Interrelationships between education, occupational class, income and sickness absence

Eur J Public Health. 2010 Jun;20(3):276-80. doi: 10.1093/eurpub/ckp162. Epub 2009 Oct 20.


Background: Socio-economic position measures, such as education, occupational class and income, are well-known determinants of health. However, previous studies have not paid attention to mutual interrelationships between these socio-economic position measures and medically confirmed sickness absence.

Methods: The study is a register-based study. The participants were municipal employees of the City of Helsinki aged 25-59 years in 2003. There were 21,599 women and 5841 men participants. Three socio-economic position measures were used, namely three-level education, four-level occupational class and gross individual income quartiles. Main outcome measure was medically confirmed sickness absence spells of 4 days or longer. Inequality indices were calculated using Poisson regression analysis.

Results: High education, occupational class and individual income were all consistently associated with lower sickness absence rates among both women and men. After mutual adjustment, education and occupational class remained independent determinants of sickness absence. The association of individual income with sickness absence was practically explained by temporally preceding education and occupational class.

Conclusions: Our results indicate that education and occupational class-rather than income-are strong determinants of sickness absence. Education, occupational class and income are complementary socio-economic position measures. To better inform sickness absence policy, future studies should aim to establish whether the observed socio-economic differences reflect broader differences in ill-health, lifestyle and working conditions.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Educational Status*
  • Female
  • Finland
  • Health Status Indicators*
  • Humans
  • Income*
  • Interpersonal Relations
  • Male
  • Middle Aged
  • Occupations / classification*
  • Poisson Distribution
  • Registries
  • Sex Distribution
  • Sick Leave / statistics & numerical data*
  • Socioeconomic Factors