Bayesian Network Analysis of Cigarette Smoking and E-cigarette Use in U.S. Population Samples

Bayesian Network Analysis of Cigarette Smoking and E-cigarette Use in U.S. Population Samples

Presented at the 2024 BayesiaLab Conference in Cincinnati on April 12, 2024.


There are approximately 1.1 billion tobacco smokers in the world who either suffer from or are at risk of smoking-related diseases such as cancers and cardiorespiratory conditions. Smokers who quit, especially at earlier ages, will gain back several years of life from smoking-related premature mortality. Quitting smoking, however, is difficult despite the availability of evidence-based quit methods, including medications and behavioral counseling. Electronic cigarettes (e-cigs) are being used by an increasing number of smokers, and the question arises whether e-cigs can help tobacco smokers quit. Data from randomized controlled trials (RCTs) suggests that e-cigs can serve as a substitute for cigarettes and are more effective than nicotine replacement therapies (e.g., nicotine patches, gum, lozenges) for smoking cessation. Data from observational studies are less clear about the association between the use of e-cigs and quitting smoking. We present results from two nationally representative US survey studies in which we examine factors that are associated with cigarette smoking and the use of e-cigs and whether the use of e-cigs is associated with quitting smoking (National Health Interview Survey – NHIS; Population Assessment of Tobacco and Health – PATH). Unsupervised BN learning of the 2022 NHIS survey showed a clustering of factors normally associated with cigarette smoking, including poverty and educational levels, depression, anxiety, disability, alcohol consumption, sexual orientation, nativity, and veteran status. There were only two direct paths from educational attainment and nativity to smoking status. E-cig use was directly associated with smoking status and age, suggesting that the determinants of use differ somewhat between cigarettes and e-cigs. Longitudinal data from the PATH study set up as a cross-lagged BN model, showed negative associations between cigarette and e-cig use at each successive year across 6 years, indicating a cumulative impact of e-cig use on smoking cessation. We will discuss challenges and options regarding longitudinal data analysis via Bayesian networks.

Presentation Video

Presentation Slides

About the Presenters

Dr. Niaura is a Professor of Social and Behavioral Sciences and Epidemiology and Chair of the Department of Epidemiology at the School of Global Public Health, New York University. From 2009-2017, he was Director of Research at the Schroeder Institute, Truth Initiative (formerly the Legacy Foundation) in Washington, DC. He has extensive expertise in tobacco dependence and treatment, and he has published over 400 peer-reviewed articles and several book chapters in this area. His interests include studying the biobehavioral substrates of tobacco dependence, evaluating behavioral and pharmacological treatments for cessation, and understanding and addressing public health disparities in tobacco-related burdens of illness and disability. He has been the Principal Investigator (PI) or co-investigator of over 70 NIH-funded grants, and he is the former President of the Society of Nicotine and Tobacco Research. He is currently a co-I of a large, multicenter initiative: the Population Assessment of Tobacco and Health Study (PATH, funded by the National Institute on Drug Abuse/Center for Tobacco Products, FDA), a national, longitudinal cohort study of more than 40,000 users and non-users of tobacco products ages 12+, including adolescents and young adults.

Shu (Violet) Xu, Ph.D.
Clinical Assistant Professor
Department of Biostatistics
School of Global Public Health
New York University

My work represents a balance of both statistical and applied aspects of quantitative methodology. My primary quantitative interests include evaluating and developing statistical methods for longitudinal data analysis. Specifically, My research focuses on various aspects of latent growth models, missing data methods, and causal inference models.

I have served as an Investigator/Biostatistician on more than 10 federally or locally funded research projects. I was PI of an NIH/NCI supplement award through the University of Michigan/Georgetown Center for the Assessment of the Public Health Impact of Tobacco Regulations (3U54CA229974), and the project aimed to examine the longitudinal effect of e-cigarette exposure on subsequent tobacco use patterns using conventional and causal mediation methods. I was also a co-investigator of an NIH/NCI R21 award (1R21CA260423-01). This project aims to assess the longitudinal impact of e-cigarette flavor, device, and marketing exposure on tobacco use and health outcomes using propensity score weighting and causal mediation methods. I am PI of an on-going NIH NIDA/FDA K01 award (1K01DA058408). This project aims to develop and implement causal machine-learning methods to inform tobacco regulatory sciences.

I have collaborated with substance use, family, and health researchers to advance and share my knowledge of quantitative methodology and pursue a better understanding of the social sciences and public health. I have conducted research with the Family Translational Research Group at New York University and the Methodology Center at Pennsylvania State University.

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