Predicting job loss in those off sick

Occup Med (Lond). 2008 Mar;58(2):99-106. doi: 10.1093/occmed/kqm141. Epub 2008 Jan 22.


Background: Evidence shows incapacity benefit claimants (those off sick >26 weeks) are at greatest risk of long-term job loss.

Aim: To develop a screening tool to select those at risk of job loss, defined as failure to return to work among those off sick. The screening tool was for use in the Job Retention and Rehabilitation Pilot of the Department for Work and Pensions.

Methods: A literature review identified risks for long-term incapacity and job loss as multifactorial. Potential predictors for return to work were then assembled into a set of questions and tested by a prospective study in general practice surgeries and a retrospective study of occupational health records of local authority employees referred for sickness absence management, using univariate and multivariate logistic regression analysis.

Results: Univariate logistic regression analysis of the retrospective study produced odds ratios with 95% confidence intervals for each question (where P <or= 0.05) and a C-index was then constructed for their predictive power. Five questions holding the greatest predictive power were subjected to multivariate analysis and in the final model had a high C-index of 0.90 (0.5 = no predictive power, 1.0 = perfect prediction). They formed the screening tool. The questions cover self-assessment of ability to return to work after current sick leave, of ability to do current job in 6 months' time, sick leave in past year, current age and whether awaiting a consultation or treatment.

Conclusion: A screening tool identifying those most at risk of job loss has been produced.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Analysis of Variance
  • Employment / statistics & numerical data
  • Forecasting
  • Humans
  • Prospective Studies
  • Retrospective Studies
  • Risk Assessment / methods
  • Risk Factors
  • Sick Leave*
  • Surveys and Questionnaires*
  • Unemployment / statistics & numerical data*
  • United Kingdom / epidemiology