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Journal of Public Health

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01-06-2010 | Original Article

The fraction of disability pensions attributable to smoking and obesity. Results from a 15-year follow-up study

Authors: Thomas Lund, Merete Labriola, Helene Feveile, Karl Bang Christensen

Published in: Journal of Public Health | Issue 3/2010

Abstract

Objective

The aim of this study was to estimate the fraction of permanent disability pensions among the working population in Denmark that can be attributed to differences in health behaviour.

Methods

A total of 8,287 employees were interviewed regarding health behaviour, work environment and general health. They were followed in a national register on granted disability pensions. Cox regression analysis was performed, and attributable fractions were calculated.

Results

The analysis showed a 48% and 79% risk increase for female heavy and moderate smokers, respectively (attributable fraction 19.7%), and a 74% risk increase for BMI < 18.5 (attributable fraction 6.1%). Male heavy smokers had a 67% excess risk of disability pension (attributable fraction 15.7%).

Conclusions

Smoking was an independent disability pension predictor regardless of age, work environment factors and baseline general health status. Workplace-based smoking cessation could substantially decrease permanent disability retirement from work.

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Title: The fraction of disability pensions attributable to smoking and obesity. Results from a 15-year follow-up study
Authors: Thomas Lund
Merete Labriola
Helene Feveile
Karl Bang Christensen
Publication date: 01-06-2010
Publisher: Springer-Verlag
Published in: Journal of Public Health / Issue 3/2010
Print ISSN: 2198-1833
Electronic ISSN: 1613-2238
DOI: https://doi.org/10.1007/s10389-009-0304-3

Keynote webinar | Spotlight on medication adherence

Springer Medicine

The fraction of disability pensions attributable to smoking and obesity. Results from a 15-year follow-up study

Abstract

Objective

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

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