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Published on: February 12, 2015
Smoking patterns based on birth-cohort-specific histories from 1965 to 2013, with projections to 2041
Douglas G Manuel1, Andrew S Wilton2, Carol Bennett3
1Ottawa Hospital Research Institute, ICES, Statistics Canada, and the Department of Family Medicine and the School of Epidemiology, Public Health and Preventative Medicine at the University of Ottawa.
Background:
Characterizing smoking patterns over time is essential for evaluating the impact of tobacco control interventions and predicting smoking-related mortality. Beginning with a 1920s birth cohort, smoking histories (i.e., estimates of smoking initiation and cessation, and prevalence of current and former smokers) were generated.
Data:
The Ontario sample (n = 238,411) of the 2003 to 2013 cycles of the Canadian Community Health Survey, which is conducted biennially, was used to obtain cross-sectional information on current smoking behaviour.
Methods:
Age at smoking initiation and age at smoking cessation were used to construct smoking histories for each respondent, up to the survey date. An age-period-cohort model was generated and used to examine survival differences by smoking status. Using the model, and adjusting for survival differences in smoking status, the prevalence of current, former and never smokers was estimated in cohorts from 1920 to 1985. Smoking initiation, cessation and intensity were then estimated for age-specific distributions of each birth cohort. These rates were projected forward through to 2041. Smoking patterns by highest level of education were generated using education-stratified models.
Results:
Smoking histories show clear trends over time by sex, cohort and age. If current patterns persist, smoking prevalence is projected to decline to single digits (below 10%) by 2023 for women and 2040 for men.
Discussion:
Birth-cohort-specific smoking histories can be generated using cross-sectional health surveys. These cohort histories can describe smoking patterns over time and into the future. In turn, these histories can be used in micro-simulation models to evaluate historic or planned tobacco control interventions, and to project smoking prevalence.
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