Different domains of health functioning as predictors of sickness absence--a prospective cohort study.
OBJECTIVE
The aim of this study was to examine different domains of health functioning as predictors of sickness absence.


METHODS
The Short Form 36 (SF-36) is one of the best known instruments measuring various domains of physical and mental health functioning. A questionnaire including the SF-36 was mailed to 40-60-year-old employees of the City of Helsinki in 2000-2002. For the subsequent three years, sickness absence episodes >2 weeks were derived from the employer's register. The predictive ability of the eight subscales and two component summaries of the SF-36 were compared using regression methods and receiver operating characteristic (ROC) curve analysis.


RESULTS
All eight SF-36 subscales and the two component summaries predicted the occurrence of sickness absence over the follow-up period. Among women, bodily pain was the strongest predictor, with 1 standard deviation increase in bodily pain increasing the occurrence of sickness absence by 77% [95% confidence interval (95% CI) 68-86%]. Role limitations due to emotional problems were the weakest predictor of sickness absence (29%, 95% CI 23-36%). Among men, the results were similar to those of women. In both genders, the area under the ROC curve was largest for bodily pain, general health, and physical functioning and lowest for mental health and role limitation due to emotional problems.


CONCLUSIONS
The subscales measuring physical domains of functioning were more strongly associated with sickness absence than the mental subscales. In particular, ability to perform daily activities, pain, and general health were important predictors of sickness absence >2 weeks.
Hjelt-Institute, Department of Public Health, PO Box 41, FIN-00014 University of Helsinki, Finland. mikko.t.laaksonen@helsinki.fi
Mikko
M
Laaksonen
Sanna-Mari
SM
Kääriä
Päivi
P
Leino-Arjas
Eero
E
Lahelma
D000041:Absenteeism; D000328:Adult; D005260:Female; D005387:Finland / Q000453:epidemiology; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008875:Middle Aged; D010146:Pain / Q000453:epidemiology; D011446:Prospective Studies; D012372:ROC Curve; D012044:Regression Analysis; D018570:Risk Assessment; D012307:Risk Factors; D018582:Sick Leave / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires