

  • Adult
  • Age Distribution
  • Cohort Studies
  • Female
  • Forecasting*
  • Humans
  • Longitudinal Studies
  • Male
  • Occupational Health*
  • Recurrence
  • Risk Factors
  • Sex Distribution
  • Sick Leave / economics
  • Sick Leave / statistics & numerical data*
  • Socioeconomic Factors
  • Work Capacity Evaluation