Modeling predicted that tobacco control policies targeted at lower educated will reduce the differences in life expectancy
Journal of Clinical Epidemiology , Volume 59 - Issue 9 p. 1002- 1008
Background and Objective: To estimate the effects of reducing the prevalence of smoking in lower educated groups on educational differences in life expectancy. Methods: A dynamic Markov-type multistate transition model estimated the effects on life expectancy of two scenarios. A "maximum scenario" where educational differences in prevalence of smoking disappear immediately, and a "policy target-scenario" where difference in prevalence of smoking is halved over a 20-year period. The two scenarios were compared to a reference scenario, where smoking prevalences do not change. Five Dutch cohort studies, involving over 67,000 participants aged 20 to 90 years, provided relative mortality risks by educational level, and smoking habits were assessed using national data of more than 120,000 persons. Results: In the reference scenario, the difference in life expectancy at age 40 between highest and lowest educated groups was 5.1 years for men and 2.7 years for women. In the "maximum scenario" these differences were reduced to 3.6 years for men and 1.7 years for women (reduction ≈30%), and in the "policy target-scenario" differences were 4.7 years for men and 2.4 years for women (reduction ≈10%). Conclusion: Theoretically, educational differences in life expectancy would be reduced by 30% at maximum, if variations in smoking prevalence were eliminated completely. In practice, tobacco control policies that are targeted at the lower educated may reduce the differences in life expectancy by approximately 10%.
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|Journal of Clinical Epidemiology|
|Organisation||Erasmus MC: University Medical Center Rotterdam|
Bemelmans, W.J, van Lenthe, F.J, Hoogenveen, R.T, Kunst, A.E, Deeg, D.J.H, van den Brandt, P.A, … Verschuren, W.M.M. (2006). Modeling predicted that tobacco control policies targeted at lower educated will reduce the differences in life expectancy. Journal of Clinical Epidemiology, 59(9), 1002–1008. doi:10.1016/j.jclinepi.2006.02.008