Reactions to a Hypothetical Ban of Open-System Electronic Cigarettes Among People Who Currently Use Electronic Cigarettes.

INTRODUCTION: Policies limiting electronic cigarette (ECIG) device and liquid characteristics have been considered to prevent dependence potential and youth product appeal. "Open-system" ECIGs allow people to adjust device and liquid characteristics, which may undermine these policies. This study examined anticipated reactions to a policy prohibiting the sale of open-system ECIG devices in the United States using concept mapping. METHODS: In 2020, adults who reported ECIG use in the past 30 days (n = 70; 45.1% women; mean age = 33.0, SD = 10.6) recruited from a research registry of individuals from across the United States provided statements into a shared list that completed a prompt: "If open-system vaping devices were no longer sold in the US and only closed-system vaping devices were available, what is a specific reaction or response you would have?" at a study website. Participants rated statements (1-7) on how true the statements were for them. Multidimensional scaling and hierarchical cluster analyses were used to identify thematic clusters. Mean ratings of statements were calculated and compared based on the type of ECIG used and current cigarette smoking status. RESULTS: Participants generated 85 unique statements. The analysis identified eight themes that were organized into two broad groups. The first group of five clusters described behavioral responses to the hypothetical policy (Loopholes, Switch to Closed-System ECIGs, Legal Approaches to Use Open-System ECIGs, ECIG Use Cessation, Switch to non-ECIG Products). The second group of three clusters described psychological responses to the hypothetical policy (Closed-System ECIG Cons, Psychological Responses, Policy Outcome Concerns). CONCLUSIONS: While a ban on open-system ECIG devices may have positive impacts on public health, policy loopholes, and consumer behaviors may undermine the policy. Efforts to support policy enforcement could increase policy effectiveness. IMPLICATIONS: Open-system electronic cigarettes (ECIGs) that allow people who use ECIGs to modify ECIG device and liquid characteristics may limit the effectiveness of policies that establish product standards which attempt to limit nicotine delivery and flavor content in ECIG liquids. This study identified predicted reactions to a hypothetical policy prohibiting the sale of open-system ECIGs in the United States. Results suggest that people who use ECIGs may view a policy prohibiting open-system ECIGs unfavorably, but would be willing to use closed-system ECIGs. However, many ECIG users may attempt to find loopholes in the policy or alternative sources to purchase open-system ECIGs if open-systems were banned. Future research should identify strategies for tobacco regulatory policy enforcement to increase effectiveness.

Hidden flaws in e-cigarette industry-funded studies.

Electronic cigarette (e-cigarette) use has increased since e-cigarettes were introduced to the market nearly 20 years ago. Researchers continue to conduct studies to understand the health risks and benefits of e-cigarettes to inform health education and promotion efforts as well as public policy. Studies funded by the tobacco industry examining the potential risks and benefits of e-cigarettes have also been conducted and are sometimes published in the scientific literature. Frequently, tobacco and e-cigarette industry-funded researchers report findings that contradict research funded by other sources. While many industry-funded studies may appear methodologically sound at first glance, in some cases, industry-funded studies include methodological flaws that result in misleading conclusions. The tobacco industry's use of biased research to influence tobacco-related policy decisions in the past is well-documented. This commentary provides specific examples of recent e-cigarette research funded by the tobacco/e-cigarette industry in which methodological flaws result in misleading conclusions that support industry goals. Given the long history of biased research conducted by the tobacco industry, there is a need to assess whether research funded by the e-cigarette industry similarly contains methodological flaws. We emphasise the need for tobacco and e-cigarette-funded research to be scrutinised by non-industry-funded subject matter experts and call for journals to not consider manuscripts that have received support from the tobacco or e-cigarette industry.

Waterpipe Tobacco Brands and Flavors Sold Online in the USA.

INTRODUCTION: Flavor additives are commonly used in combustible tobacco products to mask harshness and increase appeal. However, research on the availability of flavored waterpipe tobacco (WT) is lacking, yet is important to support implementation of policies. METHODS: We completed a comprehensive online search in 2020 to identify WT brands and flavors sold online in the USA. We conducted a descriptive content analysis categorizing flavors as explicit (i.e., clear taste/flavor) or concept (i.e., no clear taste/flavor); and coded for 23 flavor descriptors (e.g., fruit, mint/menthol, tobacco). Flavor names were double-coded and discrepancies were resolved by a third coder. RESULTS: We identified 66 WT brands with 118 product lines (i.e., sub-brands). We found 2953 flavors, including 1871 unique flavors. Brands averaged 45 flavors (range: 1-183). Almost three quarters (73.5%, n = 2171) used explicit flavor names and 26.5% (n = 782) used concept flavor names. Concept flavors varied widely, and included names such as 1001 Nights and California Dream. The most common flavor descriptors were fruit (54.1%) and mint/menthol (12.5%). Tobacco was rarely (0.2%) used as a flavor descriptor. Flavor descriptors also included location (10.7%), color (11.1%), candy (6.3%), cool/ice (5.3%), and alcohol (5.5%). CONCLUSIONS: WT is available in 1871 unique flavors, likely contributing to product appeal and use. Like other tobacco products, fruit and mint/menthol are common flavors. Given the significant health consequences associated with WT smoking and the role of flavors in product use, regulatory action specifically targeting WT flavors is urgently needed.

Changes in Nicotine Dependence Among Smokers Using Electronic Cigarettes to Reduce Cigarette Smoking in a Randomized Controlled Trial.

INTRODUCTION: How nicotine dependence will be affected when current smokers initiate electronic cigarette (e-cigarette) use to reduce cigarette smoking is unknown. This study evaluated cigarette, e-cigarette, and total nicotine dependence more than 6 months among smokers reducing cigarette consumption by replacing with e-cigarettes. AIMS AND METHODS: Adult cigarette smokers were randomized to one of four conditions (36 mg/ml e-cigarette, 8 mg/ml e-cigarette, 0 mg/ml e-cigarette, or cigarette-substitute [CS] [provided at no cost]) and instructed to reduce their cigarette smoking by 75% at 1 month. Participants completed follow-up at 1, 3, and 6 months. The Penn State Nicotine Dependence Index (PSNDI) measured dependence on cigarettes (PSCDI) and e-cigarettes (PSECDI). Urine cotinine measured total nicotine exposure. Linear mixed effects models for each outcome were conducted and included interaction terms between visit and condition. RESULTS: Participants (n = 520) were 58.8% female, 67.3% White, and 48.0 years old. At baseline, the median number of cigarettes smoked per day was 17.3 and the mean PSCDI score was 13.4, with no significant differences between conditions. Participants in the e-cigarette conditions reported significantly lower PSCDI scores, compared with baseline, and with the CS condition at all follow-up visits. Those in the 36 mg/ml e-cigarette condition reported greater PSECDI scores at 6 months, compared with baseline and the 0 mg/ml and 8 mg/ml conditions. At all follow-up visits, there were no differences in total nicotine exposure compared to baseline, nor between any conditions. CONCLUSIONS: E-cigarette use was associated with reduced cigarette dependence, compared to the CS, without significant increases in total nicotine exposure. IMPLICATIONS: Initiation of electronic cigarette use while continuing to smoke could potentially increase nicotine dependence. In this randomized trial aimed at helping smokers to reduce their cigarette intake, we found that use of an e-cigarette was associated with a reduction in cigarette dependence and an increase in e-cigarette dependence (in the condition with the highest nicotine concentration only), with no long term increase in total nicotine dependence or nicotine exposure.

Electronic Cigarette Nicotine Flux, Nicotine Yield, and Particulate Matter Emissions: Impact of Device and Liquid Heterogeneity.

INTRODUCTION: Electronic cigarettes (ECIGs) heat a nicotine-containing liquid to produce an inhalable aerosol. ECIG power (wattage) and liquid nicotine concentration are two factors that predict nicotine emission rate ("flux"). These factors can vary greatly across devices and users. AIMS AND METHODS: The purpose of this study was to examine ECIG device and liquid heterogeneity in "real world" settings and the association with predicted nicotine flux, nicotine yield, and total particulate matter (TPM) emissions. Past 30-day ECIG users (n = 84; mean age = 23.8 years [SD = 9.6]) reported device and liquid characteristics. Device power was measured via multimeter, device display screens, or obtained via labeling. Liquid nicotine concentration was obtained via labeling or through chemical analysis. Predicted nicotine flux, nicotine yield, and TPM associated with 10 4-second puffs were calculated for participants' primary devices. RESULTS: Participants' primary devices were box mods (42.9%), disposable vapes (20.2%), and pod mods (36.9%). Most participants (65.5%) reported not knowing their primary device wattage. Rebuildable/box mods had the greatest power range (11.1-120.0 W); pod mod power also varied considerably (4.1-21.7 W). Unlike device wattage, most participants (95.2%) reported knowing their liquid nicotine concentration, which ranged from 3.0 to 86.9 mg/ml (M = 36.0, SD = 29.3). Predicted nicotine flux varied greatly across products (range =12.0-160.1 µg/s, M = 85.6 µg/s, SD = 34.3). Box mods had the greatest variability in wattage and predicted nicotine flux, nicotine yield, and TPM yield. CONCLUSIONS: ECIG device and liquid heterogeneity influence nicotine and other toxicant emissions. Better measurement of ECIG device and liquid characteristics is needed to understand nicotine and toxicant emissions and to inform regulatory policy. IMPLICATIONS: ECIG device and liquid heterogeneity cause great variability in nicotine flux and toxicants emitted. These data demonstrate the need to examine device and liquid characteristics to develop empirically informed, health-promoting regulatory policies. Policies may include setting product standards such that ECIG products cannot (1) have nicotine fluxes much greater than that of a cigarette to decrease the risk of dependence, (2) have nicotine fluxes that are very low and thus would have minimal appeal to cigarette smokers and may serve as starter products for youth or nontobacco users, and (3) emit large amounts of particulate matter and other toxicants.

Electronic cigarette use intensity measurement challenges and regulatory implications.

Assessing tobacco use intensity allows researchers to examine tobacco use in greater detail than assessing ever or current use only. Tobacco use intensity measures have been developed that are specific to tobacco products, such as asking smokers to report number of cigarettes smoked per day. However, consensus on electronic cigarette use intensity measures that can be used for survey research has yet to be established due to electronic cigarette product and user behavior heterogeneity. While some survey measures that attempt to assess electronic cigarette use intensity exist, such as examining number of 'times' using an electronic cigarette per day, number of puffs taken from an electronic cigarette per day, volume of electronic cigarette liquid consumed per day, or nicotine concentration of electronic cigarette liquid, most measures have limitations. Challenges in electronic cigarette measurement often stem from variations across electronic cigarette device and liquid characteristics as well as the difficulty that many electronic cigarette users have regarding answering questions about their electronic cigarette device, liquid, or behavior. The inability for researchers to measure electronic cigarette use intensity accurately has important implications such as failing to detect unintended consequences of regulatory policies. Development of electronic cigarette use intensity measures, though not without its challenges, can improve understanding of electronic cigarette use behaviors and associated health outcomes and inform development of regulatory policies.

Impact of Canada's menthol cigarette ban on quitting among menthol smokers: pooled analysis of pre-post evaluation from the ITC Project and the Ontario Menthol Ban Study and projections of impact in the USA.

INTRODUCTION: Between 2015 and 2018, Canada banned menthol cigarettes. This study pooled data from two pre-post cohort studies (the Ontario Menthol Ban Study, and the International Tobacco Control Policy Evaluation (ITC) Canada Survey, conducted in seven provinces) to derive more precise estimates of the impact of Canada's menthol ban on quitting and to apply these estimates to project the impact of a menthol ban in the USA. METHODS: Weighted multivariable logistic analyses compared post-ban quit success of menthol smokers with non-menthol smokers (for daily smokers and for all (daily + non-daily) smokers), controlling for sex, age, ethnicity, education, baseline smoking status, baseline cigarettes per day and study regions. Projections to the USA were created by multiplying the effect size of the Canadian menthol ban on quitting (percentage of increased quitting among menthol smokers) by the number of menthol smokers overall and among African Americans, from the 2019 National Survey on Drug Use and Health. RESULTS: After the menthol cigarette ban, menthol smokers were more likely than non-menthol smokers to have quit smoking among daily smokers (difference=8.0%; 95% CI: 2.4% to 13.7%,p=0.005) and all (daily+non-daily) smokers (difference=7.3%; 95% CI: 2.1% to 12.5%,p=0.006). The projected number of smokers who would quit after a US menthol ban would be 789 724 daily smokers (including 199 732 African Americans) and 1 337 988 daily+non-daily smokers (including 381 272 African Americans). CONCLUSIONS: This pooled analysis of Canada's menthol cigarette ban provides the foundation for estimating the impact of menthol bans in the USA and other countries. Projections suggest that a US menthol cigarette ban would have a substantial impact on increasing quitting.

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

BACKGROUND: Co-use of tobacco and cannabis and dual use of cigarettes and e-cigarettes are very common among young adults. However, it is unclear whether co-use of cigarettes, e-cigarettes, and/or cannabis is associated with higher levels of nicotine dependence than cigarette-only use. We investigated the relationship between cigarette/nicotine dependence and co-use of tobacco and cannabis among 4 groups of cigarette smokers aged 18-35: cigarette-only smokers, cigarette-e-cigarette (CIG-ECIG) co-users, cigarette-cannabis (CIG-CAN) co-users, and cigarette-e-cigarette-cannabis (CIG-ECIG-CAN) co-users. METHODS: Data were from a 2018 cross-sectional survey based on a national convenience sample of smokers aged 18-35 (n = 315). Cigarette/nicotine dependence was measured by the Fagerstrom Test of Nicotine Dependence (FTND) and e-cigarette dependence was measured by the Penn State E-cigarette Dependence Index. Bivariate analyses examined sociodemographic and tobacco/other substance use characteristics by co-use status and multivariable linear regression assessed the relationship between co-use and nicotine dependence. RESULTS: In the sample, 27.6% were cigarette-only smokers, 24.8% were CIG-ECIG, 27.6% were CIG-CAN, and 20.0% were CIG-ECIG-CAN co-users. Significant differences were observed in sociodemographic and tobacco/other substance use characteristics by co-use status. E-cigarette co-users had low e-cigarette dependence, but moderate FTND scores. In adjusted analyses, only CIG-ECIG co-use was associated with higher FTND scores compared to cigarette-only smoking. However, CIG-ECIG and CIG-ECIG-CAN co-use were associated with higher FTND scores compared to CIG-CAN co-use. CONCLUSIONS: Co-use of cigarettes and e-cigarettes was associated with greater nicotine dependence among smokers aged 18-35. Additional research is needed to understand the underlying mechanisms of these relationships and inform prevention efforts.

Electronic Cigarette Users' Reactions and Responses to a Hypothetical Nicotine Concentration Reduction in Electronic Cigarette Liquids.

Background: Regulations limiting nicotine in electronic cigarettes (e-cigarettes) have been proposed or implemented. Little is known about e-cigarette users' reactions to reducing e-cigarette liquid nicotine concentration. Methods: We used concept mapping to describe e-cigarette users' reactions to a 50% reduction in the nicotine concentration of their e-cigarette liquids. In 2019, current e-cigarette users who used e-cigarette liquid with greater than 0 mg/ml nicotine concentration completed an online study. Participants (n = 71, mean age = 34.9 (SD = 11.0), 50.7% women), brainstormed statements completing a prompt: "If the e-liquid that I use now in my e-cigarette/vaping device was only available in half the nicotine concentration or amount that I use now, a specific action I would take or a specific reaction I would have is…", Participants then sorted a final list of 67 statements into piles of similar content and rated statements on how true the statements would be for them. Multidimensional scaling and hierarchical cluster analyses identified thematic clusters. Results: Eight clusters were identified: (1) Replacement Product Seeking, (2) Mental Preparations and Expectations, (3) Use the New Liquid, (4) Information Seeking, (5) Compensation Behaviors, (6) Opportunity for E-Cigarette Reduction, (7) Physical and Psychological Effects, and (8) Replacement with non-E-Cigarette Products and Behaviors. Cluster ratings suggested many participants would search for other e-cigarette products/liquids to replace their current liquids, but using other tobacco products (e.g., cigarettes) may be less likely. Conclusions: If nicotine concentrations were decreased in e-cigarette liquids, e-cigarette users may attempt to purchase different e-cigarette products or modify their products to achieve desired effects.

Increased JUUL Emissions from Initial Puffs after Removing and Reinserting Pod.

Standard laboratory electronic cigarette (ECIG) puffing protocols that do not consider user behaviors, such as removing and resinserting a pod, may underestimate emissions. This study compared JUUL emissions from four 10-puff bout procedures. We generated ECIG aerosol in a chamber using a JUUL device and measured concentrations of particulate matter ≤2.5 µm in diameter (PM2.5). The JUUL pod was removed and reinserted 0 times, 1 time, 4 times, and 9 times in experiments 1-4, respectively. Mean real-time PM2.5 concentration was 65.06 µg/m3 (SD = 99.53) for experiment 1, 375.50 µg/m3 (SD = 346.45) for experiment 2, 501.94 µg/m3 (SD = 450.00) for experiment 3, and 834.69 µg/m3 (SD = 578.34) for experiment 4. In this study, removing and reinserting a JUUL pod resulted in greater PM2.5 concentrations compared to puffing protocols in which the JUUL pod was not removed and reinserted. ECIGs should be examined and evaluated based on ECIG users' real-world behaviors.

A national assessment of on-premise drinking establishments near public universities: Drink prices, drink specials, indoor tobacco use, and state-level alcohol laws.

BACKGROUND: Inexpensive drinks and price promotions increase alcohol consumption and have been observed at on-premise drinking establishments near large colleges. Some bars may sell tobacco products and allow indoor tobacco use to encourage patrons to stay and drink more. This study examined drink prices/specials and associated practices of on-premise drinking establishments including tobacco sales and policies regarding tobacco use. METHODS: In 2018, telephone calls about prices/practices were made to 403 randomly selected bars/nightclubs within 2 miles of large residential universities in each U.S. state. The Alcohol Policy Information System provided data on state-level alcohol laws. Multivariable linear and logistic regression models examined associations between alcohol prices/specials, state laws, and establishment practices. RESULTS: The average price for the least expensive draft beer and a vodka shot at each location were $3.62 (SD = $1.15) and $4.77 (SD = $1.16), respectively. Most establishments (65%) had happy hour specials, 6% had 2-for-1 specials, 91% sold food, 9% sold cigarettes, 8% allowed smoking indoors, and 18% permitted electronic cigarette (e-cigarette) use indoors. Allowing e-cigarette use indoors (b = -0.54) and selling cigarettes (b = -0.79) were associated with lower vodka prices; allowing cigarette smoking indoors (b = -0.46) was associated with lower beer prices. Lower beer prices (OR = 1.38), selling food (OR = 2.97), and no state law banning happy hour specials altogether (OR = 4.24) or with full-day price reduction exemptions (OR = 12.74) were associated with higher odds of having happy hour specials. Allowing e-cigarette use indoors was associated with having 2-for-1 specials (OR = 6.38). CONCLUSION: In bars near large public universities, beers and shots were often available for less than $5 and drink specials were prevalent. Further, some establishments allowed tobacco use indoors and/or sold cigarettes. Laws that increase alcohol taxes, set minimum drink prices, and ban the sale and indoor use of tobacco products at on-premise drinking locations are important harm reduction tools.

Electronic cigarette users' reactions and responses to a hypothetical ban of flavoured electronic cigarette liquids.

BackgroundRegulations have been proposed to limit e-cigarette flavours, but limited research has examined potential impacts of such policies. This study examined adult e-cigarette users' reactions to a hypothetical e-cigarette flavour ban. METHODS: In 2019, a convenience sample of current e-cigarette users in the USA (n=81, 53.1% women, mean age=37.6, 59.3% dual users of cigarettes) completed an online concept mapping study. Participants provided statements describing anticipated reactions to a hypothetical policy in which only tobacco, menthol or unflavoured e-cigarettes were available for purchase. Seventy-one unique statements were generated. Participants sorted statements into thematic groups and rated statements on how likely they would be to have each reaction. Multidimensional scaling was used to identify thematic clusters of statements. RESULTS: Twelve clusters were identified: negative reaction, take action against flavour limitation, youth prevention effectiveness perception, tolerance, acceptance, willingness to try new flavours, maintain vaping, reduce vaping, new flavours as vaping cessation transition, alternative sources for banned flavours, do-it-yourself mixing behaviours and alternative tobacco products. The highest rated cluster (negative reaction) described being angry or upset that flavours were banned, while the lowest rated clusters related to quitting/reducing e-cigarette use or switching to other tobacco products. Non-tobacco or non-menthol/mint flavoured e-cigarette users had higher ratings for clusters describing negative sentiment for the hypothetical policy. CONCLUSIONS: Some e-cigarette users may dislike an e-cigarette flavour ban; however, some e-cigarette users would likely be willing to use tobacco, menthol or unflavoured e-cigarette liquids with lower likelihood of quitting vaping or switching to other tobacco products.

Electrical features, liquid composition and toxicant emissions from 'pod-mod'-like disposable electronic cigarettes.

INTRODUCTION: Use of flavoured pod-mod-like disposable electronic cigarettes (e-cigarettes) has grown rapidly, particularly among cost-sensitive youth and young adults. To date, little is known about their design characteristics and toxicant emissions. In this study, we analysed the electrical and chemical characteristics and nicotine and pulmonary toxicant emission profiles of five commonly available flavoured disposable e-cigarettes and compared these data with those of a JUUL, a cartridge-based e-cigarette device that pod-mod-like disposables emulate in size and shape. METHODS: Device construction, electrical power and liquid composition were determined. Machine-generated aerosol emissions including particulate matter, nicotine, carbonyl compounds and heavy metals were also measured. Liquid and aerosol composition were measured by high-performance liquid chromatography, gas chromatography-mass spectrometry/flame ionisation detection, and inductively coupled plasma mass spectrometry. RESULTS: We found that unlike JUUL, disposable devices did not incorporate a microcontroller to regulate electrical power to the heating coil. Quality of construction varied widely. Disposable e-cigarette power ranged between 5 and 9 W and liquid nicotine concentration ranged between 53 and 85 mg/mL (~95% in the protonated form). In 15 puffs, total nicotine yield for the disposables ranged between 1.6 and 6.7 mg, total carbonyls ranged between 28 and 138 µg, and total metals ranged between 1084 and 5804 ng. JUUL emissions were near the floors of all of these ranges. CONCLUSIONS: Disposable e-cigarettes are designed with high nicotine concentration liquids and are capable of emitting much higher nicotine and carbonyl species relative to rechargeable look-alike e-cigarettes. These differences are likely due to the lower quality in construction, unreliable labelling and lack of temperature control regulation that limits the power during operation. From a public health perspective, regulating these devices is important to limit user exposure to carbonyls and nicotine, particularly because these devices are popular with youth and young adults.

Assessing toxicant emissions from e-liquids with DIY additives used in response to a potential flavour ban in e-cigarettes.

SIGNIFICANCE: Electronic cigarettes (e-cigarettes) aerosolise liquids that contain nicotine, propylene glycol, glycerol and appealing flavours. In the USA, regulations have limited the availability of flavoured e-cigarettes in pod-based systems, and further tightening is expected. In response, some e-cigarette users may attempt to make their e-liquids (do-it-yourself, DIY). This study examined toxicant emissions from several aerosolised DIY e-liquids. METHODS: DIY additives were identified by reviewing users' responses to a hypothetical flavour ban, e-cigarette internet forums and DIY mixing internet websites. They include essential oils, cannabidiol, sucralose and ethyl maltol. E-liquids with varying concentrations and combinations of additives and tobacco and menthol flavours were prepared and were used to assess reactive oxygen species (ROS), carbonyl and phenol emissions in machine-generated aerosols. RESULTS: Data showed that adding DIY additives to unflavoured, menthol-flavoured or tobacco-flavoured e-liquids increases toxicant emissions to levels comparable with those from commercial flavoured e-liquids. Varying additive concentrations in e-liquids did not have a consistently significant effect on the tested emissions, yet increasing power yielded significantly higher ROS, carbonyl and phenol emissions for the same additive concentration. Adding nicotine to DIY e-liquids with sucralose yielded increase in some emissions and decrease in others, with freebase nicotine-containing e-liquid giving higher ROS emissions than that with nicotine salt. CONCLUSION: This study showed that DIY additives can impact aerosol toxicant emissions from e-cigarettes and should be considered by policymakers when restricting commercially available flavoured e-liquids.

Prior Daily Menthol Smokers More Likely to Quit 2 Years After a Menthol Ban Than Non-menthol Smokers: A Population Cohort Study.

BACKGROUND AND AIMS: The province of Ontario, Canada, banned the use of menthol-flavored tobacco products as of January 1, 2017. This study aims to assess the longer-term impact of a menthol ban on smoking behavior at 2 years, which is unknown. METHODS: Population cohort study with baseline survey (n = 1821) conducted September-December 2016 and follow-up survey January-August 2019 among current smokers in Ontario (16+) prior to the menthol ban. Poisson regression was used to assess the probability of quitting smoking by pre-ban menthol status, controlling for differences in smoking and demographic characteristics, with multiple imputations used to address missing data. FINDINGS: Menthol smokers were more likely to report having quit smoking (12% [daily menthol] and 10% [occasional menthol] vs. 3% [non-menthol]; p < .001) than non-menthol smokers in the 2 years after a menthol ban. After adjustment for smoking and demographic characteristics, daily menthol smokers had higher likelihood of quitting smoking (adjusted relative risk [ARR] 2.08; 95% confidence interval [CI] 1.20-3.61) and reported more quit attempts (ARR 1.45; 95% CI 1.15-1.82). Among those who attempted to quit, menthol smoking was not associated with relapse (daily ARR = 0.96; 95% CI: 0.86, 1.07; occasional ARR = 0.99; 95% CI: 0.90, 1.08). However, there was a statistically significant interaction among menthol users who reported using other flavored tobacco products 1 year after the ban (ARR = 0.26 [95% CI: 0.08, 0.90]). CONCLUSIONS: The study found increased probability of quitting among daily menthol smokers and more quit attempts among daily and occasional menthol smokers compared with non-menthol smokers in Ontario 2 years after the implementation of a menthol ban. IMPLICATIONS: This study examines quitting behavior 2 years after a menthol ban in Ontario, Canada. Those who were daily menthol smokers prior to the ban were more likely to quit smoking and make more quit attempts in the 2 years after the ban. While there was no difference in the likelihood of relapse between menthol and non-menthol smokers among those who attempt to quit, there were indications that pre-ban daily menthol smokers who used other tobacco products after the ban were likely to quit.

Health Claims, Marketing Appeals, and Warnings on Popular Brands of Waterpipe Tobacco Packaging Sold in the United States.

INTRODUCTION: Waterpipe tobacco (WT) smoking is associated with misperceptions of harm, especially among users. WT packaging contains imagery, flavor descriptors, and text claims that may contribute to misperceptions. The study goal was to characterize visual and text elements of WT packaging. AIMS AND METHODS: Using data from the U.S. Population Assessment on Tobacco and Health Study Wave 2 (October 2014-October 2015), we identified the 10 most popular WT brands. For each brand, we identified available flavors, including flavor collections with unique packaging elements. We randomly selected 10 flavors per brand for purchase (March-April 2018). We conducted descriptive content analysis to code all textual and visual design elements of each package. RESULTS: Over half (54%) of WT packages had modified risk tobacco product (MRTP) claims prohibited by federal law, including substance-free MRTP claims (43%) and the descriptor "natural" (11%). No MRTP reduced exposure or reduced risk claims were found. Over a quarter (26%) of packaging including one or more of terms that may imply reduced harm including "fresh," "premium," "quality," and "pure." All packages included a text-only warning, yet none appeared on the primary display panel. Almost all packaging (99%) included imagery, with 72% including flavor imagery. The majority of packages (72%) included a smoking cue. The most popular marketing appeals were "well-made" (57%), "enjoyable" (55%), and "patriotic" (47%). CONCLUSIONS: Prohibited MRTP claims, other descriptors, and flavor imagery are common on WT packaging, despite federal law. Future research is needed to evaluate if this marketing contributes to misperceptions of reduced harm. IMPLICATIONS: Tobacco packaging is used to convey health-related messages, both explicitly and implicitly; however, information about WT packaging is virtually nonexistent. We conducted a content analysis of WT packaging from the 10 most popular US brands. Over half (54%) of packages had prohibited MRTP claims and over a quarter (26%) included one or more descriptors that may be perceived as implying reduced harm. Use of imagery, including smoking cues, was common. The widespread use of prohibited MRTP claims, other descriptors, and imagery on WT packaging may contribute to misperceptions of reduced harm.

Characterization of Electronic Cigarette Warning Statements Portrayed in YouTube Videos.

INTRODUCTION: In 2018, the United States Food and Drug Administration (FDA) required that electronic cigarette (e-cigarette) manufacturers, packagers, importers, distributors, and retailers display an addictive or alternate warning statement on e-cigarette visual advertisements. Few studies have investigated the FDA-mandated and other warnings on social media. This study examined the prevalence and content of warning statements in e-cigarette-related YouTube videos. METHODS: In 2019, The Virginia Commonwealth University Center for the Study of Tobacco Products conducted bi-monthly (February-June) YouTube searches by relevance and view count to identify e-cigarette-related videos. Overall, 178 videos met the inclusion criteria. Staff coded each video for the presence of a visual/verbal warning statement, warning statement type (eg, FDA-mandated, addiction/tobacco, safety/toxic exposure, health effects), sponsorship, and tobacco product characteristics. A data extraction tool collected the video URL, title, upload date, and number of views, likes/dislikes, and comments. RESULTS: Only 5.1% of videos contained FDA-mandated and 21.9% contained non-mandated warnings. All videos with FDA-mandated and 46.2% of non-mandated warnings were represented visually. Only 13.1% of industry-sponsored videos uploaded after the mandate effective date had an FDA-mandated warning statement and videos with FDA-mandated and non-mandated (v. no) warnings had significantly fewer views, likes, dislikes, and comments. Among all non-mandated warnings, 31.3% featured an addiction/tobacco, 18.8% a safety/toxic exposure, and 37.5% a health effects warning. CONCLUSIONS: The prevalence of FDA-mandated warning statements in e-cigarette related YouTube videos was low. FDA enforcement of the warning statement mandate on YouTube could increase the public's understanding of the addictive nature of nicotine in e-cigarettes. IMPLICATIONS: The FDA has the authority to regulate the advertisement and promotion of e-cigarettes on the Internet. These data can inform future FDA requirements related to the language content and visual representation of addiction/tobacco, safety/exposure, and health effects warning statements that appear in YouTube videos and other visual social media popular among young people. Such data would help consumers make informed decisions about purchasing e-cigarette products, using e-cigarettes, and avoiding unintentional harm related to e-cigarettes. In addition, these data may help social media platforms make decisions on whether they will prohibit advertisements that promote or facilitate the sale of tobacco products.

"Taking Up a New Problem": Context and Determinants of Pod-Mod Electronic Cigarette Use Among College Students.

In this article, we explore the context and determinants of pod-mod electronic cigarette (ECIG) use among college students aged 18 to 24 in the United States. We conducted semi-structured interviews with 55 students (M age = 20 years, 56% female, 65% White) on three campuses in the Northwest, Midwest, and Southeast. We performed a thematic analysis. Students described ECIG use by peers, increased ECIG exposure and prevalence in college, and school-related stress as reasons for vaping. Pod-mod features such as convenience, design, easy concealment, and flavors made vaping more appealing. Negative aspects of vaping included addiction, cost, and possible health consequences. Most viewed vaping as a college behavior and intended to quit after graduation. Our results show that the college environment plays a major role in young adult ECIG use. These findings can inform the development of targeted interventions to prevent and control uptake of ECIGs by young adults.

User-Perceived Negative Respiratory Symptoms Associated with Electronic Cigarette Use.

INTRODUCTION: Electronic cigarettes (ECIGs) expose users to an aerosol containing chemicals, which could affect the respiratory system negatively. This study examined negative respiratory symptoms associated with ECIG use. METHODS: In 2019, adult current ECIG users from 24 US states who reported experiencing negative respiratory symptoms from ECIG use (n = 49; 44.9% women; mean age = 35.2, SD = 11.5) completed an online survey and brainstormed statements that completed the prompt: "A specific negative effect or symptom related to my breathing, nose, mouth, throat, or lungs that I have experienced from vaping/using my e-cigarette is..." Participants sorted the final list of 56 statements into groups of similar content and rated statements on how true they were for them. Multidimensional scaling analysis identified thematic clusters. RESULTS: Eight ECIG use respiratory symptom clusters identified in analysis included Mucus and Congestion, Fatigue, Throat Symptoms, Breathing Problems, Mouth Symptoms, Chest Symptoms, Illness Symptoms, and Nose and Sinus Symptoms. Highly rated (ie, most common) symptoms included dry throat or mouth, fatigue during physical activity, coughing, shortness of breath, excessive phlegm, and bad taste in mouth. Mean cluster ratings did not differ based on lifetime cigarette smoking status (100 lifetime cigarettes smoked), but current cigarette smokers (ie, dual users) rated the Fatigue, Breathing Problems, Mucus and Congestion, and Nose and Sinus Symptoms clusters higher than noncurrent cigarette smokers. CONCLUSIONS: Participant-identified respiratory symptoms perceived to be ECIG related, many similar to cigarette smoking symptoms. Future research should assess if these symptoms are associated with other negative health outcomes. IMPLICATIONS: ECIG use exposes users to chemicals that may have negative health impacts on the respiratory system. Limited research has examined the broad range of negative respiratory symptoms associated with e-cigarette use. This study identified that ECIG-cigarette users perceive their ECIG use to be associated with negative respiratory symptoms. Many e-cigarette user-reported negative respiratory symptoms are similar to those associated with cigarette smoking, though some appear unique to e-cigarette use. Future research should continue to monitor respiratory symptoms reported by ECIG users and whether these are associated with health outcomes over time.

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.