Do Tobacco Companies Have an Incentive to Promote "Harm Reduction" Products?: The Role of Competition.

INTRODUCTION: Some cigarette companies have started to talk about replacing cigarettes with less harmful alternatives, which might include nicotine vaping products (NVPs), heated tobacco products (HTPs), and oral nicotine delivery products. We consider market competition as a primary driver of whether cigarette companies follow through on their stated intentions. AIMS AND METHODS: We focus on the behavior of cigarette companies in the United States. We compare competition in the pre- and post-2012 time periods, analyze the impact of the growth in NVPs on smoking prevalence and cigarette company profits, and examine the potential future role of competition. RESULTS: Since 2006, consumers have broadened their use of non-combustible nicotine delivery products (NCNDPs) to include, inter alia, NVPs, HTPs, and oral nicotine pouches. U.S. cigarette companies have acquired major stakes in each of these product categories which corresponds to a period of rapidly declining adult smoking prevalence, especially among younger adults (ages 18-24 years). The shifting dynamics of the nicotine product marketplace are also reflected in cigarette company stock prices. While cigarette companies are likely to promote HTPs and nicotine delivery products over NVPs, their incentives will be directly related to competition from independent firms, which in turn will depend on government regulation. CONCLUSIONS: Although cigarette companies will back alternatives to combusted tobacco when threatened by competition, the prospects for their lasting conversion to NCNDPs will depend on the extent of such competition, which will be influenced by government regulation of tobacco products. IMPLICATIONS: Regulations that limit competition from independent firms while also protecting cigarette company profits risk slowing or even reversing recent declines in smoking, especially among youth and young adults. Regulations that reduce the appeal and addictiveness of combusted tobacco products, such as higher cigarette taxes or a reduced nicotine standard, will encourage smokers to quit and/or switch to less harmful non-combusted forms of tobacco. The regulation of non-combustible nicotine delivery products and cigarettes should be proportionate to their relative risks, so that smokers have incentives to switch from combustibles to safer alternatives, and cigarette companies have incentives to promote safer products.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.