Evaluating trends in cigarette and HTP use in Japan and measurement issues in the National Health and Nutrition Survey.

INTRODUTION: Studies have reported that the rapid rise in heated tobacco product (HTP) sales in Japan accompanied an accelerated decline in cigarette sales. However, these studies do not distinguish whether those who previously smoked cigarettes became dual users with HTPs (smoking fewer cigarettes) or instead switched completely to HTPs. If HTPs present lower health risks than cigarettes, replacing cigarettes with HTPs is more likely to improve public health than cigarette users continuing as dual users. METHODS: To evaluate the role of HTP introduction relative to smoking prevalence, we examine trends in cigarette prevalence as related to trends in HTP use using Japan's National Health and Nutrition Survey (NHNS) from 2011 to 2019. We develop measures of relative changes in smoking prevalence use by age and gender in the pre-HTP and post-HTP periods. We then analyse prevalence data by year using joinpoint regression to statistically distinguish changes in trend. RESULTS: Compared with the pre-HTP 2011-2014 period, cigarette prevalence decreased more rapidly during the post-HTP 2014-2017 period, particularly among younger age groups. However, the changing format of NHNS questions limits our ability to determine the impact on smoking prevalence, particularly after 2017. CONCLUSIONS: While suggesting that HTPs helped some people who smoke to quit smoking, this study also shows the difficulties in eliciting accurate survey responses about product use and distinguishing the impact of a potentially harm-reducing product in an environment subject to rapidly evolving patterns of use.

The smoking and vaping model, A user-friendly model for examining the country-specific impact of nicotine VAPING product use: application to Germany.

BACKGROUND: Simulation models play an increasingly important role in tobacco control. Models examining the impact of nicotine vaping products (NVPs) and smoking tend to be highly specialized and inaccessible. We present the Smoking and Vaping Model (SAVM),a user-friendly cohort-based simulation model, adaptable to any country, that projects the public health impact of smokers switching to NVPs. METHODS: SAVM compares two scenarios. The No-NVP scenario projects smoking rates in the absence of NVPs using population projections, deaths rates, life expectancy, and smoking prevalence. The NVP scenario models vaping prevalence and its impact on smoking once NVPs became popular. NVP use impact is estimated as the difference in smoking- and vaping-attributable deaths (SVADs) and life-years lost (LYLs) between the No-NVP and NVP scenarios. We illustrate SAVM's adaptation to the German adult ages 18+ population, the Germany-SAVM by adjusting the model using population, mortality, smoking and NVP use data. RESULTS: Assuming that the excess NVP mortality risk is 5% that of smoking, Germany-SAVM projected 4.7 million LYLs and almost 300,000 SVADs averted associated with NVP use from 2012 to 2060. Increasing the excess NVP mortality risk to 40% with other rates constant resulted in averted 2.8 million LYLs and 200,000 SVADs during the same period. CONCLUSIONS: SAVM enables non-modelers, policymakers, and other stakeholders to analyze the potential population health effects of NVP use and public health interventions.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

The role of flavored electronic nicotine delivery systems in smoking cessation: A systematic review.

Background: Electronic nicotine delivery systems (ENDS) come in numerous flavors and may aid smoking cessation. This systematic review examines evidence on the role of ENDS flavors in smoking cessation. Methods: We searched EMBASE OVID, PsychInfo, and Medline databases for studies that: 1) examined cigarette cessation outcomes for persons using ENDS (intent, attempts, and success) and 2) reported results separated by respondent's ENDS flavor used. We extracted crude and adjusted odds ratios for associations between cessation outcomes and types of ENDS flavors used (nontobacco vs. tobacco/unflavored; nontobacco and nonmenthol vs. tobacco/unflavored and menthol). We did not consider cessation outcomes among people not using ENDS. We evaluated the evidence using the GRADE approach, focusing on consistency and reliability of findings across studies. Results: 29 studies met inclusion criteria, producing 36 odds ratios (ORs) comparing cessation outcomes across ENDS flavor groups. Three ORs examined quit intent, five examined quit attempts, and 28 examined quit success. Using GRADE, we reached Low levels of certainty that there was not an association between ENDS flavor use and intention to quit smoking or making a quit attempt. There were Very Low levels of certainty that nontobacco flavored versus tobacco/unflavored ENDS use was not associated with smoking cessation success, with similar findings for nonmenthol and nontobacco compared to tobacco and menthol flavored ENDS. Conclusion: The evidence about the role of different flavored ENDS use and smoking cessation outcomes is inconclusive, reflecting highly heterogeneous study definitions and methodological limitations. More high-quality evidence, ideally from randomized controlled trials, is required.

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.

The actual and anticipated effects of restrictions on flavoured electronic nicotine delivery systems: a scoping review.

OBJECTIVE: To synthesize the outcomes of policy evaluations of flavoured electronic nicotine delivery systems (ENDS) restrictions. DATA SOURCES: PubMed, Scopus, Embase and Web of Science before May 3, 2022. STUDY SELECTION: Studies that report sales, behaviour, or compliance outcomes related to implemented or hypothetical ENDS flavour restrictions. DATA EXTRACTION: Restriction details, whether implemented or hypothetical, whether additional products were restricted, jurisdictional level, study locations, and outcomes classified by sales, behaviour, and compliance. DATA SYNTHESIS: We included 30 studies. Of those, 26 were conducted exclusively in the US, two in India, and two surveyed respondents in multiple countries, including the US. Twenty-one evaluated implemented restrictions, while nine considered hypothetical restrictions. Five studies evaluated product sales, 17 evaluated behaviour, and 10 evaluated compliance, with two studies reporting multiple outcomes. Two studies reported an increase and one a reduction in cigarette sales following restrictions, while three reported reductions in ENDS sales. Behavioural studies presented a mixed view of the impacts of regulations on ENDS and cigarette use. However, the use of disparate outcomes limits the comparability of studies. Studies of hypothetical restrictions suggest decreased ENDS use, increased cigarette use, and increased use of illicit markets. Studies of compliance with flavoured product restrictions that included ENDS found that 6-39% of stores sold restricted flavoured products post-restrictions. Online stores remain a potential source of restricted products. CONCLUSION: Our findings highlight the need for additional research on the impacts of ENDS restrictions. Research should further evaluate the impact of restrictions on youth and adult use of nicotine and tobacco products in addition to the effects of restrictions in countries beyond the US to enable a robust consideration of the harm-benefit trade-off of restrictions.

A Decision-Theoretic Public Health Framework for Heated Tobacco and Nicotine Vaping Products.

Markets for nicotine vaping products (NVPs) and heated tobacco products (HTPs) have grown as these products became positioned as harm-reduction alternatives to combusted tobacco products. Herein, we present a public health decision-theoretic framework incorporating different patterns of HTP, NVP, and cigarette use to examine their impacts on population health. Our framework demonstrates that, for individuals who would have otherwise smoked, HTP use may provide public health benefits by enabling cessation or by discouraging smoking initiation and relapse. However, the benefits are reduced if more harmful HTP use replaces less harmful NVP use. HTP use may also negatively impact public health by encouraging smoking by otherwise non-smokers or by encouraging initiation or relapse into smoking. These patterns are directly influenced by industry behavior as well as public policy towards HTPs, NVPs, and cigarettes. While substantial research has been devoted to NVPs, much less is known about HTPs. Better information is needed to more precisely define the health risks of HTPs compared to cigarettes and NVPs, the relative appeal of HTPs to consumers, and the likelihood of later transitioning to smoking or quitting all products. While our analysis provides a framework for gaining that information, it also illustrates the complexities in distinguishing key factors.

Public health impact of a US menthol cigarette ban on the non-Hispanic black population: a simulation study.

INTRODUCTION: With the US Food and Drug Administration recently proposing to implement a ban on menthol cigarettes, it is critical to estimate the potential public health effects of such a ban. With high rates of menthol cigarette use and important smoking-related health disparity implications, the impact of the ban on the non-Hispanic black (NHB) population merits strong consideration. METHODS: We apply the previously developed Menthol Smoking and Vaping Model to the NHB population. A status quo scenario is developed using NHB-specific population, smoking and vaping initiation, cessation and death rates. Estimates from a recent expert elicitation on behavioural impacts of a menthol cigarette ban on the NHB population are used to develop a menthol ban scenario implemented in 2021. The public health impacts of the menthol ban are estimated as the difference between smoking and vaping attributable deaths (SVADs) and life years lost (LYLs) in the status quo and the menthol ban scenarios from 2021 to 2060. RESULTS: Under the menthol ban scenario, overall smoking is projected to decline by 35.7% in 2026 and by 25.3% in 2060 relative to the status quo scenario. With these reductions, SVADs are estimated to fall by about 18.5% and LYLs by 22.1%, translating to 255 895 premature deaths averted, and 4.0 million life years gained over a 40-year period. CONCLUSIONS: A menthol cigarette ban will substantially reduce the smoking-associated health impact on the NHB population, thereby reducing health disparities.

Scheduling patient appointment in an infusion center: a mixed integer robust optimization approach.

Infusion centers are experiencing greater demand, resulting in long patient wait times. The duration of chemotherapy treatment sessions often varies, and this uncertainty also contributes to longer patient wait times and to staff overtime, if not managed properly. The impact of such long wait times can be significant for cancer patients due to their physical and emotional vulnerability. In this paper, a mixed integer programming infusion appointment scheduling (IAS) mathematical model is developed based on patient appointment data, obtained from a cancer center of an academic hospital in Central Virginia. This model minimizes the weighted sum of the total wait times of patients, the makespan and the number of beds used through the planning horizon. A mixed integer programming robust slack allocation (RSA) mathematical model is designed to find the optimal patient appointment schedules, considering the fact that infusion time of patients may take longer than expected. Since the models can only handle a small number of patients, a robust scheduling heuristic (RSH) is developed based on the adaptive large neighborhood search (ALNS) to find patient appointments of real size infusion centers. Computational experiments based on real data show the effectiveness of the scheduling models compared to the original scheduling system of the infusion center. Also, both robust approaches (RSA and RSH) are able to find more reliable schedules than their deterministic counterparts when infusion time of patients takes longer than the scheduled infusion time.