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  • Use of E-cigarettes by Individuals with mental health conditions

    BACKGROUND: Individuals with mental health conditions (MHC) have disproportionately high tobacco-related morbidity and mortality due to high smoking prevalence rates. As high consumers of cigarettes, smokers with MHC may consider using e-cigarettes as an alternative form of nicotine delivery. OBJECTIVE: Examination of the susceptibility to use e-cigarettes by individuals with MHC. METHODS: A US population survey with a national probability sample (n=10?041) was used to assess ever use and current use of regular cigarettes, e-cigarettes, and US Food and Drug Administration-approved pharmacotherapy for smoking cessation. Survey respondents provided information about whether they had been diagnosed with an anxiety disorder, depression, or other MHC. RESULTS: Individuals with MHC were more likely to have tried e-cigarettes (14.8%) and to be current users of e-cigarettes (3.1%) than those without MHC (6.6% and 1.1%, respectively; p<0.01). Ever smokers with MHC were also more likely to have tried approved pharmacotherapy (52.2% vs 31.1%, p<0.01) and to be currently using these products (9.9% vs 3.5%, p<0.01) than those without MHC. Additionally, current smokers with MHC were more susceptible to future use of e-cigarettes than smokers without MHC (60.5% vs 45.3%, respectively, p<0.01). CONCLUSIONS: Smokers with MHC are differentially affected by the rise in popularity of e-cigarettes. Clinical interventions and policies for tobacco control on e-cigarettes should take into account the possible outcomes and their implications for this priority population.

  • Using Peer Crowds to Segment Black Youth for Smoking Intervention

    Studies of peer crowds show promise for enhancing public health promotion and practice through targeting. Distinct images, role models, and social norms likely influence health behaviors of different peer crowds within health disparity groups. We describe peer crowds identified by Black young people and determine whether identification with them is associated with smoking. Data from Black young people aged 13 to 20 years in Richmond, Virginia, were collected via interview and online survey (N = 583). We identified the number and type of peer crowds using principal components analysis; associations with smoking were analyzed using Pearson chi-square tests and logistic regression. Three peer crowds were identified—“preppy,” “mainstream,” and “hip hop.” Youth who identify with the hip hop peer crowd were more likely to smoke and have friends who smoke and less likely to hold antitobacco attitudes than those identifying with preppy or mainstream crowds. Identifying with the hip hop crowd significantly increased the odds of smoking, controlling for demographic factors (odds ratio = 1.97; 95% confidence interval = 1.03-3.76). Tobacco prevention efforts for Black youth and young adults should prioritize the hip hop crowd. Crowd identity measures can aid in targeting public health campaigns to effectively engage those at highest risk.

  • Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products

    Background: Social media platforms such as Twitter are rapidly becoming key resources for public health surveillance applications, yet little is known about Twitter users’ levels of informedness and sentiment toward tobacco, especially with regard to the emerging tobacco control challenges posed by hookah and electronic cigarettes. Objective: To develop a content and sentiment analysis of tobacco-related Twitter posts and build machine learning classifiers to detect tobacco-relevant posts and sentiment towards tobacco, with a particular focus on new and emerging products like hookah and electronic cigarettes. Methods: We collected 7362 tobacco-related Twitter posts at 15-day intervals from December 2011 to July 2012. Each tweet was manually classified using a triaxial scheme, capturing genre, theme, and sentiment. Using the collected data, machine-learning classifiers were trained to detect tobacco-related vs irrelevant tweets as well as positive vs negative sentiment, using Naïve Bayes, k-nearest neighbors, and Support Vector Machine (SVM) algorithms. Finally, phi contingency coefficients were computed between each of the categories to discover emergent patterns. Results: The most prevalent genres were first- and second-hand experience and opinion, and the most frequent themes were hookah, cessation, and pleasure. Sentiment toward tobacco was overall more positive (1939/4215, 46% of tweets) than negative (1349/4215, 32%) or neutral among tweets mentioning it, even excluding the 9% of tweets categorized as marketing. Three separate metrics converged to support an emergent distinction between, on one hand, hookah and electronic cigarettes corresponding to positive sentiment, and on the other hand, traditional tobacco products and more general references corresponding to negative sentiment. These metrics included correlations between categories in the annotation scheme (phihookah-positive=0.39; phie-cigs-positive=0.19); correlations between search keywords and sentiment (?24=414.50, P<.001, Cramer’s V=0.36), and the most discriminating unigram features for positive and negative sentiment ranked by log odds ratio in the machine learning component of the study. In the automated classification tasks, SVMs using a relatively small number of unigram features (500) achieved best performance in discriminating tobacco-related from unrelated tweets (F score=0.85). Conclusions: Novel insights available through Twitter for tobacco surveillance are attested through the high prevalence of positive sentiment. This positive sentiment is correlated in complex ways with social image, personal experience, and recently popular products such as hookah and electronic cigarettes. Several apparent perceptual disconnects between these products and their health effects suggest opportunities for tobacco control education. Finally, machine classification of tobacco-related posts shows a promising edge over strictly keyword-based approaches, yielding an improved signal-to-noise ratio in Twitter data and paving the way for automated tobacco surveillance applications.

  • Wanna know about vaping? Patterns of message exposure, seeking and sharing information across media platforms

    Background Awareness and use of electronic cigarettes has rapidly grown in the USA recently, in step with increased product marketing. Using responses to a population survey of US adults, we analysed demographic patterns of exposure to, searching for and sharing of e-cigarette-related information across media platforms. Methods An online survey of 17?522 US adults was conducted in 2013. The nationally representative sample was drawn from GfK Group's KnowledgePanel plus off-panel recruitment. Fixed effects logit models were applied to analyse relationships between exposure to, searching for and sharing of e-cigarette-related information and demographic characteristics, e-cigarette and tobacco use, and media behaviours. Results High levels of awareness about e-cigarettes were indicated (86% aware; 47% heard through media channels). Exposure to e-cigarette-related information was associated with tobacco use, age, gender, more education, social media use and time spent online. Although relatively small proportions of the sample had searched for (?5%) or shared (?2%) e-cigarette information, our analyses indicated demographic patterns to those behaviours. Gender, high income and using social media were associated with searching for e-cigarette information; lesbian, gay and bisexual and less education were associated with sharing. Current tobacco use, age, being Hispanic and time spent online were associated with both searching and sharing. Conclusions US adults are widely exposed to e-cigarette marketing through the media; such marketing may differentially target specific demographic groups. Further research should longitudinally examine how exposure to, searching for and sharing of e-cigarette information relate to subsequent use of e-cigarettes and/or combustible tobacco.

  • What online communities can tell us about electronic cigarettes and hookah use: a study using text mining and visualization techniques

    Background: The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective: This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods: In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results: In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions: This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a “hobbyist culture” that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design.

  • When you hit the blunt too hard: Influx of organic conversation on cigarillo and marijuana co-use on Twitter

    SRNT, Chicago, IL, 2016. Kostygina G, Tran H, Shi Y, Emery S. When you hit the blunt too hard: Influx of organic conversation on cigarillo and marijuana co-use on Twitter. Poster Session #5, Poster #136, Saturday, March 5.

  • Why Do We Still Smoke?

    Press Coverage

  • Winning Twitter, but losing the election: Media campaign lessons from California’s Prop 29

    Feng M, Szczypka G, Emery S (2015, June). Winning Twitter, but losing the election: Media campaign lessons from California’s Prop 29. 68th Annual Conference of the World Association for Public Opinion Research, Buenos Aires, Argentina.

  • Winning Twitter, but losing the election: Media campaign lessons from California’s Prop 29.

    Poster presented at the California Tobacco Control Program (CTCP), Tobacco-Related Disease Research Program (TRDRP) and the Tobacco-Use Prevention Education Program (TUPE) Joining Forces Conference, Sacramento, CA.

  • Working Paper Series #18026: The Impact of the 2009 Federal Tobacco Excise Tax Increase on Youth Tobacco Use