Educational differences in disability retirement among young employees in Helsinki, Finland

Eur J Public Health. 2016 Apr;26(2):318-22. doi: 10.1093/eurpub/ckv226. Epub 2015 Dec 17.


Background: Disability retirement (DR) among young employees is an increasing problem affecting work life and public health, given the potential major loss of working time. Little is known about educational differences in the risk of DR among young employees, despite the need for such knowledge in targeting preventive measures. We examined the association between education and DR due to any cause and to mental and non-mental causes among young employees.

Methods: Personnel register data of the City of Helsinki from the years 2002-2013 for 25-to-34-year-old employees (n= 41225) were linked to register data from the Finnish Centre for Pensions on DR (n= 381), and from Statistics Finland on education. Education was categorised into four hierarchical groups. The mean follow-up time was 5.7 years. Cox regression analysis was used.

Results: There were 381 DR events and of the events, over 70% were due to mental disorders and 72% were temporary. A consistent educational gradient was found. Those with a basic education were at the highest risk of DR due to any cause (HR 4.64, 95% CI 3.07, 7.02), and to mental (HR 4.79, 95% CI 2.89, 7.94) and non-mental causes (HR 4.32, 95% CI 2.10, 8.91).

Conclusions: DR due to any cause, and to mental and non-mental causes, followed a clear educational gradient. Early intervention, treatment and rehabilitation with a view to maintaining work ability are needed among young employees, especially those with low education. Adapting working conditions to their health and work ability may also help to avoid premature exit from work.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Age Factors
  • Educational Status*
  • Female
  • Finland / epidemiology
  • Health Status*
  • Humans
  • Male
  • Mental Health / statistics & numerical data*
  • Pensions / statistics & numerical data*
  • Regression Analysis
  • Risk Factors
  • Sick Leave / statistics & numerical data*
  • Socioeconomic Factors