case study of e cigarettes

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• Published: 22 October 2022

A systematic review of the effects of e-cigarette use on lung function

• Lucy Honeycutt 1 ,
• Katherine Huerne 1 , 2 ,
• Alanna Miller 1 ,
• Erica Wennberg 1 ,
• Kristian B. Filion 1 , 3 ,
• Roland Grad 1 , 4 ,
• Andrea S. Gershon 5 ,
• Carolyn Ells   ORCID: orcid.org/0000-0002-4593-454X 1 , 2 , 4 ,
• Genevieve Gore 6 ,
• Andrea Benedetti 3 , 7 ,
• Brett Thombs 1 , 3 , 8 &
• Mark J. Eisenberg   ORCID: orcid.org/0000-0002-1296-0661 1 , 3 , 9  

npj Primary Care Respiratory Medicine volume  32 , Article number:  45 ( 2022 ) Cite this article

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• Epidemiology

Given the increasing use of e-cigarettes and uncertainty surrounding their safety, we conducted a systematic review to determine the effects of e-cigarettes on measures of lung function. We systematically searched EMBASE, MEDLINE, and PsycINFO databases via Ovid, the Cochrane CENTRAL database, and the Web of Science Core from 2004 until July 2021, identifying 8856 potentially eligible studies. A total of eight studies (seven studying immediate effects and one long-term effects, 273 total participants) were included. The risk of bias was assessed using the Risk of Bias in Non-randomized Studies—of Interventions (ROBINS-I) and Cochrane risk of bias tools. These studies suggest that vaping increases airway resistance but does not appear to impact forced expiratory volume in one second (FEV 1) , forced vital capacity (FVC), or FEV 1 /FVC ratio. However, given the limited size and follow-up duration of these studies, larger, long-term studies are required to further determine the effects of e-cigarettes on lung function.

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Introduction.

The first electronic cigarette (e-cigarette) was patented and marketed in 2004 1 . Since then, e-cigarette use (or “vaping”) has grown exponentially across the globe 2 . As the use of vaping devices evolves with policy, the consequences of vaping on health are becoming an increasingly important public health issue. E-cigarettes are being studied for harm reduction in individuals who use cigarettes and as a smoking cessation aid, as they are believed to be less harmful to health than smoking 3 . However, there is increasing evidence demonstrating adverse respiratory effects of vaping compared to vaping abstinence. In particular, an outbreak of E-Cigarette and Vaping-Associated Lung Illness (EVALI) brought the short-term respiratory consequences of vaping into question, especially if cannabis or THC-containing products are used 4 . Other short-term respiratory changes that have been linked to vaping include increased airway resistance 5 , breathing difficulty 6 , and transient lung inflammation 7 . Vaping has also been associated with chronic respiratory conditions such as asthma 8 and chronic bronchitis 9 . Despite these reports, the short- and long-term respiratory safety of vaping is still largely unknown. Several small studies have examined the effects of e-cigarettes on lung function, including measures such as forced expiratory volume in one second (FEV 1 ), forced vital capacity (FVC), and airway resistance. However, no evidence syntheses have been completed on this topic. Therefore, we conducted a systematic review to determine the effects of vaping on measures of lung function.

Our systematic review was conducted following a protocol developed prior to initiating the review, which was registered on the PROSPERO register of systematic reviews ( CRD42021227121 ) 10 . This systematic review is reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 11 .

Search strategy and study selection

Using a search strategy (Supplementary Tables 1 – 5 ) developed by an experienced health sciences librarian (G.G.), we systematically searched EMBASE, MEDLINE, and PsycINFO databases via Ovid, the Cochrane CENTRAL database, and the Web of Science Core from 2004 (the year of the first e-cigarette patent) until July 12, 2021. We additionally conducted a gray literature search by searching the websites of key governmental and public health organizations (the World Health Organization, Health Canada, the US Centers for Disease Control and Prevention, the US Food and Drug Administration, the Canadian Center on Substance Use and Addiction, the European Centre for Disease Prevention and Control, and the European Public Health Association). Additional articles were identified by manually searching the reference lists of included publications as well as SCOPUS and Google Scholar (first ten pages). Articles were included if they reported quantitative primary data on changes in lung function associated with vaping, defined as the use of any device that functions by transforming an e-liquid to an aerosol using metal coils, among human participants of any age. Studies of cells and those conducted in animals were excluded. Studies using heat-not-burn devices were also excluded, as these do not meet the above definition of vaping. Eligible studies included randomized controlled trials (RCTs), non-randomized studies of interventions (NRSIs), and cohort studies; cross-sectional studies and case reports were excluded. We included studies that used non-users of both vaping devices and conventional cigarettes as a comparison group and those that used a pre- and post-design in which individuals acted as their own controls. Inclusion was not restricted by language or country of publication. Abstracts and conference proceedings were included if sufficient data could be extracted from these publications.

Search results were downloaded from databases into reference management software (EndNote X9) or manually added (e.g., for gray literature results). Duplicates were removed in EndNote and entries were uploaded to Covidence (Veritas Health Innovation, Melbourne, Australia), a systematic review software. Two reviewers (L.H. and K.H.) independently screened the titles and abstracts of all identified publications for eligibility. Citations considered potentially eligible by either reviewer, based on the pre-specified review inclusion/exclusion criteria (Supplementary Table 6 ), were retrieved for full-text screening and assessed for inclusion. The reasons for exclusion after full-text review were annotated in Covidence and any disagreements were resolved by consensus or a third reviewer (A.H-L.).

Data extraction

Two independent reviewers (L.H and K.H.) extracted methodological, demographic, and outcome data from included studies in duplicate; disagreements were detected in Covidence and were resolved by consensus or, if necessary, by a third reviewer (A.H-L.). Extracted data included study characteristics (first author, journal, year of publication, years(s) of data collection, funding, data source, study design, recruitment strategy, duration of follow-up, country of origin, sample size); population characteristics (sex, gender, age, race, ethnicity, socioeconomic status, dose/frequency of e-cigarette use, conventional cigarette smoking status, smoked cannabis use); and vaping behavior, including the type of vaping device used (e.g., disposable e-cigarette vs. pod device such as JUUL), vaping products used (e.g., nicotine cartridges exclusively vs. THC cartridges exclusively vs. dual use of nicotine and THC products), and source of the vaping product (informal [i.e., friends, family members, or dealers] vs. commercial [i.e., vape shops, stores, dispensaries]).

Initially, extracted outcomes of primary interest were respiratory signs and symptoms, as these are important to patients and are the early signs of respiratory disease. Secondary outcomes included: findings on lung function; Computed tomography (CT) findings of emphysema, airway remodeling, and small airway loss; respiratory-related quality of life and exercise limitations; incidence and/or prevalence of respiratory disease as well as exacerbations of previous respiratory disease; and health care resource use including respiratory disease-related ambulatory care, emergency department visits, and hospitalization. Given the limited number of studies available and the heterogeneity of the data extracted from these studies, no meta-analysis was conducted.

Risk of bias

The risk of bias in included publications was assessed independently by two reviewers (L.H. and K.H.), and discrepancies were resolved by consensus or, if necessary, by a third reviewer (A.H-L.). The risk of bias of included non-randomized studies (pre-post studies, NRSI with non-vaping reference group, cohort study) was assessed using the Risk of Bias in Non-randomized Studies—of Interventions (ROBINS-I) tool 12 . The ROBINS-I tool evaluates intervention-specific outcomes for a study through seven domains which assess the risk of bias pre-intervention, at-intervention, and post-intervention. For each outcome of interest extracted from an included study, the risk of bias within each domain was reported as “low”, “moderate”, “serious”, or “critical”. Included RCTs were assessed using the Cochrane Collaboration’s Tool for Assessing Risk of Bias (ROB V1) 13 . Similar to ROBINS-I, this tool evaluates the risk of bias through the assessment of five domains; for each outcome of interest extracted from an included study, the risk of bias for each domain was reported as “low risk of bias”, “high risk of bias”, or “unclear risk of bias.” All eligible publications were included in the qualitative synthesis regardless of their assessed risk of bias.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

As our search did not identify studies which focused on the broad outcomes detailed above, we chose to limit our focus to studies on lung function. Our database searches identified 14,307 potentially eligible studies (Fig. 1 ). After duplicates were removed, 8856 titles and abstracts were assessed. After this initial screening, 44 full texts were retrieved and reviewed in further detail, yielding eight studies eligible for inclusion.

PRISMA flow diagram of included studies assessing the effect of e-cigarettes on lung function.

Study and participant characteristics

Of the eight included studies (273 total participants), seven 14 , 15 , 16 , 17 , 18 , 19 , 20 involved short-term exposure to e-cigarettes with immediate outcome assessment, and the remaining study followed vapers and non-vapers over 3.5 years 21 (Table 1 ). This prospective cohort study examined 21 participants (12 nonsmokers and nine vapers) at means of 12 (standard deviation: 1), 24 (2), and 42 (2) months after baseline 21 . Of the seven short-term studies, four were NRSIs (three pre-post studies 14 , 15 , 16 and one NRSI with a non-vaping reference group 20 ) and three were RCTs 17 , 18 , 19 . Among these seven studies, two included 70–80 participants 14 , 15 and five included 10–30 participants 16 , 17 , 18 , 19 , 20 . Exposures varied in terms of e-cigarettes, e-liquids, and vaping session timings. Most studies did not expand on their definition of “non-smoker/non-vaper” 15 , 16 , 18 , 19 , 20 , 21 , but two studies clarified that these participants were never-smokers 14 , 17 . One of these two studies further elaborated that participants had no exposure to tobacco products or e-cigarettes 17 . Few studies gave detailed information on the type of e-cigarette used. Three studies reported a specific brand or product (Blu 17 , eGo 16 , Joytech elips-C series 18 , Puff bar 20 ). Polosa et al. listed some of the various e-cigarettes used by participants throughout the longitudinal study, including standard refillable (eGo style products) and more advanced refillable (Provari, Innokin, Joytech, eVIC, Avatar Puff) 21 . The remaining studies did not report a specific brand, though one study described the e-cigarette used as a “1 st generation e-cigarette popular in Greece” 15 . All studies clarified whether the e-cigarettes used during the study contained nicotine.

The included RCTs ( n  = 3) 17 , 18 , 19 had an unclear risk of bias, with each study demonstrating an unclear risk of bias in 3+ domains (Table 2 ). This was primarily due to missing information in the manuscripts required to make an adequate judgment, such as a lack of detail surrounding randomization. The risk associated with the blinding of participants and personnel was judged to be low for all 3 included RCTs. These studies were not blinded, and one was placebo-controlled. However, it was judged that this lack of blinding would not influence measures of lung function. Of the included non-randomized studies ( n  = 5) 14 , 15 , 16 , 20 , 21 , four 14 , 15 , 16 , 20 were judged to be at moderate risk of bias and one 21 was found to have a serious risk of bias (Table 3 ). The most consistent source of bias in these studies was bias due to confounding, with only one 16 study judged to have a low risk of bias due to confounding. Of the remaining four studies, three 14 , 15 , 20 were found to have a moderate risk of bias due to confounding and one 21 was found to be at serious risk of bias due to confounding, with important confounding variables not accounted for in the design or analysis.

Effects of E-cigarette use on lung function

Seven studies 14 , 15 , 16 , 17 , 18 , 19 , 20 reported immediate measures of lung function after short-term exposure to e-cigarettes (Table 4 ), including FEV 1 , FVC, and FEV 1 /FVC. Two studies, Boulay et al. and Staudt. et al. suggested no changes in FEV 1 or FEV 1 /FVC after vaping among nonsmokers 17 , 19 . Kizhakke Puliyakote et al. observed lower baseline FEV 1 and FEV 1 /FVC values among nonsmokers compared to vapers 20 . Coppeta et al. found a decrease in FEV 1 and FEV 1 /FVC among nonsmokers after 1 min of vaping; however, these values returned to baseline after 15 min 16 .

Airway resistance and specific airway conductance after 10 min of vaping were measured in two 14 , 15 of the seven short-term studies (Table 4 ). Both Palamidas et al. 2013 and 2017 suggested that vaping increased airway resistance and decreased specific airway conductance among nonsmokers and smokers with and without respiratory disease. Oxygen saturation was assessed in four studies 15 , 17 , 19 , 20 . Three studies suggested no changes after vaping, with only Palamidas et. al. 2017 suggesting decreased oxygen saturation following vaping among smokers with and without asthma 15 .

Long-term changes (3.5 years) in lung function measurements were assessed in only one small ( n  = 21) study (Polosa 2017) 21 . This study suggested that FEV 1 , FVC, FEV 1 /FVC, and forced mid-expiratory flow (FEF 25-75 ) did not change over time among vapers and non-vapers (Table 5 ).

This systematic review was designed to determine the effect of vaping on measures of lung function. We found that there were only eight studies in the literature assessing this issue, all of which were small, and only one examined longer-term outcomes (3.5 years follow-up). In general, these studies suggest that there are no acute changes associated with vaping. However, airway resistance and conductance may be influenced by e-cigarettes, with two studies reporting changes in these values in multiple population subgroups. It is important to note that there were few studies available for this systematic review and that most of these studies focused on the acute effects of vaping; therefore, these results are suggestive but not definitive, and future research must be conducted in this area. Furthermore, three of the included studies had an unclear risk of bias, four had a moderate risk of bias, and one had a serious risk of bias, which further limits the interpretation of this review’s findings.

In addition to the limitations above, this review lacks subgroup analyses or a meta-analysis. This is due to the heterogeneity of the included studies, both in terms of study design and outcomes. Few studies were eligible for this review due to the variation in study designs and definitions of e-cigarettes and smoking status. For example, some studies included both conventional cigarette smokers and nonsmokers in their definition of “non-vapers” and did not analyze data separately based on conventional smoking status. Other studies used a “sham” vaping session for controls where either an e-cigarette with an empty cartridge (i.e., without e-liquid) or second-hand smoke were used. More commonly, studies were conducted on smokers only, without nonsmokers as a comparison group. Future studies could analyze subgroups based on both smoking and vaping status to allow for a more detailed quantitative analysis.

E-cigarettes are becoming more popular for recreational use and are being studied for harm reduction among smokers as a smoking cessation aid, as they are believed to be less harmful to health than smoking. However, there are limited data available and virtually no long-term studies assessing how prolonged e-cigarette use could impact lung function. As the use of vaping devices evolves and becomes more widespread, the health consequences of vaping are becoming an increasingly important public health issue. This is a knowledge gap that must be addressed. Knowledge of the safety of e-cigarettes, particularly their long-term safety, will inform public health policy and e-cigarette regulations, as well as the guidance clinicians, offer to their patients on smoking harm reduction. For these policies, regulations, and guidelines to be developed, we must understand how e-cigarettes can influence one’s health. This includes establishing the effects of e-cigarettes on clinical outcomes such as respiratory symptoms (cough, dyspnea), measures of lung function, and risk of developing respiratory disease. Further research is required to elucidate the short- and long-term consequences of vaping to determine whether e-cigarettes are truly a “safer” alternative to traditional cigarettes for smoking cessation or for recreational use. Future studies should be long-term, have large sample sizes, and may include different types of e-cigarettes as well as conventional cigarettes for comparison. In addition, it is important for future research to include clinical outcomes as described above. This will allow for better translation of the research findings to help inform clinical decision-making.

Data availability

No additional data were available, as this study is a knowledge synthesis that relied on aggregate, published results available in the public domain. Any inquiries should be directed to the corresponding author.

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Acknowledgements

The authors would like to thank Jenna Glidden and Andrea Hebert-Losier for their assistance with study screening, data abstraction, and risk of bias assessment. The authors would also like to thank Francesca Frati, who peer-reviewed the search strategy. This work was funded by the Canadian Institutes for Health Research (#HEV-172891). The funder of the study had no role in study design, data collection, data analysis, data interpretation, writing of the report, or decision to submit for publication. Dr. Filion is supported by a Senior Research Scholar award from the Fonds de recherche du Québec – Santé and a William Dawson Scholar award from McGill University. Dr. Thombs was supported by a Tier 1 Canada Research Chair.

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Lady Davis Institute for Medical Research, Jewish General Hospital/McGill University, Montreal, QC, Canada

Lucy Honeycutt, Katherine Huerne, Alanna Miller, Erica Wennberg, Kristian B. Filion, Roland Grad, Carolyn Ells, Brett Thombs & Mark J. Eisenberg

Biomedical Ethics Unit, Departments of Medicine and Social Studies of Medicine, and Division of Experimental Medicine, McGill University, Montreal, QC, Canada

Katherine Huerne & Carolyn Ells

Departments of Medicine and of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada

Kristian B. Filion, Andrea Benedetti, Brett Thombs & Mark J. Eisenberg

Department of Family Medicine, McGill University, Montreal, QC, Canada

Roland Grad & Carolyn Ells

Division of Respirology, Department of Medicine, Sunnybrook Health Sciences Centre and the University of Toronto, Toronto, ON, Canada

Andrea S. Gershon

Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada

Genevieve Gore

Respiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada

Andrea Benedetti

Departments of Psychiatry, Psychology, and Biomedical Ethics Unit, McGill University, Montreal, QC, Canada

Brett Thombs

Division of Cardiology, Jewish General Hospital/McGill University, Montreal, QC, Canada

Mark J. Eisenberg

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Contributions

G.G. performed the search. L.H. and K.H. screened studies, extracted data, and performed a risk of bias assessment of included studies. L.H. drafted the manuscript. All authors contributed to the study design and interpretation of results, revised the manuscript for important intellectual content, and approved the final version of the manuscript. M.J.E. supervised the study and is the guarantor.

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Correspondence to Mark J. Eisenberg .

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Honeycutt, L., Huerne, K., Miller, A. et al. A systematic review of the effects of e-cigarette use on lung function. npj Prim. Care Respir. Med. 32 , 45 (2022). https://doi.org/10.1038/s41533-022-00311-w

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An updated overview of e-cigarette impact on human health

• Patrice Marques   ORCID: orcid.org/0000-0003-0465-1727 1 , 2 ,
• Laura Piqueras   ORCID: orcid.org/0000-0001-8010-5168 1 , 2 , 3 &
• Maria-Jesus Sanz   ORCID: orcid.org/0000-0002-8885-294X 1 , 2 , 3  

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The electronic cigarette ( e-cigarette ), for many considered as a safe alternative to conventional cigarettes, has revolutionised the tobacco industry in the last decades. In e-cigarettes , tobacco combustion is replaced by e-liquid heating, leading some manufacturers to propose that e-cigarettes have less harmful respiratory effects than tobacco consumption. Other innovative features such as the adjustment of nicotine content and the choice of pleasant flavours have won over many users. Nevertheless, the safety of e-cigarette consumption and its potential as a smoking cessation method remain controversial due to limited evidence. Moreover, it has been reported that the heating process itself can lead to the formation of new decomposition compounds of questionable toxicity. Numerous in vivo and in vitro studies have been performed to better understand the impact of these new inhalable compounds on human health. Results of toxicological analyses suggest that e-cigarettes can be safer than conventional cigarettes, although harmful effects from short-term e-cigarette use have been described. Worryingly, the potential long-term effects of e-cigarette consumption have been scarcely investigated. In this review, we take stock of the main findings in this field and their consequences for human health including coronavirus disease 2019 (COVID-19).

Electronic nicotine dispensing systems (ENDS), commonly known as electronic cigarettes or e-cigarettes , have been popularly considered a less harmful alternative to conventional cigarette smoking since they first appeared on the market more than a decade ago. E-cigarettes are electronic devices, essentially consisting of a cartridge, filled with an e-liquid, a heating element/atomiser necessary to heat the e-liquid to create a vapour that can be inhaled through a mouthpiece, and a rechargeable battery (Fig.  1 ) [ 1 , 2 ]. Both the electronic devices and the different e-liquids are easily available in shops or online stores.

Effect of the heating process on aerosol composition. Main harmful effects documented. Several compounds detected in e-cigarette aerosols are not present in e-liquid s and the device material also seems to contribute to the presence of metal and silicate particles in the aerosols. The heating conditions especially on humectants, flavourings and the low-quality material used have been identified as the generator of the new compounds in aerosols. Some compounds generated from humectants (propylene glycol and glycerol) and flavourings, have been associated with clear airways impact, inflammation, impairment of cardiovascular function and toxicity. In addition, some of them are carcinogens or potential carcinogens

The e-liquid typically contains humectants and flavourings, with or without nicotine; once vapourised by the atomiser, the aerosol (vapour) provides a sensation similar to tobacco smoking, but purportedly without harmful effects [ 3 ]. However, it has been reported that the heating process can lead to the generation of new decomposition compounds that may be hazardous [ 4 , 5 ]. The levels of nicotine, which is the key addictive component of tobacco, can also vary between the commercially available e-liquids, and even nicotine-free options are available. For this particular reason, e-cigarettes are often viewed as a smoking cessation tool, given that those with nicotine can prevent smoking craving, yet this idea has not been fully demonstrated [ 2 , 6 , 7 ].

Because e-cigarettes are combustion-free, and because most of the damaging and well-known effects of tobacco are derived from this reaction, there is a common and widely spread assumption that e-cigarette consumption or “vaping” is safer than conventional cigarette smoking. However, are they risk-free? Is there sufficient toxicological data on all the components employed in e-liquids ? Do we really know the composition of the inhaled vapour during the heating process and its impact on health? Can e-cigarettes be used to curb tobacco use? Do their consumption impact on coronavirus disease 2019 (COVID-19)? In the present review, we have attempted to clarify these questions based on the existing scientific literature, and we have compiled new insights related with the toxicity derived from the use of these devices.

Effect of e-cigarette vapour versus conventional cigarette exposure: in vivo and in vitro effects

Numerous studies have been performed to evaluate the safety/toxicity of e-cigarette use both in vivo and in in vitro cell culture.

One of the first studies in humans involved the analysis of 9 volunteers that consumed e-cigarettes , with or without nicotine, in a ventilated room for 2 h [ 8 ]. Pollutants in indoor air, exhaled nitric oxide (NO) and urinary metabolite profiles were analysed. The results of this acute experiment revealed that e-cigarettes are not emission-free, and ultrafine particles formed from propylene glycol (PG) could be detected in the lungs. The study also suggested that the presence of nicotine in e-cigarettes increased the levels of NO exhaled from consumers and provoked marked airway inflammation; however, no differences were found in the levels of exhaled carbon monoxide (CO), an oxidative stress marker, before and after e-cigarette consumption [ 8 ]. A more recent human study detected significantly higher levels of metabolites of hazardous compounds including benzene, ethylene oxide, acrylonitrile, acrolein and acrylamide in the urine of adolescent dual users ( e-cigarettes and conventional tobacco consumers) than in adolescent e-cigarette -only users (Table 1 ) [ 9 ]. Moreover, the urine levels of metabolites of acrylonitrile, acrolein, propylene oxide, acrylamide and crotonaldehyde, all of which are detrimental for human health, were significantly higher in e-cigarette -only users than in non-smoker controls, reaching up to twice the registered values of those from non-smoker subjects (Table 1 ) [ 9 ]. In line with these observations, dysregulation of lung homeostasis has been documented in non-smokers subjected to acute inhalation of e-cigarette aerosols [ 10 ].

Little is known about the effect of vaping on the immune system. Interestingly, both traditional and e-cigarette consumption by non-smokers was found to provoke short-term effects on platelet function, increasing platelet activation (levels of soluble CD40 ligand and the adhesion molecule P-selectin) and platelet aggregation, although to a lesser extent with e-cigarettes [ 11 ]. As found with platelets, the exposure of neutrophils to e-cigarette aerosol resulted in increased CD11b and CD66b expression being both markers of neutrophil activation [ 12 ]. Additionally, increased oxidative stress, vascular endothelial damage, impaired endothelial function, and changes in vascular tone have all been reported in different human studies on vaping [ 13 , 14 , 15 , 16 , 17 ]. In this context, it is widely accepted that platelet and leukocyte activation as well as endothelial dysfunction are associated with atherogenesis and cardiovascular morbidity [ 18 , 19 ]. In line with these observations the potential association of daily e-cigarettes consumption and the increased risk of myocardial infarction remains controversial but benefits may occur when switching from tobacco to chronic e-cigarette use in blood pressure regulation, endothelial function and vascular stiffness (reviewed in [ 20 ]). Nevertheless, whether or not e-cigarette vaping has cardiovascular consequences requires further investigation.

More recently, in August 2019, the US Centers for Disease Control and Prevention (CDC) declared an outbreak of the e-cigarette or vaping product use-associated lung injury (EVALI) which caused several deaths in young population (reviewed in [ 20 ]). Indeed, computed tomography (CT scan) revealed local inflammation that impaired gas exchange caused by aerosolised oils from e-cigarettes [ 21 ]. However, most of the reported cases of lung injury were associated with use of e-cigarettes for tetrahydrocannabinol (THC) consumption as well as vitamin E additives [ 20 ] and not necessarily attributable to other e-cigarette components.

On the other hand, in a comparative study of mice subjected to either lab air, e-cigarette aerosol or cigarette smoke (CS) for 3 days (6 h-exposure per day), those exposed to e-cigarette aerosols showed significant increases in interleukin (IL)-6 but normal lung parenchyma with no evidence of apoptotic activity or elevations in IL-1β or tumour necrosis factor-α (TNFα) [ 22 ]. By contrast, animals exposed to CS showed lung inflammatory cell infiltration and elevations in inflammatory marker expression such as IL-6, IL-1β and TNFα [ 22 ]. Beyond airway disease, exposure to aerosols from e-liquids with or without nicotine has also been also associated with neurotoxicity in an early-life murine model [ 23 ].

Results from in vitro studies are in general agreement with the limited number of in vivo studies. For example, in an analysis using primary human umbilical vein endothelial cells (HUVEC) exposed to 11 commercially-available vapours, 5 were found to be acutely cytotoxic, and only 3 of those contained nicotine [ 24 ]. In addition, 5 of the 11 vapours tested (including 4 that were cytotoxic) reduced HUVEC proliferation and one of them increased the production of intracellular reactive oxygen species (ROS) [ 24 ]. Three of the most cytotoxic vapours—with effects similar to those of conventional high-nicotine CS extracts—also caused comparable morphological changes [ 24 ]. Endothelial cell migration is an important mechanism of vascular repair than can be disrupted in smokers due to endothelial dysfunction [ 25 , 26 ]. In a comparative study of CS and e-cigarette aerosols, Taylor et al . found that exposure of HUVEC to e-cigarette aqueous extracts for 20 h did not affect migration in a scratch wound assay [ 27 ], whereas equivalent cells exposed to CS extract showed a significant inhibition in migration that was concentration dependent [ 27 ].

In cultured human airway epithelial cells, both e-cigarette aerosol and CS extract induced IL-8/CXCL8 (neutrophil chemoattractant) release [ 28 ]. In contrast, while CS extract reduced epithelial barrier integrity (determined by the translocation of dextran from the apical to the basolateral side of the cell layer), e-cigarette aerosol did not, suggesting that only CS extract negatively affected host defence [ 28 ]. Moreover, Higham et al . also found that e-cigarette aerosol caused IL-8/CXCL8 and matrix metallopeptidase 9 (MMP-9) release together with enhanced activity of elastase from neutrophils [ 12 ] which might facilitate neutrophil migration to the site of inflammation [ 12 ].

In a comparative study, repeated exposure of human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates led to alterations in morphology, suppression of proliferation and induction of apoptosis, with changes in all three parameters greater in cells exposed to CS condensate [ 29 ]. Likewise, both e-cigarette aerosol and CS extract increased cell death in adenocarcinomic human alveolar basal epithelial cells (A549 cells), and again the effect was more damaging with CS extract than with e-cigarette aerosol (detrimental effects found at 2 mg/mL of CS extract vs. 64 mg/mL of e-cigarette extract) [ 22 ], which is in agreement with another study examining battery output voltage and cytotoxicity [ 30 ].

All this evidence would suggest that e-cigarettes are potentially less harmful than conventional cigarettes (Fig.  2 ) [ 11 , 14 , 22 , 24 , 27 , 28 , 29 ]. Importantly, however, most of these studies have investigated only short-term effects [ 10 , 14 , 15 , 22 , 27 , 28 , 29 , 31 , 32 ], and the long-term effects of e-cigarette consumption on human health are still unclear and require further study.

Comparison of the degree of harmful effects documented from e-cigarette and conventional cigarette consumption. Human studies, in vivo mice exposure and in vitro studies. All of these effects from e-cigarettes were documented to be lower than those exerted by conventional cigarettes, which may suggest that e-cigarette consumption could be a safer option than conventional tobacco smoking but not a clear safe choice

Consequences of nicotine content

Beyond flavour, one of the major issues in the e-liquid market is the range of nicotine content available. Depending on the manufacturer, the concentration of this alkaloid can be presented as low , medium or high , or expressed as mg/mL or as a percentage (% v/v). The concentrations range from 0 (0%, nicotine-free option) to 20 mg/mL (2.0%)—the maximum nicotine threshold according to directive 2014/40/EU of the European Parliament and the European Union Council [ 33 , 34 ]. Despite this normative, however, some commercial e-liquids have nicotine concentrations close to 54 mg/mL [ 35 ], much higher than the limits established by the European Union.

The mislabelling of nicotine content in e-liquids has been previously addressed [ 8 , 34 ]. For instance, gas chromatography with a flame ionisation detector (GC-FID) revealed inconsistencies in the nicotine content with respect to the manufacturer´s declaration (average of 22 ± 0.8 mg/mL vs. 18 mg/mL) [ 8 ], which equates to a content ~ 22% higher than that indicated in the product label. Of note, several studies have detected nicotine in those e-liquids labelled as nicotine-free [ 5 , 35 , 36 ]. One study detected the presence of nicotine (0.11–6.90 mg/mL) in 5 of 23 nicotine-free labelled e-liquids by nuclear magnetic resonance spectroscopy [ 35 ], and another study found nicotine (average 8.9 mg/mL) in 13.6% (17/125) of the nicotine-free e-liquids as analysed by high performance liquid chromatography (HPLC) [ 36 ]. Among the 17 samples tested in this latter study 14 were identified to be counterfeit or suspected counterfeit. A third study detected nicotine in 7 of 10 nicotine-free refills, although the concentrations were lower than those identified in the previous analyses (0.1–15 µg/mL) [ 5 ]. Not only is there evidence of mislabelling of nicotine content among refills labelled as nicotine-free, but there also seems to be a history of poor labelling accuracy in nicotine-containing e-liquids [ 37 , 38 ].

A comparison of the serum levels of nicotine from e-cigarette or conventional cigarette consumption has been recently reported [ 39 ]. Participants took one vape from an e-cigarette , with at least 12 mg/mL of nicotine, or inhaled a conventional cigarette, every 20 s for 10 min. Blood samples were collected 1, 2, 4, 6, 8, 10, 12 and 15 min after the first puff, and nicotine serum levels were measured by liquid chromatography-mass spectrometry (LC–MS). The results revealed higher serum levels of nicotine in the conventional CS group than in the e-cigarette group (25.9 ± 16.7 ng/mL vs. 11.5 ± 9.8 ng/mL). However, e-cigarettes containing 20 mg/mL of nicotine are more equivalent to normal cigarettes, based on the delivery of approximately 1 mg of nicotine every 5 min [ 40 ].

In this line, a study compared the acute impact of CS vs. e-cigarette vaping with equivalent nicotine content in healthy smokers and non-smokers. Both increased markers of oxidative stress and decreased NO bioavailability, flow-mediated dilation, and vitamin E levels showing no significant differences between tobacco and e-cigarette exposure (reviewed in [ 20 ]). Inasmuch, short-term e-cigarette use in healthy smokers resulted in marked impairment of endothelial function and an increase in arterial stiffness (reviewed in [ 20 ]). Similar effects on endothelial dysfunction and arterial stiffness were found in animals when they were exposed to e-cigarette vapor either for several days or chronically (reviewed in [ 20 ]). In contrast, other studies found acute microvascular endothelial dysfunction, increased oxidative stress and arterial stiffness in smokers after exposure to e-cigarettes with nicotine, but not after e-cigarettes without nicotine (reviewed in [ 20 ]). In women smokers, a study found a significant difference in stiffness after smoking just one tobacco cigarette, but not after use of e-cigarettes (reviewed in [ 20 ]).

It is well known that nicotine is extremely addictive and has a multitude of harmful effects. Nicotine has significant biologic activity and adversely affects several physiological systems including the cardiovascular, respiratory, immunological and reproductive systems, and can also compromise lung and kidney function [ 41 ]. Recently, a sub-chronic whole-body exposure of e-liquid (2 h/day, 5 days/week, 30 days) containing PG alone or PG with nicotine (25 mg/mL) to wild type (WT) animals or knockout (KO) mice in α7 nicotinic acetylcholine receptor (nAChRα7-KO) revealed a partly nAChRα7-dependent lung inflammation [ 42 ]. While sub-chronic exposure to PG/nicotine promote nAChRα7-dependent increased levels of different cytokines and chemokines in the bronchoalveolar lavage fluid (BALF) such as IL-1α, IL-2, IL-9, interferon γ (IFNγ), granulocyte-macrophage colony-stimulating factor (GM-CSF), monocyte chemoattractant protein-1 (MCP-1/CCL2) and regulated on activation, normal T cell expressed and secreted (RANTES/CCL5), the enhanced levels of IL-1β, IL-5 and TNFα were nAChRα7 independent. In general, most of the cytokines detected in BALF were significantly increased in WT mice exposed to PG with nicotine compared to PG alone or air control [ 42 ]. Some of these effects were found to be through nicotine activation of NF-κB signalling albeit in females but not in males. In addition, PG with nicotine caused increased macrophage and CD4 + /CD8 + T-lymphocytes cell counts in BALF compared to air control, but these effects were ameliorated when animals were sub-chronically exposed to PG alone [ 42 ].

Of note, another study indicated that although RANTES/CCL5 and CCR1 mRNA were upregulated in flavour/nicotine-containing e-cigarette users, vaping flavour and nicotine-less e-cigarettes did not significantly dysregulate cytokine and inflammasome activation [ 43 ].

In addition to its toxicological effects on foetus development, nicotine can disrupt brain development in adolescents and young adults [ 44 , 45 , 46 ]. Several studies have also suggested that nicotine is potentially carcinogenic (reviewed in [ 41 ]), but more work is needed to prove its carcinogenicity independently of the combustion products of tobacco [ 47 ]. In this latter regard, no differences were encountered in the frequency of tumour appearance in rats subjected to long-term (2 years) inhalation of nicotine when compared with control rats [ 48 ]. Despite the lack of carcinogenicity evidence, it has been reported that nicotine promotes tumour cell survival by decreasing apoptosis and increasing proliferation [ 49 ], indicating that it may work as a “tumour enhancer”. In a very recent study, chronic administration of nicotine to mice (1 mg/kg every 3 days for a 60-day period) enhanced brain metastasis by skewing the polarity of M2 microglia, which increases metastatic tumour growth [ 50 ]. Assuming that a conventional cigarette contains 0.172–1.702 mg of nicotine [ 51 ], the daily nicotine dose administered to these animals corresponds to 40–400 cigarettes for a 70 kg-adult, which is a dose of an extremely heavy smoker. We would argue that further studies with chronic administration of low doses of nicotine are required to clearly evaluate its impact on carcinogenicity.

In the aforementioned study exposing human gingival fibroblasts to CS condensate or to nicotine-rich or nicotine-free e-vapour condensates [ 29 ], the detrimental effects were greater in cells exposed to nicotine-rich condensate than to nicotine-free condensate, suggesting that the possible injurious effects of nicotine should be considered when purchasing e-refills . It is also noteworthy that among the 3 most cytotoxic vapours for HUVEC evaluated in the Putzhammer et al . study, 2 were nicotine-free, which suggests that nicotine is not the only hazardous component in e-cigarettes [ 24 ] .

The lethal dose of nicotine for an adult is estimated at 30–60 mg [ 52 ]. Given that nicotine easily diffuses from the dermis to the bloodstream, acute nicotine exposure by e-liquid spilling (5 mL of a 20 mg/mL nicotine-containing refill is equivalent to 100 mg of nicotine) can easily be toxic or even deadly [ 8 ]. Thus, devices with rechargeable refills are another issue of concern with e-cigarettes , especially when e-liquids are not sold in child-safe containers, increasing the risk of spilling, swallowing or breathing.

These data overall indicate that the harmful effects of nicotine should not be underestimated. Despite the established regulations, some inaccuracies in nicotine content labelling remain in different brands of e-liquids . Consequently, stricter regulation and a higher quality control in the e-liquid industry are required.

Effect of humectants and their heating-related products

In this particular aspect, again the composition of the e-liquid varies significantly among different commercial brands [ 4 , 35 ]. The most common and major components of e-liquids are PG or 1,2-propanediol, and glycerol or glycerine (propane-1,2,3-triol). Both types of compounds are used as humectants to prevent the e-liquid from drying out [ 2 , 53 ] and are classified by the Food and Drug Administration (FDA) as “Generally Recognised as Safe” [ 54 ]. In fact, they are widely used as alimentary and pharmaceutical products [ 2 ]. In an analysis of 54 commercially available e-liquids , PG and glycerol were detected in almost all samples at concentrations ranging from 0.4% to 98% (average 57%) and from 0.3% to 95% (average 37%), respectively [ 35 ].

With regards to toxicity, little is known about the effects of humectants when they are heated and chronically inhaled. Studies have indicated that PG can induce respiratory irritation and increase the probability of asthma development [ 55 , 56 ], and both PG and glycerol from e-cigarettes might reach concentrations sufficiently high to potentially cause irritation of the airways [ 57 ]. Indeed, the latter study established that one e-cigarette puff results in a PG exposure of 430–603 mg/m 3 , which is higher than the levels reported to cause airway irritation (average 309 mg/m 3 ) based on a human study [ 55 ]. The same study established that one e-cigarette puff results in a glycerol exposure of 348–495 mg/m 3 [ 57 ], which is close to the levels reported to cause airway irritation in rats (662 mg/m 3 ) [ 58 ].

Airway epithelial injury induced by acute vaping of PG and glycerol aerosols (50:50 vol/vol), with or without nicotine, has been reported in two randomised clinical trials in young tobacco smokers [ 32 ]. In vitro, aerosols from glycerol only-containing refills showed cytotoxicity in A549 and human embryonic stem cells, even at a low battery output voltage [ 59 ]. PG was also found to affect early neurodevelopment in a zebrafish model [ 60 ]. Another important issue is that, under heating conditions PG can produce acetaldehyde or formaldehyde (119.2 or 143.7 ng/puff at 20 W, respectively, on average), while glycerol can also generate acrolein (53.0, 1000.0 or 5.9 ng/puff at 20 W, respectively, on average), all carbonyls with a well-documented toxicity [ 61 ]. Although, assuming 15 puffs per e-cigarette unit, carbonyls produced by PG or glycerol heating would be below the maximum levels found in a conventional cigarette combustion (Table 2 ) [ 51 , 62 ]. Nevertheless, further studies are required to properly test the deleterious effects of all these compounds at physiological doses resembling those to which individuals are chronically exposed.

Although PG and glycerol are the major components of e-liquids other components have been detected. When the aerosols of 4 commercially available e-liquids chosen from a top 10 list of “ Best E-Cigarettes of 2014” , were analysed by gas chromatography-mass spectrometry (GC–MS) after heating, numerous compounds were detected, with nearly half of them not previously identified [ 4 ], thus suggesting that the heating process per se generates new compounds of unknown consequence. Of note, the analysis identified formaldehyde, acetaldehyde and acrolein [ 4 ], 3 carbonyl compounds with known high toxicity [ 63 , 64 , 65 , 66 , 67 ]. While no information was given regarding formaldehyde and acetaldehyde concentrations, the authors calculated that one puff could result in an acrolein exposure of 0.003–0.015 μg/mL [ 4 ]. Assuming 40 mL per puff and 15 puffs per e-cigarette unit (according to several manufacturers) [ 4 ], each e-cigarette unit would generate approximately 1.8–9 μg of acrolein, which is less than the levels of acrolein emitted by a conventional tobacco cigarette (18.3–98.2 μg) [ 51 ]. However, given that e-cigarette units of vaping are not well established, users may puff intermittently throughout the whole day. Thus, assuming 400 to 500 puffs per cartridge, users could be exposed to up to 300 μg of acrolein.

In a similar study, acrolein was found in 11 of 12 aerosols tested, with a similar content range (approximately 0.07–4.19 μg per e-cigarette unit) [ 68 ]. In the same study, both formaldehyde and acetaldehyde were detected in all of the aerosols tested, with contents of 0.2–5.61 μg and 0.11–1.36 μg, respectively, per e-cigarette unit [ 68 ]. It is important to point out that the levels of these toxic products in e-cigarette aerosols are significantly lower than those found in CS: 9 times lower for formaldehyde, 450 times lower for acetaldehyde and 15 times lower for acrolein (Table 2 ) [ 62 , 68 ].

Other compounds that have been detected in aerosols include acetamide, a potential human carcinogen [ 5 ], and some aldehydes [ 69 ], although their levels were minimal. Interestingly, the existence of harmful concentrations of diethylene glycol, a known cytotoxic agent, in e-liquid aerosols is contentious with some studies detecting its presence [ 4 , 68 , 70 , 71 , 72 ], and others finding low subtoxic concentrations [ 73 , 74 ]. Similar observations were reported for the content ethylene glycol. In this regard, either it was detected at concentrations that did not exceed the authorised limit [ 73 ], or it was absent from the aerosols produced [ 4 , 71 , 72 ]. Only one study revealed its presence at high concentration in a very low number of samples [ 5 ]. Nevertheless, its presence above 1 mg/g is not allowed by the FDA [ 73 ]. Figure  1 lists the main compounds detected in aerosols derived from humectant heating and their potential damaging effects. It would seem that future studies should analyse the possible toxic effects of humectants and related products at concentrations similar to those that e-cigarette vapers are exposed to reach conclusive results.

Impact of flavouring compounds

The range of e-liquid flavours available to consumers is extensive and is used to attract both current smokers and new e-cigarette users, which is a growing public health concern [ 6 ]. In fact, over 5 million middle- and high-school students were current users of e-cigarettes in 2019 [ 75 ], and appealing flavours have been identified as the primary reason for e-cigarette consumption in 81% of young users [ 76 ]. Since 2016, the FDA regulates the flavours used in the e-cigarette market and has recently published an enforcement policy on unauthorised flavours, including fruit and mint flavours, which are more appealing to young users [ 77 ]. However, the long-term effects of all flavour chemicals used by this industry (which are more than 15,000) remain unknown and they are not usually included in the product label [ 78 ]. Furthermore, there is no safety guarantee since they may harbour potential toxic or irritating properties [ 5 ].

With regards to the multitude of available flavours, some have demonstrated cytotoxicity [ 59 , 79 ]. Bahl et al. evaluated the toxicity of 36 different e-liquids and 29 different flavours on human embryonic stem cells, mouse neural stem cells and human pulmonary fibroblasts using a metabolic activity assay. In general, those e-liquids that were bubblegum-, butterscotch- and caramel-flavoured did not show any overt cytotoxicity even at the highest dose tested. By contrast, those e-liquids with Freedom Smoke Menthol Arctic and Global Smoke Caramel flavours had marked cytotoxic effects on pulmonary fibroblasts and those with Cinnamon Ceylon flavour were the most cytotoxic in all cell lines [ 79 ]. A further study from the same group [ 80 ] revealed that high cytotoxicity is a recurrent feature of cinnamon-flavoured e-liquids. In this line, results from GC–MS and HPLC analyses indicated that cinnamaldehyde (CAD) and 2-methoxycinnamaldehyde, but not dipropylene glycol or vanillin, were mainly responsible for the high cytotoxicity of cinnamon-flavoured e-liquids [ 80 ]. Other flavouring-related compounds that are associated with respiratory complications [ 81 , 82 , 83 ], such as diacetyl, 2,3-pentanedione or acetoin, were found in 47 out of 51 aerosols of flavoured e-liquids tested [ 84 ] . Allen et al . calculated an average of 239 μg of diacetyl per cartridge [ 84 ]. Assuming again 400 puffs per cartridge and 40 mL per puff, is it is possible to estimate an average of 0.015 ppm of diacetyl per puff, which could compromise normal lung function in the long-term [ 85 ].

The cytotoxic and pro-inflammatory effects of different e-cigarette flavouring chemicals were also tested on two human monocytic cell lines—mono mac 6 (MM6) and U937 [ 86 ]. Among the flavouring chemicals tested, CAD was found to be the most toxic and O-vanillin and pentanedione also showed significant cytotoxicity; by contrast, acetoin, diacetyl, maltol, and coumarin did not show any toxicity at the concentrations assayed (10–1000 µM). Of interest, a higher toxicity was evident when combinations of different flavours or mixed equal proportions of e-liquids from 10 differently flavoured e-liquids were tested, suggesting that vaping a single flavour is less toxic than inhaling mixed flavours [ 86 ]. Also, all the tested flavours produced significant levels of ROS in a cell-free ROS production assay. Finally, diacetyl, pentanedione, O-vanillin, maltol, coumarin, and CAD induced significant IL-8 secretion from MM6 and U937 monocytes [ 86 ]. It should be borne in mind, however, that the concentrations assayed were in the supra-physiological range and it is likely that, once inhaled, these concentrations are not reached in the airway space. Indeed, one of the limitations of the study was that human cells are not exposed to e-liquids per se, but rather to the aerosols where the concentrations are lower [ 86 ]. In this line, the maximum concentration tested (1000 µM) would correspond to approximately 80 to 150 ppm, which is far higher than the levels found in aerosols of some of these compounds [ 84 ]. Moreover, on a day-to-day basis, lungs of e-cigarette users are not constantly exposed to these chemicals for 24 h at these concentrations. Similar limitations were found when five of seven flavourings were found to cause cytotoxicity in human bronchial epithelial cells [ 87 ].

Recently, a commonly commercialized crème brûlée -flavoured aerosol was found to contain high concentrations of benzoic acid (86.9 μg/puff), a well-established respiratory irritant [ 88 ]. When human lung epithelial cells (BEAS-2B and H292) were exposed to this aerosol for 1 h, a marked cytotoxicity was observed in BEAS-2B but not in H292 cells, 24 h later. However, increased ROS production was registered in H292 cells [ 88 ].

Therefore, to fully understand the effects of these compounds, it is relevant the cell cultures selected for performing these assays, as well as the use of in vivo models that mimic the real-life situation of chronic e-cigarette vapers to clarify their impact on human health.

The e-cigarette device

While the bulk of studies related to the impact of e-cigarette use on human health has focused on the e-liquid components and the resulting aerosols produced after heating, a few studies have addressed the material of the electronic device and its potential consequences—specifically, the potential presence of metals such as copper, nickel or silver particles in e-liquids and aerosols originating from the filaments and wires and the atomiser [ 89 , 90 , 91 ].

Other important components in the aerosols include silicate particles from the fiberglass wicks or silicone [ 89 , 90 , 91 ]. Many of these products are known to cause abnormalities in respiratory function and respiratory diseases [ 89 , 90 , 91 ], but more in-depth studies are required. Interestingly, the battery output voltage also seems to have an impact on the cytotoxicity of the aerosol vapours, with e-liquids from a higher battery output voltage showing more toxicity to A549 cells [ 30 ].

A recent study compared the acute effects of e-cigarette vapor (with PG/vegetable glycerine plus tobacco flavouring but without nicotine) generated from stainless‐steel atomizer (SS) heating element or from a nickel‐chromium alloy (NC) [ 92 ]. Some rats received a single e-cigarette exposure for 2 h from a NC heating element (60 or 70 W); other rats received a similar exposure of e-cigarette vapor using a SS heating element for the same period of time (60 or 70 W) and, a final group of animals were exposed for 2 h to air. Neither the air‐exposed rats nor those exposed to e-cigarette vapor using SS heating elements developed respiratory distress. In contrast, 80% of the rats exposed to e-cigarette vapor using NC heating units developed clinical acute respiratory distress when a 70‐W power setting was employed. Thus, suggesting that operating units at higher than recommended settings can cause adverse effects. Nevertheless, there is no doubt that the deleterious effects of battery output voltage are not comparable to those exerted by CS extracts [ 30 ] (Figs.  1 and 2 ).

E-cigarettes as a smoking cessation tool

CS contains a large number of substances—about 7000 different constituents in total, with sizes ranging from atoms to particulate matter, and with many hundreds likely responsible for the harmful effects of this habit [ 93 ]. Given that tobacco is being substituted in great part by e-cigarettes with different chemical compositions, manufacturers claim that e -cigarette will not cause lung diseases such as lung cancer, chronic obstructive pulmonary disease, or cardiovascular disorders often associated with conventional cigarette consumption [ 3 , 94 ]. However, the World Health Organisation suggests that e-cigarettes cannot be considered as a viable method to quit smoking, due to a lack of evidence [ 7 , 95 ]. Indeed, the results of studies addressing the use of e-cigarettes as a smoking cessation tool remain controversial [ 96 , 97 , 98 , 99 , 100 ]. Moreover, both FDA and CDC are actively investigating the incidence of severe respiratory symptoms associated with the use of vaping products [ 77 ]. Because many e-liquids contain nicotine, which is well known for its powerful addictive properties [ 41 ], e-cigarette users can easily switch to conventional cigarette smoking, avoiding smoking cessation. Nevertheless, the possibility of vaping nicotine-free e-cigarettes has led to the branding of these devices as smoking cessation tools [ 2 , 6 , 7 ].

In a recently published randomised trial of 886 subjects who were willing to quit smoking [ 100 ], the abstinence rate was found to be twice as high in the e-cigarette group than in the nicotine-replacement group (18.0% vs. 9.9%) after 1 year. Of note, the abstinence rate found in the nicotine-replacement group was lower than what is usually expected with this therapy. Nevertheless, the incidence of throat and mouth irritation was higher in the e-cigarette group than in the nicotine-replacement group (65.3% vs. 51.2%, respectively). Also, the participant adherence to the treatment after 1-year abstinence was significantly higher in the e-cigarette group (80%) than in nicotine-replacement products group (9%) [ 100 ].

On the other hand, it is estimated that COPD could become the third leading cause of death in 2030 [ 101 ]. Given that COPD is generally associated with smoking habits (approximately 15 to 20% of smokers develop COPD) [ 101 ], smoking cessation is imperative among COPD smokers. Published data revealed a clear reduction of conventional cigarette consumption in COPD smokers that switched to e-cigarettes [ 101 ]. Indeed, a significant reduction in exacerbations was observed and, consequently, the ability to perform physical activities was improved when data was compared with those non-vapers COPD smokers. Nevertheless, a longer follow-up of these COPD patients is required to find out whether they have quitted conventional smoking or even vaping, since the final goal under these circumstances is to quit both habits.

Based on the current literature, it seems that several factors have led to the success of e-cigarette use as a smoking cessation tool. First, some e-cigarette flavours positively affect smoking cessation outcomes among smokers [ 102 ]. Second, e-cigarettes have been described to improve smoking cessation rate only among highly-dependent smokers and not among conventional smokers, suggesting that the individual degree of nicotine dependence plays an important role in this process [ 97 ]. Third, the general belief of their relative harmfulness to consumers' health compared with conventional combustible tobacco [ 103 ]. And finally, the exposure to point-of-sale marketing of e-cigarette has also been identified to affect the smoking cessation success [ 96 ].

Implication of e-cigarette consumption in COVID-19 time

Different reports have pointed out that smokers and vapers are more vulnerable to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infections or more prone to adverse outcomes if they suffer COVID-19 [ 104 ]. However, while a systematic review indicated that cigarette smoking is probably associated with enhanced damage from COVID-19, a meta-analysis did not, yet the latter had several limitations due to the small sample sizes [ 105 ].

Interestingly, most of these reports linking COVID-19 harmful effects with smoking or vaping, are based on their capability of increasing the expression of angiotensin-converting enzyme 2 (ACE2) in the lung. It is well known that ACE2 is the gate for SARS-CoV-2 entrance to the airways [ 106 ] and it is mainly expressed in type 2 alveolar epithelial cells and alveolar macrophages [ 107 ]. To date, most of the studies in this field indicate that current smokers have higher expression of ACE2 in the airways (reviewed by [ 108 ]) than healthy non-smokers [ 109 , 110 ]. However, while a recent report indicated that e-cigarette vaping also caused nicotine-dependent ACE2 up-regulation [ 42 ], others have revealed that neither acute inhalation of e-cigarette vapour nor e-cigarette users had increased lung ACE2 expression regardless nicotine presence in the e-liquid [ 43 , 110 ].

In regard to these contentions, current knowledge suggests that increased ACE2 expression is not necessarily linked to enhanced susceptibility to SARS-CoV-2 infection and adverse outcome. Indeed, elderly population express lower levels of ACE2 than young people and SARS-CoV-2/ACE2 interaction further decreases ACE2 expression. In fact, most of the deaths provoked by COVID-19 took place in people over 60 years old of age [ 111 ]. Therefore, it is plausible that the increased susceptibility to disease progression and the subsequent fatal outcome in this population is related to poor angiotensin 1-7 (Ang-1-7) generation, the main peptide generated by ACE2, and probably to their inaccessibility to its anti-inflammatory effects. Furthermore, it seems that all the efforts towards increasing ACE2 expression may result in a better resolution of the pneumonic process associated to this pandemic disease.

Nevertheless, additional complications associated to COVID-19 are increased thrombotic events and cytokine storm. In the lungs, e-cigarette consumption has been correlated to toxicity, oxidative stress, and inflammatory response [ 32 , 112 ]. More recently, a study revealed that while the use of nicotine/flavour-containing e-cigarettes led to significant cytokine dysregulation and potential inflammasome activation, none of these effects were detected in non-flavoured and non-nicotine-containing e-cigarettes [ 43 ]. Therefore, taken together these observations, e-cigarette use may still be a potent risk factor for severe COVID-19 development depending on the flavour and nicotine content.

In summary, it seems that either smoking or nicotine vaping may adversely impact on COVID-19 outcome. However, additional follow up studies are required in COVID-19 pandemic to clarify the effect of e-cigarette use on lung and cardiovascular complications derived from SARS-CoV-2 infection.

Conclusions

The harmful effects of CS and their deleterious consequences are both well recognised and widely investigated. However, and based on the studies carried out so far, it seems that e-cigarette consumption is less toxic than tobacco smoking. This does not necessarily mean, however, that e-cigarettes are free from hazardous effects. Indeed, studies investigating their long-term effects on human health are urgently required. In this regard, the main additional studies needed in this field are summarized in Table 3 .

The composition of e-liquids requires stricter regulation, as they can be easily bought online and many incidences of mislabelling have been detected, which can seriously affect consumers’ health. Beyond their unknown long-term effects on human health, the extended list of appealing flavours available seems to attract new “never-smokers”, which is especially worrying among young users. Additionally, there is still a lack of evidence of e-cigarette consumption as a smoking cessation method. Indeed, e-cigarettes containing nicotine may relieve the craving for smoking, but not the conventional cigarette smoking habit.

Interestingly, there is a strong difference of opinion on e-cigarettes between countries. Whereas countries such as Brazil, Uruguay and India have banned the sale of e-cigarettes , others such as the United Kingdom support this device to quit smoking. The increasing number of adolescent users and reported deaths in the United States prompted the government to ban the sale of flavoured e-cigarettes in 2020. The difference in opinion worldwide may be due to different restrictions imposed. For example, while no more than 20 ng/mL of nicotine is allowed in the EU, e-liquids with 59 mg/dL are currently available in the United States. Nevertheless, despite the national restrictions, users can easily access foreign or even counterfeit products online.

In regard to COVID-19 pandemic, the actual literature suggests that nicotine vaping may display adverse outcomes. Therefore, follow up studies are necessary to clarify the impact of e-cigarette consumption on human health in SARS-CoV-2 infection.

In conclusion, e-cigarettes could be a good alternative to conventional tobacco cigarettes, with less side effects; however, a stricter sale control, a proper regulation of the industry including flavour restriction, as well as further toxicological studies, including their chronic effects, are warranted.

Availability of data and materials

Not applicable.

Abbreviations

Angiotensin-converting enzyme 2

Angiotensin 1-7

Bronchoalveolar lavage fluid

Cinnamaldehyde

US Centers for Disease Control and Prevention

Carbon monoxide

Chronic obstructive pulmonary disease

Coronavirus disease 2019

Cigarette smoke

Electronic nicotine dispensing systems

e-cigarette or vaping product use-associated lung injury

Food and Drug Administration

Gas chromatography with a flame ionisation detector

Gas chromatography-mass spectrometry

Granulocyte–macrophage colony-stimulating factor

High performance liquid chromatography

Human umbilical vein endothelial cells

Interleukin

Interferon γ

Liquid chromatography-mass spectrometry

Monocyte chemoattractant protein-1

Matrix metallopeptidase 9

α7 Nicotinic acetylcholine receptor

Nickel‐chromium alloy

Nitric oxide

Propylene glycol

Regulated on activation, normal T cell expressed and secreted

Reactive oxygen species

Severe acute respiratory syndrome coronavirus 2

Stainless‐steel atomizer

Tetrahydrocannabinol

Tumour necrosis factor-α

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Acknowledgements

The authors gratefully acknowledge Dr. Cruz González, Pulmonologist at University Clinic Hospital of Valencia (Valencia, Spain) for her thoughtful suggestions and support.

This work was supported by the Spanish Ministry of Science and Innovation [Grant Number SAF2017-89714-R]; Carlos III Health Institute [Grant Numbers PIE15/00013, PI18/00209]; Generalitat Valenciana [Grant Number PROMETEO/2019/032, Gent T CDEI-04/20-A and AICO/2019/250], and the European Regional Development Fund.

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Marques, P., Piqueras, L. & Sanz, MJ. An updated overview of e-cigarette impact on human health. Respir Res 22 , 151 (2021). https://doi.org/10.1186/s12931-021-01737-5

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DOI : https://doi.org/10.1186/s12931-021-01737-5

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Respiratory Research

ISSN: 1465-993X

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• Published: 10 June 2024

Do sociodemographic risk profiles for adolescents engaging in weekly e-cigarette, cigarette, and dual product use differ?

• Katelyn Battista 1 ,
• Karen A Patte 2 ,
• Terrance J Wade 2 ,
• Adam G. Cole 3 ,
• Tara Elton-Marshall 4 ,
• Kristen M Lucibello 2 ,
• William Pickett 2 , 5 &
• Scott T Leatherdale 1  

BMC Public Health volume  24 , Article number:  1558 ( 2024 ) Cite this article

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E-cigarette use represents a contemporary mode of nicotine product use that may be changing the risk profile of participating adolescents. Understanding differences in sociodemographic characteristics of adolescents engaging in contemporary e-cigarette use and traditional cigarette use is important for effectively developing and targeting public health intervention programs. The objective of this study was to identify and compare sociodemographic risk profiles for exclusive e-cigarette use and dual-product use among a large sample of Canadian youth.

A survey of 46,666 secondary school students in the 2021-22 wave of the COMPASS study measured frequency of past month e-cigarette and cigarette use as well as age, sex, gender, racial or ethnic background, spending money, relative family affluence, and having one’s own bedroom. Rates of cigarette-only, e-cigarette-only, and dual product use were calculated, and separate classification trees were run using the CART algorithm to identify sociodemographic risk profiles for weekly dual-product use and weekly e-cigarette-only use.

Over 13% of adolescents used only e-cigarettes at least weekly, 3% engaged in weekly dual e-cigarette and cigarette use, and less than 0.5% used only cigarettes. Available spending money was a common predictor of dual-product and e-cigarette-only use. Gender diverse youth and youth with lower perceived family affluence were at higher risk for dual-product use, while white and multiethnic adolescents were at greater risk of e-cigarette-only use. Two high-risk profiles were identified for e-cigarette-only use and four high-risk profiles were identified for dual product use.

Conclusions

This study used a novel modelling approach (CART) to identify combinations of sociodemographic characteristics that profile high-risk groups for exclusive e-cigarette and dual-product use. Unique risk profiles were identified, suggesting that e-cigarettes are attracting new demographics of adolescents who have not previously been considered as high-risk for traditional cigarette use.

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E-cigarettes have overtaken cigarettes as the most popular nicotine product used by Canadian youth. Among a large sample of Ontario youth in grades 9-12, past 30-day e-cigarette use increased from 7.6% in 2013-14 to 25.7% in 2018-19, while corresponding cigarette use declined from 11.0% to 7.9%. [ 1 ] More recent nationally representative estimates from 2021-22 showed that 29% of grade 7-12 students had tried e-cigarettes, while only 14% had ever tried smoking cigarettes. [ 2 ] This decrease in cigarette smoking and corresponding increase in e-cigarette use suggests that youth risk-taking via nicotine product use is changing. The frequency with which adolescents use e-cigarettes and the rates of dual e-cigarette and cigarette use are two concerning patterns. Canadian estimates from 2019 suggest that 47.9% of youth aged 12-17 who use e-cigarettes do so at least weekly, and 23.8% do so daily. [ 3 ] The same study found that 5.3% of 15-17 year olds have used both e-cigarettes and cigarettes, with e-cigarette use preceding cigarette use in two-thirds of dual users. [ 3 ] Dual-product use is concerning because it may increase total nicotine exposure, leading to increased health risks and risk of nicotine dependence. [ 4 , 5 ] Despite this, few Canadian studies have examined frequent dual product use.

As a contemporary form of nicotine product use, e-cigarette vaping may attract different sociodemographic groups than traditional cigarette smoking, which can have consequences for targeted public health initiatives. Previous studies have shown various similarities and differences in the sociodemographic risk profiles of e-cigarette and cigarette users. Prevalence of both e-cigarette and cigarette use has generally been higher among males and older adolescents [ 1 , 3 , 6 ]; however, recent estimates suggest a trend toward more similar rates of use among girls and boys. [ 2 ] Males are more likely to engage in exclusive e-cigarette use [ 7 , 8 ] or poly-tobacco use [ 7 , 9 ] while females are more likely to use cigarettes exclusively. [ 7 ] E-cigarette use is also more likely among youth of higher household income [ 3 , 7 ] while dual-product use is more likely than exclusive e-cigarette use among youth of lower perceived socioeconomic status (SES). [ 8 ] Differences have also been observed by race and ethnicity, with e-cigarette use more common among white and Hispanic adolescents than among Black or Asian adolescents. [ 1 , 7 , 8 , 9 ] Dual-product use also appears more likely among white adolescents than among Black, Hispanic, or other minority ethnicity adolescents. [ 7 , 9 ] However, there remains a lack of population-level research into rates of e-cigarette and dual-product use among certain sociodemographic groups, in particular gender diverse youth and youth of Asian and Middle Eastern minority ethnicities.

One important limitation of previous studies [ 7 , 8 , 9 ] examining sociodemographic risk factors for e-cigarette and dual-product use is that the incremental risk of each sociodemographic characteristic is considered separately. In reality, various sociodemographic characteristics will have intersecting influences that need to be accounted for to properly portray risk profiles. The current study addresses this limitation by examining risk profiles through a classification and regression tree (CART) approach [ 10 ] that models complex interactions among risk factors using a tree structure. The objective of the current study is to identify and compare sociodemographic risk profiles for exclusive e-cigarette use and dual-product use among a large sample of Canadian youth. This study focuses on a measure of at least weekly use to identify adolescents at highest risk for problematic e-cigarette and/or conventional cigarette use.

Study design and participants

The current study was completed as part of the Contemporary Risk-taking by Canadian Youth (RISCY) study. RISCY uses a youth-informed approach involving a continual feedback cycle with youth advisory committees to explore how adolescent risk-taking is changing over time and to identify those who may be inequitably affected by risk-taking and its health consequences. The RISCY study brings together two of the largest Canadian youth health surveys: the Health Behaviour in School-aged Children (HBSC) study, and the Cannabis, Obesity, Mental health, Physical activity, Alcohol, Smoking, Sedentary behaviour (COMPASS) study, to incorporate new measures of risk-taking into Canadian surveillance initiatives. RISCY has received ethics clearance from Brock University (REB#22-315).

The COMPASS study, used here, is an ongoing, prospective cohort survey study of Canadian secondary school students in Ontario, Alberta, British Columbia, and Quebec. COMPASS uses purposive sampling to recruit whole-school samples. Ethics approval for COMPASS has been obtained from the University of Waterloo (ORE#30118), Brock University (REB#18-099), CIUSSS de la Capitale-Nationale–Université Laval (#MP-13-2017-1264), and all participating school boards. Informed consent was obtained from participants and from parents/guardians of children. Additional details about the COMPASS study are available in print [ 11 ] and online [ 12 ].

The current study uses student-level sociodemographic and substance use data from the 2021-22 wave of COMPASS study. The sample includes 50,189 students in grades 9-12 (secondary 3-5 in Quebec) from 167 schools, corresponding to a typical age range of 13-18 years old. The COMPASS student survey is an online, self-administered, anonymous questionnaire completed within a school-specific two-week period, with optional allocated class time. [ 13 ] Using active-information, passive-consent protocols, parents/guardians of all eligible students were sent study permission information via email and/or automated school phone system a minimum of two weeks prior to the survey start date, with the option to actively withdraw permission for their child(ren) to participate. Additionally, students could decline participation at any time prior to submitting survey responses. The participation rate in 2021-22 was 68.4%.

E-cigarette and cigarette use measures

Cigarette use.

Ever use of cigarettes was measured using the yes/no question, “Have you ever tried a cigarette, even just a few puffs?” and past 30-day use was measured using the question “On how many of the last 30 days did you smoke one or more cigarettes?”, with response options of “None”, “1 day”, “2 to 3 days”, “4 to 5 days”, “6 to 10 days”, “11 to 20 days”, “21 to 29 days”, and “30 days (every day)”. These measures align with frequency measures previously used in the Youth Smoking Survey [ 14 ]. Cigarette smoking self-report has been shown to be an accurate indicator of cigarette use prevalence for Canadian adolescents [ 15 ].

E-cigarette use

Ever use of e-cigarettes was measured using the yes/no question, “Have you ever tried a vape, also known as an e-cigarette? (e.g., JUUL, Vype, Suorin, Smok)” and past 30-day use was measured using the question “On how many of the last 30 days did you use a vape?”, with response options of “None”, “1 day”, “2 to 3 days”, “4 to 5 days”, “6 to 10 days”, “11 to 20 days”, “21 to 29 days”, and “30 days (every day)”. These measures were developed to align with frequency measures for cigarette smoking. E-cigarette use self-report has been shown to be a valid indicator of e-cigarette use in adolescents and young adults [ 16 , 17 ].

Use frequency classifications

For e-cigarette and cigarette use separately, participants who responded “No” to the question on ever use were classified as “Never” users and those who indicated “Yes” to ever use but responded “None” to past 30-day use were classified as “Non-current” users. Among participants who indicated any past 30-day use, use on 1-3 days was classified as “Infrequent” while use on four or more days was classified as “Weekly”. The cut-off of four or more days was chosen based on sample size considerations for the low number of cigarette users in this study. This cut-off provides a sufficient sample of cigarette users to ensure model stability in prediction of dual product use while still differentiating potentially problematic use from one-off or very infrequent use.

Sociodemographic measures

Age was measured using the question “How old are you today?”, with response options of “12 years or younger”, “13 years”, “14 years”, “15 years”, “16 years”, “17 years”, “18 years”, and “19 years or older”.

Gender identity

Participants were asked to indicate both their biological sex and current gender. Sex was measured using the question “What sex were you assigned at birth?” with response options for “Female”, “Male”, and “I prefer not to say”. Gender was measured using the question “Which gender do you most identity with?” with response options for “Girl/Woman”, “Non-binary person”, “Two-spirit”, “Boy/Man”, “I describe my gender differently”, and “I prefer not to say”. Missing values were assigned to participants who selected “I prefer not to say”. Gender identity was classified using a two-step process based on World Health Organization recommendations [ 18 ]. Participants who answered “Female” and “Girl/Woman” were classified as cisgender girl, those who answered “Male” and “Boy/Man” were classified as cisgender boy, and those who answered other combinations of sex and gender were classified as gender diverse.

Race/ethnicity

Participant racial/ethnic background was measured using the question “Which race category best describes you? (Mark all that apply)” with response options for “Black”, “East Asian”, “Latino”, “Middle Eastern”, “South Asian”, “Southeast Asian”, “White”, “Another category”, “I do not know”, and “I prefer not to say”. Participants selecting more than one option were classified as multiethnic. This measure aligns with the Canadian Institute for Health Information guidance on race-based data collection [ 19 ].

Available spending money

Participants were asked two questions related to the amount and source of individual weekly spending money. Amount of spending money was measured using the question “About how much money do you usually get each week to spend on yourself or to save?” with response options of “Zero”, “$1 to $5”, “$6 to $10”, “$11 to $20”, “$21 to $40”, “$41 to $100”, “More than $100”, and “I do not know how much money I get each week”. Participants who responded “I do not know” were classified as missing for analysis purposes. Source of spending money was measured using the question “Where do you get money to spend on yourself or to save? (Mark all that apply)” with response options “I do not usually get any money to spend on myself or to save”, “My parents/guardians give me money (e.g., an allowance)”, “I get a paycheque from a job (working evenings or weekends at a restaurant, store, etc.)”, and “I get paid cash for occasional work (babysitting, mowing lawns, shovelling snow, etc.)”. Participants selecting more than one source option were classified as having multiple sources of spending money.

Family financial affluence

Relative family financial affluence was measured using the question “Would you say that you and your family are more or less financially comfortable than the average student in your class?” with response options for “More comfortable”, “As comfortable”, and “Less comfortable”. Participants were also asked the yes/no question “In your house, do you have your own bedroom?”, which is a component of the Family Affluence Scale [ 20 ] and can indicate lower family financial affluence. This measure has not been validated as a standalone indicator of socioeconomic status but was included in the current study because it is also hypothesized as a potential indicator of available private space to engage in substance use. The complete Family Affluence Scale was not available in the 2021-22 COMPASS student survey.

Participants with missing data on e-cigarette or cigarette use ( n = 3,523; 7.0% of sample) were excluded from the analysis, resulting in a final analytic sample of 46,666 students from 167 schools. A contingency table of e-cigarette and cigarette use was calculated to assess dual product use. Participants were classified according to their weekly (i.e., four or more times in the last 30 days) product use as either dual-product users (use of both e-cigarettes and cigarettes), e-cigarette-only users, cigarette-only users, or infrequent/non-users. Sociodemographic characteristics were reported for the total sample, as well as by product use classification. Separate t-tests (continuous variables) and chi-square tests (categorical variables) were used to calculate the statistical significance of difference in means and proportions between each user group relatively to infrequent/non-users. Missing values were excluded from tests. For categorical variables with more than two categories, chi-square test residuals were examined to explore which sociodemographic categories most contributed to the differences between user groups (residual tables not presented).

Separate classification trees were constructed using Classification and Regression Tree (CART) analysis [ 10 ] to identify sociodemographic risk profiles for weekly dual-product use and weekly e-cigarette-only use. The CART algorithm divides the sample into subgroups by iteratively choosing the sociodemographic variables and cut points that provide maximum separation between groups with respect to probability of e-cigarette or dual product use. Overviews of the CART method in the context of public health are available [ 21 , 22 ]. A stable classification tree for cigarette-only use could not be constructed due to the small number of cigarette-only users. Infrequent/non-users were used as the reference group for all tree models. All covariates were included as predictors in each classification model and missing values were accounted for using surrogate splitting variables. [ 23 ] Due to class proportion imbalance in rates of weekly use, a weighted loss function proportional to the class imbalance was used to improve model sensitivity. The Gini index was used to measure node impurity for splitting, and tree depth was capped at four levels of splits to avoid over-complexity. Area under the receiver operating characteristic curve (AUC) was used as the criterion for final tree selection, with pruning performed to mitigate overfitting using 10-fold cross-validation to select the smallest tree having an AUC within one standard error of the maximum AUC (i.e., the “1-SE” rule [ 10 ]). Terminal nodes with weekly use probabilities higher than the root node were classified as “high-risk” groups. To perform the CART analysis, “rpart” [ 24 ] routine within the “caret” [ 25 ] package was used in R software version 4.3.0 [ 26 ].

Rates of e-cigarette and cigarette use

Table 1 shows contingent rates of e-cigarette and cigarette use. Examining marginal product use, 42.0% of adolescents had ever tried e-cigarettes with 16.2% using at least weekly, while 19.9% of adolescents had ever tried cigarettes with only 3.4% using at least weekly. Examining dual product use, nearly all adolescents who used cigarettes also used e-cigarettes at an equal or greater frequency. Weekly use rates were 3.0% for dual-product use, 13.2% for e-cigarette-only use, and 0.4% for cigarette-only use.

Sample sociodemographic characteristics by weekly product use

Table 2 shows sample sociodemographic characteristics. The sample comprised 48.9% cisgender girls, 45.6% cisgender boys, and 5.5% gender diverse adolescents, and the average age was 15.5 (SD 1.1). The sample was 68.4% white, 21.0% had no weekly spending money, and 63.9% considered themselves to have average relative family financial comfort.

Table 2 shows sample characteristics across product use groups as well as statistical significance levels of differences in sample proportions relative to the infrequent/non-user group. Average age was similar across groups of weekly product users and approximately 0.3-0.4 years higher than the infrequent/non-user group. A disproportionately high percentage of dual-product and cigarette-only users were gender diverse relative to infrequent/non-users (19.4% and 20.7% vs. 5.1%). A higher proportion of e-cigarette-only users identified as white ethnicity (77.3% vs. 67.4%) and higher proportions of dual-product and cigarette-only use identified as Black, Latino, Middle Eastern, or multiethnic, relative to infrequent/non-users. Lower proportions of dual product, e-cigarette only, and cigarette only users identified as East Asian, South Asian, or Southeast Asian. A higher proportion of dual-product and e-cigarette only users had over $100 per week in available spending money (50.9% and 50.4% vs. 31.7%), primarily from a paycheque. A disproportionately high percentage of dual-product and cigarette-only users perceived their family to be relatively less financially comfortable (19.1% and 18.7% vs. 7.1%) and did not have their own bedroom (13.1% and 13.4% vs. 6.4%).

Risk profiles of weekly dual-product users

Figure 1 shows the classification tree predicting weekly dual use of e-cigarettes and cigarettes. Gender identity emerged as a key differentiator, with probability of dual-product use four times higher in gender diverse adolescents than cisgender adolescents. Individual weekly spending money and family financial comfort emerged as opposing risk factors, with higher probability of dual-product use among those with higher individual spending money but relatively lower family financial comfort. Differences by race/ethnicity also emerged for some subgroups of cisgender adolescents, with much lower probability of dual-product use among East Asian, South Asian, and Southeast Asian adolescents.

Classification tree* predicting weekly dual-product use of e-cigarettes and cigarettes vs. infrequent or non-use

* Pr = within-node probability of use; the percentage under the node refers to the percentage of the analytic sample contained within the node 

Seven unique risk profiles were identified corresponding to the seven terminal tree nodes, and four groups were classified as “high-risk”, with probability of dual product use higher than the root node rate of 3.4%. The highest-risk group comprised gender diverse adolescents, who had a 12.0% probability of dual-product use. The second high-risk group comprised cisgender adolescents with over $40 per week in available spending money and relatively less family financial comfort, who had an 8.9% probability of dual-product use. The third and fourth high-risk groups both comprised cisgender adolescents of Black, Latino, Middle Eastern, multiethnic, white, or another ethnicity. Those with spending money under $40 per week and relatively less family financial comfort had a 5.6% probability of dual product use, while those with spending money over $40 per week but average or more family financial comfort had a 4.0% probability of dual-product use.

Risk profiles of weekly e-cigarette-only users

Figure 2 shows the classification tree predicting weekly e-cigarette-only use. Available weekly spending money emerged as a key differentiator, with higher probability of e-cigarette-only use among those with over $20 per week in available spending money. Differences by ethnicity also emerged, with much higher probability of e-cigarette-only use among white, multiethnic, and Latino adolescents. Gender identity and family financial comfort also emerged as differentiators of use among subgroups of white and multiethnic adolescents with low spending money, with higher probability of use among cisgender girls with lower family financial comfort.

Classification tree* predicting weekly e-cigarette-only use vs. infrequent or non-use

* Pr = within-node probability of use; the percentage under the node refers to the percentage of the analytic sample contained within the node

Eight unique risk profiles were identified corresponding to the eight terminal tree nodes, and two groups were classified as “high-risk” with probability of dual product use higher than the root node rate of 13.8%. The highest risk group comprised cisgender girls of white, multiethnic, or another ethnicity who had spending money under $20 per week and relatively low family financial comfort: the probability of e-cigarette-only use in this group was 19.7%. The second-highest risk group comprised adolescents of Latino, white, multiethnic, or another ethnicity with over $20 per week in spending money, who had a 19.2% probability of e-cigarette-only use.

This study examined differences in sociodemographic risk profiles for adolescents engaging in contemporary (i.e. e-cigarette vaping) and traditional (i.e. cigarette smoking) forms of nicotine product use. Nearly all cigarette users in this study also used e-cigarettes. Over 16% of youth in the sample used e-cigarettes at least weekly with 3% engaging in dual e-cigarette and cigarette use, while less than 0.5% of youth used only cigarettes. These findings are in line with past 30-day dual product use estimates of 5.3% in Canadian grade 7-12 students [ 3 ] and 2.7% to 8.9% (weighted average 5.3%) for grade 8-12 students in the United States [ 9 ]. While youth with available spending money had higher probabilities of both dual-product and exclusive e-cigarette use, risk profiles differed on other characteristics, suggesting that e-cigarettes are attracting different demographics of adolescents from those at high-risk for conventional cigarette use. Additionally, the sociodemographic risk factors identified for dual-product use but not e-cigarette-only use —namely, identifying as gender diverse and having lower family socioeconomic position— are consistent with risk factors traditionally associated with cigarette smoking [ 27 , 28 , 29 ], suggesting a shift among these traditional at-risk groups toward contemporary modes of use.

The largest high-risk group for exclusive e-cigarette use included white, Latino, and multiethnic adolescents who had over $20 in available weekly spending money. The CART analysis found higher probabilities of e-cigarette use among white and Hispanic adolescents compared to those of Black ethnicity, and this is consistent with other recent findings [ 7 , 8 , 9 ]; however, studies from the United States have found relatively lower rates of e-cigarette use among Hispanic adolescents compared to white students [ 7 ]. The other key differentiating factor for this risk group is available spending money, which has well-established associations to youth substance use [ 30 , 31 , 32 ]. Notably, the risk profiles differentiated on spending money rather than on family-level affluence. The increased risk associated with spending money could potentially be related to adolescents’ ability to purchase e-cigarettes rather than their overall socioeconomic status. Notably, this high-risk sociodemographic group comprised over half of the study sample and did not differentiate on gender or age, suggesting that e-cigarette use is widespread across many demographic groups who can access vaping devices. The results of the current study highlight the need for broad, universal strategies to limit e-cigarette availability and access across sociodemographic groups.

Four risk profiles were identified for youth at high risk of dual-product use. Gender diverse youth were the highest risk group, with rates of weekly dual-product use more than four times higher than cisgender adolescents. These findings are consistent with a recent review [ 27 ] showing higher rates of both e-cigarette and cigarette use among gender diverse youth, with evidence that these higher rates may be attributable to experienced gender minority stressors (e.g., discrimination, victimization) [ 27 ]. Gender diverse youth report stress relief and conforming to peer social norms as primary reasons for smoking and vaping [ 33 ]. Past population-level Canadian studies have not distinguished gender diverse youth in gender-stratified estimates of e-cigarette or cigarette use. The results of the current study highlight the need to represent gender diverse youth in national estimates that inform needs-based prevention initiatives. Notably, the elevated risk for gender diverse youth was specific to dual-product use as opposed to exclusive e-cigarette use. Given their unique stressors, tailored anti-tobacco campaigns may be more effective for gender diverse youth [ 33 ]. As an important caveat, this high-risk group comprised only 5.5% of the current sample, and so initiatives that target only this group are unlikely to significantly reduce population-level rates of e-cigarette and cigarette use; inclusive programming and multi-targeted initiatives are likely needed.

Cisgender adolescents in high-risk groups for dual product use had either high levels of individual spending money or lower relative family affluence, with the highest risk group having both attributes. The opposite directionality of association for these two SES proxy measures seems initially paradoxical. As previously discussed as a driver for e-cigarette-only use, higher individual spending money may increase risk of dual-product use through increased access and ability to purchase substances. In contrast, the findings regarding lower family affluence align with a previous study that found increased risk specifically for dual-product use compared to exclusive e-cigarette use among adolescents with lower family affluence [ 8 ]. This unique association between lower family affluence and dual-product use could suggest different influences behind decisions to use cigarettes and e-cigarettes. For example, the association between lower family affluence and youth cigarette use has been partially attributed to parents’ smoking behaviours [ 34 ]. Additionally, lower SES has been associated with greater exposure to cigarette advertising, while higher SES was associated with greater exposure to e-cigarette advertising among youth [ 35 ]. General tobacco prevention programming may benefit youth of lower relative family affluence; however, a better understanding of the drivers of dual-product use among this group is needed.

This study used a novel modelling approach to address a research gap in understanding adolescent risk profiles for problematic cigarette, e-cigarette, and dual use; however, several limitations are noteworthy. While this study included a large sample of Canadian youth, the sampling design was not representative and therefore any generalizations should be made with caution. While this study included a more comprehensive measure of gender identity than previous Canadian research, the sample required collapsing all gender diverse adolescents into one category. Also, no absolute measure of family-level SES (e.g., household income) was available. Not having one’s own bedroom is one indicator that can be associated with household material affluence [ 20 ], but this measure did not emerge as an important differentiator of risk in this study. Additionally, the measures of cigarette and e-cigarette use did not assess quantity of consumption, and the measure of e-cigarette use did not distinguish between nicotine-containing and nicotine-free products. The chosen frequency cut-off of four or more days in the past 30 days was selected to represent approximately weekly use, though it is not known if actual use was evenly distributed across weeks. This measure is also less stringent than the commonly used criterion for frequent use of 20+ days due to limitations in the sample size of cigarette smokers in this study. From a modelling standpoint, the CART algorithm does not account for the clustered nature of participants within schools, which could influence choice of split; however, modelling limitations for categorical outcomes prevented accounting for this clustered sampling design in the decision tree models. The resulting decision tree models also had modest fit statistics, with AUC values ranging from 0.63 to 0.67. CART analysis does not use statistical tests of determine if probabilities are statistically significantly different from each other, and therefore differences are understood as descriptive in nature rather than inferential. Thus, while the decision tree models were able to identify sociodemographic groups with varying probabilities of use, the included sociodemographic factors don’t fully explain differences between users and non-users. Future research should examine additional behavioural, interpersonal, and contextual drivers of use.

This study used a novel modelling approach (CART) to identify combinations of sociodemographic characteristics that profile high-risk groups for exclusive e-cigarette and dual-product use. Most weekly users in this study exclusively used e-cigarettes, and available spending money was a key driver of e-cigarette and dual-product use across several sociodemographic groups. Nearly all traditional cigarette users also used e-cigarettes, with gender diverse and less affluent adolescents in high-risk groups for dual product use. The unique risk profiles identified for exclusive e-cigarette use and dual-product use suggest that new demographics of adolescents are at risk for problematic e-cigarette use.

Availability of data and materials

The datasets used in the current study are available upon successful completion of a COMPASS data usage application, available at https://uwaterloo.ca/compass-system/information-researchers

Abbreviations

classification and regression tree

Cannabis, Obesity, Mental health, Physical activity, Alcohol, Substance use, Sedentary behaviour

Contemporary risk-taking in Canadian Youth

Socioeconomic status

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Acknowledgements

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The Contemporary risk-taking in Canadian Youth (RISCY) study is funded by the Canadian Institutes for Health Research (PJT-488331). The COMPASS study has been supported by a bridge grant from the CIHR Institute of Nutrition, Metabolism and Diabetes (INMD) through the “Obesity – Interventions to Prevent or Treat” priority funding awards (OOP-110788; awarded to STL), an operating grant from the CIHR Institute of Population and Public Health (IPPH) (MOP-114875; awarded to STL), a CIHR project grant (PJT-148562; awarded to STL), a CIHR bridge grant (PJT-149092; awarded to KAP/STL), a CIHR project grant (PJT-159693; awarded to KAP), and by a research funding arrangement with Health Canada (#1617-HQ-000012; contract awarded to STL), and a project grant from the CIHR Institute of Population and Public Health (IPPH) (PJT-180262; awarded to STL and KAP). The COMPASS-Quebec project additionally benefits from funding from the Ministère de la Santé et des Services sociaux of the province of Québec, and the Direction régionale de santé publique du CIUSSS de la Capitale-Nationale. KAP is the Canada Research Chair in Child Health Equity and Inclusion. KML is supported by a Social Sciences and Humanities Research Council (SSHRC) Postdoctoral Fellowship.

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W.P., K.P., and S.L. conceived the study design. T.W. and K.P. conceived the manuscript. All authors substantively contributed to the analysis plan and interpretation of data. K.B. wrote the main manuscript text and completed the analysis. T.W., A.C., and T.E.M. substantively revised the manuscript. All authors critically reviewed the manuscript and approved the submitted version.

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Battista, K., Patte, K.A., Wade, T.J. et al. Do sociodemographic risk profiles for adolescents engaging in weekly e-cigarette, cigarette, and dual product use differ?. BMC Public Health 24 , 1558 (2024). https://doi.org/10.1186/s12889-024-18813-2

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DOI : https://doi.org/10.1186/s12889-024-18813-2

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eTable 1. Global Appraisal of Individual Needs–Short Screener (GAIN-SS) Items

eTable 2. Association Between Baseline e-Cigarette Use and Continued Cigarette Use (Continued Smoking Measure II) in 3 Years, After Cigarette Initiation a Year After the Baseline

eTable 3. Association Between Baseline e-Cigarette Use and Continued Cigarette Use (Continued Smoking Measure III) in 3 Years, After Cigarette Initiation a Year After the Baseline

eTable 4. Association Between Baseline e-Cigarette Use and Continued Cigarette Use in 3 Years, After Cigarette Initiation a Year After the Baseline, With Alternative Measures of Continued Use

eTable 5. Association Between Baseline Past 12-Month e-Cigarette Use and Continued Cigarette Use in 3 Years, After Cigarette Initiation a Year After the Baseline

eTable 6. Association Between Baseline e-Cigarette Use and Continued Cigarette Use in 3 Years, After Cigarette Initiation a Year After the Baseline, Without Survey Weights

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• e-Cigarette and Cigarette Use Among Youth: Gateway or Common Liability? JAMA Network Open Invited Commentary March 27, 2023 Cristine D. Delnevo, PhD, MPH

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Sun R , Méndez D , Warner KE. Association of Electronic Cigarette Use by US Adolescents With Subsequent Persistent Cigarette Smoking. JAMA Netw Open. 2023;6(3):e234885. doi:10.1001/jamanetworkopen.2023.4885

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Association of Electronic Cigarette Use by US Adolescents With Subsequent Persistent Cigarette Smoking

• 1 Department of Health Policy and Organization, School of Public Health, University of Alabama at Birmingham, Birmingham
• 2 Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor
• Invited Commentary e-Cigarette and Cigarette Use Among Youth: Gateway or Common Liability? Cristine D. Delnevo, PhD, MPH JAMA Network Open

Question   Among adolescents who initiate smoking after electronic cigarette (e-cigarette) use, is their baseline e-cigarette use status associated with continued smoking a few years later?

Findings   This cohort study using data from a national sample of 8671 cigarette-naive adolescents found that youth who had used e-cigarettes at baseline, compared with those who had not, had higher odds of continuing smoking 2 years following initiating smoking the year after baseline. However, the absolute risks of continued smoking for both baseline e-cigarette users and nonusers were very small, as were the differences in absolute risks.

Meaning   Although the significantly higher odds of continued smoking among e-cigarette users suggest a potentially important problem, the small magnitude of absolute risks and the minor risk differences in continued smoking between baseline e-cigarette users and nonusers indicate a much less consequential problem: few adolescents are likely to report continued smoking after initiation regardless of baseline e-cigarette use.

Importance   Many studies have reported a positive association of youth electronic cigarette (e-cigarette) use with subsequent cigarette smoking initiation, but it remains unclear whether e-cigarette use is associated with continued cigarette smoking after initiation.

Objective   To assess the association of youth baseline e-cigarette use with their continued cigarette smoking 2 years after initiation.

Design, Setting, and Participants   The Population Assessment of Tobacco and Health (PATH) Study is a national longitudinal cohort study. This sample consisted of youth who participated in waves 3, 4, and 5 of the study (wave 3 was from October 2015 to October 2016, wave 4 was from December 2016 to January 2018, and wave 5 was from December 2018 to November 2019) and had never used cigarettes (cigarette-naive) by wave 3. The current analysis used multivariable logistic regressions in August 2022 to assess the association between e-cigarette use among cigarette-naive adolescents aged 12 to 17 years in 2015 and 2016 and subsequent continued cigarette smoking. PATH uses audio computer-assisted self-interviewing and computer-assisted personal interviewing to collect data.

Exposures   Ever and current (past 30-day) use of e-cigarettes in wave 3.

Main Outcomes and Measures   Continued cigarette smoking in wave 5 after initiating smoking in wave 4.

Results   The current sample included 8671 adolescents who were cigarette naive in wave 3 and also participated in waves 4 and 5; 4823 of the participants (55.4%) were aged 12 to 14 years, 4454 (51.1%) were male, and 3763 (51.0%) were non-Hispanic White. Overall, regardless of e-cigarette use, few adolescents (362 adolescents [4.1%]) initiated cigarette smoking at wave 4, and even fewer (218 participants [2.5%]) continued smoking at wave 5. Controlling for multiple covariates, the adjusted odds ratio of baseline ever e-cigarette use, compared with never e-cigarette use, was 1.81 (95% CI, 1.03 to 3.18) for continued smoking measured as past 30-day smoking at wave 5. However, the adjusted risk difference (aRD) was small and not significant. The aRD was 0.88 percentage point (95% CI, −0.13 to 1.89 percentage points) for continued smoking, with the absolute risk being 1.19% (95% CI, 0.79% to 1.59%) for never e-cigarette users and 2.07% (95% CI, 1.01% to 3.13%) for ever e-cigarette users. Similar results were found using an alternative measure of continued smoking (lifetime ≥100 cigarettes and current smoking at wave 5) and using baseline current e-cigarette use as the exposure measure.

Conclusions and Relevance   In this cohort study, absolute and relative measures of risks yielded findings suggesting very different interpretations of the association. Although there were statistically significant odds ratios of continued smoking comparing baseline e-cigarette users with nonusers, the minor risk differences between them, along with the small absolute risks, suggest that few adolescents are likely to continue smoking after initiation regardless of baseline e-cigarette use.

Since 2014, electronic cigarettes (e-cigarettes) have become the most popular nicotine or tobacco product among adolescents in the US. 1 - 4 After the peak in 2019, the prevalence of current e-cigarette use (any use in the past 30 days) in high school students who completed the National Youth Tobacco Survey decreased to 14.1% in 2022. 2 - 5

An important concern about the popularity of adolescent e-cigarette use is that it may lead cigarette-naive adolescents to try cigarettes. These adolescents could become addicted to nicotine and start smoking regularly, leading to well-known smoking-related health consequences in the future. 6 On the basis of a recent meta-analysis, 7 many longitudinal studies of adolescents in the US 8 - 17 and other countries 18 - 24 have reported positive associations between e-cigarette use and subsequent smoking initiation. However, all of these studies (except one 9 ) examined only smoking initiation at follow-up, evaluating ever smoking of cigarettes. These studies provide no insight into whether these adolescents, after initiating smoking, become regular or established smokers.

Most adolescents who have ever tried smoking do not become regular or established smokers. 25 Adolescent smoking is commonly conceptualized as progressing through a sequence of developmental states, defined by smoking frequency and intensity. 26 Experimenters have tried a cigarette but do not smoke regularly. 27 Established smokers are those who smoke regularly and have smoked at least 100 cigarettes during their lifetime. 28 Data from the 2015 National Health Interview Survey indicate that among individuals aged 25 years who had ever tried a cigarette, only 36% reported lifetime use of more than 100 cigarettes. 29 That number includes many who never smoked regularly or no longer do so.

It is important to distinguish experimenters from regular or established smokers. For adolescents who initiate smoking after e-cigarette use but do not continue smoking, their risks of adverse smoking-related health outcomes are negligible. However, if these adolescents become long-term smokers, then the health consequences could be substantial. Because most previous studies investigated only the association between e-cigarette use and subsequent smoking initiation, 8 , 10 - 24 the association between e-cigarette use and continued smoking after initiation remains unclear.

To address this gap in the literature, we examined whether cigarette-naive adolescents who had used e-cigarettes at baseline, compared with those who had not used e-cigarettes, were more likely to continue smoking 2 years following initiating smoking the year after baseline. We used longitudinal data on adolescents from the 3 most recent waves (waves 3, 4, and 5) of the Population Assessment of Tobacco and Health (PATH) Study. We assessed baseline e-cigarette use in 2 ways: ever use and current use. On the basis of cigarette smoking status in waves 4 and 5, we then constructed 5 different measures of continued cigarette smoking. In assessing the prospective association between e-cigarette use and continued smoking, we controlled for multiple independent risk factors, including sociodemographics, environmental factors, other substance use, cigarette susceptibility, and mental health measures.

The PATH Study is a US national longitudinal cohort study of youth and adults from the civilian, noninstitutionalized population. The survey uses a 4-stage, stratified probability sample design to select participants to answer questions on tobacco use and how it affects their health. The first wave of data collection took place in 2013, and annual or biennial follow-up surveys have been conducted since then. Follow-up surveys for individual participants were conducted during a target data collection period, starting with 1 month before the anniversary month of the individual’s prior survey and ending 1 month after the anniversary month. Adolescents were recruited for the PATH Study after written informed consent was given by their parents. The wave 3 weighted response rate was 83.3%. 30 Our sample consisted of youth aged 12 to 17 years in 2015 and 2016 who participated in waves 3, 4, and 5 of the study (wave 3 was from October 2015 to October 2016, wave 4 was from December 2016 to January 2018, and wave 5 was from December 2018 to November 2019) and had never used cigarettes (cigarette-naive) by wave 3. We also included adolescents who turned 18 years old in wave 4 or 5 from the adult surveys. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines for cohort studies. The University of Alabama at Birmingham institutional review board exempted this study from review because it used deidentified data.

The independent variable of interest is self-reported e-cigarette use in wave 3, assessed by 2 different measures: ever use and current (past 30-day) use. Cigarette smoking was assessed in waves 4 and 5, from both the youth and adult surveys. Among cigarette-naive adolescents in wave 3, their initiation of cigarette smoking was determined by self-reported ever use of cigarettes in wave 4. In wave 5, to assess continued cigarette use among wave 4 smoking initiators, we constructed 5 different binary measures of cigarette smoking representing, in order, progressively more substantial commitment to smoking: (1) any use in the past 12 months (continued smoking measure [CSM]–I); (2) any use in the past 30 days (CSM-II); (3) established use, which we defined as 100 cigarettes or more in the respondent’s lifetime and smoking currently (CSM-III); (4) 100 or more lifetime cigarettes and smoking 5 or more days in the past 30 days (CSM-IV); and (5) 100 or more lifetime cigarettes and smoking 20 or more days in the past 30 days (CSM-V).

Sociodemographic variables included age (12-14 vs 15-17 years), sex (male vs female), race and ethnicity (Hispanic, non-Hispanic Black, non-Hispanic White, and non-Hispanic other [ie, American Indian or Alaska Native, Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese, other Asian, Native Hawaiian, Guamanian or Chamorro, Samoan, and other Pacific Islander]), highest parental education (high school or general educational development or less, some college, and college or higher), annual household income (<$50 000, $50 000-$100 000, and >$100 000), and school grades (less than mostly Bs vs mostly Bs and higher). All responses were self-reported. Race and ethnicity were analyzed in this study because they are factors associated with smoking.

Exposure to smoking was measured via family tobacco use, secondhand smoke exposure, and peer cigarette smoking. Family tobacco use (0 vs 1) was evaluated by asking whether anyone living with the respondent uses cigarettes, smokeless tobacco, cigars, cigarillos, filtered cigars, or any other form of tobacco. Secondhand smoke exposure (0 vs 1) was categorized by any nonzero answer to the question, “During the past seven days, about how many hours were you around others who were smoking?” Peer cigarette use (0 vs 1) was scored 1 if participants reported any positive number to the question asking, “How many of your best friends smoke cigarettes?”

Ever other tobacco product use (0 vs 1), excluding cigarettes and e-cigarettes, was defined by any positive response to questions asking about ever use of cigar, pipe, hookah, snus, smokeless tobacco, bidi, kretek, or dissolvable tobacco. Past 12-month use of alcohol (0 vs 1) and cannabis (0 vs 1) were assessed separately by the question, “In the past 12 months, have you used alcohol (or marijuana hash, THC, grass, pot or weed)?”

On the basis of previous literature, 31 we constructed a binary variable of susceptibility to cigarette smoking. Participants were asked about potential future use of cigarettes: “Have you ever been curious about smoking a cigarette?”, “Do you think that you will try a cigarette soon?”, “If one of your best friends were to offer you a cigarette, would you smoke it?”, and “Do you think you will smoke a cigarette in the next year?” Participants answering “not at all curious” to the first question and “definitely not” to the last 3 were considered not susceptible to cigarettes, whereas the rest were considered susceptible.

We also included measures of internalizing and externalizing mental health problems, which are important factors associated with substance use. 32 , 33 Respondents were asked the last time they experienced any of 4 internalizing disorder symptoms and any of 7 externalizing disorder symptoms. The number of reported symptoms within the past year was summed and then coded into a 3-level severity measure: low, moderate, and high. 34 See eTable 1 in Supplement 1 for details.

The analyses were conducted in August 2022 using Stata statistical software version 17 (StataCorp), with the Fay method of balanced repeated replication to estimate variance. Stata’s svy command was used to incorporate survey weights. The income variable had the most missing data (6.6%), whereas others had few missing values (<5.0%). Because of the negligible impact of missing data, 35 we conducted complete case analysis. We performed multivariable logistic regressions to examine the association between e-cigarette use in wave 3 and continued cigarette use 3 years later in wave 5, following smoking initiation in wave 4, controlling for the variables identified above.

We report our main results as adjusted odds ratios (aORs) as well as adjusted risk differences (aRDs) and absolute risks using Stata’s margins command. Two-tailed P  < .05 by the Pearson χ 2 test or Wald test was considered significant. An association that appears large according to ratios may be negligible when applied to the absolute risks and may, therefore, be misleading. As a result, Holmberg et al 36 recommend reporting both the ratios and the actual risks.

In sensitivity analyses, we present the associations between e-cigarette use and continued cigarette use CSM-II and CSM-III in the main results and the associations with other continued use measures (CSM-I, CSM-IV, and CSM-V) as sensitivity checks. In addition to ever and current use of e-cigarettes at baseline, we add past 12-month e-cigarette use as a sensitivity check.

Among the 8671 adolescents included in the analysis, 4823 (55.4%) were aged 12 to 14 years, 4454 (51.1%) were male, and 3763 (51.0%) were non-Hispanic White ( Table 1 ). A total of 842 participants (9.6%) reported ever e-cigarette use, and 138 (1.6%) reported current use. Highest parental education was high school or general educational development or less (3057 participants [31.7%]), some college (2700 participants [31.2%]), and college or higher (2733 participants [37.0%]). There were 3 categories for annual household income: less than $50 000 (3956 participants [43.7%]), between $50 000 and $100 000 (2082 participants [26.7%]), and more than $100 000 (2060 participants [29.7%]). A total of 2383 participants (25.7%) reported school grades less than mostly Bs, and 6228 (74.3%) reported mostly Bs or higher. In total, 2535 participants (29.1%) lived with family members who used tobacco, 2393 (28.2%) were exposed to secondhand smoke, and 1403 (15.7%) had best friends who smoked cigarettes. A small fraction (438 participants [5.3%]) had ever used other tobacco products (excluding cigarettes and e-cigarettes) and used cannabis (207 participants [2.5%]) in the past 12 months, but 1508 (18.9%) reported past 12-month alcohol use. Less than one-third (2575 participants [29.6%]) were susceptible to cigarettes. Approximately one-half (4393 participants [50.4%]) had a low score for internalizing problems, and 3792 (42.9%) had a low score for externalizing problems. We found significant differences in almost all of these characteristics (excluding sex) between ever and never baseline e-cigarettes users. Similarly, most of these characteristics differed significantly between current e-cigarette users and non–current users.

In Table 2 , we show the prevalence of cigarette initiation at wave 4 and continued use in wave 5. Overall, few adolescents started smoking cigarettes in wave 4, and even fewer continued smoking in wave 5. A total of 362 cigarette-naive adolescents in wave 3 (4.1% of all cigarette-naive adolescents) initiated cigarette use in wave 4. Among them, 218 (2.5%) continued smoking cigarettes using CSM-I, 133 (1.5%) using CSM-II, 60 (0.8%) using CSM-III, 27 (0.4%) using CSM-IV, and 12 (0.2%) using CSM-V.

Table 3 presents the prevalence of 2 of the continued cigarette use measures (CSM-II and CSM-III) by sample characteristics. Using CSM-II, among adolescents who had ever used e-cigarettes in wave 3, 6.0% (95% CI, 4.5%-8.0%) initiated smoking cigarettes in wave 4 and continued smoking in wave 5, compared with 1.1% (95% CI, 0.8%-1.3%) among never e-cigarette users. Using CSM-III, 3.6% (95% CI, 2.5%-5.1%) of ever e-cigarette users continued smoking, compared with 0.5% (95% CI, 0.3%-0.7%) of never e-cigarette users. Similarly, continued smoking prevalence in wave 5 was higher among current e-cigarette users in wave 3. On the basis of continued use measures CSM-II and CSM-III, 9.4% (95% CI, 5.1%-16.8%) and 7.4% (95% CI, 3.6%-14.6%) of current e-cigarette users in wave 3 continued smoking, respectively, compared with 1.4% (95% CI, 1.2%-1.7%) and 0.7% (0.3%-0.9%) of non–current users. In addition, age, race and ethnicity, school grades, family tobacco use, secondhand smoke, peer cigarette use, ever use of other tobacco products, past 12-month use of alcohol and cannabis, cigarette susceptibility, and scores for internalizing and externalizing problems were all factors significantly associated with both continued use measures.

Table 4 shows the association of baseline e-cigarette use and 2 of our continued cigarette smoking measures (CSM-II and CSM-III), adjusting for all study covariates. With CSM-II, baseline ever e-cigarette use was associated with a 0.88 percentage point increase (95% CI, −0.13 to 1.89 percentage points) in reported continued smoking, from 1.19% (95% CI, 0.79% to 1.59%) for never e-cigarette users to 2.07% (95% CI, 1.01% to 3.13%) for ever e-cigarette users. Baseline current e-cigarette use was associated with a 1.88 percentage points increase (95% CI, −0.66 to 4.41 percentage points) in continued smoking, from 1.30% (95% CI, 0.90% to 1.70%) among noncurrent e-cigarette users to 3.18% (95% CI, 0.57% to 5.79%) among current users. The aRDs of using CSM-III were 0.60 percentage point (95% CI, −0.18 to 1.38 percentage points) for ever e-cigarette use and 1.79 percentage points (95% CI, −0.51 to 4.09 percentage points) for current use. Note that none of the aRDs was statistically significant. On the other hand, all aORs were statistically significant. The aORs were 1.81 (95% CI, 1.03 to 3.18) and 2.66 (95% CI, 1.07 to 6.63), respectively, for ever and current use of e-cigarettes with CSM-II. The aORs were 2.24 (95% CI, 1.06 to 4.72) and 4.59 (95% CI, 1.39 to 15.16), respectively, for ever and current use of e-cigarettes with CSM-III.

We present the complete regression results of our main analyses in eTables 2 and 3 in Supplement 1 . The results of sensitivity analyses are similar to those in Table 4 . See eTables 4 and 5 in Supplement 1 . Note that when using CSM-IV and CSM-V as the outcome, some findings were not significant because of the few participants reporting continued use. We also calculated associations between e-cigarette use and continued cigarette use without survey weights (eTable 6 in Supplement 1 ), and the results were very similar.

In general, in this cohort study, regardless of e-cigarette use status, few cigarette-naive adolescents (4.1%) initiated cigarette smoking and even fewer (≤2.5%) continued to smoke at all in 3 years. As seen in Table 2 , the progressively more rigorous definitions of continued smoking (CSM-I to CSM-V) yielded successively lower prevalence in PATH wave 5, decreasing to 0.2% for CSM-V, more than 100 lifetime cigarettes and smoking 20 or more days in the past 30 days. Note that even our most demanding measure of regular smoking included many respondents who smoked less than every day.

According to the aORs in our analysis, we found that cigarette-naive adolescents who had used e-cigarettes at baseline, compared with those who had not, had significantly higher odds of continuing smoking 2 years after their initial smoking. In contrast, however, the aRDs were not significant, although baseline e-cigarette use was still associated with increases in risks of continued smoking. The magnitude of the risk difference was also very small, as were the absolute risks of continued smoking.

These seemingly conflicting findings are both accurate. Holmberg et al 36 have observed that measures of ratios could change substantially with outcome prevalence, whereas measures of difference stay the same. In our study of continued smoking, because of the low outcome prevalence (1.5% with CSM-II and 0.6% with CSM-III), the observed small difference in absolute risks leads to a large value in terms of ratios. The association can be statistically significant and large using ratio measures without having a meaningful size of change of the outcome measure. Similarly, because relatively few adolescents initiate cigarette smoking in general, studies investigating the association of e-cigarettes with cigarettes that only report their findings as ratios may have exaggerated the importance of the association in the minds of readers. To fully and accurately describe this relationship, future studies should report absolute risks, as well as relative measures in ratios.

Although a positive association between e-cigarette use and cigarette initiation has been reported by many studies, at the population level, the prevalence of past 30-day cigarette smoking among youth and young adults has steadily decreased in the era of e-cigarettes. 37 This apparent inconsistency between individual-level and population-level results can be explained by our findings. On the one hand, there is a positive association between e-cigarette use and subsequent smoking in terms of aORs. On the other hand, given the low prevalence of smoking, the absolute risks of continued smoking for both e-cigarette users and nonusers and the risk differences between them are very small. The change in the prevalence of cigarette smoking due to e-cigarette use at the population level is, thus, also very small.

The important question is whether e-cigarette use may lead substantial proportions of cigarette-naive adolescents to become long-term smokers. Consistent with previous findings showing that few youth experimenters become regular or established smokers, 25 , 29 our results show that many adolescents who initiated smoking did not report continued smoking 2 years later. Considering our CSMs (CSM-II to CSM-V), the vast majority did not. In addition, a recent study by Méndez et al 38 revealed that the adult smoking cessation rate in the US has continued to increase, from 4.2% in 2008 to 2013 to 5.4% to 2014 to 2019. This large increase exceeded the rate expected on the basis of the trend over time. 38 The cessation rate has become especially large for young adults, and the smoking initiation rate has decreased greatly during the same period. As a result, it is likely that smoking prevalence, especially among young adults, will decline further in the coming years.

This study has a few limitations. First, our results using data from PATH waves 3 through 5 (2015-2019) may not hold for data from other years. One possible reason is changes in e-cigarette prevalence. Youth e-cigarette use increased between 2017 and 2019 and then decreased after 2019. Future studies are needed to examine whether the association between e-cigarette use and continued cigarette smoking changes over time. Another limitation is with respect to the study period of continued smoking. We only assessed continued smoking 3 years after the baseline survey and 2 years after smoking initiation. It is possible that the prevalence of continued smoking could change if we used a longer study period. Future analysis can be conducted once PATH wave 6 data become available. Furthermore, there is the possibility of response bias, which is a common problem with self-reported data. However, Bachman et al 39 found evidence supporting the validity of self-reported data.

In this cohort study of cigarette-naive respondents, we examined the association of adolescent e-cigarette use at baseline with continued cigarette smoking 3 years later, following smoking initiation a year after baseline. Our findings suggest very different interpretations of the association as measured by absolute and relative risk. The minor risk differences in continued smoking among baseline e-cigarette users and nonusers, together with the small magnitude of absolute risks for both groups, suggest that regardless of baseline e-cigarette use, few adolescents were likely to report continued smoking after initiation.

Accepted for Publication: January 22, 2023.

Published: March 27, 2023. doi:10.1001/jamanetworkopen.2023.4885

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Sun R et al. JAMA Network Open .

Corresponding Author: Ruoyan Sun, PhD, Department of Health Policy and Organization, School of Public Health, University of Alabama at Birmingham, 310C Ryals Public Health Bldg, 1665 University Blvd, Birmingham, AL 35294 ( [email protected] ).

Author Contributions: Dr Sun had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Sun, Warner.

Acquisition, analysis, or interpretation of data: Sun, Méndez.

Drafting of the manuscript: Sun.

Critical revision of the manuscript for important intellectual content: Méndez, Warner.

Statistical analysis: Sun.

Obtained funding: Méndez, Warner.

Supervision: Méndez.

Conflict of Interest Disclosures: None reported.

Funding/Support: Drs Méndez and Warner received support from the National Cancer Institute of the National Institutes of Health (NIH) and the Food and Drug Administration’s (FDA) Center for Tobacco Products (award number U54CA229974).

Role of the Funder/Sponsor: The funder had no 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.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the FDA.

Data Sharing Statement: See Supplement 2 .

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E-Cigarettes: An Epidemic Among Youth

Walker is in your office for his annual Texas Health Steps preventive medical checkup. As part of your routine practice, you speak privately with Walker’s mother and then with Walker. You sit at eye-level with Walker, briefly explain your approach to confidentiality, and ask if he has any concerns to share. Walker shrugs and shakes his head no. “There are some topics I discuss with all my teenaged patients,” you say. “Is it okay to talk about them?” After Walker agrees, you screen him for substance use, asking specifically about cigarettes, alcohol, and marijuana and other drugs. “No, I don’t do any of that,” he says. “I try to stay healthy, and I lift weights.” You are aware that e-cigarette use is increasing dramatically among adolescents so you ask Walker if has tried e-cigarettes. “You mean vaping? Oh yeah, I do that,” Walker says. “But that’s not smoking. It’s only vaping. It’s safer than smoking cigarettes.”

According to a 2019 American Academy of Pediatrics (AAP) policy statement about e-cigarettes, how should you respond to Walker’s remarks?

The AAP recommends that primary care providers take four steps in their clinical practice to help end the e-cigarette epidemic among youth.

Screen patients for e-cigarette use, just as you did with Walker. E-cigarette use (known as vaping) among youth is at epidemic levels in the United States. About 3 million high school students used e-cigarettes in 2018, a 78 percent increase in just one year (Centers for Disease Control and Prevention [CDC], 2018). E-cigarette use in middle school increased almost 50 percent. E-cigarettes “are the most commonly used tobacco product among youth,” yet they are unsafe for children and adolescents (AAP, 2019). In addition, youth who vape “are much more likely to go on to use traditional cigarettes—a product that kills half its long-term users” (Ibid).

Screen patients for exposure to e-cigarettes and advertising. More than three-quarters of high school and middle school students saw or heard e-cigarette ads in 2016 (AAP, 2019). Exposure to such ads “increases intention to use e-cigarettes,” according to the AAP, and youth are at higher risk of transitioning to conventional tobacco products through experimental use of e-cigarettes.

Provide counseling during clinical exams to help prevent e-cigarette use by patients. Primary care providers have a responsibility to educate youth about the health risks and long-term consequences of using e-cigarettes. E-cigarette liquid contains nicotine, which is highly addictive, and other toxic chemicals. Numerous toxicants and carcinogens have been found in e-cigarette solutions (AAP, 2019). E-cigarettes are especially dangerous for youth because the human brain is still developing until about age 25 and nicotine can damage brain development. In addition, secondhand vapor from e-cigarettes can harm those who are involuntarily exposed, including unborn babies and children, and can pollute indoor air. Third-hand vapor clings to surfaces and dust and can become a pollutant as well.

The AAP recommends that pediatricians counsel families, caregivers, and patients that homes, cars, and places where children and adolescents live, learn, play, work, and visit should have comprehensive tobacco-free bans on e-cigarettes and other tobacco products.

Why It Matters

As a primary care provider, you serve as a credible source of important health information for your patients. You can help bust dangerous myths about e-cigarettes by providing patients and families with accurate information about e-cigarettes and the damage they can do to individual and public health. “The increasing use of e-cigarettes among youth threatens 5 decades of public health gains in successfully deglamorizing, restricting, and decreasing the use of tobacco products” (AAP, 2019).

For more information, read the AAP Policy Statement E-Cigarettes and Similar Devices .

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Harvard Business School Case Study Explores E-Cigarettes

By QS Contributor

Harvard Business School has recently announced a new MBA case study which will see business and public health students at the school analyzing the growing popularity of the electronic cigarette market as well as looking at the tension between public health and the marketing of such a product. The case will explore the ways in which tobacco companies and regulators are responding, while also considering how businesses can ensure corporate social responsibility and profit.

The new MBA case study, entitled ‘E-Cigarettes: Marketing Versus Public Health’, written by Harvard Business School associate Margaret L. Rodriguez, explores the seemingly innocent consumer market of the e-cigarette in addressing the worries of public health advocates, who assert that the so-called cure to harmful cigarette addiction is not a cure at all, enabling further addiction and acting as a gateway for new – as well as reformed – smokers.

While the benefits of e-cigarettes are clear – there are none of the harmful chemicals and toxins which can be found in normal cigarettes – the popularity of the e-cigarette, along with company takeovers from big tobacco firms, has meant there are definite issues to be examined, not only from health regulators, but also future business leaders who can learn much about corporate social responsibility in business and marketing, while all the while turning a profit.

Existing statistics indicate that electronic cigarettes have led to a net decrease in traditional smoking of 2.2 million in the US, or 5% of the smoking population. But these statistics only tell half of the story, failing to highlight the influence that the marketing of e-cigarettes has had. A crucial point made on the case in terms of corporate social responsibility is that the tobacco industry itself has profited hugely from the success of e-cigarettes, with the vast majority of e-cigarette companies now owned by big tobacco companies.

Corporate social responsibility becomes bigger focus in MBA case study and programs

John A. Quelch, a professor in business administration at Harvard Business School, who also holds a joint appointment with the Harvard School of Public Health, will be teaching the new MBA case study to all who enroll in the course, ‘Consumers, Corporations and Public Health’, debuting at the business school and the School of Public Health next year. Talking to Harvard Business School Working Knowledge about the value of learning about the issues surrounding the e-cigarette market, Quelch explains; “One of the themes in the course is the tension that exists, quite understandably, between regulators and commercial interests. Most people are used to hearing about that in the context of financial regulation, but similar issues apply in other sectors of the economy including health care.”

One of the many issues in play, says Quelch, is that health regulators are currently in a dilemma: Do they employ a light hand in an attempt to decrease the number of existing smokers, or do they apply stricter regulations in order to put a stop to new smokers picking up the habit. “Put crudely,” says Quelch, “how many nicotine addicts is it worth the risk of creating to have one tobacco smoker quit?”

Quelch’s overall aim on this specific MBA case study is for students to think more deeply about corporate social responsibility and how issues of public health impact business decisions. He hopes to do this by pointing out the dichotomies and consequences of opening up a new market which simultaneously tries to tackle and reinforce consumer addiction for commercial purposes.

This article was originally published in April 2014 . It was last updated in September 2019

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• http://orcid.org/0000-0003-2740-5633 Eric K Soule 1 , 2 ,
• Matthew E Rossheim 3 ,
• Melvin D Livingston 4 ,
• Cassidy R LoParco 5 ,
• Kayla K Tillett 3 ,
• http://orcid.org/0000-0002-8277-5437 Thomas Eissenberg 2 ,
• Steve Sussman 6
• 1 Department of Health Education and Promotion , East Carolina University , Greenville , North Carolina , USA
• 2 Virginia Commonwealth University , Richmond , Virginia , USA
• 3 University of North Texas Health Science Center , Fort Worth , Texas , USA
• 4 Emory University , Atlanta , Georgia , USA
• 5 George Washington University , Washington , District of Columbia , USA
• 6 Departments of Preventive Medicine and Psychology, and School of Social Work , University of Southern California , Pasadena , California , USA
• Correspondence to Dr Eric K Soule, Department of Health Education and Promotion, East Carolina University, Greenville, NC 27858, USA; soulee18{at}ecu.edu

Electronic cigarette (e-cigarette) use has increased since e-cigarettes were introduced to the market nearly 20 years ago. Researchers continue to conduct studies to understand the health risks and benefits of e-cigarettes to inform health education and promotion efforts as well as public policy. Studies funded by the tobacco industry examining the potential risks and benefits of e-cigarettes have also been conducted and are sometimes published in the scientific literature. Frequently, tobacco and e-cigarette industry-funded researchers report findings that contradict research funded by other sources. While many industry-funded studies may appear methodologically sound at first glance, in some cases, industry-funded studies include methodological flaws that result in misleading conclusions. The tobacco industry’s use of biased research to influence tobacco-related policy decisions in the past is well-documented. This commentary provides specific examples of recent e-cigarette research funded by the tobacco/e-cigarette industry in which methodological flaws result in misleading conclusions that support industry goals. Given the long history of biased research conducted by the tobacco industry, there is a need to assess whether research funded by the e-cigarette industry similarly contains methodological flaws. We emphasise the need for tobacco and e-cigarette-funded research to be scrutinised by non-industry-funded subject matter experts and call for journals to not consider manuscripts that have received support from the tobacco or e-cigarette industry.

• tobacco industry
• electronic nicotine delivery devices

https://doi.org/10.1136/tc-2024-058609

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Contributors ES and MER wrote the original draft of the manuscript and all other authors provided critical review of the manuscript. All authors approved the final version.

Funding ES’s effort is supported by grant number P50MD017319 from the National Institute on Minority Health and Health Disparities of the National Institutes of Health. ES’s and TE’s effort are supported by grant number U54DA036105 from the National Institute on Drug Abuse of the National Institutes of Health and the Center for Tobacco Products of the US Food and Drug Administration. This content is solely the responsibility of the authors and does not necessarily represent the views of the NIH or the FDA.

Competing interests TE is a paid consultant in litigation against the tobacco industry and also the electronic cigarette industry and is named on one patent for a device that measures the puffing behaviour of electronic cigarette users and a patent application for a smoking cessation intervention. TE and ES are named on a patent application for a smartphone app that determines electronic cigarette device and liquid characteristics.

Provenance and peer review Not commissioned; externally peer reviewed.

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The Effect of E-Cigarette Flavor Bans on Tobacco Use

Advocates for sales restrictions on flavored e-cigarettes argue that flavors appeal to young people and lead them down a path to nicotine addiction. This study is among the first to examine the effect of state and local restrictions on the sale of flavored electronic nicotine delivery system (ENDS) products on youth and young adult tobacco use. Using data from the State and National Youth Risk Behavior Surveys, we find that the adoption of an ENDS flavor restriction reduces frequent and everyday youth ENDS use by 1.2 to 2.5 percentage points. Auxiliary analyses of the Behavioral Risk Factor Surveillance System show similar effects on ENDS use for young adults ages 18-20. However, we also detect evidence of an unintended effect of ENDS flavor restrictions that is especially clear among 18-20-year-olds: inducing substitution to combustible cigarette smoking. Finally, there is no evidence that ENDS flavor restrictions affect ENDS use among adults aged 21 and older or non-tobacco-related health behaviors such as binge drinking and illicit drug use.

Dr. Sabia acknowledges research support for this work from the Center for Health Economics & Policy Studies (CHEPS) at San Diego State University (SDSU), which has received grants from the Charles Koch Foundation. This study was funded with a grant from Global Action to End Smoking (formerly known as the Foundation for a Smoke-Free World), an independent, U.S. nonprofit 501(c)(3) grantmaking organization, accelerating science-based efforts worldwide to end the smoking epidemic. Global Action played no role in designing, implementing, data analysis, or interpretation of the study results, nor did Global Action edit or approve any presentations or publications from the study. The contents, selection, and presentation of facts, as well as any opinions expressed, are the sole responsibility of the authors and should not be regarded as reflecting the positions of Global Action to End Smoking. Global Action’s mission is to end combustible tobacco use, which remains the leading preventable cause of death globally. The organization collaborates with academic and research centers and others to accelerate life-saving research and educational projects. Global Action does not seek or accept funding from companies that produce tobacco or non-medicinal nicotine products. The charitable gift agreement (the “Pledge Agreement”) between the organization and its prior funder, PMI Global Services Inc., was terminated in September 2023. To complement the termination of the Pledge Agreement, the organization’s Board of Directors established a new policy to not accept or seek any tobacco or non-medicinal nicotine industry funding. Dr. Sabia also acknowledges research support from a subcontract by Georgia State University – via a grant received from the National Institute on Drug Abuse of the National Institutes of Health under Award Number R01DA045016 – to support earlier exploratory work on this project. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health. We thank Caterina Muratori, Anthony Chuo, and Christian Pryfogle for outstanding research assistance. The authors note that after this paper was completed, we learned of a closely related paper by Dhaval Dave, Daniel Dench, Michael Grossman, Selen Özdoğan, and Henry Saffer. Their paper studies similar questions and reaches similar conclusions as our study. All errors are the authors’. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health or of the National Bureau of Economic Research.

Dr. Sabia acknowledges research support for this work from the Center for Health Economics & Policy Studies (CHEPS) at San Diego State University (SDSU), which has received grants from the Charles Koch Foundation. This study was funded with a grant from Global Action to End Smoking (formerly known as Foundation for Smoke-Free World), an independent, U.S. nonprofit 501(c)(3) grantmaking organization, accelerating science-based efforts worldwide to end the smoking epidemic. Global Action played no role in designing, implementing, data analysis, or interpretation of the study results, nor did Global Action edit or approve any presentations or publications from the study. The contents, selection, and presentation of facts, as well as any opinions expressed, are the sole responsibility of the authors and should not be regarded as reflecting the positions of Global Action to End Smoking. Global Action’s mission is to end combustible tobacco use, which remains the leading preventable cause of death globally. The organization collaborates with academic and research centers and others to accelerate life-saving research and educational projects. Global Action does not seek or accept funding from companies that produce tobacco or non-medicinal nicotine products. The charitable gift agreement (the “Pledge Agreement”) between the organization and its prior funder, PMI Global Services Inc., was terminated in September 2023. To complement the termination of the Pledge Agreement, the organization’s Board of Directors established a new policy to not accept or seek any tobacco or non-medicinal nicotine industry funding. Dr. Sabia also acknowledges research support from a subcontract by Georgia State University in 2023 – via a grant received from the National Institute on Drug Abuse of the National Institutes of Health under Award Number R01DA045016 – to support exploratory work on this project. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Institutes of Health.

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74th session of the WHO Regional Committee for Europe

Peering through tobacco’s smokescreen: young people fight for clarity and freedom

“It’s really important for my generation to feel like they’re fighting for something, like they’re part of a trend, they’re part of a movement. And we see that this works.”

So says youth leader Karina Mocanu, who oversees a group of young people dedicated to improving tobacco control in Europe. The European Network for Smoking and Tobacco Prevention (ENSP) launched ENSPNext, the group that Karina coordinates, in response to an alarming rise in tobacco use among youth in Europe.

The group aims to help young people recognize the manipulative tactics used by an industry keen to exploit them and foster a lifelong nicotine addiction.

“When you’re addicted to something, you aren’t free to do whatever you want because you depend on a product and need to spend money on that product,” Karina points out. “It isn’t cool to consume, it’s a trap. We aim to empower young people to take back their freedom.”

Karina also believes that her generation needs to understand that, in fighting against the tobacco industry, they are also contributing to a good cause.

“Tobacco impacts so many aspects of our lives and there are so many reasons for which we need to fight,” she explains when asked about what motivates her. “These include impacts on our mental health and the environment, issues of poverty and food insecurity, alongside health effects and industry manipulation. I really believe that tobacco is not a niche sector at all.”

A history of deception

Dr Raouf Alebshehy is Managing Editor of Tobacco Tactics, part of the Tobacco Control Research Group at the University of Bath in the United Kingdom. Highlighting the need to inform young people about the tobacco industry’s dirty tactics, he details how the industry has influenced the narrative around tobacco from as far back as the 1950s by funding research results favourable to their products and using their own science to address policy-makers.

“There was a time when tobacco advertisements shamelessly claimed that tobacco is not harmful. Over decades, the industry pushed against the proven facts: that tobacco is harmful and addictive, tobacco causes cancer, second-hand smoking is harmful, and tobacco control measures are effective and absolutely needed,” Dr Alebshehy explains.

“The industry is always keeping track of the latest public health developments and working to undermine the public health narrative.”

Deception by the tobacco industry is documented. For example, the industry marketed light cigarettes decades ago to make them appear safer and to attract users. A court case in the United States of America forced the industry to reveal documents showing that they were aware of the dangers of nicotine addiction at the time. 

In response to the tobacco epidemic, the WHO Framework Convention on Tobacco Control (FCTC) came into force in 2005, containing evidence-based measures to protect present and future generations from the harmful effects of tobacco. Sales of cigarettes started to slide.

Dr Alebshehy details how the industry responded to these new pressures with a different set of tactics. He points out that studies show that 9 out of 10 tobacco smokers start before the age of 18, which clarifies why the tobacco industry now targets young people with marketing: so they start smoking as early as possible.

Controlling the narrative

Dr Alebshehy continues, “The industry started targeting youth by sponsoring attractive events like Formula One and integrating smoking into movies and Netflix shows, and on social media. They know that if you see a message several times in different places, it becomes normalized, and you might be affected.”

He notes that, while cigarette sales are starting to decline in some countries, the increasing trend for heated tobacco products and e-cigarettes, especially among young people, is very alarming.

“The industry is now pushing the narrative that they are moving towards a ‘smoke-free’ world, and they are funding organizations to promote this, but meanwhile they are still expanding,” he emphasizes. 

“They seize every opportunity to increase their profits by hooking in new nicotine users and expanding the market. To this end, they are investing in producing new, addictive products including water pipes, snus, e-cigarettes and heated tobacco products.”

Dr Alebshehy points to the tobacco industry’s attempts to interfere at this year’s Conference of the Parties (COP10), which is the governing body of the WHO FCTC. Karina highlights that COP10 took young people into consideration at a higher level for the first time. 

“Within my community, we are all unhappy with the slow pace at which the authorities act,” she says. “I see that there are rarely concrete, proactive measures initiated by those in charge, and almost always delayed reactions to the damage already caused by different actors.” 

Karina adds, “Even though the numbers of young people involved in this field are still small, we want to continue to encourage young researchers to research tobacco control, so we can produce our own evidence and base our advocacy actions on that. We should not be a passive generation.”

Environmental activism

Karina has experience of how the latest tobacco industry tactics are impacting young consumers. She says disposable tobacco and nicotine products are ubiquitous on the platforms they see and marketed directly at them. For example, even though the addition of menthol, along with its cooling properties, has been banned from tobacco products in Europe since 2020, the market has been flooded with novel tobacco and nicotine products that make use of bright colours and flavours designed to broaden their appeal.

“These products smell like perfume or like something you would eat. Influencers on social media are promoting e-cigarettes that are super slim and stylish and will match your outfit. Nicotine pouches are tiny and come in a range of appealing colours and designs to fit neatly in your bag. These are the things that attract children and young people, not the product itself.”

Since tobacco not only harms human health but also damages our environment, Karina believes that an effective way to fight this marketing is for the tobacco control sector to engage with environmental activists. She says while talking about tobacco’s health effects on teenagers as individuals has a limited impact, information about how tobacco is polluting the planet resonates loudly with Generation Z.

“During negotiations for the United Nations treaty on plastic control, our community advocated for a ban on cigarette butts on the basis that they contain plastic and are statistically the most littering thing on the planet. Similarly, disposable tobacco and nicotine products will soon be banned in France and Belgium, mainly on the basis that they pollute the planet. Focusing on the links between tobacco and the health of the world is crucial,” she insists. 

“It might take years or even decades to see the damage we are doing to our own health, but at least we can clearly see the effects of climate change in the present moment. I think that’s a good enough reason to act now.”

Focusing on change

Another way to make an impact on the younger generation, Karina feels, is to teach them to take action against consumerism generally and to live in a more holistic, balanced way. She is hopeful that the latest internet trends appear to show more young people becoming aware of what happens around them and trying to act.

“Educating yourself is important. Learn to be in a bar or at the shops without having to consume everything. Tobacco and nicotine products are not a cool way of life, and neither is alcohol. It’s cool to be healthy and to feel good,” she says.

“People should also understand that tobacco control is not just a sector for experts and it’s not only about developing studies,” Karina continues. “I’m trying to involve my family, my little sister. It’s about everyone creating platforms, including researchers and academics, to create synergies and change the narrative.”

However, while delivering information and reaching audiences matters, Karina thinks that teaching young people the skills to search for trustworthy information themselves is even more crucial.

“What I think is important with my generation is just to encourage critical thinking and to provide some guidance on how to navigate online information effectively. Tell them, ‘Look, if you’re not sure about something, do your own research and a good way to do this is by searching for independent and reliable resources such as WHO.’”

Child and adolescent health

WHO Framework Convention on Tobacco Control (WHO FCTC)

The European Network for Smoking and Tobacco Prevention (ENSP)

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Factors associated with dual use of tobacco and electronic cigarettes: A case control study

Affiliations.

• 1 Onassis Cardiac Surgery Center, Greece; University of Patras, Greece. Electronic address: [email protected].
• 2 ABICH S.r.l, Biological and Chemical Toxicology Research Laboratory, Italy.
• 3 Onassis Cardiac Surgery Center, Greece.
• PMID: 25687714
• DOI: 10.1016/j.drugpo.2015.01.006

Background: Many electronic cigarette (EC) users reduce cigarette consumption without completely quitting. It is important to assess the characteristics and experiences of these users, commonly called "dual users", in comparison with EC users who have completely substituted smoking (non-smoking vapers).

Methods: A questionnaire was uploaded in an online survey tool. EC users were invited to participate irrespective of their current smoking status. Dual users were matched for age and gender with non-smoking vapers.

Results: From 19,441 participants, 3682 were dual users. After random 1:1 matching with non-smoking vapers (all of whom were former smokers), 3530 participants in each group were compared. Dual users had longer smoking history, lower daily cigarette consumption and similar cigarette dependence compared to non-smoking vapers. Their daily consumption was reduced after initiation of EC use from 20 to 4 cigarettes per day. Most of them were using ECs daily, however, more were occasional EC users compared to non-smoking vapers. Use of advanced (third generation) devices and daily liquid consumption was lower in dual users compared to non-smoking vapers. The most important reason for initiating EC use was to reduce smoking and exposure of family members to smoke for both groups, but higher scores were given to "avoid smoking ban in public places" by dual users compared to non-smoking vapers. The strongest predictors of being dual user from multivariate analysis were: higher risk perception for ECs (OR=2.27, 95% CI=1.40-3.68), use of first-generation EC devices (OR=1.98, 95% CI=1.47-2.66), use of prefilled cartomizers (OR=1.94, 95% CI=1.23-3.06) and occasional use of ECs (OR=1.62, 95% CI=1.21-2.17).

Conclusions: The results of this case-control study indicate that higher risk perceptions about, and less frequent use of, ECs was associated with dual use of ECs and tobacco cigarettes. Since this is a cross-sectional survey, which explores association but not causation, longitudinal studies are warranted to further explore the reasons for dual use.

Keywords: Dual use; Electronic cigarette; Harm reduction; Nicotine; Public health; Smoking; Tobacco.

Copyright © 2015 Elsevier B.V. All rights reserved.

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Evaluating trends in recruitment challenges in vape shop research, e-cigarette product characteristics and use among shop customers from 2019 to 2023: A mixed-methods study

1 Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, United States

Artur Galimov

Ellen galstyan, jennifer b. unger, lourdes baezconde-garbanati, steven sussman, associated data.

Monthly-aggregated data supporting this research are available from the authors on reasonable request.

INTRODUCTION

Brick-and-mortar vape shops specialize in the sale of e-cigarettes and remain a primary source for purchasing emerging e-cigarette products. New regulatory policies have been implemented at local-, state- and federal-level; the retail environment at vape shops and product preferences among vape shop customers shifted accordingly.

From 2019 to 2023, we collected anonymous interview data from vape shop customers (n=572) from 83 vape shops in Southern California. We aggregated the data by month and treated each month as the unit of analysis to document changes in recruitment efforts among the vape shops in relation to major policy implementations over 4 years. We also examined the systematic fluctuations and trends in customers’ e-cigarette product preferences and nicotine content in these products.

The monthly average shop-level consent rate was 52.9% (SD=8.7), with an overall decreasing trend over time. It was necessary for our data collection team to approach a greater number of vape shops to obtain consent with implementation of various state and federal tobacco regulations and following COVID-19. We observed an increase in the purchase of disposable products and nicotine concentrations in the products, while the average use frequency remained the same.

CONCLUSIONS

Our findings demonstrated that user preferences, product characteristics and challenges in research involving vape shops are closely associated to changes in regulations. We documented a dramatic increase in nicotine concentration in products. Future policies restricting the amount of nicotine in tobacco products at the federal level are necessary to protect consumers from further nicotine addiction. This study provides documentation over time of the drastic increases in nicotine concentration among e-cigarette users as a result of the fluctuations in the product market. Regulating nicotine content in tobacco products could safeguard against further unsafe modifications in e-cigarettes and other types of tobacco products.

The vape shop retail environment has rapidly evolved and shifted with changes in e-cigarette policies and regulations 1 . Marketing strategies of manufacturers and retailers have swiftly responded to the ever-changing consumer landscape – including varying e-cigarette consumer demand throughout the COVID-19 pandemic – and the changing of federal and state tobacco regulations 2 - 4 . Brick-and-mortar vape shop retailers specialize in the sale and promotion of e-cigarettes and e-liquids, and remain one of the primary sources to purchase vaping products 5 , 6 . Further, vape shop employees serve as one of the main points of contact for those interested in trying e-cigarettes for the first time 7 .

The evolution of e-cigarette products and contained nicotine

E-cigarette products have proliferated since the mid 2000s 8 , and are available in a variety of flavors and nicotine concentrations 9 . Vaping devices have also undergone substantial transformations from thin, disposable cigarette-resembling, ‘1st-generation’ devices to the newest modern-looking, closed-system disposable pod devices 2 . First-generation devices were designed to mimic traditional cigarettes, making them attractive to current smokers. These then evolved into the second-generation tubular-shaped, larger tank style, refill-able and rechargeable devices. Modular (box) products gained popularity for several years (the third generation) until JUUL introduced the fourth-generation pod-style devices in 2015 10 . The more recent pod products are smaller than previous (box) mod devices (thus offering concealability) and are intended for immediate use. Further, closed-system disposable pod devices (e.g. ‘Flum’) offer a sleek design with an extensive range of e-juice flavors and high nicotine concentration levels ranging 20–70 mg/mL 10 - 12 .

Overview of federal- and state-wide e-cigarette product regulations from 2016 to 2022

The US Food and Drug Administration (FDA) established regulatory authority over e-cigarettes in 2016 because of their classification as tobacco products 1 . Since then, the federal government has implemented additional regulations affecting e-cigarette products and retail practices (e.g. the purchasing age of tobacco products was raised to 21 years of age) 13 . In February 2020, the US FDA enforced a regulation governing the manufacturing, distribution, marketing, and sale of prefilled cartridge-based flavored e-cigarettes, with the exemption of tobacco and menthol flavors 14 , 15 . In July 2020, the US FDA requested that 10 prominent e-cigarette manufacturers withdraw their flavored disposable e-cigarette products from the market due to the absence of the necessary premarket authorization 16 . Still, with ample availability, low pricing, and improved features [e.g. rechargeable battery technology, higher number of puffs (up to 7000) per device], novel vaping devices are easily accessible and continue to gain popularity ( Figure 1 ).

Federal and state-wide e-cigarette regulations during 2019–2023

In addition to the evolution of vaping devices, notable modifications in the nicotine formulation and type within e-cigarette products have been observed. Salt-based nicotine was introduced in 2015 and made popular in pod-style devices 17 . Synthetic nicotine e-cigarettes have emerged as an alternative to tobacco-derived nicotine vaping products and are marketed as containing ‘tobacco-free nicotine’, adding confusion and dubious claims of reduced risk in such products 18 . In response to the increased use of synthetic nicotine, US Congress passed a law that went into effect in April 2022, updating the Tobacco Control Act to include synthetic nicotine as a tobacco product and granting the FDA authority to regulate tobacco products containing nicotine from ‘any source’ 19 .

In November 2022, California approved Proposition 31, a referendum to the 2020 California Senate Bill (SB) 793 that prohibits the retail sale of flavored tobacco products. Flavored tobacco products are defined in the bill as ‘any tobacco product that imparts a characterizing flavor other than tobacco’ (including menthol) 20 . As of 21 December 2022, e-cigarette retailers in California are now required to stop selling, offering for sale, and possessing with the intent to sell, flavored tobacco products 21 .

Changes in vape shop retail environment

With the newly implemented federal- and state-wide regulations during this period, substantial changes have taken place in the e-cigarette market, including the type of e-cigarette products sold, and in the way vape shops operate. There are little data on how these changes in the tobacco product landscape (e.g. the evolution of devices and nicotine content) are associated with changes in vape shop customers’ product preferences (e.g. device type, flavor, nicotine level). Our research team has been in the unique position to observe these changes over time at brick- and-mortar retailers, as we have built rapport and collected data in the tobacco retail environment since 2014 2 , 22 - 27 . Along with the policy changes, our data collection team has encountered levels of enthusiasm, expressed by vape shop owners and employees in participating in research projects, which waxed and waned over the years, with an overall decreasing trend. We are one of the very few research teams in the forefront of monitoring and tracking the vaping industry since the early days of its inception, prior to the changes in regulation.

Aims and hypotheses

Given our research team’s vantage point in conducting vape shop research over the past 9 years, we aim to document changes (successes and challenges) in recruitment efforts among the vape shop retailer environment amidst major policy changes during our observation period. Based on increasing regulations on e-cigarette products and the industry coinciding with the COVID-19 pandemic, we hypothesized that vape shop owners and employees would be more guarded and hesitant about participating in research, documented through changes in our vape shop recruitment efforts and related challenges. We also describe the systematic fluctuations and trends in: 1) e-cigarette product preference; 2) e-liquid nicotine concentration in mg/mL; and 3) frequency of use among vape shop customers during our observation period. We summarize the patterns in e-cigarette product use in a prospective manner to demonstrate that specific types of products would gain in popularity while others become nearly obsolete. In addition, we contextualize our findings with quotes during our recruitment process with vape shop owners/employees, interviews with customers, and with the product photos taken as part of data collection.

Sample and data collection

All data were collected in the Southern California region from April 2019 through February 2023, and the sample consisted of 572 customer interviews within 83 vape shops, 11 of which required multiple visits to enable data collection from a sufficient number of customers available at the time of the visit. All interactions with the vape shop retailers were thoroughly documented, including the multiple attempts to obtain shop-level consent. Shop employees, managers, or owners were approached by data collectors and given information about the research study. They provided shop-level consent for data collectors to be in their shop location and approach customers as they exited the vape shop after making a purchase. Customers provided verbal consent and participated in a 15-minute structured intercept interview survey and were provided with a $35 gift card as participation compensation upon completion. The study was approved by the research institution’s Institutional Review Board (#HS-18-00732). In-person data collection paused during surges of COVID-19 infection and/or pandemic-related restrictions from April 2020 to November 2021. In order to protect privacy of recruited vape shops, all store names have been anonymized.

Vape shop level measures

We maintained detailed records of our consent attempts at each candidate vape shop and noted reasons for refusal, if provided. We recorded the number of vape shops that we approached to obtain the shop-level consent and those that provided us with consent. All qualitative shop-based data were collected by documenting detailed notes directly after each consent attempt and/or visit to the shop. We also documented artifacts (e.g. posted flyers and their contents) at vape shops that were temporarily or permanently closed during or following COVID-19. We also report the content of any spontaneous communication with vape shop owners or employees in verbatim in the Results.

Vape shop customer level measures

The most frequently used type of vaping device (in the past 30-days) was assessed with the question: ‘What type of e-cigarette device do you use most often?’ [Response options: pen, box mod, disposable, pod mod (salt-based), pod mod (free-based), other]. Past 30-day vaping frequency was assessed with the question: ‘In the past 30 days, on how many days did you use e-cigarettes?’ (1–30 days). Preferred device/e-liquid nicotine level was assessed with the open-ended item: ‘How many mg per mL of nicotine does your favorite brand/flavor have?’ (e.g. 0, 3, 6, 50, etc.).

Data analysis

We aggregated data by each month and treated each month as the unit of analysis. We employed a repeated cross-sectional design to document descriptive trends in shop-level recruitment efforts and vape shop customer-level variables during the observation period. Due to pragmatic and logistical reasons – including COVID-19-related restrictions – posed during the data collection period, there were a very small number of vape shops that were approached and agreed to participate for a large number of observational monthly-periods. Thus, our data structure does not render itself to a formal time-series analysis. With our method, we summarized the trend in the outcomes of interest instead, rather than to make an inference to the vape shop population.

Vape shop recruitment effort and success

As shown in Figures 2A and ​ and2B, 2B , the number of shops our data collection team approached to obtain consent had to be substantially increased following the COVID-19 pandemic, peaking around March–April 2022 (about 40 shops), tapering off toward the end of 2022. To maintain comparable levels of shop-level consent rate, our research team was required to approach nearly twice as many vape shops, as represented by the red line which peaked during the first 6 months of 2022 ( Figure 2A ). The average monthly shop consent rate was 52.9% (SD=8.7), with an overall decreasing trend following the COVID-19 pandemic and changes in policy (except the end of 2022). In November 2022, our recruitment effort was concluding, therefore, we approached only 3 stores, all of whom consented (see the trendline Figure 2B ).

A) Changes in recruitment effort and success

B) Shop-level consenting trend and policies

Our field notes corroborate the fluctuations in the receptiveness among vape shop owners or employees as policy changes unfolded. Examples of artifacts or refusal responses that our team encountered which align with the shop-level consent rate are listed below:

• Flyer on door read: ‘[Shop] will be permanently closed due to the recent regulations that have passed .’ (Oct. 2019)
• Flyer on the front door mentions localized policy: ‘ [City] Ordinance No. 19-1940. An ordinance of the city council … prohibits the retail sale of electronic cigarettes, retail sale of flavored tobacco products and other vaping devices. Please help us stay in business … Only tobacco flavors are for sale .’ (Feb. 2020)
• Employee implies that customers have decreased due to localized policies, and the shop has been ‘ already affected by a vape ban ’. (Mar. 2020)
• Employee stated that the ‘ business has been extremely slow ’. (Dec. 2021)
• Employee stated that there is ‘ already too much going on ’, referring to the California flavor ban – SB 793. (Jan. 2023)

Customer recruitment and success

We approached customers as they exited the store, attempting to recruit all customers (n=1012) present during the period of our presence in the shop. Eligible customers – those who had used vaping products in the past 30 days – were invited to participate in a 15-minute interview. Data collectors used scripts to verbally administer structured questionnaire items. Out of 946 eligible customers, 572 (60.5%) agreed to participate and completed the survey. The 66 customers who did not meet the eligibility criteria were excluded. The primary reasons for declining to participate in our study included: not having time (e.g. going back to their workplace, 42%), not being interested (41%), and not speaking English language (2%).

Relative e-cigarette product categories purchased over time

We observed distinct changes in the distribution of e-cigarette products purchased at vape shops from 2019 to 2023. As can be seen in Figure 3 , the distributional pattern demonstrates that the predominately used products changed with time. Specifically, box mod products (represented by light blue bars) were most popular in early 2019 and began to dwindle down as closed-system disposable products (yellow bars) became popular around March 2020, and continued their dominance through the end of the project observation period.

Distributional changes in e-cigarette product categories

Salt-based products (green bars) were used at somewhat consistent rates throughout the project period and were primarily found in disposable and pod devices. Vape pens (deep blue bars), which constituted a small proportion of products used, were scattered throughout, and appear to peak in January 2023. Free-based products (in red) also make up a low proportion but were used somewhat consistently throughout the project period ( Figure 3 ).

We also observed changes in types and intensity of the nicotine content within disposable vaping devices over the years. As seen in the photos of disposable products purchased by vape shop customer participants, more recent disposable products include synthetic nicotine with a warning label that emphasized ‘tobacco-free nicotine’ (Supplementary file Figure; all the photos taken in 2022, before the flavor ban went into effect in California).

Changes in nicotine levels and use frequency

The aggregated data corroborates our field observation in which the average nicotine level in mg/mL (shown as a dark red line in Figure 4 ) started to increase substantially in February 2020 (39.9 mg/mL from 12.5 mg/mL in April 2019) with the upward trend continuing throughout the remainder of the project period (40.6 mg/mL in January 2023). This correlates directly with the dramatic increase in the purchase of disposable pod devices, as noted in Figure 3 . However, despite the increase in nicotine level contained in products, the reported average days used in the past 30-day stays relatively unchanged over the project period (indicating nearly daily use, shown in gold line).

Changes in nicotine level and 30-day use over time

In this study, we summarized various changes observed at vape shops and e-cigarette use characteristics over a span of 4 years, as policies and regulations have become increasingly restrictive affecting the e-cigarette marketplace. Specifically, we documented the challenges in data collection with vape shop retailers in Southern California, reflected by our research team’s shop-level recruitment strategies and efforts in relation to major federal and state policies implemented from 2019 to 2023. We also described the systematic fluctuations and trends in vape shop customers’ e-cigarette product preferences and nicotine contents over the same period. Given the constant evolution of vaping devices and e-cigarette characteristics, it is critical that regulatory agencies are informed about the latest trends in the e-cigarette market and the contributing factors associated with these changes. Changes to tobacco product characteristics can be a result of tobacco manufacturers’ attempt at complying with new policy regulations 28 . Monitoring and surveillance of emerging product characteristics and trends help policymakers and regulatory authorities to identify areas of priority for enforcement, as well as consider refinements in future regulatory efforts. A recent review of 104 studies published in 2022 concluded that high variety of flavors and high nicotine level concentrations may lead to higher abuse potential and appeal of e-cigarettes among users 29 . Further, e-cigarette flavors have been linked with increased use frequency among youth and adolescents 30 , 31 . Similarly, nicotine concentration levels – potentially bidirectionally related to e-cigarette dependence – could prove to be a highly concerning yet regulatable factor to curb nicotine addiction among established adult users.

Our findings merit further investigation of changes in the e-cigarette marketplace. With the variety and pace of changes in e-cigarette characteristics, future researchers need to examine how changes in e-cigarette characteristics affect nicotine level intake and use frequency, nicotine dependence, and harm perceptions among vape shop customers over time. This study provides insight into the developments of e-cigarette product preferences and behavioral changes among a large cohort of vape shop customers. The once most popular box mod e-cigarettes became rarely-purchased products by early 2020, around when disposable products began steadily gaining popularity through early 2023.

The social and policy climate has shifted in recent years and has contributed to changes within the e-cigarette product landscape. This important change in cultural climate was also illustrated by the changes in recruitment efforts as we report in this study. This is most likely due to stricter and more prevalent changes in tobacco policies in both the federal (e.g. FDA deeming rule) and localized levels of enforcement (e.g. city and/or statewide flavor bans), which were corroborated by our recruitment data. Recruitment effort had to increase, which was prevalent in all research since the pandemic. Our team attempted to contact a substantially greater number of shops (not needed in the past) and experienced additional challenges as new policies were introduced and implemented. Shops were concerned about going out of business due to sales restrictions and were reluctant to take on other activities such as being involved in a university-based research project. The current study could describe possible current and future challenges conducting research with tobacco retailers, even though such research is very much needed to further protect communities. Partnering with communities and the retailers in socially and economically disadvantaged areas might help to patch the disconnect that may exist between policymakers, researchers, and the vaping community. The partnership between our team and the vape shop retailer community enabled us to complete data collection as proposed, despite the challenges described in the current study.

Limitations

This study has several limitations. Our study design and available data structure do not allow statistical inferences, as the same set of vape shops were not followed over time and we had small vape shop sample sizes per aggregation period used to summarize the trends. Further, our findings might not be generalizable to vape shops in regions outside Southern California, as well as e-cigarette users who purchase their e-cigarette products online or through other types of brick-and-mortar retail outlets, including youth (aged <18 years) and those outside Southern California. Given the nature of the self-reported customer data, the findings may have been influenced by recall and social desirability biases. As new regulations and policies were introduced, shop-level recruitment was affected as some shops were hesitant to provide consent to allow recruitment at their shops or share additional information about reasons for declining to participate with researchers, leading to potential selection bias.

Implications

As product characteristics evolve and nicotine concentrations rapidly and continually increase in vaping products, it is important to consider stricter and more thorough regulations to circumvent such changes. This study provides documentation over time of the drastic increases in nicotine concentration among e-cigarette users as a result of the fluctuations in the product market. Regulating the nicotine content in tobacco products could provide a safeguard for future fluxes among e-cigarettes and other types of tobacco products. These measures may limit desirability among new users of e-cigarette products and protect future generations of potential users.

The US FDA’s regulation of e-cigarette products has greatly changed the way the e-cigarette market operates and the availability of novel e-cigarette products available to users. Recent flavor restrictions of e-cigarette products warrant additional research to examine the changing practices of e-cigarette manufacturers and retailers post flavor-ban restrictions. It is important to continue surveillance of tobacco retailers to learn about new products that may be sold after flavor restriction policies are enacted. The US FDA recently announced plans for a proposed rule to establish a maximum level of nicotine in cigarettes, which may be applied to other tobacco products 32 . This will help policymakers establish additional regulatory policies to reduce the sales of new unauthorized tobacco products.

Supplementary Material

Funding statement.

FUNDING Research reported in this publication was supported by National Cancer Institute and FDA Center for Tobacco Products (CTP) Awards (NCI/FDA Grant #U54CA180905).

CONFLICTS OF INTEREST

The authors have each completed and submitted an ICMJE form for disclosure of potential conflicts of interest. The authors declare that they have no competing interests, financial or otherwise, related to the current work. All the authors report that since the initial planning of the work this study was supported by the NCI/FDA Grant #U54CA180905.

ETHICAL APPROVAL AND INFORMED CONSENT

Ethical approval was obtained from the Institutional Review Board, Keck School of Medicine, University of Southern California (Approval number: #HS-18-00732; Date: 13 September 2019). Participants provided informed consent.

DATA AVAILABILITY

Provenance and peer review.

Not commissioned; externally peer reviewed.

Steve Sussman, Editorial Board member of the journal, had no involvement in the peer-review or acceptance of this article and had no access to information regarding its peer-review. Full responsibility for the editorial process for this article was delegated to a handling editor of the journal.