Characteristics of Adolescents' and Young Adults' Exposure to and Engagement with Nicotine and Tobacco Product Content on Social Media.

To inform policy and messaging, this study examined characteristics of adolescents' and young adults' (AYAs') exposure to and engagement with nicotine and tobacco product (NTP) social media (SM) content. In this cross-sectional survey study, AYAs aged 13-26 (N=1,163) reported current NTP use, SM use frequency, and exposure to and engagement with SM content promoting and opposing NTP use (i.e. frequency, source[s], format[s], platform[s]). Participants who used NTPs (vs. did not use) were more likely to report having seen NTP content (p-values<.001). Prevalent sources were companies/brands (46.6%) and influencers (44.4%); prevalent formats were video (65.4%) and image (50.7%). Exposure to content promoting NTP use was prevalent on several popular platforms (e.g. TikTok, Instagram, Snapchat); exposure to content opposing NTP use was most prevalent on YouTube (75.8%). Among those reporting content engagement (i.e. liking, commenting on, or sharing NTP content; 34.6%), 57.2% engaged with influencer content. Participants reported engaging with content promoting and opposing NTP use on popular platforms (e.g. TikTok, Instagram, YouTube). Participants with (versus without) current NTP use were significantly more likely to use most SM platforms and to report NTP content exposure and engagement (p-values<.05). Results suggest that NTP education messaging and enforcement of platforms' content restrictions are needed.

Causal inference in tobacco research: a public health challenge.

Causal inference represents a rapidly expanding interdisciplinary subfield that involves various assumptions, study designs, and estimation strategies, allowing researchers to establish causal relationships from both clinical trials and observational data. In tobacco research, numerous studies address causal questions, including the contentious issue of whether vaping in nonsmoking youth leads to smoking initiation, known as the "gateway effect." Determining the effectiveness and safety of many health interventions will continue to rely on observational [mainly longitudinal] data because randomized trials are not always feasible, ethical, or timely. Therefore, review articles that are synthesizing evidence on the gateway effects of electronic nicotine delivery systems [ENDS] on subsequent cigarette smoking must also consider observational studies as first-rate evidence that can help bring together the polarized tobacco research community and help better understand the "gateway effect." In addition, this will help ongoing efforts to rigorously prevent ENDS use by youth while expanding the cessation potential of ENDS among adult established smokers who are unwilling to quit otherwise. In this commentary, we discuss causal inference tobacco research as one of the public health challenges and provide some recommendations/implications.

Measuring vaping-related expectancies in young adults: Psychometric evaluation of the Electronic Nicotine Vaping Outcomes (ENVO) scale.

OBJECTIVE: Electronic cigarettes are the most commonly used tobacco products by young adults. Measures of beliefs about outcomes of use (i.e., expectancies) can be helpful in predicting use, as well as informing and evaluating interventions to impact use. METHODS: We surveyed young adult students (N = 2296, Mean age=20.0, SD=1.8, 64 % female, 34 % White) from a community college, a historically black university, and a state university. Students answered ENDS expectancy items derived from focus groups and expert panel refinement using Delphi methods. Factor Analysis and Item Response Theory (IRT) methods were used to understand relevant factors and identify useful items. RESULTS: A 5-factor solution [Positive Reinforcement (consists of Stimulation, Sensorimotor, and Taste subthemes, α = .92), Negative Consequences (Health Risks and Stigma, α = .94), Negative Affect Reduction (α = .95), Weight Control (α = .92), and Addiction (α = .87)] fit the data well (CFI=0.95; TLI=0.94; RMSEA=0.05) and was invariant across subgroups. Factors were significantly correlated with relevant vaping measures, including vaping susceptibility and lifetime vaping. Hierarchical linear regression demonstrated factors were significant predictors of lifetime vaping after controlling for demographics, vaping ad exposure, and peer/family vaping. IRT analyses indicated that individual items tended to be related to their underlying constructs (a parameters ranged from 1.26 to 3.18) and covered a relatively wide range of the expectancies continuum (b parameters ranged from -0.72 to 2.47). CONCLUSIONS: A novel ENDS expectancy measure appears to be a reliable measure for young adults with promising results in the domains of concurrent validity, incremental validity, and IRT characteristics. This tool may be helpful in predicting use and informing future interventions. IMPLICATIONS: Findings provide support for the future development of computerized adaptive testing of vaping beliefs. Expectancies appear to play a role in vaping similar to smoking and other substance use. Public health messaging should target expectancies to modify young adult vaping behavior.

Identifying prospective subpopulations of combustible and electronic cigarette dual users in the United States via finite mixture modeling.

AIMS: To identify subpopulations of dual users of combustible and electronic cigarettes using current smoking and vaping behaviors. DESIGN: Secondary analysis of baseline data from a randomized controlled trial testing a smoking cessation intervention for dual users. Finite mixture modeling of frequency, quantity, and dependence on combustible and electronic cigarettes was used to identify classes. Demographics and additional smoking and vaping variables were used to further characterize the classes. SETTING: United States. PARTICIPANTS: A total of 2896 adults who smoked weekly for the past year and vaped weekly for the past month. MEASUREMENTS: Self-report baseline measures assessed demographics and smoking and vaping behaviors and characteristics including days of use per week, frequency of use within a day, time to first use after waking, urges to smoke, smoking cessation motivation, self-efficacy to abstain from smoking, months since vaping initiation, reasons for initiating and maintaining vaping, and future plans to stop vaping. FINDINGS: Eight probabilistic classes were identified and well-defined (relative entropy = 0.95, Lo-Mendell-Rubin adjusted likelihood ratio test P < 0.0001; class probabilities 0.89-0.97). In general, classes crossed two levels of smoking with four levels of vaping. The largest class (31%) had relatively high levels of smoking (72% daily, 56% 11+ cigarettes per day [CPD], 96% within 30 minutes of waking) and vaping (74% daily, 100% 20+ electronic-CPD, 74% <30 minutes). The next largest class (27%) had relatively high levels of vaping (93% daily, 100% 20+ electronic-CPD, 82% <30 minutes) and very low levels of smoking (28% daily, 12% 11+ CPD, 0% <30 minutes). The six smaller classes (3%-13%) also had distinct smoking and vaping behaviors. All eight classes exhibited distinguishing characteristics beyond current smoking and vaping behaviors. CONCLUSIONS: Dual users of combustible and electronic cigarettes are not a homogeneous population, having eight well-defined prospective subpopulations.

Emotion dysregulation, transdiagnostic vulnerabilities, and e-cigarette expectancies in a young adult sample.

OBJECTIVE: Use of e-cigarettes is more common among youth with psychological distress. Negative affect reduction outcome expectancies (beliefs about ability of a product to reduce unpleasant emotional states) and emotional transdiagnostic vulnerabilities (maladaptive responses to emotional states common to multiple forms of emotional psychopathology) are both positively related to tobacco susceptibility and use. However, it is unclear if expectancies mediate the effects between emotional issues and use. METHOD: College students aged 18 to 24 (N = 216; 79.6% female; 36.6% African American) completed an online survey with measures on e-cigarette negative affect reduction outcome expectancies (ENAROE), e-cigarette use, and the following emotional transdiagnostic vulnerabilities (ETV): difficulties in emotion regulation, distress tolerance (DT), positive/negative urgency, and positive/negative affect. RESULTS: In individual predictor path analysis models, ENAROE mediated the relationship between ETVs and e-cigarette use. Expectancies (ENAROE) mediated 47.3% of the total relationship between positive urgency and use. Significant effects were found for a comprehensive model, in which negative urgency was related to higher ENAROE (b = 0.47, 95% BCCI [0.04, 0.96]), controlling for all other transdiagnostic vulnerabilities. Higher positive urgency was related to an increased likelihood of current use. CONCLUSIONS: This study provides a novel examination of expectancies as a potential mediator for current e-cigarette use via emotional vulnerabilities. Negative Affect Reduction outcome expectancies mediated the relationship between vulnerabilities and e-cigarette use, indicating that the association between positive urgency and e-cigarette use may be explained partially though emotional expectancies. Thus, expectancies are important targets for prevention and further ETV research.

Dripping Technology Use Among Young Adult E-Cigarette Users.

BACKGROUND: Young adults are increasingly using electronic nicotine delivery systems (ENDS). The standard ENDS device involves an electric coil that heats a wick to vaporize an e-liquid solution. "Dripping" is another method that involves applying e-liquid directly to the coil. Dripping increases risk of harmful toxic compounds in vapor aerosols. Despite evidence of high levels of dripping among adolescents, young adult prevalence is unknown. METHODS: Young adults aged 18 to 24 completed an online survey assessing vaping and dripping status, type of devices used, tobacco use, and vaping expectancies. Among ever-vapers, univariate and multivariate logistic regression models assessed whether demographics predicted dripping and use of dripping devices. Multivariate Analysis of Variance compared never- and ever-drippers on expectancies for vaping outcomes. RESULTS: Over 2 in 5 young adult ever-vapers (43.7%) reported dripping. A multivariate regression model found that male gender (AOR = 1.83), identifying as White (AOR = 2.37), and use of other tobacco products (hookah; AOR = 1.91; cigars; AOR = 2.26; cigarettes; AOR = 2.51) were associated with dripping. E-cigarette users who reported lifetime dripping were more likely to consider vaping stimulating, socially facilitating, and flavorful. They ascribed lower health risks to vaping and felt it could reduce negative affect, weight gain, nicotine cravings, and boredom (all P < .05). CONCLUSION: Dripping was a highly prevalent behavior among this sample of young adults, particularly among White males. These findings point to the importance of gaining a greater understanding of the drivers and consequences of vaping and dripping behavior.

Targeted smoking cessation for dual users of combustible and electronic cigarettes: a randomised controlled trial.

BACKGROUND: Although many smokers use electronic cigarettes (e-cigarettes) to quit smoking, most continue to smoke while vaping. This dual use might delay cessation and increase toxicant exposure. We aimed to test the efficacy of a self-help intervention designed to help dual users to quit smoking. METHODS: In this three-arm randomised controlled trial we recruited individuals in the USA using Facebook and multimedia advertisements. Included participants were 18 years or older, smoked at least weekly in the preceding year, and vaped at least weekly in the preceding month. We used computer generated randomisation with balanced-permuted blocks (block size 10, with 2-4-4 ratio) to allocate participants to assessment only (ASSESS group), generic smoking cessation self-help booklets (GENERIC group), or booklets targeting dual users (eTARGET group). Individuals in the generic or targeted intervention groups received monthly cessation materials for 18 months, with assessments every 3 months for 24 months. The main outcome was self-reported 7-day point-prevalence smoking abstinence at each assessment point. All randomly allocated participants were included in primary analyses using generalised estimating equations for each of 20 datasets created by multiple imputation. Analysis of the χ2s produced an F test. The trial is registered with ClinicalTrials.gov, NCT02416011, and is now closed. FINDINGS: Between July 12, 2016, and June 30, 2017, we randomly assigned 2896 dual users (575 to assessment, 1154 to generic intervention, and 1167 to targeted self-help). 7-day point-prevalence smoking abstinence increased from 14% at 3 months to 42% at 24 months (F7,541·7=67·1, p<0·0001) in the overall sample. Targeted self-help resulted in higher smoking abstinence than did assessment alone throughout the treatment period (F1,973·8=10·20, p=0·0014 [α=0·017]). The generic intervention group had abstinence rates between those of the assessment and targeted groups, but did not significantly differ from either when adjusted for multiple comparisons (GENERIC vs eTARGET F1,1102·5=1·79, p=0·18 [α=0·05]; GENERIC vs ASSESS F1,676·7=4·29, p=0·039 [α=0·025]). Differences between study groups attenuated after the interventions ended. INTERPRETATION: A targeted self-help intervention with high potential for dissemination could be efficacious in promoting smoking cessation among dual users of combustible cigarettes and e-cigarettes. FUNDING: National Institute on Drug Abuse, National Cancer Institute.

Development of a Risk Behavior Diagnosis Scale for E-cigarette Use.

Objectives: Adolescent use of electronic cigarettes has risen dramatically, prompting concerns about the health effects. There is need for brief measures to assess adolescents' perceived threat and efficacy related to e-cigarette use and cessation. A 12-item Likert-type scale was modeled after the Risk Behavior Diagnosis Scale and designed to assess threat (ie, severity and susceptibility of threat) and efficacy (ie, self-efficacy and response efficacy) as they relate to e-cigarette use. Methods: The scale was administered online to a developmental sample of 674 adolescents to examine internal consistency and factor structure. Participants (52.1% female, M age = 14.6) were representative of the surrounding community (60% non-Hispanic white; 27% non-Hispanic black; 8% Hispanic). Results: Factor analysis and Velicer's minimum average partial test revealed 2 factors (as expected), which explained 68% of the variance. Analyses revealed strong internal consistency, with Cronbach's alpha of .93 overall and alphas of .92 and .87 for threat and efficacy subscales, respectively. The measure also exhibited good convergent and discriminant validity with other constructs. Conclusions: The measure demonstrates strong preliminary reliability and validity for a developmental sample of adolescents.

Development of a Self-Help Smoking Cessation Intervention for Dual Users of Tobacco Cigarettes and E-Cigarettes.

Most users of electronic cigarettes (e-cigarettes) report initiating use to quit combustible cigarettes. Nevertheless, high levels of dual use (i.e., using both combustible cigarettes and e-cigarettes) occur among adults. Using formative data from in-depth interviews and employing learner verification, we adapted an existing, validated self-help smoking-cessation intervention (Stop Smoking for Good; SSFG) to create a targeted intervention for dual users, If You Vape: A Guide to Quitting Smoking (IYV). In Phase I, in-depth interviews (n = 28) were conducted to assess relevance of the existing SSFG materials (10 booklets, nine pamphlets) and identify new content for the booklets. Next, for Phase II, learner verification interviews (n = 20 dual users) were conducted to assess their appeal and acceptability. Several key themes emerged from the Phase I in-depth interviews. Findings led to the inclusion of e-cigarette-specific strategies used by successful quitters such as gradually reducing nicotine levels, switching from tobacco flavor to alternative flavors, and limiting e-cigarette use to places one would normally smoke (i.e., not expanding use). Suggestions from Phase II learner verification included broadening the visual appeal for a younger, more diverse demographic, expanding tips for quitting smoking via e-cigarettes, and expanding terminology for e-cigarette devices. Beginning with an efficacious self-help intervention, we used a systematic process to develop a version specifically for dual users.

Vaping, smartphones, and social media use among young adults: Snapchat is the platform of choice for young adult vapers.

BACKGROUND: Tobacco use often begins or stabilizes in young adulthood. Approximately 90% of young adults use social media and over 80% own a smartphone. Retailers of electronic nicotine delivery systems (ENDS) have targeted smartphones and social media with adverting campaigns. Despite evidence of ENDS advertising on social media and smartphones, few studies have examined associations between exposure to vaping advertisements on smartphones, social media use, and ENDS use (i.e., vaping) among young adults. METHODS: College students aged 18-24 from a large public university (N = 1047) completed online surveys about vaping. The survey measured frequency of vaping advertisement exposure, smartphone use, social media use, and vaping behaviors. Hierarchical logistic regression assessed whether demographics, vaping advertisement exposure, smartphone use, and social media use predicted ever vaping compared to never vaping. RESULTS: The four most commonly used platforms (used "daily") were Snapchat (80.0%), Instagram (73.4%), YouTube (59.7%), and Facebook (54.3%). Use of Snapchat, Instagram, and Facebook were all significantly associated with higher rates of vaping advertisement exposure via smartphones. Exposure to vaping advertisements on smartphones was associated with ever vaping (AOR: 1.30, 95% CI = 1.05-1.60). Of the social media platforms examined, only Snapchat use frequency was associated with higher odds of ever vaping (AOR: 1.22, 95% CI = 1.10-1.36). CONCLUSIONS: Exposure to advertisements via smartphones and use of Snapchat were associated with higher rates of vaping for young adults. Social media and smartphone use should be further investigated for young adult impact.

Longitudinal Analysis of Associations Between Reasons for Electronic Cigarette Use and Change in Smoking Status Among Adults in the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Electronic cigarette (ECIG) use and changes in cigarette smoking status may be influenced by self-reported reasons for using ECIGs. METHODS: We analyzed adult current and former cigarette smokers who were also current or former ECIG users at wave 1 (n = 3044) using wave 1 and wave 2 Population Assessment of Tobacco and Health Study data (2013-2015). Prevalence of reporting 13 reasons for ECIG use at wave 1 was examined and weighted logistic regressions were conducted predicting smoking status changes from wave 1 to wave 2. RESULTS: Reasons for ECIG use ranged from 18.1% (people in the media or public figures use them) to 82.5% (they might be less harmful to people around me than cigarettes). From wave 1 to wave 2, 27.2% of former smokers (n = 249) became current smokers and 11.6% of current smokers (n = 246) became former smokers. Among wave 1 former smokers, using ECIGs because of the availability of flavors (AOR = 0.57, 95% CI = 0.39-0.85) or because they don't smell (AOR = 0.64, 95% CI = 0.42-0.97) was associated with lower odds of relapse to smoking, but using ECIGs because using them helps people quit smoking (AOR = 1.55, 95% CI = 1.01-2.38) was associated with greater odds of relapse. Among wave 1 current smokers, using ECIGs because they can be used where smoking is not allowed (AOR = 0.56, 95% CI = 0.38-0.85) was associated with reduced odds of quitting cigarettes. CONCLUSIONS: Some reasons for ECIG use are associated with changes in self-reported smoking status. Researchers should examine ECIG user characteristics when assessing associations between ECIG use and smoking status transitions. IMPLICATIONS: Given that certain reasons for ECIG use, such as using ECIGs in locations are where smoking is not allowed, may inhibit smoking reduction, policies may be developed to prevent ECIG use in locations where smoking is banned. In addition, because certain reasons for ECIG use may aid in relapse prevention, such as availability of desired flavors, efforts should be made to identify ECIG device characteristics that are appealing to smokers but not youth or nontobacco users. These results provide support for future research on reasons for ECIG use to inform regulatory policies.

How Does Smoking and Nicotine Dependence Change After Onset of Vaping? A Retrospective Analysis of Dual Users.

INTRODUCTION: Although some smokers switch to exclusive use of electronic cigarettes (e-cigarettes), others become dual users of combustible cigarettes and e-cigarettes. Little is known about how the onset of vaping affects the use of and dependence on combustible cigarettes or total nicotine use and dependence, which may influence health-related and cessation outcomes. Using self-report data of current combustible and e-cigarette use and retrospective recall of pre-vaping smoking in a sample of dual users, the aims of this study were (1) to compare pre- and post-vaping number of cigarettes per day and combustible cigarette dependence; (2) to compare pre- and post-vaping total nicotine use frequency (number of vaping sessions and cigarettes smoked per day), and total nicotine dependence; and (3) to examine predictors of nicotine dependence. METHODS: We used baseline data from a smoking cessation trial with 2896 dual users. Nicotine use frequency and the Heaviness of Smoking Index were used as measures of nicotine use and dependence, respectively. RESULTS: Participants decreased cigarettes/day from pre- (M = 19.24, SD = 9.01) to post-vaping (M = 11.15, SD = 8.02, p < .0001) and combustible cigarette dependence declined from pre- (M = 3.55, SD = 1.51) to post-vaping (M = 2.11, SD = 1.60, p < .0001). Total daily nicotine use frequency increased after initiating vaping (M = 19.25, SD = 9.01 vs. M = 29.46, SD = 8.61; p < .0001), as did total nicotine dependence (M = 3.55, SD = 1.51 vs. M = 4.68, SD = 1.38; p < .0001). Hierarchical regression analyses indicated that variables associated with greater overall nicotine dependence included: younger age, lower education, more years smoking, higher pre-vaping nicotine dependence, using e-cigarettes more days per month, more puffs per vaping session, higher e-liquid nicotine concentration, and longer vaping history. CONCLUSIONS: Dual use leads to a reduction in the number of combustible cigarettes, but total nicotine use and dependence increases. IMPLICATIONS: In dual users, a reduction in smoking following onset of vaping may offer some harm reduction via reduction in cigarette intake. However, the increase in total nicotine use and dependence could affect the ability to quit either or both products.

Vaping Expectancies: A Qualitative Study among Young Adult Nonusers, Smokers, Vapers, and Dual Users.

BACKGROUND: "Expectancies," or beliefs about outcomes, robustly correlate with and predict several behaviors including electronic nicotine delivery system ("e-cigarette") use. However, there is limited qualitative research available regarding relevant e-cigarette vaping expectancies. OBJECTIVES: The present study used a qualitative approach to derive and refine e-cigarette expectancy themes among young adults. METHODS: We conducted 12 focus groups and two individual interviews with young adult nonusers, e-cigarette vapers, cigarette smokers, and dual users to assess beliefs about the effects of e-cigarettes. After a series of open-ended questions, follow-up questions assessed reactions to domains previously examined in expectancy measures for cigarette smoking and e-cigarette vaping. The constant comparative method was used to derive themes from transcripts. RESULTS: Four main themes (Positive Reinforcement, Social Benefits, Negative Affect Reduction, Negative Consequences) emerged from the results. Each theme contained three associated subthemes (Positive Reinforcement: Sensorimotor Experiences, Taste, Stimulation; Social Benefits: Social Facilitation, Influence on Others, Convenience; Negative Affect Reduction: Stress Reduction, Appetite Reduction, Boredom Reduction; and Negative Consequences: Health Risks, Addiction, Secondhand Effects). CONCLUSIONS/IMPORTANCE: Previously identified smoking expectancies appear relevant for young adult vaping, with some notable refinements. Positive reinforcement aspects encompassed aerosol clouds, vaping tricks, and unique flavors. Social benefits included influencing others via social media and competitive activity, as well as the convenience of use in a variety of places. Negative affect reduction was controversial among user groups, but vaping was seen as more interesting than smoking and thus more effective at boredom reduction. Young adults were uncertain regarding negative consequences, but appreciated a potential for secondhand effects. Measure refinement via qualitative research and future field testing can enhance our understanding of this relatively new behavior, supporting tobacco control surveillance, marketing/labeling regulations, and counter-advertising development/evaluation.

Vaping characteristics and expectancies are associated with smoking cessation propensity among dual users of combustible and electronic cigarettes.

BACKGROUND AND AIMS: Most e-cigarette users who also smoke combustible cigarettes (dual users) begin vaping to quit smoking, yet only a subset succeeds. We hypothesized that reinforcing characteristics of e-cigarettes (vaping reinforcement) would positively predict smoking cessation propensity (SCP) among dual users. DESIGN: Secondary analysis of cross-sectional baseline data from dual users in an ongoing smoking cessation trial. Exploratory and confirmatory factor analysis (EFA and CFA) created latent variables for vaping reinforcement and SCP. A structural equation modeling (SEM) approach was used to test the hypothesis. SETTING: United States. PARTICIPANTS: A national sample of dual users of combustible and electronic cigarettes who smoke and vape at least once per week (n = 2896) were enrolled (63% male; mean age = 29.9 years) into a randomized controlled trial in which they would receive either smoking cessation materials or no smoking cessation materials. MEASUREMENTS: Vaping reinforcement was indexed by vaping frequency (days/week vaping, times/day vaping, puffs/e-cigarette use), e-cigarette characteristics [numbers of modifications and tobacco or non-tobacco flavors, nicotine content (mg) and positive e-cigarette expectancies]. SCP was measured by items of confidence, commitment to being smoke-free, cessation motivation (contemplation ladder), change in cigarettes per day since beginning e-cigarette use and negative smoking expectancies. FINDINGS: Four factors emerged from the EFA: vaping propensity (vaping frequency, positive expectancies), vaping enthusiasm (e-cigarette modifications, using non-tobacco flavors, puffs per use), nicotine/tobacco flavor (nicotine strength, tobacco flavors) and SCP (negative expectancies about smoking, motivation to quit smoking, reduction in smoking). A CFA upheld the exploratory factor structure [root mean square error of approximation (RMSEA) = 0.046, CFI = 0.91]. An SEM with the three vaping latent variables directly predicting SCP had good model fit (RMSEA = 0.030, CFI = 0.97) with a positive relationship of vaping propensity (0.509, P < 0.001), and small negative relationships of vaping enthusiasm (-0.158, P = 0.014) and nicotine/tobacco flavor (-0.230, P < 0.001). CONCLUSIONS: Among e-cigarette users who also smoke combustible cigarettes, frequent vaping combined with positive e-cigarette expectancies appears to predict greater smoking cessation propensity. However, vaping enthusiasm (measured by e-cigarette modifications, using non-tobacco flavors and puffs per use), higher nicotine content and use of tobacco flavored solution may reduce cessation propensity.

Basic Science and Public Policy: Informed Regulation for Nicotine and Tobacco Products.

Introduction: Scientific discoveries over the past few decades have provided significant insight into the abuse liability and negative health consequences associated with tobacco and nicotine-containing products. While many of these advances have led to the development of policies and laws that regulate access to and formulations of these products, further research is critical to guide future regulatory efforts, especially as novel nicotine-containing products are introduced and selectively marketed to vulnerable populations. Discussion: In this narrative review, we provide an overview of the scientific findings that have impacted regulatory policy and discuss considerations for further translation of science into policy decisions. We propose that open, bidirectional communication between scientists and policy makers is essential to develop transformative preventive- and intervention-focused policies and programs to reduce appeal, abuse liability, and toxicity of the products. Conclusions: Through these types of interactions, collaborative efforts to inform and modify policy have the potential to significantly decrease the use of tobacco and alternative nicotine products and thus enhance health outcomes for individuals. Implications: This work addresses current topics in the nicotine and tobacco research field to emphasize the importance of basic science research and provide examples of how it can be utilized to inform public policy. In addition to relaying current thoughts on the topic from experts in the field, the article encourages continued efforts and communication between basic scientists and policy officials.

A randomized controlled trial of a smoking cessation self-help intervention for dual users of tobacco cigarettes and E-cigarettes: Intervention development and research design.

Electronic Nicotine Delivery Systems, also called electronic cigarettes or e-cigarettes, have been available for over a decade and use has been increasing dramatically. The primary reported reasons for use are to aid smoking cessation or reduction, yet a significant proportion appear to be long-term users of both products ("dual users"). Dual users may be motivated to quit smoking and might benefit from a behavioral intervention for smoking cessation. This paper describes the intervention development, as well as the design, methods, and data analysis plans for an ongoing randomized controlled trial (RCT). Formative research and learner verification were conducted to create a usable, understandable, and acceptable self-help intervention targeting dual users. The efficacy is being tested in an RCT with current dual users (N=2900) recruited nationally and randomized to one of three conditions. The Assessment Only (ASSESS) group only completes assessments. The Generic Self-Help (GENERIC) group receives non-targeted smoking cessation booklets and supplemental materials sent monthly over 18months. The e-cigarette Targeted Self-Help (eTARGET) group receives the newly developed intervention (targeted booklets and supplemental materials) sent over the same period. All participants complete self-report surveys every 3months over 2years. The primary study outcome is self-reported 7-day point prevalence abstinence. Cost-effectiveness metrics for the GENERIC and eTARGET interventions will also be calculated.

Patterns of youth tobacco and polytobacco usage: The shift to alternative tobacco products.

BACKGROUND: Despite significant declines in youth cigarette smoking, overall tobacco usage remains over 20% as non-cigarette tobacco product usage is increasingly common and polytobacco use (using 1+ tobacco product) remains steady. OBJECTIVES: The present study was designed to identify patterns of youth tobacco use and examine associations with sociodemographic characteristics and tobacco dependence. METHODS: The current analysis uses Latent Class Analysis (LCA) to examine the 6,958 tobacco users (n = 2,738 female) in the National Youth Tobacco Survey (2012 and 2013). We used as indicators past month use of tobacco products (cigarettes, cigars, smokeless tobacco, e-cigarettes, hookah, snus, pipes, bidis, and kreteks) and regressed resulting classes on sociodemographic characteristics and tobacco dependence. RESULTS: Nine classes emerged: cigarette smokers (33.4% of sample, also included small probabilities for use of cigars and e-cigarettes), cigar smokers (16.8%, nearly exclusive), smokeless tobacco users (12.3%, also included small probabilities for cigarettes, cigars, snus), hookah smokers (11.8%), tobacco smokers/chewers (10.7%, variety of primarily traditional tobacco products), tobacco/hookah smokers (7.2%), tobacco/snus/e-cig users (3.3%), e-cigarette users (2.9%,), and polytobacco users (1.7%, high probabilities for all products). Compared to cigarette smokers, tobacco/hookah smokers and hookah smokers were more likely to report Hispanic ethnicity. Polytobacco users were more likely to report dependence (AOR:2.77, 95% CI:[1.49-5.18]), whereas e-cigarette users were less likely (AOR:0.49, 95% CI:[0.24-0.97]). CONCLUSION: Findings are consistent with other research demonstrating shifts in adolescent tobacco product usage towards non-cigarette tobacco products. Continuous monitoring of these patterns is needed to help predict if this shift will ultimately result in improved public health.

E-cigarette use in adults: a qualitative study of users' perceptions and future use intentions.

BACKGROUND: There has been an exponential increase in the prevalence of e-cigarette use, particularly among youth. However, adult use is also rising, and there have been relatively few qualitative studies with adult users to understand their reasons for use and future use intentions. Such information is needed to inform both prevention and cessation approaches. METHOD: Thirty-one e-cigarette users participated in one of several focus groups assessing the appeal of e-cigarettes as well as comparisons to combustible cigarettes and approved smoking cessation aids. We also obtained perspectives on future use intentions and interest in e-cigarette cessation interventions. Verbatim transcripts were analyzed using the constant comparative method. RESULTS: Participants reported several aspects of e-cigarette appeal as compared to approved cessation treatment options. These included similarities to combustible cigarettes, fewer side effects, and control of e-cigarettes to suit personal preferences. Participants were split on whether they preferred flavors that mimicked or contrasted with their combustible cigarettes (i.e., tobacco vs. alternative flavors, such as candy). Some participants who were unmotivated to quit smoking reported an unanticipated disinterest in continuing use of combustible cigarettes shortly after initiating e-cigarettes. Despite strong interest in reducing nicotine dosage, the majority did not intend to fully discontinue e-cigarettes. CONCLUSIONS: Understanding e-cigarette users' perspectives can inform policy and treatment development. Regulatory and policy initiatives will need to balance the appealing characteristics of e-cigarettes with the potential for negative public health outcomes.

The impact of adolescent exposure to medical marijuana laws on high school completion, college enrollment and college degree completion.

BACKGROUND: There is concern that medical marijuana laws (MMLs) could negatively affect adolescents. To better understand these policies, we assess how adolescent exposure to MMLs is related to educational attainment. METHODS: Data from the 2000 Census and 2001-2014 American Community Surveys were restricted to individuals who were of high school age (14-18) between 1990 and 2012 (n=5,483,715). MML exposure was coded as: (i) a dichotomous "any MML" indicator, and (ii) number of years of high school age exposure. We used logistic regression to model whether MMLs affected: (a) completing high school by age 19; (b) beginning college, irrespective of completion; and (c) obtaining any degree after beginning college. A similar dataset based on the Youth Risk Behavior Survey (YRBS) was also constructed for confirmatory analyses assessing marijuana use. RESULTS: MMLs were associated with a 0.40 percentage point increase in the probability of not earning a high school diploma or GED after completing the 12th grade (from 3.99% to 4.39%). High school MML exposure was also associated with a 1.84 and 0.85 percentage point increase in the probability of college non-enrollment and degree non-completion, respectively (from 31.12% to 32.96% and 45.30% to 46.15%, respectively). Years of MML exposure exhibited a consistent dose response relationship for all outcomes. MMLs were also associated with 0.85 percentage point increase in daily marijuana use among 12th graders (up from 1.26%). CONCLUSIONS: Medical marijuana law exposure between age 14 to 18 likely has a delayed effect on use and education that persists over time.