Chemical analysis of fresh and aged Australian e-cigarette liquids.

OBJECTIVES: To assess the chemical composition of electronic cigarette liquids (e-liquids) sold in Australia, in both their fresh and aged forms. DESIGN, SETTING: Gas chromatography-mass spectrometry analysis of commercial e-liquids sold in Australia (online and physical stores). MAIN OUTCOME MEASURES: Chemical composition of 65 Australian e-liquids - excipients/solvents, flavouring chemicals, other known e-liquid constituents (including nicotine), and polycyclic aromatic hydrocarbons - before and after an accelerated ageing process that simulated the effects of vaping. RESULTS: The measured levels of propylene glycol and glycerol often diverged from those recorded on the e-liquid label. All e-liquids contained one or more potentially harmful chemicals, including benzaldehyde, menthol, trans-cinnamaldehyde, and polycyclic aromatic hydrocarbons. Nicotine or nicotyrine were detected in a small proportion of e-liquids at extremely low concentrations. CONCLUSIONS: Australian e-liquids contain a wide variety of chemicals for which information on inhalation toxicity is not available. Further analyses are required to assess the potential long term effects of e-cigarette use on health.

ICP-MS Determination of 23 Elements of Potential Health Concern in Liquids of e-Cigarettes. Method Development, Validation, and Application to 37 Real Samples.

The lack of interest in the determination of toxic elements in liquids for electronic cigarettes (e-liquids) has so far been reflected in the scarce number of accurate and validated analytical methods devoted to this aim. Since the strong matrix effects observed for e-liquids constitute an exciting analytical challenge, the main goal of this study was to develop and validate an ICP-MS method aimed to quantify 23 elements in 37 e-liquids of different flavors. Great attention has been paid to the critical phases of sample pre-treatment, as well as to the optimization of the ICP-MS conditions for each element and of the quantification. All samples exhibited a very low amount of the elements under investigation. Indeed, the sum of their average concentration was of ca. 0.6 mg kg-1. Toxic elements were always below a few tens of a µg per kg-1 and, very often, their amount was below the relevant quantification limits. Tobacco and tonic flavors showed the highest and the lowest concentration of elements, respectively. The most abundant elements came frequently from propylene glycol and vegetal glycerin, as confirmed by PCA. A proper choice of these substances could further decrease the elemental concentration in e-liquids, which are probably barely involved as potential sources of toxic elements inhaled by vapers.

Incidence of Cigarette Smoking Relapse Among Individuals Who Switched to e-Cigarettes or Other Tobacco Products.

Importance: Although e-cigarettes are not approved as a cessation device, many who smoke believe that e-cigarettes will help them quit cigarette smoking successfully. Objective: To assess whether people who recently quit smoking and who had switched to e-cigarettes or another tobacco product were less likely to relapse to cigarette smoking compared with those who remained tobacco free. Design, Setting, and Participants: This cohort study analyzed a nationally representative sample of US households that participated in 4 waves of the Population Assessment of Tobacco and Health Study (conducted 2013 through 2017), combining 2 independent cohorts each with 3 annual surveys. Eligible participants were individuals who smoked at baseline, had recently quit at the first follow-up, and completed the second follow-up survey. Exposures: Use of e-cigarettes or alternate tobacco products at follow-up 1 after recently quitting smoking. Main Outcomes and Measures: Weighted percentage of participants with over 12 months abstinence by follow-up 2. Results: Of a total of 13 604 participants who smoked cigarettes at baseline, 9.4% (95% CI, 8.7%-10.0%) recently had quit smoking (mean age, 41.9; 95% CI, 39.7-46.6 years; 641 [43.2%] women) Of these, 22.8% (95% CI, 19.7%-26.0%) had switched to e-cigarettes, with 17.6% (95% CI, 14.8%-20.5%) using them daily. A total of 37.1% (95% CI, 33.7%-40.4%) used a noncigarette tobacco product and 62.9% (95% CI, 59.6%-66.3%) were tobacco free. Rates of switching to e-cigarettes were highest for those who were in the top tertile of tobacco dependence (31.3%; 95% CI, 25.0%-37.7%), were non-Hispanic White (26.4%; 95% CI, 22.3%-30.4%), and had higher incomes (annual income ≥$35 000, 27.5%; 95% CI, 22.5%-32.4% vs <$35 000, 19.3%; 95% CI, 16.3%-22.3%). At follow-up 2, unadjusted relapse rates were similar among those who switched to different tobacco products (for any tobacco product: successfully quit, 41.5%; 95% CI, 36.2%-46.9%; relapsed with significant requit, 17.0%; 95% CI, 12.4%-21.6%; currently smoking, 36.2%; 95% CI, 30.9%-41.4%). Controlled for potential confounders, switching to any tobacco product was associated with higher relapse rate than being tobacco free (adjusted risk difference, 8.5%; 95% CI, 0.3%-16.6%). Estimates for those who switched to e-cigarettes, whether daily or not, were not significant. While individuals who switched from cigarettes to e-cigarettes were more likely to relapse, they appeared more likely to requit and be abstinent for 3 months at follow-up 2 (17.0%; 95% CI, 12.4%-21.6% vs 10.4%; 95% CI, 8.0%-12.9%). Conclusions and Relevance: This large US nationally representative study does not support the hypothesis that switching to e-cigarettes will prevent relapse to cigarette smoking.

Examining the sources of evidence in e-cigarette policy recommendations: A citation network analysis of international public health recommendations.

BACKGROUND: Public health policies and recommendations aim to be informed by the best available evidence. Evidence underpinning e-cigarettes policy recommendations has been necessarily limited due to the novelty of the technology and the lack of long-term epidemiological studies and trials. Some public health bodies have actively encouraged e-cigarette use whilst others have raised concerns over introducing new health risks and renormalising tobacco smoking. Using citation network analysis we investigated the author conflicts of interest and study funding statements within sources of evidence used by public health bodies when making recommendations about e-cigarette policy. METHODS: We conducted citation network analysis of public health recommendation documents across four purposively selected diverse jurisdictions: WHO, UK, Australia, and USA. We extracted all citations from 15 public health recommendation documents, with more detailed data collected for influential citations (used in 3+ recommendation documents). We analysed the relationships between the sources of evidence used across jurisdictions using block modelling to determine if similar groups of documents were used across different jurisdictions. We assessed the frequency and nature of conflicts of interest. RESULTS: 1700 unique citations were included across the 15 public health recommendation documents, with zero to 923 citations per document (median = 63, IQR = 7.5-132). The evidence base underpinning public health recommendations did not systematically differ across jurisdictions. Of the 1700 citations included, the majority were journal articles (n = 1179). Across 1081 journal articles published between 1998-2018, 200 declared a conflict of interest, 288 contained no mention of conflicts of interest, and 593 declared none. Conflicts of interest were reported with tobacco (3%; n = 37 journal articles of 1081), e-cigarette (7%; n = 72), and pharmaceutical companies (12%; n = 127), with such conflicts present even in the most recent years. There were 53 influential citations, the most common study type was basic science research without human subjects (e.g. examination of aerosols and e-liquids) (n = 18) followed by systematic review (n = 10); with randomised control trial being least common (n = 4). Network analysis identified clusters of highly-cited articles with a higher prevalence of conflicts of interest. CONCLUSION: Public health bodies across different jurisdictions drew upon similar sources of evidence, despite articulating different policy approaches to e-cigarettes. The evidence drawn upon, including the most influential evidence, contained substantial conflicts of interest (including relationships with e-cigarette and tobacco industries). Processes to explicitly manage conflicts of interest arising from the underlying evidence base may be required when developing public health recommendations.

Nicotine dosimetry and stability in cambridge filter PADs (CFPs) following different smoking regime protocols and storage conditions.

Despite the growing numbers of studies on cigarettes and electronic nicotine delivery products (ENDs), no standard assessment of nicotine stability in various matrix post exposure is currently available. The aim of the present study was to evaluate the optimal standard condition to store Cambridge Filter Pads (CFPs) before chemical analysis in order to guarantee the titer of nicotine.We further performed data normalization according to different smoking or vaping runs. Smoke and vapor generated respectively by a reference tobacco cigarette (1R6F) and ENDs under different exposure regimes (ISO, HCI and CRM81) were collected on CFPs as total particulate matter (TPM) and subsequently analyzed for nicotine content. For each exposure, some CFPs were analyzed at time zero, whereas the others were stored under different conditions for nicotine assessment after 30 days. Principal Component Analysis (PCA) showed the best correlation between nicotine on CFPs and TPM for normalization. This study suggests that different exposure regimes and products can affect the preservation of nicotine titer on CFPs while samples storage at -80 °C may prevent the loss of nicotine. Finally, normalization of nicotine with TPM is strongly recommended for regulatory purpose.

Electronic Cigarette Users' Perspective on the COVID-19 Pandemic: Observational Study Using Twitter Data.

BACKGROUND: Previous studies have shown that electronic cigarette (e-cigarette) users might be more vulnerable to COVID-19 infection and could develop more severe symptoms if they contract the disease owing to their impaired immune responses to viral infections. Social media platforms such as Twitter have been widely used by individuals worldwide to express their responses to the current COVID-19 pandemic. OBJECTIVE: In this study, we aimed to examine the longitudinal changes in the attitudes of Twitter users who used e-cigarettes toward the COVID-19 pandemic, as well as compare differences in attitudes between e-cigarette users and nonusers based on Twitter data. METHODS: The study dataset containing COVID-19-related Twitter posts (tweets) posted between March 5 and April 3, 2020, was collected using a Twitter streaming application programming interface with COVID-19-related keywords. Twitter users were classified into two groups: Ecig group, including users who did not have commercial accounts but posted e-cigarette-related tweets between May 2019 and August 2019, and non-Ecig group, including users who did not post any e-cigarette-related tweets. Sentiment analysis was performed to compare sentiment scores towards the COVID-19 pandemic between both groups and determine whether the sentiment expressed was positive, negative, or neutral. Topic modeling was performed to compare the main topics discussed between the groups. RESULTS: The US COVID-19 dataset consisted of 4,500,248 COVID-19-related tweets collected from 187,399 unique Twitter users in the Ecig group and 11,479,773 COVID-19-related tweets collected from 2,511,659 unique Twitter users in the non-Ecig group. Sentiment analysis showed that Ecig group users had more negative sentiment scores than non-Ecig group users. Results from topic modeling indicated that Ecig group users had more concerns about deaths due to COVID-19, whereas non-Ecig group users cared more about the government's responses to the COVID-19 pandemic. CONCLUSIONS: Our findings show that Twitter users who tweeted about e-cigarettes had more concerns about the COVID-19 pandemic. These findings can inform public health practitioners to use social media platforms such as Twitter for timely monitoring of public responses to the COVID-19 pandemic and educating and encouraging current e-cigarette users to quit vaping to minimize the risks associated with COVID-19.

Compliance in Controlled E-cigarette Studies.

INTRODUCTION: E-cigarette studies have found that the use of a variety of flavors and customizable devices results in greater use frequency and user satisfaction. However, standardized research e-cigarettes are being developed as closed systems with limited flavor options, potentially limiting user satisfaction. In this study, we explore protocol compliance in an e-cigarette study using a standardized, assigned device with puff time and duration tracking (controlled e-cigarette) and potential limitations that controlled devices and e-liquids can introduce. METHODS: In a crossover study, 49 young adult e-cigarette users were recruited using convenience sampling and assigned a controlled e-cigarette device and flavored or unflavored e-liquids on standardized protocols. E-cigarette use frequency (number of puffs per day, collected from the device) and serum cotinine levels were obtained at each of three study visits over 3 weeks. The correlation of cotinine and e-cigarette use over the preceding week was calculated at each study visit. RESULTS: Correlation of nicotine intake, as measured by serum cotinine, and puff time, as measured by puffs count and duration from the e-cigarette device, as an indicator of study protocol compliance, substantially declined after the first week of the study and were no longer correlated in the remaining study weeks (R2 = 0.53 and p ≤ .01 in week 1, R2 < 0.5 and p > .05 for remaining weeks). CONCLUSIONS: There is an emerging need for controlled e-cigarette exposures studies, but low compliance in the use of assigned devices and e-liquids may be a limitation that needs to be mitigated in future studies. IMPLICATIONS: This study is the first to analyze compliance with instructions to use a standardized e-cigarette device with puff time and duration tracking (controlled e-cigarette) across all subjects and an assigned e-liquid flavor over a 3-week period. We find that protocol compliance, as measured by correlations between e-cigarette use measures and cotinine levels, was only achieved in the first week of the study and declined thereafter. These findings indicate that the assignment of a study device and instruction to only use the study device with assigned e-liquid flavor may not be sufficient to ensure participant compliance with the study protocol. We suggest that additional measures, including behavioral and biological markers, are needed to ensure sole use of the study e-cigarette and e-liquid and to be able to interpret results from controlled e-cigarette studies.

Investigating the Attitudes of Adolescents and Young Adults Towards JUUL: Computational Study Using Twitter Data.

BACKGROUND: Increases in electronic nicotine delivery system (ENDS) use among high school students from 2017 to 2019 appear to be associated with the increasing popularity of the ENDS device JUUL. OBJECTIVE: We employed a content analysis approach in conjunction with natural language processing methods using Twitter data to understand salient themes regarding JUUL use on Twitter, sentiment towards JUUL, and underage JUUL use. METHODS: Between July 2018 and August 2019, 11,556 unique tweets containing a JUUL-related keyword were collected. We manually annotated 4000 tweets for JUUL-related themes of use and sentiment. We used 3 machine learning algorithms to classify positive and negative JUUL sentiments as well as underage JUUL mentions. RESULTS: Of the annotated tweets, 78.80% (3152/4000) contained a specific mention of JUUL. Only 1.43% (45/3152) of tweets mentioned using JUUL as a method of smoking cessation, and only 6.85% (216/3152) of tweets mentioned the potential health effects of JUUL use. Of the machine learning methods used, the random forest classifier was the best performing algorithm among all 3 classification tasks (ie, positive sentiment, negative sentiment, and underage JUUL mentions). CONCLUSIONS: Our findings suggest that a vast majority of Twitter users are not using JUUL to aid in smoking cessation nor do they mention the potential health benefits or detriments of JUUL use. Using machine learning algorithms to identify tweets containing underage JUUL mentions can support the timely surveillance of JUUL habits and opinions, further assisting youth-targeted public health intervention strategies.

Current E-Cigarette Research in the Context of Asthma.

PURPOSE OF REVIEW: The purpose of this review is to integrate recent research on the respiratory immune effects of e-cigarettes with the pathogenesis of asthma to better understand how e-cigarettes may affect asthmatics and to note critical knowledge gaps regarding the effects of e-cigarettes on asthmatics. RECENT FINDINGS: Human, rodent, and cell culture studies show that key cellular functions of epithelial cells, macrophages, and neutrophils are altered by e-cigarette exposure. Because respiratory immunity is already dysregulated in asthma, further alteration of cellular function by e-cigarettes could impact asthma development, severity, and/or exacerbations. Future research is needed to more directly investigate this relationship using controlled human exposure studies and exposure of cell culture or animal models of asthma to e-cigarettes.

Characterizing Social Media Messages Related to Underage JUUL E-Cigarette Buying and Selling: Cross-Sectional Analysis of Reddit Subreddits.

BACKGROUND: Stopping the epidemic of e-cigarette use among youth has become the common goal of both regulatory authorities and health departments. JUUL is currently the most popular e-cigarette brand on the market. Young people usually obtain and exchange information about JUUL with the help of social media platforms. Along with the rising prevalence of JUUL, posts about underage JUUL buying and selling have appeared on social media platforms such as Reddit, which sharply increase the risk of minors being exposed to JUUL. OBJECTIVE: This study aims to analyze Reddit messages about JUUL buying and selling among the users of the UnderageJuul subreddit, and to further summarize the characteristics of those messages. The findings and insights can contribute to a better understanding of the patterns of underage JUUL use, and help public health officials provide timely education and guidance to minors who have intentions of accessing JUUL. METHODS: We used a novel cross-subreddit method to analyze the Reddit messages on 2 subreddits. From July 9, 2017, to January 7, 2018, we collected data from the UnderageJuul subreddit, which was created for underage JUUL use discussion. The data set included 716 threads, 2935 comments, and 844 Reddit users (ie, Redditors). We collected our second data set, comprising 23,840 threads and 162,106 comments posted between July 9, 2017, and January 8, 2019, from the JUUL subreddit. We conducted analyses including the following: (1) annotation of users with buying/selling intention, (2) posting patterns discovery and topic comparison, and (3) posting activeness observation of discovered Redditors. Term frequency-inverse document frequency and regular expression-enhanced keyword search methods were applied during the content analysis to extract the posting patterns. The public posting records of the discovered users on the JUUL subreddit during the year after the UnderageJuul subreddit was shut down were analyzed to determine whether they were still active and interested in obtaining JUUL. RESULTS: Our study revealed the following: (1) Among the 716 threads on the UnderageJuul subreddit, there were 214 threads related to JUUL sale and 168 threads related to JUUL purchase, which accounted for 53.5% (382/714) of threads. (2) Among the 844 Redditors of the UnderageJuul subreddit, 23.82% (201/844) of users were annotated with buying intention, and 21.10% (178/844) of users were annotated with selling intention. There were 34 users with buying/selling intention that self-reported as being <21 years old. (3) The most common key phrases used in selling threads were "WTS," "want to sell," "for sale," and "selling" (154/214, 72.0%). The most common key phrases used in buying threads were "look for/get JUUL/pods" (58/168, 34.5%) and "WTB" (53/168, 31.5%). (4) The most important concern that UnderageJuul Redditors had in obtaining JUULs was the price (311/1306, 23.81%), followed by the delivery service (68/1306, 5.21%). (5) The most popular flavors among the users with buying/selling intention were mango, cucumber, and mint. The flavor preferences remained consistent on both subreddits. Adverse symptoms related to the mango flavor were reported by 3 users on the JUUL subreddit. (6) In total, 24.4% (49/201) of users wanted to buy JUULs and 46.6% (83/178) of users wanted to sell JUULs, including 11 self-reported underage users, who also participated in the discussions on the JUUL subreddit. (7) Within one year of the UnderageJuul subreddit shutting down, there were 40 users who continued to post 186 threads on the JUUL subreddit, including 10 threads indicating buying/selling willingness that were posted shortly after the UnderageJuul subreddit was closed. CONCLUSIONS: There were overlapping users active in the JUUL and UnderageJuul subreddits. The buying/selling-related content appeared in multiple venues with certain posting patterns from July 9, 2017, to January 7, 2018. Such content might lead to a high risk of health problems for minors, such as nicotine addiction. Based on these findings, this study provided some insights and suggestions that might contribute to the decision-making processes of regulators and public health officials.

A Social Media Study on the Associations of Flavored Electronic Cigarettes With Health Symptoms: Observational Study.

BACKGROUND: In recent years, flavored electronic cigarettes (e-cigarettes) have become popular among teenagers and young adults. Discussions about e-cigarettes and e-cigarette use (vaping) experiences are prevalent online, making social media an ideal resource for understanding the health risks associated with e-cigarette flavors from the users' perspective. OBJECTIVE: This study aimed to investigate the potential associations between electronic cigarette liquid (e-liquid) flavors and the reporting of health symptoms using social media data. METHODS: A dataset consisting of 2.8 million e-cigarette-related posts was collected using keyword filtering from Reddit, a social media platform, from January 2013 to April 2019. Temporal analysis for nine major health symptom categories was used to understand the trend of public concerns related to e-cigarettes. Sentiment analysis was conducted to obtain the proportions of positive and negative sentiment scores for all reported health symptom categories. Topic modeling was applied to reveal the topics related to e-cigarettes and health symptoms. Furthermore, generalized estimating equation (GEE) models were used to quantitatively measure potential associations between e-liquid flavors and the reporting of health symptoms. RESULTS: Temporal analysis showed that the Respiratory category was consistently the most discussed health symptom category among all categories related to e-cigarettes on Reddit, followed by the Throat category. Sentiment analysis showed higher proportions of positive sentiment scores for all reported health symptom categories, except for the Cancer category. Topic modeling conducted on all health-related posts showed that 17 of the top 100 topics were flavor related. GEE models showed different associations between the reporting of health symptoms and e-liquid flavor categories, for example, lower association of the Beverage flavors with Respiratory compared with other flavors and higher association of the Fruit flavors with Cardiovascular than other flavors. CONCLUSIONS: This study identified different potential associations between e-liquid flavors and the reporting of health symptoms using social media data. The results of this study provide valuable information for further investigation of the health effects associated with different e-liquid flavors.

User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.

BACKGROUND: The number of electronic cigarette (e-cigarette) users has been increasing rapidly in recent years, especially among youth and young adults. More e-cigarette products have become available, including e-liquids with various brands and flavors. Various e-liquid flavors have been frequently discussed by e-cigarette users on social media. OBJECTIVE: This study aimed to examine the longitudinal prevalence of mentions of electronic cigarette liquid (e-liquid) flavors and user perceptions on social media. METHODS: We applied a data-driven approach to analyze the trends and macro-level user sentiments of different e-cigarette flavors on social media. With data collected from web-based stores, e-liquid flavors were classified into categories in a flavor hierarchy based on their ingredients. The e-cigarette-related posts were collected from social media platforms, including Reddit and Twitter, using e-cigarette-related keywords. The temporal trend of mentions of e-liquid flavor categories was compiled using Reddit data from January 2013 to April 2019. Twitter data were analyzed using a sentiment analysis from May to August 2019 to explore the opinions of e-cigarette users toward each flavor category. RESULTS: More than 1000 e-liquid flavors were classified into 7 major flavor categories. The fruit and sweets categories were the 2 most frequently discussed e-liquid flavors on Reddit, contributing to approximately 58% and 15%, respectively, of all flavor-related posts. We showed that mentions of the fruit flavor category had a steady overall upward trend compared with other flavor categories that did not show much change over time. Results from the sentiment analysis demonstrated that most e-liquid flavor categories had significant positive sentiments, except for the beverage and tobacco categories. CONCLUSIONS: The most updated information about the popular e-liquid flavors mentioned on social media was investigated, which showed that the prevalence of mentions of e-liquid flavors and user perceptions on social media were different. Fruit was the most frequently discussed flavor category on social media. Our study provides valuable information for future regulation of flavored e-cigarettes.

Association between changes in harm perceptions and e-cigarette use among current tobacco smokers in England: a time series analysis.

BACKGROUND: There is a decreasing trend in the proportion of individuals who perceive e-cigarettes to be less harmful than conventional cigarettes across the UK, Europe and the US. It is important to assess whether this may influence the use of e-cigarettes. We aimed to estimate, using a time series approach, whether changes in harm perceptions among current tobacco smokers have been associated with changes in the prevalence of e-cigarette use in England, with and without stratification by age, sex and social grade. METHODS: Respondents were from the Smoking Toolkit Study, which involves monthly cross-sectional household surveys of individuals aged 16+ years in England. Data were aggregated monthly on ~ 300 current tobacco smokers between 2014 and 2019. The outcome variable was the prevalence of e-cigarette use. The explanatory variable was the proportion of smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes. Covariates were cigarette (vs. non-cigarette combustible) current smoking prevalence, past-year quit attempt prevalence and national smoking mass media expenditure. Unadjusted and adjusted autoregressive integrated moving average with exogeneous variables (ARIMAX) models were fitted. RESULTS: For every 1% decrease in the mean prevalence of current tobacco smokers who endorsed the belief that e-cigarettes are less harmful than combustible cigarettes, the mean prevalence of e-cigarette use decreased by 0.48% (ßadj = 0.48, 95% CI = 0.25-0.71, p < .001). Marginal age and sex differences were observed, whereby significant associations were observed in older (but not in young) adults and in men (but not in women). No differences by social grade were detected. CONCLUSIONS: Between 2014 and 2019 in England, at the population level, monthly changes in the prevalence of accurate harm perceptions among current tobacco smokers were strongly associated with changes in e-cigarette use.