Recent changes in effects of predictors of tobacco cessation in formal treatment

INTRODUCTION: Several variables have been shown to predict outcomes of smoking cessation treatment; however, it is possible that these associations have changed over time due to cohort effects, contextual changes, and evolving patterns of nicotine use. This study aimed to investigate changes in associations between smoking cessation outcome and baseline variables. METHODS: In this analysis, we used 8 years of data (2016 to 2024) that comprised 107,204 unique enrollees in a large, nicotine replacement-therapy based smoking cessation program in Ontario, Canada to investigate changes in associations between outcomes and baseline variables. We used generalized additive models to conduct multiplicity-corrected tests of interactions and used a backwards stepwise process to arrive at a final model. RESULTS: We found large increases in cessation probability over time for younger people (at age 26, absolute adjusted change from study beginning to end = 9.7%, 95% CI = 5.0%, 14.4%) and those who used e-cigarettes daily (absolute adjusted change = 10.8%, 95% CI = 1.7%, 19.8%), as well as a compression of differences across levels of income. CONCLUSIONS: These findings suggest that associations between some variables and cessation outcomes have changed markedly over time. Improved outcomes for youth and people who use e-cigarettes may be related to changes in the prevalence, social acceptability, and reasons for use of different nicotine products. IMPLICATIONS: These findings underscore the importance of ongoing data collection and the need to consider time-varying effects in clinical and public health decision-making. Reliance on historical predictors without consideration of cohort and contextual changes may lead to suboptimal treatment recommendations. They are also relevant to ongoing discussions regarding the complex relationship between tobacco smoking and use of other nicotine products, suggesting that the role of e-cigarettes in cessation should be continually evaluated as patterns of use and population characteristics change.