Background: Population models have been developed to evaluate the impact of new tobacco products on the overall population. Reliable input parameters such as longitudinal tobacco use transitions are needed to quantify the net population health impact including the number of premature deaths prevented, additional life years, and changes in cigarette smoking prevalence. Methods: This secondary analysis assessed transition patterns from PATH wave 1 (2013–14) to wave 2 (2014–15) among adult exclusive cigarette smokers, exclusive e-cigarette users, and dual users. Transition probabilities were calculated by taking into account factors including cigarette smoking and e-cigarette use histories and experimental or established use behaviors. Multinomial logistic regression models were constructed to further evaluate factors associated with transition patterns. Results: Differential transition probabilities emerged among study subgroups when taking into account cigarette smoking and e-cigarette use histories and experimental or established use behaviors. For example, overall 45% of exclusive e-cigarette users in wave 1 continued using e-cigarettes exclusively in wave 2. However, we observed approximately 11 to 14% of wave 1 exclusive experimental e-cigarette users continued to use e-cigarette exclusively in wave 2, compared to about 62% of exclusive established e-cigarette users. The history of cigarette smoking and e-cigarette use is another important factor associated with transition patterns. Among experimental e-cigarette users, 7.5% of individuals without a history of cigarette smoking transitioned to exclusive cigarette smoking, compared to 30% of individuals with a history of cigarette smoking. Additionally, 1.3% of exclusive cigarette smokers in wave 1 transitioned to exclusive e-cigarette use, with the highest transition probability (3.7%) observed in the established cigarette smoker with a history of e-cigarette use subgroup. Conclusions: Product use histories and current use behaviors are important factors influencing transitions between product use states. Given that experimental users’ transition behaviors may be more variable and more influenced by tobacco use history, long-term predictions made by population models could be improved by the use of transition probabilities from established users. As transition patterns might be changing over time, long-term transition patterns can be examined through analysis of future waves of PATH data.

Computational models are valuable tools for predicting the population effects prior to Food and Drug Administration (FDA) authorization of a modified risk claim on a tobacco product. We have developed and validated a population model using best modeling practices. Our model consists of a Markov compartmental model based on cohorts starting at a defined age and followed up to a specific age accounting for 29 tobacco-use states based on a cohort members transition pathway. The Markov model is coupled with statistical mortality models and excess relative risk ratio estimates to determine survival probabilities from use of smokeless tobacco. Our model estimates the difference in premature deaths prevented by comparing Base Case (“world-as-is”) and Modified Case (the most likely outcome given that a modified risk claim is authorized) scenarios. Nationally representative transition probabilities were used for the Base Case. Probabilities of key transitions for the Modified Case were estimated based on a behavioral intentions study in users and nonusers. Our model predicts an estimated 93,000 premature deaths would be avoided over a 60-year period upon authorization of a modified risk claim. Our sensitivity analyses using various reasonable ranges of input parameters do not indicate any scenario under which the net benefit could be offset entirely.

Background: Lifetime established use criteria are essential to distinguish tobacco product experimental and established users; however, these criteria are not as well defined for some tobacco product categories. The objective of this analysis is to examine various lifetime use criteria to facilitate an objective characterization of experimental versus established use behaviors. Methods: Our analyses were conducted using the Population Assessment of Tobacco and Health (PATH) Study Waves 1 to 5 datasets. We first examined the level of agreement between non-numerical (having smoked/used the product fairly regularly) and numerical ( having smoked/used the product 20/50/100 times (e.g., use occasions) or product units) lifetime established use criteria among ever-, current-, and former-tobacco product users of various tobacco products. Logistic regression models were constructed to compare demographics, socioeconomic status, and tobacco product use patterns among current experimental versus current established users. Longitudinal analyses were performed to investigate the association between numerical or non-numerical criteria and discontinuation of past 30-day use at follow-up waves. Results: For tobacco product categories other than cigarette, we identified the following numerical thresholds as the objective characterization of established use: 50 for cigars and pipe products and 20 times for hookah, electronic nicotine products, smokeless, snus, and dissolvable tobacco products, based on level of agreement with the non-numerical criterion (kappa coefficients ranging from 0.3 to 0.6) and also with consideration of existing consensus. Significant differences were observed in demographics, socioeconomic status, tobacco product use and discontinuation of past 30-day use patterns when comparing current experimental and current established users with either non-numerical or numerical criteria. Conclusions: This study is the first PATH data analysis to derive quantitative thresholds of lifetime established use criteria to characterize experimental versus established use behaviors. Consistent applications of the lifetime established use criteria in data analysis and reporting would improve harmonization in research findings.

OBJECTIVES We developed and validated a computational model to assess the potential health impact of a new tobacco product in the U.S. market. METHODS An Agent-Based Model (ABM) framework was used to estimate changes in tobacco use prevalence and premature deaths based on the difference between modified (counterfactual) and base case (status quo) scenarios. The hypothetical population transitions between different tobacco-use states based on their attributes and transition probabilities over the simulation period. A transition sub-model coupled with mortality sub-models and excess relative risk (ERR) ratio estimates determine survival probability over time. To demonstrate the functionality and capability of our ABM, we modeled a scenario to simulate the population health impact a new tobacco product on the U.S. market. We also demonstrated sensitivity analyses by adjusting key input parameters. RESULTS Our simulation, based on modified- and base-case hypothetical populations using reliable and publicly available input sources, predicts a net benefit to the population with a decrease in premature deaths and cigarette smoking prevalence. CONCLUSION Our computational model, leveraging ABM to assess population impact, is a fit-for-purpose tool for predicting public health outcomes.

BACKGROUND. Much speculation remains as to whether menthol increases the inherent dependence potential of cigarettes and interferes with the ability to quit smoking. This research evaluates differences in cigarette dependence and smoking cessation between adults who smoke (AS) menthol and non-menthol cigarettes, particularly non-Hispanic Black AS. METHODS. Data were drawn from the Population Assessment of Tobacco and Health (PATH) Study Waves 4-5 among AS 18+ (n = 7,007 [1,127 non-Hispanic Black AS]). Baseline characteristics, dependence outcomes (e.g., heaviness of smoking index, tobacco dependence index), and cessation rates were compared between AS by menthol use and race/ethnicity. Cessation rates by menthol preference and respondent characteristics were further assessed using backward stepwise logistic regressions. RESULTS. AS who prefer menthol cigarettes (MAS) had lower cigarette dependence and smoked fewer cigarettes per day than non-menthol preferring AS (NMAS); this was also true among non-Hispanic White AS. No difference in cessation rates were observed between MAS and NMAS. AS had lower odds of cessation if they were older, non-White, reported smoking every day, and had moderate/high dependence. AS with college or higher education or planning to quit had higher odds of cessation. Among all non-Hispanic Black AS, there was lower odds of cessation among everyday smokers reporting moderate cigarette addiction, and a higher odds of cessation among higher-income and current users of smoke-free products. Menthol use was not a significant correlate of cessation. CONCLUSIONS. Menthol smoking was not associated with higher dependence or smoking cessation. Further, results suggest that household income and cigarette smoking frequency are potential barriers to cessation among non-Hispanic Black AS.

Summary: Introduction: Descriptive Consumer-Reported Outcome Measures (CROM) are self-reported survey outcome measures intended to measure observable characteristics and behaviors in research on tobacco- and nicotine-containing products (TNPs). The wide variety of available TNPs and the continued development of new TNPs require a constant evolution of measures to assess TNP use and related constructs adequately. The guidelines aim to provide recommendations on the selection, development, implementation, and analysis of Descriptive CROM in research on TNPs. Methods: The best practices and guidelines on Descriptive CROM were developed via an iterative and consensus-driven approach by the Descriptive CROM working group within the Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) CROM Task Force. This involved reviewing relevant national and international surveys, peer-reviewed publications, publicly available guidelines, and best practices published by professional institutes in related fields. The Descriptive CROM working group also collaborated with other CORESTA subgroups and relevant subject matter experts representing public health, academia, and the tobacco industry to continuously update the guidelines. Results: The resulting guidelines contain four main sections. First, we introduce the Descriptive CROM domain framework and provide definitions and descriptions of TNP categories and use patterns. Second, based on review of existing Descriptive CROM from national and international surveys, recommendations on Descriptive CROM for TNP research are provided. Third, the guidelines provide recommendations related to the development, modification, and adaptation of Descriptive CROM. This section includes a multi-stage process for CROM development, including item generation and qualitative and quantitative assessments of the CROM. The final section on Descriptive CROM implementation includes recommendations for best practices from the CORESTA organization for study design and development, data analysis, and reporting of Descriptive CROM data. Conclusions: The recommendations on the development, modification, and application of Descriptive CROM are grounded in scientific rationale and developed with consensus from the CORESTA TNP research community. The consensus Descriptive CROM measures, along with consistent definitions, could facilitate comparisons across studies, aggregation of data sets, and improve harmonization in research findings. As best practices and guidelines may evolve over time, the CORESTA CROM Task Force will continue to update the guidelines based on the dynamic TNP landscape and regulatory requirements to advance TNP research.