David Méndez1, Jamie Tam1, Gary A Giovino2, Alexander Tsodikov3, Kenneth E Warner1. 1. Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI. 2. Department of Community Health and Health Behavior, School of Public Health and Health Professions, University at Buffalo, SUNY, Buffalo, NY. 3. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI.
Abstract
INTRODUCTION: We examine the trajectory of adult smoking prevalence in the United States over the period 1990-2014 to investigate whether the smoking cessation rate has changed during this period. METHODS: We employ a dynamic model of smoking prevalence, and data from the National Health Interview Survey (NHIS) and the National Survey on Drug Use and Health (NSDUH), to estimate the adult cessation rate in 6-year intervals. We use weighted nonlinear least squares to perform the estimation. We then employ a meta-regression model to test whether the cessation rate has increased. RESULTS: The annual cessation rate has increased from 2.4% in 1990 to 4.5% in 2014 according to the NHIS data, and from 3.2% in 2002 to 4.2% in 2014 according to the NSDUH data. The increasing trend is statistically significant (p value = 1.57×10-6) and the two independent surveys produced nearly identical results, which makes it unlikely that our findings are a product of chance. CONCLUSIONS: Our analysis finds that the smoking cessation rate in the United States has almost doubled since 1990. This increase is responsible for at least 2 million fewer smokers in 2014. If current conditions persist, by the year 2020 the increase in cessation rates will be responsible for 3.5 million fewer smokers. Our findings can assist in predicting the future path of the smoking epidemic and determining the correct allocation of resources to eradicate it. IMPLICATIONS: We show that the adult smoking cessation rate has greatly increased since 1990. We demonstrate this by studying prevalence trajectories from two independent population surveys, which yielded nearly identical results. Different from other studies, we focus on permanent quit rates (net of relapses) which we estimate from a dynamic model of prevalence. Our results do not stem from self-reported quitting behavior, but from the analysis of observed prevalence and its inherent variability. Our findings can contribute to predicting the future path of the smoking epidemic and to determining the optimal allocation of resources to eradicate it.
INTRODUCTION: We examine the trajectory of adult smoking prevalence in the United States over the period 1990-2014 to investigate whether the smoking cessation rate has changed during this period. METHODS: We employ a dynamic model of smoking prevalence, and data from the National Health Interview Survey (NHIS) and the National Survey on Drug Use and Health (NSDUH), to estimate the adult cessation rate in 6-year intervals. We use weighted nonlinear least squares to perform the estimation. We then employ a meta-regression model to test whether the cessation rate has increased. RESULTS: The annual cessation rate has increased from 2.4% in 1990 to 4.5% in 2014 according to the NHIS data, and from 3.2% in 2002 to 4.2% in 2014 according to the NSDUH data. The increasing trend is statistically significant (p value = 1.57×10-6) and the two independent surveys produced nearly identical results, which makes it unlikely that our findings are a product of chance. CONCLUSIONS: Our analysis finds that the smoking cessation rate in the United States has almost doubled since 1990. This increase is responsible for at least 2 million fewer smokers in 2014. If current conditions persist, by the year 2020 the increase in cessation rates will be responsible for 3.5 million fewer smokers. Our findings can assist in predicting the future path of the smoking epidemic and determining the correct allocation of resources to eradicate it. IMPLICATIONS: We show that the adult smoking cessation rate has greatly increased since 1990. We demonstrate this by studying prevalence trajectories from two independent population surveys, which yielded nearly identical results. Different from other studies, we focus on permanent quit rates (net of relapses) which we estimate from a dynamic model of prevalence. Our results do not stem from self-reported quitting behavior, but from the analysis of observed prevalence and its inherent variability. Our findings can contribute to predicting the future path of the smoking epidemic and to determining the optimal allocation of resources to eradicate it.
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