Awareness of and receptivity to FDA's point-of-sale tobacco public education campaign.

The purpose of the study was to assess awareness of and receptivity to FDA's point-of-sale (POS) tobacco public education campaign for adult cigarette smokers called Every Try Counts; it was the first multi-county POS campaign in the U.S. The design was a county-level treatment-control three-wave longitudinal design. The setting was 15 treatment and 15 control counties. Subjects were smokers ages 25 to 54 (N = 3,628). 4,145 individuals screened in as eligible; 3,628 (87.5% response rate) completed the Wave 1 questionnaire (Wave 2: n = 2,812; Wave 3: n = 2,571; retention 70.9%). Measures were self-reported brand and ad awareness (saw any ad a few times or more) and receptivity (5-item perceived effectiveness scale). The analysis included descriptive analyses of receptivity; bivariate analyses of awareness by treatment group; and covariate- and time-adjusted logistic regression models to determine changes in awareness attributable to the campaign. Receptivity was moderate and differed significantly by race/ethnicity. As was the case for all waves, at wave 3, ad awareness was significantly higher in treatment (53.3%) than control counties (36.1%, p < .05). In regression models, brand (OR = 1.53, 95% CI: 1.26-1.86) and ad (OR = 1.74, 95% CI: 1.39-2.16) awareness were significantly higher in treatment than control counties. Every Try Counts generated a moderate level of receptivity and attention from cigarette smokers. Limitations include self-reports of campaign awareness and generalizability to a small number of U.S. counties.

The Impact of the Tips from Former Smokers® Campaign on Reducing Cigarette Smoking Relapse.

Evidence-based mass-reach health communication campaigns can increase tobacco cessation, use of cessation resources such as quitlines, and change tobacco-related social norms. These interventions have been associated with a lower likelihood of cigarette smoking relapse in studies conducted internationally; however, no studies have assessed this outcome for a national campaign in the United States. This study examined the relationship between Tips from Former Smokers® (Tips®) campaign exposure and the odds of cigarette smoking relapse among adults who formerly smoked. Using data from the 2014 to 2019 Tips longitudinal campaign surveys, we estimated first episode of relapse (versus remaining a former smoker) as a function of Tips gross rating points (GRPs, a measure of media exposure). Higher levels of Tips GRPs were associated with lower odds of relapse (aOR = 0.63, 95% CI: 0.50-0.78). These results suggest that the Tips campaign may reduce smoking relapse, in addition to the established effect of increasing smoking cessation. Former smokers can be considered a secondary target audience for smoking cessation mass media campaigns, and mass media campaigns could be considered a component of smoking relapse prevention efforts.

Cannabis legalization and driving under the influence of cannabis in a national U.S. Sample.

The relationship between cannabis legalization and traffic safety remains unclear. Physiological measures of cannabis impairment remain imperfect. This analysis used self-report data to examine the relationship between cannabis legalization and driving under the influence of cannabis (DUIC). Using a cross-sectional national sample (2016-2017) of 1,249 past-30-day cannabis users, we regressed self-reported DUIC (driving within three hours of "getting high") on cannabis legalization (recreational and medical (recreational), medical only (medical), or no legal cannabis), adjusting for demographics, days of use (past 30 days), days of use*legal status, calibration weights, and geographic clustering. The risk of DUIC in recreational (risk ratio [RR] = 0.41, 95% confidence interval (CI):0.23-0.72) and medical (RR = 0.39, 95% CI:0.20-0.79) states was lower than in states without legal cannabis, with one exception. Among frequent cannabis users (≥20 days per month), there was a significantly lower risk of DUIC for those living in recreational states (RR = 0.70, 95% CI: 0.49-0.99), but not for those living in medical states (RR = 0.87, 95% CI: 0.60-1.24), compared to users living in states without legal cannabis. The risk of self-reported DUIC was lower in recreational and medical cannabis states compared to states without legal cannabis. The only exception was for frequent users in medical states, for whom there was no difference in risk compared to frequent users living in states without legal cannabis.

Effect of menthol cigarette and other menthol tobacco product bans on tobacco purchases in the RTI iShoppe virtual convenience store.

OBJECTIVE: To test how a potential US ban of menthol products or replacement with 'green' products and ads could influence tobacco purchases. METHODS: US adult menthol smokers (N=1197) were recruited via an online panel and randomly assigned to complete a shopping task in one of four versions (experimental conditions) of the RTI iShoppe virtual store: (1) no ban, (2) replacement of menthol cigarettes and ads with green replacement versions, (3) menthol cigarette ban and (4) all menthol tobacco product ban. Logistic regressions assessed the effect of condition on tobacco purchases. RESULTS: Participants in the menthol cigarette ban (OR=0.67, 95% CI 0.48 to 0.92) and all menthol product ban conditions (OR=0.60, 95% CI 0.43 to 0.83) were less likely to purchase cigarettes of any type than participants in the no ban condition. Participants in the green replacement (OR=1.74, 95% CI 1.13 to 2.70), menthol cigarette ban (OR=3.40, 95% CI 2.14 to 5.41) and all menthol product ban conditions (OR=3.14, 95% CI 1.97 to 5.01) were more likely to purchase a cigarette brand different from their usual brand than participants in the no ban condition. CONCLUSIONS: Our findings suggest that menthol bans could have great public health impact by reducing cigarette purchases. However, tobacco marketing strategies, such as creating green (or other replacement) versions of menthol cigarettes, may undermine public health benefits of a menthol ban by prompting purchases of non-menthol cigarettes. Our findings highlight the importance of taking tobacco marketing tactics into consideration in tobacco product regulation.

Differential Relationship between Tobacco Control Policies and U.S. Adult Current Smoking by Poverty.

The study's purpose was to identify differences in the relationship between tobacco control policies and smoking by poverty. We matched state smoke-free air law coverage (SFALs), tobacco control funding (TCF), and cigarette taxes with individual current smoking and demographics from supplements to the Current Population Survey (1985-2015). We regressed (logistic) smoking on policy variables, poverty (<138% of poverty line versus ≥138% of poverty line), interactions of policy and poverty, and covariates, presenting beta coefficients instead of odds ratios because it is difficult to interpret interactions using odds ratios (they are ratios of odds ratios). We coded SFALs as (1) proportion of state covered by 100% workplace, restaurant and bar laws (SFAL-All) or (2) proportion of state covered by workplace laws (SFAL-WP) and proportion covered by restaurant or bar laws (SFAL-RB). In the SFAL-All model, SFAL-All (Beta coeff: -0.03, 95% CI: -0.06, -0.002), tax (Coeff: -0.06, 95% CI: -0.07, -0.05), and TCF (Coeff: -0.01, 95% CI: -0.01, -0.001) were associated with less smoking. In this model, the interaction of SFAL-All by poverty was significant (Coeff: 0.08, 95% CI: 0.02, 0.13). In the SFAL-WP/RB model, SFAL-RB (Coeff: -0.05, 95% CI: -0.08, -0.02), tax (Coeff: -0.05, 95% CI: -0.06, -0.04), and TCF (Coeff: -0.01, 95% CI: -0.01, -0.00) were significant. In the same model, SFAL-WP (Coeff: 0.09, 95% CI: 0.03, 0.15), SFAL-RB (Coeff: -0.14, 95% CI: -0.19, -0.09), and TCF (Coeff: 0.01, 95% CI: 0.00, 0.02) interacted with poverty. Tax by poverty was of borderline significance in this model (Coeff = 0.02, 95% CI: -0.00, 0.04, p = 0.050). Among adults, SFALs, TCF, and tax were associated with less current smoking, and SFALs and TCF had differential relationships with smoking by poverty.

Antismoking Advertisements and Price Promotions and Their Association With the Urge to Smoke and Purchases in a Virtual Convenience Store: Randomized Experiment.

BACKGROUND: Point of sale (POS) advertising is associated with smoking initiation, current smoking, and relapse among former smokers. Price promotion bans and antismoking advertisements (ads) are 2 possible interventions for combating POS advertising. OBJECTIVE: The purpose of this analysis was to determine the influence of antismoking ads and promotions on urges to smoke and tobacco purchases. METHODS: This analysis examined exposure to graphic (graphic images depicting physical consequences of tobacco use) and supportive (pictures of and supportive messages from former smokers) antismoking ads and promotions in a virtual convenience store as predictors of urge to smoke and buying tobacco products among 1200 current cigarette smokers and 800 recent quitters recruited via a Web-based panel (analytical n=1970). We constructed linear regression models for urge to smoke and logistic regression models for the odds of purchasing tobacco products, stratified by smoking status. RESULTS: The only significant finding was a significant negative relationship between exposure to supportive antismoking ads and urge to smoke among current smokers (beta coefficient=-5.04, 95% CI -9.85 to -0.22; P=.04). There was no significant relationship between graphic antismoking ads and urge to smoke among current smokers (coefficient=-3.77, 95% CI -8.56 to 1.02; P=.12). Neither relationship was significant for recent quitters (graphic: coefficient=-3.42, 95% CI -8.65 to 1.81; P=.15 or supportive: coefficient=-3.82, 95% CI -8.99 to 1.36; P=.20). There were no significant differences in urge to smoke by exposure to promotions for current smokers (coefficient=-1.06, 95% CI -4.53 to 2.41; P=.55) or recent quitters (coefficient=1.76, 95% CI -2.07 to 5.59; P=.37). There were also no differences in tobacco purchases by exposure to graphic (current smokers: coefficient=0.93, 95% CI 0.67 to 1.29; P=.66 and recent quitters: coefficient=0.73, 95% CI 0.44 to 1.19; P=.20) or supportive (current smokers: coefficient=1.05, 95% CI 0.75 to 1.46; P=.78 and recent quitters: coefficient=0.73, 95% CI 0.45 to 1.18; P=.20) antismoking ads or price promotions (current smokers: coefficient=1.09, 95% CI 0.86 to 1.38; P=.49 and recent quitters: coefficient=0.90, 95% CI 0.62 to 1.31; P=.60). CONCLUSIONS: The results of this analysis support future research on the ability of supportive antismoking ads to reduce urges to smoke among current cigarette smokers. Research on urges to smoke has important tobacco control implications, given the relationship between urge to smoke and smoking cigarettes, time to next smoke, and amount smoked.

Warning Labels on Sugar-sweetened Beverages: An Eye Tracking Approach.

Objectives: In this study, we examined visual attention of a warning label on a sugar-sweetened beverage (SSB) and its effects on visual attention to SSB product descriptors and perceptions of SSB using eye tracking technology. Methods: We had 180 young adults view an image of a generic soda can with or without a text warning on a computer monitor. Results: Participants spent less time looking at marketing elements on the can in the "Warning" condition compared to the "No warning" (control) condition. Compared to the control, participants in the "Warning" condition viewed the sugar-sweetened beverage as less healthy (1.78 warning vs 2.21 control, p < .01) and believed that drinking SSBs contributed to diabetes (5.70 warning vs 5.27 control, p < .01). Visual attention to warning label was associated with correct recall of the warning and opting out of purchasing the can. Conclusions: Textual warning on SSB reduced visual attention to marketing elements on the can. Although there were few statistically significant differences between the conditions on most measures of product appeal or risk perception, warnings increased some perceived risks of SSBs indicating that warning labels on SSBs might be a promising strategy in informing consumers, particularly young adults, about risks of added sugars.

Smokers' Attention to Point-of-Sale Antismoking Ads: An Eye-tracking Study.

OBJECTIVES: We used eye-tracking to examine smokers' visual attention in one of 4 antismoking ad contexts (alone, next to cigarette ad, tobacco display, or cooler). Participants viewed 4 ad types (graphic, intended emotive, and benefits of quitting-graphic ads, and benefits of quitting-informational ads), each with 3 areas of interest (AOI) (anti-ad image, anti-ad text, and other text). METHODS: Current smokers (N = 153) viewed ads for 10 seconds each. Multivariable random effect linear regressions with post-test comparisons (with sidak-adjusted p-values) were used to test for differences in fixations and dwell time by ad context and type while adjusting for covariates. Visual attention was adjusted by percentage of anti-ad area taken up by each AOI. RESULTS: Adjusting for covariates, there were no differences by ad context (p > .05). Fixations and dwell time were greatest for the image of the benefits of quitting-graphic ad, the text of the graphic ad, and the other text of the intended emotive ad (all ps < .005). Conclusions: Visual attention to antismoking ads did not vary by ad context but varied significantly by ad type.

Examining Daily Electronic Cigarette Puff Topography Among Established and Nonestablished Cigarette Smokers in their Natural Environment.

Introduction: Understanding exposures and potential health effects of e-cigarettes is complex. Users' puffing behavior, or topography, affects function of e-cigarette devices (eg, coil temperature) and composition of their emissions. Users with different topographies are likely exposed to different amounts of any harmful or potentially harmful constituents (HPHCs). In this study, we compare e-cigarette topographies of established cigarette smokers and nonestablished cigarette smokers. Methods: Data measuring e-cigarette topography were collected using a wireless hand-held monitoring device in users' everyday lives over 1 week. Young adult (aged 18-25) participants (N = 20) used disposable e-cigarettes with the monitor as they normally would and responded to online surveys. Topography characteristics of established versus nonestablished cigarette smokers were compared. Results: On average, established cigarette smokers in the sample had larger first puff volume (130.9 mL vs. 56.0 mL, p < .05) and larger puff volume per session (1509.3 mL vs. 651.7 mL, p < .05) compared with nonestablished smokers. At marginal significance, they had longer sessions (566.3 s vs. 279.7 s, p = .06) and used e-cigarettes more sessions per day (5.3 s vs. 3.5 s, p = .14). Established cigarette smokers also used e-cigarettes for longer puff durations (3.3 s vs. 1.8 s, p < .01) and had larger puff volume (110.3 mL vs. 54.7 mL, p < .05) compared with nonestablished smokers. At marginal significance, they had longer puff interval (38.1 s vs. 21.7 s, p = .05). Conclusions: Our results demonstrate that topography characteristics differ by level of established cigarette smoking. This suggests that exposure to constituents of e-cigarettes depends on user characteristics and that specific topography parameters may be needed for different user populations when assessing e-cigarette health effects. Implications: A user's topography affects his or her exposure to HPHCs. As this study demonstrates, user characteristics, such as level of smoking, can influence topography. Thus, it is crucial to understand the topography profiles of different user types to assess the potential for population harm and to identify potentially vulnerable populations. This study only looked at topography of cigarette smokers using disposable e-cigarettes. Further research is needed to better understand potential variation in e-cigarette topography and resulting exposures to HPHCs among users of different e-cigarette devices and liquids.

New product trial, use of edibles, and unexpected highs among marijuana and hashish users in Colorado.

OBJECTIVE: This study examines the relationships between trial of new marijuana or hashish products and unexpected highs, and use of edible products and unexpected highs. METHODS: We conducted an online survey of 634 adult, past-year marijuana users in Colorado. We used logistic regression models to examine the relationship between new product trial or edible use and unexpected highs. RESULTS: In the first year that recreational marijuana was legal in Colorado, 71.4% of respondents tried a new marijuana or hashish product, and 53.6% used an edible product. Trial of new products was associated with greater odds of experiencing an unexpected high after controlling for age, gender, education, mental health status, current marijuana or hashish use, and mean amount of marijuana or hashish consumed in the past month (OR=2.13, p<0.001). Individuals who reported having used edibles had greater odds of experiencing an unexpected high, after controlling for the same set of variables (OR=1.56, p<0.05). CONCLUSION: People who try new marijuana or hashish products, or use edible marijuana or hashish products, are at greater risk for an unexpected high. It is possible that some negative outcomes associated with marijuana use and unexpected highs may be averted through a better understanding of how to use product packaging to communicate with consumers.

Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers.

BACKGROUND: E-cigarettes have rapidly increased in popularity in recent years, driven, at least in part, by marketing and word-of-mouth discussion on Twitter. Given the rapid proliferation of e-cigarettes, researchers need timely quantitative data from e-cigarette users and smokers who may see e-cigarettes as a cessation tool. Twitter provides an ideal platform for recruiting e-cigarette users and smokers who use Twitter. Online panels offer a second method of accessing this population, but they have been criticized for recruiting too few young adults, among whom e-cigarette use rates are highest. OBJECTIVE: This study compares effectiveness of recruiting Twitter users who are e-cigarette users and smokers who have never used e-cigarettes via Twitter to online panelists provided by Qualtrics and explores how users recruited differ by demographics, e-cigarette use, and social media use. METHODS: Participants were adults who had ever used e-cigarettes (n=278; male: 57.6%, 160/278; age: mean 34.26, SD 14.16 years) and smokers (n=102; male: 38.2%, 39/102; age: mean 42.80, SD 14.16 years) with public Twitter profiles. Participants were recruited via online panel (n=190) or promoted tweets using keyword targeting for e-cigarette users (n=190). Predictor variables were demographics (age, gender, education, race/ethnicity), e-cigarette use (eg, past 30-day e-cigarette use, e-cigarette puffs per day), social media use behaviors (eg, Twitter use frequency), and days to final survey completion from survey launch for Twitter versus panel. Recruitment method (Twitter, panel) was the dependent variable. RESULTS: Across the total sample, participants were recruited more quickly via Twitter (incidence rate ratio=1.30, P=.02) than panel. Compared with young adult e-cigarette users (age 18-24 years), e-cigarette users aged 25 to 34 years (OR 0.01, 95% CI 0.00-0.60, P=.03) and 35 to 44 years (OR 0.01, 95% CI 0.00-0.51, P=.02) were more likely to be recruited via Twitter than panel. Smokers aged 35 to 44 years were less likely than those aged 18 to 24 years to be recruited via Twitter than panel (35-44: OR 0.03, 95% CI 0.00-0.49, P=.01). E-cigarette users who reported a greater number of e-cigarette puffs per day were more likely to be recruited via Twitter than panel compared to those who reported fewer puffs per day (OR 1.12, 95% CI 1.05-1.20, P=.001). With each one-unit increase in Twitter usage, e-cigarette users were 9.55 times (95% CI 2.28-40.00, P=.002) and smokers were 4.91 times (95% CI 1.90-12.74, P=.001) as likely to be recruited via Twitter than panel. CONCLUSIONS: Twitter ads were more time efficient than an online panel in recruiting e-cigarette users and smokers. In addition, Twitter provided access to younger adults, who were heavier users of e-cigarettes and Twitter. Recruiting via social media and online panel in combination offered access to a more diverse population of participants.

Reasons for current E-cigarette use among U.S. adults.

E-cigarette use has increased rapidly among U.S. adults. However, reasons for use among adults are unclear. We assessed reasons for e-cigarette use among a national sample of U.S. adults. Data were collected via online surveys among U.S. adults aged 18 or older from April through June 2014. Descriptive and multivariate regression analyses were conducted to assess reasons for e-cigarette use among 2448 current e-cigarette users, by sociodemographic characteristics and product type. Assessed reasons included cessation/health, consideration of others, convenience, cost, curiosity, flavoring, and simulation of conventional cigarettes. Among current e-cigarette users, 93% were also current cigarette smokers. The most common reasons for e-cigarette use were cessation/health (84.5%), consideration of others (71.5%), and convenience (56.7%). The prevalence of citing convenience (adjusted prevalence ratio [aPR]=1.49) and curiosity (aPR=1.54) as reasons for e-cigarette use were greater among current cigarette smokers than nonsmokers (P<0.05). The prevalence of citing flavoring as a reason for use was greater among adults aged 18 to 24 (aPR=2.02) than 55 or older (P<0.05). Tank use was associated with greater prevalence of citing every assessed reason except convenience and curiosity. Cessation- and health-related factors are primary reasons cited for e-cigarette use among adults, and flavorings are more commonly cited by younger adults. Efforts are warranted to provide consumers with accurate information on the health effects of e-cigarettes and to ensure that flavoring and other unregulated features do not promote nicotine addiction, particularly among young adults.

Association between self-reports of being high and perceptions about the safety of drugged and drunk driving.

This study examines the relationship between self-reports of being high on marijuana and perceptions about driving high or drunk. Data were collected in 2014 from an online convenience sample of adult, past 30-day marijuana and hashish users in Colorado and Washington (n = 865). Respondents were asked, "Were you high or feeling the effects of marijuana or hashish when you took this survey?" Logistic regression was used to assess the relationship between being high and beliefs about driving high, controlling for demographics and marijuana use. Respondents who reported being high at the time of survey administration had higher odds of agreeing with the statements, "I can safely drive under the influence of marijuana" (OR = 3.13, P < 0.001) and "I can safely drive under the influence of alcohol" (OR = 3.71, P < 0.001) compared with respondents who did not report being high. Respondents who were high also had higher odds of being open to driving high under certain circumstances. Being high may influence perceptions about the safety of drugged and drunk driving. The effectiveness of public health messages to prevent drugged and drunk driving may depend in part on how persuasive they are among individuals who are high.

Correlates of Marijuana Drugged Driving and Openness to Driving While High: Evidence from Colorado and Washington.

AIMS: A potential unintended consequence of legalizing recreational marijuana is increased marijuana-related driving impairment. Some states where recreational marijuana is legal have begun implementing interventions to mitigate driving under the influence (DUI) of marijuana, including media campaigns to increase knowledge about DUI laws. However, little is known about the associations between knowledge of DUI laws and marijuana DUI behavior. In this study, we provide new data from a survey of marijuana users in Colorado and Washington to examine associations between marijuana drugged driving and two potential behavioral precursors of marijuana DUI. We also explore other factors that may influence marijuana DUI. METHODS: Data are from an online survey of marijuana users in Colorado and Washington. Respondents who reported any marijuana use in the past 30 days (n = 865) served as the analytic sample. We examined prevalence of two behavioral outcomes: (1) any driving of a motor vehicle while high in the past year and (2) driving a motor vehicle within 1 hour of using marijuana 5 or more times in the past month. Additional outcomes measuring willingness to drive while high were also assessed. Logistic regressions were used to estimate each outcome as a function of two multi-item scales measuring knowledge of the legal consequences of driving high and perceptions that driving while high is not safe. Additional covariates for potential confounders were included in each model. RESULTS: Prevalence of past-year driving while under the influence of marijuana was 43.6% among respondents. The prevalence of driving within 1 hour of using marijuana at least 5 times in the past month was 23.9%. Increased perception that driving high is unsafe was associated with lower odds of past-year marijuana DUI (OR = 0.31, P < 0.01) and lower past-month odds of driving 5 or more times within 1 hour of using marijuana (OR = 0.26, P < 0.01). Increased knowledge of marijuana DUI laws was also associated with lower odds of each of these outcomes (OR = 0.63, P < 0.01, OR = 0.69, P = 0.02, respectively). Post-estimation Wald tests confirmed the negative associations with marijuana DUI were greater in magnitude for safety perceptions than knowledge of DUI laws. Increased perceptions that driving while high is unsafe was associated with significantly lower willingness to drive after using marijuana while increased knowledge of marijuana DUI laws was not associated with these outcomes. CONCLUSIONS: Despite recent interventions targeting public awareness of the legal consequences of marijuana DUI, our results suggest that knowledge of these laws is a weaker predictor of DUI behavior than perceptions that driving high is unsafe. In addition, safety perceptions predict decreased openness to driving high while knowledge of DUI laws was not associated with openness. These findings suggest that interventions for reducing the incidence of marijuana DUI are likely to be more successful by targeting safety perceptions related to marijuana DUI rather than knowledge of DUI laws. We caution that because these data are limited to an online convenience sample, results may not be generalizable beyond our sample.