Explaining educational differences in sickness absence: a population-based follow-up study.
OBJECTIVE
There is a marked socioeconomic gradient in sickness absences, but the causes of this gradient are poorly understood. This study examined the role of health and work-related factors as determinants of educational differences in long-term sickness absence in an 8-year follow-up.


METHODS
The study comprised a population-based sample of 5835 Finns aged 30-64 years (participation 89%, N=3946) in a register-based 8-year follow-up. This is a novel method to predict the population average of sickness absence days per working year (DWY) based on the expected outcome values using Poisson and gamma regression models.


RESULTS
The difference in the DWY between the lowest and highest educational level was clear among both men (3.2 days/year versus 8.0 days/year) and women (women 4.4 days/year versus 10.1 days/year). Adjusting for physical working conditions, health status and health behavior, and obesity attenuated the differences. Psychosocial working conditions had only a minor effect on the association. After adjusting for health and work-related factors, the difference attenuated by 1.8 days and 2.6 days among men and women, respectively.


CONCLUSIONS
Our results suggest that improvements in physical working conditions and reducing smoking, particularly among employees with a low level of education, may markedly reduce educational differences in sickness absence.
National Institute for Health and Welfare, Department of Health, Functional Capacity and Welfare, P.O. Box 30, 00271 Helsinki, Finland. risto.kaikkonen@thl.fi.
Risto
R
Kaikkonen
Tommi
T
Härkänen
Ossi
O
Rahkonen
Raija
R
Gould
Seppo
S
Koskinen
D000328:Adult; D001499:Bayes Theorem; D003430:Cross-Sectional Studies; D004522:Educational Status; D005260:Female; D005387:Finland / Q000453:epidemiology; D005500:Follow-Up Studies; D015438:Health Behavior; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D008297:Male; D008875:Middle Aged; D016012:Poisson Distribution; D012044:Regression Analysis; D017678:Sex Distribution; D018582:Sick Leave / Q000191:economics / Q000706:statistics & numerical data*; D012931:Social Environment; D012959:Socioeconomic Factors; D016526:Workload / Q000706:statistics & numerical data*; D017132:Workplace