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

Regulatory Strategies for Preventing and Reducing Nicotine Vaping Among Youth: A Systematic Review

INTRODUCTION

Many jurisdictions have implemented different regulatory strategies to reduce vaping among youth. The objective of this systematic review is to synthesize the evidence of the effectiveness of different regulatory strategies for preventing and reducing nicotine vaping among youth.

METHODS

Five electronic databases were searched from January 1, 2004 to July 17, 2022 for primary studies examining state/provincial or national regulations targeting vaping among youth (aged 12-21 years) in high-income countries. The primary outcome was vaping prevalence. Included studies were qualitatively synthesized through systematic review.

RESULTS

The systematic review included 30 studies. There was insufficient evidence to recommend age restrictions (n=16), restrictions on location of use (n=1), and mixed/combined regulations (n=3). Flavor bans (n=4), sales licenses (n=2), and taxation (n=2) were generally shown to be associated with decreased rates of youth vaping. Warning labels (n=2) were associated with a decreased desire to initiate vaping. Included studies had moderate-to-serious risks of bias.

DISCUSSION

Although several regulatory interventions have been shown to be effective at reducing vaping among youth, evidence is insufficient to recommend a specific type of regulation. Regulatory authorities could implement various regulations targeting the price, accessibility, and desirability (i.e., flavors and packaging) of E-cigarettes.

Health, safety, and socioeconomic impacts of cannabis liberalization laws: An evidence and gap map

Background

Globally, cannabis laws and regulations are rapidly changing. Countries are increasingly permitting access to cannabis under various decriminalization, medicalization, and legalization laws. With strong economic, public health, and social justice incentives driving these domestic cannabis policy reforms, liberalization trends are bound to continue. However, despite a large and growing body of interdisciplinary research addressing the policy-relevant health, safety, and socioeconomic consequences of cannabis liberalization, there is a lack of robust primary and systematic research that comprehensively investigates the consequences of these reforms.

Objectives

This evidence and gap map (EGM) summarizes the empirical evidence on cannabis liberalization policies. Primary objectives were to develop a conceptual framework linking cannabis liberalization policies to relevant outcomes, descriptively summarize the empirical evidence, and identify areas of evidence concentration and gaps.

Search Methods

We comprehensively searched for eligible English-language empirical studies published across 23 academic databases and 11 gray literature sources through August 2020. Additions to the pool of potentially eligible studies from supplemental sources were made through November 2020.

Selection Criteria

The conceptual framework for this EGM draws upon a legal epidemiological perspective highlighting the causal effects of law and policy on population-level outcomes. Eligible interventions include policies that create or expand access to a legal or decriminalized supply of cannabis: comprehensive medical cannabis laws (MCLs), limited medical cannabidiol laws (CBDLs), recreational cannabis laws (RCLs), industrial hemp laws (IHLs), and decriminalization of cultivations laws (DCLs). Eligible outcomes include intermediate responses (i.e., attitudes/behaviors and markets/environments) and longer-term consequences (health, safety, and socioeconomic outcomes) of these laws.

Data Collection and Analysis

Both dual screening and dual data extraction were performed with third person deconfliction. Primary studies were appraised using the Maryland Scientific Methods Scale and systematic reviews were assessed using AMSTAR 2.

Main Results

The EGM includes 447 studies, comprising 438 primary studies and nine systematic reviews. Most research derives from the United States, with little research from other countries. By far, most cannabis liberalization research focuses on the effects of MCLs and RCLs. Studies targeting other laws-including CBDLs, IHLs, and DCLs-are relatively rare. Of the 113 distinct outcomes we documented, cannabis use was the single most frequently investigated. More than half these outcomes were addressed by three or fewer studies, highlighting substantial evidence gaps in the literature. The systematic evidence base is relatively small, comprising just seven completed reviews on cannabis use (3), opioid-related harms (3), and alcohol-related outcomes (1). Moreover, we have limited confidence in the reviews, as five were appraised as minimal quality and two as low quality.

Authors’ Conclusions

More primary and systematic research is needed to better understand the effects of cannabis liberalization laws on longer-term-and arguably more salient-health, safety, and socioeconomic outcomes. Since most research concerns MCLs and RCLs, there is a critical need for research on the societal impacts of industrial hemp production, medical CBD products, and decriminalized cannabis cultivation. Future research should also prioritize understanding the heterogeneous effects of these laws given differences in specific provisions and implementation across jurisdictions.

E-cigarette-inclusive smoke-free policies, excise taxes, tobacco 21 and changes in youth e-cigarette use: 2017-2019

BACKGROUND

We examined whether the implementation of electronic cigarette (e-cigarette) policies at the state level (e-cigarette-inclusive smoke-free (ESF) policies, excise taxes on e-cigarettes and raising tobacco legal purchasing age to 21 years (T21)) affected recent upward trends in youth e-cigarette use.

METHODS

Data were from participants from 34 US states who completed the Youth Risk Behavior Survey (YRBS) state surveys in 2017 and 2019 (n=278 271). States were classified as having or not having ESF policies, any e-cigarette excise tax and T21 policies by 1 January 2019. Participants reported ever, past 30-day and frequent (≥20 days) e-cigarette use; past 30-day combustible cigarette smoking; and age, sex and race/ethnicity. Weighted multivariable logistic regression models assessed whether changes in e-cigarette use over time differed by policy status, adjusting for participants' demographics and combustible cigarette smoking.

RESULTS

Prevalence of ever and past 30-day youth e-cigarette use in states with ESF policies decreased during 2017-2019, while the prevalence of these measures in states without ESF policies increased. States with T21 policies showed non-significant changes in prevalence of ever and past 30-day youth e-cigarette use, whereas states without T21 policies showed significant increases in ever and past 30-day youth e-cigarette use. States with ESF and T21 policies showed slower increases in youth frequent e-cigarette use. E-cigarette excise taxes were not associated with decreasing prevalence of youth e-cigarette use.

CONCLUSIONS

State-level ESF and T21 policies could be effective for limiting growth of youth e-cigarette use despite an overall national increase. Higher e-cigarette excise tax rates may be needed to effectively reduce youth e-cigarette use.

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

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A national survey sampled current cannabis users.

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Users in medical cannabis states were less likely to report driving high.

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Users in recreational states were less likely to report driving high.

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Likelihood of driving high varied by frequency of cannabis use.

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)1.

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.

Exploring Patterns of the Use of Electronic Nicotine Delivery Systems among Adolescents in High-Risk Appalachian (U.S.A) Communities

Background: Electronic nicotine delivery systems (ENDS) use among adolescents in the United States (U.S.) has surpassed conventional tobacco products (CTPs), including cigarettes. Increasingly, ENDS are used concurrently with CTPs and substances such as cannabis. However, few studies involve Central Appalachia, a region with historically high rates of tobacco and other substance use. Objective:  To examine prevalence of concurrent use of ENDS and cannabis among school-going adolescents in Appalachian Tennessee and delineate associations between ENDS use and substance-related risk behavior (cannabis use), social relations (peer use), and school-related risk behavior (academic performance). Methods: Data were obtained from a survey conducted with youth aged 13-17 years in 2018 in a county in Appalachian Tennessee (n = 280). A multivariable logistic regression model was fit to evaluate associations between ENDS and cannabis use, and other factors. Results: Overall, lifetime ENDS and cannabis prevalence estimates were 31.1% and 18.6%, respectively. Lifetime ENDS users had increased odds of also being lifetime cannabis users [OR = 9.22, 95% confidence interval (CI): 3.44-24.75]. Lifetime ENDS users had increased odds of reporting ENDS use among peers [OR = 12.11; 95% CI: 5.40-27.12] and lower academic performance (OR associated with mostly C or D vs. A grades was 4.28, 95% CI: 1.68-10.90). Conclusion: This study found an association between ENDS and cannabis use among adolescents in Appalachian Tennessee exists. Additionally, peer use and academic performance were associated with ENDS use. The findings have implications for public health intervention planning to address not only ENDS but also substance use among Appalachian youth.

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.

Electronic Cigarette Use Among Youth: Understanding Unique Risks in a Vulnerable Population

PURPOSE OF REVIEW

This review discusses prevalence rates of electronic (e-)cigarette use among youth and factors that likely contribute to their growing popularity among this population. Trends shaping the e-cigarette landscape, the appeal of e-cigarettes among youth, perceptions contributing to the initiation of e-cigarettes, available assessments capturing the usage of and attitudes towards e-cigarettes, and e-cigarette policies and regulations are reviewed.

RECENT FINDINGS

E-cigarette use among this vulnerable group may relate to factors associated with the promotion of social status, individuality, and enjoyment, along with low perceptions of risk and harm. Measures assessing factors unique to e-cigarette use among youth (e.g., individuality) still need to be developed and validated. Effects of existing regulations to limit youth access to e-cigarettes may be limited, and shortcomings of current policy measures are discussed with recommendations.

SUMMARY

The rise of e-cigarette use among youth culminated through a perfect storm of clever marketing targeting youth appeal, innovations in more effective nicotine delivery systems, capitalizing on increased susceptibility of the adolescent brain, and regulatory gaps. Understanding risk and protective factors specific to this vulnerable group, which can be gleaned in part by psychometrically valid assessments, could inform regulatory strategies and prevention programming efforts. Yet, few validated measures exist that assess attitudes, behaviors, and patterns of e-cigarette use that are specific to youth. Ultimately, it is incumbent upon policymakers to create comprehensive regulations that prioritize harm reduction and can evolve in lockstep with the constantly changing e-cigarette product landscape.

Young people's use of e-cigarettes in Wales, England and Scotland before and after introduction of EU Tobacco Products Directive regulations: a mixed-method natural experimental evaluation

BACKGROUND

Young people's experimentation with e-cigarettes has increased in recent years, although regular use remains limited. EU Tobacco Products Directive (TPD) regulations introduced packet warnings, advertising restrictions, and regulated nicotine strength from 2016, in part due to concerns regarding use by young people. This paper examines e-cigarette use trajectories before and after TPD.

METHODS

E-cigarette use data were obtained from School Health Research Network/Health Behaviour in School-aged Children surveys in Wales and Smoking Drinking and Drug Use surveys in England. Data from Wales were analysed using segmented logistic regression, with before and after regression analyses of English data. Semi-structured group interviews included young people aged 14-16 years in Wales, England and Scotland in 2017 and 2018.

RESULTS

In Wales, ever use of e-cigarettes increased over time, but under a range of assumptions, growth did not appear to continue post-TPD. A small and non-significant change in trend was observed post-implementation (OR=0.96; 95%CI=0.91 to 1.01), which increased in size and significance after adjusting for ever smoking (OR=0.93; 95%CI=0.88 to 0.98). There was little increase in regular e-cigarette use from 2015 to 2017 in Wales. However, ever and regular use increased from 2014 to 2016 in England. Young people in all nations described limited interactions with components of TPD, while describing e-cigarette use as a 'fad', which had begun to run its course.

CONCLUSIONS

This study provides preliminary evidence that young people's e-cigarette experimentation may be plateauing in UK nations. The extent to which this arises from regulatory changes, or due to a fad having begun to lose its appeal among young people in the UK countries, remains unclear. These trends contrast to those observed in North America, where newer products whose EU market entry and marketing have been impacted by TPD, have gained traction among young people. Long-term monitoring of e-cigarette use trends and perceptions among young people remain vital.

Research on Youth and Young Adult Tobacco Use, 2013-2018, From the Food and Drug Administration-National Institutes of Health Tobacco Centers of Regulatory Science

The Tobacco Regulatory Science Program is a collaborative research effort between the National Institutes of Health (NIH) and the Food and Drug Administration (FDA). In 2013, the NIH funded 14 Tobacco Centers of Regulatory Science (TCORS), which serve as partners in establishing research, training, and professional development programs to guide FDA. Each of the fourteen TCORS, and two other NIH-funded research programs, the Center for the Evaluation of Nicotine in Cigarettes (CENIC) and the Consortium on Methods Evaluating Tobacco (COMET), pursued specific research themes relevant to FDA's priorities. A key mandate for FDA is to reduce tobacco use among young people. This article is a review of the peer-reviewed research, including published and in-press manuscripts, from the TCORS, CENIC, and COMET, which provides specific data or other findings on youth (ages 10-18 years) and/or young adults (ages 18-34 years), from 2013 to 2018. Citations of all TCORS, CENIC, and COMET articles from September 2013 to December 2017 were collected by the TCORS coordinating center, the Center for Evaluation and Coordination of Training and Research. Additional citations up to April 30, 2018 were requested from the principal investigators. A scoring rubric was developed and implemented to assess study type, primary theme, and FDA priority area addressed by each article. The major subareas and findings from each priority area are presented. There were 766 articles in total, with 258 (34%) focusing on youth and/or young adults. Findings relevant to FDA from this review concern impact analysis, toxicity, health effects, addiction, marketing influences, communications, and behavior.

IMPLICATIONS

The Tobacco Centers of Regulatory Science, CENIC, and COMET have had a high output of scientific articles since 2013. These Centers are unique in that the FDA supports science specifically to guide future regulatory actions. The 258 articles that have focused on youth and/or young adults are providing data for regulatory actions by the FDA related to the key priority areas such as the addictiveness of non-cigarette products, the effects of exposure to electronic cigarette marketing on initiation and cessation, and the impact of flavored products on youth and young adult tobacco use. Future regulations to reduce tobacco use will be guided by the cumulative evidence. These Centers are one innovative mechanism to promote important outcomes to advance tobacco regulatory science.

County Smoke-Free Laws and Cigarette Smoking Among U.S. Adults, 1995-2015

INTRODUCTION

Tobacco use remains the leading cause of preventable death worldwide. Though research on smoke-free policies in the U.S. exists at the state or national level, there is limited evidence on such policies at the county level. This study examined the association between changes in county-level comprehensive smoke-free laws and smoking behavior among U.S. adults.

METHODS

Data were used from the 1995/1996 to the 2014/2015 Tobacco Use Supplement to the Current Population Survey, analyzed in 2019. Changes in county smoke-free law population coverage over time were used as a natural experiment. Quasi-experimental analyses were performed to examine the association between changes in county-level comprehensive smoke-free laws and smoking behavior (smoking status and number of cigarettes smoked).

RESULTS

During the study period, current smoking prevalence decreased from 21.6% to 11.9%. Specifically, the prevalence of every day smoking decreased from 17.1% to 9.1% and some days smoking decreased from 4.4% to 2.9%. The number of cigarettes smoked per day for every day smokers decreased from 18.5 to 13.6, and from 5.9 to 4.1 for those who smoked some days. Comprehensive smoke-free legislation was associated with lower odds of cigarette smoking (AOR=0.76, 95% CI=0.74, 0.79). Adults living in counties with comprehensive smoke-free policies smoked fewer cigarettes per day both for every day smokers (β= -1.55, p<0.0001) and some days smokers (β= -0.79, p<0.0001).

CONCLUSIONS

County smoke-free policies in the U.S. may have contributed significantly to the reduction in smoking prevalence as well as the reduction in the number of cigarettes smoked among continuing cigarette smokers.

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.

Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances: Findings from state policy evaluations

Background: The past decade has seen unprecedented shifts in the cannabis policy environment, and the public health impacts of these changes will hinge on how they affect patterns of cannabis use and the use and harms associated with other substances.Objectives: To review existing research on how state cannabis policy impacts substance use, emphasizing studies using methods for causal inference and highlighting gaps in our understanding of policy impacts on evolving cannabis markets.Methods: Narrative review of quasi-experimental studies for how medical cannabis laws (MCLs) and recreational cannabis laws (RCLs) affect cannabis use and use disorders, as well as the use of or harms from alcohol, opioids, and tobacco.Results: Research suggests MCLs increase adult but not adolescent cannabis use, and provisions of the laws associated with less regulated supply may increase adult cannabis use disorders. These laws may reduce some opioid-related harms, while their impacts on alcohol and tobacco use remain uncertain. Research on RCLs is just emerging, but findings suggest little impact on the prevalence of adolescent cannabis use, potential increases in college student use, and unknown effects on other substance use.Conclusions: Research on how MCLs influence cannabis use has advanced our understanding of the importance of heterogeneity in policies, populations, and market dynamics, but studies of how MCLs relate to other substance use often ignore these factors. Understanding effects of cannabis laws requires greater attention to differences in short- versus long-term effects of the laws, nuances of policies and patterns of consumption, and careful consideration of appropriate control groups.

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.