2024 Funding Cycle Awardees

View other award years: 2024 • 2022 • 2021 • 2020 • 2019

Please note: Listing describes appointments and affiliations at the time of award. Please check our Trainees and Alumni pages for current appointments and affiliations.

Title: “Exploring Multiplicative Interactions of Marketing and Disparity Identities on Tobacco Use Behaviors”

PI: Dr. Zongshuan Duan, Assistant Professor at Georgia State University

Abstract and accomplishments +

Dr. Zongshuan DuanGeorgia State University

Abstract

Background: The evolving US tobacco marketplace employs diversified marketing strategies, disproportionately targeting disadvantaged populations. Despite existing knowledge on tobacco-related disparities, literature faces several limitations. First, there is limited research exploring the role of intersectionality in disparities. Second, there is insufficient knowledge of the broad spectrum of tobacco products and multiple tobacco product (MTP) use among minorities and those with intersecting minority identities, particularly in longitudinal or nationally representative studies. Third, limited research has examined the potential multiplicative interaction role of marketing exposure, which may exacerbate disparities in tobacco use among minorities and those with intersecting minority identities. Addressing these gaps is crucial for developing targeted policies and interventions to promote health equity. This proposal aims to examine whether and to what extent tobacco marketing and disparity identities may multiplicatively affect their tobacco use behaviors among US youth and young adults. Specific Aims: In this pilot study, we will focus on analyzing population-based longitudinal data from the Population Assessment of Tobacco and Health (PATH) study, to address 2 aims:
Aim 1: Examine tobacco use behaviors over time (i.e., tobacco product use, MTP use, flavored product use) among youth and young adults representing disparity identities and intersecting disparity identities.
Aim 2: Examine whether and to what extent exposure to tobacco marketing may differentially affect tobacco use behaviors among youth and young adults representing minorities and intersecting disparity identities.

 

Title: “Understanding the Role of Social Network Dynamics on Adolescent Tobacco and Nicotine Use”

PI: Dr. Clinton H. Durney, Research Methodologist at British Columbia Cancer Research Institute

Abstract and accomplishments +

Dr. Clinton H. DurneyBritish Columbia Cancer Research Institute

Abstract

Tobacco use is a significant public health concern, often beginning during adolescence and early adulthood. Existing research frequently examines tobacco and nicotine use at a broad level, without considering the impact of social interactions. This leaves a gap in our understanding of how knowledge, attitudes, perceptions, and behaviors related to tobacco use spread within social networks. Adolescents are heavily influenced by their social circles, both in-person and online, making them particularly vulnerable to peer influence, viral marketing, and targeted products like flavored tobacco. Recognizing the critical role of network characteristics — such as the arrangement and number of connections, as well as other topological features — in shaping tobacco use dynamics is essential. Understanding these influences is crucial for developing effective prevention and intervention strategies tailored to the unique vulnerabilities of youth. Specific Aims:

1. Investigate Peer Influence:
   • We will study how the behaviors and characteristics of friends and social connections affect the initiation and progression of nicotine use among teenagers.
   • Using data from large national surveys, we aim to identify key factors that influence teen tobacco use. This includes examining how peer pressure, perceptions of health risks, and the popularity of different tobacco products affect their choices.
   • We will assess how the interaction between peer influence and peer selection impacts youth decisions regarding tobacco use.
2. Model Social Networks:
   • We will develop a dynamic social network model to simulate how social connections evolve over time and how these changes impact tobacco use among teens.
   • This model will help us understand the role of different network features, such as the number and arrangement of connections and the formation of friend groups, in shaping tobacco use behaviors.
   • By analyzing these network characteristics, we aim to determine how they influence the likelihood of adolescent’s initiation of tobacco products.
3. Explore Key Influences:
   • We will delve deeper into how various factors, such as environmental influences and psychosocial aspects, interact with peer influences to shape adolescent smoking behaviors.
   • Our model will incorporate these factors to refine the probabilities of transitioning to tobacco use within social networks.
   • We will simulate different theoretical scenarios to explore how changes in social networks and smoking habits co-evolve. This includes examining how friends' smoking behaviors influence individual decisions and how network restructuring processes impact overall tobacco use trends.

By examining these social dynamics, we aim to gain comprehensive insights into the factors driving tobacco use among adolescents. This knowledge will be crucial in developing targeted strategies to reduce tobacco use initiation and promote cessation among youth.

 

Title: “Estimating Unbiased and Precise Effects of Oral Nicotine Product Use on Inhaled Tobacco Product Use Persistence and Progression in Adolescence”

PI: Dr. Dae Hee Han, Postdoctoral Scholar at University of Southern California

Abstract and accomplishments +

Dr. Dae Hee HanUniversity of Southern California

Abstract

Background: Non-tobacco oral nicotine products (ONP) are a relatively new type of commercial tobacco product and one of the fastest-growing commercial tobacco product categories in the U.S. market. During 2019–2022, U.S. sales of nicotine pouches, one of the most prevalent ONPs among U.S. adolescents, have increased by 540% (from 126 million units to 808 million units). ONPs may be of particular interest to young people who use inhaled tobacco products (ITPs; e.g., combustible cigarettes and e-cigarettes) because ONPs can be used discreetly where ITP use is not allowed. Due to these concerns and adverse health effects of ONP use (e.g., oral toxicity), it is important to understand whether ONP-ITP co-use promotes ITP use persistence and progression among adolescents who use ITPs. Inverse probability weighting (IPW) has been one of the most widely used approaches to reduce confounding effects in epidemiologic and public health research. Augmented inverse probability weighting (AIPW) combines both the properties of the regression-based estimator (balancing differences in outcomes) and IPW estimator (balancing differences in exposure) and is therefore a “doubly robust” method in that it requires only either the propensity or outcome model to be correctly specified but not both. Machine learning can be used to enhance AIPW (AIPW-ML) to produce more accurate standard error estimates, narrowing the confidence interval (CI) for exposure effect estimates, improving statistical accuracy and efficiency. Yet, AIPW-ML has never been applied in tobacco regulatory science research, including analysis of ONP use effects on subsequent ITP use patterns. Specific Aims:
Aim 1: Examine the association of ONP and ITP co-use with subsequent ITP use persistence and progression using a conventional IPW approach. The average treatment effects of ONP-ITP co-use on ITP persistence and progression among adolescents will be estimated using a conventional inverse probability weighting approach in which participants are weighted by the inverse probability of their treatment assignment (i.e., ONP co-use). Hypothesis: Among adolescents who used ITPs at baseline, those with (vs. without) baseline ONP co-use would be more likely to continue to use ITPs and increase ITP use frequency at follow-up.
Aim 2: Examine the association of ONP-ITP co-use with subsequent ITP use persistence and progression using machine learning enhanced augmented inverse probability weighting (AIPW-ML) and compare estimates to Aim 1 results. We will use AIPW-ML that estimates less biased average treatment effects of adolescent co-use of ONP and ITP on ITP use persistence and progression. We will use the ensemble (i.e., super learner) algorithm for machine leaning analysis. Hypothesis: 95% CIs of ONP co-use effect estimates using AIPW-ML will be narrower than those estimated using the conventional IPW.

 

Title: “A Machine Learning Approach to Cluster Similar Flavors of Electronic Nicotine Delivery Systems”

PI: Dr. Mona Issabakhsh, Research Instructor at Georgetown University

Abstract and accomplishments +

Dr. Mona IssabakhshGeorgetown University

Abstract

Background: Despite a significant decline in smoking rates, cigarette use remains a leading cause of preventable mortality in the US, responsible for 480,000 deaths annually. Smoking uptake prevention and smoking cessation are among the most cost-effective and powerful solutions for tobacco-related disease prevention. Of US adult tobacco users, 24% use electronic nicotine delivery systems (ENDS). ENDS are available in hundreds of flavors beyond tobacco, which is one of the most commonly reported reasons for their use. The wide variety of ENDS flavors is a persistent concern among policymakers since studies indicate that flavored ENDS attract youth to ENDS use with the risk of subsequent cigarette initiation. Flavored ENDS use may also encourage cigarette smoking cessation; flavors, such as fruit, dessert, and menthol, have been reported to attract adult smokers trying to quit or reduce cigarette use. Studies, however, reported outcomes ranging from no effect to a significant positive effect for the association between flavored ENDS use and smoking cessation. The range of outcomes reported in the literature may stem from the wide variety of ENDS flavors and the heterogeneity of their users. Therefore, a systematic classification of ENDS flavors (considering flavor types, user characteristics, and ENDS devices) can improve empirical studies and simulation models' accuracy and increase the comparability of research results. The proposed research will apply machine learning (ML) clustering models to distinguish related clusters of ENDS flavors using the Nielson consumer panel data. Specific Aims:
Aim 1: Develop a flavored ENDS clustering model based on flavor types, devices, and user characteristics. To develop a clustering model, first, a variable selection step will be performed, which determines essential features to assign the most similar flavors to the same cluster while assigning the least similar flavors to separate clusters. Variables will include flavor types, ENDS devices, and consumers’ characteristics and will be inputted into the variable selection analysis to find the best subset of variables to develop an optimized clustering model. The most significant variables will be selected using an efficient tree-based algorithm that applies a “decision tree” structure to determine variable importance.
Aim 2: Train the flavored ENDS clustering model. Using the selected important variables, the dissimilarities between ENDS flavors will be calculated using the Gower distance method, a measure used to calculate the distance between two data points whose attribute has a mix of categorical and numerical values. The sample will be divided into a training and a testing dataset. The training dataset will be used to “train” the model, while the testing dataset (which shares the same distribution and properties as the training dataset) will be used to test the model's accuracy.
Aim 3: Test and validate the flavored ENDS clustering model. The trained model will cluster the testing set instances (i.e., unclustered data). The model's performance will then be evaluated by assigning the most similar flavors of ENDS to the same cluster or, in other words, minimizing dissimilarity within each cluster. While the model will be internally validated using a testing set, external validation can be done by developing ENDS clusters using the latent class analysis (LCA) statistical approach and comparing the results to the literature that has developed ENDS flavor categories.

 

Title: “Impact of Flavored E-Cigarette Use and Nicotine Metabolism on Systemic Inflammatory Biomarkers of Cardiovascular Disease Risk”

PI: Dr. Nancy Jao, Assistant Professor at Rosalind Franklin University of Medicine and Science

Abstract and accomplishments +

Dr. Nancy JaoRosalind Franklin University of Medicine and Science

Abstract

Background: In 2020, the United States (US) Food and Drug Administration (FDA) issued a new enforcement policy against the sale of certain flavored e-cigarette (EC) products in an effort to limit the popularity of flavored ECs among youth, especially fruit and mint flavors. However, the overall use of flavored ECs has continued to increase since the implementation of the FDA policy. Additionally, despite the efforts to curb flavored EC use among youth, the 2022 National Youth Tobacco Survey found that 84.9% of high and middle school students who use ECs prefer flavored ECs, including a rise in menthol-flavored EC use. With the continued rise of flavored ECs, it remains vital for the FDA to continue to prioritize understanding how flavored EC use can impact overall public health. Although the use of combustible cigarettes has been long connected to increased risk for cardiovascular disease (CVD), the investigation of the short- and long-term health effects of EC use is still emerging. One important gap in the literature is the understanding of e-cigarette use with subclinical CVD outcomes in well-defined longitudinal cohorts. Although basic science studies have shown that flavoring in EC products can lead to increases in inflammatory response and dysfunction beyond the effects of nicotine, no studies to date have examined whether flavored EC use may increase CVD risk compared to unflavored (i.e., tobacco) EC use. Individual differences in nicotine processing not only influence smoking behavior but also have direct implications for adverse health outcomes. As individuals adjust their smoking behavior based on their rate of nicotine processing, individual differences in nicotine processing have been shown to play a role in influencing clinical outcomes. The nicotine metabolite ratio (NMR) is a validated phenotypic biomarker measurement of nicotine processing and clearance. However, while flavored EC use has been shown to suppress NMR to prolong the duration of nicotine bioavailability, no studies to date have examined how flavored EC use may impact NMR to impact levels of systemic inflammation. Specific Aims: The proposed study will examine the potential role of flavored EC use and NMR on biomarkers of CVD risk in the Population Assessment of Tobacco and Health (PATH) Study conducted by NIH/FDA. Specifically, the proposed study will examine population-level differences in systemic biomarkers of inflammation and nicotine processing between individuals who use flavored vs. unflavored ECs. The specific aims of this project are to:
Aim 1: Assess the direct and interactive effects of EC type (flavored vs. unflavored) and NMR on levels of systemic inflammation at Wave 1. Hypothesis: Individuals who use flavored ECs with slower NMR will have higher levels of systemic inflammation.
Aim 2: Compare the level of risk for CVD diagnosis based on flavored EC use and NMR at baseline and follow up waves. Hypothesis: Individuals who use flavored ECs and have slower NMR will have a greater likelihood of CVD diagnosis at Wave 1 and follow up Waves.

 

Title: “Predicting Response to a U.S. Nicotine Reduction Standard Among Medical and Psychiatric Priority Populations”

PI: Dana Rubenstein, MHS, Medical Student at Duke University School of Medicine

Abstract and accomplishments +

Dana Rubenstein, MHSDuke University School of Medicine

Abstract

Background: Tobacco use is the leading preventable cause of death and disability in the U.S. Populations experiencing medical and psychiatric comorbidities use tobacco at disproportionately high prevalences and experience the greatest burden of related morbidity and mortality. For instance, adults with serious mental illness (SMI), adults with chronic pain, and adults with human immunodeficiency virus (HIV) are 2-4 times more likely to smoke cigarettes than the general population. This disproportionately high tobacco use results in people with SMI losing an average of 25 years of potential life, compared to 10 years in the general smoking population. Well-treated individuals with HIV may also lose more potential life-years from smoking than from HIV. One public policy approach that may reduce smoking and improve outcomes in these priority populations and in the US overall is a nicotine reduction standard (NRS). It is based on nearly a decade of clinical trials indicating improved smoking outcomes for individuals randomized to very low nicotine content (VLNC) versus normal nicotine content cigarettes. This policy was announced as a potential proposed rule from the US Food and Drug Administration (FDA) in 2022 and is under consideration. While the net-public health impact of an NRS is anticipated to be groundbreaking (e.g. estimated to drop the U.S. smoking prevalence from 12% to <2%), it is less clear if the benefits of this policy will be equitably distributed (e.g. will all populations reach <2% smoking prevalence). Clinical trials examining the impact of an NRS found positive benefits among several tobacco disparity populations, including individuals with SMI and individuals with opioid use disorder. Nevertheless, the long-term effects of an NRS in these populations, as well as in other understudied populations with medical/psychiatric comorbidities and disproportionately high tobacco burden is unknown. Specific Aims: The objective of this study is to extend the Mendez-Warner simulation model (a well-established model of smoking prevalence and public health effects) to medical and/or psychiatric priority populations (e.g. people with SMI, people with substance use disorders, people with disabilities, people with chronic pain, and people with HIV) to examine the potential effects of an NRS in these groups. Specifically:
Aim 1: Acquire relevant input parameters (e.g. smoking prevalence, net cessation rate, and initiation rate) for each population of interest using national survey datasets such as the National Health Interview Survey (NHIS).
Aim 2: Use the Mendez-Warner simulation model to predict the smoking prevalence and public health harms from 2022-2100 in each subpopulation with and without an NRS.
Aim 3: Evaluate the potential benefits of NRS in these priority subpopulations. In particular,

• Aim 3.2: Compare projected outcomes for each subpopulation to the overall US population.
• Aim 3.1: Compare these scenarios (NRS versus no NRS) to quantify the potential public health benefits of an NRS for each subpopulation.