"Better Than Any DARE Program": Qualitative Analysis of Adolescent Reactions to EVALI Television Storylines.

Introduction. Research suggests that awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) among adolescents is associated with increased harm perception of e-cigarettes. The depiction of EVALI on three primetime medical dramas offers an opportunity to examine the use of these storylines for tobacco prevention education. Methods. We conducted four focus groups with seventh- and eighth-grade students at an urban middle school. Participants viewed three clips of scenes followed by a facilitated discussion as to the influence of the clips on knowledge and perceptions of e-cigarettes and the use of clips for tobacco prevention education. Two research assistants double-coded notes from the focus groups using a qualitative content analysis approach. Results. Our final sample included 78 adolescents; we obtained self-reported demographic information for 75. The majority of participants were 13 to 14 years of age (82.7%) and identified as cisgender female (52.0%) and Black (52.0%). No participants had knowledge of EVALI prior to viewing the clips. Comments made both during and after watching suggest the clips may have reinforced knowledge and perceptions of harm; participants stated that the clips could be a useful intervention tool. Viewing the clips also generated unprompted discussion about flavored products, tobacco advertising, other television programs, and marijuana. Conclusions. Clips featuring the depiction of EVALI on medical dramas may be an effective tool for raising awareness of e-cigarette use-related harms. These results offer a promising first step for future collaborative research between public health, adolescents, and schools to develop tobacco prevention education utilizing these clips.

Viewer Reactions to EVALI Storylines on Popular Medical Dramas: A Thematic Analysis of Twitter Messages.

Previous research has found an association between awareness of e-cigarette, or vaping, product-use associated lung injury (EVALI) and lower intention to use e-cigarettes among young people. This study utilized Twitter data to evaluate if the January 2020 depiction of EVALI on New Amsterdam, Chicago Med, and Grey's Anatomy-three popular primetime medical dramas-could be a potential innovative avenue to raise awareness of EVALI. We obtained tweets containing e-cigarette-related search strings from 1/21/2020 to 02/18/2020 and filtered these with storyline-specific keywords, resulting in 1,493 tweets for qualitative coding by two trained human coders. Content codes were informed by prior research, theories of narrative influence, and e-cigarette related outcomes. Of 641 (42.9%) relevant tweets, the most frequent content codes were perceived realism (n = 292, 45.6%) and negative response (n = 264, 41.2%). A common theme among these tweets was that storylines were unrealistic because none of the characters with EVALI used THC-containing products. Approximately 12% of tweets (n = 78) mentioned e-cigarette knowledge and 28 (4.4%) mentioned behavior, including quitting e-cigarettes because of viewing the storylines. Implications for health communication research utilizing social media data and maximizing the achievement of positive health-related outcomes for storylines depicting current health topics are discussed.

Characterizing the Influence of Television Health Entertainment Narratives in Lay Populations: A Scoping Review.

OBJECTIVE: To conduct a scoping review of published literature examining the influence of health storylines from fictional television programs on viewers. DATA SOURCE: We performed literature searches in Medline, PsycINFO, and Mass Media Complete in October 2021, and examined bibliographies of included articles and conducted forward searching using Web of Science with included articles. STUDY INCLUSION AND EXCLUSION CRITERIA: Selected studies were required to be original research published in English, involve exposure to fictional television programming by individuals not in the medical field, and assess associations between exposure and health-related outcomes. DATA EXTRACTION: Article screening and data abstraction were performed by two independent researchers using DistillerSR (Cohen's κ range: .73-1.00). DATA SYNTHESIS: We analyzed and qualitatively described the data using methods of scoping reviews described by PRISMA-ScR. RESULTS: Of 5,537 unique records identified, 165 met inclusion criteria. The most frequently studied program was ER (n = 22, 13.3%). Most studies had adult participants (n = 116, 70.3%) and used quantitative methods (n = 136, 82.4%). The most frequently examined health topics were sexual behavior (n = 28, 17.0%) and mental health (n = 28, 17.0%). Exposure had a positive influence on viewers' health-related outcomes in 28.5% (n = 47) of studies. CONCLUSION: Health storylines on fictional television influence viewers. Future research could address gaps identified in this review to further elucidate the influence of this programming on health promotion and disease prevention.

Characterizing online social support for alcohol use disorder: A mixed-methods approach.

BACKGROUND: Online social media communities are increasingly popular venues for discussing alcohol use disorder (AUD) and recovery. Little is known about distinct contexts of social support that are exchanged in this milieu, which are critical to understanding the social dynamics of online recovery support. METHODS: We randomly selected one post per day over the span of a year from the StopDrinking recovery forum. Direct responses to posts were double coded within an established theoretical framework of social support. Within a mixed-methods research framework, we quantified the linguistic characteristics of 1386 responses (i.e., text length, complexity, and sentiment) and qualitatively explored themes within and among different types of social support. RESULTS: Emotional support was most prevalent (74% of responses) and appeared as the sole form of support in 38% of responses. Emotionally supportive responses were significantly shorter, less complex, and more positively valenced than other support types. Appraisal support was also common in 55% of responses, while informational support was identified in only 17%. There was substantial overlap among support types, with 40% of responses including two or more types. Salient themes included the common use of community-specific acronyms in emotional support. Appraisal support conveyed feedback about attitudes and behaviors that are perceived as (un-) favorable for AUD recovery. Informational support responses were composed primarily of recommendations for self-help literature, clinical treatment approaches, and peer recovery programs. CONCLUSIONS: Social support in this sample was primarily emotional in nature, with other types of support included to provide feedback and guidance (i.e., appraisal support) and supplemental recovery resources (i.e., informational support). The provided social support framework can be helpful in characterizing community dynamics among heterogeneous online AUD recovery support forums. This framework could also be helpful in considering changes in support approaches that correspond to progress in recovery.

COVID-19 Vaccine Hesitancy, Acceptance, and Promotion Among Healthcare Workers: A Mixed-Methods Analysis.

Even with vaccine mandates, COVID-19 vaccine hesitancy remains a concern among healthcare workers, in part due to their role in promoting vaccination among patients and communities. To examine COVID-19 vaccine hesitancy, acceptance, and promotion among healthcare workers, we conducted a mixed-methods analysis of (1) survey responses about COVID-19 vaccination and (2) Twitter messages (i.e., tweets) relevant to COVID-19 vaccination and healthcare. A total of 540 hospital employees completed the survey. Those that completed less than 80% of the survey or did not endorse employment at the hospital were excluded, resulting in a total of 511 valid responses; 93.2% reported receiving at least one dose of a COVID-19 vaccine. Approximately 1/3 of vaccinated individuals indicated they posted about receiving the vaccine on social media. Simultaneously, we analyzed a sample of 3845 tweets; 2299 (60%) were relevant to COVID-19 vaccination and 1863 (81%) were coded as authored by an individual. Of tweets authored by an individual, 6% (n = 106) were authored by a healthcare provider/health sciences student. Among relevant tweets, the most frequent code across all sentiment categories was related to the pharmaceutical industry (n = 529 tweets, 28%; n = 33, 31% of tweets authored by healthcare workers). Triangulation of results found themes including vaccine access, trust, and vaccine safety or negative health impacts. Results suggest that promoting the sharing of COVID-19 vaccine personal narratives on social media, combined with interventions targeting specific reasons for COVID-19 vaccine hesitancy and emphasizing freedom from fear once vaccinated could be effective at reducing COVID-19 vaccine hesitancy among this population.

Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content.

BACKGROUND: Misinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use-associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use. OBJECTIVE: Twitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages ("tweets") related to ENDS and COVID-19 to inform public health messaging. METHODS: Approximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19-related keywords, resulting in 67,321 original tweets. A 5% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories. RESULTS: A total of 1930 (57.3%) tweets were coded as relevant to the research topic. Half (n=1008, 52.2%) of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter (n=479, 24.8%) of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories. CONCLUSIONS: Discussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets.

BACKGROUND: Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices. OBJECTIVE: This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes. METHODS: Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy. RESULTS: Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%). CONCLUSIONS: This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

E-Cigarette-Related Nicotine Misinformation on Social Media.

Background. Twitter provides an opportunity to examine misperceptions about nicotine and addiction as they pertain to electronic nicotine delivery systems (ENDS). The purpose of this study was to systematically examine a sample of ENDS-related tweets that presented information about nicotine or addiction for the presence of potential misinformation.Methods. A total of 10.1 million ENDS-related tweets were obtained from April 2018 through March 2019 and were filtered for unique tweets containing keywords for nicotine and addiction. A subsample (n = 3,116) were human coded for type of account (individual, group, commercial, or news) and presence of potential misinformation.Results. Of tweets that presented ENDS-related nicotine or addiction information (n = 904), 41.7% (n = 377) contained potential misinformation coded as anti-vaping exaggeration, pro-vaping exaggeration, nicotine is not addictive or is never harmful, or unproven health benefits.Conclusions. Anti-vaping exaggeration tweets distorted or embellished claims about ENDS nicotine and addiction; pro-vaping exaggeration tweets misinterpreted results from scientific studies. Misinformation that nicotine is not addictive or is never harmful or has unproven health benefits appeared less but are potentially problematic. ENDS-related messaging should be designed to be easily understood by the public and monitored to detect the spread of misinterpretation or misinformation on social media.

Re-evaluating standards of human subjects protection for sensitive health data in social media networks.

This study addresses ethical questions about conducting health science research using network data from social media platforms. We provide examples of ethically problematic areas related to participant consent, expectation of privacy, and social media networks. Further, to illustrate how researchers can maintain ethical integrity while leveraging social media networks, we describe a study that demonstrates the ability to use social media to identify individuals affected by cancer. We discuss best practices and ethical guidelines for studying social media network data, including data collection, analysis, and reporting.

Protective Factors Against Vaping and Other Tobacco Use.

BACKGROUND AND OBJECTIVES: Vaping has gained popularity among adolescents despite negative health consequences. Few studies have focused on factors that may protect against vaping. We sought to determine if future orientation, parental monitoring, school connectedness, and social support are associated with decreased risk of vaping and other forms of tobacco use. METHODS: Data were obtained via anonymous school-based health behavior surveys among ninth- through 12th-graders in Pittsburgh, PA (n = 2487). Protective factors were assessed through validated Likert scale instruments. The primary outcome was recent (past 30-day) vaping. Additional outcomes included other forms of tobacco use and intention to quit tobacco products. Poisson regression models examined associations between protective factors and vaping and tobacco use outcomes. RESULTS: Mean age was 15.7 years, 1446 (58.1%) respondents were female, and 671 youth (27.0%) reported recent vaping. Positive future orientation and high parental monitoring were associated with significantly lower prevalence of recent vaping (adjusted prevalence ratio: 0.84 [95% confidence interval: 0.73-0.97] and adjusted prevalence ratio: 0.73 [95% confidence interval: 0.62-0.85], respectively). There were no significant relationships between social support or school connectedness and vaping. All 4 protective factors were inversely associated with other forms of tobacco use. No factors were significantly associated with intent to quit tobacco products. CONCLUSIONS: Findings reveal significant inverse associations between future orientation, parental monitoring, and vaping but no relationship between protective factors and intent to quit tobacco products. Developing interventions to foster protective factors in youth and their parental supports may inform primary prevention efforts to reduce vaping and other tobacco use.

Best Practices for Modeling Egocentric Social Network Data and Health Outcomes.

OBJECTIVE/AIM: We describe best practices for modeling egocentric networks and health outcomes using a five-step guide. BACKGROUND: Social network analysis (SNA) is common in social science fields and has more recently been used to study health-related topics including obesity, violence, substance use, health organizational behavior, and healthcare utilization. SNA, alone or in conjunction with spatial analysis, can be used to uniquely evaluate the impact of the physical or built environment on health. The environment can shape the presence, quality, and function of social relationships with spatial and network processes interacting to affect health outcomes. While there are some common measures frequently used in modeling the impact of social networks on health outcomes, there is no standard approach to social network modeling in health research, which impacts rigor and reproducibility. METHODS: We provide an overview of social network concepts and terminology focused on egocentric network data. Egocentric, or personal networks, take the perspective of an individual who identifies their own connections (alters) and also the relationships between alters. RESULTS: We describe best practices for modeling egocentric networks and health outcomes according to the following five-step guide: (1) model selection, (2) social network exposure variable and selection considerations, (3) covariate selection related to sociodemographic and health characteristics, (4) covariate selection related to social network characteristics, and (5) analytic considerations. We also present an example of SNA. CONCLUSIONS: SNA provides a powerful repertoire of techniques to examine how relationships impact attitudes, experiences, and behaviors-and subsequently health.

Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial.

BACKGROUND: After the US Surgeon General declared youth electronic cigarette (e-cigarette) use an epidemic in 2018, the number of youth e-cigarette users continued to surge, growing from 3.8 million in 2018 to over 5 million 2019. Youth who use e-cigarettes are at a substantially higher risk of transitioning to traditional cigarettes, becoming regular cigarette smokers, and increasing their risk of developing tobacco-related cancer. A majority of youth are misinformed about e-cigarettes, often believing they are not harmful or contain no nicotine. Middle school students using e-cigarettes have been affected by its normalization leading to influence by their peers. However, social and group dynamics can be leveraged for a school-based peer-led intervention to identify and recruit student leaders to be anti-e-cigarette champions to prevent e-cigarette initiation. This study outlines a project to use social network analysis to identify student opinion-leaders in schools and train them to conduct anti-e-cigarette programming to their peers. METHODS: In the 2019-2020 academic school year, 6th grade students from nine schools in the Pittsburgh area were recruited. A randomized controlled trial (RCT) was conducted with three arms-expert, elected peer-leader, and random peer-leader-for e-cigarette programming. Sixth grade students in each school completed a network survey that assessed the friendship networks in each class. Students also completed pre-intervention and post-intervention surveys about their intention-to-use, knowledge, and attitudes towards e-cigarettes. Within each peer-led arm, social network analysis was conducted to identify peer-nominated opinion leaders. An e-cigarette prevention program was administered by (1) an adult content-expert, (2) a peer-nominated opinion leader to assigned students, or (3) a peer-nominated opinion leader to random students. DISCUSSION: This study is the first to evaluate the feasibility of leveraging social network analysis to identify 6th grade opinion leaders to lead a school-based e-cigarette intervention. TRIAL REGISTRATION: ClinicalTrials.gov NCT04083469 . Registered on September 10, 2019.

Instagram's #JUUL: who's posting what.

Exposure to e-cigarette (e-cig) marketing through traditional and online media can increase the risk of e-cig use among adolescents. Though popular e-cig brand JUUL has scaled back its social media promotions, outside vendors and individual users create posts about JUUL unregulated. Instagram's image-focus offers an opportunity to document promotions that may appeal to adolescents. To assess the visual representation of JUUL on social media, we systematically classified discussion topics and themes of posts to Instagram. Of 25,428 unique Instagram images using the hashtag #JUUL retrieved between June 18 and July 18, 2018, a random subsample of 3,000 images (approximately 12% of the full data set) was drawn for human coding and analyses. Coded categories included relevance to JUUL, type of image, image appeal, and user type. Based on the number of relevant images, percentages of image type and appeal for each user type were calculated. There were 583 images that were coded as relevant. The majority of image types (64%) were classified as product (n =; 374). The most frequently coded type of appeal was flavors, found in 54% of images with appeal (n =; 149), followed by cartoons, found in 21% (n =; 57). Vendors were responsible for posting most of the images (n =; 291), followed by e-cig enthusiasts (n =; 99). Continued surveillance of e-cig products, such as JUUL, remains necessary to provide a foundation for potential regulatory protections against marketing and promotions that may appeal to youth.

Electronic Vapor Product Use and Violence Victimization Among a Nationally Representative Sample of Adolescents.

PURPOSE: We examined associations between electronic vapor product use (vaping) and violence victimization (adolescent relationship abuse (ARA), youth violence, bullying, and experiencing all three types of violence). METHODS: Data were drawn from the 2017 National Youth Risk Behavioral Survey. Logistic regression examined associations between vaping and violence victimization, adjusting for sex, age, race, identification as a sexual minority, and use of other substances. RESULTS: After adjusting for demographic characteristics, vaping was significantly and positively associated with ARA, youth violence, bullying, and experiencing all three types of violence (ARA: adjusted odds ratio (aOR) 3.0, 95% CI: 2.5-3.5; youth violence: aOR 3.0, CI: 2.4-3.6; bullying: aOR 1.5, CI: 1.4-1.7; all three types of violence: 10, CI: 6.3, 15.8). When use of other substances was added into the model, these associations attenuated, and, for bullying, lost significance. CONCLUSIONS: Vaping is associated with increased violence victimization, particularly for youth experiencing ARA, youth violence, and polyvictimization. Further longitudinal studies are needed to determine directionality of these associations and guide prevention efforts.

Smartphone health apps for tobacco Cessation: A systematic review.

BACKGROUND: Given the low retention and lack of persistent support by traditional tobacco cessation programs, evidence-based smartphone app-supported interventions can be an important tobacco control component. The objective of this systematic review was to identify and evaluate the types of studies that use smartphone apps for interventions in tobacco cessation. METHODS: We conducted a systematic review of PubMed (1946-2019), EMBASE (1974-2019), and PsycINFO (1806-2019) databases with keywords related to smartphone-supported tobacco cessation. Included articles were required to meet 3 baseline screening criteria: 1) be written in English, 2) include an abstract, and 3) be a full, peer-reviewed manuscript. The criteria for the second level of review were: 1) primary outcome of tobacco cessation, 2) intervention study, and 3) smartphone app as primary focus of study. RESULTS: Of 1973 eligible manuscripts, 18 met inclusion criteria. Most studies (n = 17) recruited adult participants (18 + years); one included teens (16 + years). Tobacco cessation was usually self-reported (n = 11), compared to biochemical verification (n = 3) or both (n = 4). There were 11 randomized controlled trials, 4 of which reported statistically significant results, and 7 single-arm trials that reported a mean abstinence rate of 33.9%. DISCUSSION: The majority of studies that use tobacco cessation apps as an intervention delivery modality are mostly at the pilot/feasibility stage. The growing field has resulted in studies that varied in methodologies, study design, and inclusion criteria. More consistency in intervention components and larger randomized controlled trials are needed for tobacco cessation smartphone apps.

Implementation of a cluster randomized controlled trial: Identifying student peer leaders to lead E-cigarette interventions.

E-cigarette use has been increasing among middle school students. Intervention programs to prevent e-cigarette initiation administered by authority figures are met with more resistance from youth compared to peer-led programs. Therefore, this study aimed to assess the feasibility, acceptability, and implementation process of using social network analysis (SNA) to identify student peer leaders in schools and train them to deliver e-cigarette prevention programming to their peers. Nine schools were recruited to participate in the study during the 2019-2020 school year. Schools were assigned to one of three conditions: (1) expert; (2) peer-random (selected peer-leaders would teach to random students); and (3) peer-fixed (selected peer-leaders would teach to assigned students based on nominations). Study participation varied by day due to school attendance, with 686 participants at baseline and 608 at posttest. Almost all students who did not complete the study resulted from the interruption of schools being closed due to COVID-19. Implementation issues fell into three categories: (1) scheduling, (2) day-of logistics, and (3) student group dynamics. Overall, the results showed positive satisfaction among teachers, who unanimously found the program appropriate for the grade-level and that peer-leaders worked well within their groups. Peer-led students-both random and assigned-reported having more fun and willing to tell friends to try the program compared to expert-led students. This study demonstrated the feasibility of implementing a peer-led e-cigarette prevention program for 6th grade students, using SNA to provide intervention rigidity and validity.

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study.

BACKGROUND: Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets. OBJECTIVE: This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments. METHODS: We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance. RESULTS: LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks. CONCLUSIONS: We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Integrating Social Dynamics Into Modeling Cigarette and E-Cigarette Use.

Background. The use of electronic cigarettes (e-cigarette) offers potential to facilitate cigarette smoking cessation, yet potentially increases risk of cigarette smoking initiation. This relationship has been primarily modeled in mathematical ways that often do not represent real-world complexities, which could inform decisions regarding local prevention programs or policies. Aims. To develop a model of cigarette and e-cigarette use that combines current research on tobacco use and incorporates real-world geographic and demographic data. Method. We used a platform for developing agent-based models with demographic information representative of the population in Pennsylvania. We developed three models of cigarette and e-cigarette use. The primary outcome for each was the total number of users for cigarette, e-cigarette, and total nicotine. The first model applied current cigarette and e-cigarette data, the second tested the effect of implementing a program of e-cigarette education and policies, and the third considered a social contagion factor, where local schools functioned as a transmission vector. Results. The baseline and social contagion models found an overall decline in cigarette use but an increase in e-cigarette and total nicotine use. The education/policies model had declines in all categories. Sensitivity analysis suggested the importance of nuanced e-cigarette/cigarette interactions when modeling tobacco use. Discussion. Public health campaigns that focus on reducing youth e-cigarette usage can have a large effect. Social contagion should be strongly considered when studying e-cigarette spread. Conclusion. Targeted public health campaigns focused on reducing school prevalence of e-cigarette use may be particularly valuable.