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.

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: This study employed a three-product agent-based model (ABM) to evaluate the potential population-level effects of granting market authorization for on! ^® nicotine pouches, an oral tobacco-derived nicotine product. Previous research has typically relied on simplified two-product models comparing cigarette use in a “Base Case” with a “Modified Case” scenario, limiting the ability to reflect real-world multi-product tobacco use patterns. To address this, the ABM in this study incorporated cigarettes and smokeless tobacco (ST) products in the Base Case and added the on! ^® nicotine pouches as the Test Product in the Modified Case. Methods: Developed in MATLAB ^® version 9.2, the model consisted of a transition sub-model, which simulated annual changes in product use based on national survey data and Test Product-specific studies, and a mortality sub-model, which linked survival outcomes to product transitions using an excess relative risk estimate of 5% for the Test Product relative to cigarettes. The simulation ran over an 80-year time horizon, predicting individual-level agent dynamics and annual transitions. Results: Model outputs indicated that introducing the Test Product could prevent approximately 476,000 premature deaths, reduce cigarette prevalence by 0.6 percentage points, and ST product use by 0.3 percentage points, while increasing Test Product use by 1.6 percentage points. Conclusion: These findings suggest that regulatory authorization of on! ^® nicotine pouches could yield a net public health benefit by lowering all-cause mortality and reducing the prevalence of use of tobacco products associated with greater health risks.