文章基本信息

标题：Lifelong smoking trajectories of Northern Finns are characterized by sociodemographic and lifestyle differences in a 46-year follow-up
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作者：Petteri Oura ; Ina Rissanen ; Juho-Antti Junno 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-10
DOI：10.1038/s41598-020-73334-3
出版社：Springer Nature
摘要：Smoking remains among the leading causes of mortality worldwide. Obtaining a comprehensive understanding of a population’s smoking behaviour is essential for tobacco control. Here, we aim to characterize lifelong smoking patterns and explore underlying sociodemographic and lifestyle factors in a population-based birth cohort population followed up for 46 years. Our analysis is based on 5797 individuals from the Northern Finland Birth Cohort 1966 who self-reported their tobacco smoking behaviour at the ages of 14, 31 and 46. Data on sex, education, employment, body mass index, physical activity, alcohol consumption, and substance addiction were also collected at the follow-ups. We profile each individual’s annual smoking history from the age of 5 to 47, and conduct a latent class trajectory analysis on the data. We then characterize the identified smoking trajectory classes in terms of the background variables, and compare the heaviest smokers with other classes in order to reveal specific predictors of non-smoking and discontinued smoking. Six smoking trajectories are identified in our sample: never-smokers (class size 41.0%), youth smokers (12.6%), young adult quitters (10.8%), late adult quitters (10.5%), late starters (4.3%), and lifetime smokers (20.7%). Smoking is generally associated with male sex, lower socioeconomic status and unhealthier lifestyle. Multivariable between-class comparisons identify unemployment (odds ratio [OR] 1.28–1.45) and physical inactivity (OR 1.20–1.52) as significant predictors of lifetime smoking relative to any other class. Female sex increases the odds of never-smoking and youth smoking (OR 1.29–1.33), and male sex increases the odds of adult quitting (OR 1.30–1.41), relative to lifetime smoking. We expect future initiatives to benefit from our data by exploiting the identified predictors as direct targets of intervention, or as a means of identifying individuals who may benefit from such interventions.
其他摘要：Abstract Smoking remains among the leading causes of mortality worldwide. Obtaining a comprehensive understanding of a population’s smoking behaviour is essential for tobacco control. Here, we aim to characterize lifelong smoking patterns and explore underlying sociodemographic and lifestyle factors in a population-based birth cohort population followed up for 46 years. Our analysis is based on 5797 individuals from the Northern Finland Birth Cohort 1966 who self-reported their tobacco smoking behaviour at the ages of 14, 31 and 46. Data on sex, education, employment, body mass index, physical activity, alcohol consumption, and substance addiction were also collected at the follow-ups. We profile each individual’s annual smoking history from the age of 5 to 47, and conduct a latent class trajectory analysis on the data. We then characterize the identified smoking trajectory classes in terms of the background variables, and compare the heaviest smokers with other classes in order to reveal specific predictors of non-smoking and discontinued smoking. Six smoking trajectories are identified in our sample: never-smokers (class size 41.0%), youth smokers (12.6%), young adult quitters (10.8%), late adult quitters (10.5%), late starters (4.3%), and lifetime smokers (20.7%). Smoking is generally associated with male sex, lower socioeconomic status and unhealthier lifestyle. Multivariable between-class comparisons identify unemployment (odds ratio [OR] 1.28–1.45) and physical inactivity (OR 1.20–1.52) as significant predictors of lifetime smoking relative to any other class. Female sex increases the odds of never-smoking and youth smoking (OR 1.29–1.33), and male sex increases the odds of adult quitting (OR 1.30–1.41), relative to lifetime smoking. We expect future initiatives to benefit from our data by exploiting the identified predictors as direct targets of intervention, or as a means of identifying individuals who may benefit from such interventions.