We examined the transitions of adult dual e-cigarette/cigarette users in the USA in relation to nicotine dependence (ND) symptoms, interest in quitting, and history of cardiovascular disease (CVD).
We used the Population Assessment of Tobacco and Health study Waves 1 and 3 (2013–2016) in a longitudinal analysis of adults (≥ 18 years). Dual past-month users of e-cigarettes/cigarettes were identified from Wave 1 and followed for tobacco use transitions 2 years later (Wave 3).
Among 1870 adult dual users at Wave 1, 25.7% (95% CI 23.5–28.2) were dual users 2 years later, 12.1% (95% CI 10.6–13.7) reported no past-month tobacco use, 7.0% (95% CI 5.6–8.9) e-cigarette mono-use, and 55.2% (95% CI 52.4–58.0) cigarette mono-use. In the regression analysis, greater ND severity was associated with decreased relative risk of no past-month tobacco use (RRR 0.29; 95% CI 0.12–0.71). Interest in quitting and CVD factors were not associated with no past-month tobacco or e-cigarette mono-use.
Dual users who are nicotine dependent are less likely to transition to cessation. To quit cigarette use, other cessation resources may be necessary to support the needs of cigarette smokers who use e-cigarettes, particularly those at risk of continuing cigarette smoking or those with smoking-related illnesses.
The spread of e-cigarette use in many countries has brought front and center the debate about their place in reducing the burden of tobacco-related disease (National Academies of Sciences, Engineering and Medicine; NASEM 2018). Two questions arise. The first is what will be the net impact on smoking rates? Thus, if as is claimed (McNeill et al. 2018), that e-cigarettes might help adult smokers quit, is this counterbalanced by youth initiation? (Warner and Mendez 2018) The second and more fundamental question is whether e-cigarettes can contribute to tobacco harm reduction (THR). This is based on the premise that adult smokers who cannot do without nicotine may be able to reduce their risk by shifting from conventional cigarettes or quitting altogether (Abrams et al. 2018; Khoudigian et al. 2016; Andler et al. 2014). Indeed, evidence from a randomized controlled trial shows that when accompanied by behavioral support, e-cigarettes in health-care setting were more effective for smoking cessation than nicotine replacement therapy (Hajek et al. 2019).
From this perspective, advocating e-cigarettes to those suffering from smoking-related diseases but who are unable to quit, such as patients with cardiovascular disease (CVD), could be beneficial if it enables them to transition to sole e-cigarette use or, even better, ultimately to abstain from both. Yet outside of the confines of tightly controlled clinical trials, clinicians trying to help such individuals are currently in uncharted territory without any definitive evidence to support their decision-making (Brandon et al. 2015; Drummond and Upson 2014). Although reports from the USA Preventive Services Task Force suggest that there is insufficient evidence to recommend e-cigarettes as a cessation tool (USPSTF 2015), clinicians also run the risk of exposing their patients to additional risk either by advising the use of a potentially ineffective or even hazardous (Alzahrani et al. 2018) cessation tool, or by withholding what could be a valuable means to help them reduce their harm (NASEM 2018). Additionally, with tobacco use identified as one of the modifiable risk factors for CVD (USDHHS 2014) and current research suggesting that e-cigarettes have acute adverse effects on the cardiovascular system (Bhatnagar 2016), it is important to examine tobacco use transitions in this subpopulation that is particularly vulnerable to the effects of tobacco use.
What is needed is research on the potential e-cigarettes might have to help adult smokers reduce their harm in a real-world population setting, using appropriate study design and extended follow-up (Barrington-Trimis et al. 2015; Dutra and Glantz 2014). The 2018 report on e-cigarettes by the NASEM emphasized that because the e-cigarette phenomenon is relatively recent, “the majority of studies lack sufficient duration of follow-up to study the naturalistic cigarette smoking progression sequence” (NASEM 2018).
In 2011, the NIH and FDA initiated the Population Assessment of Tobacco and Health study (PATH), as the first population cohort to study tobacco use since Congress gave FDA the authority to regulate tobacco products in 2009 (USDHHS 2020; Hyland et al. 2017). Studies from PATH Waves 1 and 2 began answering some important questions about e-cigarette transitions during the first year of follow-up (Coleman et al. 2018; Verplaetse et al. 2018). For example, Coleman and colleagues found that majority of adult dual users in Wave 1 (87.8%) either continued dual use or relapsed to cigarette-only smoking at 1-year follow-up, compared to 12.1% who discontinued cigarette smoking or continued with e-cigarettes (Coleman et al. 2018). With the recent availability of Wave 3 data from PATH, we have the opportunity to look at an extended 2-year follow-up of PATH’s data to examine a question that is central to the THR debate: can e-cigarettes help adult smokers who are unable to quit reduce their harm in a real-world setting? This allows us to answer the key question facing clinicians faced with patients who are heavy smokers who need but are unable to quit. Therefore, we investigated this question among adult dual e-cigarette/cigarette users who are nicotine dependent, interested in quitting, and have a clinical condition such as cardiovascular disease (CVD) that warrants their quitting, transition to sole e-cigarette use, or total abstinence for an extended follow-up period.
The PATH Study is a collaboration between the NIH and FDA to inform the FDA’s regulatory approach to different tobacco products in the USA (USDHHS 2020). PATH is an annual, nationally representative longitudinal study of persons 12 years and older who are residents of households, and noninstitutionalized civilians. Details have been described elsewhere (Hyland et al. 2017). The weighted response rate for household screening was 54%. Subsequently, following household screening, the response rate was 74.0% at Wave 1, yielding 32,320 participants, 83.2% at Wave 2, yielding 28,362 participants, and 78.4% at Wave 3, yielding 28,148 participants. We examined tobacco use among adults who reported past-month (past 30 days) dual use of e-cigarette/cigarette at Wave 1 (2013–2014) and had follow-up information at Wave 3 (2015–2016). We examined three main transitions (from Waves 1 and 3) among these dual users (cessation, harm reduction, cigarette transition), seeking their associations with the following characteristics at Wave 1: (1) ND symptoms, (2) interest in quitting, and (3) clinical CVD factors, in addition to baseline socioeconomic and behavioral factors (Cohn et al. 2015). The Institutional Review Board of Florida International University reviewed the study and deemed it exempt.
Assessment of tobacco use
The PATH study enquires about several tobacco products including cigarettes, e-cigarettes, cigars, cigarillos, little filtered cigar, hookah, and smokeless tobacco (USDHHS 2020). Since combined e-cigarette/cigarette use is currently the most common and important e-cigarette use pattern (Owusu et al. 2019) from a policy and regulatory standpoint, we defined past-month dual users as those who reported past 30-day use of e-cigarettes and cigarettes in Wave 1, regardless of other tobacco products used. We followed these dual users 2 years later, defining three main trajectories based on their transitioning to: (1) no past-month use of any tobacco/nicotine product (cessation transition);(2) past-month mono-e-cigarette use (harm reduction transition); or (3) past-month mono-cigarette use (cigarette transition) at Wave 3. Only participants with complete information on dual use were included in this study (n = 1870).
Demographic and behavioral factors
Demographic variables included age, sex, sexual orientation, race/ethnicity, education, income, employment status, census region and body mass index (BMI) which were categorized as shown in Table 1 (USDHHS 2020; Cohn et al. 2015).
Behavioral factors assessed in Wave 1 included age at first exposure to a tobacco product, duration of tobacco product use, other tobacco use, marijuana use, and alcohol consumption. Age at first exposure to tobacco products was categorized into < 18, and ≥ 18 years. Duration of tobacco use was derived by subtracting the age at first exposure to tobacco product from participant’s age at Wave 1 and included as a continuous variable. Alcohol consumption and marijuana use were assessed from the questions pertaining to past-month drinking and marijuana use categorized into “yes” and “no.”
Nicotine dependence (ND) symptoms
ND assessment was based on several questions asked at Wave 1 for each tobacco product reported by participants. Since PATH did not include full scales for ND, but rather a selection of items from different scales (Liu et al. 2017; DiFranza et al. 2002; Heatherton et al. 1991), we opted for items that cover major domains of ND (e.g., craving, withdrawal, latency to smoke upon awakening, smoking heaviness) (Baker et al. 2013), simple to use in our modeling, and have been shown repeatedly to yield good measurement of ND (Baker et al. 2007). These were (1) time to first [product] use after waking up?; (2) do you consider yourself addicted to [product]?; (3) do you ever have strong cravings to smoke or use [product]?; (4) in the past 12 months, did you find it difficult to keep from smoking or using [product] in places where it was prohibited?; (5) have you ever felt like you really needed to use a [product]?; (6) did you cut down on activities that were enjoyable or important to you because [product] was not permitted at the activity?; and (7) frequency of tobacco use among past-month dual users categorized into everyday (regular), versus some-day (not regular) use for both cigarette and e-cigarette (USDHHS 2020). All ND variables were dichotomized into a binary score with “0” indicating “no” and “1” indicating “yes” as shown in Table 2. From these variables, we created a cumulative ND severity variable based on the number of endorsements of the 7 ND items used (range 0–7).
Interest in quitting
We used two items from PATH to assess interest in quitting. The first was interest in quitting (scale of 1–10, with 1, being not interested in quitting, and 10, being extremely interested). We categorized this variable into tertiles: 1–3, 4–7, and 8–10. The second was past year quit attempts, measured as the “number of times tried to quit smoking/using tobacco product(s) in the past 12 months.” This was categorized into 0 (no quit attempt) and ≥ 1 (one or more quit attempts) (USDHHS 2020).
History of CVD-related illness
As our aim was to have an example of the potential effect of tobacco-associated clinical conditions on e-cigarette related transitions, we picked reports of the history of CVD and related conditions that are especially relevant to tobacco cessation and harm reduction. They were based on self-reported positive response to the questions asking, “Has a doctor, nurse or other health professionals ever told you that you have….” for diabetes mellitus (DM), heart attack (myocardial infarction, MI), high blood pressure, and high cholesterol (yes, no) (USDHHS 2020). These CVD factors were assessed from participants at Wave 1 of the PATH study.
We calculated descriptive statistics for the main transitions of dual tobacco use between Waves 1 and 3 (cessation, harm reduction, and cigarette transition). An outcome variable was derived to indicate respondents’ tobacco use transition at Wave 3 (0 = continued dual use, 1 = cessation (no tobacco use), 2 = harm reduction (mono-e-cigarette use), 3 = cigarette transition (mono-cigarette use).
The replicate weights provided by the PATH Study were used to obtain unbiased point and variance estimates using Fay’s Method of Balanced Repeated Replication, with the Fay coefficient value of 0.3, as recommended by the PATH Study team (USDHHS 2020). Weighted percentages for demographic and behavioral factors, ND symptoms, interest in quitting, and clinical CVD factors at Wave 1, were reported by with their corresponding 95% confidence intervals (CI) according to tobacco transitions at Wave 3. We used SVY procedures to report the p values for the differences among the tobacco use transitions and demographic and behavioral factors, ND symptoms, interest in quitting, and clinical CVD factors, respectively.
We applied multinomial logistic regression models to test the bivariate associations between the demographic, behavioral, ND, interest in quitting, and clinical CVD factors at Wave 1 with the tobacco use transitions between Waves 1 and 3. We reported unadjusted relative risk ratios (RRR) with their corresponding 95% CI. For the multivariate predictors of main transitions among dual users (cessation, harm reduction, and cigarette transition) with continued dual use as reference, we fitted a multinomial regression model, adjusting for variables with inclusion p < 0.2 from the bivariate analysis. Due to modest to moderate correlation and collinearity between individual variables within each domain (e.g., ND), we created dichotomous domain summary variables for ND symptoms [i.e., endorsing at least one symptom for the presence of an ND symptom (yes) vs. the absence of symptoms (no)], interest in quitting scoring (i.e., a score of 2 and above or reporting any quit attempt in the past year versus score of 1 on quitting scale or no quit attempt), and clinical CVD factors (i.e., responding positively to any clinical condition vs. no). These were categorized according to no endorsement of any ND, no interest in quitting/quit attempt, or no history of CVD factor as “no,” and any endorsement of any of the domain components as “yes” for each domain.
Adjusted RRRs with their corresponding 95% CIs were calculated and reported. Finally, we tested the preplanned two-way interactions between the three domains of ND symptoms, interest in quitting, and history of CVD factors. The final analytic sample included 845 participants in the adjusted model following exclusion of participants with missing data on some of the assessed variables. Associations were considered statistically significant at the alpha level of 0.05. Analyses were performed using SVY procedures in STATA version 15.1 (StataCorp, College Station, TX).
Transitions of dual use at 2-year follow-up
Table 1 shows the descriptive statistics of dual use transitions between Waves 1 and 3 of PATH by main demographic and behavioral factors. Overall, 12.1% (95% CI 10.6–13.7) of dual users followed the cessation transition (i.e., to no past-month use of any tobacco product), 7.0% (95% CI 5.6–8.9) to the harm reduction transition (i.e., to mono-e-cigarette use), 55.2% (95% CI 52.4–58.0) were in the cigarette transition (i.e., to mono-cigarette use), and 25.7% (95% CI 23.3–28.2) remained as dual users at 2-year follow-up.
Dual-use transitions according to ND, interest in quitting, and clinical CVD factors
Overall, the majority of dual users reported at least one ND symptom (96.7%, 95% CI 95.9–97.4) and the most common symptom was tobacco use within 30 min (86.4%, 95% CI 84.3–88.1) and addiction to tobacco (86.2%, 95% CI 84.0–88.2) (Table 2). In terms of ND severity, 57.8% (95% CI 55.2–60.4) reported 4 to 5 ND symptoms. Over half of the participants (57.9%, 95% CI 54.8–60.9) had interest in quitting scores of 8–10, and 83.3% (95% CI 80.5–85.8) reported one or more quit attempts in the past year. Participants who reported at least one ND symptom were more likely to report transitioning to cigarette smoking (56.6%, 95% CI 53.4–59.7), while those who reported no ND symptoms were more likely to report cessation (49.8%, 95% CI 35.7–64.0) at 2-year follow-up (p < 0.0001). Dual users who reported an interest in quitting at Wave 1 were less likely to follow the cessation transition (10.7%, 95% CI 8.9–12.9) at Wave 3 and more likely to remain as dual users (28.0%, 95% CI 25.2–31.0) (p = 0.05). (Table 2).
A little over a third of participants (35.3%, 95% CI 32.4–38.3) overall reported a history of at least one clinical CVD-related factor at Wave 1. The distribution of the clinical conditions across the tobacco use transitions was not statistically significant, but those who reported at least one condition were less likely to follow the cessation transition (9.2%, 95% CI 7.0–11.9) (p = 0.07) (Table 2).
Predictors of cessation, harm reduction, and cigarette transitions
In the bivariate analyses, among dual users at Wave 1, younger age at first tobacco exposure (0.46, 95% CI 0.29–0.73) and longer duration of tobacco use (0.98, 95% CI 0.96–0.99) were associated with decreased relative risks of cessation transition at Wave 3 (p < 0.05; Table 4). Higher education (0.64, 95% CI 0.51-–0.80) was associated with decreased relative risks of transitioning to cigarette smoking, while other tobacco use (1.64, 95% CI 1.18–2.29) and past-month marijuana use (1.74, 95% CI 1.14–2.65) were associated with increased relative risks of cessation transitions (Table 3).
The 3 main domains [ND symptoms (0.08, 95% CI 0.03–0.19), interest in quitting (0.26, 95% CI 0.10–0.74), and clinical conditions (0.59, 95% CI 0.40–0.88)] were associated with decreased relative risks of cessation compared to continuing dual use (Table 3). Similarly, participants who were in the higher categories of ND symptoms severity compared to 0–3 symptoms had a decreased relative risk of cessation (4–5 symptoms: 0.23, 95% CI 0.14–0.38; and 6–7 symptoms: 0.18, 95% CI 0.11–0.31) and harm reduction transitions (4–5 symptoms: 0.56, 95% CI 0.35–0.90; and 6–7 symptoms: 0.29, 95% CI 0.14–0.61) (Table 3).
In the final adjusted multivariate model, those who reported the age of first exposure to tobacco product of < 18 years had a decreased relative risk of cessation transition (0.44, 95% CI 0.20–0.97) compared to ≥ 18 years. Also, ND severity of 4–5 symptoms (0.38, 95% CI 0.18–0.81) and 6–7 symptoms were associated with decreased relative risks of cessation (0.29, 95% CI 0.12–0.71) transition compared to dual use (Table 4). Interest in quitting and clinical CVD factors were not significantly associated with either cessation (1.30, 95% CI: 0.37–4.56 and 0.95, 95% CI: 0.44–2.05, respectively) or harm reduction (0.98, 95% CI: 0.08–11.27 and 0.75, 95% CI: 0.30–1.87, respectively) transitions. The interactions between ND symptoms, interest in quitting, and clinical CVD factors domains were not statistically significant (all p > 0.05).
These findings are important because dual e-cigarette/cigarette use has become the most common tobacco use pattern involving e-cigarettes in the USA, with 55% of e-cigarette users also smoking (Sung et al. 2018). We have two main findings, each helping to inform the debate on the potential role of e-cigarettes in tobacco control.
The first is that among adult dual smokers, followed over 2 years in the PATH cohort study, 19.1% followed either cessation (12.1%) or harm reduction (7.0%) transitions, compared to 55.2% transitioning to cigarette use, and 25.7% continuing dual use. In other words, the majority transitioned to cigarette and less than one in five transitioned to a less harmful situation (i.e., to e-cigarettes or no tobacco use). Second, individuals who are highly addicted to nicotine were least likely either to quit or transition to a harm reduction scenario with the use of e-cigarettes.
These findings are important because they challenge certain widely aired views. One is that smokers taking up e-cigarettes are beginning a journey to reduced harm or even cessation (Glantz and Bareham 2018). However, the available evidence has often been marred by selectiveness of samples and outcomes, contradictory evidence, and mostly lacked length of follow-up to answer this question (NASEM 2018; Kalkhoran and Glantz 2016). There are many accounts of individuals who claim benefit in using e-cigarettes either to quit or reduce their harm by moving exclusively to e-cigarettes (Notley et al. 2018). Our findings confirm that such individuals exist. However, a majority of dual users transitioned to exclusive cigarette smoking, with a substantial minority remaining dual users. The latter is especially important given concerns that continued dual use may be associated with greater adverse health effects than with either on their own (Wang et al. 2018).
Another notion is that e-cigarettes may be especially useful to those who are highly addicted to nicotine, a group that includes many who have proven resistant to other interventions (Selya et al. 2018). Previous research suggests that dual users have greater dependence symptoms and cessation intentions than exclusive cigarette smokers (Rostron et al. 2016); however, one study found no impact of dual use on quit attempts (Etter and Eissenberg 2016). Additionally, another study found that nicotine dependence did not appear to moderate the use of e-cigarettes for reduction or cessation of cigarette smoking (Selya et al. 2018). E-cigarettes may be effective in smoking cessation for dependent smokers who seek help when combined with behavioral counseling compared to nicotine replacement therapy (Hajek et al. 2019). As a result, other cessation resources remain important in helping dependent smokers who want to quit (Babb et al. 2017).
Our findings are consistent with the few earlier, although smaller studies, with the closest in design being an Italian study that included 223 dual users followed over 2 years, and found that 14.3% followed the cessation transition, 12.5% the harm reduction transition, 16.6% continued dual use, and 57.4% transitioned to cigarettes (Manzoli et al. 2016). Another study from the USA included 151 dual users among a larger sample of smokers, but while 43.7% were still dual users at 2 years, the other data reported do not allow direct comparison with the present study (Zhuang et al. 2016).
These findings are of direct relevance to clinicians confronted with the dilemma of whether or not to advise their tobacco smoking patients who need to quit but cannot, to try e-cigarettes (Brandon et al. 2015). As noted, the notion that e-cigarettes can be the best option for those who could not or will not quit otherwise has been central in the THR debate (Abrams et al. 2018; Warner 2018). While we did not find a statistically significant association between the CVD factors domain and cessation, probably due to low power to detect statistical significance, emerging evidence suggests that concurrent e-cigarette/cigarette use is associated with an increased risk of heart disease compared to each individually (Alzahrani et al. 2018). Additionally, studies suggest that the accessibility and availability of e-cigarettes may fuel nicotine addiction and promote dual use (Bhatnagar 2016).
We observed a positive relationship between other tobacco use/marijuana use and cessation in the bivariate analysis. While some studies suggest that using other tobacco products during cessation could maintain nicotine levels following craving and withdrawal symptoms that make quitting difficult (USDHHS 2012; Dugas et al. 2020), others report that using other tobacco products may help lessen ND symptoms during cessation, allowing smokers adapt easily to the neurobiological changes seen with quitting (Dugas et al. 2020). Furthermore, similar to our study findings, previous studies suggest that marijuana use at baseline did not predict smoking cessation (Humfleet et al. 1999), a finding that is inconsistent with available research indicating a negative effect of marijuana use on cessation (Ford et al. 2002; Schauer et al. 2017).
Strengths and limitations
This study has some limitations. As PATH did not include full scales for ND measurement, we limited our assessment of ND to a subset of available questions (USDHHS 2020). Previous research has demonstrated that individual items, several included here (e.g., time of first tobacco product; frequency of use), yield comparable measurements to the full scales (Baker et al. 2007). Second, although this analysis provides useful information on the transition from dual use over two time-points, we did not analyze participants’ behaviors between waves. However, our main aim was to track real-world evolution of dual use over an extended period of time rather than the dynamics of changes occurring during this period or factors influencing them. Third, tobacco use, interest in quitting, and history of CVD were based on self-reports. Previous research, however, shows a good correlation between self-report of tobacco use and biomarkers of tobacco exposure, or clinical CVD and medical records (Yuji et al. 2004). Interest in quitting, moreover, was correlated with past year quit attempts (r = 0.20; p < 0.0001). Fourth, since this is a population-based study rather than a cessation trial, it was not possible to apply a definition of cessation based on prolonged abstinence as is used in intervention studies. Using such a definition would have led to even fewer people classified as the cessation or harm reduction transitions. Thus, if anything, our findings exaggerate the probability of cessation. Fifth, we had a number of missing data for some variables in our study, which may bias estimates; however, the PATH study involves weighting to account for nonresponse (Hyland et al. 2017; USDHHS 2020). Sixth, we were unable to assess the associations between the transition categories with ND symptoms, interest in quitting, and CVD clinical factors among only daily users of e-cigarettes in our study population. Due to the small number of participants (n = 106), some variables had relatively small cell sizes (sparse data) to achieve convergence in the regression models or to draw meaningful inferences. Finally, it is also possible that not all those with CVD or DM understood that smoking was detrimental to their health, and they may vary in their use of other cessation means (e.g., medications). While this concern is legitimate, it is unlikely that smokers with a history of MI for example would not be aware of how bad smoking is for their health. Also, the use of other cessation means should have led to moving away from the null effect we found. Notwithstanding, our study based on a representative sample of the US adult population, longitudinal design, extended follow-up, and detailed use history of different tobacco products offers a unique opportunity to answer critical questions related to the role of e-cigarettes to help smokers quit or reduce their harm in a real-world setting.
The present study shows that approximately one out of four dual users continues as dual users 2 years later. Nicotine dependence severity and early age (< 18 years) of the first exposure to tobacco were associated with decreased relative risks of following a cessation transition at 2-year follow-up, while interest in quitting and history of illness were not associated with favorable transitions toward cessation or harm reduction.
In order to quit cigarette use, other cessation resources may be important to support the needs of cigarette smokers who use e-cigarettes, particularly those at risk of continuing cigarette smoking or those with smoking-related illnesses. It seems that, with or without e-cigarettes, cessation and harm reduction success are very challenging in this population.
Abrams DB, Glasser AM, Pearson JL, Villanti AC, Collins LK, Niaura RS (2018) Harm minimization and tobacco control: reframing societal views of nicotine use to rapidly save lives. Annu Rev Public Health 39:193–213
Alzahrani T, Pena I, Temesgen N, Glantz SA (2018) Association between electronic cigarette use and myocardial infarction. Am J Prev Med. https://doi.org/10.1016/j.amepre.2018.05.004
Andler R, Guignard R, Wilquin J, Beck F, Richard J, Nguyen-Thanh V (2014) Electronic cigarette use in France in 2014. Int J Public Health 61:159–165
Babb S, Malarcher A, Schauer G, Asman K, Jamal A (2017) Quitting smoking among adults: United States, 2000–2015. MMWR Morb Mortal Wkly Rep 65:1457–1464. https://doi.org/10.15585/mmwr.mm6552a1
Baker TB, Piper ME, McCarthy DM, Bolt DM, Smith SS, Kim SY et al (2007) Time to first cigarette in the morning as an index of ability to quit smoking: implications for nicotine dependence. Nicotine Tob Res 9:S555–S570
Baker TB, Breslau N, Covey L, Shiffman S (2013) DSM criteria for tobacco use disorder and tobacco withdrawal: a critique and proposed revisions for DSM-5. Addiction 107:263–275
Barrington-Trimis JL, Berhane K, Unger JB, Cruz TB, Huh J, Leventhal AM et al (2015) psychosocial factors associated with adolescent electronic cigarette and cigarette use. Pediatrics 136:308–317
Bhatnagar A (2016) E-cigarettes and cardiovascular disease risk: evaluation of evidence, policy implications, and recommendations. Curr Cardiovasc Risk Rep 10:24
Brandon TH, Goniewicz ML, Hanna NH, Hatsukami DK, Herbst RS, Hobin JA et al (2015) Electronic nicotine delivery systems: a policy statement from the American association for cancer research and the American society of clinical oncology. Clin Cancer Res. https://doi.org/10.1158/1078-0432.CCR-14-2544
Cohn A, Villanti A, Richardson A, Rath JM, Williams V, Stanton C et al (2015) The association between alcohol, marijuana use, and new and emerging tobacco products in a young adult population. Addict Behav 48:79–88
Coleman B, Rostron B, Johnson SE, Persoskie A, Pearson J, Stnaton C et al (2018) Transitions in electronic cigarette use among adults in the Population Assessment of Tobacco and Health (PATH) Study, Waves 1 and 2 (2013–2015). Tob Control. https://doi.org/10.1136/tobaccocontrol-2017-054174
DiFranza J, Savageau J, Rigotti N, Fletcher K, Ockene JK, McNeill AD et al (2002) Development of symptoms of tobacco dependence in youth: 30 month follow up data from the DANDY study. Tob Control 11:228–235
Drummond MB, Upson D (2014) Electronic cigarettes: potential harms and benefits. Ann Am Thorac Soc 11:236–242
Dugas EN, Sylvestre MP, Wellman RJ, O’Loughlin J (2020) Does use of other tobacco products change when cigarette smoking status changes: a descriptive study of young adults. Addict Behav 107:106404
Dutra LM, Glantz SA (2014) Electronic cigarettes and conventional cigarette use among U.S. adolescents: a cross-sectional study. JAMA Pediatr 168:610–617
Etter J, Eissenberg T (2016) Dependence levels in users of electronic cigarettes, nicotine gums and tobacco cigarettes. Drug Alcohol Depend 147:68–75
Ford DE, Vu HT, Anthony JC (2002) Marijuana use and cessation of tobacco smoking in adults from a community sample. Drug Alcohol Depend 67:243–248
Glantz SA, Bareham DW (2018) E-cigarettes: use, effects on smoking, risks, and policy implications. Annu Rev Public Health 39:215–235
Hajek P, Phillips-Waller A, Przulj D, Pesola F, Smith KM, Bisal N et al (2019) A randomized trial of E-cigarettes versus nicotine-replacement therapy. N Engl J Med 380:629–637
Heatherton T, Kozlowski L, Frecker R, Fagerstrom K (1991) The Fagerstrom test for nicotine dependence: a revision of the fagerstrom tolerance questionnaire. Br J Addict 86:1119–1127
Humfleet G, Munoz R, Sees K, Reus V, Hall S (1999) History of alcohol or drug problems, current use of alcohol or marijuana, and success in quitting smoking. Addict Behav 24:149–154
Hyland A, Ambrose BK, Conway KP, Borek N, Lambert E, Carusi C et al (2017) Design and methods of the population assessment of tobacco and health (PATH) study. Tob Control 26:371–378. https://doi.org/10.1136/tobaccocontrol-2016-052934
Kalkhoran S, Glantz SA (2016) E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir Med 4:116–128
Khoudigian S, Devji T, Lyyvyn L, Campbell K, Hopkins R, O’Reilly D (2016) The efficacy and short-term effects of electronic cigarettes as a method for smoking cessation: a systematic review and a meta-analysis. Int J Public Health 61:257–267
Liu G, Wasserman E, Kong L, Foulds J (2017) A comparison of nicotine dependence among exclusive E-cigarette and cigarette users in the PATH study. Prev Med 104:86–91
Manzoli L, Flacco ME, Ferrante M, La Vecchia C, Siliquini R, Ricciardi W et al (2016) Cohort study of electronic cigarette use: effectiveness and safety at 24 months. Tob Control 26:284–292
McNeill A, Brose LS, Calder R, Bauld L Robson D (2018) Evidence review of e-cigarettes and heated tobacco products 2018. A report commissioned by Public Health England. London: Public Health England
National Academies of Sciences, Engineering and Medicine (NASEM) (2018) Public health consequences of e-cigarettes. National Academies Press, Washington, DC. https://doi.org/10.17226/24952
Notley C, Ward E, Dawkins L, Holland R (2018) The unique contribution of e-cigarettes for tobacco harm reduction in supporting smoking relapse prevention. Harm Reduct J 15:31
Owusu D, Huang J, Weaver SR, Pechacek TF, Ashley DL, Nayak P et al (2019) Patterns and trends of dual use of e-cigarettes and cigarettes among US adults, 2015–2018. Prev Med Rep. https://doi.org/10.1016/j.pmedr.2019.101009
Rostron BL, Schroeder MJ, Ambrose BK (2016) Dependence symptoms and cessation intentions among US adult daily cigarette, cigar and e-cigarette users, 2012–2013. BMC Public Health 16:814
Schauer GL, King BA, McAfee KTA (2017) Prevalence, correlates, and trends in tobacco use and cessation among current, former, and never adult marijuana users with a history of tobacco use, 2005–2014. Addict Behav 73:165–171
Selya AS, Dierker L, Rose JS, Hedeker D, Mermelstein RJ (2018) the role of nicotine dependence in e-cigarettes’ potential for smoking reduction. Nicotine Tob Res 20:1272–1277
Sung HY, Wang Y, Yao T, Lightwood J, Max W (2018) Polytobacco use and nicotine dependence symptoms among US adults, 2012–2014. Nicotine Tob Res 20(suppl1):S88–S98
United States Department of Health and Human Services (2020) 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. Inter-university Consortium for Political and Social Research [distributor], 2020-06-24. https://doi.org/10.3886/ICPSR36231.v25. Accessed 29 June 2020
U.S. Department of Health and Human Services (2012) Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
US Department of Health and Human Services (2014) The Health Consequences of Smoking-50 years of Progress: A Report of the Surgeon General. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health
U.S. Preventive Services Task Force (2015) Tobacco smoking cessation in adults including pregnant women: behavioral and pharmacotherapy interventions. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/tobacco-use-in-adults-and-pregnant-women-counseling-and-interventions. Accessed 6 April 2020
Verplaetse TL, Moore KE, Pittman BP, Roberts W, Oberleitner LM, Peltier MR et al (2018) Intersection of e-cigarette use and gender on transitions in cigarette smoking status: findings across waves 1 and 2 of the Population Assessment of Tobacco and Health (PATH) study. Nicotine Tob Res. https://doi.org/10.1093/ntr/nty187
Wang JB, Olgin JE, Nah G, Vittinghoff E, Cataldo JK, Pletcher MJ et al (2018) Cigarette and e-cigarette dual use and risk of cardiopulmnonary symptoms in the Heart eHeart Study. PLoS ONE 13:e0198681
Warner KE (2018) How to think: not feel—about tobacco harm reduction. Nicotine Tob Res. https://doi.org/10.1093/ntr/nty084
Warner KE, Mendez D (2018) E-cigarettes: comparing the possible risks of increasing smoking initiation with the potential benefits of increasing smoking cessation. Nicotine Tob Res. https://doi.org/10.1093/ntr/nty062
Yuji O, Urban L, Mahoney D, Jacobsen S, Rodeheffer R (2004) Agreement between self-reported questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 57:1010–1096
Zhuang YL, Cummins SE, Sun JY, Zhu SH (2016) Long-term e-cigarette use and smoking cessation: a longitudinal study with US population. Tob Control 25:i90–i95
The authors thank the investigators, the staff, and the participants of the Population Assessment of Tobacco and Health study.
OO is supported by the NIDA T32DA043449 Grant. ZB and TL are supported by FIU-Research Center in Minority Institution (Grant U54MD012393-01). WM is supported by National Institutes of Health (Grants R01-DA035160, R01-TW010654, R01-DA042477) and the NIDA T32DA043449 Grant. The funding sources had no role in the study design, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.
Conflict of interest
The authors declare that they have no conflict of interest.
The Population Assessment of Tobacco and Health study was conducted by Westat and approved by Westat’s institutional review board. The current study was reviewed and deemed exempt by the Institutional Review Board of Florida International University.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Osibogun, O., Bursac, Z., Mckee, M. et al. Cessation outcomes in adult dual users of e-cigarettes and cigarettes: the Population Assessment of Tobacco and Health cohort study, USA, 2013–2016. Int J Public Health 65, 923–936 (2020). https://doi.org/10.1007/s00038-020-01436-w
- Dual use
- Harm reduction
- Tobacco cessation