Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jan 1.
Published in final edited form as: Addict Behav. 2021 Sep 22;124:107124. doi: 10.1016/j.addbeh.2021.107124

E-cigarette use and change in plans to quit cigarette smoking among adult smokers in the United States: Longitudinal findings from the PATH Study 2014-2019

Karin A Kasza 1, Kathryn C Edwards 2, Andrew Anesetti-Rothermel 3, MeLisa R Creamer 4, K Michael Cummings 5, Raymond S Niaura 6, Akshika Sharma 1, Stephanie R Pitts 3, Sara K Head 3, Colm D Everard 4,7, Dorothy K Hatsukami 8, Andrew Hyland 1
PMCID: PMC8511329  NIHMSID: NIHMS1742927  PMID: 34598012

Abstract

Introduction:

Much of the population-based e-cigarette use and cigarette cessation literature is restricted to smokers who have expressed intention to quit smoking, though experimental studies suggest e-cigarette use might motivate some smokers to change their quit intentions. We used U.S. nationally representative data to evaluate whether e-cigarette use by smokers initially not planning to ever quit is associated with change in plans to quit.

Methods:

Longitudinal Population Assessment of Tobacco and Health (PATH) Study data collected between 2014–2019 were analyzed. Main analyses were conducted among adult daily cigarette smokers not currently using e-cigarettes with no plans to ever quit smoking (n=2,366 observations from n=1,532 individuals). Generalized estimating equations were used to evaluate the association between change in e-cigarette use and change in plans to quit smoking within the next six months, over three assessment pairs.

Results:

Daily cigarette smokers with no plans to quit had a higher rate of change to plan to quit if at follow-up they used e-cigarettes daily (41.4%, 95% CI: 27.1-57.3%) versus not at all (12.4%, 95% CI: 10.6-14.5%; aOR= 5.7, 95% CI: 2.9-11.2). Rate of change to plan to quit did not statistically differ between those who at follow-up used e-cigarettes some days versus not at all.

Conclusions:

Among adult daily cigarette smokers initially not planning to ever quit, subsequent daily e-cigarette use is associated with subsequent plans to quit smoking. Population-level research on e-cigarette use that is focused on smokers already motivated to quit may limit a complete evaluation of the smoker population.

1. Introduction

The population-level impact of electronic nicotine delivery systems (‘e-cigarettes’) on net cigarette smoking cessation rates depends in part on the extent to which cigarette smokers completely substitute e-cigarettes for cigarettes.1 Much of the population-based research on e-cigarette use and smoking cessation is focused on cigarette smokers who are already motivated to try to quit smoking.2-4 However, some experimental studies suggest that offering unmotivated smokers access to stop smoking medications (i.e., nicotine replacement therapy (NRT)5 or varenicline6) or e-cigarettes7,8 might motivate them to change their intention to quit or to quit9. Wagener and colleagues7 found that experimentation with e-cigarette use was associated with increased readiness to quit among a convenience sample of cigarette smokers uninterested in quitting, Carpenter and collegues8 found that trial use of e-cigarettes was associated with increases in motivation to quit among smokers with low motivation in a randomized naturalistic trial, and Foulds and collegues9 found in a clinical trial among smokers with no plans to quit but who were interested in reducing smoking, that use of e-cigarettes with nicotine delivery similar to cigarettes was effective in helping smokers to quit.

Cigarette smokers not planning to quit smoking tend to have higher nicotine dependence, lower quitting self-efficacy, and be of lower socioeconomic status (SES) than their counterparts who do plan to quit.10 However, although experimental studies suggest that these unmotivated smokers could experience a motivational benefit from e-cigarette use, there have been no population-based studies that have investigated the association between e-cigarette use and change in plans to quit, which is needed to understand the range of potential impacts of e-cigarettes on net cigarette cessation rates.11 We addressed this gap in the literature by using U.S. nationally-representative data from the Population Assessment of Tobacco and Health (PATH) Study to evaluate the association between change in e-cigarette use and change in plans to quit cigarette smoking among adult daily cigarette smokers who initially did not plan to ever quit cigarette smoking for good.

2. Methods

2.1. Participants

The PATH Study is an ongoing, nationally representative, longitudinal cohort study in the U.S. Data used in this paper were collected October 2014-October 2015 (Wave (W) 2), October 2015-October 2016 (W3), December 2016-January 2018 (W4), and two years later from December 2018-November 2019 (W5), using audio computer-assisted self-interviews administered in English or Spanish. (Data collected during Wave 1 (2013-2014) were not used due to subsequent key changes in items assessing e-cigarette use.) The PATH Study was conducted by Westat and approved by the Westat Institutional Review Board. All respondents in this paper provided informed consent. Details regarding the PATH Study design and methods12-14 and demographic and tobacco use distributions15 are published elsewhere. Details on interviewing procedures, questionnaires, sampling, weighting, response rates, and accessing the data are available at https://doi.org/10.3886/Series606.16

We conducted our main analysis among adult daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit smoking for good (n=2,366 observations (nobs) contributed by 1,532 individuals). For comparison, we also included adult daily cigarette smokers not using e-cigarettes who did have plans to quit smoking for good (nobs=8,603 contributed by n=4,708 individuals). All estimates are weighted to represent the resident population of the U.S. ages 18+ years at the time of data collection who were in the civilian, noninstitutionalized population in 2013/14.

2.2. Measures

2.2.1. Sample-defining measures and key predictor measure

At each interview, respondents were asked separately whether they currently smoke cigarettes and whether they currently use e-cigarettes (i.e., any electronic nicotine products) every day, some days, or not at all. At each interview, cigarette smokers were asked: “Do you plan to ever quit [cigarettes/tobacco]1 for good?” and those who answered ‘yes’ were asked: “When do you plan to quit [cigarettes/tobacco] for good?” with response options being: in the next 7 days, in the next 30 days, in the next 6 months, in the next year, more than one year from now. We restricted our main analysis to those who at baseline wave (where ‘baseline wave’ refers to the first wave of each wave pair, described in Statistical analyses section) were currently smoking cigarettes ‘every day,’ were currently using e-cigarettes ‘not at all’ (though they could have used e-cigarettes in the past), and currently did not plan to ever quit cigarettes/tobacco for good (though they could have had plans to quit in the past). Our key predictor measure was e-cigarette use at follow-up wave using a three-level e-cigarette use variable at follow-up: 1) no use, 2) someday use, or 3) daily use.

2.2.2. Outcome measure

We evaluated change to plan to quit smoking within the next 6 months (i.e., change to plan to quit in the next 7 days, next 30 days, or next 6 months [to capture ‘near-term’ quitting plans]) at follow-up wave, where we included all those who had quit cigarette smoking at follow-up as part of the numerator. We also conducted a sensitivity analysis where we excluded from the sample those who had quit cigarette smoking at follow-up.

2.2.3. Descriptive characteristics

The following descriptive characteristics were included in analyses for adjustment purposes (each assessed at baseline wave of each wave pair): biological sex, race/ethnicity (non-Hispanic white, non-Hispanic Black, non-Hispanic other racial group including multi-racial groups, Hispanic), age group (18-24, 25-39, 40-54, 55-69, 70+ years), cigarettes smoked per day (<10, 10-19, 20-29, 30+ cigarettes per day), educational attainment (less than high school/general equivalency diploma, high school graduate, some college/associates degree, Bachelor’s degree or more, not reported), annual household income (<$25,000, $25,000-$74,999, $75,000+, not reported), and wave pair.

2.3. Statistical analyses

First, we evaluated differences in descriptive characteristics between daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit for good at baseline, compared to their counterparts who were planning to ever quit for good at baseline, using generalized estimating equations (GEE) goodness-of-fit chi-squared tests for bivariate logistic regression models. Next, among those not planning to ever quit for good at baseline, we used GEE to evaluate the association between change in e-cigarette use and change in plans to quit smoking over three waves pairs: W2-W3, W3-W4, and W4-W5. That is, at the baseline wave of each wave pair, we restricted our sample to adult daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit smoking for good, and at the follow-up wave for each wave pair, we assessed change to plan to quit within the next 6 months as a function of change in e-cigarette use. Analyses were adjusted for descriptive characteristics.

GEE allows for the assessment of differences in descriptive characteristics and change between baseline and follow-up waves from all wave pairs in a single analysis while statistically controlling for interdependence among observations contributed by the same individuals.17-18 We used GEE logistic regression models specifying the unstructured covariance and within-person correlation matrices and the binomial distribution of the dependent variable using the logit link function. Analyses were weighted using the Wave 5 ‘all-waves’ weights to produce nationally representative estimates, and variances were computed using the balanced repeated replication method19 with Fay’s adjustment set to 0.3.20 All analyses were conducted using STATA V16.0 software (StataCorp LP: College Station, TX.). The syntax created to run weighted GEE analyses and calculate adjusted odd ratios (aORs) and confidence intervals (CIs) has been published elsewhere.21 Analyses were run on the W2–W5 Restricted Use Files (https://doi.org/10.3886/ICPSR36231).

3. Results

Overall, 17.4% (95% CI: 16.4%-18.5%) of adult cigarette smokers in the U.S. were daily smokers who were not using e-cigarettes and did not plan to ever quit smoking for good (15.1 million observations, 95%CI: 14.0-16.1). Descriptive characteristics among this group of smokers are shown in Table 1 alongside characteristics of daily cigarette smokers who were planning to quit smoking. Smokers with no plans to ever quit were more likely to be male, non-Hispanic white, older, have lower educational attainment, have lower household income, and smoke more cigarettes per day compared to their counterparts who were planning to quit smoking for good.

Table 1.

Descriptive characteristics of adult daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit smoking for good at baseline wave, compared to their counterparts who were planning to quit smoking for good at baseline wave

Daily cigarette smokers with no plans to ever quit smoking for good (nobs=2,366)
 Daily cigarette smokers with plans to ever quit smoking for good (nobs=8,603)
 p-value
Descriptive characteristics % 95%CI % 95%CI
Sex Male 56.6 53.8-59.3 52.5 50.7-54.3 0.039
Female 43.4 40.7-46.2 47.5 45.7-49.3
Race Non-Hispanic white 75.6 72.9-78.1 69.2 67.4-70.9 <.001
Non-Hispanic Black 10.1 8.7-11.8 15.6 14.2-17.0
Non-Hispanic other 4.2 3.3-5.5 5.2 4.5-6.0
Hispanic 10.0 8.1-12.3 10.0 9.0-11.1
Age Group 18-24 7.2 6.3-8.3 9.0 8.3-9.7 <.001
25-39 24.1 21.6-26.8 35.2 33.6-36.9
40-54 30.8 27.2-34.5 30.7 29.1-32.3
55-69 29.2 26.0-32.6 22.3 20.8-23.8
70-90 8.7 6.6-11.4 2.8 2.2-3.6
Cigarettes per day <10 20.5 18.2-22.9 27.2 25.7-28.7 <.001
10-19 29.3 26.2-32.5 36.6 35.0-38.3
20-29 37.7 34.8-40.8 29.3 27.7-30.9
30+ 12.6 10.7-14.6 6.9 6.1-7.7
Educational attainment Less than High School/GED 36.4 33.6-39.4 28.2 26.6-29.8 <.001
High School graduate 34.2 31.0-37.7 27.4 25.7-29.1
Some college/associates degree 22.6 19.9-25.6 34.7 32.9-36.5
Bachelor’s degree or more 6.1 4.5-8.1 9.4 8.6-10.3
Not reported 0.6† 0.3-1.3 0.4 0.3-0.5
Annual household income <$25,000 55.4 52.6-58.3 45.5 43.8-47.3 <.001
$25,000-$74,999 30.4 27.7-33.2 37.4 35.9-38.9
$75,000+ 6.9 5.5-8.7 12.7 11.4-14.1
Not reported 7.2 5.8-9.0 4.4 3.9-5.0
Open in a new tab

Table notes. ns are unweighted and reflect the number of observations (nobs); %s and 95%CIs are weighted using the Wave 5 all-wave weights for longitudinal analyses. p-values are indicated for GEE goodness-of-fit chi-squared tests for bivariate logistic regression models to examine associations between descriptive characteristics and plans to quit smoking. Sample includes those who aged into the adult cohort over the course of the study period. Smokers were asked, “Do you plan to ever quit [cigarettes/tobacco] for good?” Respondents who were current established users of cigarettes and current established users of any other non-e-cigarette product (11.3%; 95% CI: 10.5%-12.1% of all daily cigarette smokers) were asked about intending to quit using tobacco generally rather than specifically about intending to quit smoking cigarettes.

†

Estimate is based on a denominator sample size of less than 50 or its relative standard error is greater than 30%.

3.1. Change to plan to quit smoking within the next 6 months as a function of change in e-cigarette use.

Among daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit smoking at baseline wave, 13.2% (95% CI: 11.4-15.2%) changed to plan to quit within the next 6 months at follow-up wave (Table 2). This rate of change was significantly higher among those who used e-cigarettes daily at follow-up (41.4%, 95% CI: 27.1-57.3%; though the CI was wide with nobs=57) compared to those who did not use e-cigarettes at follow-up (12.4%, 95% CI: 10.6-14.5%; aOR= 5.7, 95% CI: 2.9-11.2). The rate of change to plan to quit did not statistically differ between those who used e-cigarettes some days compared to no use at follow-up. Findings from the sensitivity analysis in which we excluded those who quit smoking at follow-up, and from the analysis in which we excluded those who were asked about their plans to quit using ‘tobacco’ instead of specifically about their plans to quit smoking cigarettes, were each consistent with findings shown in Table 2 (see footnotes).

Table 2.

Change to plan to quit cigarette smoking within the next 6 months as a function of change in e-cigarette use between baseline wave and follow-up wave

Change to plan to quit within the next 6 months2 at follow-up
Sample at baseline wave Change in e-cigarette use at follow-up nobs % 95%CI aOR‡ 95%CI
Daily cigarette smokers who were not using e-cigarettes and had no plans to ever quit for good1 at baseline wave Overall (nobs=2,366) 329 13.2 11.4-15.2 - -
No use (nobs=2,167) 286 12.4 10.6-14.5 ref
Someday use(nobs=142) 19 14.8 8.8-23.8 1.3 0.7-2.4
Daily use (nobs=57) 24 41.4 27.1-57.3 5.7 2.9-11.2
Open in a new tab

Table notes. ns are unweighted and reflect numbers of observations (nobs); %s, aORs, and 95%CIs are weighted using the Wave 5 all-wave weights for longitudinal analyses. Sample includes those who aged into adults over the course of the study period.

GEE logistic regression analyses were used to assess the association between change in e-cigarette use and change in plans to quit cigarette smoking between baseline wave and follow-up wave over three periods of time (i.e., Wave 2–Wave 3, Wave 3–Wave 4, and Wave 4–Wave 5), including up to three change data points per individual and statistically controlling for the correlation among observations contributed by the same individuals. Wald Chi2(22)= 84.1, p<.001 for the model shown in the table.

1

Cigarette smokers were asked, “Do you plan to ever quit [cigarettes/tobacco] for good?” Cigarette smokers who were current users of any other non-e-cigarette tobacco product and had ever used that tobacco product fairly regularly were asked about planning to quit using “tobacco” rather than specifically about planning to quit smoking cigarettes. Findings among the subset of cigarette smokers who were asked specifically about their plans to quit cigarettes (86.8%, 95%CI: 85.0-88.5) were similar to those shown above in the table; prevalence of change in plans to quit as a function of uptake of e-cigarette use was 12.8% (95%CI:10.8-15.0) for no use, 16.5% (95%CI: 8.8-28.6) for someday use, and 52.5%† (95%CI: 34.1-70.2) for daily use; Adjusted GEE logistic regression analysis with referent group ‘no use’ yielded aOR=1.5 (95%CI: 0.7-3.4) for someday use and aOR=9.0 (95%CI: 4.1-20.1) for daily use. GEE models were fitted specifying the unstructured covariance and within-person correlation matrices, Wald Chi2(21)= 87.6, p<.001. Lastly, prevalence of change in plans to quit as a function of uptake of any e-cigarette use (i.e., someday use or daily use) among the main sample was 22.6% (95%CI: 16.2-30.6); Adjusted GEE logistic regression analysis with referent group ‘no use’ yielded aOR=2.2 (95%CI: 1.4-3.4); GEE model was fitted specifying the unstructured covariance and within-person correlation matrices, Wald Chi2(21)= 74.4, p<.001.

2

For the change in plans outcome assessed at follow-up, smokers were asked, “Do you plan to ever quit [cigarettes/tobacco] for good?” and those who answered ‘yes’ were asked; “When do you plan to quit [cigarettes/tobacco] for good?” These items referred to cigarettes or tobacco depending on other product use (see above footnote); those who had quit cigarette smoking were included in the numerator. We also conducted a sensitivity analysis where we excluded from the sample those who had quit cigarette smoking, and the prevalence of change in plans to quit as a function of uptake of e-cigarette use was 7.4% (95%CI: 6.2-9.0) for no use, 11.8% (95%CI: 6.7-20.1) for someday use, and 31.9%† (95%CI: 15.1-55.2) for daily use; GEE adjusted logistic regression analysis with referent group ‘no use’ yielded aOR=1.6 (95%CI: 0.8-3.2) for someday use, and aOR=6.0 (95%CI: 2.0-17.7) for daily use. GEE models were fitted specifying the unstructured covariance and within-person correlation matrices, Wald Chi2(22)= 58.0, p<.001 for the sensitivity analysis model.

‡

Analyses were adjusted for biological sex, race/ethnicity (non-Hispanic white, non-Hispanic Black, non-Hispanic other racial group including multi-racial groups, Hispanic), age group (18-24, 25-39, 40-54, 55-69, 70+ years), cigarettes smoked per day (<10, 10-19, 20-29, 30+cigarettes per day), educational attainment (less than high school/general equivalency diploma, high school graduate, some college/associates degree, Bachelor’s degree or more, not reported), annual household income (<$25,000, $25,000-$74,999, $75,000+, not reported), and wave pair; all covariates were assessed at baseline wave of each wave pair.

†

Estimate is based on a denominator sample size of less than 50 or its relative standard error is greater than 30%.

4. Discussion

This U.S. population-based study indicates that daily cigarette smokers initially not planning to quit smoking and not using e-cigarettes had higher odds of changing their quit intentions if they used e-cigarettes daily vs. not at all at follow-up. This finding is consistent with growing experimental literature suggesting that giving unmotivated cigarette smokers access to cigarette alternatives such as e-cigarettes or NRT can increase their motivation to quit.5-8

Whether initially unmotivated cigarette smokers who change their quit intentions alongside becoming daily e-cigarette users will later follow through and succeed in quitting cigarettes, or will become long-term dual users, for example, is an important area for future research. However, long-standing theory suggests that any factor that is positively associated with taking a step toward planning to quit smoking could ultimately yield a population-level cessation benefit.11 As such, our findings have potential implications for the design and interpretation of studies to understand the net risk/benefit potential of e-cigarettes for smoking cessation since many past population studies have been restricted to smokers initially motivated to quit.2-4

While the fraction of cigarette smokers not planning to ever quit is a relatively small segment of the cigarette smoker population in the U.S. (17% of adult smokers), and only 2.2% (95% CI: 1.6%-2.9%) of this segment of cigarette smokers took up daily e-cigarette use, our findings suggest that a potential benefit of e-cigarette use at this motivational stage of the cessation process could have a disproportionately greater impact on smokers who are non-Hispanic white, older, smoke more cigarettes per day, and have less education and lower incomes than the general population of smokers. While these differences may suggest potential for greater benefit among those of lower SES, they also suggest potential for increasing racial/ethnic disparities in cessation outcomes; further research would be useful to investigate these questions.

Limitations of this study include use of self-reported data and use of point measurements of plans to ever quit smoking. Plans to quit may be generally unstable over time so future research that maps out patterns of changes in plans to quit over multiple time points may provide further context for situating these initial findings. Also, our findings do not speak to temporality or causality. That is, our analysis of these observational data cannot determine whether daily vaping caused a change in quit intentions or whether a change in quit intentions may have prompted e-cigarette use. Rather, we report that changes in behaviors and quit intentions co-occur, and future research should consider unmotivated smokers when investigating potential for benefit of e-cigarette use in cigarette cessation outcomes.

4.1. Conclusions

Using nationally representative data, we found that adult daily cigarette smokers in the U.S. with no plans to ever quit smoking experienced a nearly six-fold greater odds of changing to plan to quit smoking alongside changing to using e-cigarettes daily (over a three-fold higher relative risk). However, with only 2.2% (95% CI: 1.6%-2.9%) of this segment of cigarette smokers taking up daily e-cigarette use, the extent of any potential motivational benefit from e-cigarette use among this group of smokers appears largely unrealized in the U.S.

Highlights:

  • We evaluated adult daily cigarette smokers initially not planning to ever quit

  • Subsequent daily e-cigarette use was related to changing plans to quit smoking

  • E-cigarette research focused only on smokers motivated to quit limits full evaluation

Funding Statement:

This manuscript is supported with Federal funds from the National Institute on Drug Abuse, National Institutes of Health, and the Center for Tobacco Products, Food and Drug Administration (FDA), Department of Health and Human Services, under contracts to Westat (Contract Nos. HHSN271201100027C and HHSN271201600001C).

Disclosures:

K. Michael Cummings provides expert testimony on the health effects of smoking and tobacco industry tactics in lawsuits filed against the tobacco industry. He has also received payment as a consultant to Pfizer, Inc., for services on an external advisory panel to assess ways to improve smoking cessation delivery in health care settings. Raymond Niaura has served as a paid consultant to the Government of Canada via a contract with Industrial Economics Inc., has received an honorarium for a virtual meeting from Pfizer Inc. within the past 3 years, and was an unpaid grant reviewer for the Foundation for a Smoke Free World.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Publisher's Disclaimer: Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the U.S. Department of Health and Human Services or any of its affiliated institutions or agencies.

All authors agree to be accountable for this work.

1

Cigarette smokers who were current users of any other non-e-cigarette tobacco product and had ever used that tobacco product ‘fairly regularly’ were asked about intending to quit using “tobacco” rather than specifically about intending to quit smoking cigarettes. Only 13% (95% CI: 12%-15%) of all daily smokers categorized as not planning to ever quit were asked about their quit intentions using the word “tobacco” rather than the word “cigarettes.” Results from a sensitivity analysis in which we excluded those who were asked about their quit intentions using the word “tobacco” are shown in the Table 2 footnotes.

REFERENCES

  • 1.National Academies of Sciences Engineering and Medicine. Public health consequences of e-cigarettes. Washington, DC: The National Academies Press; 2018. [PubMed] [Google Scholar]
  • 2.Pierce JP, Benmarhnia T, 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(9):e0237938. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Benmarhnia T, Pierce JP, Leas E, et al. Can e-cigarettes and pharmaceutical aids increase smoking cessation and reduce cigarette consumption? Findings from a nationally representative cohort of American smokers. Am J Epidemiol. 2018;187(11):2397–2404. 10.1093/aje/kwy129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Buu A, Hu YH, Piper ME, Lin HC. The association between e-cigarette use characteristics and combustible cigarette consumption and dependence symptoms: Results from a national longitudinal study. Addict Behav. 2018. September;84 69–74. doi: 10.1016/j.addbeh.2018.03.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Carpenter MJ, Hughes JR, Gray KM, et al. Nicotine therapy sampling to induce quit attempts among smokers unmotivated to quit: A randomized clinical trial. Arch Intern Med. 2011;171(21):1901–1907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Carpenter MJ, Gray KM, Wahlquist AE, et al. A pilot randomized clinical trial of remote Varenicline sampling to promote treatment engagement and smoking cessation. Nicotine Tob Res. (in press). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wagener TL, Meier E, Hale JL, et al. Pilot investigation of changes in readiness and confidence to quit smoking after e-cigarette experimentation and 1 week of use. Nicotine Tob Res. 2014; 16(1):108–114. [DOI] [PubMed] [Google Scholar]
  • 8.Carpenter MJ, Heckman BW, Wahlquist AE, et al. A naturalistic, randomized pilot trial of e-cigarettes: Uptake, exposure, and behavioral effects. Cancer Epidemiol Biomarkers Prev. 2017; 26(12); 1795–803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Foulds J, Cobb CO, Yen M-S, et al. Effect of Electronic Nicotine Delivery Systems on Cigarette Abstinence in Smokers with no Plans to Quit: Exploratory Analysis of a Randomized Placebo-Controlled Trial. medRxiv 2021.06.22.21259359; doi: 10.1101/2021.06.22.21259359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Siahpush M, McNeill A, Borland R. et al. Socioeconomic variations in nicotine dependence, self-efficacy, and intention to quit across four countries: findings from the International Tobacco Control (ITC) Four Country Survey. Tob Control. 2006;15(Suppl III):iii71–iii75. doi: 10.1136/tc.2004.008763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Prochaska J & Diclemente C (1983). Stages and Processes of Self-Change of Smoking - Toward An Integrative Model of Change. Journal of consulting and clinical psychology. 51. 390–5. 10.1037//0022-006X.51.3.390. [DOI] [PubMed] [Google Scholar]
  • 12.Hyland A, Ambrose BK, Conway KP, et al. Design and methods of the Population Assessment of Tobacco and Health (PATH) Study. Tob Control. 2017;26:371–378. doi: 10.1136/tobaccocontrol-2016-052934. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Piesse A, Opsomer J, Dohrmann S, et al. Longitudinal uses of the Population Assessment of Tobacco and Health Study. Tobacco Regulatory Science. 2021;7(1):3–16. doi: 10.18001/TRS.7.1.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Tourangeau R, Yan T, Sun H, et al. Population Assessment of Tobacco and Health (PATH) reliability and validity study: Selected reliability and validity estimates. Tobacco Control. 2019;28(6), 663–668. doi: 10.1136/tobaccocontrol-2018-054561. [DOI] [PubMed] [Google Scholar]
  • 15.Kasza KA, Ambrose BK, Conway KP, et al. Tobacco-product use by adults and youths in the United States in 2013 and 2014. N Engl J Med. 2017. January 26;376(4):342–353. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.United States Department of Health and Human Services. National Institutes of Health. National Institute on Drug Abuse, and United States Department of Health and Human Services. Food and Drug Administration. Center for Tobacco Products. Population Assessment of Tobacco and Health (PATH) Study [United States] Restricted-Use Files, User Guide. ICPSR36231-v21 Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-February-23. 10.3886/ICPSR36231.userguide. [DOI] [Google Scholar]
  • 17.Liang K-Y, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73:13–22. [Google Scholar]
  • 18.Hardin JW, Hilbe JM. Generalized estimating equations. Boca Raton, FL: Chapman and Hall/CRC, 2003. [Google Scholar]
  • 19.McCarthy PJ. Pseudoreplication: Further evaluation and applications of the balanced half-sample technique. 1969. [PubMed] [Google Scholar]
  • 20.Judkins DR. Fay’s method for variance estimation. Journal of Official Statistics. 1990;6(3):223. [Google Scholar]
  • 21.Kasza KA, Edwards K, Tang Z, et al. Correlates of tobacco product initiation among youth and adults in the USA: Findings from the PATH Study Waves 1–3 (2013–2016). Tob Control. 2020;29:s191–s202. doi: 10.1136/tobaccocontrol-2020-055671. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES