These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Predictors of disability pension in long-term sickness absence: results from a population-based and prospective study in Norway 1994-1999.
    Author: Gjesdal S, Ringdal PR, Haug K, Maeland JG.
    Journal: Eur J Public Health; 2004 Dec; 14(4):398-405. PubMed ID: 15542877.
    Abstract:
    BACKGROUND: While several socio-demographic predictors of disability pension (DP) have been identified, less is known about the importance of the medical aspects. METHODS: A representative sample of Norwegian long-term sickness absentees, 2043 women and 1585 men, with detailed diagnostic information based on the International Classification of Primary Care (ICPC) was followed up for 5 years. The date of granting DP was obtained from the Norwegian DP-register and used as the dependent variable in Cox multivariate regression analysis. Medical and socio-demographic factors were entered as explanatory variables. RESULTS: Kaplan-Meier estimates of the 5 year risk of DP were 22.9% for the full sample, 22.5% for men and 23.3% for the women. Men on sick leave for mental health disorders had an increased disability risk. Except for pregnancy-related cases, which carried a very low risk for future DP, there was no significant difference between the main diagnostic groups among women. Previous sickness absence increased the disability risk but was significant only for total absence above 20 weeks in the 4 years preceding inclusion. Age was the strongest predictor of future DP. Increasing income decreased the risk, bur not linearly. Unemployment status in the year preceding inclusion increased disability risk for women, but not for men. Among cases with musculoskeletal disorders (54.5% of the sample), subgroups with different disability risks were identified in Cox' regression analysis, with a gender-specific pattern. CONCLUSION: In addition to previously known socio-demographic predictors, medical variables were important in identifying sickness absentees with an increased risk of DP.
    [Abstract] [Full Text] [Related] [New Search]