Methodological considerations when assessing symptoms of nicotine dependence by cigarette and e-cigarette use behavior and brand.

Letter to the Editor re: Adjei et al., 2024. Symptoms of nicotine dependence by e-cigarette and cigarette use behavior and brand: A population-based, nationally representative cross-sectional study. Drug and Alcohol Dependence 255, 111059 concerning issues of context, citations, non-psychometrically validated dependence items, and disclosure transparency.

What Substances Are Adolescents Vaping? Estimating Nicotine-Specific and Cannabis-Specific Vaping from US National Youth Surveys.

BACKGROUND: Youth use of electronic cigarettes ("e-cigarettes") is an ongoing concern. Vaping is usually assumed to be of nicotine, but survey responses may also include vaping of non-nicotine substances (particularly cannabis), which can impose different risks. The current study quantifies the proportions of nicotine-specific and cannabis-specific vaping among adolescents. METHODS: Data from the Monitoring the Future (MTF) 2021 survey were analyzed with respect to the substance vaped: nicotine, cannabis, or flavoring only. Past-30-day (P30D) nicotine-specific vaping (exclusively, or also having vaped other substances) and cannabis-specific vaping were calculated. Results were also broken down by grade level, cigarette smoking history (current, former, or never), and frequency of e-cigarette use (<20 vs. 20+ days in P30D). RESULTS: Among 4706 P30D e-cigarette users, 84.2% vaped nicotine; the remaining 15.8% reported not vaping any nicotine in P30D. Cannabis vaping was reported by 48.4% of P30D vapers. Vaping both nicotine and cannabis in P30D was more common in current and former smokers than in never-smokers, and in frequent vapers (of any substance). Conversely, never-smokers were more commonly vaping cannabis but not nicotine, compared to current and former smokers. Supplementary NYTS 2022 analyses were consistent with these findings. CONCLUSIONS: A notable proportion of self-reported P30D vapers reported not vaping nicotine; nearly half vaped cannabis. Capturing the variety of substances used in vaping devices is imperative for accurate public health surveillance of both nicotine and cannabis vaping among US adolescents, considering their different respective harms and regulatory frameworks.

Comment on Wang et al. Consumption of JUUL vs. Other E-Cigarette Brands among U.S. E-Cigarette Users: Evidence from Wave 5 of the PATH Study. Int. J. Environ. Res. Public Health 2022, 19, 10837.

This comment is to express concern about an article by Wang et al. [...].

Re: "Impact of the e-cigarette era on cigarette smoking among youth in the United States: A population-level study".

This is a letter to the editor of Preventive Medicine responding to Harrell et al.'s "Impact of the e-cigarette era on cigarette smoking among youth in the United States: A population-level study." (Harrell MB, Mantey DS, Baojiang C, Kelder SH, Barrington-Trimis J. Impact of the e-cigarette era on cigarette smoking among youth in the United States: A population-level study. Preventive Medicine 2022;164:107265).

Population-level counterfactual trend modelling to examine the relationship between smoking prevalence and e-cigarette use among US adults.

BACKGROUND: Studies have suggested that some US adult smokers are switching away from smoking to e-cigarette use. Nationally representative data may reflect such changes in smoking by assessing trends in cigarette and e-cigarette prevalence. The objective of this study is to assess whether and how much smoking prevalence differs from expectations since the introduction of e-cigarettes. METHODS: Annual estimates of smoking and e-cigarette use in US adults varying in age, race/ethnicity, and sex were derived from the National Health Interview Survey. Regression models were fitted to smoking prevalence trends before e-cigarettes became widely available (1999-2009) and trends were extrapolated to 2019 (counterfactual model). Smoking prevalence discrepancies, defined as the difference between projected and actual smoking prevalence from 2010 to 2019, were calculated, to evaluate whether actual smoking prevalence differed from those expected from counterfactual projections. The correlation between smoking discrepancies and e-cigarette use prevalence was investigated. RESULTS: Actual overall smoking prevalence from 2010 to 2019 was significantly lower than counterfactual predictions. The discrepancy was significantly larger as e-cigarette use prevalence increased. In subgroup analyses, discrepancies in smoking prevalence were more pronounced for cohorts with greater e-cigarette use prevalence, namely adults ages 18-34, adult males, and non-Hispanic White adults. CONCLUSION: Population-level data suggest that smoking prevalence has dropped faster than expected, in ways correlated with increased e-cigarette use. This population movement has potential public health implications.

Estimating E-Cigarette Use Prevalence among US Adolescents UsingVaping-Related Online Search Trends.

Introduction: Adolescent e-cigarette use is a developing phenomenon. Greater surveillance of underage use is necessary to inform e-cigarette policy and mitigate adolescent e-cigarette use. Accurate prevalence estimates for adolescent e-cigarette use are provided by large national surveys. However, these surveys are costly and provide only annual estimates. To obtain more affordable estimates faster and more frequently, novel methods are required. Methods: Online search term popularity data were taken from Google Trends. Interest in vaping-related search terms were followed monthly from January 2011 to November 2020. Time-lagged zero-normalized cross-correlations were performed between the Google data and current (past 30 day) high-school e-cigarette use prevalence estimates from the National Youth Tobacco Survey (NYTS). The search interest data were then calibrated to the NYTS data to estimate adolescent e-cigarette use prevalence using online searches. Results: Maximum correlation coefficients of 0.979 for "vapes" and 0.938 for "vape" were obtained when search interest lagged use prevalence by one month, and 0.970 for "vape pen" when the lag was two months (p < 0.001 for all). Calibrating the search term data to NYTS provided a high-school current e-cigarette use prevalence estimate of 12.1-18.4% for November 2020, suggesting adolescent use of e-cigarettes has continued to decline since the NYTS estimate of 19.6% for January-March 2020. Conclusions: Online search trend data may provide reasonably reliable and more frequent estimates of adolescent e-cigarette use prevalence at substantially lower costs than traditional surveys. Such additional data may help to assess immediate impacts of policies and events.

Trends in electronic cigarette use and conventional smoking: quantifying a possible 'diversion' effect among US adolescents.

BACKGROUND AND AIMS: The impact of electronic cigarettes (ECs) on nicotine use is hotly debated: some fear that ECs are a 'catalyst' to conventional smoking, while others argue that they divert adolescents from the more harmful product. This study used simulation modeling to evaluate the plausibility of catalyst and diversion hypotheses against real-world data. DESIGN: A simulation model represented life-time exclusive EC use, exclusive conventional smoking and dual use as separate subpopulations. The 'catalyst' effect was modeled as EC use increasing dual use initiation (i.e. EC users also start smoking). The 'diversion' effect was modeled as EC use decreasing exclusive cigarette initiation. The model was calibrated using data from the US National Youth Tobacco Survey (NYTS). The plausibility of each scenario was evaluated by comparing simulated trends with NYTS data. This is the first study, to our knowledge, to estimate the magnitude of a diversion effect through simulation. SETTING: United States. PARTICIPANTS AND MEASUREMENTS: Adolescents aged 12-17 years in NYTS, a cross-sectional study from 2000 to 2019 (n = 12 500 to 31 000 per wave). Exclusive cigarette use, exclusive EC use and dual use of both products were defined using cumulative life-time criteria (100+ cigarettes smoked and/or > 100 days vaped). FINDINGS: A null model (no catalyst or diversion) over-predicts NYTS smoking by up to 87%. Under the conservative assumption that the catalyst effect accounts for all dual use, an exponential decay constant of 19.6% EC users/year initiating smoking is required; however, this further over-predicts actual smoking by up to 109%. A diversion effect with an exponential decay constant of 55.4%/year or 65.4%/year, with the maximum possible opposing catalyst effect also active, is required optimally to match NYTS smoking trends (root mean square error = 286 632 versus 391 396 in the null model). CONCLUSIONS: A simulation model shows that a substantial diversion effect is needed to explain observed nicotine use trends among US adolescents, and it must be larger than any possible opposing catalyst effect, if present.

Electronic cigarettes, nicotine use trends and use initiation ages among US adolescents from 1999 to 2018.

BACKGROUND AND AIMS: Recent nicotine use trends raise concerns that electronic cigarettes (ECs) may act as a gateway to cigarettes among adolescents. The aims of this study were to examine prevalence trends of exclusive EC use, exclusive cigarette use and dual use to determine the corresponding ages of initiation and to investigate hypothetical trends in total nicotine use and cigarette use in the absence of ECs among US adolescents. DESIGN: Observational study using data from the National Youth Tobacco Survey (NYTS) to statistically model trends in the prevalences of each user group and their initiation ages. Projections from counterfactual models based on data from 1999 to 2009 (before EC introduction) were compared with actual trends based on data from 1999 to 2018. Rigorous error analyses were applied, including Theil proportions. SETTING: USA. PARTICIPANTS AND MEASUREMENTS: Adolescents aged 12-17 years who were established exclusive cigarette users (≥ 100 cigarettes smoked and ≤ 100 days vaped), established exclusive EC users (< 100 cigarettes smoked and > 100 days vaped) and established dual users (≥ 100 cigarettes smoked and > 100 days vaped), based on cumulative life-time exposure (n ≈ 12 500-31 000 per wave). FINDINGS: Exclusive cigarette use prevalence declined from 1999 to 2018, while exclusive EC use and dual use prevalences increased since their introduction in 2009. The age of cigarette initiation began a slight increase after 2014, whereas the age for EC use remained approximately constant and was higher than that of cigarettes. The counterfactual comparison results were consistent with ECs not increasing the number of US adolescent nicotine users, and in fact diverting adolescents from cigarettes. CONCLUSIONS: Electronic cigarettes may have offset conventional smoking among US adolescents between 2010 and 2018 by maintaining the total nicotine use prevalence and diverting them from more harmful conventional smoking. Additionally, electronic cigarette users appear to initiate at older ages relative to conventional smokers, which is associated with lower risk.