Compliance With US Federal Regulations on Waterpipe Tobacco Warnings on Packaging.

This cohort study reports the rate and degree of compliance with the Food and Drug Administration­mandated warning labels among waterpipe tobacco brands and products.

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.

Use of real-time monitors to evaluate the potential exposure of secondhand electronic cigarette particulate matter inside vehicles.

Electronic cigarette (ECIG) use continues to be highly prevalent, especially among youth and young adults. Potential exposure from secondhand ECIG particulate matter (PM) places bystanders in danger of inhaling harmful substances, especially in confined spaces. This study was conducted to measure the potential exposure from secondhand ECIG PM exposure in vehicles, with participants completing a 30-min ECIG use session in their own vehicle with their preferred ECIG device. Sessions included a 5-min, 10-puff directed bout (30-s interpuff interval), followed by a 25-min ad libitum bout in which participants could take as many puffs as desired. Real-time PM1, PM2.5, and PM10 (the 50% efficiency mass cut-off of that passes through a size-selective inlet at 1 µm, 2.5 µm, and 10 µm aerodynamic diameters, respectively) measurements were captured during the sessions using portable PM monitors (MiniWRAS, pDR, SidePak, and GeoAir2 low-cost monitors). A total of 56 participants with valid measurements were included in the study, with a total of 13 unique ECIG device brands, including Vuse Alto, Box Air Bar, ElfBar, Esco Bar, Aegis Legend, Hyde Edge, JUUL, Kang Onee Stick, Kang Onee Stick Plus, Nord X, Nord 2, Nord 3, and Vaporesso. During the 5-min directed bout, the highest real-time PM2.5 mean concentrations were 175 µg/m3 for the MiniWRAS, 1050 µg/m3 for pDR and 3314 µg/m3 for SidePak. The filter measurements were not detectable in most experiments, except for two participants, with one taking 205 puffs and the other taking 285 puffs, approximately 10 times the mean (30) puffs of all participants. The evaluation of GeoAir2 with the MiniWRAS showed a wide range of Pearson correlation coefficient (r) values, ranging from -0.03 to 1.00, for the 13 ECIG brands. The mass median diameter (0.31 µm-3.42 µm) and geometric standard deviation (2.47-8.21) were different based on the participants for the same ECIG brand.

Secondhand electronic cigarette aerosol in vehicles impacts indoor air quality.

BACKGROUND: Electronic cigarette (ECIG) use in vehicles represents a public health concern due to the potential for exposure to high concentrations of particulate matter (PM) and other toxicants. This study examined the impact of ECIG use on air quality in vehicles. METHODS: People who reported current ECIG use (n=60; mean age=20.5, SD=2.3) completed a brief survey and a 30-min ECIG use session in their own vehicle. Using a protocol similar to clinical laboratory studies involving tobacco use, participants took 10 directed puffs (i.e., a directed bout with one puff every 30s for 5min) followed by a 25-min ad libitum period in which participants took as many puffs as desired. PM 2.5µm in diameter or smaller (PM2.5) were measured using aerosol monitors set up to sample air from the breathing zone of the passenger seat and total puffs were recorded. The association between peak PM2.5 concentration and puff count was examined. RESULTS: Participants took a median 18 total puffs during the sessions. Median PM2.5 concentrations increased from 4.78µg/m3 at baseline to 107.40µg/m3 after the directed bout. Median peak PM2.5 concentration was 464.48µg/m3 and ranged from 9.56µg/m3 to 143,503.91µg/m3 (IQR=132.72-1604.68). After removing two extreme outliers for puff count and PM2.5 concentrations, puff count was significantly correlated with peak PM2.5 concentration during the ad libitum bout (r=0.32, p=0.015). CONCLUSIONS: ECIG use in vehicles impacts air quality negatively and may pose health risks to those present in vehicles when ECIG use is occurring.

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.

Absence of Age Verification for Online Purchases of Cannabidiol and Delta-8: Implications for Youth Access.

PURPOSE: This study assessed the age verification process for purchasing and shipping cannabidiol (CBD) and Delta-8 tetrahydrocannabinol products from online retailers. METHODS: We purchased CBD and Delta-8 products online from 20 brick-and-mortar shops located in the United States that sold products online and shipped products to consumers. We documented online age verifications at the time of purchase and whether identification or a signature was required at delivery. RESULTS: 37.5% of the CBD and 70.0% of the Delta-8 websites required the customer to confirm their age (18 + or 21+ years). Age verification or contact with the customer was not requested at the point of home delivery for all products. DISCUSSION: Methods for age verification at time of purchase are self-reported and easily circumvented. Policies and policy enforcement are needed to prevent youth access to CBD and Delta-8 products from online sources.

Reactions to a Hypothetical Menthol Cigarette Ban among Sexual- and Gender-Minoritized Communities: A Concept Mapping Study.

Menthol cigarette use is disproportionately higher among sexual- and gender-minoritized (SGM; 36%) individuals compared to cisgender, heterosexual (29%), individuals. The FDA has announced intentions to ban menthol in cigarettes, citing these use and health disparities as partial motivation. This study identified potential outcomes of a menthol cigarette ban among SGM individuals who smoke menthol cigarettes (N = 72). Potential outcomes were identified via concept mapping using the prompt: "If menthol in cigarettes was banned, a specific action I would take related to my tobacco use is…" Participants generated 82 response statements, sorted them, and rated them on personal relevance. Eight thematic clusters were identified: (1) Thoughtful Consideration of the Ban, (2) Negative Reactions to the Ban, (3) Positive Aspects of the Ban, (4) Strategies to Reduce Cravings, (5) Intent to Quit and Cessation Strategies, (6) Support-Seeking and Engagement in Positive Behaviors, (7) Strategies to Maintain Menthol-Flavored Product Use, and (8) Substance Use Alternatives to Menthol Cigarettes. Cluster differences based on sociodemographic factors, smoking behavior, and quitting interest were identified. Results provide insight into potential responses to a menthol cigarette ban and can contribute to public health prevention and intervention efforts, messaging campaigns, and support services for SGM people who smoke menthol cigarettes, specifically.

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.

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.

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.

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.

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.

Predictors of electronic cigarette dependence among non-smoking electronic cigarette users: User behavior and device characteristics.

INTRODUCTION: ECIGs differ in their ability to deliver nicotine to the user and, consequently, they may differ in their ability to produce dependence. This study examined individual device characteristics, device type, and user behaviors as predictors of ECIG dependence in a sample of never-smoking ECIG users. METHODS: Participants (N = 134) completed an online survey that assessed demographics, ECIG use behavior, and ECIG dependence as measured via the Penn State Electronic Nicotine Dependence Index (PSECDI) and E-cigarette Dependence Scale (EDS-4). Participants uploaded a picture of their personal ECIG device/liquid, which was coded by raters to identify product features. Multivariable linear regressions examined device characteristics (e.g., adjustable power, nicotine concentration) and device type (e.g., vape pen, mod, pod, modern disposable) as predictors of dependence controlling for demographics and user behaviors (e.g., ECIG use duration and frequency, other tobacco use). RESULTS: Longer durations of ECIG use and more use days/week were associated significantly with higher PSECDI (ß's = 0.91 and 1.90, respectively; p's < 0.01) and EDS-4 scores (ß's = 0.16 and 0.28, respectively; p's < 0.01). Higher nicotine concentrations were associated with higher PSECDI scores only (ß = 0.07, p =.011). Dependence scores did not differ as a function of ECIG device types after controlling for covariates. CONCLUSIONS: ECIG dependence was observed among the never-smoking ECIG users in this sample, regardless of their ECIG device/liquid features. Findings suggest that regulatory efforts aimed at reducing the dependence potential of ECIGs in never smokers should focus on overall nicotine emissions rather than product features.

Effects of E-Cigarette Liquid Ratios on the Gravimetric Filter Correction Factors and Real-Time Measurements.

Electronic cigarettes (ECIGs) generate high concentrations of particulate matter (PM), impacting the air quality inhaled by humans through secondhand exposure. ECIG liquids are available commercially and some users create their own "do-it-yourself" liquids, and these liquids often vary in the amounts of their chemical ingredients, including propylene glycol (PG) and vegetable glycerin (VG). Previous studies have quantified PM concentrations in ECIG aerosol generated from liquids containing different PG/VG ratios. However, the effects of these ratios on aerosol instrument filter correction factors needed to measure PM concentrations accurately have not been assessed. Thus, ECIG aerosol filter correction factors for multiple aerosol instruments (SMPS + APS, MiniWRAS, pDR, and SidePak) were determined for five different PG/VG ratios 1) 0PG/100VG, 2) 15PG/85VG, 3) 50PG/50VG, 4) 72PG/28VG, and 5) 90PG/10VG and two different PM sizes, PM1 (1 µm and smaller) and PM2.5 (2.5 µm and smaller). ECIG aerosols were generated inside a controlled exposure chamber using a diaphragm pump and a refillable ECIG device for all the ratios. In addition, the aerosol size distribution and mass median diameter were measured for all five ECIG ratios. PM2.5 correction factors (5-7.6) for ratios 1, 2, 3, and 4 were similar for the SMPS + APS combined data, and ratios 1, 2, 3 were similar for the MiniWRAS (~2), pDR (~0.5), and SidePak (~0.24). These data suggest different correction factors may need to be developed for aerosol generated from ECIGs with high PG content. The higher correction factor values for the 90PG/10VG ratio may have resulted from greater PG volatility relative to VG and sensor losses. The correction factors (ratios 1-4) for PM2.5 were SMPS + APS data (4.96-7.62), MiniWRAS (2.02-3.64), pDR (0.50-1.07), and SidePak (0.22-0.40). These data can help improve ECIG aerosol measurement accuracy for different ECIG mixture ratios.

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.

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.

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.

Combinations of electronic nicotine delivery system device and liquid characteristics among U.S. adults.

INTRODUCTION: The combination of electronic nicotine delivery systems (ENDS) device and liquid characteristics affect user nicotine and toxicant exposure, however population-level national survey studies have not reported device and liquid features jointly nor in detail. We examined combinations of characteristics of devices and liquids used by adult ENDS users. METHODS: U.S. adults (21+) using ENDS at least five days/week (n = 1,179) completed an online survey in 2020 and uploaded photos of their most used device and liquid. Device/liquid combinations were analyzed to determine and describe the most common pairings. Data were weighted to the U.S. population of adult daily ENDS users. RESULTS: Five device/liquid combinations accounted for 87 % of those reported. The most prevalent combination, reported by 36.2 %, was re-useable devices with adjustable settings and a tank refilled with a free-base nicotine liquid with a median nicotine concentration of 5.2 mg/mL. The second most frequent combination - re-usable devices with no adjustable settings and a nicotine salt liquid in a disposable pod or cartridge - was reported by 22.8 % and the median nicotine concentration was 49.4 mg/mL. Wattage, voltage and resistance varied across device/liquid combination. Overall, 66.5 % refilled their device's tank or pod and 64.2 % had the ability to manipulate device characteristics (power, airflow, and/or coil). CONCLUSIONS: Analyzing ENDS device/liquid combinations rather than each separately can allow researchers to better evaluate relationships between use and key outcomes, such as quitting cigarettes and abuse liability. Additional research may reveal whether certain combinations are more effective at helping smokers quit and which device/liquid combinations should remain on the market.