You are here

Article Text

Article menu
Original research
Effectiveness of e-cigarettes as aids for smoking cessation: evidence from the PATH Study cohort, 2017–2019
  1. Ruifeng Chen1,
  2. John P Pierce1,2,
  3. Eric C Leas1,
  4. Tarik Benmarhnia3,
  5. David R Strong1,2,
  6. Martha M White2,
  7. Matthew Stone1,
  8. Dennis R Trinidad1,
  9. Sara B McMenamin1,
  10. Karen Messer1,2
  1. 1 Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA
  2. 2 Moores Cancer Center, University of California San Diego, La Jolla, California, USA
  3. 3 Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USA
  1. Correspondence to Dr John P Pierce, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, USA; jppierce{at}ucsd.edu

Abstract

Objective To assess the effectiveness of e-cigarettes in smoking cessation in the USA from 2017 to 2019, given the 2017 increase in high nicotine e-cigarette sales.

Methods In 2017, the PATH Cohort Study included data on 3578 previous year smokers with a recent quit attempt and 1323 recent former smokers. Respondents reported e-cigarettes or other products used to quit cigarettes and many covariates associated with e-cigarette use. Study outcomes were 12+ months of cigarette abstinence and tobacco abstinence in 2019. We report weighted unadjusted estimates and use propensity score matched analyses with 1500 bootstrap samples to estimate adjusted risk differences (aRD).

Results In 2017, 12.6% (95% CI 11.3% to 13.9%) of recent quit attempters used e-cigarettes to help with their quit attempt, a decline from previous years. Cigarette abstinence for e-cigarette users (9.9%, 95% CI 6.6% to 13.2%) was lower than for no product use (18.6%, 95% CI 16.0% to 21.2%), and the aRD for e-cigarettes versus pharmaceutical aids was −7.3% (95% CI −14.4 to –0.4) and for e-cigarettes versus any other method was −7.7% (95% CI −12.2 to –3.2). Only 2.2% (95% CI 0.0% to 4.4%) of recent former smokers switched to a high nicotine e-cigarette. Subjects who switched to e-cigarettes appeared to have a higher relapse rate than those who did not switch to e-cigarettes or other tobacco, although the difference was not statistically significant.

Conclusions Sales increases in high nicotine e-cigarettes in 2017 did not translate to more smokers using these e-cigarettes to quit smoking. On average, using e-cigarettes for cessation in 2017 did not improve successful quitting or prevent relapse.

  • cessation
  • electronic nicotine delivery devices
  • Surveillance and monitoring
  • addiction

Data availability statement

The data are in a Restricted Use File that is available to approved researchers. National Addiction and HIV Data Archive Program. Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files (ICPSR 36231). NIH; National Institute on Drug Abuse.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/tobaccocontrol-2021-056901

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    Electronic cigarettes (e-cigarettes), which were first sold in the USA in 2007, had become a popular cessation aid for US smokers by 2014–2016.1 2 From 2013 to 2017 US sales of e-cigarettes almost doubled,3 which was associated with rapid uptake among adolescents.4 If there was a similar increase in e-cigarette usage attributed to smoking cessation (either as a cessation aid or an alternative nicotine source) and effectiveness was demonstrated, we would expect that successful cigarette cessation would increase in the population.

    Randomised clinical trials (RCTs) are the optimal design to assess the efficacy of e-cigarettes as smoking cessation aids. To date, a number of RCTs have addressed the role of e-cigarettes as an aid to quitting cigarettes, and a recent systematic review concluded, with moderate certainty, that e-cigarettes improve cessation by an estimated four additional successful quitters per 100 quit attempters when compared with nicotine replacement therapy (NRT).5 However, RCTs are usually conducted under optimal conditions, which means that they may not translate to the effectiveness of the product in community settings.6 Analyses of the Population Assessment of Tobacco and Health (PATH) Study7 have not found that e-cigarettes improve cessation.8 9

    To date, no trials have been reported that test the hypothesis that cigarette smokers are able to switch to e-cigarettes and maintain their nicotine habit without relapsing to cigarette smoking. A recent PATH Study analysis found that those who switched to e-cigarettes between 2014 and 2016 were more likely to relapse to cigarette smoking by 2017 than those who were free from all tobacco including e-cigarettes between 2014 and 2016.10 However, the e-cigarette market has changed dramatically since 2016. JUUL Labs introduced nicotine salt technology in 2015 and high nicotine concentration pods (ie, 5% nicotine by weight).11 On the back of an innovative marketing campaign, JUUL became the most popular US e-cigarette in 201712 13 when over 50% of all e-cigarette products sold had high (>4%) nicotine concentrations.3 Increasing the nicotine concentration in e-cigarette liquid increases nicotine exposure for users,14–16 and high nicotine JUUL users have blood nicotine concentrations similar to cigarette smokers, which some argue may be a prerequisite for successfully switching to e-cigarettes.17 Thus, in 2017, recent former smokers had the opportunity to switch to e-cigarettes with a much higher nicotine concentration than was possible for those in earlier years, which could reduce relapse to cigarette smoking.

    The PATH Study is a nationally representative longitudinal study that can address questions on the effectiveness of e-cigarettes in reducing cigarette smoking. However, for longitudinal studies to address whether a product may cause an outcome such as smoking cessation requires careful analysis. The critical point is that groups must be as comparable as possible across variables that might be related to the study outcome.18 In RCTs, randomisation of product usage usually achieves this effect. In observational studies it is necessary to control for the variables associated with using e-cigarettes, particularly those that are also associated with longer term cigarette cessation (eg, motivation to quit). Some published analyses of PATH Study data19–21 have not required that the control group has a recent quit attempt. Given that e-cigarettes are seen as a popular way to quit cigarettes,1 such an analytical decision means that the control group will be very different from the e-cigarette user group as it will include many people who are not trying to quit, thus significantly biasing the conclusions in favour of an e-cigarette effect.22

    In this paper, our starting population are PATH Study respondents who were established smokers in 2016. To address the hypothesis that e-cigarettes are an effective cigarette cessation aid, we limit our consideration to those who reported a quit attempt in the year prior to the 2017 (W4) survey and compare how cessation aids used were associated with 12+ months of cigarette/tobacco abstinence at the 2019 (W5) survey (see study flowchart in online supplemental file 1). To address whether switching to e-cigarettes improves maintenance of cigarette abstinence, we focus on those who were recent former smokers in 2017 (W4) and compare relapse to cigarette smoking in 2019 (W5) among those who switched to e-cigarettes versus those who did not use any tobacco or e-cigarette product.

    Methods

    Data sources

    The PATH Study is a US nationally representative cohort study. A screener survey of a stratified address-based sample of households oversampled tobacco users, young adults aged 18–24 and African Americans for the adult cohort.7 The first four survey waves (W1–4) were at annual intervals starting in 2013–14 (W1), and W5 (2019) was conducted ~2 years after W4 (2017). The initial household screener had a 54% response rate and the adult survey response rates were 74.0%, 83.2%, 78.4% and 73.5% for W1–4, respectively. Among initial screened households, 27 757 adults were interviewed at W4 and an additional new replenishment sample of 6065 adults were added to the cohort to adjust for attrition and reset the cohort sample size, thus reducing the magnitude of weighting required to provide population estimates.23 The weighted response rate for W4 replenishment household screener was 52.8% and the response rate of the adult survey was 68.0% at W4 and 88.0% at W5. The Westat Institutional Review Board approved the study and all respondents provided written informed consent. Data were obtained from available restricted use files.23

    Study sample

    The W4 (2017) total sample included both a continuing cohort and an added refreshment sample (see online supplemental file 1). For longitudinal analyses requiring earlier data we are limited to the continuing cohort subset (those with W1–W3 data). For each PATH survey, lifetime 100+ cigarette smokers were asked if they “currently smoke every day, some days, or not at all”.23 Thus, in this paper the continuing cohort are drawn from those who were current daily or some-day smokers at W3 (2016). For the added refreshment sample at W4 (2017), we assessed previous year smoking from: “Around this time 12 months ago, did you smoke cigarettes every day, some days or not at all?”.

    To investigate whether e-cigarettes are an effective cigarette cessation aid, we identified recent quit attempters from the W4 question: “In the past 12 months, have you tried to quit cigarettes completely?” A positive response was made by 3578 previous year established smokers. To investigate whether switching to e-cigarettes helps prevent relapse to cigarettes, we identified recent former smokers at W4 from a “not at all” response to the current cigarette smoking question among previous year established smokers (n=1323).

    Use of e-cigarette or other products

    To identify products used to help quit attempts, W4 quit attempters were asked: “Thinking back to the last time you tried to quit cigarettes in the past 12 months”, followed by three separate types of questions: “did you use an e-cigarette/(other non-cigarette tobacco product) to help you quit?”; “did you use a nicotine patch, gum, inhaler, nasal spray, lozenge or pill?”; and “did you use Chantix, varenicline, Wellbutrin, Zyban or bupropion?”.

    To identify recent former smokers who had switched to an alternative nicotine source, we used the current use question (responses of every day, some days or not at all) for each of the following products: e-cigarettes, cigars, cigarillo, filtered cigars, pipes, hookah, snus and smokeless products. E-cigarette users were asked: “What concentration of nicotine do you usually use?” with eight response categories ranging from 0% to 4+%, as well as don't know.

    Study outcome

    At W5 (2019) current cigarette and other tobacco use was assessed from responses to the current use question for each product. To assess duration of abstinence from cigarettes, recent former smokers were asked: “In the past 12 months, have you smoked a cigarette/(used product), even one or two puffs/times?” Cigarette abstinence includes those who were using e-cigarettes or other tobacco products. Tobacco abstinence requires abstinence from all tobacco and e-cigarettes. This question was asked for all tobacco products as well as e-cigarettes. Duration of abstinence came from the question: “About how long has it been since you last smoked a cigarette/puffed from an electronic nicotine product?”

    Study covariates

    PATH Study investigators identified and measured potential confounders for e-cigarette and cessation analyses and demonstrated that these were mismatched between e-cigarette users and control participants.9 Most of these variables were best measured when participants were still smokers at W3 (2016) and are only available for the continuing cohort. They include sociodemographic variables (age, sex, education, race, ethnicity, income), cigarette smoking status (daily or non-daily), tobacco dependence index,24 time since last quit attempt, cigarette consumption, e-cigarette use status (any use or no use), interest in quitting cigarettes, self-efficacy about quitting, smoke-free home, exposure to smoking, perceived harm of cigarettes and e-cigarettes, cigarette pack-years, age began regular smoking, insurance status and health-related covariates (external/internal mental health symptoms, existence of smoking-related disease). Questions for each covariate and univariate distributions by product used in the quit attempt are shown in online supplemental file 2,3.

    To test whether switching to e-cigarettes prevented relapse, we used the same set of covariates with the following exceptions: (1) we added duration of cigarette abstinence at W4 (2017); (2) we changed the source of the smoke-free home measure from W3 (2016) to W4 (2017). Details of these covariates with univariate distributions by product used are shown in online supplemental file 4,5.

    Statistical analyses

    All analyses were conducted in R (version 3.6.1). For unadjusted analyses using total samples (continuing + refreshment), estimates were weighted using W4 single wave weights23 and variance estimates for confidence intervals were calculated using replicate weights constructed using a balanced repeated replications procedure with Fay adjustment (ρ=0.3).7 Sample characteristics were explored using weighted proportions with 95% confidence limits. The adjusted analyses were restricted to the continuing cohort only and used W1–W5 longitudinal survey weights.23

    For the adjusted propensity score matching analysis we created 1500 bootstrap samples for each hypothesis test. Within each bootstrap sample we used simple imputation (R package ‘Mice’) for missing data from all the covariates, and we identified the optimal set of covariates prior to estimating the propensity score as follows. To select variables we used the LASSO with the Akaike Information Criterion (AIC).25 26 The optimal set of covariates was the one that returned the smallest AIC. Then, for each exposure separately, we calculated a propensity score for each participant by estimating the unweighted probability of membership in the e-cigarette use group using logistic regression adjusting for the optimised set of covariates. Using the estimated propensity score, we matched up to two controls for each case (nearest neighbour matching using R package ‘Matchit’)27 within the a priori calliper distance of 0.1. Cases that did not have a match meeting these criteria were omitted from the sample (<10% for each matching). For each matched bootstrap sample we used logistic regression with survey weights (R package ‘survey’) to estimate the average risk difference between the two matched groups for each outcome. The model included an indicator of the matched pair (or triple) and an indicator of use of e-cigarettes or not. The risk difference was estimated by the bootstrap mean estimate and the confidence intervals were calculated using the 95% bootstrap quantiles. To assess e-cigarettes as a cigarette cessation aid we compared 12+ months of cigarette abstinence between (1) any e-cigarette for quit attempt versus anyone who did not use an e-cigarette; and (2) any e-cigarette versus NRT or pharmaceutical aid only for quit attempt. We also compared those who used e-cigarettes only versus NRT or pharmaceutical aid only in a sensitivity analysis. To assess if e-cigarettes prevent relapse to cigarettes, we estimated the risk difference in rates of relapse to cigarette smoking between any e-cigarette versus no e-cigarette at W4. Current use of NRT and pharmaceutical aids was only collected in relation to the last quit attempt.

    Results

    Characteristics of tobacco use among recent quit attempters

    There were no differences between the continuing cohort and the combined continuing cohort and refreshment sample (ie, total W4 sample) in any of the following key measures (table 1). In 2017 (W4), 32.8% (95% CI 31.8% to 33.9%) of previous year established smokers reported a recent quit attempt in the year prior to W4 and 12.4% (95% CI 11.6% to 13.3%) were recent former smokers at W4. Among recent quit attempters, 12.6% (95% CI 11.3% to 13.9%) reported using e-cigarettes to help in their last quit attempt (8.7% e-cigarettes only, 3.2% e-cigarettes and NRT/pharmaceutical aid, 0.5% e-cigarettes and other tobacco products, 0.2% used 3+ products); 2.5% (95% CI 1.9% to 3.1%) used non-e-cigarette tobacco products (2.1% non-e-cigarette tobacco products only); 20.6% (95% CI 18.9% to 22.3%) used NRT or a pharmaceutical aid only and 64.3% (95% CI 62.4% to 66.1%) did not use any product.

    View this table:
    Table 1

    Characteristics of PATH Study Wave 4 tobacco use

    Among recent former cigarette smokers in 2017 (W4), 15.3% had switched to e-cigarettes (daily: 9.1% (95% CI 7.1% to 11.0%); non-daily: 6.2% (95% CI 4.7% to 7.7%); 10.4% e-cigarettes only) and 15.9% (95% CI 13.6% to 18.2%) reported use of another tobacco product (11.5% cigar family, 2.9% smokeless, 3.6% other or multiple products) and 68.8% (95% CI 65.9% to 71.8%) reported not using any tobacco or e-cigarette. Among those who had switched to e-cigarettes, only 2.2% (95% CI 0.0% to 4.4%) reported using e-cigarettes with concentration >4% (see online supplemental file 6) and 1.9% (95% CI 0.4% to 3.4%) reported using JUUL e-cigarettes. This supplement also presents the 2019 (W5) data for recent former smokers who switched to e-cigarettes as this proportion increased to 22.0% (95% CI 19.6% to 24.5%) compared with the 15.3% observed at W4, with 19.9% of them using high nicotine content e-cigarettes.

    Characteristics of recent quit attempters who used e-cigarettes

    The use of e-cigarettes to aid a quit attempt was higher in 18–50-year-old subjects than in those aged 50+ years, higher in those who had attended college than in those who did not complete high school, higher in non-Hispanic white people than in other race ethnicities, higher in those with incomes >$35 000 than in those with lower incomes, higher in 2016 (W3) daily smokers than in non-daily smokers and higher in 2016 (W3) e-cigarette users (table 2). Similar use patterns were observed for recent former smokers (see online supplemental file 3, 5), although the lower sample size of recent former smokers resulted in some wide confidence intervals.

    View this table:
    Table 2

    Characteristics of recent quit attempters reported at PATH Wave 4 by use of non-cigarette tobacco products on last quit attempt prior to Wave 4

    Successful quitting at W5 among quit attempters in year prior to W4

    Unadjusted successful quitting in the total samples (continuing + refreshment)

    Among those who used e-cigarettes in their last quit attempt prior to W4 (2017), 9.9% (95% CI 6.6% to 13.2%) were abstinent from cigarettes for 12+ months but not all tobacco at W5, which was lower than those who used NRT or pharmaceutical aid only (15.2%, 95% CI 12.3% to 18.1%) or those who did not use any product in the quit attempt (18.6%, 95% CI 16.0% to 21.2%), with similar patterns between the total sample and the continuing cohort (table 3). Considering abstinence for 12+ months from all tobacco including e-cigarettes, the proportion who used e-cigarettes for the quit attempt (3.5%, 95% CI 1.5% to 5.5%) was considerably lower than those who used NRT or pharmaceutical aid only (12.5%, 95% CI 9.6% to 15.4%) or who did not use any product when attempting to quit (13.9%, 95% CI 11.4% to 16.5%). For both abstinence from cigarettes and abstinence from all tobacco (including e-cigarettes), our data suggest that those who used e-cigarettes to help them quit had a similar outcome to those who used another non-cigarette combustible (eg, cigar) or smokeless tobacco product (eg, snus) (table 3).

    View this table:
    Table 3

    Abstinence for 12+ months at Wave 5 among smokers who tried to quit prior to Wave 4 according to products used to assist during last quit attempt prior to Wave 4

    Among recent former smokers who had switched to daily use of e-cigarettes in 2017 (W4), 43.2% (95% CI 32.5% to 54.0%) had successfully quit cigarette smoking by 2019 (W5), which was similar to those who used e-cigarettes on a non-daily basis or to those who switched to another tobacco product, whether daily or non-daily (table 4). All estimates of successful quitting for those who switched to another nicotine source were below the lower confidence bound for those who reported no tobacco use in 2017 (W4) (52.9%, 95% CI 47.8% to 58.0%), although confidence intervals overlapped. Among those who had relapsed between 2017 (W4) and 2019 (W5), 15–20% had made another quit attempt (re-quit) and were abstinent at the time of the 2019 (W5) survey, although there were no differences across categories in the duration of these re-quit attempts.

    View this table:
    Table 4

    Unadjusted cigarette smoking status at Wave 5 among recent former cigarette smokers* by use of non-cigarette tobacco products assessed at Wave 4

    Adjusted successful quitting in the continuing cohort

    Propensity score matching achieved comparable study groups for variables associated with e-cigarette use at W4 (2017) (see online supplemental file 7-9). However, the perception that e-cigarettes were less harmful than cigarettes fell from 23.8% (95% CI 23.1% to 24.5%) in 2016 (W3) to 16.4% (95% CI 15.9% to 17.0%) in 2019 (W5) (see online supplemental file 10). Among quit attempters, those who used an e-cigarette as an aid had a lower 12+ month cigarette abstinence rate than those who did not (adjusted risk difference (aRD) −7.7, 95% CI −12.2 to −3.2). Similarly, using an e-cigarette as an aid resulted in a lower 12+ month cigarette abstinence rate than using NRT or a pharmaceutical aid (aRD −7.3, 95% CI −14.4 to −0.4) (figure 1A). When the outcome was 12+ months abstinence from cigarettes, e-cigarettes or any other tobacco product, these results were essentially the same with the aRD showing that e-cigarette use had between 7.4% and 6.4% lower abstinence than either not using e-cigarettes or using a pharmaceutical aid (figure 1B). The sensitivity analysis estimating the aRD between e-cigarette only users and NRT or pharmaceutical aid only users produced similar results.

    Figure 1
    Figure 1

    The adjusted risk difference (RD) in the rate of 12+ months of cigarette/tobacco abstinence for quit attempters by comparing the use of e-cigarettes versus no product use and the use of e-cigarettes versus use of nicotine replacement therapy (NRT) or pharmaceutical aid only during the last quit attempt in the year prior to Wave 4. (A) 12+ months of cigarette abstinence; (B) 12+ months of tobacco abstinence. Analyses using propensity score matching followed by logistic regression adjustment. Bootstrap samples were created to make statistical inference (details given in the section on Statistical Analyses). Covariates used for propensity score matching include: age, sex, education, race, ethnicity, income, cigarette smoking status at W3, time since last quit attempt, tobacco dependence index, cigarette consumption at W3, duration of previous quit attempt reported at W4, interest in quitting cigarettes, self-efficacy about quitting, smoke-free home, exposure to smoking, perceived harm of cigarettes and e-cigarettes, cigarette pack-years, age began regular smoking, insurance status, external mental health symptoms, internal mental health symptoms and existence of smoking-related disease. Missing data were imputed using simple imputation for each bootstrap sample. Cigarette abstinence does not include abstinence from e-cigarettes or other tobacco products. Tobacco abstinence includes no use of e-cigarette, cigar, cigarillo, filtered cigar, pipe, hookah, snus and smokeless tobacco.

    Propensity score matching achieved highly comparable groups among recent former smokers who had switched to e-cigarettes compared with those who had not (online supplemental file 7). The e-cigarette group appeared to have a higher relapse rate by W5 (2019) than those who did not use any tobacco or e-cigarette product (aRD 9.4%, 95% CI −5.0% to 22.8%); however, this did not reach statistical significance.

    Discussion

    In this analysis of the most recent PATH Study data, smokers who reported using e-cigarettes to help them in their most recent cigarette quit attempt were less rather than more likely than other quit attempters to achieve either successful cigarette cessation or to become tobacco and e-cigarette free. Rather than e-cigarettes adding four additional successful cigarette quitters per 100 quit attempters compared with pharmaceutical aid users as concluded by a systematic review of RCT data,5 in this study e-cigarette use was associated with seven fewer successful quitters per 100 quit attempters. Furthermore, switching to e-cigarettes did not reduce the risk of relapse to cigarette smoking compared with other recent former smokers. Instead, nearly 60% of recent former smokers who were daily e-cigarette users had relapsed to cigarette smoking by 2019 (W5).

    Between 2013 and 2018 there was a rapid increase in both the number of e-cigarette products available in the USA (now >800) and in the total unit sales, with over 40% sales growth between 2016 and 2017 alone.3 This rapid growth has been attributed to the introduction and effective marketing of high nicotine e-cigarettes, initially by JUUL Labs.28 The high nicotine JUUL e-cigarette has been noted as the closest match to cigarettes in both nicotine delivery and user satisfaction,29 which should make it one of the best candidates as a product to which smokers could switch in order to maintain their nicotine habit.30 Thus, it was surprising that, just as sales for JUUL were surging in the marketplace, the use of e-cigarettes as a cessation aid fell from 17.4% of recent quit attempters in PATH W38 to 12.4% at PATH W4. However, by 2019 this situation had changed, at least among recent former smokers, with 22% switching to e-cigarettes and ~4% using high nicotine concentration e-cigarettes. Our analysis suggests that the 2017 JUUL marketing campaigns were not effective in encouraging smokers to use JUUL products to help with quit attempts, unlike their effectiveness in encouraging young people to initiate nicotine use with their products.4 31 32 However, when we looked ahead to 2019, recent former smokers had started using high nicotine e-cigarettes. The effectiveness of high nicotine e-cigarettes at preventing relapse will require another follow-up PATH survey.

    This study has both advantages and limitations. The PATH Study is a large cohort of a representative sample of the US population with a rigorous methodology, including biological samples to validate self-reported cigarette smoking.7 In previous reports, biomarker concentrations indicate that self-reporting is valid.33 This study included a large group of potential confounders that were measured prior to the target quit attempt and propensity score matching was used to achieve highly comparable groups. Each PATH survey collects detailed current use of a comprehensive set of tobacco products and detailed duration of abstinence of recently used products, allowing a comparison of the effectiveness of a wide range of potential products to help smokers quit. However, this study is observational and the exposure variable was not under experimental control. While our analytical design adjusted for potential confounding variables, other variables that were unmeasured confounders limit causal inference.

    Conclusion

    In 2017, a time of rapid growth in e-cigarette sales in the USA and increasing nicotine content in e-cigarette liquids, no such growth was seen in the use of e-cigarettes for cessation. In this study, smokers trying to quit or interested in switching to another nicotine delivery system were not early adopters of the high nicotine e-cigarettes such as JUUL, which have been reported as the closest products to resembling the experience of cigarette smoking. This analysis did not show a cessation benefit from using e-cigarettes either to help a cessation attempt or as a substitute for cigarette smoking. However, there is evidence that cigarette smokers were starting to use high nicotine e-cigarettes by 2019 and further follow-up in PATH is needed to see whether these changes result in future cessation benefit.

    What this paper adds

    What is already known on this subject?

    • Randomised clinical trials indicate e-cigarettes have efficacy in helping smokers quit

    • US cohort studies have not demonstrated effectiveness in the real world

    • Starting in 2017, JUUL high nicotine e-cigarettes became the most popular e-cigarette brand and overall e-cigarette sales increased markedly

    What important gaps in knowledge exist on this topic?

    • The influence of the increased nicotine content of e-cigarettes on US smokers’ ability to quit cigarette smoking is not known

    What this study adds

    • Despite a large increase in e-cigarette sales, the proportion who used e-cigarettes to help quit cigarettes declined and in 2017 only 2.2% of recent former smokers were using high nicotine e-cigarettes

    • Those who used e-cigarettes to aid their cigarette quit attempt in the year prior to the 2017 survey were less likely to have successfully quit by 2019 compared with those who used a pharmaceutical aid or no product at all

    • E-cigarette use did not prevent recent former smokers from relapsing to cigarettes

    • However, the usage of high nicotine e-cigarettes for cessation increased in 2019, suggesting that this question needs to be addressed again in the 2021 PATH survey

    Data availability statement

    The data are in a Restricted Use File that is available to approved researchers. National Addiction and HIV Data Archive Program. Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files (ICPSR 36231). NIH; National Institute on Drug Abuse.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants but IRB for University of California San DiegoProject #181462 exempted this study Participants gave informed consent to participate in the study before taking part.

    References

    1. ↵
      2. Caraballo RS ,
      3. Shafer PR ,
      4. Patel D , et al
      . Quit methods used by US adult cigarette smokers, 2014-2016. Prev Chronic Dis 2017;14:E32.doi:10.5888/pcd14.160600 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28409740
      OpenUrlCrossRefPubMed
    2. ↵
      2. Rigotti NA
      . Randomized trials of e-cigarettes for smoking cessation. JAMA 2020;324:1835–7.doi:10.1001/jama.2020.18967 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33170225
      OpenUrlPubMed
    3. ↵
      2. Romberg AR ,
      3. Miller Lo EJ ,
      4. Cuccia AF , et al
      . Patterns of nicotine concentrations in electronic cigarettes sold in the United States, 2013-2018. Drug Alcohol Depend 2019;203:1–7.doi:10.1016/j.drugalcdep.2019.05.029 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31386973
      OpenUrlCrossRefPubMed
    4. ↵
      2. Miech R ,
      3. Johnston L ,
      4. O'Malley PM , et al
      . Adolescent vaping and nicotine use in 2017–2018: U.S. national estimates. N Engl J Med 2019;380:192–3.doi:10.1056/NEJMc1814130 pmid:http://www.ncbi.nlm.nih.gov/pubmed/30554549
      OpenUrlPubMed
    5. ↵
      2. Hartmann-Boyce J ,
      3. McRobbie H ,
      4. Lindson N , et al
      . Electronic cigarettes for smoking cessation. Cochrane Database Syst Rev 2020;10:CD010216.doi:10.1002/14651858.CD010216.pub pmid:http://www.ncbi.nlm.nih.gov/pubmed/33052602
      OpenUrlPubMed
    6. ↵
      2. Motschman CA ,
      3. Gass JC ,
      4. Wray JM , et al
      . Selection criteria limit generalizability of smoking pharmacotherapy studies differentially across clinical trials and laboratory studies: a systematic review on varenicline. Drug Alcohol Depend 2016;169:180–9.doi:10.1016/j.drugalcdep.2016.10.018 pmid:http://www.ncbi.nlm.nih.gov/pubmed/27863344
      OpenUrlPubMed
    7. ↵
      2. Hyland A ,
      3. Ambrose BK ,
      4. Conway KP , et al
      . Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tob Control 2017;26:371–8.doi:10.1136/tobaccocontrol-2016-052934 pmid:http://www.ncbi.nlm.nih.gov/pubmed/27507901
      OpenUrlAbstract/FREE Full Text
    8. ↵
      2. Chen R ,
      3. Pierce JP ,
      4. Leas EC , et al
      . Use of electronic cigarettes to aid long-term smoking cessation in the United States: prospective evidence from the PATH Cohort Study. Am J Epidemiol 2020;189:1529–37.doi:10.1093/aje/kwaa161 pmid:32715314
      OpenUrlPubMed
    9. ↵
      2. Pierce JP ,
      3. Benmarhnia T ,
      4. Chen R , et al
      . Role of e-cigarettes and pharmacotherapy during attempts to quit cigarette smoking: the PATH Study 2013–16. PLoS One 2020;15:e0237938.doi:10.1371/journal.pone.0237938 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32877429
      OpenUrlCrossRefPubMed
    10. ↵
      2. Pierce JP ,
      3. Chen R ,
      4. Kealey S , et al
      . Incidence of cigarette smoking relapse among individuals who switched to e-cigarettes or other tobacco products. JAMA Netw Open 2021;4:e2128810.doi:10.1001/jamanetworkopen.2021.28810 pmid:http://www.ncbi.nlm.nih.gov/pubmed/34665239
      OpenUrlPubMed
    11. ↵
      2. Prochaska JJ ,
      3. Vogel EA ,
      4. Benowitz N
      . Nicotine delivery and cigarette equivalents from vaping a JUULpod. Tob Control 2022;31:e88–93. doi:10.1136/tobaccocontrol-2020-056367 pmid:33762429
      OpenUrlAbstract/FREE Full Text
    12. ↵
      2. King BA ,
      3. Gammon DG ,
      4. Marynak KL , et al
      . Electronic cigarette sales in the United States, 2013-2017. JAMA 2018;320:1379–80.doi:10.1001/jama.2018.10488 pmid:http://www.ncbi.nlm.nih.gov/pubmed/30285167
      OpenUrlCrossRefPubMed
    13. ↵
      2. Craver R
      . Juul expands e-cig market share gap with Reynolds' Vuse. Winston-Salem Journal, 2018. Available: https://journalnow.com/business/juul-expands-e-cig-market-share-gap-with-reynolds-vuse/article_0bb4d442-fc0f-5c00-8b05-29bbf95dc985.html [Accessed 08 Sep 2021].
    14. ↵
      2. Lopez AA ,
      3. Hiler MM ,
      4. Soule EK , et al
      . Effects of electronic cigarette liquid nicotine concentration on plasma nicotine and puff topography in tobacco cigarette smokers: a preliminary report. Nicotine Tob Res 2016;18:720–3.doi:10.1093/ntr/ntv182 pmid:http://www.ncbi.nlm.nih.gov/pubmed/26377515
      OpenUrlCrossRefPubMed
    15. ↵
      2. Soule E ,
      3. Bansal-Travers M ,
      4. Grana R , et al
      . Electronic cigarette use intensity measurement challenges and regulatory implications. Tob Control 2023;32:124–9. doi:10.1136/tobaccocontrol-2021-056483 pmid:34059553
      OpenUrlAbstract/FREE Full Text
    16. ↵
      2. Talih S ,
      3. Balhas Z ,
      4. Eissenberg T , et al
      . Effects of user puff topography, device voltage, and liquid nicotine concentration on electronic cigarette nicotine yield: measurements and model predictions. Nicotine Tob Res 2015;17:150–7.doi:10.1093/ntr/ntu174 pmid:http://www.ncbi.nlm.nih.gov/pubmed/25187061
      OpenUrlCrossRefPubMed
    17. ↵
      2. Goldenson NI ,
      3. Fearon IM ,
      4. Buchhalter AR , et al
      . An open-label, randomized, controlled, crossover study to assess nicotine pharmacokinetics and subjective effects of the JUUL system with three nicotine concentrations relative to combustible cigarettes in adult smokers. Nicotine Tob Res 2021;23:947–55.doi:10.1093/ntr/ntab001 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33486526
      OpenUrlPubMed
    18. ↵
      2. Stratton KR ,
      3. Kwan LY ,
      4. Eaton DL
      . National Academies of Sciences Engineering and Medicine (U.S). Committee on the review of the health effects of electronic nicotine delivery systems. Public health consequences of e-cigarettes. Washington, DC: The National Academies Press, 2018.
    19. ↵
      2. Berry KM ,
      3. Reynolds LM ,
      4. Collins JM , et al
      . E-Cigarette initiation and associated changes in smoking cessation and reduction: the Population Assessment of Tobacco and Health Study, 2013–2015. Tob Control 2019;28:42–9.doi:10.1136/tobaccocontrol-2017-054108 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29574448
      OpenUrlAbstract/FREE Full Text
    20. ↵
      2. Glasser AM ,
      3. Vojjala M ,
      4. Cantrell J , et al
      . Patterns of e-cigarette use and subsequent cigarette smoking cessation over 2 years (2013/2014–2015/2016) in the Population Assessment of Tobacco and Health Study. Nicotine Tob Res 2021;23:669–77.doi:10.1093/ntr/ntaa182 pmid:http://www.ncbi.nlm.nih.gov/pubmed/32939555
      OpenUrlPubMed
    21. ↵
      2. Kalkhoran S ,
      3. Chang Y ,
      4. Rigotti NA
      . Electronic cigarette use and cigarette abstinence over 2 years among U.S. smokers in the Population Assessment of Tobacco and Health Study. Nicotine Tob Res 2020;22:728–33.doi:10.1093/ntr/ntz114 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31298296
      OpenUrlCrossRefPubMed
    22. ↵
      2. Pierce JP ,
      3. Leas EC ,
      4. Benmarhnia T , et al
      . E-Cigarettes and cessation: the introduction of substantial bias in analyses of PATH Study. Nicotine Tob Res 2021;23:876–7.doi:10.1093/ntr/ntaa234 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33188408
      OpenUrlPubMed
    23. ↵
      1. National Addiction & HIV Data Archive Program
      . Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files (ICPSR 36231). Available: https://www.icpsr.umich.edu/icpsrweb/NAHDAP/studies/36231 [Accessed 09 Sep 2021].
    24. ↵
      2. Strong DR ,
      3. Pearson J ,
      4. Ehlke S , et al
      . Indicators of dependence for different types of tobacco product users: descriptive findings from wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) Study. Drug Alcohol Depend 2017;178:257–66.doi:10.1016/j.drugalcdep.2017.05.010 pmid:http://www.ncbi.nlm.nih.gov/pubmed/28675817
      OpenUrlPubMed
    25. ↵
      2. Tibshirani R
      . Regression shrinkage and selection via the LASSO. J R Stat Soc Series B 1996;58:267–88.doi:10.1111/j.2517-6161.1996.tb02080.x
      OpenUrl
    26. ↵
      2. Heinze G ,
      3. Wallisch C ,
      4. Dunkler D
      . Variable selection - a review and recommendations for the practicing statistician. Biom J 2018;60:431–49.doi:10.1002/bimj.201700067 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29292533
      OpenUrlPubMed
    27. ↵
      2. Ho DE ,
      3. Imai K ,
      4. King G , et al
      . Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Political Analysis 2007;15:199–236.doi:10.1093/pan/mpl013
      OpenUrlCrossRefWeb of Science
    28. ↵
      2. Jackler RK ,
      3. Ramamurthi D
      . Nicotine arms race: JUUL and the high-nicotine product market. Tob Control 2019;28:623–8.doi:10.1136/tobaccocontrol-2018-054796 pmid:http://www.ncbi.nlm.nih.gov/pubmed/30733312
      OpenUrlAbstract/FREE Full Text
    29. ↵
      2. Phillips-Waller A ,
      3. Przulj D ,
      4. Smith KM , et al
      . Nicotine delivery and user reactions to Juul EU (20 mg/ml) compared with Juul US (59 mg/ml), cigarettes and other e-cigarette products. Psychopharmacology 2021;238:825–31.doi:10.1007/s00213-020-05734-2 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33270145
      OpenUrlPubMed
    30. ↵
      2. Patterson JG ,
      3. LaPolt DT ,
      4. Miranda AR , et al
      . Switching stories: user testimonials on juul.com continue to contradict JUUL's switch ≠ cessation narrative. Tob Control 2021;30:e37–40.doi:10.1136/tobaccocontrol-2020-055816 pmid:http://www.ncbi.nlm.nih.gov/pubmed/33154067
      OpenUrlAbstract/FREE Full Text
    31. ↵
      2. Johnston LD ,
      3. Miech RA ,
      4. O'Malley PM
      . Monitoring the future national survey results on drug use, 1975-2020: overview, key findings on adolescent drug use. Institute for Social Research, 2021.
    32. ↵
      1. Centers for Disease Control and Prevention
      . Surgeon General’s Advisory on E-cigarette Use Among Youth. Available: https://www.cdc.gov/tobacco/basic_information/e-cigarettes/surgeon-general-advisory/index.html [Accessed 09 Sep 2021].
    33. ↵
      2. Rostron BL ,
      3. Corey CG ,
      4. Chang JT , et al
      . Associations of cigarettes smoked per day with biomarkers of exposure among U.S. adult cigarette smokers in the Population Assessment of Tobacco and Health (PATH) Study wave 1 (2013–2014). Cancer Epidemiol Biomarkers Prev 2019;28:1443–53.doi:10.1158/1055-9965.EPI-19-0013 pmid:http://www.ncbi.nlm.nih.gov/pubmed/31239264
      OpenUrlAbstract/FREE Full Text

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

    • Press release

    • Press release

    Footnotes

    • Twitter @MatthewDavStone

    • Contributors JPP is responsible for the overall content and is the guarantor of this paper. JPP and RC conceptualised and designed the study, drafted the initial manuscript and reviewed and revised the manuscript. JPP and TB acquired funding for the study. TB and KM had input into conceptualisation and supervised the methodology and all analyses undertaken. They also reviewed and revised the manuscript for important intellectual content. ECL, SBM, DRS, MDS, DT and MMW had input into the study conceptualisation and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

    • Funding Supported by the National Institutes of Health (grant R01CA234539) and by the Tobacco-Related Disease Research ProgramProgramme of the University of California, Office of the President (grants 28IR-0066 and T31IR-1584).

    • Disclaimer Neither funding source had any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Further, the funders of the PATH Study had no role in the analysis or interpretation of the data, its preparation, review or approval of this manuscript or decision to submit it for publication. All data used are available in a restricted public use file.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.