E-cigarette use and onset of first cigarette smoking among adolescents: An empirical test of the 'common liability' theory

Electronic cigarettes and subsequent use of cigarettes in young people: An evidence and gap map

BACKGROUND AND AIMS

The use of e-cigarettes may influence later smoking uptake in young people. Evidence and gap maps (EGMs) are interactive on-line tools that display the evidence and gaps in a specific area of policy or research. The aim of this study was to map clusters and gaps in evidence exploring the relationship between e-cigarette use or availability and subsequent combustible tobacco use in people aged < 30 years.

METHODS

We conducted an EGM of primary studies and systematic reviews. A framework and an interactive EGM was developed in consultation with an expert advisory group. A systematic search of five databases retrieved 9057 records, from which 134 studies were included. Systematic reviews were appraised using AMSTAR-2, and all included studies were coded into the EGM framework resulting in the interactive web-based EGM. A descriptive analysis of key characteristics of the identified evidence clusters and gaps resulted in this report.

RESULTS

Studies were completed between 2015 and 2023, with the first systematic reviews being published in 2017. Most studies were conducted in western high-income countries, predominantly the United States. Cohort studies were the most frequently used study design. The evidence is clustered on e-cigarette use as an exposure, with an absolute gap identified for evidence looking into the availability of e-cigarettes and subsequent cessation of cigarette smoking. We also found little evidence analysing equity factors, and little exploring characteristics of e-cigarette devices.

CONCLUSIONS

This evidence and gap map (EGM) offers a tool to explore the available evidence regarding the e-cigarette use/availability and later cigarette smoking in people under the age of 30 years at the time of the search. The majority of the 134 reports is from high-income countries, with an uneven geographic distribution. Most of the systematic reviews are of lower quality, suggesting the need for higher-quality reviews. The evidence is clustered around e-cigarette use as an exposure and subsequent frequency/intensity of current combustible tobacco use. Gaps in evidence focusing on e-cigarette availability, as well as on the influence of equity factors may warrant further research. This EGM can support funders and researchers in identifying future research priorities, while guiding practitioners and policymakers to the current evidence base.

Socio-demographic Correlates of Electronic Cigarette and Cannabis Co-use Among Naïve and Tobacco Adolescent Users

The increasing co-use of e-cigarette and cannabis among youth has become a public health challenge. The present analyses aimed to identify prevalence and correlates of past-month co-use of e-cigarettes and cannabis among adolescents with and without prior tobacco use. For this panel study, 5 years of cross-sectional data (2014-2018) were used from 8th, 10th-, and 12th-grade adolescents in the Monitoring the Future study, a nationally representative survey of U.S. students. We examined prevalence and correlates of e-cigarettes and cannabis co-use among adolescents who had ever used tobacco (n = 15,136) and among those who had never used tobacco (n = 56,525). Adolescents who had ever used tobacco showed significantly higher rates of e-cigarettes and cannabis co-use compared to adolescents who had never used tobacco (17.1% vs. 2.2%, p < 0.01). Results from adjusted multinomial regression models showed that overall, Black and Hispanic adolescents tobacco users were less likely than Whites to co-use e-cigarettes and cannabis. Black adolescents who had used tobacco previously were more likely than Whites to have used cannabis exclusively. Black and Hispanic tobacco-naïve adolescents were more likely than Whites to have used cannabis exclusively, while Black tobacco-naïve adolescents were less likely to use e-cigarettes exclusively or co-use e-cigarettes and cannabis. Overall, males and twelve graders were more likely than males and eight graders to use or co-use cannabis or e-cigarettes, respectively. Among lifetime tobacco users, higher levels of parental education were associated with co-use of cannabis and e-cigarettes. Racial/ethnic-specific patterns of e-cigarette and cannabis co-use depends on adolescents' prior experience with tobacco. The higher rates of use and co-use of e-cigarettes and cannabis among prior tobacco users suggest that targeted interventions are needed for this group. Identified socio-demographic groups at higher risk of co-use of e-cigarettes and cannabis need to be further studied.

Sociodemographic, personal, peer, and familial predictors of e-cigarette ever use in ESPAD Ireland: A forward stepwise logistic regression model

INTRODUCTION

E-cigarette ever use has risen significantly in recent years in Ireland, similar to trends elsewhere in Europe, the United States, and Asia-Pacific region. Results from ESPAD Ireland (European School Survey Project on Alcohol and other Drugs) show teenage e-cigarette ever use increased from 18% (2015) to 37% (2019). Given this increase, our aim is to profile e-cigarette ever users and never users in this age group; to examine sociodemographic, personal, peer, and familial factors associated with e-cigarette ever use; and to suggest appropriate measures to reduce use.

METHODS

A nationally representative stratified random sample of 50 ESPAD schools was surveyed in 2019, with 3495 students aged 15–17 years. Bivariate and multivariable logistic regression analyses were performed using Stata version 16.

RESULTS

E-cigarette ever use was significantly associated with ever smoking (AOR=4.15; 95% CI: 1.29–13.41), ever cannabis use (AOR=2.21; 95% CI: 1.11–4.41) and ever inhalants use (AOR=2.51; 95% CI: 1.07–5.88). Children of university-educated mothers had significantly higher odds of e-cigarette ever use (AOR=3.46; 95% CI: 1.40–8.54). Associated with reduced AORs were reading books for enjoyment (AOR=0.32; 95% CI: 0.16–0.64), living in households where smoking was regulated (AOR=0.53; 95% CI: 0.30–0.94), and perceiving moderate risk in trying e-cigarettes once or twice (AOR=0.20; 95% CI: 0.07–0.67).

CONCLUSIONS

E-cigarette ever use is part of a pattern of teenage polysubstance use including cigarette smoking, providing some support for the common liability theory. Regulation of smoking in the home, reading for enjoyment, and perceiving risk from e-cigarette use are associated with decreased likelihood of ever use, and higher parental education with increased likelihood. Thus, health education emphasizing the role of parents and risks of e-cigarette use is indicated to reduce the rise in e-cigarette ever use in teenagers.

Youth susceptibility to tobacco use: is it general or specific?

BACKGROUND

Susceptibility to tobacco use predicts tobacco use onset among youth. The current study aimed to estimate the extent of overlap in susceptibilities across various tobacco products, investigate sociopsychological correlates with susceptibilities, and examine whether the relationship linking susceptibility with the onset of use is product-specific or is accounted for by a general susceptibility-onset relationship.

METHODS

The study population consisted of US youth 12-17 years old who had never used a tobacco product, sampled in the longitudinal Population Assessment of Tobacco and Health study wave 4 (Dec. 2016-Jan. 2018; n = 10,977). Tobacco product-specific susceptibility at wave 4 was assessed via questions about curiosity, likelihood to try, and likelihood of use if a best friend offered. The onset of use of various tobacco products was defined as first use occurring between the wave 4 and wave 4.5 (Dec. 2017-Dec. 2018) assessments (n = 8841). Generalized linear regression and structural equation models were used for data analysis.

RESULTS

There is a large degree of overlap in susceptibilities across tobacco products (65% of tobacco-susceptible youth were susceptible to more than one tobacco product). Tobacco-susceptible youths were more likely to have recently used cannabis, consumed alcohol, or to have been associated with tobacco-using peers. Structural equation models suggest that the susceptibility-onset relationship largely operates in a non-product-specific manner after accounting for the general susceptibility-to-tobacco-onset relationship.

CONCLUSIONS

Youth susceptibility to tobacco use overlaps widely across different tobacco products and other risky behaviors. Findings from this study support a holistic approach towards the prevention of risk behaviors, supplemented by product-specific strategies when needed.

Reducing the smoking-related health burden in the USA through diversion to electronic cigarettes: a system dynamics simulation study

BACKGROUND

Electronic cigarettes ("e-cigarettes") have altered tobacco smoking trends, and their impacts are controversial. Given their lower risk relative to combustible tobacco, e-cigarettes have potential for harm reduction. This study presents a simulation-based analysis of an e-cigarette harm reduction policy set in the USA.

METHODS

A system dynamics simulation model was constructed, with separate aging chains representing people in different stages of use (both of combustible cigarettes and e-cigarettes). These structures interact with a policy module to close the gap between actual (simulated) and goal numbers of individuals who smoke, chosen to reduce the tobacco-attributable death rate (i.e., mostly combustible cigarette-attributable, but conservatively allowing e-cigarette-attributable deaths) to that due to all accidents in the general population. The policy is two-fold, removing existing e-liquid flavor bans and providing an informational campaign promoting e-cigarettes as a lower-risk alternative. Realistic practical implementation challenges are modeled in the policy sector, including time delays, political resistance, and budgetary limitations. Effects of e-cigarettes on tobacco smoking occur through three mechanisms: (1) diversion from ever initiating smoking; (2) reducing progression to established smoking; and (3) increasing smoking cessation. An important unintended effect of possible death from e-cigarettes was conservatively included.

RESULTS

The base-case model replicated the historical exponential decline in smoking and the exponential increase in e-cigarette use since 2010. Simulations suggest tobacco smoking could be reduced to the goal level approximately 40 years after implementation. Implementation obstacles (time delays, political resistance, and budgetary constraints) could delay and weaken the effect of the policy by up to 62% in the worst case, relative to the ideal-case scenario; however, these discrepancies substantially decreased over time in dampened oscillations as negative feedback loops stabilize the system after the one-time "shock" introduced by policy changes.

CONCLUSIONS

The simulation suggests that the promotion of e-cigarettes as a harm-reduction policy is a viable strategy, given current evidence that e-cigarettes offset or divert from smoking. Given the strong effects of implementation challenges on policy effectiveness in the short term, accurately modeling such obstacles can usefully inform policy design. Ongoing research is needed, given continuing changes in e-cigarette use prevalence, new policies being enacted for e-cigarettes, and emerging evidence for substitution effects between combustible cigarettes and e-cigarettes.

Further investigation of gateway effects using the PATH study

Background: Interest exists in whether youth e-cigarette use (“vaping”) increases risk of initiating cigarette smoking. Using Waves 1 and 2 of the US PATH study we previously reported adjustment for vaping propensity using Wave 1 variables explained about 80% of the unadjusted relationship. Here data from Waves 1 to 3 are used to avoid over-adjustment if Wave 1 vaping affected variables recorded then.

Methods: Main analyses M1 and M2 concerned Wave 2 never smokers who never vaped by Wave 1, linking Wave 2 vaping to Wave 3 smoking initiation, adjusting for predictors of vaping based on Wave 1 data using differing  propensity indices.  M3 was similar but derived the index from Wave 2 data.  Sensitivity analyses excluded Wave 1 other tobacco product users, included other product use as another predictor, or considered propensity for smoking or any tobacco use, not vaping. Alternative analyses used exact age (not previously available) as a confounder not grouped age, attempted residual confounding adjustment by modifying predictor values using data recorded later, or considered interactions with age.

Results: In M1, adjustment removed about half the excess OR (i.e. OR–1), the unadjusted OR, 5.60 (95% CI 4.52-6.93), becoming 3.37 (2.65-4.28), 3.11 (2.47-3.92) or 3.27 (2.57-4.16), depending whether adjustment was for propensity as a continuous variable, as quintiles, or the variables making up the propensity score. Many factors had little effect: using grouped or exact age; considering other products; including interactions; or using predictors of smoking or tobacco use rather than vaping. The clearest conclusion was that analyses avoiding over-adjustment explained about half the excess OR, whereas analyses subject to over-adjustment explained about 80%.

Conclusions: Although much of the unadjusted gateway effect results from confounding, we provide stronger evidence than previously of some causal effect of vaping, though doubts still remain about the completeness of adjustment.