Predictive factors for disability pension--an 11-year follow up of young persons on sick leave due to neck, shoulder, or back diagnoses

Scand J Public Health. 2001 Jun;29(2):104-12.


Aims: Although back diagnoses are recurrent and the main diagnoses behind sickness absence and disability pension surprisingly few longitudinal studies have been performed. This study identifies predictive factors for disability pension among young persons initially sick-listed with back diagnoses.

Methods: An 11-year prospective cohort study was conducted, including all individuals in a Swedish city who, in 1985, were aged 25-34 and sick-listed > or =28 days owing to neck, shoulder, or back diagnoses (n = 213). The following data was obtained: disability pension, emigration, and death for 1985-96, sickness absence for 1982-84, and demographics in 1985 regarding sex, income, occupation, marital status, diagnosis, socioeconomic group, and citizenship. Cox regression and life tables were used in the analyses.

Results: In 1996, i.e. within 11 years, 22% of the individuals (27% of the women and 14% of the men) had been granted disability pension. The relative risk for disability pension was higher for women (2.4; p = 0.010), persons with foreign citizenship (3.6; p=0.009), and those who had had >14 sick-leave days per spell during the three years before inclusion, compared to those with <7 days/spell (3.1; p=0.003).

Conclusions: This cohort of young persons proved to be a high-risk group for disability pension. Some of the factors known to predict long-time sickness absence also predict disability pension in a cohort of already sick-listed persons.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Back / physiopathology*
  • Female
  • Follow-Up Studies
  • Humans
  • Insurance, Disability
  • Life Tables
  • Longitudinal Studies
  • Male
  • Neck / physiopathology*
  • Occupations / classification
  • Pensions / statistics & numerical data*
  • Proportional Hazards Models
  • Risk Factors
  • Shoulder / physiopathology*
  • Sick Leave / statistics & numerical data*
  • Survival Analysis
  • Sweden / epidemiology
  • Work Capacity Evaluation*