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.

Perceived Safety, Not Perceived Legality, Mediates the Relationship Between Cannabis Legalization and Drugged Driving.

Cannabis legalization has rapidly spread throughout the United States and is associated with multiple public health outcomes, including driving under the influence of cannabis (DUIC). To improve understanding of the relationship between legalization and DUIC, we tested two potential mediators of this relationship: perceived safety and perceived legality of driving high. We analyzed data from 1,236 current (past 30-day) cannabis users who were recruited from states with recreational, medical only, or no legal cannabis between 2016 and 2017 using address-based and social media samples. Using a generalized linear model and adjusting for cannabis legalization, demographics, living in a state with a cannabis-specific drugged driving law, frequency of cannabis use, and weights, we found that perceived safety (risk ratio [RR] = 2.60, 95% CI [1.88, 3.58]), but not perceived legality (RR = 0.96, 95% CI [0.67, 1.37]), was significantly associated with DUIC. Perceived safety mediated the relationship between legalization and DUIC (Coeff: -0.12, 95% CI [-0.23, -0.01]). Models stratified by frequency of cannabis use yielded results consistent with those of pooled models except that, for frequent users, cannabis-specific driving laws were associated with a significantly lower risk of DUIC (RR = 0.64, 95% CI [0.44, 0.92]). Agencies developing cannabis-focused drugged driving educational campaigns should consider the potential role of perceived safety of driving high in DUIC campaigns.

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.

Associations between smoking trajectories, smoke-free laws and cigarette taxes in a longitudinal sample of youth and young adults.

Cigarette smoking patterns vary within the population, with some individuals remaining never smokers, some remaining occasional users, and others progressing to daily use or quitting. There is little research on how population-level tobacco control policy interventions affect individuals within different smoking trajectories. We identified associations between tobacco control policy interventions and changes across different smoking trajectories among adolescents and young adults. Using 15 annual waves of data drawn from the National Longitudinal Survey of Youth 1997 (NLSY97), we applied a group-based trajectory model to identify associations between days smoked per month, comprehensive smoke-free laws, cigarette tax rates, and known socio-demographic risk factors for membership in different smoking trajectories. Comprehensive smoke-free laws were associated with reduced risk of initiation and reductions in days smoked per month for all trajectories other than occasional users. Higher tax rates were associated with reduced risk of initiation and days smoked for all trajectories other than established users. Overall, population-based tobacco control policies, particularly comprehensive smoke-free laws, were associated with reduced smoking. Tobacco taxes primarily reduced risk of initiation and use among never smokers, experimenters, and quitters, consistent with previous research suggesting that tobacco manufacturers lower prices after tax increases to reduce the cost of continued smoking for established users. These results provide support for expanding smoke-free laws and establishing a minimum tobacco floor price, which could improve public health by reducing the risk of initiation as well as use among occasional and established smokers.

Effects of e-cigarette use on cigarette smoking among U.S. youth, 2004-2018.

OBJECTIVE: To determine if the declining trend in U.S. youth cigarette smoking changed after e-cigarettes were introduced, and if youth e-cigarette users would have been likely to smoke cigarettes based on psychosocial and demographic predictors of smoking. METHODS: An interrupted time series analysis was used for cross-sectional data from the 2004 to 2018 National Youth Tobacco Surveys (NYTS) to assess changes in cigarette and e-cigarette use over time. A multivariable logistic regression model used 2004-2009 NYTS data on psychosocial risk factors to predict individual-level cigarette smoking risk from 2011 to 2018. Model-predicted and actual cigarette smoking behavior were compared. RESULTS: The decline in current cigarette smoking slowed in 2014 (-0.75 [95% CI: -0.81, -0.68] to -0.26 [95% CI: -0.40, -0.12] percentage points per year). The decline in ever cigarette smoking accelerated after 2012 (-1.45 [95% CI: -1.59, -1.31] to -1.71 [95% CI: -1.75, -1.66]). Ever and current combined cigarette and/or e-cigarette use declined during 2011-2013 and increased during 2013-2014 with no significant change during 2014-2018 for either variable. The psychosocial model estimated that 69.0% of current cigarette smokers and 9.3% of current e-cigarette users (who did not smoke cigarettes) would smoke cigarettes in 2018. CONCLUSIONS: The introduction of e-cigarettes was followed by a slowing decline in current cigarette smoking, a stall in combined cigarette and e-cigarette use, and an accelerated decline in ever cigarette smoking. Traditional psychosocial risk factors for cigarette smoking suggest that e-cigarette users do not fit the traditional risk profile of cigarette smokers.

Association of tobacco control policies with cigarette smoking among school youth aged 13-15 years in the Philippines, 2000-2015.

INTRODUCTION: In 2003, the Philippines implemented legislation that prohibited the sale of tobacco products to youth, placed text warning labels on tobacco products, and prohibited tobacco smoking in public places. This study assessed if this legislation was associated with reduced cigarette smoking among youth. METHODS: Data came from the 2000-2015 Philippines Global Youth Tobacco Survey (GYTS), a nationally representative, cross-sectional survey of students aged 13-15 years. GYTS data were used to determine associations between tobacco control legislation and current, past 30-day, current cigarette smoking (CCS). Logistic regression models were adjusted for age, sex, current other tobacco product use (COTPU), and price per cigarette stick (PPCS). RESULTS: In the unadjusted model, the 2003 legislation was not associated with CCS (OR=0.77; 95% CI: 0.54-1.10). After adjusting for covariates, it was negatively associated (AOR=0.65; 95% CI: 0.53-0.80). Being 15 years old (OR=1.31; 95% CI: 1.08-1.58), male (OR=2.54; 95% CI: 2.17-2.98), and COTPU (OR=4.12; 95% CI: 3.47-4.91) were positively associated with CCS in unadjusted models. In adjusted models, being 14 years old (AOR=1.29; 95% CI: 1.08-1.53), 15 years old (AOR=1.55; 95% CI: 1.31-1.84), male (AOR=2.49; 95% CI: 2.13-2.91), and COTPU (AOR=3.96; 95% CI: 3.32-4.73), were associated with CCS. PPCS was not associated with CCS in either the unadjusted (OR=1.32; 95% CI: 0.82-2.11) or adjusted (AOR=1.32; 95% CI: 0.79-2.18) models. CONCLUSIONS: After adjusting for covariates, the 2003 tobacco control legislation was associated with lower current cigarette smoking, but price per cigarette stick was not.

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.

Imitating waterpipe: Another tobacco industry attempt to create a cigarette that seems safer.

INTRODUCTION: The use of waterpipe (also known as hookah) increased between 2011 and 2016 in the US and globally, especially among youth and young adults. The aim of this study is to examine the tobacco industry's interest, involvement and role in proliferating waterpipe-like products and its technology. METHODS: We searched the University of California San Francisco Truth Tobacco Industry Documents beginning with the search terms "hookah', 'waterpipe', 'narghile', 'shisha', 'hooka', 'e-hookah', 'electronic hookah', 'water filtration', and 'hubble-bubble' and then expanded the search using snowball sampling. Over 1500 documents were reviewed, and 39 were included in this analysis. RESULTS: This review focuses on several companies' addition of water to the filter of one cigarette holder and to two cigarettes in an attempt to make these products seem safer. In 1954, the Aquafilter Corporation created and patented a filtered cigarette holder named Aquafilter, which the documents reveal was closely monitored by multiple tobacco companies. In 1965, the American Tobacco Company developed the Waterford cigarette. In 1987, Japan Tobacco patented the Rivage cigarette. Waterford and Rivage were very similar products that contained crushable water capsules designed to "wet" the cigarettes' filter. CONCLUSION: Companies have been attempting to incorporate the water filtrating aspect of waterpipe into cigarette products. Ultimately, several tobacco companies adapted the technology from these devices to create cigarettes with crushable flavor capsules. Given the tobacco industry's history of resurrecting products, the industry may attempt to incorporate water filtration and other aspects of waterpipe into future products to attempt to make them appear safer.

Revolution or redux? Assessing IQOS through a precursor product.

BACKGROUND: Philip Morris International (PMI) currently claims that its heated tobacco product, IQOS, reduces health risk by reducing users' exposure to harmful and potentially harmful constituents present in tobacco smoke. Given the tobacco industry's long history of misrepresenting and obfuscating research, independent assessment of PMI's claims is important. Analysis of Accord, a failed but strikingly similar precursor to IQOS, may help contextualise PMI's claims in its Modified Risk Tobacco Product (MRTP) application. METHODS: We analysed previously secret internal Philip Morris (PM) and PMI documents, public communications and MRTP application. RESULTS: PM marketed Accord as a 'cleaner' tobacco product in an attempt to address smokers' growing health concerns without making explicit health claims. While PM communications asserted that Accord reduced users' exposure to harmful constituents, company scientists and executives consistently stressed to both regulators and the public that such reductions did not render Accord safer. IQOS's design and marketing are similar to Accord's. On the basis of aerosol chemistry data, IQOS reduces user exposure to some compounds compared with Accord but raises them for others. DISCUSSION: IQOS appears to be a variant of Accord without consistent improvements in exposure to aerosol toxic compounds. In contrast to PM's past claims for Accord, PMI now claims in its MRTP application that IQOS reduces health risk. This shift in stance is likely not the result of any toxicological difference between Accord and IQOS, but rather a change in the social and regulatory landscape permitting these claims.

Medical cannabis legalization and state-level prevalence of serious mental illness in the National Survey on Drug Use and Health (NSDUH) 2008-2015.

Although research has established a link between cannabis legalization and use, and cannabis use and mental health, the relationship between medical cannabis legalization and mental health remains uncharacterized. This analysis investigated the relationship between state medical cannabis laws (restrictive, i.e. covering a narrow set of medical conditions; or liberal, i.e. covering a broad range of medical conditions), whether the law permits patients to petition their physician to approve medical cannabis use for specific medical conditions, and state prevalence of serious mental illness (SMI) in the National Survey of Drug Use and Health 2008-2015. In a covariate-adjusted meta-regression, liberal laws were significantly associated with higher prevalence of SMI (Coeff = 0.003, SE = 0.001, p < .001). Restrictive laws (Coeff = 0.001, SE = 0.001, p = .285) and the ability to petition physician approval (Coeff = -0.001, SE = 0.001, p = .140) were non-significant. When added to the model, state past-year cannabis use was significantly associated with higher prevalence of SMI (Coeff = 0.037, SE = 0.015, p = .018), liberal laws remained significant (Coeff = 0.002, SE = 0.001, p = .015), and restrictive laws (Coeff = -0.0001, SE = 0.001, p = .945) and the ability to petition a physician (Coeff = 0.001, SE = 0.001, p = .290) remained non-significant. Medical cannabis laws are likely related to state mental health, and a higher prevalence of cannabis use partially explains this relationship.

Impact of E-Cigarette Minimum Legal Sale Age Laws on Current Cigarette Smoking.

PURPOSE: The purpose of this study was to use individual-level data to examine the relationship between e-cigarette minimum legal sale age (MLSA) laws and cigarette smoking among U.S. adolescents, adjusting for e-cigarette use. METHODS: In 2016 and 2017, we regressed (logistic) current (past 30-day) cigarette smoking (from 2009-2014 National Youth Tobacco Surveys [NYTS]) on lagged (laws enacted each year counted for the following year) and unlagged (laws enacted January-June counted for that year) state e-cigarette MLSA laws prohibiting sales to youth aged <18 or <19 years (depending on the state). Models were adjusted for year and individual- (e-cigarette and other tobacco use, sex, race/ethnicity, and age) and state-level (smoke-free laws, cigarette taxes, medical marijuana legalization, income, and unemployment) covariates. RESULTS: Cigarette smoking was not significantly associated with lagged MLSA laws after adjusting for year (odds ratio [OR] = .87, 95% confidence interval [CI]: .73-1.03; p = .10) and covariates (OR = .85, .69-1.03; p = .10). Unlagged laws were significantly and negatively associated with cigarette smoking (OR = .84, .71-.98, p = .02), but not after adjusting for covariates (OR = .84, .70-1.01, p = .07). E-cigarette and other tobacco use, sex, race/ethnicity, age, and smoke-free laws were associated with cigarette smoking (p <.05). Results unadjusted for e-cigarette use and other tobacco use yielded a significant negative association between e-cigarette MLSA laws and cigarette smoking (lagged: OR = .78, .64-.93, p = .01; unlagged: OR = .80, .68-.95, p = .01). CONCLUSIONS: After adjusting for covariates, state e-cigarette MLSA laws did not affect youth cigarette smoking. Unadjusted for e-cigarette and other tobacco use, these laws were associated with lower cigarette smoking.

Thirty-day smoking in adolescence is a strong predictor of smoking in young adulthood.

Thirty-day smoking, although a widely used measure of adolescent smoking (age 12-16), has been questioned as an accurate measure of young adult (age 26-30) smoking behavior, particularly when critiquing studies linking use of e-cigarettes with subsequent cigarette smoking. We used logistic regression to test two measures of 30-day adolescent smoking as predictors of young adult smoking in the National Longitudinal Survey of Youth 1997. Adjusting for psychosocial covariates, compared to those who smoked zero days in the past 30 days in adolescence, odds of any past-30-day smoking in young adulthood ranged from 2.85 (95% CI: 1.85-4.37) for those who smoked 1 day to 4.81 (3.50-6.59) for those who smoked daily as adolescents, and adjusted odds of daily smoking in young adulthood ranged from 1.99 (1.24-3.18) to 4.69 (3.42-6.43). Compared with adolescent never smokers, adjusted odds of any past-30-day smoking in young adulthood among adolescent former smokers was 2.11 (1.77-2.53), and among adolescent current smokers, ranged from 3.03 (2.22-4.14) for those who smoked 1-5 cigarettes per month to 8.19 (5.80-11.55) for those who smoked daily. Adjusted odds of daily smoking in young adulthood were 2.49 (2.12-2.91) for adolescent former smokers and, among adolescent current smokers, ranged from 2.54 (1.92-3.37) for those who smoked 1-5 cigarettes per month to 8.65 (6.06-12.35) for those who smoked daily. There is a strong dose-response relationship between 30-day smoking in adolescence-even a single day in the month-and 30-day and daily smoking in young adulthood.

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.

Differences in Adolescent E-cigarette and Cigarette Prevalence in Two Policy Environments: South Korea and the United States.

Introduction: In the context of different regulatory environments, different patterns of e-cigarette use have emerged among adolescents worldwide. The United States and South Korea are two examples, the latter of which has maintained much more extensive regulation of e-cigarettes. Methods: This analysis compares the prevalence of e-cigarette and conventional cigarette use between 2011 and 2015 from the Korean Youth Risk Behavior Web-based Survey and the U.S. National Youth Tobacco Survey, both nationally representative samples of middle and high school students that use similar questions. Results: E-cigarette prevalence (past 30 days) among South Korean adolescents decreased from 4.7% in 2011 (95% confidence interval [CI]: 4.4-5.0) to 4.0% (3.7-4.3) in 2015 but increased dramatically among US adolescents from 0.9% (0.7-1.2) to 11.2% (9.9-12.7). Cigarette prevalence (past 30 days) decreased in South Korea from 12.1% (11.6-12.7) to 7.8% (CI: 7.3-8.3) and in the United States from 11.1% (9.5-12.6) to 6.1% (5.1-7.3). Combined prevalence of cigarette and e-cigarette use (adjusting for dual users) decreased in South Korea from 13.2% (12.7-13.8) to 8.5% (8.0-9.1) but increased in the United States from 11.3% (9.7-12.9) to 14.0% (12.4-15.7). Conclusions: In South Korea, where e-cigarettes are extensively regulated, adolescent e-cigarette use remained stable at a low level, whereas in the United States, where e-cigarette regulation has been limited, e-cigarette use increased. Combined e-cigarette plus cigarette use declined in South Korea whereas it increased in the United States. The restrictive policies in South Korea likely contributed to lower overall product use. Implications: This paper compared the strength of regulation of South Korea and the United States with the prevalence of adolescents' e-cigarette and cigarette use and suggests that more restrictive policies likely to contribute to lower e-cigarette use and overall use of e-cigarette and cigarette.

Beyond experimentation: Five trajectories of cigarette smoking in a longitudinal sample of youth.

The first goal of this study was to identify the most appropriate measure of cigarette smoking for identifying unique smoking trajectories among adolescents; the second goal was to describe the resulting trajectories and their characteristics. Using 15 annual waves of smoking data in the National Longitudinal Survey of Youth 1997 (NLSY97), we conducted an exploratory latent class growth analysis to determine the best of four outcome variables for yearly smoking (cigarettes per day on days smoked, days smoked per month, mean cigarettes per day, and total cigarettes per month) among individuals aged 12 to 30 (n = 8,791). Days smoked per month was the best outcome variable for identifying unique longitudinal trajectories of smoking and characteristics of these trajectories that could be used to target different types of smokers for prevention and cessation. Objective statistics were used to identify four trajectories in addition to never smokers (34.1%): experimenters (13.6%), quitters (8.1%), early established smokers (39.0%), and late escalators (5.2%). We identified a quitter and late escalator class not identified in the only other comparable latent class growth analysis. Logistic regressions were used to identify the characteristics of individuals in each trajectory. Compared with never smokers, all trajectories except late escalators were less likely to be black; experimenters were more likely to be out of school and unemployed and drink alcohol in adolescence; quitters were more likely to have a mother with a high school degree/GED or higher (versus none) and to use substances in adolescence and less likely to have ever married as a young adult; early established smokers were more likely to have a mother with a high school diploma or GED, be out of school and unemployed, not live with both parents, have used substances, be depressed, and have peers who smoked in adolescence and to have children as young adults and less likely to be Hispanic and to have ever married as young adults; and late escalators were more likely to be Hispanic, drink alcohol, and break rules in adolescence and less likely to have ever married as young adults. Because of the number of waves of data analyzed, this analysis provided a clearer temporal depiction of smoking behavior and more easily distinguishable smoking trajectories than previous analyses. Tobacco control interventions need to move beyond youth-focused approaches to reach all smokers.

E-cigarettes and National Adolescent Cigarette Use: 2004-2014.

BACKGROUND: E-cigarette use is rapidly increasing among adolescents in the United States, with some suggesting that e-cigarettes are the cause of declining youth cigarette smoking. We hypothesized that the decline in youth smoking changed after e-cigarettes arrived on the US market in 2007. METHODS: Data were collected by using cross-sectional, nationally representative school-based samples of sixth- through 12th-graders from 2004-2014 National Youth Tobacco Surveys (samples ranged from 16 614 in 2013 to 25 324 in 2004). Analyses were conducted by using interrupted time series of ever (≥1 puff) and current (last 30 days) cigarette smoking. Logistic regression was used to identify psychosocial risk factors associated with cigarette smoking in the 2004-2009 samples; this model was then applied to estimate the probability of cigarette smoking among cigarette smokers and e-cigarette users in the 2011-2014 samples. RESULTS: Youth cigarette smoking decreased linearly between 2004 and 2014 (P = .009 for ever smoking and P = .05 for current smoking), with no significant change in this trend after 2009 (P = .57 and .23). Based on the psychosocial model of smoking, including demographic characteristics, willingness to wear clothing with a tobacco logo, living with a smoker, likelihood of smoking in the next year, likelihood of smoking cigarettes from a friend, and use of tobacco products other than cigarettes or e-cigarettes, the model categorized <25% of current e-cigarette-only users (between 11.0% in 2012 and 23.1% in 2013) as current smokers. CONCLUSIONS: The introduction of e-cigarettes was not associated with a change in the linear decline in cigarette smoking among youth. E-cigarette-only users would be unlikely to have initiated tobacco product use with cigarettes.

Philip Morris research on precursors to the modern e-cigarette since 1990.

BACKGROUND: Use of electronic cigarettes (e-cigarettes) is increasing rapidly. Chinese pharmacist Hon Lik is frequently cited as inventing the modern e-cigarette in 2003. However, tobacco companies have developed electronic nicotine delivery systems since at least 1963. METHODS: We searched the University of California San Francisco Truth (formerly Legacy) Tobacco Industry Documents beginning with the terms 'electric cigarette' and 'electronic cigarettes', 'e-cigarette', 'smokeless cigarettes', 'nicotine aerosol', 'tobacco aerosol', and 'vaping' and then expanded the search using snowball sampling. We focused our analysis on Philip Morris (PM) documents discussing technology that aerosolised a nicotine solution because these devices resembled modern e-cigarettes. Over 1000 documents were reviewed; 40 were included in the final analysis. RESULTS: PM started developing a nicotine aerosol device in 1990 to address the health concerns and decreased social acceptability of smoking that were leading smokers to switch to nicotine replacement therapy. PM had developed a capillary aerosol generator that embodied basic e-cigarette technology in 1994, but in the mid-to-late 1990s focused on applying its aerosol technology to pharmaceutical applications because of uncertainty of how such products might affect potential Food and Drug Administration regulation of tobacco products. In 2001, PM resumed its work on a nicotine aerosol device, and in 2013, NuMark (a division of Altria, PM's parent company) released the MarkTen, a nicotine aerosol device. CONCLUSIONS: Rather than a disruptive technology, PM developed e-cigarette technology to complement, not compete with, conventional cigarettes and evade tobacco control regulations.

Association of Smoke-Free Laws With Lower Percentages of New and Current Smokers Among Adolescents and Young Adults: An 11-Year Longitudinal Study.

IMPORTANCE: Smoke-free laws are associated with a lower prevalence of smoking. OBJECTIVE: To quantify the effect of 100% smoke-free laws on the smoking behavior of adolescents and young adults in a longitudinal analysis. DESIGN, SETTING, AND PARTICIPANTS: Pooled logistic regression and zero-inflated negative binomial regression analysis of participants in the National Longitudinal Survey of Youth 1997 (data from 1997 to 2007), with complete data on initiation of smoking (n = 4098) and number of days respondents reported smoking in the past 30 days (n = 3913). EXPOSURES: Laws for 100% smoke-free workplaces, laws for 100% smoke-free bars, and state cigarette taxes. MAIN OUTCOMES AND MEASURES: Smoking initiation (first report of smoking cigarette), current (for 30 days) smoking, and number of days respondents reported smoking in the past 30 days among current smokers. RESULTS: Laws for 100% smoke-free workplaces, but not bars, were associated with significantly lower odds of initiating smoking (odds ratio, 0.66 [95% CI, 0.44-0.99]). Laws for 100% smoke-free bars were associated with lower odds of being a current smoker (odds ratio, 0.80 [95% CI, 0.71-0.90]) and fewer days of smoking (incidence rate ratio, 0.85 [95% CI, 0.80-0.90]) among current smokers. Taxes were associated with a lower percentage of new smokers but not current smokers among adolescents and young adults. The effect of smoke-free workplace laws on smoking initiation is equivalent to a $1.57 (in 2007 dollars) tax increase. Smoke-free bar laws are associated with lower rates of current smoking, as well as a decrease in the number of days reported smoking among current smokers. CONCLUSIONS AND RELEVANCE: Smoke-free laws are an important tobacco control tool. They not only protect bystanders from secondhand smoke but also contribute to less smoking among adolescents and young adults.

Worksite safety climate, smoking, and the use of protective equipment by blue-collar building workers enrolled in the MassBUILT smoking cessation trial.

OBJECTIVE: To assess potential contributors to high injury rates and smoking prevalence among construction workers, we investigated the association of safety climate with personal protective equipment use, and smoking behaviors. METHODS: Logistic regression models estimated risk ratios for personal protective equipment use and smoking using data from participants in MassBUILT smoking cessation intervention (n = 1725). RESULTS: Contractor safety climate was negatively associated with the use of dust masks (rate ratio [RR], 0.88; 95% confidence interval [CI], 0.83 to 0.94), respirators (RR, 0.82; 95% CI, 0.75 to 0.89), general equipment (RR, 0.98; 95% CI, 0.95 to 1.00), and fall protection (RR, 0.94; 95% CI, 0.91 to 0.98) and positively associated with current smoking (RR, 1.12; 95% CI, 1.01 to 1.25) but not smoking cessation. Coworker safety climate was negatively associated with the use of dust masks (RR, 0.87; 95% CI, 0.82 to 0.92), respirators (RR, 0.80; 95% CI, 0.74 to 0.87), general equipment (RR, 0.96; 95% CI, 0.94 to 0.98), fall (RR, 0.92; 95% CI, 0.89 to 0.96), and hearing protection (RR, 0.88; 95% CI, 0.83 to 0.93) but not smoking. CONCLUSIONS: Worksite safety climate may be important for personal protective equipment use and smoking, but further research is needed.