Prediction of sickness absence: development of a screening instrument.
OBJECTIVE
To develop a concise screening instrument for early identification of employees at risk for sickness absence due to psychosocial health complaints.


METHODS
Data from the Maastricht Cohort Study on "Fatigue at Work" were used to identify items to be associated with an increased risk of sickness absence. The analytical procedures univariate logistic regression, backward stepwise linear regression, and multiple logistic regression were successively applied. For both men and women, sum scores were calculated, and sensitivity and specificity rates of different cut-off points on the screening instrument were defined.


RESULTS
In women, results suggested that feeling depressed, having a burnout, being tired, being less interested in work, experiencing obligatory change in working days, and living alone, were strong predictors of sickness absence due to psychosocial health complaints. In men, statistically significant predictors were having a history of sickness absence, compulsive thinking, being mentally fatigued, finding it hard to relax, lack of supervisor support, and having no hobbies. A potential cut-off point of 10 on the screening instrument resulted in a sensitivity score of 41.7% for women and 38.9% for men, and a specificity score of 91.3% for women and 90.6% for men.


CONCLUSIONS
This study shows that it is possible to identify predictive factors for sickness absence and to develop an instrument for early identification of employees at risk for sickness absence. The results of this study increase the possibility for both employers and policymakers to implement interventions directed at the prevention of sickness absence.
Duijts
S F A
SF
Department of Epidemiology, Maastricht University, Netherlands. sfa.duijts@epid.unimaas.nl
Kant
I J
IJ
Landeweerd
J A
JA
Swaen
G M H
GM
D000041:Absenteeism; D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D005222:Mental Fatigue / Q000517:prevention & control*; D008875:Middle Aged; D009784:Occupational Diseases / Q000175:diagnosis*; D009786:Occupational Health Services / Q000458:organization & administration*; D012044:Regression Analysis; D012307:Risk Factors; D018582:Sick Leave / Q000706:statistics & numerical data*; D013315:Stress, Psychological / Q000517:prevention & control