Twitter Sentiment About the US Federal Tobacco 21 Law: Mixed Methods Analysis.

BACKGROUND: On December 20, 2019, the US "Tobacco 21" law raised the minimum legal sales age of tobacco products to 21 years. Initial research suggests that misinformation about Tobacco 21 circulated via news sources on Twitter and that sentiment about the law was associated with particular types of tobacco products and included discussions about other age-related behaviors. However, underlying themes about this sentiment as well as temporal trends leading up to enactment of the law have not been explored. OBJECTIVE: This study sought to examine (1) sentiment (pro-, anti-, and neutral policy) about Tobacco 21 on Twitter and (2) volume patterns (number of tweets) of Twitter discussions leading up to the enactment of the federal law. METHODS: We collected tweets related to Tobacco 21 posted between September 4, 2019, and December 31, 2019. A 2% subsample of tweets (4628/231,447) was annotated by 2 experienced, trained coders for policy-related information and sentiment. To do this, a codebook was developed using an inductive procedure that outlined the operational definitions and examples for the human coders to annotate sentiment (pro-, anti-, and neutral policy). Following the annotation of the data, the researchers used a thematic analysis to determine emergent themes per sentiment category. The data were then annotated again to capture frequencies of emergent themes. Concurrently, we examined trends in the volume of Tobacco 21-related tweets (weekly rhythms and total number of tweets over the time data were collected) and analyzed the qualitative discussions occurring at those peak times. RESULTS: The most prevalent category of tweets related to Tobacco 21 was neutral policy (514/1113, 46.2%), followed by antipolicy (432/1113, 38.8%); 167 of 1113 (15%) were propolicy or supportive of the law. Key themes identified among neutral tweets were news reports and discussion of political figures, parties, or government involvement in general. Most discussions were generated from news sources and surfaced in the final days before enactment. Tweets opposing Tobacco 21 mentioned that the law was unfair to young audiences who were addicted to nicotine and were skeptical of the law's efficacy and importance. Methods used to evade the law were found to be represented in both neutral and antipolicy tweets. Propolicy tweets focused on the protection of youth and described the law as a sensible regulatory approach rather than a complete ban of all products or flavored products. Four spikes in daily volume were noted, 2 of which corresponded with political speeches and 2 with the preparation and passage of the legislation. CONCLUSIONS: Understanding themes of public sentiment-as well as when Twitter activity is most active-will help public health professionals to optimize health promotion activities to increase community readiness and respond to enforcement needs including education for retailers and the general public.

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.

Miscommunication about the US federal Tobacco 21 law: a content analysis of Twitter discussions.

OBJECTIVE: Tobacco 21 is a law that sets the minimum legal sales age of tobacco products to 21. On 20 December 2019, the USA passed a federal Tobacco 21 law. The objective of this study is to explore Twitter discussions about the federal Tobacco 21 law in the USA leading up to enacted. METHODS: Twitter messages about Tobacco 21 posted between September and December 2019 were collected via RITHM software. A 2% sample of all collected tweets were double coded by independent coders using a content analysis approach. RESULTS: Findings included three content categories of tweets (news, youth and young adults and methods of avoiding the law) with eight subcodes. Most news tweets incorrectly described the law as a purchase law (54.7%). However, Tobacco 21 is in fact a sales law-it only includes penalties for tobacco retailers who sell to under-age purchasers. About one-fourth (27%) of the tweets involved youth and young adults, with some claiming the law would reduce youth smoking and others doubting its ability to limit youth access to tobacco products. Few tweets (2.5%) mentioned methods of circumventing the policy, such as having an older peer purchase tobacco. CONCLUSIONS: As several countries explore raising their age of sale of tobacco laws to 21, they should couple policy enactment with clear and accurate communication about the law. Compliance agencies at all levels (eg, local, regional, national) can use social media to identify policy loopholes and support vulnerable populations throughout the policy implementation process.

Policy and Behavior: Comparisons between Twitter Discussions about the US Tobacco 21 Law and Other Age-Related Behaviors.

To combat the e-cigarette epidemic among young audiences, a federal law was passed in the US that raised the minimum legal sales age of tobacco to 21 years (commonly known as Tobacco 21). Little is known about sentiment toward this law. Thus, the purpose of our study was to systematically explore trends about Tobacco 21 discussions and comparisons to other age-restriction behaviors on Twitter. Twitter data (n = 4628) were collected from September to December of 2019 that were related to Tobacco 21. A random subsample of identified tweets was used to develop a codebook. Two trained coders independently coded all data, with strong inter-rater reliability (κ = 0.71 to 0.93) found for all content categories. Associations between sentiment and content categories were calculated using χ2 analyses. Among relevant tweets (n = 955), the most common theme­the disjunction between ages for military enlistment and tobacco use­was found in 17.8% of all tweets. Anti-policy sentiment was strongly associated with the age of military enlistment, alcohol, voting, and adulthood (p < 0.001 for all). Opposition to Tobacco 21 propagates on social media because the US federal law does not exempt military members. However, the e-cigarette epidemic may have fueled some support for this law.

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.

Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series.

Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.

Vape Shop Proliferation and Noncompliance in Pennsylvania: A Pre- and Post-tax Analysis.

BACKGROUND: The growing use of electronic nicotine delivery systems (ENDS) among adolescents is a public health concern. Taxation of these products is a viable approach to reduce ENDS use, particularly among adolescents. Opponents of taxation posit that it puts specialty retailers (ie, vape shops) out of business, thereby reducing availability of ENDS for adult smokers seeking harm reduction. Pennsylvania enacted substantial ENDS taxes in October 2016. This study sought to examine (1) the prevalence of Pennsylvania vape shops before and after ENDS taxes were enacted and (2) ENDS retail licensing compliance among vape shops. METHODS: We employed standardized searches for vape shops in Pennsylvania on the Yelp business-listing platform a month prior to and for 18 consecutive months following the imposition of ENDS taxes. We then compared listings to a public database of ENDS-related retail licenses to determine compliance status. RESULTS: The number of listed vape shops increased in a linear fashion by a magnitude of 23%. In addition, when we compared a final listing of retailers to data from the state tax authority, we found roughly a quarter (22%-29%) of vape shops to be noncompliant with maintaining a valid ENDS retail license. CONCLUSIONS: Overall, ENDS taxation in Pennsylvania has not appeared to reduce prevalence of vape shops as anticipated. However, stricter enforcement of the tax law is necessary to ensure compliance among retailers. These findings have implications for implementation and enforcement of ENDS tax policy nationwide, including states that currently lack such policies.

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.

JUUL on Twitter: Analyzing Tweets About Use of a New Nicotine Delivery System.

BACKGROUND: Initial reports suggest that JUUL, a popular e-cigarette, is being used in schools and other locations in which cigarette smoking is illegal or discouraged. However, there is little scholarly research documenting this. We aimed to make a systematic analysis of JUUL use themes and sentiment on Twitter. METHODS: Data were collected from Twitter's Filtered Streams Application Programming Interface from April 12, 2018 to May 10, 2018. This yielded 67,934 tweets, from which a random sample of 2% was selected for coding. The final dataset included 1209 tweets. Inter-rater reliability ranged κ = 0.64-0.85. RESULTS: The majority (71.5%) of tweets expressed positive sentiment toward JUUL. JUUL use in places where cigarette smoking is illegal or discouraged appeared in 111 tweets (9.2%); approximately one-third of these tweets referring to using the device in school. Nearly 20% of tweets mentioned using the device at home and/or directly in front of responsible adults. CONCLUSIONS: This study confirms anecdotal reports of JUUL use in places where cigarette smoking is illegal or discouraged. Positive sentiment about use of JUUL suggests that the product is being normalized among young people. It may be valuable for educators to discuss the addictive nature of nicotine delivered through JUUL with younger populations.

I wake up and hit the JUUL: Analyzing Twitter for JUUL nicotine effects and dependence.

BACKGROUND: JUUL-a novel electronic nicotine delivery system (ENDS)-comprises most of the ENDS market share. Additionally, JUUL has a high nicotine content and utilizes a patented nicotine salt formulation aimed to speed absorption. Many JUUL users are not aware of the nicotine content and therefore may not be expecting acute nicotine effects or potential for dependence. This study sought to analyze Twitter messages ("tweets") regarding nicotine, symptoms of dependence, and withdrawal related to JUUL use. METHODS: Data were collected from Twitter's Filtered Streams interface 4/11-6/16/2018 by retrieving tweets matching the terms "juul," "juuls," and "juuling" that also used words consistent with nicotine effects, symptoms of dependence, and withdrawal. A random 5% subsample (n = 1986) was coded by 2 independent coders. Cohen's κ for inter-rater reliability ranged 0.62-1.00 for all coded variables. Tweets were assessed using a qualitative content analysis approach. RESULTS: A total of 335 tweets mentioned dependence-related themes, including use upon waking and compulsion to use. A total of 189 tweets mentioned themes related to nicotine, with almost 15% of these tweets describing physical effects. Additionally, 42 tweets mentioned themes related to quitting JUUL and/or withdrawal from JUUL. DISCUSSION: This qualitative analysis suggests that users of JUUL are experiencing symptoms of nicotine exposure and dependence. Considering the high nicotine content of JUUL and the rising popularity among young people, more research around initiation of and dependence on JUUL, as well as the impact of recent FDA policy changes, should be conducted.

It's not all about autism: The emerging landscape of anti-vaccination sentiment on Facebook.

BACKGROUND: Due in part to declining vaccination rates, in 2018 over 20 states reported at least one case of measles, and over 40,000 cases were confirmed in Europe. Anti-vaccine posts on social media may be facilitating anti-vaccination behaviour. This study aimed to systematically characterize (1) individuals known to publicly post anti-vaccination content on Facebook, (2) the information they convey, and (3) the spread of this content. METHODS: Our data set consisted of 197 individuals who posted anti-vaccination comments in response to a message promoting vaccination. We systematically analysed publicly-available content using quantitative coding, descriptive analysis, social network analysis, and an in-depth qualitative assessment. The final codebook consisted of 26 codes; Cohen's κ ranged 0.71-1.0 after double-coding. RESULTS: The majority (89%) of individuals identified as female. Among 136 individuals who divulged their location, 36 states and 8 other countries were represented. In a 2-mode network of individuals and topics, modularity analysis revealed 4 distinct sub-groups labelled as "trust," "alternatives," "safety," and "conspiracy." For example, a comment representative of "conspiracy" is that poliovirus does not exist and that pesticides caused clinical symptoms of polio. An example from the "alternatives" sub-group is that eating yogurt cures human papillomavirus. Deeper qualitative analysis of all 197 individuals' profiles found that these individuals also tended to post material against other health-related practices such as water fluoridation and circumcision. CONCLUSIONS: Social media outlets may facilitate anti-vaccination connections and organization by facilitating the diffusion of centuries old arguments and techniques. Arguments against vaccination are diverse but remain consistent within sub-groups of individuals. It would be valuable for health professionals to leverage social networks to deliver more effective, targeted messages to different constituencies.

Tobacco cessation mobile app intervention (Just Kwit! study): protocol for a pilot randomized controlled pragmatic trial.

BACKGROUND: Adolescence and young adulthood are critical times of initiation and progression to daily use of tobacco. However, it is difficult to recruit young adults to traditional smoking cessation and retention rates are typically low. Smartphone cessation applications (apps) can provide real-time responses to smoking urges and related cues, which are known to be important factors in lapse and relapse. Given the popularity of smartphones among young adults and the considerably higher download rates of commercially developed apps compared to research-based apps, there is a need to design pragmatic studies that evaluate commercial tobacco cessation apps. The aims of this pilot study are to assess the impact on tobacco cessation of using a smartphone app compared with usual care and to generate feasibility data to inform a future fully powered clinical trial. METHODS: We will conduct an open randomized controlled trial with parallel groups. Participants will be selected from hospitalized patients and must be aged 18-30 years, interested in cessation, smoked > 5 cigarettes/day over the past 30 days, and own an Apple or Android smartphone. Participants who are eligible will be randomized to either a smartphone experimental group or patient-initiated follow up (usual care). As this study seeks to assess feasibility, the primary data will include (1) recruitment rates, (2) retention rates, and (3) adherence, measured through user engagement with the app. DISCUSSION: This pilot trial will be the first to evaluate a commercially available smartphone app for tobacco cessation in a hospitalized setting. Data generated by this study can be used for larger fully powered trials such as comparative effectiveness studies against apps developed by academics or health scientists based on behavioral theories, or cost-effectiveness analyses of mobile interventions. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03538678 . Registered on 28 May 2018.

Online data to contextualize waterpipe tobacco smoking establishments surrounding large US universities.

Waterpipe tobacco smoking has grown in popularity among US college students and is associated with serious health risks. Much of the waterpipe tobacco smoking takes place in establishments such as "hookah bars" or in lounge settings. Web-based data platforms such as Yelp have demonstrated utility in locating these establishments but are prone to over- and underestimation. The purpose of this study was to optimize strategies for algorithmically estimating the prevalence of waterpipe tobacco smoking establishments. We conducted searches for potential waterpipe tobacco smoking establishments near highly residential US universities (N = 41). Of 521 potential establishments, independent coders confirmed 257 as permitting waterpipe tobacco smoking. We compared four strategies for using Yelp metadata to estimate the number of confirmed waterpipe tobacco smoking establishments by location. An accuracy-weighted approach generated estimates that closely matched confirmed data without significant over- or underestimation. The use of algorithms such as these may dramatically improve the feasibility and efficacy of future research linking environmental data and health outcomes.

Waterpipe tobacco use in college and non-college young adults in the USA.

BACKGROUND: Waterpipe tobacco smoking (WTS or 'hookah') is common among adolescents and college students in the USA. However, there has not yet been a large-scale, nationally representative study independently examining WTS among young adults who are not in college. OBJECTIVE: This study sought to examine associations between attitudes, normative beliefs, certain socio-demographic factors and current WTS among young adults not in college and compare them to young adults in college. METHODS: A total of 3131 US adults ages 18-30 completed an online survey about WTS behaviour, attitudes, normative beliefs and relevant socio-demographic factors. Multivariable logistic regression was used to examine independent associations between these variables and current WTS stratified by student status. RESULTS: Ever WTS was reported by 29% of young adults not in college and by 35% of those in college, and current use rates were 3 and 7%, respectively. Multivariable models demonstrated that positive attitudes and perceived peer acceptability of WTS were significantly associated with increased current WTS for both young adults not in college [adjusted odds ratio (AOR) = 2.72; 95% CI: 2.00-3.71 and AOR = 2.02; 95% CI: 1.50-2.71, respectively] and young adults in college (AOR = 3.37; 95% CI: 2.48-4.58 and AOR = 2.05; 95% CI: 1.49-2.83, respectively). The magnitude of these associations was not significantly different when comparing individuals in college and not in college. CONCLUSIONS: Among young adults, WTS is common in non-college-based populations as well as in college-based populations. Therefore, prevention programming should extend to all young adults, not only to those in college.

#Hookahlife: The Rise of Waterpipe Promotion on Instagram.

INTRODUCTION: Hookah smoking is becoming increasingly popular worldwide, especially among young adults. The growth of social media has also enabled sharing of opinions, experiences, and marketing related to hookah via user-generated content. AIM: To evaluate the portrayal and promotion of hookah on Instagram and to highlight public health challenges created by this content. METHOD: A qualitative content analysis was performed on a sample of 301 Instagram posts made with the hashtags #hookah or #shisha in October 2016. RESULTS: Of the 301 coded posts, 279 were relevant to hookah. These posts were most commonly made by hookah lounges/bars (29.2%) and individual users (27.6%). Posters were most commonly from Russia (38.5%), the United States (18.6%), and Germany (10.7%). A total of 63.8% of the posts were promotional in nature; 19.7% of posts contained hashtags that expressed hookah identity or community (e.g., #hookahlife); 35.5% of posts associated nightlife activities with hookah smoking and 33.0% portrayed hookah smoking as a social activity. Only one post (0.4%) mentioned that hookah might have negative health effects. Likes were more common on posts depicting novel rather than traditional hookah designs ( p = .027). CONCLUSIONS: Images from Instagram may help in understanding health behaviors such as hookah smoking. Our results suggest that domestic and international hookah promotion is common on Instagram. Instagram posts cast hookah in an overwhelmingly positive light and may normalize hookah use through associations with nightlife, community, and hookah identity. Policy makers and researchers should explore approaches for reducing the volume of promotional posts and tailoring counter-marketing to the specific themes presented on social media.

JUUL: Spreading Online and Offline.

PURPOSE: The increasing popularity of the JUUL electronic nicotine delivery system among youth poses several potential public health concerns. Social media can be used to better understand the spread of information related to JUUL. This study examined whether adolescents (age < 18) are following JUUL's official Twitter account and subsequently sharing (retweeting) JUUL's posts to their followers. We also assessed various patterns in which adolescents share information on JUUL with each other. METHODS: We collected 3,239 tweets from JUUL's official Twitter account (@JUULVapor) for one full year from February 2017 to January 2018. These tweets were shared by Twitter users to their followers 1,124times by 721 unique users. Users were determined to be either adolescent (age <18) or adult (≥18) based on a systematic procedure involving double coding. We used social network analysis to explore the relationships between users retweeting JUUL. RESULTS: One-quarter (25%) of users were determined to be adolescents. Social network analysis revealed a maximum path length of 4 and a positive assortativity (r = .29). There were 25 (9%) instances of an adolescent retweeting content from another adolescent, 35 (12%) of an adolescent retweeting from an adult, and 30 (11%) of an adult retweeting from an adolescent. CONCLUSIONS: JUUL's official Twitter account is being followed-and its messages are being shared-by adolescents. Rigorous policies and prevention programs are needed to curb adolescent exposure to JUUL content and discussions online.

Toward Real-Time Infoveillance of Twitter Health Messages.

There is growing interest in conducting public health research using data from social media. In particular, Twitter "infoveillance" has demonstrated utility across health contexts. However, rigorous and reproducible methodologies for using Twitter data in public health are not yet well articulated, particularly those related to content analysis, which is a highly popular approach. In 2014, we gathered an interdisciplinary team of health science researchers, computer scientists, and methodologists to begin implementing an open-source framework for real-time infoveillance of Twitter health messages (RITHM). Through this process, we documented common challenges and novel solutions to inform future work in real-time Twitter data collection and subsequent human coding. The RITHM framework allows researchers and practitioners to use well-planned and reproducible processes in retrieving, storing, filtering, subsampling, and formatting data for health topics of interest. Further considerations for human coding of Twitter data include coder selection and training, data representation, codebook development and refinement, and monitoring coding accuracy and productivity. We illustrate methodological considerations through practical examples from formative work related to hookah tobacco smoking, and we reference essential methods literature related to understanding and using Twitter data.

Comparison of toxicant load from waterpipe and cigarette tobacco smoking among young adults in the USA.

OBJECTIVES: To form population-level comparisons of total smoke volume, tar, carbon monoxide and nicotine consumed from waterpipe tobacco smoking (WTS) and cigarette smoking using data from a nationally representative sample of smokers and non-smokers aged 18-30 years. METHODS: In March and April 2013, we surveyed a nationally representative sample of 3254 US young adults to assess the frequency and volume of WTS and cigarette smoking. We used Monte Carlo analyses with 5000 repetitions to estimate the proportions of toxicants originating from WTS and cigarette smoking. Analyses incorporated survey weights and used recent meta-analytic data to estimate toxicant exposures associated with WTS and cigarette smoking. RESULTS: Compared with the additive estimates of WTS and cigarette smoking combined, 54.9% (95% CI 37.5% to 72.2%) of smoke volume was attributed to WTS. The proportions of tar attributable to WTS was 20.8% (95% CI 6.5% to 35.2%), carbon monoxide 10.3% (95% CI 3.3% to 17.3%) and nicotine 2.4% (95% CI 0.9% to 3.8%). CONCLUSIONS: WTS accounted for over half of the tobacco smoke volume consumed among young US adult waterpipe and cigarette smokers. Toxicant exposures to tar, carbon monoxide and nicotine were lower, but still substantial, for WTS alone compared with WTS and cigarette smoking. Public health and policy interventions to reduce harm from tobacco smoking in young US adults should explicitly address WTS toxicant exposures.

Quantifying population-level health benefits and harms of e-cigarette use in the United States.

BACKGROUND: Electronic cigarettes (e-cigarettes) may help cigarette smokers quit smoking, yet they may also facilitate cigarette smoking for never-smokers. We quantify the balance of health benefits and harms associated with e-cigarette use at the population level. METHODS AND FINDINGS: Monte Carlo stochastic simulation model. Model parameters were drawn from census counts, national health and tobacco use surveys, and published literature. We calculate the expected years of life gained or lost from the impact of e-cigarette use on smoking cessation among current smokers and transition to long-term cigarette smoking among never smokers for the 2014 US population cohort. RESULTS: The model estimated that 2,070 additional current cigarette smoking adults aged 25-69 (95% CI: -42,900 to 46,200) would quit smoking in 2015 and remain continually abstinent from smoking for ≥7 years through the use of e-cigarettes in 2014. The model also estimated 168,000 additional never-cigarette smoking adolescents aged 12-17 and young adults aged 18-29 (95% CI: 114,000 to 229,000), would initiate cigarette smoking in 2015 and eventually become daily cigarette smokers at age 35-39 through the use of e-cigarettes in 2014. Overall, the model estimated that e-cigarette use in 2014 would lead to 1,510,000 years of life lost (95% CI: 920,000 to 2,160,000), assuming an optimistic 95% relative harm reduction of e-cigarette use compared to cigarette smoking. As the relative harm reduction decreased, the model estimated a greater number of years of life lost. For example, the model estimated-1,550,000 years of life lost (95% CI: -2,200,000 to -980,000) assuming an approximately 75% relative harm reduction and -1,600,000 years of life lost (95% CI: -2,290,000 to -1,030,000) assuming an approximately 50% relative harm reduction. CONCLUSIONS: Based on the existing scientific evidence related to e-cigarettes and optimistic assumptions about the relative harm of e-cigarette use compared to cigarette smoking, e-cigarette use currently represents more population-level harm than benefit. Effective national, state, and local efforts are needed to reduce e-cigarette use among youth and young adults if e-cigarettes are to confer a net population-level benefit in the future.