Can pre-placement health assessments predict subsequent sickness absence?
BACKGROUND
Sickness absence is a growing economic problem, due largely to the financial losses it incurs. The ability to identify employees likely to take greater than average sickness absence may provide managers with useful information at the pre-placement stage.


OBJECTIVE
To confirm whether specific risk factors identified at the pre-placement health assessment could predict subsequent sickness absence.


METHODS
A total of 400 National Health Service pre-placement health questionnaires were analysed to allocate employees to low-, medium- or high-risk categories for subsequent sickness absence, using the risk table developed by C. J. M. Poole (Can sickness absence be predicted at the pre-placement health assessment? Occup Med (Lond) 1999; 49:337-339) [1]. Subsequent sickness absence was analysed to assess if there was an association between the allocated category and sickness absence taken.


RESULTS
Mean sickness absence hours per 1000 h worked were 22.5 (95% CI 18.2-27.2) in the low-risk group, 33.6 (27.2-40.7) in the medium-risk group and 44.7 (25.1-69.9) in the high-risk group (analysis of variance, P <or= 0.002), demonstrating a statistically significant difference in sickness absence taken in subsequent years.


CONCLUSIONS
The results confirmed Poole's hypothesis that future sickness absence can be predicted at the pre-placement health assessment. Certain risk factors, namely female sex, age, smoking, history of at least two previous episodes of low-back pain and previous days sickness absence identified at pre-placement assessment, predict a greater than average subsequent sickness absence. However, the best model using identified risk factors only predicted 10-12% of the variation in sickness absence.
Lucey
Suzanne P
SP
Whitefriars Occupational Health Department, United Bristol Healthcare Trust, Whitefriars, NHS, Lewins Mead, Bristol BS2 8BQ, UK. sluceymed@yahoo.co.uk
D000041:Absenteeism; D000328:Adult; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D010560:Personnel Selection; D018570:Risk Assessment / Q000379:methods*; D018582:Sick Leave / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires / Q000592:standards