The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach

Abstract

Abstract Background Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public’s health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. Methods We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009–2017 period using data from the 2009–2018 National Health Interview Surveys. We focused on cessation rates in the 25–44, 45–64 and 65 + age groups. Results The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25–44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25–44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45–64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. Conclusions The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

Keywords

• Kalman filter
• Age-group-specific cessation rates
• United States
• Dynamic mathematical model
• Smoking prevalence