research papers on vaping

• Open access
• Published: 18 May 2021

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  

Respiratory Research volume  22 , Article number:  151 ( 2021 ) Cite this article

395k Accesses

129 Citations

358 Altmetric

Metrics details

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

Hiemstra PS, Bals R. Basic science of electronic cigarettes: assessment in cell culture and in vivo models. Respir Res. 2016;17(1):127.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Bertholon JF, Becquemin MH, Annesi-Maesano I, Dautzenberg B. Electronic cigarettes: a short review. Respiration. 2013;86(5):433–8.

Article   CAS   PubMed   Google Scholar  

Rowell TR, Tarran R. Will chronic e-cigarette use cause lung disease? Am J Physiol Lung Cell Mol Physiol. 2015;309(12):L1398–409.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Herrington JS, Myers C. Electronic cigarette solutions and resultant aerosol profiles. J Chromatogr A. 2015;1418:192–9.

Hutzler C, Paschke M, Kruschinski S, Henkler F, Hahn J, Luch A. Chemical hazards present in liquids and vapors of electronic cigarettes. Arch Toxicol. 2014;88(7):1295–308.

Pokhrel P, Herzog TA, Muranaka N, Fagan P. Young adult e-cigarette users’ reasons for liking and not liking e-cigarettes: a qualitative study. Psychol Health. 2015;30(12):1450–69.

Article   PubMed   PubMed Central   Google Scholar  

Harrell PT, Simmons VN, Correa JB, Padhya TA, Brandon TH. Electronic nicotine delivery systems (“e-cigarettes”): review of safety and smoking cessation efficacy. Otolaryngol Head Neck Surg. 2014;151(3):381–93.

Schober W, Szendrei K, Matzen W, Osiander-Fuchs H, Heitmann D, Schettgen T, et al. Use of electronic cigarettes (e-cigarettes) impairs indoor air quality and increases FeNO levels of e-cigarette consumers. Int J Hyg Environ Health. 2014;217(6):628–37.

Rubinstein ML, Delucchi K, Benowitz NL, Ramo DE. Adolescent exposure to toxic volatile organic chemicals from E-cigarettes. Pediatrics. 2018;141(4):e20173557.

Article   PubMed   Google Scholar  

Staudt MR, Salit J, Kaner RJ, Hollmann C, Crystal RG. Altered lung biology of healthy never smokers following acute inhalation of E-cigarettes. Respir Res. 2018;19(1):78.

Nocella C, Biondi-Zoccai G, Sciarretta S, Peruzzi M, Pagano F, Loffredo L, et al. Impact of tobacco versus electronic cigarette smoking on platelet function. Am J Cardiol. 2018;122(9):1477–81.

Higham A, Rattray NJW, Dewhurst JA, Trivedi DK, Fowler SJ, Goodacre R, et al. Electronic cigarette exposure triggers neutrophil inflammatory responses. Respir Res. 2016;17(1):56.

Antoniewicz L, Bosson JA, Kuhl J, Abdel-Halim SM, Kiessling A, Mobarrez F, et al. Electronic cigarettes increase endothelial progenitor cells in the blood of healthy volunteers. Atherosclerosis. 2016;255:179–85.

Carnevale R, Sciarretta S, Violi F, Nocella C, Loffredo L, Perri L, et al. Acute impact of tobacco vs electronic cigarette smoking on oxidative stress and vascular function. Chest. 2016;150(3):606–12.

Vlachopoulos C, Ioakeimidis N, Abdelrasoul M, Terentes-Printzios D, Georgakopoulos C, Pietri P, et al. Electronic cigarette smoking increases aortic stiffness and blood pressure in young smokers. J Am Coll Cardiol. 2016;67(23):2802–3.

Franzen KF, Willig J, Cayo Talavera S, Meusel M, Sayk F, Reppel M, et al. E-cigarettes and cigarettes worsen peripheral and central hemodynamics as well as arterial stiffness: a randomized, double-blinded pilot study. Vasc Med. 2018;23(5):419–25.

Caporale A, Langham MC, Guo W, Johncola A, Chatterjee S, Wehrli FW. Acute effects of electronic cigarette aerosol inhalation on vascular function detected at quantitative MRI. Radiology. 2019;293(1):97–106.

von Hundelshausen P, Schmitt MM. Platelets and their chemokines in atherosclerosis-clinical applications. Front Physiol. 2014;5:294.

Google Scholar  

Landmesser U, Hornig B, Drexler H. Endothelial function: a critical determinant in atherosclerosis? Circulation. 2004;109(21 Suppl 1):Ii27-33.

PubMed   Google Scholar  

Münzel T, Hahad O, Kuntic M, Keaney JF, Deanfield JE, Daiber A. Effects of tobacco cigarettes, e-cigarettes, and waterpipe smoking on endothelial function and clinical outcomes. Eur Heart J. 2020;41:4057–70.

Javelle E. Electronic cigarette and vaping should be discouraged during the new coronavirus SARS-CoV-2 pandemic. Arch Toxicol. 2020;94(6):2261–2.

Husari A, Shihadeh A, Talih S, Hashem Y, El Sabban M, Zaatari G. Acute exposure to electronic and combustible cigarette aerosols: effects in an animal model and in human alveolar cells. Nicotine Tob Res. 2016;18(5):613–9.

Zelikoff JT, Parmalee NL, Corbett K, Gordon T, Klein CB, Aschner M. Microglia activation and gene expression alteration of neurotrophins in the hippocampus following early-life exposure to E-cigarette aerosols in a murine model. Toxicol Sci. 2018;162(1):276–86.

Putzhammer R, Doppler C, Jakschitz T, Heinz K, Forste J, Danzl K, et al. Vapours of US and EU market leader electronic cigarette brands and liquids are cytotoxic for human vascular endothelial cells. PLoS One. 2016;11(6):e0157337.

Bernhard D, Pfister G, Huck CW, Kind M, Salvenmoser W, Bonn GK, et al. Disruption of vascular endothelial homeostasis by tobacco smoke: impact on atherosclerosis. Faseb J. 2003;17(15):2302–4.

Newby DE, Wright RA, Labinjoh C, Ludlam CA, Fox KA, Boon NA, et al. Endothelial dysfunction, impaired endogenous fibrinolysis, and cigarette smoking: a mechanism for arterial thrombosis and myocardial infarction. Circulation. 1999;99(11):1411–5.

Taylor M, Jaunky T, Hewitt K, Breheny D, Lowe F, Fearon IM, et al. A comparative assessment of e-cigarette aerosols and cigarette smoke on in vitro endothelial cell migration. Toxicol Lett. 2017;277:123–8.

Herr C, Tsitouras K, Niederstraßer J, Backes C, Beisswenger C, Dong L, et al. Cigarette smoke and electronic cigarettes differentially activate bronchial epithelial cells. Respir Res. 2020;21(1):67.

Alanazi H, Park HJ, Chakir J, Semlali A, Rouabhia M. Comparative study of the effects of cigarette smoke and electronic cigarettes on human gingival fibroblast proliferation, migration and apoptosis. Food Chem Toxicol. 2018;118:390–8.

Otreba M, Kosmider L. E-cigarettes: voltage- and concentration-dependent loss in human lung adenocarcinoma viability. J Appl Toxicol. 2018;38(8):1135–43.

Chaumont M, Bernard A, Pochet S, Melot C, El Khattabi C, Reye F, et al. High-wattage E-cigarettes induce tissue hypoxia and lower airway injury: a randomized clinical trial. Am J Respir Crit Care Med. 2018;198(1):123–6.

Chaumont M, van de Borne P, Bernard A, Van Muylem A, Deprez G, Ullmo J, et al. Fourth generation e-cigarette vaping induces transient lung inflammation and gas exchange disturbances: results from two randomized clinical trials. Am J Physiol Lung Cell Mol Physiol. 2019;316(5):L705–19.

European Parliament and the council of the European Union. Directive 2014/40/EU. 2014 (updated April 29, 2014). https://ec.europa.eu/health//sites/health/files/tobacco/docs/dir_201440_en.pdf . Accessed 17 April 2020.

Cameron JM, Howell DN, White JR, Andrenyak DM, Layton ME, Roll JM. Variable and potentially fatal amounts of nicotine in e-cigarette nicotine solutions. Tob Control. 2014;23(1):77–8.

Hahn J, Monakhova YB, Hengen J, Kohl-Himmelseher M, Schussler J, Hahn H, et al. Electronic cigarettes: overview of chemical composition and exposure estimation. Tob Induc Dis. 2014;12(1):23.

Omaiye EE, Cordova I, Davis B, Talbot P. Counterfeit electronic cigarette products with mislabeled nicotine concentrations. Tob Regul Sci. 2017;3(3):347–57.

Buettner-Schmidt K, Miller DR, Balasubramanian N. Electronic cigarette refill liquids: child-resistant packaging, nicotine content, and sales to minors. J Pediatr Nurs. 2016;31(4):373–9.

Jackson R, Huskey M, Brown S. Labelling accuracy in low nicotine e-cigarette liquids from a sampling of US manufacturers. Int J Pharm Pract. 2019;28(3):290–4.

Yingst JM, Foulds J, Veldheer S, Hrabovsky S, Trushin N, Eissenberg TT, et al. Nicotine absorption during electronic cigarette use among regular users. PLoS One. 2019;14(7):e0220300.

Farsalinos KE, Romagna G, Tsiapras D, Kyrzopoulos S, Voudris V. Evaluation of electronic cigarette use (vaping) topography and estimation of liquid consumption: implications for research protocol standards definition and for public health authorities’ regulation. Int J Environ Res Public Health. 2013;10(6):2500–14.

Mishra A, Chaturvedi P, Datta S, Sinukumar S, Joshi P, Garg A. Harmful effects of nicotine. Indian J Med Paediatr Oncol. 2015;36(1):24–31.

Wang Q, Sundar IK, Li D, Lucas JH, Muthumalage T, McDonough SR, et al. E-cigarette-induced pulmonary inflammation and dysregulated repair are mediated by nAChR α7 receptor: role of nAChR α7 in SARS-CoV-2 Covid-19 ACE2 receptor regulation. Respir Res. 2020;21(1):154.

Lee AC, Chakladar J, Li WT, Chen C, Chang EY, Wang-Rodriguez J, et al. Tobacco, but not nicotine and flavor-less electronic cigarettes, induces ACE2 and immune dysregulation. Int J Mol Sci. 2020;21(15):5513.

Article   CAS   PubMed Central   Google Scholar  

England LJ, Bunnell RE, Pechacek TF, Tong VT, McAfee TA. Nicotine and the developing human: a neglected element in the electronic cigarette debate. Am J Prev Med. 2015;49(2):286–93.

Yuan M, Cross SJ, Loughlin SE, Leslie FM. Nicotine and the adolescent brain. J Physiol. 2015;593(16):3397–412.

Holbrook BD. The effects of nicotine on human fetal development. Birth Defects Res C Embryo Today. 2016;108(2):181–92.

Sanner T, Grimsrud TK. Nicotine: carcinogenicity and effects on response to cancer treatment—a review. Front Oncol. 2015;5:196.

Waldum HL, Nilsen OG, Nilsen T, Rørvik H, Syversen V, Sanvik AK, et al. Long-term effects of inhaled nicotine. Life Sci. 1996;58(16):1339–46.

Cucina A, Dinicola S, Coluccia P, Proietti S, D’Anselmi F, Pasqualato A, et al. Nicotine stimulates proliferation and inhibits apoptosis in colon cancer cell lines through activation of survival pathways. J Surg Res. 2012;178(1):233–41.

Wu SY, Xing F, Sharma S, Wu K, Tyagi A, Liu Y, et al. Nicotine promotes brain metastasis by polarizing microglia and suppressing innate immune function. J Exp Med. 2020;217(8):e20191131.

Roemer E, Stabbert R, Rustemeier K, Veltel DJ, Meisgen TJ, Reininghaus W, et al. Chemical composition, cytotoxicity and mutagenicity of smoke from US commercial and reference cigarettes smoked under two sets of machine smoking conditions. Toxicology. 2004;195(1):31–52.

Mayer B. How much nicotine kills a human? Tracing back the generally accepted lethal dose to dubious self-experiments in the nineteenth century. Arch Toxicol. 2014;88(1):5–7.

Brown CJ, Cheng JM. Electronic cigarettes: product characterisation and design considerations. Tob Control. 2014;23(Suppl 2):ii4-10.

Food and Drug Administration. SCOGS (Select Committee on GRAS Substances). 2019 (updated April 29, 2019). https://www.accessdata.fda.gov/scripts/fdcc/index.cfm?set=SCOGS&sort=Sortsubstance&order=ASC&startrow=251&type=basic&search= . Accessed 14 April 2020.

Wieslander G, Norback D, Lindgren T. Experimental exposure to propylene glycol mist in aviation emergency training: acute ocular and respiratory effects. Occup Environ Med. 2001;58(10):649–55.

Choi H, Schmidbauer N, Sundell J, Hasselgren M, Spengler J, Bornehag CG. Common household chemicals and the allergy risks in pre-school age children. PLoS One. 2010;5(10):e13423.

Kienhuis AS, Soeteman-Hernandez LG, Bos PMJ, Cremers HWJM, Klerx WN, Talhout R. Potential harmful health effects of inhaling nicotine-free shisha-pen vapor: a chemical risk assessment of the main components propylene glycol and glycerol. Tob Induc Dis. 2015;13(1):15.

Renne RA, Wehner AP, Greenspan BJ, Deford HS, Ragan HA, Westerberg RB, et al. 2-Week and 13-week inhalation studies of aerosolized glycerol in rats. Inhal Toxicol. 1992;4(2):95–111.

Article   CAS   Google Scholar  

Behar R, Wang Y, Talbot P. Comparing the cytotoxicity of electronic cigarette fluids, aerosols and solvents. Tob Control. 2018;27(3):325.

Massarsky A, Abdel A, Glazer L, Levin ED, Di Giulio RT. Neurobehavioral effects of 1,2-propanediol in zebrafish (Danio rerio). Neurotoxicology. 2018;65:111–24.

Geiss O, Bianchi I, Barrero-Moreno J. Correlation of volatile carbonyl yields emitted by e-cigarettes with the temperature of the heating coil and the perceived sensorial quality of the generated vapours. Int J Hyg Environ Health. 2016;219(3):268–77.

Counts ME, Morton MJ, Laffoon SW, Cox RH, Lipowicz PJ. Smoke composition and predicting relationships for international commercial cigarettes smoked with three machine-smoking conditions. Regul Toxicol Pharmacol. 2005;41(3):185–227.

Agency for Toxic Substances & Disease Registry. Toxicological Profile for Formaldehyde. 2019 (updated September 26, 2019). https://www.atsdr.cdc.gov/ToxProfiles/tp.asp?id=220&tid=39 . Accessed 9 April 2020.

Agency for Toxic Substances & Disease Registry. Toxicological Profile for Acrolein. 2019 (updated September 26, 2019). https://www.atsdr.cdc.gov/toxprofiles/TP.asp?id=557&tid=102 . Accessed 9 April 2020

Moghe A, Ghare S, Lamoreau B, Mohammad M, Barve S, McClain C, et al. Molecular mechanisms of acrolein toxicity: relevance to human disease. Toxicol Sci. 2015;143(2):242–55.

Seitz HK, Stickel F. Acetaldehyde as an underestimated risk factor for cancer development: role of genetics in ethanol metabolism. Genes Nutr. 2010;5(2):121–8.

Faroon O, Roney N, Taylor J, Ashizawa A, Lumpkin MH, Plewak DJ. Acrolein health effects. Toxicol Ind Health. 2008;24(7):447–90.

Goniewicz ML, Knysak J, Gawron M, Kosmider L, Sobczak A, Kurek J, et al. Levels of selected carcinogens and toxicants in vapour from electronic cigarettes. Tob Control. 2014;23(2):133–9.

Farsalinos KE, Voudris V. Do flavouring compounds contribute to aldehyde emissions in e-cigarettes? Food Chem Toxicol. 2018;115:212–7.

Kavvalakis MP, Stivaktakis PD, Tzatzarakis MN, Kouretas D, Liesivuori J, Alegakis AK, et al. Multicomponent analysis of replacement liquids of electronic cigarettes using chromatographic techniques. J Anal Toxicol. 2015;39(4):262–9.

Etter JF, Zather E, Svensson S. Analysis of refill liquids for electronic cigarettes. Addiction. 2013;108(9):1671–9.

Etter JF, Bugey A. E-cigarette liquids: constancy of content across batches and accuracy of labeling. Addict Behav. 2017;73:137–43.

Varlet V, Farsalinos K, Augsburger M, Thomas A, Etter JF. Toxicity assessment of refill liquids for electronic cigarettes. Int J Environ Res Public Health. 2015;12(5):4796–815.

McAuley TR, Hopke PK, Zhao J, Babaian S. Comparison of the effects of e-cigarette vapor and cigarette smoke on indoor air quality. Inhal Toxicol. 2012;24(12):850–7.

Cullen KA, Gentzke AS, Sawdey MD, Chang JT, Anic GM, Wang TW, et al. e-Cigarette use among youth in the United States, 2019. JAMA. 2019;322(21):2095–103.

Villanti AC, Johnson AL, Ambrose BK, Cummings KM, Stanton CA, Rose SW, et al. Flavored tobacco product use in youth and adults: findings from the first wave of the PATH Study (2013–2014). Am J Prev Med. 2017;53(2):139–51.

Food and Drug Administration. Vaporizers, E-Cigarettes, and other Electronic Nicotine Delivery Systems (ENDS) 2020 (updated April 13, 2020). https://www.fda.gov/tobacco-products/products-ingredients-components/vaporizers-e-cigarettes-and-other-electronic-nicotine-delivery-systems-ends . Accessed 15 April 2020

Omaiye EE, McWhirter KJ, Luo W, Tierney PA, Pankow JF, Talbot P. High concentrations of flavor chemicals are present in electronic cigarette refill fluids. Sci Rep. 2019;9(1):2468.

Bahl V, Lin S, Xu N, Davis B, Wang YH, Talbot P. Comparison of electronic cigarette refill fluid cytotoxicity using embryonic and adult models. Reprod Toxicol. 2012;34(4):529–37.

Behar R, Davis B, Wang Y, Bahl V, Lin S, Talbot P. Identification of toxicants in cinnamon-flavored electronic cigarette refill fluids. Toxicol In Vitro. 2014;28(2):198–208.

Morgan DL, Flake GP, Kirby PJ, Palmer SM. Respiratory toxicity of diacetyl in C57BL/6 mice. Toxicol Sci. 2008;103(1):169–80.

Hubbs AF, Cumpston AM, Goldsmith WT, Battelli LA, Kashon ML, Jackson MC, et al. Respiratory and olfactory cytotoxicity of inhaled 2,3-pentanedione in Sprague-Dawley rats. Am J Pathol. 2012;181(3):829–44.

Vas CA, Porter A, Mcadam K. Acetoin is a precursor to diacetyl in e-cigarette liquids. Food Chem Toxicol. 2019;133:110727.

Allen JG, Flanigan SS, LeBlanc M, Vallarino J, MacNaughton P, Stewart JH, et al. Flavoring chemicals in E-cigarettes: diacetyl, 2,3-pentanedione, and acetoin in a sample of 51 products, including fruit-, candy-, and cocktail-flavored E-cigarettes. Environ Health Perspect. 2016;124(6):733–9.

Park RM, Gilbert SJ. Pulmonary impairment and risk assessment in a diacetyl-exposed population: microwave popcorn workers. J Occup Environ Med. 2018;60(6):496–506.

Muthumalage T, Prinz M, Ansah KO, Gerloff J, Sundar IK, Rahman I. Inflammatory and oxidative responses induced by exposure to commonly used e-cigarette flavoring chemicals and flavored e-liquids without nicotine. Front Physiol. 2017;8:1130.

Sherwood CL, Boitano S. Airway epithelial cell exposure to distinct e-cigarette liquid flavorings reveals toxicity thresholds and activation of CFTR by the chocolate flavoring 2,5-dimethypyrazine. Respir Res. 2016;17(1):57.

Pinkston R, Zaman H, Hossain E, Penn AL, Noël A. Cell-specific toxicity of short-term JUUL aerosol exposure to human bronchial epithelial cells and murine macrophages exposed at the air–liquid interface. Respir Res. 2020;21(1):269.

Williams M, Villarreal A, Bozhilov K, Lin S, Talbot P. Metal and silicate particles including nanoparticles are present in electronic cigarette cartomizer fluid and aerosol. PLoS One. 2013;8(3):e57987.

Mikheev VB, Brinkman MC, Granville CA, Gordon SM, Clark PI. Real-time measurement of electronic cigarette aerosol size distribution and metals content analysis. Nicotine Tob Res. 2016;18(9):1895–902.

Williams M, Bozhilov K, Ghai S, Talbot P. Elements including metals in the atomizer and aerosol of disposable electronic cigarettes and electronic hookahs. PLoS One. 2017;12(4):e0175430.

Kleinman MT, Arechavala RJ, Herman D, Shi J, Hasen I, Ting A, et al. E-cigarette or vaping product use-associated lung injury produced in an animal model from electronic cigarette vapor exposure without tetrahydrocannabinol or vitamin E oil. J Am Heart Assoc. 2020;9(18):e017368.

Patnode CD, Henderson JT, Thompson JH, Senger CA, Fortmann SP, Whitlock EP. Behavioral counseling and pharmacotherapy interventions for tobacco cessation in adults, including pregnant women: a review of reviews for the U.S. preventive services task force. Ann Intern Med. 2015;163(8):608–21.

Messner B, Bernhard D. Smoking and cardiovascular disease: mechanisms of endothelial dysfunction and early atherogenesis. Arterioscler Thromb Vasc Biol. 2014;34(3):509–15.

Bansal V, Kim K-H. Review on quantitation methods for hazardous pollutants released by e-cigarette (EC) smoking. Trends Analyt Chem. 2016;78:120–33.

Mantey DS, Pasch KE, Loukas A, Perry CL. Exposure to point-of-sale marketing of cigarettes and E-cigarettes as predictors of smoking cessation behaviors. Nicotine Tob Res. 2019;21(2):212–9.

Selya AS, Dierker L, Rose JS, Hedeker D, Mermelstein RJ. The role of nicotine dependence in E-cigarettes’ potential for smoking reduction. Nicotine Tob Res. 2018;20(10):1272–7.

Kalkhoran S, Glantz SA. E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir Med. 2016;4(2):116–28.

Levy DT, Yuan Z, Luo Y, Abrams DB. The relationship of e-cigarette use to cigarette quit attempts and cessation: insights from a large, nationally representative U.S. survey. Nicotine Tob Res. 2017;20(8):931–9.

Article   PubMed Central   Google Scholar  

Hajek P, Phillips-Waller A, Przulj D, Pesola F, Myers Smith K, Bisal N, et al. A randomized trial of E-cigarettes versus nicotine-replacement therapy. N Engl J Med. 2019;380(7):629–37.

Polosa R, Morjaria JB, Caponnetto P, Prosperini U, Russo C, Pennisi A, et al. Evidence for harm reduction in COPD smokers who switch to electronic cigarettes. Respir Res. 2016;17(1):166.

Litt MD, Duffy V, Oncken C. Cigarette smoking and electronic cigarette vaping patterns as a function of e-cigarette flavourings. Tob Control. 2016;25(Suppl 2):ii67–72.

Palmer AM, Brandon TH. How do electronic cigarettes affect cravings to smoke or vape? Parsing the influences of nicotine and expectancies using the balanced-placebo design. J Consult Clin Psychol. 2018;86(5):486–91.

Majmundar A, Allem JP, Cruz TB, Unger JB. Public health concerns and unsubstantiated claims at the intersection of vaping and COVID-19. Nicotine Tob Res. 2020;22(9):1667–8.

Berlin I, Thomas D, Le Faou A-L, Cornuz J. COVID-19 and smoking. Nicotine Tob Res. 2020;22(9):1650–2.

Wrapp D, Wang N, Corbett KS, Goldsmith JA, Hsieh C-L, Abiona O, et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020;367(6483):1260–3.

Wang K, Gheblawi M, Oudit GY. Angiotensin Converting Enzyme 2: A Double-Edged Sword. Circulation. 2020;142(5):426–8.

Sharma P, Zeki AA. Does vaping increase susceptibility to COVID-19? Am J Respir Crit Care Med. 2020;202(7):1055–6.

Brake SJ, Barnsley K, Lu W, McAlinden KD, Eapen MS, Sohal SS. Smoking upregulates angiotensin-converting enzyme-2 receptor: a potential adhesion site for novel coronavirus SARS-CoV-2 (Covid-19). J Clin Med. 2020;9(3):841.

Zhang H, Rostamim MR, Leopold PL, Mezey JG, O’Beirne SL, Strulovici-Barel Y, et al. Reply to sharma and zeki: does vaping increase susceptibility to COVID-19? Am J Respir Crit Care Med. 2020;202(7):1056–7.

Cheng H, Wang Y, Wang GQ. Organ-protective effect of angiotensin-converting enzyme 2 and its effect on the prognosis of COVID-19. J Med Virol. 2020;92(7):726–30.

Lerner CA, Sundar IK, Yao H, Gerloff J, Ossip DJ, McIntosh S, et al. Vapors produced by electronic cigarettes and e-juices with flavorings induce toxicity, oxidative stress, and inflammatory response in lung epithelial cells and in mouse lung. PLoS ONE. 2015;10(2):e0116732.

Download references

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.

Author information

Authors and affiliations.

Department of Pharmacology, Faculty of Medicine, University of Valencia, Avda. Blasco Ibañez 15, 46010, Valencia, Spain

Patrice Marques, Laura Piqueras & Maria-Jesus Sanz

Institute of Health Research INCLIVA, University Clinic Hospital of Valencia, Valencia, Spain

CIBERDEM-Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders, ISCIII, Av. Monforte de Lemos 3-5, 28029, Madrid, Spain

Laura Piqueras & Maria-Jesus Sanz

You can also search for this author in PubMed   Google Scholar

Contributions

All authors discussed and agreed to the scope of the manuscript and contributed to the development of the manuscript at all stages. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Maria-Jesus Sanz .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors of the manuscript declare no conflicts of interest and take sole responsibility for the writing and content of the manuscript. None of the authors have been involved in legal or regulatory matters related to the contents of this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

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

Download citation

Received : 22 October 2020

Accepted : 03 May 2021

Published : 18 May 2021

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

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Electronic cigarette
• E-cigarette
• Flavourings
• Smoking cessation tool

Respiratory Research

ISSN: 1465-993X

• General enquiries: [email protected]

• Systematic Review
• Open access
• Published: 21 November 2022

The prevalence of electronic cigarettes vaping globally: a systematic review and meta-analysis

• Hadi Tehrani 1 , 2   na1 ,
• Abdolhalim Rajabi 3   na1 ,
• Mousa Ghelichi- Ghojogh 4 ,
• Mahbobeh Nejatian 5 &
• Alireza Jafari 6  

Archives of Public Health volume  80 , Article number:  240 ( 2022 ) Cite this article

20k Accesses

38 Citations

13 Altmetric

Metrics details

The purpose of this systematic review study was to determine the national, regional, and global prevalence of electronic cigarettes (e-cigarettes) vaping.

The articles were searched in July 2020 without a time limit in Web of Science (ISI), Scopus, PubMed, and Ovid-MEDLINE. At first, the titles and abstracts of the articles were reviewed, and if they were appropriate, they entered the second stage of screening. In the second stage, the whole articles were reviewed and articles that met the inclusion criteria were selected. In this study, search, selection of studies, qualitative evaluation, and data extraction were performed by two authors independently, and any disagreement between the two authors was reviewed and corrected by a third author.

In this study, the lifetime and current prevalence of e-cigarettes vaping globally were 23% and 11%, respectively. Lifetime and current prevalence of e-cigarettes vaping in women were 16% and 8%, respectively. Also, lifetime and current prevalence of e-cigarettes vaping in men were 22% and 12%, respectively. In this study, the current prevalence of e-cigarettes vaping in who had lifetime smoked conventional cigarette was 39%, and in current smokers was 43%. The lifetime prevalence of e-cigarettes vaping in the Continents of America, Europe, Asia, and Oceania were 24%, 26%, 16%, and 25%, respectively. The current prevalence of e-cigarettes vaping in the Continents of America, Europe, Asia, and Oceania were 10%, 14%, 11%, and 6%, respectively.

Conclusions

Based on the results of this study, it can be concluded that the popularity of e-cigarettes is increasing globally. Therefore, it is necessary for countries to have more control over the consumption and distribution of e-cigarettes, as well as to formulate the laws prohibiting about the e-cigarettes vaping in public places. There is also a need to design and conduct information campaigns to increase community awareness about e-cigarettes vaping.

Peer Review reports

Electronic cigarettes (e-cigarettes) are another type of tobacco that has become popular in the world in recent years. These cigarettes have batteries and heat the liquid and usually contain nicotine and other toxins [ 1 ]. In recent years, the prevalence of e-cigarettes has increased.

The results of a study by Brożek and et al. in several European countries showed that the overall prevalence of lifetime e-cigarette vaping was 43.7%, with 51.3% in men and 40.5% in women [ 2 ]. According to the results of various studies, the prevalence of e-cigarettes vaping in different countries such as France, Mexico, China, Australia, and in the United States were 25.46%, 42.42%, 24.44%, 12.52%, and 13.47%, respectively [ 3 , 4 , 5 , 6 , 7 ].

A systematic review by Pisinger and Dossing in 2014 showed that e-cigarettes can have an adverse effect on the health of individuals due to materials such as fine/ultrafine particles, volatile organic compounds, carcinogenic carbonyls, carcinogenic tobacco-specific nitrosamines, and cytotoxicity. Additionally, another major concern is the availability of novel compounds, such as propylene glycol, which are not found in conventional cigarettes with unknown impact on health [ 8 ]. The results of studies showed that using e-cigarettes may increase the risk of cardiovascular disease and respiratory disease [ 9 , 10 ].

People usually e-cigarettes vaping to quit conventional cigarettes, while some people using both types of cigarettes and are at higher risk [ 11 ]. The e-cigarettes vaping can also encourage people to initial use of conventional cigarettes and other substances [ 12 , 13 ]. The results of a systematic review study have shown that adolescents whose parents and friends vaping of e-cigarettes are more likely to be inclined towards e-cigarettes vaping in the future [ 14 ]. Therefore, this systematic and meta-analysis review study was conducted with the aims of (1) Investigating an updated estimate of the prevalence of lifetime and current e-cigarettes vaping in around the world based on countries, and (2) also demonstrate a trend of the prevalence of lifetime and current e-cigarettes vaping.

Search strategy and selection of articles

This study was a systematic review and meta-analysis to determine the national, regional and global prevalence of e-cigarettes vaping. In this study, articles were searched in July 2020 without a time limit and only in articles published in English in Web of Science (ISI), Scopus, PubMed, and Ovid-MEDLINE. Contrary to what is mentioned in the protocol, we did not use Google Scholar to search for articles. Also, the reference sections of relevant systematic review articles were checked. In this study, the phrase of “lifetime prevalence” referred to e-cigarette vaping by a person during his/her lifetime, and the phrase of “current prevalence” referred to e-cigarette vaping during the last 12 months. The search strategy was performed with the keywords of “Electronic Cigarette” OR “Electronic Nicotine” OR “E-Cigarette” OR “Vaping” OR “E-Cig” (Additional file 1 ). This study was based on the PRISMA guideline (Fig.  1 ). The protocol of this study has been registered in the PROSPERO system (registration number: CRD42020183032).

Flowchart of the systematic review process using PRISMA checklist

To select articles, first, all search results were entered into Endnote software and then reviewed by two authors separately and any disagreement was reviewed by the third author. At this stage, first, the titles and abstracts of the articles were reviewed, and if they were appropriate, they entered in the second stage of screening. In the second stage, the all articles were reviewed and articles that met the inclusion criteria were selected. The process of reviewing the selection of articles is shown in Fig.  1 .

Inclusion and exclusion criteria

Inclusion criteria included (1) papers published in English language, (2) cross-sectional, cohort, case–control, and intervention articles, (3) papers that reported the prevalence of e-cigarette vaping, and (4) papers that were published in full text. Exclusion criteria included qualitative papers, and papers that were published as review study, editorials comments, presentations or conference abstracts.

Quality assessment

Methodological quality was assessed using the Joanna Briggs Institute’s critical appraisal tool [ 15 ] for prevalence studies. This tool evaluates the extent to which a study has addressed the potential biases in its design, conduct, and analysis. Studies were examined for representativeness, sample size, recruitment, description of study participants and setting, data coverage of the identified sample, reliability of the measured condition, statistical analysis, and confounding factors. Scores ranged from 0–9 with ≤ 5 as “low/moderate quality” and > 5 as “fair quality.” All studies selected for this meta-analysis were independently assessed by two authors (A.R. and A.J), and any disagreements between the two authors were reviewed and corrected by a third author.

Data extraction

All final papers entered into the study process were extracted from a pre-prepared checklist. The checklist included the surname of the first author, year of data collection, year of paper publication, target group, age of target group, place of study, type of study, the data gathering instrument, sample size, current and lifetime prevalence of e-cigarettes vaping, the prevalence of current e-cigarettes vaping in who had lifetime smoked conventional cigarettes, or currently smoking conventional cigarettes (Table S 1 ). In this study, search, study selection, qualitative evaluation, and data extraction were conducted independently by two authors, and any disagreements between the two authors were reviewed and corrected by a third author.

Data analysis

The pooled prevalence of e-cigarettes and a 95% confidence interval (CI) was calculated with raw data in STATA version 16 (Stata Corp LP, College Station, TX, USA). A random effects models (Der-Simonian Laird method) were used to combine data from individual studies. Q test and I2 statistic were used to calculate the heterogeneity between studies. I2 describes the percentage of total variation because of between-study heterogeneity [ 16 ]. Subgroup analysis was conducted according to the continent, study design, population study, and tools of assessment of e-cigarettes. Meta-regression was performed to explore the possible sources of heterogeneity. A p -value less than 0.05 was considered to be statistically significant.

In brief, a total of 146 eligible studies were identified and included in in the final analysis from 4026 potentially relevant articles with 5,495,495 participants. A flowchart of the inclusion and exclusion criteria of articles are shown in Fig.  1 . The included studies were published between 2010 and 2020. The studies were conducted on four continents, with 67 studies in North America, 28 studies in Asia, 43 studies in Europe, and 8 studies in Australia/Oceania. Of the total studies included in this systematic review, 137 studies were cross-sectional and 9 studies were cohort studies (Table S 1 ) [ 3 , 4 , 5 , 6 , 7 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ].

The prevalence of lifetime and current e-cigarettes vaping

The results of this study showed that the lifetime and current prevalence of e-cigarettes vaping were 23% (with a confidence interval (CI) of 95%: 21–25%) and 11% (95% CI: 10–11%), respectively (Fig.  2 ). The lifetime and current prevalence of e-cigarettes vaping among women were 16% (95% CI: 15–18%) and 8% (95% CI: 0.07–0.08%), respectively (Fig.  3 ). Also, the lifetime and current prevalence of e-cigarettes vaping among men were 22% (95% CI: 20–25%) and 12% (95% CI: 11–13%), respectively (Fig.  4 ).

Pooled lifetime and current prevalence of e-cigarettes vaping in all subjects

Pooled lifetime and current prevalence of e-cigarettes vaping in women

Pooled lifetime and current prevalence of e-cigarettes vaping in men

In this study, the lifetime prevalence of e-cigarettes vaping among adolescents and school students, adults, college students, and patients were 25% (95% CI: 21–30%), 19% (95% CI: 17–21%), 26% (95% CI: 15–37%), and 29% (95% CI: 16–43%), respectively (Fig.  5 ). Also, the current prevalence of e-cigarettes vaping in adolescent and school students, adults, college students, and patients were 11% (95% CI: 10–12%), 11% (95% CI: 10–12%), 14% (95% CI: 7–22%), and 10% (95% CI: 8–11%), respectively (Fig.  5 ). The lifetime and current prevalence of e-cigarettes by subgroups in women and men can be seen in Fig S 1 and Fig S 2 .

Pooled lifetime and current prevalence of e-cigarettes vaping in all subjects by study population, continent, type of study, and tools assessment

The lifetime prevalence of e-cigarettes vaping in the continents of America, Europe, Asia, and Oceania were 24% (95% CI: 21–27%), 26% (95% CI: 21–31%), 16% (95% CI: 11–20%), and 25% (95% CI: 18–33%), respectively (Fig.  5 ). The current prevalence of e-cigarettes vaping in the continents of America, Europe, Asia, and Oceania were 10% (95% CI: 9–10%), 14% (95% CI: 10–17%), 11% (95% CI: 10–11%), and 6% (95% CI: 4–8%), respectively (Fig.  5 ).

According to the type of study, the lifetime prevalence of e-cigarettes vaping in cohort studies and cross-sectional studies were 28% (95% CI: 11–45%) and 23% (95% CI: 21–25%), respectively (Fig.  5 ). Also, based on the type of study, the current prevalence of e-cigarettes vaping in cohort studies and cross-sectional studies were 13% (95% CI: 11–16%) and 11% (95% CI: 10–11%), respectively (Fig.  5 ).

In terms of assessment tools, the lifetime prevalence of e-cigarettes vaping in studies conducted by self-report and standard questionnaire were 23% (95% CI: 21–26%) and 20% (95% CI: 15–25%), respectively (Fig.  5 ). Also, in terms of assessment tools, the current prevalence of e-cigarettes vaping in studies conducted by self-report and the standard questionnaire were 12% (95% CI: 11–12%) and 5% (95% CI: 4–6%), respectively (Fig.  5 ). In this study, the current prevalence of e-cigarettes vaping in people who had lifetime used conventional cigarettes, and in current smokers (conventional cigarettes) were 39% (95% CI: 36–42%) and 43% (95% CI: 39–47%), respectively (Fig.  6 ).

Pooled current prevalence of e-cigarettes vaping in ex-smokers and current smokers

The trend of current e-cigarettes vaping

The cumulative meta-analysis examined current e-cigarette vaping trends, which showed an upward trend from 2011 to 2014 and then a constant trend from 2014 to 2019 (Figure S 3 , Part A). The current prevalence of e-cigarettes among women first showed an upward and then a downward trends (Figure S 4 , part A). However, the current prevalence of e-cigarettes among men first showed an upward trend and then showed a constant trend (Fig S 5 , part A). The current prevalence of e-cigarettes vaping among adolescents and school students showed an upward trend. However, results of current e-cigarettes vaping among adolescents and school students showed an upward trend and among adults showed a downward trend (Fig S 6 , part A). The current prevalence of e-cigarettes vaping in continents of Americas and Asia first showed an upward trend and then showed an almost constant trend. The current prevalence of e-cigarettes vaping in Europe continent showed an upward trend (Fig S 7 , part A). The current prevalence of e-cigarettes vaping among people who had lifetime used conventional cigarettes and among current smokers (conventional cigarettes) first showed an upward trend and then showed an almost constant trend (Fig S 8 ). The current prevalence of e-cigarettes vaping by subgroups among women and men can be seen in Fig S 9 (part A) and Fig S 10 (part A). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men in each continent can be seen in Fig S 11 (part A) and Fig S 12 (part A).

The trend of lifetime e-cigarettes vaping

The cumulative meta-analysis examined the lifetime e-cigarettes vaping, which showed an upward trend from 2011 to 2019 (Fig S 3 , part B). The trend of lifetime e-cigarettes vaping among women first showed an upward trend and then showed a constant trend (Fig S 4 , part B). Also, the trend of lifetime e-cigarettes vaping among men showed an upward trend and then showed a constant trend (Fig S 5 , part B). According to the results, the lifetime e-cigarettes vaping among adolescents and school students showed an upward trend (Fig S 6 , part B). The lifetime e-cigarettes vaping in the continents of the Americas, Asia, Europe, and Oceania showed an upward trend (Fig S 7 , part B). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men can be seen in Fig S 9 (part B) and Fig S 10 (part B). The lifetime prevalence of e-cigarettes vaping by subgroups among women and men in every continent can be seen in Fig S 11 (part B) and Fig S 12 (part B).

Quality of included studies

The risk of bias and the quality of the included articles is illustrated in Table S 2 . All studies used an adequate sample size (100%) to determine the prevalence of e-cigarettes vaping. All studies (100%) used appropriate statistical analysis to measure the prevalence of e-cigarettes vaping. According to the Joanna Briggs Institute's Quality Assessment Checklist; the included articles had a score ranging from five to nine (Total nine-scored scale). Four studies scored nine out of nine (2.74%), fifty-seven studies scored seven to eight out of nine (39.04%) and the remaining eighty-five studies scored five to six out of nine score (58.22%) (Table S 2 ) [ 3 , 4 , 5 , 6 , 7 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 ].

Meta-regression analyses

Exploratory univariate meta-regression was conducted with the introduction of sample size, year of publication, tools of assessment, study design, continent, and population study for lifetime vaping and current vaping prevalence. The meta-regression coefficients for lifetime e-cigarettes vaping, 95% CI and P -value for these variables were, year of publication: β = 0.013, (95% CI: 0.0024, 0.0254, p  = 0.01), sample size: β = -1.42e −6 (95% CI: -2.05e −6 , -7.82e −7 , p  < 0.001), tools of assessment: β = -0.029, (95% CI: -0.098, 0.039, p  = 0.39), continent: β = 0.010, (95% CI: -0.011, 0.032, p  = 0.34), study design: β = -0.049, (95% CI: -0.170, 0.072, p  = 0.42), study population: β = -0.0012, (95% CI: -0.028, 0.025, p  = 0.92). The meta-regression coefficients for current e-cigarettes vaping showed that th95% CI and P -value for follow variables were, year of publication: β = 0.0065, (95% CI: 0.0037, 0.0092, p  < 0.001), sample size: β = -1.88e −6 (95% CI: -2.30e −6 , -1.46e −7 , p  < 0.001), tools of assessment: β = -0.059, (95% CI: -0.076, -0.043, p  < 0.001), continent: β = 0.005, (95% CI: -0.0013, 0.010, p  = 0.05), study design: β = -0.025, (95% CI: -0.042, -0.0075, p  = 0.005), study population: β = 0.0037, (95% CI: -0.0022, 0.0097, p  = 0.22).

This systematic review and meta-analysis study was conducted to determine the global prevalence of e-cigarettes vaping. In this study, the lifetime and current prevalence of e-cigarettes vaping in both sexes were 23% and 11%, respectively. The Europe continent had the high prevalence of e-cigarettes vaping and the lifetime and current of e-cigarettes vaping were 26 and 14 respectively. According to the results of this study, the overall trend of e-cigarettes vaping from 2011 to 2019 showed an upward trend. The current e-cigarettes vaping trend has been increasing from 2011 to 2014, and then there is a steady trend from 2014 to 2019.

Prevalence in men and women

The lifetime prevalence of e-cigarettes vaping among men and women were 22% and 16%, respectively. Also, the current prevalence of e-cigarettes vaping among men and women were 12% and 8%. In a study conducted in South Korea, the lifetime and current prevalence of e-cigarettes were 11.2% and 2% in men and 2.1% and 0.4% in women, respectively [ 117 ]. In a study conducted in Spain, the lifetime prevalence of e-cigarettes vaping among men and women were 8% and 5.3%, respectively [ 121 ]. The current prevalence of e-cigarettes vaping among Japanese men and women were 6.7% and 3.1%, respectively [ 136 ]. The lifetime prevalence of e-cigarettes vaping among Germany men and women were 9.2% and 6.7%, respectively, and the current prevalence of e-cigarettes vaping were 2.6% and 1.3%, respectively [ 115 ]. Among American men and women, the lifetime prevalence of e-cigarettes vaping were 9.6% and 7.4%, respectively, and current prevalence of e-cigarettes vaping were reported 2.6% and 2.1%, respectively [ 133 ]. Men and women use e-cigarettes for a variety of reasons. Men will start using e-cigarettes for reasons such as quitting smoking, health concerns related to conventional cigarette, and curiosity about e-cigarettes. In women, the recommendation to use e-cigarettes by family or friends is one of the important reasons for e-cigarettes vaping [ 157 ].

According to the results, the current prevalence of e-cigarettes among men first showed an upward trend and then showed a constant trend. Also, the current prevalence of e-cigarettes among women first showed an upward and then a downward trends. One of the reasons for the increasing trend of the current prevalence is the positive expectations of e-cigarettes including good taste, good social performance, and increased energy in men compared to women, while the only positive expectation of women to use e-cigarettes is weight loss due to e-cigarettes vaping [ 157 ]. The findings suggest that young women are more likely to use e-cigarettes, while pregnant women are less likely to use e-cigarettes due to the adverse effects of e-cigarettes [ 158 ]. The reason for the decrease of e-cigarettes vaping among women may be the failure of smoking consumption to help with weight loss and fitness. Also, women are generally more concerned about their health than men, and the reason for the reduced consumption may be due to greater awareness of the complications of e-cigarettes vaping.

Prevalence in adolescent’s and school students

In this study, the lifetime and current prevalence of e-cigarettes vaping among adolescents and school students were 25% and 11%, respectively. In a study conducted in Russia, the lifetime and current prevalence of e-cigarettes vaping were 28.6% and 2.2%, respectively [ 114 ]. The current prevalence of e-cigarettes vaping among adolescents and school students is very wide in different countries, such as 1% in Mexico [ 159 ] and 9.9% in the United States [ 122 ]. In other countries such as China, the United Kingdom, Canada, and Poland, the current prevalence of e-cigarettes vaping were reported 1.2%, 2.2%, 3.6% and 3.5%, respectively [ 148 , 150 , 160 ]. According to the results, the trend of lifetime and current prevalence of e-cigarettes vaping in adolescents has been increasing, for example, the lifetime prevalence rate in the UK has increased from 22% in 2014 to 25% in 2016 [ 161 ], also the current prevalence rate in the United States has increased rapidly from 1.5% in 2011 to 20.8% in 2018 [ 162 ]. In various studies, a positive relationship has been found between the amounts of monthly allowance given by parents to their adolescent children, so as much as the amount of money is higher, the probability of e-cigarettes vaping is also higher by children [ 144 , 163 , 164 , 165 ] and this factor could have been a reason to increase e-cigarettes vaping. Another reason for increasing the prevalence of e-cigarettes vaping could be the use of e-cigarettes to quitting conventional cigarette by adolescents. Therefore, this results indicate that families should pay more attention to their adolescent and children about e-cigarettes vaping. Also, as an important channel for e-cigarettes vaping education, health professionals could play an important role, especially for adolescents and school students. Additionally, banning the sale of e-cigarettes to people under 18 years may help reduce e-cigarettes vaping rates among adolescents and school students.

Prevalence in adults

In this study, the lifetime and current prevalence of e-cigarettes vaping among adults were 19% and 11%, respectively. In a study in South Korea, the lifetime and current prevalence of e-cigarettes were 6.6% and 1.1%, respectively [ 117 ]. In a study conducted in Spain, the lifetime prevalence of e-cigarettes vaping among adult men was 6.5% [ 121 ]. The current prevalence of e-cigarettes vaping in Japan has been reported to be 4.3% [ 136 ]. The current prevalence of e-cigarettes among adults varies from country to country, which can be influenced by various factors such as availability of these products and regulatory rules. For example, in China, the current prevalence of e-cigarettes vaping was 1.2%, while in the United States has been reported to be 5.5% [ 148 , 159 , 166 ]. However, the lack of laws on the sale of e-cigarettes and widespread access to tobacco in Chinese stores is a cause for concern about the increasing use of e-cigarettes, as in other countries [ 148 ]. In various studies conducted in different countries around the world, including Mexico, Australia, New Zealand, and Canada, the current prevalence of e-cigarettes has been reported to be 1.1%, 1.2%, 2.1%, and 2.9%, respectively [ 37 , 167 , 168 ]. Based on the results of this study, current prevalence of e-cigarettes vaping among adults showed decreasing trend. The downward trend in current prevalence may be due to increased awareness among adults about the harms and dangers of e-cigarettes, and the creation of regulatory laws that prohibit e-cigarette use.

Prevalence in college students

In this study, the lifetime and current prevalence of e-cigarettes in college students were 26% and 14%. In a study conducted in five European countries including Slovakia, Belarus, Poland, Russia and Lithuania, the lifetime prevalence of e-cigarettes among college students were 34.4%, 42.7%, 45%, 33.4%, and 42.7%, respectively, and the current prevalence of e-cigarettes in these five countries were 2.3%, 2.7%, 2.8%, 4%, and 3.5%, respectively [ 2 ]. In a study conducted in the United States, the lifetime and current prevalence of e-cigarettes vaping among college students were 9% and 30%, respectively [ 130 ]. In another study among health science students in Saudi Arabia, the lifetime prevalence of e-cigarettes vaping has been reported to be 27.7% [ 137 ]. In a study conducted in Pakistan on medical students, the prevalence of e-cigarettes vaping was 13.9% [ 139 ], while in another study, the current prevalence of e-cigarettes vaping was 4.4% on medical students and 12.4% on non-medical students [ 2 ]. It has been reported that the reason for the low prevalence among medical students maybe their high awareness of the dangers of e-cigarettes vaping during the period of their education course [ 2 ]. The lifetime prevalence of e-cigarettes in Malaysian college students has been reported to be 20.4% [ 143 ]. Differences prevalence of e-cigarettes vaping in studies can be due to the different target groups, differences in age groups, and method of conducted the studies. According to the results of this study, the lifetime prevalence of e-cigarettes among college students showed increasing trend and the current prevalence of consumption has been decreasing. The reasons for the declining trend of the current prevalence of e-cigarettes can be cultural differences and the creation of laws to monitor and prohibit the use of e-cigarettes. Also, the prohibition of e-cigarettes vaping in the college can be effective in reducing e-cigarettes vaping.

Prevalence by continent

In this study, the lifetime prevalence of e-cigarettes vaping was 24% in the Americas, 26% in Europe, 16% in Asia and 25% in Oceania. Also, in this study the current prevalence of e-cigarettes vaping was 10% in the Americas, 14% in Europe, 11% in Asia, and 6% in Oceania. In a study conducted in 27 European countries, the lifetime prevalence of e-cigarettes increased from 7.2% in 2012 to 11.6% in 2016 [ 169 ]. One of the reasons for the increase in consumption in this continent may be because people usually use e-cigarettes to reduce or quit conventional cigarettes, but after a period of time, they start to use e-cigarettes continuously.

In this study, the lifetime prevalence of e-cigarettes vaping in the continents of Americas, Asia, Europe, and Oceania showed an upward trend. Also, the current e-cigarettes vaping in the continents of Americas and Asia first showed an upward trend and then showed an almost constant trend, but in Europe continent, it was showed an upward trend. In general, the use of e-cigarettes is increasing across different continents, possibly due to insufficient taxation of e-cigarettes. Also, given the increase in e-cigarettes in recent years, the law may not have been enacted yet. The reason for the differences in the prevalence of e-cigarettes in different continents may be due to the enactment of laws to reduce publicity, ban sales, increase taxes and conduct information campaigns in the field.

Prevalence of e-cigarettes vaping among ex-smokers and current smokers

In this study, the current prevalence of e-cigarettes vaping in people who had lifetime used conventional cigarettes, and among current smokers were 39% and 43%, respectively. In a study conducted in Malaysia, the current prevalence of e-cigarettes vaping in who had lifetime smoked conventional cigarettes, and in current smokers conventional cigarettes were 4.3% and 8%, respectively [ 146 ]. In a study in the USA, the current prevalence of e-cigarettes vaping among current smokers has been reported to be 24.1% [ 128 ]. One of the reasons for e-cigarettes vaping among current smoker’s conventional cigarettes is the curiosity to try it, helping to quit and reduce conventional cigarette smoking. In a study conducted in Serbia, 12.8% of respondents reported that e-cigarettes vaping helped reduce their conventional cigarette smoking [ 153 ].

The current prevalence of e-cigarettes among people who had lifetime smoked conventional cigarettes or among current smokers first showed an upward trend and then t showed an almost constant trend. The reason for the increasing trend of e-cigarettes vaping may be the tendency of more smokers to quit or reduce conventional cigarettes, which can also be seen as both a threat and an opportunity. The threat aspect of this approach may be that a greater tendency to use e-cigarettes can lead to addiction to e-cigarettes. The opportunity aspect of this approach is that since most people have a tendency to quit smoking, e-cigarette can be a good option for quit or reducing conventional cigarettes.

According to the results of this study, it can be concluded that the prevalence of e-cigarettes is increasing worldwide. Therefore, it is necessary for countries to have more control over the consumption and distribution of e-cigarettes, as well as to formulate laws prohibiting the consumption of e-cigarettes in public places. Due to the increase in the prevalence of e-cigarettes among adolescents and school students, it is necessary that parents pay more attention to their children and also schools should also design and implement various educational programs to increase the awareness of adolescents and school students in this field. A broad program of behavioral, communications, and educational research is crucial to assess how youth perceive e-cigarettes and associated marketing messages, and to determine what kinds of tobacco control communication strategies and channels are most effective.

Besides, due to the high prevalence of e-cigarettes among current smokers, to quit or reduce their conventional cigarette smoking, more evidence is require in this regard and Clinical trial studies are also recommended to evaluate the benefits and harms of e-cigarettes vaping. The increase in e-cigarettes consumption in continental Europe compared to other continents indicates more detailed studies to identify the use of e-cigarettes, survey, and enact laws to ban e-cigarettes in this continent.

Limitations and strengths

This study also had its limitations. Due to the use of studies whose data are collected through self-reporting data, the results of the study may be distorted due to measurement errors such as reporting bias and reminder bias. This self-reporting can lead to the misclassification of people that applies to smoke behavior in women, who are often underreported. Another limitation of this study was that due to the smaller number of studies that reported the lifetime prevalence of e-cigarettes vaping in pregnant women (2 studies) than the studies that reported the current prevalence of e-cigarettes vaping in this group (3 studies), the lifetime prevalence rate was lower than the current prevalence rate. One of the strengths of the study is that it includes cross-sectional, cohort, case–control, and intervention studies, and examines the prevalence of e-cigarettes in worldwide, and examines both the lifetime and current prevalence of e-cigarettes. It has also examined the prevalence of e-cigarettes in different subgroups including men, women, adults, adolescents, university students, by continents, and conventional cigarette users.

Availability of data and materials

Not applicable.

Abbreviations

Electronic cigarettes

Reasoner JJ, Regier BA, Beckendorf R, McAllister RK. Update on the risks of electronic cigarettes—vaping. Ochsner J. 2020;20(1):2.

Article   PubMed   PubMed Central   Google Scholar  

Brożek GM, Jankowski M, Lawson JA, Shpakou A, Poznański M, Zielonka TM, et al. The prevalence of cigarette and E-cigarette smoking among students in Central and Eastern Europe—results of the YUPESS Study. Int J Environ Res Public Health. 2019;16(13):2297.

Article   PubMed Central   Google Scholar  

Alzahrani T, Pena I, Temesgen N, Glantz SA. Association between electronic cigarette use and myocardial infarction. Am J Prev Med. 2018;55(4):455–61.

Andler R, Guignard R, Wilquin J-L, Beck F, Richard J-B, Nguyen-Thanh V. Electronic cigarette use in France in 2014. Int J Public Health. 2016;61(2):159–65.

Article   PubMed   Google Scholar  

Wang X, Zhang X, Xu X, Gao Y. Perceptions and use of electronic cigarettes among young adults in China. Tob Induc Dis. 2019;17:17.

Barrientos-Gutierrez I, Lozano P, Arillo-Santillan E, Morello P, Mejia R, Thrasher JF. “Technophilia”: a new risk factor for electronic cigarette use among early adolescents? Addict Behav. 2019;91:193–200.

Twyman L, Watts C, Chapman K, Walsberger SC. Electronic cigarette use in New South Wales, Australia: reasons for use, place of purchase and use in enclosed and outdoor places. Aust N Z J Public Health. 2018;42(5):491–6.

Pisinger C, Døssing M. A systematic review of health effects of electronic cigarettes. Prev Med. 2014;69:248–60.

Wagener TL, Floyd EL, Stepanov I, Driskill LM, Frank SG, Meier E, et al. Have combustible cigarettes met their match? The nicotine delivery profiles and harmful constituent exposures of second-generation and third-generation electronic cigarette users. Tob Control. 2017;26(e1):e23–8.

Bhatta DN, Glantz SA. Association of e-cigarette use with respiratory disease among adults: a longitudinal analysis. Am J Prev Med. 2020;58(2):182–90.

Farsalinos KE, Poulas K, Voudris V, Le Houezec J. Prevalence and correlates of current daily use of electronic cigarettes in the European Union: analysis of the 2014 Eurobarometer survey. Intern Emerg Med. 2017;12(6):757–63.

Westling E, Rusby JC, Crowley R, Light JM. Electronic cigarette use by youth: prevalence, correlates, and use trajectories from middle to high school. J Adolesc Health. 2017;60(6):660–6.

Soneji S, Barrington-Trimis JL, Wills TA, Leventhal AM, Unger JB, Gibson LA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis. JAMA Pediatr. 2017;171(8):788–97.

Wang J-W, Cao S-S, Hu R-Y. Smoking by family members and friends and electronic-cigarette use in adolescence: a systematic review and meta-analysis. Tob Induc Dis. 2018;16:05.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Munn Z, Moola S, Riitano D, Lisy K. The development of a critical appraisal tool for use in systematic reviews addressing questions of prevalence. Int J Health Policy Manag. 2014;3(3):123.

Rajabi A, Dehghani M, Shojaei A, Farjam M, Motevalian SA. Association between tobacco smoking and opioid use: a meta-analysis. Addict Behav. 2019;92:225–35.

Gallus S, Lugo A, Pacifici R, Pichini S, Colombo P, Garattini S, et al. E-cigarette awareness, use, and harm perceptions in Italy: a national representative survey. Nicotine Tob Res. 2014;16(12):1541–8.

Article   PubMed   CAS   Google Scholar  

Rigotti NA, Harrington KF, Richter K, Fellows JL, Sherman SE, Grossman E, et al. Increasing prevalence of electronic cigarette use among smokers hospitalized in 5 US cities, 2010–2013. Nicotine Tob Res. 2015;17(2):236–44.

Fotiou A, Kanavou E, Stavrou M, Richardson C, Kokkevi A. Prevalence and correlates of electronic cigarette use among adolescents in Greece: a preliminary cross-sectional analysis of nationwide survey data. Addict Behav. 2015;51:88–92.

Kinnunen JM, Ollila H, El-Amin SE-T, Pere LA, Lindfors PL, Rimpelä AH. Awareness and determinants of electronic cigarette use among Finnish adolescents in 2013: a population-based study. Tob Control. 2015;24(e4):e264–70.

Mark KS, Farquhar B, Chisolm MS, Coleman-Cowger VH, Terplan M. Knowledge, attitudes, and practice of electronic cigarette use among pregnant women. J Addict Med. 2015;9(4):266–72.

Chivers LL, Hand DJ, Priest JS, Higgins ST. E-cigarette use among women of reproductive age: impulsivity, cigarette smoking status, and other risk factors. Prev Med. 2016;92:126–34.

Geidne S, Beckman L, Edvardsson I, Hulldin J. Prevalence and risk factors of electronic cigarette use among adolescents: data from four Swedish municipalities. Nordic Stud Alcohol Drugs. 2016;33(3):225–40.

Article   Google Scholar  

Kilibarda B, Mravcik V, Martens MS. E-cigarette use among Serbian adults: prevalence and user characteristics. Int J Public Health. 2016;61(2):167–75.

Kinnunen JM, Ollila H, Lindfors PL, Rimpelä AH. Changes in electronic cigarette use from 2013 to 2015 and reasons for use among Finnish adolescents. Int J Environ Res Public Health. 2016;13(11):1114.

Morello P, Perez A, Peña L, Lozano P, Thrasher JF, Sargent JD, et al. Prevalence and predictors of e-cigarette trial among adolescents in Argentina. Tob Prev Cessat. 2016;2:80.

Thrasher JF, Abad-Vivero EN, Barrientos-Gutíerrez I, Pérez-Hernández R, Reynales-Shigematsu LM, Mejía R, et al. Prevalence and correlates of e-cigarette perceptions and trial among early adolescents in Mexico. J Adolesc Health. 2016;58(3):358–65.

Levy DT, Yuan Z, Li Y. The prevalence and characteristics of e-cigarette users in the US. Int J Environ Res Public Health. 2017;14(10):1200.

Wagner NJ, Camerota M, Propper C. Prevalence and perceptions of electronic cigarette use during pregnancy. Matern Child Health J. 2017;21(8):1655–61.

Ruokolainen O, Ollila H, Karjalainen K. Determinants of electronic cigarette use among Finnish adults: results from a population-based survey. Nordic Stud Alcohol Drugs. 2017;34(6):471–80.

Jaber RM, Mirbolouk M, DeFilippis AP, Maziak W, Keith R, Payne T, et al. Electronic cigarette use prevalence, associated factors, and pattern by cigarette smoking status in the United States from NHANES (National Health and Nutrition Examination Survey) 2013–2014. J Am Heart Assoc. 2018;7(14):e008178.

Melka AS, Chojenta CL, Holliday EG, Loxton DJ. Predictors of E-cigarette use among young Australian women. Am J Prev Med. 2019;56(2):293–9.

Wang X, Zhang X, Xu X, Gao Y. Electronic cigarette use and smoking cessation behavior among adolescents in China. Addict Behav. 2018;82:129–34.

Giovenco DP, Casseus M, Duncan DT, Coups EJ, Lewis MJ, Delnevo CD. Association between electronic cigarette marketing near schools and e-cigarette use among youth. J Adolesc Health. 2016;59(6):627–34.

Zhu J, Shi F, Xu G, Li N, Li J, He Y, et al. Conventional cigarette and e-cigarette smoking among school personnel in shanghai, China: prevalence and determinants. Int J Environ Res Public Health. 2019;16(17):3197.

Ab Rahman J, Mohd Yusoff MF, Nik Mohamed MH, Mahadir Naidu B, Lim KH, Tee GH, et al. The Prevalence of E-Cigarette Use Among Adults in Malaysia. Asia Pac J Public Health. 2019;31(7):9–21.

Oakly A, Edwards R, Martin G. Prevalence of e-cigarette use from a nationally representative sample in New Zealand. Addict Behav. 2019;98:106024.

Lee YH, Chiang T, Kwon E, Baik S, Chang YC. Trends and sociodemographic factors of e-cigarette use among adult daily smokers in South Korea. Int J Health Plann Manage. 2020;35(4):960–9.

Mehra VM, Keethakumar A, Bohr YM, Abdullah P, Tamim H. The association between alcohol, marijuana, illegal drug use and current use of E-cigarette among youth and young adults in Canada: results from Canadian Tobacco, Alcohol and Drugs Survey 2017. BMC Public Health. 2019;19(1):1208.

Rollins L, Sokol NA, McCallum M, England L, Matteson K, Werner E, et al. Electronic cigarette use during preconception and/or pregnancy: prevalence, characteristics, and concurrent mental health conditions. J Womens Health (Larchmt). 2020;29(6):780–8.

Article   PubMed Central   CAS   Google Scholar  

Walker N, Parag V, Wong SF, Youdan B, Broughton B, Bullen C, et al. Use of e-cigarettes and smoked tobacco in youth aged 14–15 years in New Zealand: findings from repeated cross-sectional studies (2014–19). Lancet Public Health. 2020;5(4):e204–e212.

Hrywna M, Manderski MTB, Delnevo CD. Prevalence of electronic cigarette use among adolescents in new jersey and association with social factors. JAMA Netw Open. 2020;3(2):e1920961.

Pinkas J, Kaleta D, Zgliczyński WS, Lusawa A, Wrześniewska-Wal I, Wierzba W, et al. The prevalence of tobacco and e-cigarette use in Poland: a 2019 Nationwide Cross-Sectional Survey. Int J Environ Res Public Health. 2019;16(23):4820.

Amato MS, Boyle RG, Levy D. How to define e-cigarette prevalence? Finding clues in the use frequency distribution. Tob Control. 2016;25(e1):e24–9.

Babineau K, Taylor K, Clancy L. Electronic cigarette use among Irish youth: a cross sectional study of prevalence and associated factors. PLoS ONE. 2015;10(5):e0126419.

Boyle RG, Richter S, Helgertz S. Who is using and why: prevalence and perceptions of using and not using electronic cigarettes in a statewide survey of adults. Addict Behav Rep. 2019;10:100227.

PubMed   PubMed Central   Google Scholar  

Brose LS, Hitchman SC, Brown J, West R, McNeill A. Is the use of electronic cigarettes while smoking associated with smoking cessation attempts, cessation and reduced cigarette consumption? A survey with a 1-year follow-up. Addiction. 2015;110(7):1160–8.

Brown J, West R, Beard E, Michie S, Shahab L, McNeill A. Prevalence and characteristics of e-cigarette users in Great Britain: findings from a general population survey of smokers. Addict Behav. 2014;39(6):1120–5.

Chen J, Ho SY, Leung LT, Wang MP, Lam TH. School-level electronic cigarette use prevalence and student-level tobacco use intention and behaviours. Sci Rep. 2019;9(1):1–7.

Google Scholar  

Czoli CD, Hammond D, White CM. Electronic cigarettes in Canada: prevalence of use and perceptions among youth and young adults. Can J Public Health. 2014;105(2):e97–102.

De Lacy E, Fletcher A, Hewitt G, Murphy S, Moore G. Cross-sectional study examining the prevalence, correlates and sequencing of electronic cigarette and tobacco use among 11–16-year olds in schools in Wales. BMJ Open. 2017;7(2):e012784.

Dockrell M, Morrison R, Bauld L, McNeill A. E-cigarettes: prevalence and attitudes in Great Britain. Nicotine Tob Res. 2013;15(10):1737–44.

Du Y, Shih M, Shah MD, Weber MD, Lightstone AS. Prevalence and sociodemographic disparities in ever e-cigarette use among adults in Los Angeles County. Prev Med Rep. 2019;15:100904.

Elin SK. Prevalence and correlates of electronic cigarette use among a clinical sample of polysubstance users in Kentucky: long live the cigarette? Subst Use Misuse. 2019;54(2):225–35.

Fedele DA, Barnett TE, Dekevich D, Gibson-Young LM, Martinasek M, Jagger MA. Prevalence of and beliefs about electronic cigarettes and hookah among high school students with asthma. Ann Epidemiol. 2016;26(12):865–9.

Gorini G, Gallus S, Carreras G, De Mei B, Masocco M, Faggiano F, et al. Prevalence of tobacco smoking and electronic cigarette use among adolescents in Italy: Global Youth Tobacco Surveys (GYTS), 2010, 2014, 2018. Prev Med. 2020;131:105903.

Giovenco DP, Delnevo CD. Prevalence of population smoking cessation by electronic cigarette use status in a national sample of recent smokers. Addict Behav. 2018;76:129–34.

Jackson SE, Beard E, Michie S, West R, Brown J. Is the use of e-cigarettes for smoking cessation associated with alcohol consumption? A population-level survey of successful quitters in England. Addict Behav. 2020;101:106138.

Jeon C, Jung KJ, Kimm H, Lee S, Barrington-Trimis JL, McConnell R, et al. E-cigarettes, conventional cigarettes, and dual use in Korean adolescents and university students: prevalence and risk factors. Drug Alcohol Depend. 2016;168:99–103.

Kristjansson AL, Mann MJ, Smith ML. Prevalence of substance use among middle school–aged e-cigarette users compared with cigarette smokers, nonusers, and dual users: Implications for primary prevention. Substance Abuse. 2017;38(4):473–6.

Leavens EL, Lechner WV, Stevens EM, Miller MB, Meier E, Brett EI, et al. Electronic cigarette and combustible cigarette use following a campus-wide ban: prevalence of use and harm perceptions. J Am Coll Health. 2020;68(4):332–5.

Li J, Newcombe R, Walton D. The prevalence, correlates and reasons for using electronic cigarettes among New Zealand adults. Addict Behav. 2015;45:245–51.

Phyo Y, Kumar AM, Kyaw KWY, Kaung KK, Nwe ML. Prevalence of e-cigarette use among tobacco smokers in six states and regions of Myanmar. Addict Behav Rep. 2020;11:100248.

Perialathan K, Rahman AB, Lim KH, Adon Y, Ahmad A, Juatan N, et al. Prevalence and associated factors of ever use of electronic cigarettes: findings from a hospitals and health clinics study based in Malaysia. Tob Induc Dis. 2018;16:55.

Rennie LJ, Bazillier-Bruneau C, Rouëssé J. Harm reduction or harm introduction? Prevalence and correlates of e-cigarette use among French adolescents. J Adolesc Health. 2016;58(4):440–5.

Roberts W, Moore KE, Peltier MR, Verplaetse TL, Oberleitner L, Hacker R, et al. Electronic cigarette use and risk of harmful alcohol consumption in the US population. Alcohol Clin Expe Res. 2018;42(12):2385–93.

Article   CAS   Google Scholar  

Shiplo S, Czoli CD, Hammond D. E-cigarette use in Canada: prevalence and patterns of use in a regulated market. BMJ Open. 2015;5(8):e007971.

Stokes A, Collins JM, Berry KM, Reynolds LM, Fetterman JL, Rodriguez CJ, et al. Electronic cigarette prevalence and patterns of use in adults with a history of cardiovascular disease in the United States. J Am Heart Assoc. 2018;7(9):e007602.

Wilson FA, Wang Y. Recent findings on the prevalence of e-cigarette use among adults in the US. Am J Prev Med. 2017;52(3):385–90.

Yu E, Lippert AM. Race/ethnicity modifies the association between school prevalence of e-cigarette use and student-level use: results from the 2014 US National Youth Tobacco Survey. Health Place. 2017;46:114–20.

Cullen KA, Gentzke AS, Sawdey MD, Chang JT, Anic GM, Wang TW, et al. E-cigarette use among youth in the United States, 2019. JAMA. 2019;322(21):2095–103.

Obisesan OH, Mirbolouk M, Osei AD, Orimoloye OA, Uddin SI, Dzaye O, et al. Association between e-cigarette use and depression in the behavioral risk factor surveillance system, 2016–2017. JAMA Netw open. 2019;2(12):e1916800.

Nădăşan V, Foley KL, Pénzes M, Paulik E, Mihăicuţă Ş, Ábrám Z, et al. Use of electronic cigarettes and alternative tobacco products among Romanian adolescents. Int J Public Health. 2016;61(2):199–207.

Alcalá HE, Albert SL, Ortega AN. E-cigarette use and disparities by race, citizenship status and language among adolescents. Addict Behav. 2016;57:30–4.

Herbeć AA, Chang Y, Tindle HA, Rigotti NA. Smokers’ use of electronic cigarettes before, during, and in the month after hospitalization. Findings from the helping HAND 2 Study. Addict Behav. 2019;91:5–11.

Parekh T, Pemmasani S, Desai R. Risk of stroke with e-cigarette and combustible cigarette use in young adults. Am J Prev Med. 2020;58(3):446–52.

Shan L, Manzione LC, Azagba S. Psychological well-being and dual-use of cigarettes and e-cigarettes among high school students in Canada. J Affect Disord. 2020;265:357–63.

Claire AWS, Schillo BA, Lien RK, Keller PA, O’Gara E, D’Silva J, et al. Changing patterns in e-cigarette use among Minnesota adults between 2014 and 2018. Prev Med Rep. 2019;16:101014.

Chun J, Yu M, Kim J, Kim A. E-cigarette, cigarette, and dual use in Korean adolescents: a test of problem behavior theory. J Psychoactive Drugs. 2020;52(1):27–36.

Soule EK, Rossheim ME, Cavazos TC, Bode K, Desrosiers AC. Cigarette, waterpipe, and electronic cigarette use among college fraternity and sorority members and athletes in the United States. J Am Coll Health. 2021;69(5):463–9.

Aljandaleh H, Bolze C, El-Khoury Lesueur F, Melchior M, Mary-Krause M. Factors associated with electronic cigarette use among young adults: the French “Trajectoires EpidéMiologiques en POpulation”(TEMPO) cohort study. Subst Use Misuse. 2020;55(6):964–72.

Ruether T, Wissen F, Linhardt A, Aichert DS, Pogarell O, de Vries H. Electronic cigarettes—attitudes and use in Germany. Nicotine Tob Res. 2016;18(5):660–9.

Skelton E, Silberberg L, Guillaumier A, Dunlop AJ, Wilkinson RB, Bonevski B. Electronic cigarettes: ever use, current use and attitudes among alcohol and other drug clients. Drug Alcohol Rev. 2020;39(1):7–11.

Abrams LR, Kalousova L, Fleischer NL. Gender differences in relationships between sociodemographic factors and e-cigarette use with smoking cessation: 2014–15 current population survey tobacco use supplement. J Public Health. 2020;42(1):e42–50.

Ali S, King K, Vidourek R, Ashley M, Rao M. A study of electronic cigarette use among youth. J Public Health. 2018;26(4):417–24.

Anand V, McGinty KL, O’Brien K, Guenthner G, Hahn E, Martin CA. E-cigarette use and beliefs among urban public high school students in North Carolina. J Adolesc Health. 2015;57(1):46–51.

Atuegwu NC, Perez MF, Oncken C, Mead EL, Maheshwari N, Mortensen EM. E-cigarette use is associated with a self-reported diagnosis of prediabetes in never cigarette smokers: results from the behavioral risk factor surveillance system survey. Drug Alcohol Depend. 2019;205:107692.

Auf R, Trepka MJ, Selim M, Taleb ZB, De La Rosa M, Bastida E, et al. E-cigarette use is associated with other tobacco use among US adolescents. Int J Public Health. 2019;64(1):125–34.

Zhao L, Mbulo L, Palipudi K, Wang J, King B. Awareness and use of e-cigarettes among urban residents in China. Tob Induc Dis. 2019;17:53.

Azagba S. E-cigarette use, dual use of e-cigarettes and tobacco cigarettes, and frequency of cannabis use among high school students. Addict Behav. 2018;79:166–70.

Baldassarri SR, Fiellin DA, Savage ME, Madden LM, Beitel M, Dhingra LK, et al. Electronic cigarette and tobacco use in individuals entering methadone or buprenorphine treatment. Drug Alcohol Depend. 2019;197:37–41.

Barnett TE, Soule EK, Forrest JR, Porter L, Tomar SL. Adolescent electronic cigarette use: associations with conventional cigarette and hookah smoking. Am J Prev Med. 2015;49(2):199–206.

Berg CJ. Preferred flavors and reasons for e-cigarette use and discontinued use among never, current, and former smokers. Int J Public Health. 2016;61(2):225–36.

Berlin I, Nalpas B, Targhetta R, Perney P. Comparison of e-cigarette use characteristics between exclusive e-cigarette users and dual e-cigarette and conventional cigarette users: an on-line survey in France. Addiction. 2019;114(12):2247–51.

Borderud SP, Li Y, Burkhalter JE, Sheffer CE, Ostroff JS. Electronic cigarette use among patients with cancer: characteristics of electronic cigarette users and their smoking cessation outcomes. Cancer. 2014;120(22):3527–35.

Canzan F, Finocchio E, Moretti F, Vincenzi S, Tchepnou-Kouaya A, Marognolli O, et al. Knowledge and use of e-cigarettes among nursing students: results from a cross-sectional survey in north-eastern Italy. BMC Public Health. 2019;19(1):976.

Chang Y, Cho S, Kim I, Khang Y-H. Socioeconomic inequalities in e-cigarette use in Korea: comparison with inequalities in conventional cigarette use using two national surveys. Int J Environ Res Public Health. 2019;16(22):4458.

Brose LS, Brown J, Hitchman SC, McNeill A. Perceived relative harm of electronic cigarettes over time and impact on subsequent use. A survey with 1-year and 2-year follow-ups. Drug Alcohol Depend. 2015;157:106–11.

Cooper M, Case KR, Loukas A. E-cigarette use among Texas youth: results from the 2014 Texas Youth Tobacco Survey. Addict Behav. 2015;50:173–7.

Lewek P, Woźniak B, Maludzińska P, Smigielski J, Kardas P. E-cigarette use and its predictors: results from an online cross-sectional survey in Poland. Tob Induc Dis. 2019;17:79.

Eastwood B, Dockrell M, Arnott D, Britton J, Cheeseman H, Jarvis M, et al. Electronic cigarette use in young people in Great Britain 2013–2014. Public Health. 2015;129(9):1150–6.

Eastwood B, East K, Brose L, Dockrell M, Arnott D, Cheeseman H, et al. Electronic cigarette use in young people in Great Britain 2015–2016. Public Health. 2017;149:45.

Farsalinos K, Siakas G, Poulas K, Voudris V, Merakou K, Barbouni A. E-cigarette use is strongly associated with recent smoking cessation: an analysis of a representative population sample in Greece. Intern Emerg Med. 2019;14(6):835–42.

Farsalinos KE, Siakas G, Poulas K, Voudris V, Merakou K, Barbouni A. Electronic cigarette use in Greece: an analysis of a representative population sample in Attica prefecture. Harm Reduct J. 2018;15(1):20.

Gomajee R, El-Khoury F, Goldberg M, Zins M, Lemogne C, Wiernik E, et al. Association between electronic cigarette use and smoking reduction in France. JAMA Intern Med. 2019;179(9):1193–200.

Goniewicz ML, Zielinska-Danch W. Electronic cigarette use among teenagers and young adults in Poland. Pediatrics. 2012;130(4):e879–85.

Hammond D, Reid JL, Cole AG, Leatherdale ST. Electronic cigarette use and smoking initiation among youth: a longitudinal cohort study. CMAJ. 2017;189(43):E1328–36.

Jankowski M, Kaleta D, Zgliczyński WS, Grudziąż-Sękowska J, Wrześniewska-Wal I, Gujski M, et al. Cigarette and E-cigarette use and smoking cessation practices among physicians in Poland. Int J Environ Res Public Health. 2019;16(19):3595.

Jiang N, Cleland CM, Wang MP, Kwong A, Lai V, Lam TH. Perceptions and use of e-cigarettes among young adults in Hong Kong. BMC Public Health. 2019;19(1):1123.

Joung MJ, Han MA, Park J, Ryu SY. Association between family and friend smoking status and adolescent smoking behavior and e-cigarette use in Korea. Int J Environ Res Public Health. 2016;13(12):1183.

Kaleta D, Wojtysiak P, Polańska K. Use of electronic cigarettes among secondary and high school students from a socially disadvantaged rural area in Poland. BMC Public Health. 2016;16(1):703.

Kenne DR, Mix D, Banks M, Fischbein R. Electronic cigarette initiation and correlates of use among never, former, and current tobacco cigarette smoking college students. J Subst Use. 2016;21(5):491–4.

Kinouani S, Pereira E, Tzourio C. Electronic cigarette use in students and its relation with tobacco-smoking: a cross-sectional analysis of the I-Share study. Int J Environ Res Public Health. 2017;14(11):1345.

Kong G, Idrisov B, Galimov A, Masagutov R, Sussman S. Electronic cigarette use among adolescents in the Russian Federation. Subst Use Misuse. 2017;52(3):332–9.

Kotz D, Böckmann M, Kastaun S. The use of tobacco, e-cigarettes, and methods to quit smoking in Germany: a representative study using 6 waves of data over 12 months (the DEBRA Study). Dtsch Arztebl Int. 2018;115(14):235.

Kristjansson AL, Mann MJ, Sigfusdottir ID. Licit and illicit substance use by adolescent e-cigarette users compared with conventional cigarette smokers, dual users, and nonusers. J Adolesc Health. 2015;57(5):562–4.

Lee JA, Kim SH, Cho H-J. Electronic cigarette use among Korean adults. Int J Public Health. 2016;61(2):151–7.

Lee S, Grana RA, Glantz SA. Electronic cigarette use among Korean adolescents: a cross-sectional study of market penetration, dual use, and relationship to quit attempts and former smoking. J Adolesc Health. 2014;54(6):684–90.

Lee Y, Lee K-S. Association of alcohol and drug use with use of electronic cigarettes and heat-not-burn tobacco products among Korean adolescents. PLoS ONE. 2019;14(7):e0220241.

Lippert AM. Temporal changes in the correlates of US adolescent electronic cigarette use and utilization in tobacco cessation, 2011 to 2013. Health Educ Behav. 2017;44(2):254–61.

Martínez-Sánchez JM, Ballbè M, Fu M, Martín-Sánchez JC, Saltó E, Gottlieb M, et al. Electronic cigarette use among adult population: a cross-sectional study in Barcelona, Spain (2013–2014). BMJ Open. 2014;4(8):e005894.

McCabe SE, West BT, Veliz P, Boyd CJ. E-cigarette use, cigarette smoking, dual use, and problem behaviors among US adolescents: results from a national survey. J Adolesc Health. 2017;61(2):155–62.

Milicic S, Leatherdale ST. The associations between e-cigarettes and binge drinking, marijuana use, and energy drinks mixed with alcohol. J Adolesc Health. 2017;60(3):320–7.

Osei AD, Mirbolouk M, Orimoloye OA, Dzaye O, Uddin SI, Benjamin EJ, et al. Association between e-cigarette use and chronic obstructive pulmonary disease by smoking status: behavioral risk factor surveillance system 2016 and 2017. Am J Prev Med. 2020;58(3):336–42.

Osei AD, Mirbolouk M, Orimoloye OA, Dzaye O, Uddin SI, Benjamin EJ, et al. Association between e-cigarette use and cardiovascular disease among never and current combustible-cigarette smokers. Am J Med. 2019;132(8):949-54. e2.

Jackson SE, Hill E, Shahab L, Beard E, Michie S, Brown J. Prevalence and correlates of long-term e-cigarette and nicotine replacement therapy use: a prospective study in England. BMJ Open. 2019;9(10):e029252.

Roys MR, Peltier MR, Stewart SA, Waters AF, Waldo KM, Copeland AL. The association between problematic alcohol use, risk perceptions, and e-cigarette use. Am J Drug Alcohol Abuse. 2020;46(2):224–31.

Rutten LJF, Blake KD, Agunwamba AA, Grana RA, Wilson PM, Ebbert JO, et al. Use of e-cigarettes among current smokers: associations among reasons for use, quit intentions, and current tobacco use. Nicotine Tob Res. 2015;17(10):1228–34.

Seo D-C, Kwon E, Lee S, Seo J. Using susceptibility measures to prospectively predict ever use of electronic cigarettes among adolescents. Prev Med. 2020;130:105896.

Sutfin EL, McCoy TP, Morrell HE, Hoeppner BB, Wolfson M. Electronic cigarette use by college students. Drug Alcohol Depend. 2013;131(3):214–21.

Wiernik E, Airagnes G, Lequy E, Gomajee R, Melchior M, Le Faou A-L, et al. Electronic cigarette use is associated with depressive symptoms among smokers and former smokers: cross-sectional and longitudinal findings from the Constances cohort. Addict Behav. 2019;90:85–91.

Wong D, Fan W. Ethnic and sex differences in e-cigarette use and relation to alcohol use in California adolescents: the California Health Interview Survey. Public Health. 2018;157:147–52.

Zhu S-H, Zhuang Y-L, Wong S, Cummins SE, Tedeschi GJ. E-cigarette use and associated changes in population smoking cessation: evidence from US current population surveys. BMJ. 2017;358:j3262.

Corsi DJ, Lippert AM. An examination of the shift in school-level clustering of US adolescent electronic cigarette use and its multilevel correlates, 2011–2013. Health Place. 2016;38:30–8.

Du Y, Liu B, Xu G, Rong S, Sun Y, Wu Y, et al. Association of electronic cigarette regulations with electronic cigarette use among adults in the United States. JAMA Netw Open. 2020;3(1):e1920255.

Okawa S, Tabuchi T, Miyashiro I. Who uses e-cigarettes and why? E-cigarette use among older adolescents and young adults in Japan: JASTIS study. J Psychoactive Drugs. 2020;52(1):37–45.

Qanash S, Alemam S, Mahdi E, Softah J, Touman AA, Alsulami A. Electronic cigarette among health science students in Saudi Arabia. Annals of Thoracic Medicine. 2019;14(1):56.

Chan CMH, Ab Rahman J, Tee GH, Wee LH, Ho BK, Robson NZMH, et al. Perception of harms and benefits of electronic cigarettes among adult Malaysian men: a comparison by electronic cigarette use and smoking status. Asia Pac J Public Health. 2019;31(7_suppl):32S-43S.

Iqbal N, Khan ZA, Anwar SMH, Irfan O, Irfan B, Mushtaq A, et al. Electronic cigarettes use and perception amongst medical students: a cross sectional survey from Sindh, Pakistan. BMC Res Notes. 2018;11(1):188.

Hirano T, Tabuchi T, Nakahara R, Kunugita N, Mochizuki-Kobayashi Y. Electronic cigarette use and smoking abstinence in Japan: a cross-sectional study of quitting methods. Int J Environ Res Public Health. 2017;14(2):202.

Miyazaki Y, Tabuchi T. Educational gradients in the use of electronic cigarettes and heat-not-burn tobacco products in Japan. PLoS ONE. 2018;13(1):e0191008.

Sharan RN, Chanu TM, Chakrabarty TK, Farsalinos K. Patterns of tobacco and e-cigarette use status in India: a cross-sectional survey of 3000 vapers in eight Indian cities. Harm Reduct J. 2020;17:1–11.

Puteh SEW, Manap RA, Hassan TM, Ahmad IS, Idris IB, Sham FM, et al. The use of e-cigarettes among university students in Malaysia. Tob Induc Dis. 2018;16:57.

Zhu J, Li J, Xu G, Yu J, Wang Q, He Y. School-type differences in e-cigarette use and its correlates among Chinese adolescents. Tob Induc Dis. 2020;18:17.

Treur JL, Rozema AD, Mathijssen JJ, van Oers H, Vink JM. E-cigarette and waterpipe use in two adolescent cohorts: cross-sectional and longitudinal associations with conventional cigarette smoking. Eur J Epidemiol. 2018;33(3):323–34.

Ho B, Haniki NM, Jamalludin A, Samsul D, Mira K, Syafinaz AN, et al. Prevalence and characteristics of e-cigarette users among Malaysian current and ex-smokers. Malays Fam Physician. 2019;14(2):10.

PubMed   PubMed Central   CAS   Google Scholar  

Wang M, Hu R-Y, Pan J, Wang H, Yu M, Xie K-X, et al. Awareness, current use of electronic cigarettes and associated smoking factors in Zhejiang Chinese adolescents. PLoS ONE. 2019;14(10):e0224033.

Xiao L, Parascandola M, Wang C, Jiang Y. Perception and current use of e-cigarettes among youth in China. Nicotine Tob Res. 2019;21(10):1401–7.

Soteriades S, Barbouni A, Rachiotis G, Grevenitou P, Mouchtouri V, Pinaka O, et al. Prevalence of Electronic cigarette use and its determinants among 13-to-15-year-old students in Greece: results from the 2013 Global Youth Tobacco Survey (GYTS). Int J Environ Res Public Health. 2020;17(5):1671.

Hammond D, Reid JL, Rynard VL, Fong GT, Cummings KM, McNeill A, et al. Prevalence of vaping and smoking among adolescents in Canada, England, and the United States: repeat national cross sectional surveys. BMJ. 2019;365:l2219.

Erku DA, Gartner CE, Tengphakwaen U, Morphett K, Steadman KJ. Nicotine vaping product use, harm perception and policy support among pharmacy customers in Brisbane, Australia. Drug Alcohol Rev. 2019;38(6):703–11.

Tolstrup JS, Pisinger VS, Egan KK, Christensen AI. Trends in smoking and smokeless tobacco use among Danish adolescents, 1997–2014. Tob Prev Cessat. 2018;4:10.

Kilibarda B, Krstev S, Milovanovic M, Foley K. E-cigarette use in Serbia: Prevalence, reasons for trying and perceptions. Addict Behav. 2019;91:61.

Chen X, Yu B, Wang Y. Initiation of electronic cigarette use by age among youth in the US. Am J Prev Med. 2017;53(3):396–9.

Dutra LM, Glantz SA. Electronic cigarettes and conventional cigarette use among US adolescents: a cross-sectional study. JAMA Pediatr. 2014;168(7):610–7.

Farsalinos KE, Polosa R, Cibella F, Niaura R. Is e-cigarette use associated with coronary heart disease and myocardial infarction? Insights from the 2016 and 2017 National Health Interview Surveys. Ther Adv Chronic Dis. 2019;10:2040622319877741.

Piñeiro B, Correa JB, Simmons VN, Harrell PT, Menzie NS, Unrod M, et al. Gender differences in use and expectancies of e-cigarettes: online survey results. Addict Behav. 2016;52:91–7.

Ashford K, Wiggins A, Butler K, Ickes M, Rayens MK, Hahn E. E-cigarette use and perceived harm among women of childbearing age who reported tobacco use during the past year. Nurs Res. 2016;65(5):408–14.

Bozier J, Chivers EK, Chapman DG, Larcombe AN, Bastian NA, Masso-Silva JA, et al. The evolving landscape of e-cigarettes: a systematic review of recent evidence. Chest. 2020;157(5):1362–90.

Smith DM, Gawron M, Balwicki L, Sobczak A, Matynia M, Goniewicz ML. Exclusive versus dual use of tobacco and electronic cigarettes among adolescents in Poland, 2010–2016. Addict Behav. 2019;90:341–8.

Siddiqui F, Mishu M, Marshall A-M, Siddiqi K. E-cigarette use and subsequent smoking in adolescents and young adults: a perspective. Expert Rev Respir Med. 2019;13(5):403–5.

Cullen KA, Ambrose BK, Gentzke AS, Apelberg BJ, Jamal A, King BA. Notes from the field: use of electronic cigarettes and any tobacco product among middle and high school students—United States, 2011–2018. Morb Mortal Wkly Rep. 2018;67(45):1276.

Park S, Lee H, Min S. Factors associated with electronic cigarette use among current cigarette-smoking adolescents in the Republic of Korea. Addict Behav. 2017;69:22–6.

Czoli CD, Hammond D, Reid JL, Cole AG, Leatherdale ST. Use of conventional and alternative tobacco and nicotine products among a sample of Canadian youth. J Adolesc Health. 2015;57(1):123–5.

Cui Y, Forget EL, Zhu Y, Torabi M, Oguzoglu U. The effects of cigarette price and the amount of pocket money on youth smoking initiation and intensity in Canada. Can J Public Health. 2019;110(1):93–102.

Kasza KA, Ambrose BK, Conway KP, Borek N, Taylor K, Goniewicz ML, et al. Tobacco-product use by adults and youths in the United States in 2013 and 2014. N Engl J Med. 2017;376(4):342–53.

Zavala-Arciniega L, Reynales-Shigematsu LM, Lozano P, Rodríguez-Andrade MÁ, Arillo-Santillán E, Thrasher JF. Patterns of awareness and use of electronic cigarettes in Mexico, a middle-income country that bans them: Results from a 2016 national survey. Prev Med. 2018;116:211–8.

Chan G, Leung J, Gartner C, Yong H-H, Borland R, Hall W. Correlates of electronic cigarette use in the general population and among smokers in Australia-findings from a nationally representative survey. Addict Behav. 2019;95:6–10.

Filippidis FT, Laverty AA, Gerovasili V, Vardavas CI. Two-year trends and predictors of e-cigarette use in 27 European Union member states. Tob Control. 2017;26(1):98–104.

Download references

Acknowledgements

Social Development and Health Promotion Research Center funded this project.

Author information

Hadi Tehrani and Abdolhalim Rajabi contributed equally as first author.

Authors and Affiliations

Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Hadi Tehrani

Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran

Environmental Health Research Center, Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medical Sciences, Gorgan, Iran

Abdolhalim Rajabi

Metabolic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran

Mousa Ghelichi- Ghojogh

Social Determinants of Health Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

Mahbobeh Nejatian

Department of Health Education and Health Promotion, School of Health, Social Development and Health Promotion Research Center, Gonabad University of Medical Sciences, Gonabad, Iran

Alireza Jafari

You can also search for this author in PubMed   Google Scholar

Contributions

AJ and HT conceptualized the study and led the project and writing. All authors contributed to the development of the coding scheme. AJ, MGh and AR conducted the coding and analyses and drafted the methods. AR, MN, AJ and HT reviewed the codes and results. All authors contributed to the writing and revision and approved the final version of the manuscript.

Corresponding author

Correspondence to Alireza Jafari .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

Search Strategy.

Additional file 2: Table S1.

Population characteristics of the studies reported the lifetime and current prevalence of electronic cigarette (e-cigarettes) vaping among women and men.

Additional file 3: Table S2.

Qualities of studies included in the systematic review and meta-analysis.

Additional file 4: Fig S1.

Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 5: Fig S2.

Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in men.

Additional file 6: Fig S3.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping in all subjects.

Additional file 7: Fig S4.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes smoking among women.

Additional file 8: Fig S5.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping among men.

Additional file 9: Fig S6.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by study population.

Additional file 10: Fig S7.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by continent.

Additional file 11: Fig S8.

Cumulative meta-analysis of current prevalence of e-cigarettes vaping in ex-smokers and current smokers. Pooled lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 12: Fig S9.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in women.

Additional file 13: Fig S10.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in men.

Additional file 14: Fig S11.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in women in every continent.

Additional file 15: Fig S12.

Cumulative meta-analysis of lifetime and current prevalence of e-cigarettes vaping by subgroups in men in every continent.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Tehrani, H., Rajabi, A., Ghelichi- Ghojogh, M. et al. The prevalence of electronic cigarettes vaping globally: a systematic review and meta-analysis. Arch Public Health 80 , 240 (2022). https://doi.org/10.1186/s13690-022-00998-w

Download citation

Received : 11 April 2022

Accepted : 07 November 2022

Published : 21 November 2022

DOI : https://doi.org/10.1186/s13690-022-00998-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Electronic cigarette
• Electronic nicotine

Archives of Public Health

ISSN: 2049-3258

• General enquiries: [email protected]

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 May 2024

Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions

• Akihiro Kishimoto 1 ,
• Dan Wu 2 &
• Donal F. O’Shea 2  

Scientific Reports volume  14 , Article number:  9591 ( 2024 ) Cite this article

9103 Accesses

1444 Altmetric

Metrics details

• Cheminformatics
• Public health

Vaping involves the heating of chemical solutions (e-liquids) to high temperatures prior to lung inhalation. A risk exists that these chemicals undergo thermal decomposition to new chemical entities, the composition and health implications of which are largely unknown. To address this concern, a graph-convolutional neural network (NN) model was used to predict pyrolysis reactivity of 180 e-liquid chemical flavours. The output of this supervised machine learning approach was a dataset of probability ranked pyrolysis transformations and their associated 7307 products. To refine this dataset, the molecular weight of each NN predicted product was automatically correlated with experimental mass spectrometry (MS) fragmentation data for each flavour chemical. This blending of deep learning methods with experimental MS data identified 1169 molecular weight matches that prioritized these compounds for further analysis. The average number of discrete matches per flavour between NN predictions and MS fragmentation was 6.4 with 92.8% of flavours having at least one match. Globally harmonized system classifications for NN/MS matches were extracted from PubChem, revealing that 127 acute toxic, 153 health hazard and 225 irritant classifications were predicted. This approach may reveal the longer-term health risks of vaping in advance of clinical diseases emerging in the general population.

Similar content being viewed by others

Augmenting large language models with chemistry tools

Predicting equilibrium distributions for molecular systems with deep learning

Physics-informed machine learning

Introduction.

The delivery of nicotine to the lungs through the inhalation of tobacco smoke has been practiced by mankind for centuries with devastating impacts on public health 1 . Relatively recently, vaping of e-liquids has emerged as a modern variant of this ancient practice. In their original construction, the constituents of e-liquids contained only four chemical entities, nicotine, propane-1,2-diol, propane-1,2,3-triol and water, with the goal of providing a less hazardous means of nicotine delivery than tobacco leaf 2 . Their use as an aid for tobacco smoking cessation has evolved as a cornerstone of some national public health policies, though others have restricted or prohibited their use 3 .

Soon after their first commercialization in the mid 2000’s, the number of chemical entities used in vaping e-liquids dramatically increased as an array of flavours were added. Currently, at least 180 discrete chemicals are known to be in use in e-liquids, blended in various amounts to produce a specific flavour branded product 4 . A European based study identified a mean and range of 6 (±) 4 chemical flavours used per specific e-liquid product, whereas a comparable US study found a range of 22 to 47 chemical flavours per e-liquid 5 , 6 . In both studies it was found that the total flavour chemical concentration in the majority of e-liquids exceeded that of nicotine. Furthermore, several studies have shown that flavoured e-liquids are linked to a lowering of the vaping age demographic 7 . Their appeal to non-smoking teenagers and young adults has led to a divergence of opinion on their use as an aid for tobacco smoking cessation in the established smoking population 8 . Concerns are growing that vaping in younger generations jeopardizes the decline in nicotine use and also risks the emergence of future vaping induced diseases 9 . Yet, while the strongly polarized debates about the pros and cons of vaping are ongoing, the implications for long-term effects on public health, morbidity and mortality are simply unknown 10 , 11 , 12 , 13 . While the health risks from exposure to the carcinogenic chemicals in tobacco smoke are known, it can take decades of accumulative damage before clinical manifestation of disease occurs.

Intuitively for vaping, it would be reasonable to anticipate that lung exposure to a large number of chemical entities can only increase health risks. In 2019, the potential for vaping health risks became apparent when cases of acute lung injury emerged attributable to tetrahydrocannabinol vaping products. E-cigarette or vaping use-associated lung injury (EVALI) statistics from the CDC document 2807 hospitalizations and 68 deaths over one year in the United States. A single chemical additive, vitamin E acetate (VEA), has been strongly linked to the outbreak that ended once its use stopped 14 . Our previous research showed that the action of pyrolysis heating within a vaping device could transform VEA into more than ten different substances including the highly toxic gas ketene which could account for the severe lung injuries 15 . While the chemicals used for nicotine vaping are different from tetrahydrocannabinol products 16 , 17 , the number of chemicals is considerably higher. Prolonged exposure to these chemicals and their pyrolysis products makes it plausible that we are standing at the starting line of a new wave of chronic diseases that will only emerge in 15 to 20 years from now.

The chemicals used as e-liquid flavours are not specifically developed for vaping and are adopted from the food industry 18 . Much like VEA, which is also widely used in foodstuffs and cosmetics, these compounds have a good safety record for these specific uses. However, it was not envisaged that they would be used in a significantly different manner that involves heating to high temperatures with inhalation into the lungs. Remarkably, there are a myriad of different vaping devices whose operating temperature ranges are often unknowingly determined by user preferences. Studies have measured typical temperatures ranging from 100 to 400 °C depending upon factors such as power, heating coil materials, puff size and e-liquid quantity, with dry coil temperature measured above 1000 °C 19 , 20 . Pyrolysis decomposition of flavours at these temperatures could produce large numbers of unknown secondary chemical entities, thereby hugely amplifying the health risks from each flavour. In contrast to tobacco smoking, combustion products are minor in vaping so were not included in this initial study.

As hundreds of chemicals are used in tens of thousands of commercial e-liquid products, the experimental analysis of all their vaping induced chemistries and associated products could take decades of research. In this study, a holistic research strategy employing artificial intelligence (AI) was adopted to simultaneously investigate all flavours in e-liquids. AI is increasingly being used to perform chemistry tasks such as retrosynthetic route planning, the prediction of reaction outcomes and the acceleration of drug discovery 21 , 22 , 23 . Currently, a unique opportunity exists to exploit AI to anticipate vaping risks in advance of their public health impact, which may take years to emerge.

Overview of 180 e-liquid flavour chemicals

While the exact number of flavour chemicals in current worldwide e-liquid use is unclear, 180 representative chemicals known to be used as flavourings in e-liquids were chosen for this study based on literature reports 4 , 5 , 6 , 18 . Structural inspection of the chemical functional groups within the 180 flavours revealed 66 esters, 46 ketones/aldehydes, 27 alcohols/acetals, 26 aromatics/heterocycles/carbocycles and 15 carboxylic acids/amides, clearly indicating the potential for a wide range of pyrolysis reactions (Supplementary Table S1 ). Flavour structural diversity was analyzed by comparing their molecular weight, hydrogen bond donors/acceptors, topological polar surface area, number of rotatable bonds, and octanol–water partition coefficient properties 24 . A 3D visualization of the chemical space reflecting these six properties shows that compounds are clustered in a similar area, indicating moderate diversity with 85.5% of the variance accounted for by molecular weight, surface area and number of rotatable bonds (Fig.  1 A, red circles, Supplementary Dataset S1 ). Their mean molecular weight was 146.2 signifying a relative volatile set of molecules (Fig.  1 B, red distribution profile).

Chemistry diversity analysis of 180 flavour chemicals and their predicted pyrolysis products. ( A ) 3D representation of the chemical space occupied by 180 e-liquid compounds (red circles) and their discrete 4524 NN predicted pyrolysis products (grey circles). Principal component (PC) scale refers to normalized projections of the six molecular properties. ( B ) Molecular weight distribution of 180 e-liquid compounds (red distribution profile) and their discrete 4524 NN predicted pyrolysis products (grey distribution profile).

Workflow for e-liquid flavour risk identification and classification

All 180 flavours were subjected to a common workflow that blended NN pyrolysis prediction with experimental electron-impact mass spectrometry (EI-MS) data. An overview of each stage is shown in Fig.  2 , which started with transcribing the 180 chemical structures into their simplified molecular-input line-entry system (SMILES) format. A graph-convolutional neural network model was used to predict pyrolysis chemical transformations and their associated products for each flavour. Experimental MS data containing the molecular ion, associated fragmentation masses and their relative abundances were sourced for each flavour. As both pyrolysis reactions and MS fragmentations involved energy induced bond breaking; a correlation between both was anticipated. Using specifically written script, the molecular weight of each NN predicted product from each flavour was correlated against the MS fragmentation masses for that flavour. A data subset was formed containing NN-reactions with a predicted product which matched a MS fragmentation mass. Next, the GHS classification was identified for each NN/MS matched product. A second NN was used to predict activation energies (AE) for reactions producing products with the most significant health implications. Data collation generated an enumerated list of NN predicted products, MS matched products and their associated GHS hazard classification for each of the 180 flavours (Fig.  2 ). Each step was automated and could accept new compound inputs as required.

Workflow chart for the pyrolysis risk identification of vaping e-liquid components (solid arrows). Dashed arrows indicate future scope for an informative feedback into NN pyrolysis predictor.

Graph-convolutional neural network model for pyrolysis products prediction

To date, reaction prediction methods have primarily focused on synthetic transformations in which at least two reactants generate a product and a byproduct under varying experimental conditions 25 , 26 , 27 . Pyrolysis reactions differ in that a single reactant produces an array of lower molecular weight products by different transformation pathways with heat being the driving force of the reactions (Fig.  3 ).

Synthetic and pyrolysis transformations.

It was found that the previously described Weisfeiler–Lehman neural network (W–L NN) model suited our requirements as it operates by prediction of reaction centers based on bond changes for every pair of atoms in a molecule 26 . As a graph convolutional network, it can predict unimolecular pyrolysis transformations without any training data specific to pyrolysis reactions. Supervised learning of the W–L NN was achieved using US patent literature as a source of data, with pyrolysis predictions based on a training set of 354,937 reactions 26 , 28 , 29 . For this study, only first phase pyrolysis products were considered with further pyrolysis of initial pyrolysis products not included. All reactions that included flavour molecules were removed from the training data to ensure that no characteristics of these flavour molecules were leaked before their pyrolysis predictions were performed. Prevention of such data leakage allows the performance assessment of the trained W–L NN model without bias, even if a new flavour molecule is passed to the trained model. As designed, the W–L NN architecture embedded the inherent computations in the W–L graph kernel to learn atomic representations. This starts by converting chemical SMILES (notation to describe a chemical structure that can be understood by computer software) to attributed graph representations of molecules. For example, the SMILES for flavour 2,3-pentanedione being CCC(=O)C(=O)C converts to a labelled form of atoms 1 to 7 as shown in Fig.  4 A,B. Each atom representation was computed by including contributions from adjacent atoms such as atom 3 with atom 2, 4, and 5. Specifically, each atom was initialized with a feature vector f atom indicating its key properties such as atomic number, connectivity, valence, formal charge, and aromaticity. Representation of the bond order (number of chemical bonds between a pair of atoms) and connectivity of each bond was through the feature vectors f bond (Fig.  4 C). Local feature vectors were calculated for each atom based on its representation and those of other atoms directly bonded to it. Next, global atom features were produced for each atom to account for the influence of atoms not directly bonded to it. Finally, a combination of local and global feature vectors was used to predict the likelihood of bond changes for each pair of atoms (Fig.  4 D).

W–L neural network for predicting bond changes between every pair of atoms in 2,3-pentandione. (i) Molecular SMILES converted to attributed graph. (ii) Atom descriptors generated by incorporating information from neighboring atoms. (iii) Updated new atom features after iterations, calculation of atom local and global features vector and final prediction of reactivity for each pair of atoms. (iv) Calculated scores for each likelihood bond change by W–L neural network. (v) Potential products enumerated after removal of those failing chemical valence rules. (vi): W–L difference network model for ranking pyrolysis reactions enumerated based on the most probable bond changes. (vii) Predicted pyrolysis reactions ranked from 1 to 25 and their associated products (P).

In the representative input example of 2,3-pentandione, all atom pairs were “tested” to identify high probability bond breaking positions (Fig.  4 E). Up to 16 likely bond-breaking positions were identified to enumerate their possible pyrolysis transformations and associate output products. Up to five simultaneous chemically feasible bond changes per pyrolysis reaction were allowed. Any predicted products that did not comply with chemical valence rules (correct number of bonds from each atom) were removed (Fig.  4 F). Next, a W–L difference network (W–L DN) generated a probability score for each predicted pyrolysis transformation based on the differences in atom representations between the products and the original molecule (Fig.  4 G). The W–L DN then selected and ranked the twenty-five most likely transformations based on their probability scores (Fig.  4 H). Analysis of the NN output of 4500 pyrolysis predictions for the 180 flavours showed 7307 products (Supplementary Dataset S2 ). When duplicate products from the same flavour are not included, the total number was 4524. The average number of discrete products per flavour was 25.1 with a greater number predicted for compounds of larger molecular size and complexity. The top 20 predicted pyrolysis products (excluding duplicates arising from the same flavour) included alkanes, alkene, alcohols, aldehydes, acids, and aromatics, as shown in Table 1 .

Structural diversity of the 4524 predicted products was determined using the same molecular parameters used for the 180 flavours (23). The 3D chemical space visualization showed the NN-predicted pyrolysis products clustered in a similar space as their originating flavours (Fig.  1 A, grey circles, Supplementary Dataset S1 ). The expected difference was a significant shift to lower molecular weight compounds, with a mean molecular weight of 111.7 indicating the production of highly volatile organic compounds (Fig.  1 B, grey distribution profile).

Sourcing experimental EI-MS data for each e-liquid flavour

Mass spectrometry fragmentation identifies intramolecular bond breaking positions that occur as a result of molecular interaction with the applied energy from the instrument source. As pyrolysis is a heat induced bond breaking process, a correlation between both can exist 30 . Experimental EI-MS mass data was retrieved, using Python script, from the online National Institute of Standards and Technology (NIST) database for each of the 180 e-liquid flavours 31 , 32 . Data obtained included the molecular weight of all fragmentations from the parent ion and their relative abundance. Representative flavour examples for 2,3-pentandione, linalool, 2-acetyl pyridine and α-methylbenzyl acetate in Fig.  5 , show their MS fragmentation patterns and corresponding molecular weights. Specifically for 2,3-pentandione, the series of fragmentation masses (% relative abundance) of 100 (11), 57 (32), 43 (100), 42 (12), 29 (60), 27 (25) and 15 (14) can be seen which correspond to the molecular ion and the most likely bond breaking positions of the molecule (Fig.  5 ). In this way, the MS fragmentation data for each e-liquid component can act as a minable dataset to identify molecular weight alignments with the products from the NN predicted pyrolysis reactions. A 5% relative abundance threshold for each mass peak was applied to the MS data to eliminate the possibility of instrument noise or isotope contributions. The average number of mass fragmentation peaks per e-liquid component was 16.5 with the maximum at 54 and the minimum at 2. As expected, larger molecular weight compounds typically have more fragmentation mass peaks than those of lower weight.

Representative flavour EI-MS data of ( A ) 2,3-pentandione; ( B ) linalool; ( C ) 2-acetyl pyridine; ( D ) α-methylbenzyl acetate from the NIST database. Threshold (T) set at 5% relative abundance indicated by blue dotted line. Green asterisk indicates the molecular weight matches with W–L NN predicted products. Insets show structures of NN-predicted products that are molecular weight matched with an MS fragmentation.

Amalgamation of W–L NN and EI-MS data

Next, automated amalgamation of in silico NN with experimental MS data was carried out 32 . The goal of merging these two information sources was to identify the most likely pyrolysis products for each flavour, from which their health risks could be assigned. Correlation of molecular weights of NN predicted products with their experimental MS fragmentation masses identified 1169 discrete matches between the two datasets (not counting repeat matches for a flavour) (Supplementary Dataset S2 ). The average number of NN/MS matches per flavour was 6.4 with 92.8% having at least one match and 86% having more than one match. Examples of specific NN/MS matches are shown in Fig.  5 for four structurally different flavours. Green asterisks indicate the molecular weight matches in the mass spectral data with W–L NN predicted products and the insets show structures of the matched compounds. It is noteworthy that this data amalgamation was successful for a wide variety of different molecular structures and functional groups.

Encouragingly, plotting the number of MS matches against the NN rank position for each predicted product shows a clear bias towards higher rank positions, with the highest NN-rank 1 accounting for 8.7% of all matches (Fig.  6 , rank 1). Comparison of the cumulative number of matches for the top (1–5) and bottom (21–25) rank positions show that the higher positions accounted for 29% of all matches whereas the lower ranks accounted for only 15% (Fig.  6 ). These correlations indicate that NN predicted products could be substantiated through experimental MS fragmentations and that in future work MS data could be used in a hybrid supervised and reinforcement learning model. The non-matched W–L NN predicted products were not used further in this work but may serve as an informative feedback allowing future refinement of the NN pyrolysis predictor (Fig.  2 , dashed arrow).

Comparative plot of W–L NN rank position from 1 to 25 for predicted products matched with experimental EI-MS data.

Examining the most commonly matched compounds it was encouraging to find that a broad distribution of molecular classes was matched (saturated, unsaturated and aromatic hydrocarbons, aliphatic alcohols and carboxylic acids) (Table 2 ). Seventeen of the top twenty W–L NN predicted products (Table 1 ) were also in the top 20 matched compounds, further increasing confidence in the NN/MS matched predictions. Next, the health risk of each NN/MS matched product was identified.

Acquisition of risk assessment data for W–L NN and EI-MS matched products

Using specifically written Python script, the GHS classifications for each NN/MS matched product was obtained from the open-source PubChem database 32 , 33 . The script used the SMILES string of each compound as a query keyword to identify matching URLs within the site. Within each URL, the hazard statements in the GHS classification section were downloaded in JSON format. Three different classification categories were used to build each flavour risk profile (i) acute toxic; (ii) health hazard; or (iii) irritant. In addition, a category (iv) was used to group compounds not classified as either (i), (ii) or (iii) but that may have other hazard warnings, and category (v) was compounds for which a search query did not produce a result, indicating they were not in the database (Supplementary Dataset S3 ). The GHS classifications of acute toxic (127 compounds), health hazard (153 compounds) and irritant (225 compounds) accounted for 11%, 13% and 19% of classifications attributed to the dataset respectively (Fig.  7 A). Only 49% of products were not included in the categories (i) to (iii) and 8% had no classification information available. Mining classification data specifically for inhalation health hazards revealed further insights into their health risks with a representative selection of these results for a structurally diverse set of the functional compounds shown in Fig.  7 B (Supplementary Dataset S3 ). It is noteworthy that while some similarities to compounds produced by tobacco smoke exist (e.g. formaldehyde, ethylene oxide, aromatic amines), many others differ such as α,β-unsaturated carbonyl compounds (aldehydes, ketones, esters), heterocycles and phenols. This is due to the diverse chemical makeup of the individual vaping flavours, which differ from the natural products found within tobacco leaf. This indicates that, while related, vaping biomarkers and their clinical disease manifestations could differ significantly from those of tobacco smoking 34 . Furthermore, vaping biomarkers are likely to differ based on commercial e-liquid product, as the spectrum of pyrolysis products differ for each chemical flavour 35 .

( A ) Distribution of globally harmonized system classifications of W–L NN/MS matched products (for compounds with more than one classification only the most serious classification is included). ( B ) Representative examples of the structure for W–L NN/MS predicted compounds with acute toxic GHS classification and their specific inhalation hazard warning. GHS hazard statements: H330 fatal if inhaled; H331 toxic if inhaled; H335 may cause respiratory irritation; H340 may cause genetic defects; H341 suspected of causing genetic defects; H350 may cause cancer; H373 causes damage to organs through prolonged or repeated exposure. ( C ) Distribution of Cramer classifications of W–L NN/MS matched products.

Additionally, NN/MS matched products were grouped using the three Cramer classes (Supplementary Dataset S4 ) 36 . Cramer classification is a commonly used predictive approach for classifying chemicals on the basis of their expected level of oral toxicity. Cramer Class III, which represents the most severe potential toxic hazard, accounted for 35% of these compounds with Class II (moderate risk) and Class I (low risk) accounting for 8 and 57% respectively (Fig.  7 C). It was noteworthy that many of the more commonly used flavour chemicals (e.g. cis -3-hexenol, isoamyl acetate, benzaldehyde, ethyl hexanoate, cinnamaldehyde, benzyl acetate, hexyl acetate) had one or more predicted products identified as Class III. Across all 180 flavours, the most common Cramer III classifications were for benzene, ketene and ethylene oxide predicted by 16, 10 and 8 different flavours respectively (Supplementary Dataset S4 ).

W–L NN prediction of pyrolysis activation energies

The activation energy (AE) of a chemical reaction is the minimum energy required for a reaction to proceed. With respect to vaping, AEs are an excellent means of obtaining a first approximation of the thermal conditions required for pyrolysis to occur. Yet, determination of AEs is experimentally very laborious and computationally expensive, as it requires quantum chemical calculations. As such, the use of NN methods to obtain quantitative values for flavour pyrolysis reactions would be of significant value. To address this goal, a recently reported directed message passing neural network (D-MPNN) for AE predictions has been employed 37 , 38 . D-MPNN is a graph convolutional neural network, similar to that used for pyrolysis predictions described earlier, though it should be noted that other NN methods have been employed for AE predictions 39 , 40 . The training data used consisted of published gas phase energy activation data of 16,264 transformations determined by quantum chemistry calculations using B97-D3/def2-mSVP theory following the exclusion of flavour compounds to prevent data leakage of the test set 38 . AEs for 482 NN predicted reactions were determined. The reactions were chosen to reflect different transformation types and reactions that generated products classified as high health risk were prioritized (Supplementary Dataset S5 ). The outcome gave a wide range of AE values from 45 to 121 kcal/mol indicating that comparisons could be made between different degradation pathways for each flavour.

Fruit flavoured products are the most popular commercial brands for the younger vaping demographic so warrant particular attention. These compounds commonly have an ester functional group which are known to undergo thermal decomposition by different elimination and free radical β-scission reactions, both of which are plausible under vaping conditions 41 , 42 . To illustrate use of AE values, ten acetate esters with substituent containing β-hydrogens were selected for comparative data analysis (Fig.  8 ). Previously reported experimental and computational studies have mostly focused on the simplest derivatives such as ethyl acetate with others as of yet unstudied 42 , 43 , 44 , 45 . These results show that three different elimination pathways are possible to generate either acetic acid and substituted alkenes (pathway A); ketene with substituted alcohols (pathway B); or C–O cleavage resulting in the formation of two carbonyls (pathway C) 42 . Analysis of the NN predicted reactions for each of these acetates showed that these transformation pathways were common to all. Comparison of the D-MPNN derived AE values for these reactions showed that pathway A consistently predicted the lowest energy requirement for most of the acetates (Fig.  8 , table). The identification of pathway A as most favorable is consistent with literature reports and thus identifies inhalation of acetic acid and substituted alkenes as the likely health hazards 46 . It is noteworthy that of the ten different alkenes producible via AE favored pathway A, eight are GHS classified as either irritant or health hazard (ethene, hexene, 1,3-hexadiene, 2-methylpropene, 3-methyl-1-butene, 2-methyl-1-butene, 3,7-dimethylocta-1,6-diene, styrene). Additionally, it is important to recognize that due to the complex reacting conditions within a vaping device, pyrolysis would not be expected to follow a single pathway 47 . In the case of acetates, products could also occur via free radical β-scission reactions as vaping conditions have been shown to promote radical type reactions 48 , 49 .

D-MPNN derived activation energies applied to three different NN predicted pyrolysis pathways of acetate fruit flavours (ethyl acetate, butyl acetate, amyl acetate, hexyl acetate, cis -3-hexenyl acetate, isobutyl acetate, isoamyl acetate, 2-methylbutyl acetate, citronellyl acetate, 2-phenylethyl acetate). a kcal/mol, # not predicted by NN.

While these AE values are, as yet, a first approximation, taking no account of the conditions under which reactions are taking place, their importance will grow as the accuracy in predicting these values improves. It could be envisaged that they play a future role in reinforcement learning models in conjunction with MS fragmentation data (Fig.  2 , dashed arrows).

E-Liquid flavour reports

Collation of all data generated an output for each of the 180 flavours with an enumerated list of NN predicted reactions and their associated products, EI-MS matched products identified and their associated GHS hazard classifications (Supplementary Datasets S2 , S3 ). Taken together, these constitute a minable reference source that encompasses the complex and interconnected facets of vaping with the potential to be refined and adapted in the future.

The e-liquid marketplace is vast and growing, driven by increased investment by tobacco companies into vaping products 50 . The original source of flavours in e-liquids stems from food flavouring compounds so it could be anticipated that the number of compounds being used will increase over time 4 , 5 , 6 . Since their inception, an incorrect assumption has grown that the flavour ingredients used in e-liquids are designated “generally recognized as safe” (GRAS) under health regulations. However, this GRAS status only relates to human consumption via ingestion (compatible with their use in foodstuffs) and not inhalation following thermal activation 18 . While the health concerns for lung exposure to the flavours themselves are serious, what is even more concerning is the array of thermal degradation products which they generate as a consequence of their heating immediately prior to inhalation 51 . The vast majority of these degradation products remain unknown as do their health consequences from long-term exposure. From a public health perspective, the use of flavours in e-liquids can be viewed as a double-edged sword. The role for vaping flavours is cited as a support for smoking cessation for those already addicted to nicotine tobacco products, but the same flavours are the main attractant for a non-smoking younger demographic 8 , 52 .

Experimental research into the heat-induced breakdown of organic compounds has its origins in the early twentieth century with the seminal work of Hurd and others 53 . Such research was conducted to gain fundamental scientific insights into the nature of chemical bond dissociations and formations. Vaping devices can be considered as crude versions of a laboratory pyrolysis apparatus 54 . Both are designed to rapidly heat organic molecules to high temperatures, although when using an experimental apparatus the products are safely trapped, quantified and characterized whereas in vaping they are drawn into the lungs. A laboratory pyrolysis apparatus has rigorous control over temperature, is made from materials to limit radical formation and is used to study test molecules individually. In contrast, a vaping device has poor temperature control, is constructed using metal materials that induce radical reactions and simultaneously heats an array of chemical entities in an e-liquid. By its nature, experimental pyrolysis chemistry is highly complex, but within vaping this complexity is magnified due to e-liquid, device and user variabilities making it a daunting task to map all possible chemical outcomes from a vaping “experiment”.

To date, experimental studies on the thermal decomposition products from vaping flavours have focused on detecting and quantifying volatile carbonyls (VC) as they have known negative health implications 55 , 56 , 57 , 58 , 59 , 60 . Several research teams have conclusively shown that VCs such as formaldehyde, acetalaldehyde and propanaldehyde are produced in concerning quantities as a result of the vaping decomposition of flavours. The quantity of aldehydes produced is proportional to the specific commercial brands, flavour and nicotine quantity in the e-liquid, the vaping device power, and users’ puff topography 61 , 62 , 63 , 64 . Establishing which flavours produced which VCs is challenging, as the e-liquids tested were comprised of mixtures of several flavour chemicals, propane-1,2-diol, propane-1,2,3-triol (which also produce aldehydes) and nicotine. Mining our dataset allows mapping of VC producers back to specific flavour chemicals while also identifying the other chemicals co-produced with these VCs. For example, the results for acetaldehyde revealed over forty flavours as having the potential to produce it with co-products including heterocycles, aromatics, aldehydes, alkenes and alkanes (Fig.  9 , Supplementary Dataset S6 ). Sources were mostly fruit, candy and dessert flavoured products containing ester, ketone, di-ketone, aldehyde and carboxylic acid functional groups. Cross-referencing this list with the most commonly used fruit and candy flavours 4 , 5 , 6 implicates ethyl acetate, ethyl butyrate, ethyl 2-methylbutryate and 2,3-pentanedione as the more common sources of pyrolysis produced acetaldehyde. The rapid identification of chemicals of concern is an advantageous feature of this dataset which could be of assistance in focusing experimental work to confirm their generation which could in turn inform regulatory agencies.

Map showing structural classes of e-liquid flavour chemicals identified as having the potential to produce acetaldehyde (blue box) with a representative selection of co-products (red circle) and the names of the chemical flavours from which they could be produced (see Supplementary Dataset S6 ). *For ethyl esters the prediction of CH 3 CH 2 O˙ (blue box) indicates the first step in formation of acetaldehyde 47 .

Reflecting on the limitations of this study, it is important to identify areas that warrant further development. In this initial iteration of our AI-vape forecast, only the first phase of pyrolysis products has been explored. It would be expected that some pyrolysis products would themselves undergo further pyrolysis reactions and that intermolecular reactions between pyrolysis products could occur 65 . The framework we have put in place lends itself to building a second layer of prediction using the predicted products described in this work as new starting points. Generation of combustion products has not been included in this study as they are considered minor to pyrolysis products, though ambient oxygen reactions could be investigated by inclusion of O 2 as a reagent in the NN predictions 66 . The cross correlation of predicted pyrolysis products with experimental MS fragmentation data does not distinguish between different structural isomers with the same molecular weights, though in the future more elaborate MS experiments may do so if needed. A limitation of the NN-predictions is the current size of the training sets, though it is anticipated that these will continue to grow in the near future. Additionally, improvements in NN ranking of reaction predictions could be guided by predicted AEs acting as an informative feedback loop (Fig.  2 , dashed arrows). A merit of the scientific approach adopted in this work is its openness to continual refinement as chemistry related NNs evolve and that it can serve as a benchmark for other AI methods to achieve similar aims. We hope that this work motivates further research in these areas.

The aerosols produced by e-cigarette vaping contain immensely complex uncharacterized mixtures of pyrolysis products, the health implications of which are, as yet, mostly unidentified. In advance of health effects of vaping becoming apparent in the general population, AI can be exploited to give guidance to the public, policy makers and health care professionals. It was envisaged that this could be achieved through a strategy that utilizes a combination of innovative NN prediction of pyrolysis transformations and freely accessible experimental EI-MS data to construct an in silico dataset of pyrolysis products from e-liquid chemical flavours. Screening of predicted pyrolysis products against databases of chemical hazard classifications identified those of highest health risk, allowing individual flavour risk profiles to be constructed. Results show that relatively low molecular weight volatile compounds can be produced of which 24% are categorized as either acute toxic or health hazard using the GHS classification system. Collated flavour risk reports may act as an informative public health resource and assist experimental vaping research. Results show that while similarities do exist with conventional tobacco smoking, a significantly different profile of hazardous compounds emerges from vaping. As such, using tobacco smoking as the sole comparison for gauging vaping health risks is likely to give a false sense of security, especially for younger non-tobacco smokers. Regulations could be employed such that attempts to remedy nicotine addictions of older tobacco smokers does not risk the transferal of new health issues to younger generations. A protective balance needs to be struck for both cohorts rather than pitching one against the other. AI methods appear ideally suited to address the complex and multifaceted health concerns that vaping raises. As vaping is a new and unprecedented stress to the human body, with the ability to generate pyrolysis products more toxic that their parent compounds, it seems prudent to strictly limit the number of chemical entities in e-liquids.

Diversity analysis of flavours and WL-NN predicted pyrolysis products

Chemical diversity analysis was carried out using PUMA 1.0 ( http://132.248.103.152:3838/PUMA/ ) 24 . The SMILES structure of 180 flavours and 4524 NN predicted products (duplicate predicted products from the same flavour removed) were used as inputs. This platform computed six molecular properties of pharmaceutical relevance including molecular weight (MW), hydrogen bond donors (nHBDon), hydrogen bond acceptors (nHBAcc), topological polar surface area (TopoPSA), number of rotatable bonds (nRotB), and the octanol–water partition coefficient (ALogP). Then six principal components (PCs) were computed based on these molecular properties. The 3D representation of the chemical space was plotted by using Veusz 3.3.1 software using the three PCs that contributed the most proportion of the variance.

WL–NN pyrolysis reaction predictions

Supervised learning of Weisfeiler–Lehman network was achieved utilizing the US patent literature as a source of data. The starting dataset which consists of 409,035 reactions is available at: https://github.com/connorcoley/rexgen_direct/tree/master/rexgen_direct/data/ 26 . All reactions within the training data that included a flavour molecule were removed to prevent the trained W–L network from data leakage of the test set. In total, a training set of 354,937 reactions was used, on which the pyrolysis predictions were based. The Python script to remove flavour molecules from the original dataset is available at https://github.com/IBM/pyrolysis-prediction . SMILES of the 180 flavour chemicals were used as inputs for the W–L NN using the published protocols. The computational training and prediction were run using a machine with eight CPU cores (Intel Xeon E5-2690 at 2.60 GHz), one GPU (Tesla V100) and 60 GB memory. The number of iterations to train the W–L network and W-LDN was set to 140,000 mini-batches of size 20 and 1,000,000 mini-batches of a single reaction and its candidate outcomes, respectively. Accuracy of prediction tasks was determined using 40,000 test examples 26 (not in the training set) which gave 0.924 for the model to identify reaction mode when the top 25 predictions are considered and 0.9341 for ranking reactions when the top 5 predictions are considered. The total training time was 2.5 days to train both models (reaction centre and ranking) for reaction prediction. Results from 4500 pyrolysis predictions for 180 flavours gave 7307 products of which 4524 were discrete products (when duplicate products from the same flavour are not included). Average reaction prediction times were 40 ms per reaction for reaction core identification and 127 ms per reaction for ranking.

Experimental EI-MS data retrieval

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , the SMILES representations for each flavour were converted to their corresponding InChIKey and the EI-mass spectra data associated with each InChIKey was extracted from the online NIST database at https://webbook.nist.gov/chemistry/ 31 . EI-mass data including fragmentation molecular weights and relative abundance were retrieved in JCAMP format. The data for each flavour was checked manually to ensure that the correct data had been acquired for each flavour and errors corrected. Data for some flavours (2-ethyl-3-methyl pyrazine, 2-methoxy-3-methylpyrazine, acetoin, α-damascone, benzaldehyde propylene glycol acetal, benzyl alcohol, β-damascenone, cedrol, citral, ethyl lactate, ethyl vanillin propylene glycol acetal, γ-dodecalactone, γ-octalactone, menthone, neral, propenyl guaethol, tabanone, thio-menthone, trans-2-hexenylacetate, vanillin propylene glycol acetal) were either not available in the NIST database or not accessible and data was manually extracted from the NIST or from Spectrabase ( https://spectrabase.com/ ).

Correlation of EI-MS fragmentation molecular weights with W–L NN predicted products

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , the molecular weight of each W–L NN predicted product was calculated using the Descriptors.ExactMolWt method in RDKit. The value of 1 was subtracted from that weight, followed by a rounding to the nearest whole number. NN predictions with the same molecular weight as the flavour molecular ion were not included. The values obtained for each product were correlated with the EI-mass spectrum fragmentation mass data for the relevant flavour molecule available in JCAMP format. Correlation results identified 1169 discrete matches between NN predicted products with EI-MS fragmentations.

GHS classification data retrieval and cramer classifications

Using specifically written script available at https://github.com/IBM/pyrolysis-prediction , a NN/MS matched product represented as SMILES was converted to its InChIKey and all compounds matching the InChIKey in the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) were retrieved. For each compound identifier, the script retrieved the hazard keywords in the pictographs that appeared in the GHS Classification subsection. Specific inhalation hazards were searched and compiled manually. Cramer classifications were obtained by using compound SMILES inputs into the available prediction software 36 . Results of GHS classifications identified 127 NN/MS matched product predictions as acute toxic, 153 as health hazard, 225 as irritant, 566 as neither acute, health hazard nor irritant and 95 were not identified in the database.

Mapping reactions for activation energy predictions

The SMILES of reactant and NN-predicted products were used as inputs for the automated mapping algorithm available at http://mapper.grzybowskigroup.pl/marvinjs/ 67 . The full atoms reaction maps were completed by using the “map the drawing” command after adding explicit hydrogen atoms.

D-MPNN pyrolysis activation energy predictions

The original training dataset for activation energy prediction consisting of 16,365 reactions is available at https://zenodo.org/record/3715478#.Yich5BPP2Wj 37 , 38 . To prevent from data leakage of the test set, reactions involving the 180 flavour chemicals were removed using specific Python script available at https://github.com/IBM/pyrolysis-prediction . The resulting dataset consisted of default hyper-parameters and the b97d3 theory data consisting of 16,264 reactions. Accuracy for AE predictions was determined by performing a tenfold cross validation to train the model with the data split into 85% training, 5% validation and 10% test data 38 . The performance/accuracy metric for the AE is the rooted mean square error defined as \(\sqrt {\frac{1}{N}\backslash_{i = 1}^{N} \left( {y_{i} - z_{i} } \right)^{2} }\) where N is the number of examples, y i is an AE value in the i-th example, and z i is a predicted AE value in the i-the example. The average accuracy for AE prediction was determined as the root mean square error (RMSE) which was 7.53 mol −1 with standard deviation of 0.74 mol −1 . The average AE value calculated by the 10 models was used to predict AE for pyrolysis with standard deviations included.

Data availability

All data are available in the main text, Supplementary Information or GitHub ( https://github.com/IBM/pyrolysis-prediction ). Raw data files are available from the corresponding author upon request.

National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General (Centers for Disease Control and Prevention, 2014).

Google Scholar  

Sapru, S. et al. E-cigarettes use in the United States: Reasons for use, perceptions, and effects on health. BMC Public Health 20 , 1518 (2020).

Article   PubMed   PubMed Central   Google Scholar  

Fairchild, A., Healton, C., Curran, J., Abrams, D. & Bayer, R. Evidence, alarm, and the debate over e-cigarettes. Science 366 , 1318 (2019).

Article   ADS   CAS   PubMed   Google Scholar  

Krüsemann, E. J. Z. et al. Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category. Tob. Control 30 , 185–191 (2021).

Article   PubMed   Google Scholar  

Omaiye, E. E. et al. High concentrations of flavour chemicals are present in electronic cigarette refill fluids. Sci. Rep. 9 , 2468 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Krüsemann, E. J. Z. et al. GC-MS analysis of e-cigarette refill flavoring solutions: A comparison of composition between flavor categories. J. Pharm. Biomed. Anal. 118 , 113364 (2020).

Article   Google Scholar  

Miech, R., Johnston, L., O’Malley, P. M., Bachman, J. G. & Patrick, M. E. Trends in adolescent vaping, 2017–2019. N. Engl. J. Med. 381 , 15 (2019).

Yoong, S. L. et al. Prevalence of electronic nicotine delivery systems and electronic non-nicotine delivery systems in children and adolescents: A systematic review and meta-analysis. Lancet Public Health 6 , e661–e673 (2021).

Kelesidis, T. et al. Association of 1 vaping session with cellular oxidative stress in otherwise healthy young people with no history of smoking or vaping: A randomized clinical crossover trial. JAMA Pediatr. 175 , 1174–1176 (2021).

Marques, P., Piqueras, L. & Sanz, M.-J. An updated overview of e-cigarette impact on human health. Respir. Res. 22 , 151 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Tsai, M. C., Byun, M. K., Shin, J. & Crotty Alexander, L. E. Effects of e-cigarettes and vaping devices on cardiac and pulmonary physiology. J. Physiol. 598 , 5039 (2020).

Article   CAS   PubMed   Google Scholar  

Abouassali, O. et al. In vitro and in vivo cardiac toxicity of flavored electronic nicotine delivery systems. Am. J. Physiol. Heart Circ. Physiol. 320 , H133 (2021).

Chaumont, X. M. et al. Fourth generation e-cigarette vaping induces transient lung inflammation and gas exchange disturbances: Results from two randomized clinical trials. Am. J. Physiol. Lung Cell Mol. Physiol. 316 , 705–719 (2019).

Blount, B. C. et al. Lung injury response laboratory working group, vitamin E acetate in bronchoalveolar-lavage fluid associated with EVALI. N. Engl. J. Med. 382 , 697 (2020).

Wu, D. & O’Shea, D. F. Potential for release of pulmonary toxic ketene from vaping pyrolysis of vitamin E acetate. PNAS 117 , 6349 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Lynch, J. et al. Simultaneous temperature measurements and aerosol collection during vaping for the analysis of Δ 9 -tetrahydrocannabinol and vitamin E acetate mixtures in ceramic coil style cartridges. Front. Chem. 9 , 734793 (2021).

Attfield, K. R. et al. Potential of ethenone (Ketene) to contribute to electronic cigarette, or vaping, product use-associated lung injury. Am. J. Respir. Crit. Care Med. 202 , 1187–1189 (2020).

Hallagan, J. The Safety Assessment and Regulatory Authority to Use Flavor: Focus on e-cigarettes . https://www.femaflavour.org/node/24344 .

Chen, W. et al. Measurement of heating coil temperature for e-cigarettes with a “topcoil” clearomizer. PLoS ONE 13 , e0195925 (2018).

Geiss, O., Bianchi, I. & Barrero-Moreno, J. Correlation of volatile carbonyl yields emitted by e-cigarettes with the temperature of the heating coil and the perceived sensorial quality of the generated vapours. Int. J. Hyg. Environ. Health 219 , 268 (2016).

Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121 , 9816 (2021).

Ayres, L. B., Gomez, F. J. V., Linton, J. R., Silva, M. F. & Garcia, C. D. Taking the leap between analytical chemistry and artificial intelligence. Anal. Chim. Acta 1161 , 338403 (2021).

Baum, Z. J. et al. Artificial intelligence in chemistry: Current trends and future directions. J. Chem. Inf. Model. 61 , 3197 (2021).

González-Medina, M. & Medina-Franco, J. L. Platform for unified molecular analysis: PUMA. J. Chem. Inf. Model 57 , 1735 (2017).

Schwaller, P. et al. Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction. ACS Cent. Sci. 5 , 1572 (2019).

Coley, C. W. et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10 , 370 (2019).

Jin, W., Coley, C., Barzilay, R. & Jaakkola, T. Predicting organic reaction outcomes with Weisfeiler–Lehman network. NeurIPS 30 , 2604 (2017).

Lowe, D. M. Patent Reaction Extractor . https://github.com/dan2097/patent-reaction-extraction .

Lowe, D. M. Extraction of Chemical Structures and Reactions from the Literature (Doctoral Thesis) . https://doi.org/10.17863/CAM.16293 (2012).

Cao, L. et al. MolDiscovery: Learning mass spectrometry fragmentation of small molecules. Nat. Commun. 12 , 3718 (2021).

NIST Chemistry WebBook. Standard Reference Database Number 69 . https://webbook.nist.gov/chemistry/ .

Source Codes for Retrieval of Flavour EI-MS Data; Molecular Weight Correlation of W-L NN Predicted Products and EI-MS Fragmentations and Retrieval of GHS Classifications . https://github.com/IBM/pyrolysis-prediction .

PubChem is an Open Chemistry Database at the National Institutes of Health (NIH) . https://pubchem.ncbi.nlm.nih.gov/ .

Wilson, N. et al. Improving on estimates of the potential relative harm to health from using modern ENDS (vaping) compared to tobacco smoking. BMC Public Health 21 , 2038 (2021).

Singh, K. P. et al. Systemic biomarkers in electronic cigarette users: Implications for noninvasive assessment of vaping associated pulmonary injuries. ERJ Open Res. 5 , 00182 (2019).

Toxtree v3.1.0. Toxtree—Toxic Hazard Estimation by Decision Tree Approach . http://toxtree.sourceforge.net .

Grambow, C. A., Pattanaik, L. & Green, W. H. Deep learning of activation energies. J. Phys. Chem. Lett. 11 , 2992 (2020).

Grambow, C. A., Pattanaik, L. & Green, W. H. Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry. Sci. Data 7 , 137 (2020).

Lewis-Atwell, T., Townsend, P. A. & Grayson, M. N. Machine learning activation energies of chemical reactions. WIREs Comput. Mol. Sci. 12 , e1593 (2022).

Article   CAS   Google Scholar  

Raza, A. et al. A machine learning approach for predicting defluorination of pe rand polyfluoroalkyl substances (PFAS) for their efficient treatment and removal. Environ. Sci. Technol. Lett. 6 , 624 (2019).

Hurd, C. D. & Blunck, F. H. The pyrolysis of esters. J. Am. Chem. Soc. 60 , 2419 (1938).

Vargas, D. C., Salazar, S., Mora, J. R., Van Geem, K. M. & Streitwieser, D. A. Experimental and theoretical study of the thermal decomposition of ethyl acetate during fast pyrolysis. Chem. Eng. Res. Des. 157 , 153 (2020).

Porterfield, J. P. et al. Thermal decomposition of potential ester biofuels. Part I: Methyl acetate and methyl butanoate. J. Phys. Chem. A 121 , 4658–4677 (2017).

Guo, T. et al. Real-time analysis of intermediate products from non-thermal plasma degradation of ethyl acetate in air using PTR-MS: Performance evaluation and mechanism study. Chemosphere 264 , 128430 (2021).

Wang, Q.-D. Theoretical studies on the hydrogen abstraction reactions of methyl esters with HO2 radical and the following β-scission reactions. J. Phys. Org. Chem. 30 , e3668 (2017).

Article   ADS   Google Scholar  

Sutton, R. & Harr, W. E. Optimum temperatures in pyrolysis gas chromatography. Can. J. Chem. 46 , 2623 (1968).

Sun, W. et al. Experimental and modelling efforts towards a better understanding of the high-temperature combustion kinetics of C 3 –C 5 ethyl esters. Comb. Flame 185 , 173 (2017).

Goel, R. et al. Highly reactive free radicals in electronic cigarette aerosols. Chem. Res. Toxicol. 28 , 1675 (2015).

Son, Y. et al. Hydroxyl radicals in e-cigarette vapor and e-vapor oxidative potentials under different vaping patterns. Chem. Res. Toxicol. 32 , 1087 (2019).

Mathers, A., Hawkins, B. & Lee, K. Transnational tobacco companies and new nicotine delivery systems. Am. J. Public Health 109 , 227 (2019).

Hua, M. et al. Identification of cytotoxic flavour chemicals in top-selling electronic cigarette refill fluids. Sci. Rep. 9 , 2782 (2019).

Hartmann-Boyce, J. et al. Electronic cigarettes for smoking cessation. Cochrane Database of Syst. Rev. 9 , CD010216 (2021).

Hurd, C. D. The Pyrolysis of Carbon Compounds (Chemical Catalog Company, 1929).

Wentrup, C. Flash vacuum pyrolysis: Techniques and reactions. Angew. Chem. Int. Ed. 56 , 14808 (2017).

Chen, J. Y., Canchola, A. & Lin, Y.-H. Carbonyl composition and electrophilicity in vaping emissions of flavoured and unflavored e-liquids. Toxics 9 , 345 (2021).

Son, Y., Bhattarai, C., Samburova, V. & Khlystov, A. Carbonyls and carbon monoxide emissions from electronic cigarettes affected by device type and use patterns. Int. J. Environ. Res. Public Health 17 , 2767 (2020).

Gillman, I. G., Pennington, A. S. C., Humphries, K. E. & Oldham, M. J. Determining the impact of flavored e-liquids on aldehyde production during vaping. Reg. Toxicol. Pharmacol. 112 , 104588 (2020).

Kosmider, L. et al. Daily exposure to formaldehyde and acetaldehyde and potential health risk associated with use of high and low nicotine e-liquid concentrations. Sci. Rep. 10 , 6546 (2020).

Khlystov, A. & Samburova, V. Flavouring compounds dominate toxic aldehyde production during e-cigarette vaping. Environ. Sci. Technol. 50 , 13080 (2016).

Samburova, V. et al. Aldehydes in exhaled breath during e-cigarette vaping: Pilot study results. Toxics 6 , 46 (2018).

Paul Jensen, R., Luo, W., Pankow, J. F., Strongin, R. M. & Peyton, D. H. Hidden formaldehyde in e-cigarette aerosols. N. Engl. J. Med. 372 , 392 (2015).

Jensen, R. P., Strongin, R. M. & Peyton, D. H. Solvent chemistry in the electronic cigarette reaction vessel. Sci. Rep. 7 , 42549 (2017).

Wang, P. et al. A device-independent evaluation of carbonyl emissions from heated electronic cigarette solvents. PLoS ONE 12 , e0169811 (2017).

Li, Y. et al. Impact of e-liquid composition, coil temperature, and puff topography on the aerosol chemistry of electronic cigarettes. Chem. Res. Toxicol. 34 , 1640 (2021).

Narimani, M., Adams, J. & da Silva, G. Toxic chemical formation during vaping of ethyl ester flavour additives: A chemical kinetic modeling study. Chem. Res. Toxicol. 35 , 522 (2022).

Jaegers, N. R., Hu, W., Weber, T. J. & Hu, J. Z. Low-temperature (< 200 °C) degradation of electronic nicotine delivery system liquids generates toxic aldehydes. Sci. Rep. 11 , 7800 (2021).

Jaworski, W. et al. Automatic mapping of atoms across both simple and complex chemical reactions. Nat. Commun. 10 , 1434 (2019).

Download references

Acknowledgements

D.W. acknowledges the Synthesis and Solid State Pharmaceutical Centre (SSPC) and Science foundation Ireland for funding support, Grant Number 12/RC/2275_P2.

Author information

Authors and affiliations.

IBM Research - Tokyo, Shin-Kawasaki, Japan

Akihiro Kishimoto

Department of Chemistry, Royal College of Surgeons in Ireland (RCSI), Dublin 2, Ireland

Dan Wu & Donal F. O’Shea

You can also search for this author in PubMed   Google Scholar

Contributions

D.F.O. designed research; A.K. and D.W. performed research; D.F.O., A.K. and D.W. analyzed data; D.F.O., A.K. and D.W. wrote the paper.

Corresponding authors

Correspondence to Dan Wu or Donal F. O’Shea .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kishimoto, A., Wu, D. & O’Shea, D.F. Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions. Sci Rep 14 , 9591 (2024). https://doi.org/10.1038/s41598-024-59619-x

Download citation

Received : 23 August 2023

Accepted : 11 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1038/s41598-024-59619-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Search Menu
• Supplements
• Advance articles
• Editor's Choice
• Special Issues
• Author Guidelines
• Submission Site
• Why Publish With Us?
• Open Access
• About Nicotine & Tobacco Research
• About Society for Nicotine & Tobacco Research
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, conclusions, supplementary material, declaration of interests.

• < Previous

Systematic Review of Electronic Cigarette Use (Vaping) and Mental Health Comorbidity Among Adolescents and Young Adults

• Article contents
• Figures & tables
• Supplementary Data

Timothy D Becker, Melanie K Arnold, Vicky Ro, Lily Martin, Timothy R Rice, Systematic Review of Electronic Cigarette Use (Vaping) and Mental Health Comorbidity Among Adolescents and Young Adults, Nicotine & Tobacco Research , Volume 23, Issue 3, March 2021, Pages 415–425, https://doi.org/10.1093/ntr/ntaa171

• Permissions Icon Permissions

The prevalence of electronic cigarette (EC) use has risen dramatically among adolescents and young adults (AYA, ages 12–26) over the past decade. Despite extensive established relationships between combustible cigarette use and mental health problems, the mental health comorbidities of EC use remain unclear.

To provide a systematic review of existing literature on mental health comorbidities of EC use among AYA. Database searches using search terms related to EC, AYA, and mental health identified 1168 unique articles, 87 of which prompted full-text screening. Multiple authors extracted data, applied the Effective Public Health Practice Project Quality Assessment Tool to evaluate the evidence, and synthesized findings.

Forty articles met eligibility criteria ( n = 24 predominantly adolescent and 16 predominantly young adult). Analyses yielded three main categories of focus: internalizing disorders (including depression, anxiety, suicidality, eating disorders, post-traumatic stress disorder), externalizing disorders (attention-deficit/hyperactivity disorder and conduct disorder), and transdiagnostic concepts (impulsivity and perceived stress). Significant methodological limitations were noted.

Youth EC use is associated with greater mental health problems (compared with nonuse) across several domains, particularly among adolescents. Because many existing studies are cross-sectional, directionality remains uncertain. Well-designed longitudinal studies to investigate long-term mental health sequelae of EC use remain needed.

Forty recent studies demonstrate a variety of mental health comorbidities with AYA EC use, particularly among adolescents. Mental health comorbidities of EC use generally parallel those of combustible cigarette use, with a few exceptions. Future EC prevention and treatment strategies may be enhanced by addressing mental health.

The use of electronic cigarette (EC) has risen dramatically among adolescents and young adults (AYA, youth aged 12–26) over the past decade in countries around the world. 1 A nationwide survey of US high school students found that current use of EC increased from 1.5% in 2011 to 20.8% in 2018, despite a decrease in combustible cigarette (CC) use during this period. 2 In 2019, lifetime EC use among high school age youth exceeded 40% in the United States and Canada. 3

ECs are battery-powered devices that heat a liquid to produce an inhalable aerosol that creates sensations mimicking CC smoking. 4 The devices are alternatively referred to as vaporizers, vape-pens, vape pod systems, JUULs (a popular North American brand), and electronic nicotine delivery systems; inhalation may be described as vaping or blowing smoke. 5 The increasing popularity of ECs among youth has been attributed to aggressive marketing, 6 enticing flavors, 7 perceptions of lower harm, 8 , 9 social media influences, 10 and discreet designs that enable furtive use. 9

EC liquids can contain mixtures of solvents (eg, propylene glycol), nicotine, tetrahydrocannabinol or hash oil, hundreds of flavoring compounds, and trace heavy metals. 11–13 Some ECs (eg, JUUL) use nicotine salts, enabling consumption of very high doses of nicotine 14 , 15 that have been associated with high rates of continued use. 5 EC are a vehicle for nicotine use, but do not always contain nicotine. In a national survey of US high school students, a majority reported vaping only flavoring (59%–63%), followed by nicotine (13%–20%), and cannabis compounds (6%) 12 ; however, actual nicotine use may be higher than reported because subsequent studies have indicated that youth misperceive nicotine content of products they use. 5

Leading health organizations initially supported ECs as a possible smoking cessation aid for adults. 4 , 16 Though initially presumed less toxic than CC, EC use can cause carcinogen exposure, 17 respiratory toxicity, 18 declining oral health, 19 and other adverse effects. 11 Among AYA, EC use may act as a gateway to use of CCs 20 , 21 and to alcohol and illicit substances. 22 , 23 Some youth may be more susceptible to harmful effects than others.

AYA with mental illness are a population of specific concern. Adults with mental illness use tobacco products at high rates and die prematurely from tobacco-related illnesses, 24 a disparity attracting calls for further study. 25 Adolescence is a vulnerable developmental period for the onset of nicotine use and mental illness, 26 warranting special attention. Yet, to date, no article has yet to systematically review the evidence base concerning EC use and mental illness in youth.

CC use among adolescents is associated with externalizing (eg, attention-deficit/hyperactivity disorder [ADHD], oppositional defiant disorder, conduct disorder), internalizing (eg, depression, anxiety), and substance use disorders. 26–28 AYA with mental illness use nicotine at higher rates than peers without mental illness. 29 This may occur due to (1) attempts to self-medicate symptoms, such as cognitive deficits in ADHD or low mood, 30 (2) efforts to counteract sedating side effects of psychotropic medications, 30 (3) common underlying genetic or environmental risk factors for smoking and mental illness, 31 , 32 or (4) neurotoxic impacts of nicotine on mental health. 33 A combination of individual-specific factors likely contributes.

Nicotine adversely affects adolescent neurodevelopment 34 and increases the risk of cognitive and psychiatric disorders. 35 Although much of the available evidence derives from animal and preclinical research, we can nonetheless mobilize this knowledge while awaiting further clinical youth studies. During adolescence, brain regions that underlie executive functions undergo significant reorganization, 36 , 37 regulated in part by nicotinic acetylcholine receptors. 33 Evidence from animal models suggests that prolonged nicotine exposure may also induce epigenetic changes 33 and increase vulnerability to stress sensitivity. 38 , 39 These biological changes may, in part, underlie associations between adolescent nicotine use and subsequent development of mood disorders, 39 , 40 schizophrenia, 41 and substance use disorders. 33 Furthermore, reliance on nicotine to overcome challenges interferes with the development of adaptive coping skills. 42

Although nicotine remains the most commonly vaped substance, a substantial proportion of youth EC users vape cannabis 12 and nicotine vaping is highly comorbid with cannabis use among adolescents. 43 Vaped cannabis often comes in high-potency concentrates, leading to greater amounts consumed by vaping than other modes. 44 Like nicotine, cannabis use is associated with adverse mental health outcomes, including psychotic disorders, depression, worse symptoms of mania/hypomania in individuals with bipolar disorder, and suicidality. 45

We aim to assess the current evidence describing mental health comorbidities of EC use among AYA. Although prior reviews have assessed the mental health correlates of EC use among adults, 46 the evidence concerning relationships between EC use and AYA with mental illness remains unreviewed. As 99% of tobacco users initiate use before age 26, effective prevention and treatment efforts depend on understanding risks for use among AYA. 47

The research protocol was developed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 48 and registered with the International Prospective Register of Systematic Reviews (PROSPERO) (Registration ID CRD42020177159).

Data Sources and Searches

A search of studies that evaluated psychiatric comorbidities associated with EC use among adolescents and young adults was conducted on March 23, 2020, within MEDLINE, EMBASE, PsycINFO, Web of Science Core Collection, and Scopus. The search strategy included appropriate controlled vocabulary and keywords for (1) mental illness, (2) AYA (ages 12–26), and (3) EC use (see Supplementary Appendix A ). Publication date was limited from January 2011 to present, and no language or article-type restrictions were included in the search strategy. Reference lists of included studies were reviewed by hand to identify any additional studies.

Study Selection

Search results were uploaded into Covidence, 49 a systematic review software package. Two authors independently assessed articles based on title and abstract using screening criteria, with a third author resolving eligibility disagreements. We chose wide eligibility criteria ( Table 1 ), since research on mental health among EC users is just emerging. Full texts of selected articles were screened to finalize decisions on eligibility ( Figure 1 ).

Inclusion and Exclusion Criteria

AYA = adolescents and young adults; EC = electronic cigarette.

a The authors agreed to add this criterion during full-text screening because the analyses presented in these papers did not contribute significantly to answering the main research question of this review.

Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) flow diagram.

Data Extraction

The authors developed and piloted a standardized data extraction tool including first author and year of publication, study aim, participants and setting, study design, response and follow-up rates, EC measurements, mental health measures, prevalence of EC use, findings related to mental health, and covariates adjusted for in analyses. The aspects pertaining to the methods were extracted by a single author and reviewed by a second author. Results were extracted independently by two authors, who discussed each article, including additional team members as needed.

Following extraction of key data, two authors independently rated the quality of each article using the Effective Public Health Practice Project Quality Assessment Tool, a valid and reliable method for assessing a diversity of research designs. 50 Studies were rated across five domains, including selection bias, study design, confounders, data collection methods, and withdrawals and dropouts ( Table 2 , Supplementary Table 1 ). Last, a global quality rating inclusive of data in all domains was assigned.

Quality Rating of Included Studies ( n = 40)

Following data extraction and discussion of included studies, findings were qualitatively synthesized by mental illness categories. Substantial methodological heterogeneity precluded quantitative meta-analysis. Key statistics are reported.

Searches identified 1706 articles, of which 1167 were unique, and 1 article was identified by hand search. Of the 1168 articles screened, 87 met eligibility by title and abstract, of which 40 were ultimately included for qualitative synthesis ( Figure 1 ).

The included articles were published from 2015 through 2020 and pertained to 29 unique cohorts ( Supplementary Table 1 ). Most articles report on data collected between 2013 and 2017. Six cohorts were described by 17 articles, whereas the remaining 23 cohorts were described by single articles. More articles studied predominantly adolescents ( n = 24 studies, representing 16 cohorts) than young adults ( n = 16 studies, representing 13 cohorts).

Most studies were conducted in the United States ( n = 23 cohorts); others included South Korea ( n = 3 cohorts), the United Kingdom ( n = 2 cohorts), and Taiwan ( n = 1 cohort). A minority of cohorts were nationally representative ( n = 7/16, 44% of adolescent cohorts, n = 2/13, 15% of YA cohorts), two were clinical samples, 51 , 52 two focused on youth at high-risk for substance use, 53 , 54 and most others were school- or university-based samples.

More than half utilized cross-sectional designs ( n = 23 articles), although a substantial number were longitudinal ( n = 16 articles, representing 11 unique cohorts), and one reported a case series. 52 All studies used self-report measures of EC use, none of which were reported to have been established as reliable and valid. EC measures varied in assessing lifetime use, current use, age of use onset, and frequency of use. Most studies ( n = 37) referred to nicotine use in EC, whereas three explicitly investigated vaporizing other substances. 51 , 55 , 56

Mental health outcomes were subgrouped by syndrome (eg, depression, anxiety, ADHD) and age under three main categories: internalizing disorders, externalizing disorders, transdiagnostic concepts. Several additional findings that did not fit the main categories are briefly presented in Table 3 . 51 , 52 , 55–59

Additional Findings

Internalizing Disorders

Internalizing symptoms (composite), adolescents:.

Composite measures of internalizing symptoms were associated with EC use among adolescents in both the Population Assessment of Tobacco and Health (PATH) study 60–64 and a study of at-risk US high school students. 54 Quality of evidence was weak to moderate, with a mix of cross-sectional and longitudinal designs.

Cross-sectional analysis of baseline PATH data revealed that high-severity lifetime internalizing problems were similarly associated with both lifetime EC (adjusted odds ratio [aOR] = 1.6, 95% confidence interval [CI]: 1.3–1.8, p < .05) and CC use (aOR = 1.7, 95% CI: 1.5–2.0, p < .05). 61 In a 1-year follow-up longitudinal analysis of baseline nicotine-naive adolescents, high past-year internalizing problems were significantly associated with initiation of EC use (adjusted relative risk ratio [aRRR] = 1.61, 95% CI: 1.12–2.33, p < .05), but not initiation of CC-only or dual EC and CC use. 60 In a cross-sectional study of students in alternative high schools (ie, schools providing nontraditional learning experiences for youth with prior educational and/or behavioral difficulties) internalizing symptoms related significantly to EC use ( B = 0.100, standard error [SE] = 0.041, OR = 1.105, p < .05) and use frequency ( B = 0.204, SE = 0.095, β = 0.0128). 54

Young Adults:

Two articles examined and found relationships between internalizing symptoms and EC use among YA respondents in the PATH study. 62 , 65 Evidence quality was similarly weak to moderate. Similar to the adolescent PATH findings, high-severity past-year internalizing problems (compared with low severity) significantly related to current EC use (aOR = 1.97, 95% CI: 1.46–2.65, p < .001) and CC use (aOR = 1.92, 95% CI: 1.64–2.24, p < .001) in cross-sectional analysis of baseline data, 65 and high-severity lifetime internalizing problems predicted onset of EC (aOR = 1.4, 95% CI: 1.1–1.8, p < .05) and CC use (aOR = 2.2, 95% CI: 1.5–3.3, p < .05) among nonusers in a 1-year longitudinal analysis. 62

Seven studies, including four distinct national cohorts (United States, Taiwan, and Korea) 66–69 and one California-based cohort 70–72 examined associations between EC use and depression among adolescents. Most found positive associations 66–69 , 71 ,72 and one suggested a bidirectional relationship. 70 Evidence quality was weak to moderate due to cross-sectional designs, single-item measures, and minimal adjustment for confounders.

In a 1-year longitudinal analysis of a California cohort, sustained EC use was associated with the escalation of depressive symptoms over time ( b = 1.272, SE = 0.513, p = .01), and past-month use frequency was positively associated with depressive symptoms ( b = 1.611, SE = 0.782, p = .04) among sustained users. 70 The remaining studies were cross-sectional. Three national studies found EC use associated with depressive symptoms, 66 , 68 , 69 although the Taiwanese study found no relationship for exclusive EC use. 67 In the Taiwan study, depression was associated with exclusive CC use (aOR = 2.2, 95% CI: 1.1–5.0) but not EC use 67 ; however, in the Korean study depression was associated with both current EC use (current use: aOR = 2.21, 95% CI: 1.67–2.93) and CC use (current use: aOR = 2.04, 95% CI: 1.86–2.24). 69

Eight studies, among six cohorts, investigated relationships between depression and EC use, with mixed results. 21 , 53 , 59 , 73–76 Most studies were weak, due to cross-sectional designs and risk of selection bias.

A Texas-based cohort provides the strongest evidence (moderate). 59 , 75 , 76 Over 2.5 years of biannual longitudinal follow-up, depressive symptoms were significantly but modestly associated with frequency of past-month use for both EC (adjusted rate ratio [aRR] = 1.01, 95% CI: 1.00–1.03, p = .02) and CC (aRR = 1.03, 95% CI: 1.02–1.04, p < .001). 75 A cross-lagged path analysis of three waves found significant paths from Wave 1 depression to Wave 2 EC use ( B = 0.06, p < .01) and Wave 2 depression to Wave 3 EC use ( B = 0.08, p < .01), but no paths from EC use to subsequent depressive symptoms. 76

Two cross-sectional studies, among college students 58 and homeless youth smokers 53 found depressive symptoms associated with current EC use (college: aOR = 1.04, 95% CI: 1.01–1.08, p = .022 58 ; homeless: aOR = 3.06, 95% CI: 1.68–5.57, p < .05 53 ). In these studies, depression was also associated with CC use in the student cohort (aOR = 1.03, 95% CI: 1.01–1.06, p = .015), but not the homeless cohort.

Finally, two longitudinal 21 , 73 and one cross-sectional study 74 found no relationships between EC use and depression. In a 2-year follow-up of Georgia college students, depressive symptoms predicted subsequent CC use (aOR = 1.05, 95% CI: 1.02–1.09, p = .001) but not EC use. 73 In study of Virginia college students, baseline depression did not predict EC initiation during 1-year of follow-up. 21

One cross-sectional study, with weak quality evidence, assessed anxiety among adolescents, using scales for several anxiety subtypes, finding EC-only use less strongly related with anxiety than CC-only use. 71 Lifetime EC-only users had higher levels of panic disorder than lifetime nicotine abstainers, but lower levels of generalized anxiety, panic, social phobia, OCD, and anxiety sensitivity than CC-only users. 71

Four studies among three cohorts have examined anxiety among YA, yielding mostly negative results. 21 , 73 , 74 , 77 Quality of evidence was weak to moderate with risks of selection bias across studies. Studies of two longitudinal cohorts of college students, in Georgia and Virginia, followed over 1–2 years found no relationship between anxiety and subsequent EC use. 21 , 73 Among the Georgia 73 but not the Virginia cohort, 21 anxiety predicted CC use (aOR = 1.02, 95% CI: 1.00–1.04, p = .02). On a smaller scale, an ecological momentary analysis among a currently smoking subset of the Georgia cohort found no relationship between anxiety and EC use. 77 A cross-sectional study found EC use associated with generalized anxiety (likelihood ratio χ   2 = 14.0, p = .001, Cramer’s V = 0.066) in a primary unadjusted analysis that resolved with secondary analysis controlling for covariates. 74

Suicidality

Four national cross-sectional studies in the United States 66 and Korea 68 , 69 , 78 investigated suicidality, consistently finding current EC use associated with suicidal ideation, plans, and attempts. Evidence quality is again weak and is limited by cross-sectional designs, possible confounding, and single-item measures.

In an analysis of the US Youth Risk Behavior Survey (2015–2017), current EC-only use associated with past-year suicidal ideation (aOR = 1.23, 95% CI: 1.03–1.47). 66 Analyses across 3 years (2015–2017) of the Korean Youth Risk Behavior Survey found similar associations. 68 , 69 , 78 The 2016 Korean survey found significant associations between current EC use (vs. nonuse) and past-year suicidal ideation (aOR = 1.58, 95% CI: 1.31–1.89, p < .05), plans (aOR = 2.44, 95% CI: 1.94–3.08, p < .05), attempts (aOR = 2.44, 95% CI: 1.85–3.22, p < .05), and serious attempts (aOR = 3.09, 95% CI: 1.51–6.32, p < .05). 78 In the 2017 Korean survey, lifetime and current CC use, EC use, and dual CC and EC use (vs. never use) were all associated with suicidal ideation, planning, and attempts, although the magnitude of associations for CC-only users seemed consistently lower than those for EC and dual users—with greater OR, but wide CIs, limiting some comparisons between groups. Furthermore, associations between suicidality and EC use were consistently stronger among women than men. 69

No studies identified.

Eating Disorders

One South Korean study examined the comorbidity between EC use and past-month report of unhealthy weight control behaviors, including one-food dieting, fasting, diet pill use, and purging, and found significant relationships among both young men and women. 79 Although the study included a large nationally representative sample, overall quality was weak, due to a cross-sectional design, possible confounding, and single-item measures. Female lifetime and current EC adolescent users (compared with lifetime EC abstainers) had significantly higher rates of all unhealthy weight control behaviors (lifetime EC use: aORs = 1.87–2.40, current EC use: aORs = 2.32–3.76), whereas male current EC users, but not lifetime users had significantly higher rates of all unhealthy weight control behaviors (aORs = 2.05–3.18). Similar associations were found for CC use.

In one weak-quality US university-based sample, EC use was not associated with binge-eating disorder. 74

Post-traumatic Stress Disorder

Two studies were found examining relationships between aspects of post-traumatic stress disorder and EC use. 74 , 80 Findings were mixed and quality of evidence was weak, both studies used cross-sectional designs, and there was risk of sampling bias and potential confounding. Among college students, EC use significantly related to post-traumatic stress disorder (likelihood ratio χ   2 = 13.0, p = .002, Cramer’s V = 0.064) in the primary unadjusted analysis, but not after controlling for covariates. 74 In a small sample of YA, self-reported history of childhood mistreatment directly related to lifetime EC use (β = 0.19, p = .02), but not current use, a relationship that subsequent analysis found fully mediated by negative urgency, a dimension of impulsivity reflecting the tendency to act rashly while distressed (β = 0.11, p = .04). 80

Externalizing Disorders

Externalizing disorders (composite).

Analyses of adolescents in the PATH study found externalizing symptoms significantly associated with EC use. 60–64 Evidence quality was weak to moderate. In cross-sectional analysis of baseline data, high-severity lifetime externalizing problems were similarly associated with lifetime EC (aOR = 1.5, 95% CI: 1.3–1.7, p < .05) and CC use (aOR = 1.5, 95% CI: 1.3–1.7, p < .05). 61 In a 1-year longitudinal analysis of baseline nicotine-naive adolescents, high past-year externalizing problems were significantly associated with initiation of EC use (aRRR = 2.78, 95% CI: 1.76–4.40, p < .05), with relative risk ratios not significantly different from initiation of dual use (aRRR = 2.23, 95% CI: 1.15–4.31, p < .05) and CC use (aRRR = 5.59, 95% CI: 2.63–11.90, p < .05). 60

One longitudinal analysis of baseline nicotine-naive YA participants in the PATH study (moderate-quality evidence) similarly found that high-severity lifetime externalizing symptoms predicted EC onset (aOR = 1.4, 95% CI: 1.1–1.7, p < .05) at 1-year follow-up. 62 The relationship between externalizing symptoms and CC onset was not significant among these YAs.

Attention-Deficit/Hyperactivity Disorder

Two studies examined longitudinal relationships between ADHD symptoms and EC use among US high school students. 72 , 81 Both were moderate in quality, utilizing longitudinal designs with minimal attrition over 12–18 months while adjusting for covariates. Both studies found that ADHD symptoms predicted subsequent EC use, but not CC use. In a California-based cohort, overall ADHD symptoms (aOR = 1.22, 95% CI: 1.04–1.42) and hyperactivity–impulsivity subscale symptoms (aOR = 1.26, 95% CI: 1.09–1.47), but not inattentive subscale symptoms predicted initiation of EC over 18-month follow-up. 72 Similarly, in a small study of college-bound seniors, using a cross-lagged path model, ADHD symptoms at Time 1 (T1) predicted EC use at Time 2 (β = 0.206, p < .001) and ADHD symptoms at Time 2 predicted EC use at Time 3 (β = 0.350, p < .001), but EC use frequency was not associated with subsequent ADHD symptoms. 81

In contrast to the findings of adolescent samples, two studies examined ADHD symptoms and EC use among college students, both finding no associations when controlling for covariates. 73 , 74 The quality of evidence was weak-moderate in strength, due to only one longitudinal design and self-report measures. In a cross-sectional study, ADHD symptoms were significantly associated with EC use status (likelihood ratio χ   2 = 16.778, p < .001, Cramer’s V = 0.073) in the primary unadjusted analysis, but there was no significant association when controlling for covariates. 74 In a 2-year longitudinal study, neither ADHD nor any other psychological factors measured predicted EC use after controlling for covariates. 73

Conduct Disorder and Delinquency

Three articles examined conduct disorder symptoms and found significant relationships with subsequent EC use. 64 , 72 , 82 All were moderate-quality longitudinal studies, and two were nationally representative (United States, United Kingdom). An analysis of baseline nicotine-naive adolescents in the PATH study found that baseline rule-breaking tendency independently predicted EC use in the subsequent year (aOR = 1.93, 95% CI: 1.58–2.34). 64 Similarly, past 6-month delinquent behavior was associated with later EC use (aOR = 1.32, p < .001) and CC use (aOR = 1.41, p < .05) among a cohort of nicotine-naive US high school students. 72 Reports of various delinquent behaviors (eg, theft, vandalism, graffiti) were significantly higher for lifetime EC-only users (vs. never users) (aORs range 3.9–6.0, p < .001) but to less extent than among CC users and dual-EC and CC users (aORs range 5.7–11.9, p < .001). 82

Transdiagnostic Constructs

Impulsivity and executive function.

Impulsivity describes a predisposition toward rapid, unplanned actions without regard for long-term consequences and has been implicated in ADHD, conduct disorder, bipolar disorder, and personality disorders. 83 Executive function describes closely related capacities for planning, working memory, self-control, and attention shifting.

Adolescent:

Three studies examined impulsivity and EC use 71 , 84 , 85 and two studies among one cohort examined executive function. 86 , 87 These studies consistently found EC use related to impulsivity and executive function deficits. Overall, quality of evidence was weak, with nonprobability samples and cross-sectional designs.

In a cross-sectional analysis of California high school students, impulsivity was elevated similarly among EC and CC users. 71 In longitudinal analysis of British high school students, baseline impulsivity predicted onset of EC use (aOR = 1.263, 95% CI: 1.183–1.349) and CC use (aOR = 1.452, 95% CI: 1.286–1.638) at 24-month follow-up. 84 In a cross-sectional study using a mediation model, impulsivity was associated with more frequent EC use through an early age of EC initiation. 85

In a cross-sectional study of 12-year-old children in California, lifetime EC use was strongly associated with executive function deficits (aOR = 4.99, 95% CI: 1.80–13.96, p < .01), 86 with subsequent analysis finding the relationship between low inhibitory control and EC use most applicable among low-socioeconomic status respondents. 87

Four studies, also weak in overall quality, investigating EC use and various subcomponents of impulsivity (eg, sensation seeking, negative urgency, lack of premeditation, and perseverance) have had mixed results, with studies most consistently supporting a relationship between sensation seeking and EC use. 21 , 80 , 88 , 89 Two longitudinal studies 88 , 89 found relationships between sensation seeking and subsequent EC use (eg, ever JUUL use: aOR = 1.76, 95% CI: 1.52–2.05, p < .01; current use: aOR = 2.16, 95% CI: 1.81–2.58, p < .01), 89 and one cross-sectional study 80 found a correlation between sensation seeking and EC use, although relationships with other subcomponents of impulsivity were generally not significant (one small study found significance for negative urgency 80 ). One study found lack of perseverance predicted CC use (aOR = 1.52, 95% CI: 1.11–2.07, p < .05), but not EC use at 1-year follow-up. 21 In addition, in a cross-sectional study assessing impulse control disorders, EC use was related to gambling disorder (likelihood ratio χ   2 = 37.2, p = .000, Cramer’s V = 0.081), but not other impulse control disorders. 74

Perceived Stress

Perceived stress describes a heritable tendency to deem negative events as unpredictable and uncontrollable and has been implicated in anhedonic depression, anxious dysthymia, psychosis, post-traumatic stress, and various personality disorders. 90

One moderate-quality study assessed perceived stress in adolescents. 90 In a 4-year longitudinal follow-up of California teenagers, baseline (age 13) perceived stress was associated with lifetime and past-month EC use (aOR = 1.25, 95% CI: 1.07–1.47, p < .01) at age 17 as well as lifetime and past-month CC use (aOR = 1.32, 95% CI = 1.08–1.61, p < .01).

One study, weak, limited by cross-sectional design, assessed past-week perceived stress among college students, finding perceived stress associated with past 30-day EC use (aOR = 1.03, 95% CI: 1.00–1.05, p = .03) and CC use (aOR = 1.02, 95% CI: 1.00–1.04, p = .04).

Forty existing studies assess mental health comorbidities of EC use among AYA. This review of the current evidence, the first on this topic, summarizes our current knowledge base and facilitates future investigation.

Among adolescent studies, EC use is associated with internalizing problems, depression, suicidality, disordered eating, externalizing problems, ADHD, conduct disorder, impulsivity, and perceived stress, with additional limited evidence for an association with anxiety. These findings largely align with prior findings regarding mental health and CC use. 26 , 27 , 91–93 Among YA specifically, EC use has been associated with internalizing problems, externalizing problems, depression, sensation seeking, and perceived stress, whereas existing evidence does not support relationships with ADHD or anxiety.

The finding that ADHD was associated with EC use among adolescents but not YA may reflect methodological differences. Alternatively, ADHD may represent a risk factor for EC initiation among adolescents that becomes attenuated by young adulthood, due to neurobiological and psychosocial factors. Given well-established risks for substance use among AYA with untreated ADHD, 94 adolescents may gravitate toward ECs, influenced by social media 95 and availability, 96 whereas YA tend toward other substances (eg, alcohol). Brain maturation and resulting improvements in self-regulation, may also contribute to the observed difference.

Most adolescent cohorts (6/7), but only half of YA cohorts (3/6) found relationships between EC use and depression. Most of the adolescent studies were national cohorts, versus university-based samples in YA studies, and some adolescent studies used single-item measures for past-year depressive episodes. 66 , 69 These methodological differences may underly the difference in findings. Alternatively, the clear association in adults between depression and alcohol and substance use 97 again supports the hypothesis that depressed adolescents may turn to ECs whereas YAs access other substances.

Findings were similar for both EC and CC with a few notable exceptions. ADHD predicted onset of EC use but not CC use among adolescents. 72 , 81 This difference may reflect the role of sensation seeking in EC use, as youth with ADHD may be particularly attracted to their novel flavors. Although minimal associations were found between EC use and anxiety, associations were somewhat stronger for CC use and anxiety among adolescents and YA. 71 , 73 Externalizing symptoms were more strongly associated with onset of CC use than EC use among adolescents, 60 but not YA. 65 Adolescents with conduct problems may view CC use as a greater act of rebellion and risk-taking, given longstanding regulations against CC use, which have only recently begun for EC. Among YA, many high externalizing respondents were probably excluded for prior nicotine use, 62 so the negative finding may reflect that high externalizing youth had an earlier age of onset.

Implications for Practice

Clinicians should have a low threshold for providing mental health screening and referrals when treating youth using EC, as EC use may be an indicator of behavioral health risk. At this time, it seems reasonable to counsel AYA with depression and other mental health problems against vaping, warning that vaping and other substance use may exacerbate their mental illness. Although the longitudinal evidence linking vaping to subsequent psychopathology remains limited, there is some evidence of a relationship, 70 which would be consistent with relationships between CC use and mental illness, 98 and with existing models of nicotine and neurodevelopment (as described in the introduction). In addition, it is important to emphasize vaping cessation in AYA with mental illness to prevent potential progression to CC and other substance use 20–23 and associated long-term health sequelae, 24 which disproportionately affect adults with mental illness.

Further research is needed to better understand how comorbid mental illness influences uptake, use patterns, and cessation among AYA with mental illness to appropriately counsel and treat this population. There are no known effective treatments for youth EC cessation. Although EC manufacturers have created “curricula” to reduce underage abuse, these have many limitations. 99 Parents and school administrators struggle in implementing restrictions to curb use. 100 , 101 Although there exists a need for additional studies to enlarge the evidence base for adolescent CC smoking cessation, existing evidence best supports group-based behavioral interventions. 102 Adapting these programs to EC use may be effective alongside policies targeting specific problematic practices in EC marketing. 103 However, in developing interventions to mitigate EC use, it will also be important to monitor for the possible unintended consequence of diverting youth toward other, potentially riskier, substances. The results of this review highlight the importance of interventions to take into account AYA with mental illness as a special vulnerable population, which may benefit from tailored practices on both the intervention and public health policy levels.

Limitations of Evidence and Directions for Further Research

The quality of evidence among included studies varied, with several consistent limitations. The young adult studies were largely among college-based samples, raising the risk of selection bias. Given high prevalence of EC use among other groups of YA, 53 further study of high-risk YAs remains warranted. In addition, few studies have adjusted for use of other substances (see Supplementary Table 1 ), despite high comorbidity between vaping and other substance use 43 and the potential impacts of other substance use on mental health. Most studies that included substance use as a covariate still found significant relationships between EC use and mental health comorbidities. 57 , 60 , 70 , 78 , 82

Most studies were cross-sectional, or longitudinal studies with short-term follow-up. As a result, important questions about the impact of EC use on the trajectory of mental health symptoms remain unanswered. One study presented data to support a bidirectional relationship, 70 whereas two found no evidence for EC affecting subsequent mental health. 76 , 81 Given that EC use may alter cognitive and emotional health through multiple pathways, 13 further longitudinal studies remain important.

Future studies should develop more nuanced measures of EC use and establish their validity and reliability. Most studies measured either lifetime use or current use by self-report. Factors such as frequency and patterns of use, dose of nicotine (which varies considerably among products), and nicotine dependence remain relatively uninvestigated and will be important to identifying factors of youth most at risk of adverse outcomes. In addition, most studies relied on mental health screening measures, which were not designed to be diagnostic.

We expect EC use to remain an active area of investigation, given evolving legal restrictions on EC use and changing youth behavioral trends. Although youth vape numerous substances, we only found a few studies assessing vaping of cannabis and illicit drugs. In the United States, the rise of ECs over the past decade has coincided with loosening of restrictions on cannabis use. 104 Although studies indicate nicotine remains the main psychoactive substance inhaled by AYA EC users, use of cannabis in ECs is not inconsequential. 12 Like nicotine, cannabis use during adolescence influences development of depression and psychosis. 105 , 106

Although we found studies examining EC use across a range of psychopathology, we found no studies assessing psychosis. Given high rates of nicotine-associated long-term mortality and the potential etiologic role of nicotine in development of psychosis, 41 this subgroup may be most at risk of long-term adverse outcomes from EC addiction, and thus most in need of early intervention.

Limitations of Review

We acknowledge several limitations of this review. We defined inclusion criteria broadly to permit a wider view of the existing literature, but one which precluded quantitative meta-analysis, since each subcategory of results ultimately includes only a few studies, using a variety of mental health measures and covariates (see Supplementary Table 1 ). We anticipate this review will provide a horizon to permit future systematic studies to evaluate narrower questions. We excluded studies focused only on substance use disorder comorbidities, an important topic needing a dedicated review. Although we included all internalizing and externalizing mental health conditions and transdiagnostic concepts reported in this literature, we did not include search terms for transdiagnostic concepts. Our review yielded mostly US-based studies, which may in part reflect our exclusion of non-English studies; thus, it is not clear to what extent results generalize to other settings.

We identified 40 recent articles investigating the relationship between mental health and EC use among AYA. EC use correlates with several domains of AYA mental health problems. Much remains unknown about the particular use patterns of high-risk youth and the long-term neuropsychiatric sequelae of EC use during AYA development. Given the elevated rates of EC use among AYA with mental health problems, further research remains warranted.

A Contributorship Form detailing each author’s specific involvement with this content, as well as any supplementary data, are available online at https://academic.oup.com/ntr .

This research was not supported by external funding.

None declared.

Dutra   LM , Glantz SA . High international electronic cigarette use among never smoker adolescents . J Adolesc Health. 2014 ; 55 ( 5 ): 595 – 597 .

Google Scholar

Gentzke   AS , Creamer ML , Cullen KA , et al.    Vital signs: tobacco product use among middle and high school students – United States, 2011–2018 . MMWR Morb Mortal Wkly Rep. 2019;68:157 .

Hammond   D , Rynard VL , Reid JL . Changes in prevalence of vaping among youths in the United States, Canada, and England from 2017 to 2019 . JAMA Pediatr . 2020 .

Ker   S , Peckham E , Gilbody S , et al.    Everything you wanted to know about e-cigarettes and vaping but were afraid to ask: a guide for mental health clinicians . BJPsych Adv. 2019 ; 25 : 279 – 286 .

Willett   JG , Bennett M , Hair EC , et al.    Recognition, use and perceptions of JUUL among youth and young adults . Tob Control. 2019 ; 28 ( 1 ): 115 – 116 .

Mantey   DS , Cooper MR , Clendennen SL , Pasch KE , Perry CL . E-cigarette marketing exposure is associated with e-cigarette use among US youth . J Adolesc Health. 2016 ; 58 ( 6 ): 686 – 690 .

Pepper   JK , Ribisl KM , Brewer NT . Adolescents’ interest in trying flavoured e-cigarettes . Tob Control. 2016 ; 25 ( Suppl. 2 ): ii62 – ii66 .

Ambrose   BK , Rostron BL , Johnson SE , et al.    Perceptions of the relative harm of cigarettes and e-cigarettes among U.S. youth . Am J Prev Med. 2014 ; 47 ( 2 Suppl. 1 ): S53 – S60 .

Peters   RJ  Jr , Meshack A , Lin MT , Hill M , Abughosh S . The social norms and beliefs of teenage male electronic cigarette use . J Ethn Subst Abuse. 2013 ; 12 ( 4 ): 300 – 307 .

Brett   EI , Stevens EM , Wagener TL , et al.    A content analysis of JUUL discussions on social media: using Reddit to understand patterns and perceptions of JUUL use . Drug Alcohol Depend. 2019 ; 194 : 358 – 362 .

Pisinger   C , Døssing M . A systematic review of health effects of electronic cigarettes . Prev Med. 2014 ; 69 : 248 – 260 .

Miech   R , Patrick ME , O’Malley PM , Johnston LD . What are kids vaping? Results from a national survey of US adolescents . Tob Control. 2017 ; 26 ( 4 ): 386 – 391 .

Ruszkiewicz   JA , Zhang Z , Gonçalves FM , Tizabi Y , Zelikoff JT , Aschner M . Neurotoxicity of e-cigarettes . Food Chem Toxicol. 2020 ; 138 : 111245 .

Barrington-Trimis   JL , Leventhal AM . Adolescents’ use of “pod mod” e-cigarettes – urgent concerns . N Engl J Med. 2018 ; 379 ( 12 ): 1099 – 1102 .

Goniewicz   ML , Boykan R , Messina CR , Eliscu A , Tolentino J . High exposure to nicotine among adolescents who use Juul and other vape pod systems (‘pods’) . Tob Control. 2019 ; 28 ( 6 ): 676 – 677 .

Chadi   N , Hadland SE , Harris SK . Understanding the implications of the “vaping epidemic” among adolescents and young adults: a call for action . Subst Abus. 2019 ; 40 ( 1 ): 7 – 10 .

Rubinstein   ML , Delucchi K , Benowitz NL , et al.    Adolescent exposure to toxic volatile organic chemicals from e-cigarettes . Pediatrics. 2018 ; 141 : e20173557 .

Cherian   SV , Kumar A , Estrada-Y-Martin RM . E-cigarette or vaping product-associated lung injury: a review . Am J Med. 2020 ; 133 ( 6 ): 657 – 663 .

Yang   I , Sandeep S , Rodriguez J . The oral health impact of electronic cigarette use: a systematic review . Crit Rev Toxicol. 2020 ; 50 ( 2 ): 97 – 127 .

Soneji   S , Barrington-Trimis JL , Wills TA , et al.    Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis . JAMA Pediatr. 2017 ; 171 ( 8 ): 788 – 797 .

Spindle   TR , Hiler MM , Cooke ME , Eissenberg T , Kendler KS , Dick DM . Electronic cigarette use and uptake of cigarette smoking: a longitudinal examination of U.S. college students . Addict Behav. 2017 ; 67 : 66 – 72 .

Curran   KA , Burk T , Pitt PD , Middleman AB . Trends and substance use associations with e-cigarette use in US adolescents . Clin Pediatr (Phila). 2018 ; 57 ( 10 ): 1191 – 1198 .

Kandel   D , Kandel E . The Gateway Hypothesis of substance abuse: developmental, biological and societal perspectives . Acta Paediatr. 2015 ; 104 ( 2 ): 130 – 137 .

Prochaska   JJ , Das S , Young-Wolff KC . Smoking, mental illness, and public health . Annu Rev Public Health. 2017 ; 38 : 165 – 185 .

Williams   JM , Steinberg ML , Griffiths KG , Cooperman N . Smokers with behavioral health comorbidity should be designated a tobacco use disparity group . Am J Public Health. 2013 ; 103 ( 9 ): 1549 – 1555 .

Upadhyaya   HP , Deas D , Brady KT , Kruesi M . Cigarette smoking and psychiatric comorbidity in children and adolescents . J Am Acad Child Adolesc Psychiatry. 2002 ; 41 ( 11 ): 1294 – 1305 .

Chaiton   MO , Cohen JE , O’Loughlin J , Rehm J . A systematic review of longitudinal studies on the association between depression and smoking in adolescents . BMC Public Health. 2009 ; 9 : 356 .

Esmaeelzadeh   S , Moraros J , Thorpe L , et al.    Examining the association and directionality between mental health disorders and substance use among adolescents and young adults in the U.S. and Canada- a systematic review and meta-analysis . J Clin Med . 2018 ; 7 : 543 .

DeHay   T , Morris C , May MG , Devine K , Waxmonsky J . Tobacco use in youth with mental illnesses . J Behav Med. 2012 ; 35 ( 2 ): 139 – 148 .

Cavallo   DA , Krishnan-Sarin S . Nicotine use disorders in adolescents . Pediatr Clin North Am. 2019 ; 66 ( 6 ): 1053 – 1062 .

Silberg   J , Rutter M , D’Onofrio B , Eaves L . Genetic and environmental risk factors in adolescent substance use . J Child Psychol Psychiatry. 2003 ; 44 ( 5 ): 664 – 676 .

Kendler   KS , Neale MC , MacLean CJ , Heath AC , Eaves LJ , Kessler RC . Smoking and major depression. A causal analysis . Arch Gen Psychiatry. 1993 ; 50 ( 1 ): 36 – 43 .

Yuan   M , Cross SJ , Loughlin SE , Leslie FM . Nicotine and the adolescent brain . J Physiol. 2015 ; 593 ( 16 ): 3397 – 3412 .

Smith   RF , McDonald CG , Bergstrom HC , Ehlinger DG , Brielmaier JM . Adolescent nicotine induces persisting changes in development of neural connectivity . Neurosci Biobehav Rev. 2015 ; 55 : 432 – 443 .

Goriounova   NA , Mansvelder HD . Short- and long-term consequences of nicotine exposure during adolescence for prefrontal cortex neuronal network function . Cold Spring Harb Perspect Med. 2012 ; 2 ( 12 ): a012120 .

Casey   B , Jones RM , Somerville LH . Braking and accelerating of the adolescent brain . J Res Adolesc. 2011 ; 21 ( 1 ): 21 – 33 .

Casey   BJ , Heller AS , Gee DG , Cohen AO . Development of the emotional brain . Neurosci Lett. 2019 ; 693 : 29 – 34 .

Sinha   R . The role of stress in addiction relapse . Curr Psychiatry Rep. 2007 ; 9 ( 5 ): 388 – 395 .

Iñiguez   SD , Warren BL , Parise EM , et al.    Nicotine exposure during adolescence induces a depression-like state in adulthood . Neuropsychopharmacology. 2009 ; 34 ( 6 ): 1609 – 1624 .

Flensborg-Madsen Trine   T , Bay von Scholten M , Flachs EM , et al.    Tobacco smoking as a risk factor for depression. A 26-year population-based follow-up study . J Psychiatr Res. 2011 ; 45 : 143 – 149 .

Scott   JG , Matuschka L , Niemelä S , Miettunen J , Emmerson B , Mustonen A . Evidence of a causal relationship between smoking tobacco and schizophrenia spectrum disorders . Front Psychiatry. 2018 ; 9 : 607 .

Leventhal   AM , Zvolensky MJ . Anxiety, depression, and cigarette smoking: a transdiagnostic vulnerability framework to understanding emotion-smoking comorbidity . Psychol Bull. 2015 ; 141 ( 1 ): 176 – 212 .

Chadi   N , Schroeder R , Jensen JW , et al.    Association between electronic cigarette use and marijuana use among adolescents and young adults: a systematic review and meta-analysis . JAMA Pediatr. 2019 ; 173 :e192574-e192574.

Chadi   N , Minato C , Stanwick R . Cannabis vaping: understanding the health risks of a rapidly emerging trend . Paediatr Child Health. 2020 ; 25 : S16 – S20 .

National Academies of Sciences Engineering and Medicine . The Health Effects of Cannabis and Cannabinoids: The Current State of Evidence and Recommendations for Research . Washington, DC: National Academies Press ; 2017 .

Google Preview

Hefner   K , Valentine G , Sofuoglu M . Electronic cigarettes and mental illness: reviewing the evidence for help and harm among those with psychiatric and substance use disorders . Am J Addict. 2017 ; 26 ( 4 ): 306 – 315 .

US Department of Health and Human Services . Preventing Tobacco Use Among Youth and Youth Adults: A Report of the Surgeon General. Atlanta, GA : U.S. Dept. of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health ; 2012 .

Moher   D , Liberati A , Tetzlaff J , et al.    Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement . PLoS One. 2009 ; 6 : e1000097 .

Veritas Health Innovation . Covidence Systematic Review Software . 2020 .

Thomas   BH , Ciliska D , Dobbins M , Micucci S . A process for systematically reviewing the literature: providing the research evidence for public health nursing interventions . Worldviews Evid Based Nurs. 2004 ; 1 ( 3 ): 176 – 184 .

Tankersley   W , Martz G , Mekala HM . Risk factors for illicit substance vaporizer use in adolescents . Addict Disord Their Treat . 2019 ; 18 : 22 – 27 .

Faulcon   LM , Rudy S , Limpert J , Wang B , Murphy I . Adverse experience reports of seizures in youth and young adult electronic nicotine delivery systems users . J Adolesc Health. 2020 ; 66 ( 1 ): 15 – 17 .

Tucker   JS , Shadel WG , Golinelli D , Seelam R , Siconolfi D . Correlates of cigarette and alternative tobacco product use among young tobacco users experiencing homelessness . Addict Behav. 2019 ; 95 : 145 – 151 .

Miller   S , Pike J , Stacy AW , Xie B , Ames SL . Negative affect in at-risk youth: outcome expectancies mediate relations with both regular and electronic cigarette use . Psychol Addict Behav. 2017 ; 31 ( 4 ): 457 – 464 .

Frohe   T , Leeman RF , Patock-Peckham J , Ecker A , Kraus S , Foster DW . Correlates of cannabis vape-pen use and knowledge among U.S. college students . Addict Behav Rep. 2018 ; 7 : 32 – 39 .

Leventhal   AM , Bae D , Kechter A , Barrington-Trimis JL . Psychiatric comorbidity in adolescent use and poly-use of combustible, vaporized, and edible cannabis products . J Psychiatr Res. 2020 ; 124 : 91 – 98 .

Hefner   KR , Sollazzo A , Mullaney S , Coker KL , Sofuoglu M . E-cigarettes, alcohol use, and mental health: use and perceptions of e-cigarettes among college students, by alcohol use and mental health status . Addict Behav. 2019 ; 91 : 12 – 20 .

King   JL , Reboussin BA , Spangler J , Cornacchione Ross J , Sutfin EL . Tobacco product use and mental health status among young adults . Addict Behav. 2018 ; 77 : 67 – 72 .

Bandiera   FC , Loukas A , Wilkinson AV , Perry CL . Associations between tobacco and nicotine product use and depressive symptoms among college students in Texas . Addict Behav. 2016 ; 63 : 19 – 22 .

Riehm   KE , Young AS , Feder KA , et al.    Mental health problems and initiation of e-cigarette and combustible cigarette use . Pediatrics. 2019 ;144.

Conway   KP , Green VR , Kasza KA , et al.    Co-occurrence of tobacco product use, substance use, and mental health problems among youth: findings from wave 1 (2013–2014) of the population assessment of tobacco and health (PATH) study . Addict Behav. 2018 ; 76 : 208 –217.

Green   VR , Conway KP , Silveira ML , et al.    Mental health problems and onset of tobacco use among 12- to 24-year-olds in the PATH study . J Am Acad Child Adolesc Psychiatry. 2018 ; 57 ( 12 ): 944 – 954.e4 .

Sawdey   MD , Day HR , Coleman B , et al.    Associations of risk factors of e-cigarette and cigarette use and susceptibility to use among baseline PATH study youth participants (2013–2014) . Addict Behav. 2019 ; 91 : 51 – 60 .

Seo   DC , Kwon E , Lee S , Seo J . Using susceptibility measures to prospectively predict ever use of electronic cigarettes among adolescents . Prev Med. 2020 ; 130 : 105896 .

Ganz   O , Rimal RN , Cohn AM , Johnson AL , Delnevo CD , Horn K . Receptivity to tobacco advertising among young adults with internalizing problems: findings from the population assessment of tobacco and health study . Subst Use Misuse. 2020 ; 55 ( 4 ): 546 – 556 .

Chadi   N , Li G , Cerda N , Weitzman ER . Depressive symptoms and suicidality in adolescents using e-cigarettes and marijuana: a secondary data analysis from the youth risk behavior survey . J Addict Med. 2019 ; 13 ( 5 ): 362 – 365 .

Chen   YL , Wu SC , Chen YT , et al.    E-cigarette use in a country with prevalent tobacco smoking: a population-based study in Taiwan . J Epidemiol. 2019 ; 29 ( 4 ): 155 – 163 .

Jee   YJ . Comparison of emotional and psychological indicators according to the presence or absence of the use of electronic cigarettes among Korea youth smokers . Information. 2016 ; 19 : 4325 – 4532 .

Lee   Y , Lee KS . Association of depression and suicidality with electronic and conventional cigarette use in South Korean adolescents . Subst Use Misuse. 2019 ; 54 ( 6 ): 934 – 943 .

Lechner   WV , Janssen T , Kahler CW , Audrain-McGovern J , Leventhal AM . Bi-directional associations of electronic and combustible cigarette use onset patterns with depressive symptoms in adolescents . Prev Med. 2017 ; 96 : 73 – 78 .

Leventhal   AM , Strong DR , Sussman S , et al.    Psychiatric comorbidity in adolescent electronic and conventional cigarette use . J Psychiatr Res. 2016 ; 73 : 71 – 78 .

Goldenson   NI , Khoddam R , Stone MD , Leventhal AM . Associations of ADHD symptoms with smoking and alternative tobacco product use initiation during adolescence . J Pediatr Psychol. 2018 ; 43 ( 6 ): 613 – 624 .

Bierhoff   J , Haardörfer R , Windle M , Berg CJ . Psychological risk factors for alcohol, cannabis, and various tobacco use among young adults: a longitudinal analysis . Subst Use Misuse. 2019 ; 54 ( 8 ): 1365 – 1375 .

Grant   JE , Lust K , Fridberg DJ , King AC , Chamberlain SR . E-cigarette use (vaping) is associated with illicit drug use, mental health problems, and impulsivity in university students . Ann Clin Psychiatry. 2019 ; 31 ( 1 ): 27 – 35 .

Marsden   DG , Loukas A , Chen B , Perry CL , Wilkinson AV . Associations between frequency of cigarette and alternative tobacco product use and depressive symptoms: a longitudinal study of young adults . Addict Behav. 2019 ; 99 : 106078 .

Bandiera   FC , Loukas A , Li X , Wilkinson AV , Perry CL . Depressive symptoms predict current e-cigarette use among college students in Texas . Nicotine Tob Res. 2017 ; 19 ( 9 ): 1102 – 1106 .

Berg   CJ , Haardörfer R , Payne JB , et al.    Ecological momentary assessment of various tobacco product use among young adults . Addict Behav. 2019 ; 92 : 38 – 46 .

Kim   JS , Kim K . Electronic cigarette use and suicidal behaviors among adolescents . J Public Health (Bangkok). 2019 ; 1 – 7 .

Lee   Y , Lee KS . Relationship between unhealthy weight control behaviors and substance use patterns among Korean adolescents: results from the 2017 national youth risk behavior survey . Public Health. 2019 ; 174 : 56 – 64 .

Shin   SH , Conley D , Ksinan Jiskrova G , Wills TA . Adverse childhood experiences and e-cigarette use during young adulthood . Am J Addict. 2019 ; 28 ( 4 ): 303 – 310 .

Dvorsky   MR , Langberg JM . Cigarette and e-cigarette use and social perceptions over the transition to college: the role of ADHD symptoms . Psychol Addict Behav. 2019 ; 33 ( 3 ): 318 – 330 .

Staff   J , Maggs JL , Seto C , Dillavou J , Vuolo M . Electronic and combustible cigarette use in adolescence: links with adjustment, delinquency, and other substance use . J Adolesc Health. 2020 ; 66 ( 1 ): 39 – 47 .

Moeller   FG , Barratt ES , Dougherty DM , Schmitz JM , Swann AC . Psychiatric aspects of impulsivity . Am J Psychiatry. 2001 ; 158 ( 11 ): 1783 – 1793 .

Conner   M , Grogan S , Simms-Ellis R , et al.    Patterns and predictors of e-cigarette, cigarette and dual use uptake in UK adolescents: evidence from a 24-month prospective study . Addiction. 2019 ; 114 ( 11 ): 2048 – 2055 .

Bold   KW , Morean ME , Kong G , et al.    Early age of e-cigarette use onset mediates the association between impulsivity and e-cigarette use frequency in youth . Drug Alcohol Depend. 2017 ; 181 : 146 – 151 .

Pentz   MA , Shin H , Riggs N , Unger JB , Collison KL , Chou CP . Parent, peer, and executive function relationships to early adolescent e-cigarette use: a substance use pathway? Addict Behav. 2015 ; 42 : 73 – 78 .

Riggs   NR , Pentz MA . Inhibitory control and the onset of combustible cigarette, e-cigarette, and hookah use in early adolescence: the moderating role of socioeconomic status . Child Neuropsychol. 2016 ; 22 ( 6 ): 679 – 691 .

Doran   N , Tully L . Impulsivity and tobacco product use over time . Addict Behav. 2018 ; 85 : 153 – 157 .

Vallone   DM , Bennett M , Xiao H , Pitzer L , Hair EC . Prevalence and correlates of JUUL use among a national sample of youth and young adults . Tob Control. 2019 ; 28 ( 6 ): 603 – 609 .

Leventhal   AM , Urman R , Barrington-Trimis JL , et al.    Perceived stress and poly-tobacco product use across adolescence: patterns of association and gender differences . J Psychiatr Res. 2017 ; 94 : 172 – 179 .

Flory   K , Milich R , Lynam DR , Leukefeld C , Clayton R . Relation between childhood disruptive behavior disorders and substance use and dependence symptoms in young adulthood: individuals with symptoms of attention-deficit/hyperactivity disorder and conduct disorder are uniquely at risk . Psychol Addict Behav. 2003 ; 17 ( 2 ): 151 – 158 .

Wu   P , Hoven CW , Liu X , Cohen P , Fuller CJ , Shaffer D . Substance use, suicidal ideation and attempts in children and adolescents . Suicide Life Threat Behav. 2004 ; 34 ( 4 ): 408 – 420 .

Chang   G , Sherritt L , Knight JR . Adolescent cigarette smoking and mental health symptoms . J Adolesc Health. 2005 ; 36 ( 6 ): 517 – 522 .

Zulauf   CA , Sprich SE , Safren SA , Wilens TE . The complicated relationship between attention deficit/hyperactivity disorder and substance use disorders . Curr Psychiatry Rep. 2014 ; 16 ( 3 ): 436 .

Kim   Y , Emery SL , Vera L , et al.    At the speed of Juul: measuring the Twitter conversation related to ENDS and Juul across space and time (2017–2018) . Tob Control. 2020 .

McKeganey   N , Russell C , Katsampouris E , Haseen F . Sources of youth access to JUUL vaping products in the United States . Addict Behav Rep. 2019 ; 10 : 100232 .

Boden   JM , Fergusson DM . Alcohol and depression . Addiction. 2011 ; 106 ( 5 ): 906 – 914 .

Fluharty   M , Taylor AE , Grabski M , Munafò MR . The association of cigarette smoking with depression and anxiety: a systematic review . Nicotine Tob Res. 2017 ; 19 ( 1 ): 3 – 13 .

Liu   J , Halpern-Felsher B . The Juul curriculum is not the jewel of tobacco prevention education . J Adolesc Health. 2018 ; 63 ( 5 ): 527 – 528 .

Chaker   AM . Schools and parents fight a “Juul” epidemic . Wall Street Journal 2018 .

Zernike   K . ‘I can’t stop’: schools struggle with vaping explosion . New York Times 2018 .

Fanshawe   TR , Halliwell W , Lindson N , Aveyard P , Livingstone-Banks J , Hartmann-Boyce J . Tobacco cessation interventions for young people . Cochrane Database Syst Rev. 2017 ; 11 : CD003289 .

Bhalerao   A , Sivandzade F , Archie SR , Cucullo L . Public health policies on e-cigarettes . Curr Cardiol Rep. 2019 ; 21 ( 10 ): 111 .

Nicksic   NE , Do EK , Barnes AJ . Cannabis legalization, tobacco prevention policies, and cannabis use in e-cigarettes among youth . Drug Alcohol Depend. 2020 ; 206 : 107730 .

Gobbi   G , Atkin T , Zytynski T , et al.    Association of cannabis use in adolescence and risk of depression, anxiety, and suicidality in young adulthood: a systematic review and meta-analysis . JAMA Psychiatry. 2019 ; 76 ( 4 ): 426 – 434 .

Moore   TH , Zammit S , Lingford-Hughes A , et al.    Cannabis use and risk of psychotic or affective mental health outcomes: a systematic review . Lancet. 2007 ; 370 ( 9584 ): 319 – 328 .

• comorbidity
• mental health
• young adult
• electronic cigarettes

Supplementary data

Email alerts, citing articles via.

• About Nicotine & Tobacco Research
• Recommend to your Library

Affiliations

• Online ISSN 1469-994X
• Copyright © 2024 Society for Research on Nicotine and Tobacco
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Study protocol
• Open access
• Published: 10 December 2022

Delivering vaping cessation interventions to adolescents and young adults on Instagram: protocol for a randomized controlled trial

• Joanne Chen Lyu 1 ,
• Sarah S. Olson 2 ,
• Danielle E. Ramo 3 &
• Pamela M. Ling   ORCID: orcid.org/0000-0001-6166-9347 1  

BMC Public Health volume  22 , Article number:  2311 ( 2022 ) Cite this article

7183 Accesses

1 Citations

4 Altmetric

Metrics details

Adolescent and young adult use of electronic nicotine delivery systems (“vaping”) has increased rapidly since 2018. There is a dearth of evidence-based vaping cessation interventions for this vulnerable population. Social media use is common among young people, and smoking cessation groups on social media have shown efficacy in the past. The objective of this study is to describe the protocol for a randomized controlled trial (RCT) testing the efficacy of an Instagram-based vaping cessation intervention for adolescents and young adults.

Adolescents and young adults aged 13–21 residing in California who have vaped at least once per week in the past 30 days will be recruited through social media ads, community partners, and youth serving organizations. Participants will be randomly assigned to intervention or control conditions: the intervention group takes place on Instagram, where participants receive up to 3 posts per weekday for 25 days over 5 weeks; the control group will be directed to kickitca.org, a website offering links to chatline and texting cessation services operated by the California Smokers' Helpline. The primary outcome is biochemically verified 7-day point prevalence abstinence for nicotine vaping; secondary outcomes are vaping reduction by 50% or more, vaping quit attempts, readiness to quit vaping, confidence in ability to quit, desire to quit, commitment to abstinence, and use of evidence-based cessation strategies. Both the primary outcome and secondary outcomes will be assessed immediately, 3 months, and 6 months after the treatment.

This is the first RCT to test a vaping cessation intervention delivered through Instagram. If effective, it will be one of the first evidence-based interventions to address vaping among adolescents and young adults and add to the evidence base for social media interventions for this population.

Trial registration

ClinicalTrials.gov: NCT04707911, registered on January 13, 2021.

Peer Review reports

The aggressive promotion and uptake of electronic nicotine delivery systems (colloquially called “vaping”) among youth in the US [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] have led to an increase in overall nicotine use [ 8 , 10 , 11 , 12 , 13 ]. Among high school students, vaping increased 135% from 11.7% to 27.5% between 2017–2019 [ 8 , 13 ]. Despite a documented decline in e-cigarette use among young people during the COVID-19 pandemic [ 14 ], in 2021, e-cigarettes continued to be the most commonly currently used tobacco product with 11.3% of high school students and 2.8% of middle school students reporting use in the past 30 days[ 15 ]. Vaping among young people is especially concerning because compared with older adults, the brains of adolescents and young adults are more vulnerable to the harmful health effects of nicotine that most e-cigarettes contain [ 16 , 17 ]. In addition, e-cigarettes introduced in recent years have the potential to deliver equal or more nicotine compared to tobacco cigarettes [ 18 ], potentially increasing their addictiveness. Prevention programs to address adolescent vaping include prevention media campaigns and educational classroom materials, such as the Stanford Tobacco Prevention Toolkit [ 19 ] and the CATCH my breath e-cigarette educational program [ 20 ]. Fewer programs address vaping cessation for adolescents and young adults: telephone counseling and texting programs are offered [ 21 , 22 ], and Truth Initiative offers a free texting vaping intervention that has shown efficacy in a randomized trial [ 23 ].

Social media have been a part of life for young Americans with 95% of adolescents and 84% of adults ages 18 to 29 ever using social media [ 24 , 25 ]. While aggressive targeted marketing of e-cigarettes on social media such as Twitter, Instagram, and YouTube significantly contributes to high awareness of e-cigarettes and high e-cigarette use rates among youth [ 26 , 27 ], social media are also a promising channel to reach a large number of adolescents and young adults who vape without geographical restrictions to deliver vaping cessation interventions. Social media-based smoking cessation interventions for adults have demonstrated feasibility, acceptability, and early efficacy [ 28 , 29 ]. However, there are few effective and high-quality smoking cessation interventions for adolescents and young adults [ 30 , 31 ] and even less evidence on social media interventions to address vaping. To the best of our knowledge, none of the existing vaping cessation interventions for adolescents and young adults are delivered via social media. To fill this gap, we will conduct a randomized controlled trial (RCT) to test the efficacy of a social media-based intervention designed specifically to support cessation among adolescents and young adults who vape. Though vaping cessation interventions are relatively new and rigorously tested programs are limited, a meta-analysis of research on adolescent cigarette smoking cessation interventions showed that cessation programs increased the probability of quitting by approximately 46%, with higher quit rates in programs that included a motivation enhancement component, cognitive-behavioral techniques, and social influence approaches [ 32 ]. This suggests that vaping inventions utilizing these strategies may be effective to address the problem of vaping among young populations. One model has incorporated Motivational Interviewing (MI), cognitive behavioral coping skills, and the Transtheoretical Model (TTM) of behavior change (i.e., readiness to quit) to create the Tobacco Status Project (TSP) [ 31 , 33 , 34 , 35 ], a smoking cessation intervention for young adult smokers implemented entirely through private Facebook groups. Participants in TSP joined private Facebook groups where they received daily posts and were encouraged to interact with each other for 90 days [ 33 ]. Biochemically verified 7-day point prevalence abstinence at the 3-month follow-up was significantly higher in the intervention group (8.3%) compared to the control group referred to Smokefree.gov (3.2%) [ 33 ]. In our current study, we will deliver intervention materials adapted from TSP to address nicotine vaping, the adolescent and young adult target audience, and to deliver the program via the Instagram platform, which is more popular than Facebook among adolescents, being used by 72% of adolescents in 2018 [ 36 ].

This study will evaluate the efficacy of the Instagram-based vaping cessation intervention among adolescents and young adults in an RCT. The primary aim is to test the efficacy of the intervention to achieve biochemically verified nicotine vaping abstinence when the treatment ends and sustain it for 6 months after the treatment. The secondary aim is to test the effectiveness of the intervention in terms of decreasing vaping frequency, and increasing quit attempts, readiness to quit, confidence in ability to quit, desire to quit, commitment to abstinence, and use of evidence-based cessation strategies assessed immediately, 3 months, and 6 months after the treatment.

Methods/design

Study design.

This is an RCT with an intervention group and a control group. This study protocol was written according to SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials), a guideline that defines standard protocol items for clinical trials and is widely endorsed as an international standard for trial protocols [ 37 , 38 ]. The completed SPIRIT checklist is shown as Additional file 1 .

Participants

Adolescents and young adults aged 13–21 residing in California who have vaped at least once per week in the past 30 days will be recruited online via Facebook and other social media, augmented by outreach through community partners and youth serving organizations (the detailed recruitment procedure is elaborated in the Recruitment section below). This study focuses on adolescents and young adults aged 13–21 because people in this age group both need vaping cessation support and have integrated social media into daily life. Children under the age of 13 are not included because the intervention takes place on social media and popular sites all set their age limit at 13, in compliance with the Children’s Online Privacy Protection Act [ 39 ]. There are no exclusions by race/ethnicity or gender or sexual orientation. Those who are pregnant and/or currently use other tobacco products will not be excluded from participation, as they may benefit from vaping cessation.

Inclusion criteria

1. English literacy;

2. Age between 13-21 years;

3. Indicate they use social media “most” (≥ 4) days per week;

4. Have vaped at least once per week in the past 30 days;

5. Access to a computer or mobile phone with photo capability to verify abstinence from vaping;

6. Indicate they are considering quitting or are interested in quitting within the next 6 months;

7. Currently reside in California. This is because the funder for this study, the California Tobacco Related Diseases Research Program, requires the research be conducted in California.

Exclusion criteria

1. No English literacy;

2. Age under 13 or over 21 years;

3. Insufficient social media use (3 or fewer days per week);

4. Have not vaped at least once per week in past 30 days;

5. No access to computer or mobile phone with photo capability to verify abstinence from vaping;

6. Not interested in or considering quitting within the next 6 months;

7. Not California residents.

Sample size estimation

Power analyses were generated using the two-group repeated proportions module in NCSS PASS 14 [ 40 ] to compute minimum detectable effect sizes for the primary analysis. Assuming α = 0.05, power = 0.80, rho = 0.43 and a 7-day abstinence base rate of 3.2% and a quit rate of 8.3% in the treatment group from our preliminary data [ 31 ], group sample size of 234 (N total  = 468) is needed. Assuming a dropout rate of 7%, we need 500 participants.

Recruitment

A total of 500 participants will be recruited using social media campaign strategies that have been successfully employed in previous online survey and intervention studies [ 31 , 41 ], augmented by outreach through community partners and youth serving organizations. Advertisements will be targeted based on age and with keywords likely to reach e-cigarette and social media users. Ads will include an image and short text consistent with most social media advertising guidelines. Most ads will mention study incentive. Ad spending and response (clicks on ads and study enrollment numbers) will be monitored with adjustments as needed to maximize efficiency of spending. Ads will include a link to the study’s website www.quitthehitca.com , which has a short description of the study and eligibility questions that address the inclusion criteria and exclusion criteria. If respondents are eligible, then they will be taken to the informed consent page. The participant flow through the RCT is shown in Fig.  1 .

Participant flow through the randomized clinical trial

Study procedure

The state of California allows teenagers to seek treatment for substance use without parental consent [ 42 ]. Parental consent may pose a risk to teens by revealing their vaping behavior, which may make them reluctant to engage in vaping cessation interventions. Therefore, in this study, both young adult participants and adolescent participants will consent to participate in the research themselves. Participants will complete consent electronically on the study website immediately following completion of eligibility screening. The consent forms will contain a thorough description of the research project, including the procedures involved, the benefits and risks, compensation, the right to non-participation, and the Research Subject’s Bill of Rights. These documents are also available to participants at any time on the study website: www.quitthehitca.com . The amount of text in the standard consent form is burdensome to read on a mobile device, and participants may tend to skip the form. To enhance usability, especially for mobile devices, a shortened version of the consent form and a video version of the consent with key elements have been created for mobile device review and consent. Participants on mobile devices will also have the opportunity to download and save the full consent form for future viewing.

Information about the informed consent process is written in clear, concise English that can be understood at a 6th-grade reading level. Participants will be asked to contact study staff if they do not understand any part of the informed consent information.

Before being able to proceed with the study, participants will indicate on an electronic form that they understand what this study is about, that their participation is voluntary, that they are not required to complete any part of this study, and that there are risks associated with study participation. Incomplete or incorrect answers will result in the potential participant being prompted to review the full consent document before attempting the consent questions again. Potential participants will not be enrolled in the study if they fail to complete the form four times. In both the screening and baseline survey, participants are asked about location questions (zip code in the screening and county in the baseline survey). Our research staff will check the consistency of their answers and contact the participants with inconsistent answers using the contact information they provided to confirm the authenticity of the participants. In addition, participants must follow the study account on Instagram, and study personnel will review the profiles of followers for consistency with age and California location. Irregular or inconsistent answers or participants otherwise having difficulty completing the form will be contacted by trained study personnel who will review the consent material and assist with study enrollment. Study personnel may detect behavior, responses or Instagram accounts that do not appear to be authentic. Fraudulent participants identified through this process will be removed and documented. In the consent form, participants will be asked to remain in the study’s Instagram group throughout the duration of the study (about 7 months) for the intervention and for follow-up contacts. Participants will be paid $10 in the form of an electronic gift card for completing the baseline assessment. Participants will be paid $15–35 for each follow-up assessment for a possible total of $100.

Participants who sign the consent form will be asked to create a login, and then redirected to the baseline assessment. This study will recruit 500 participants in total. Those completing the baseline assessment are randomized using a computer-generated random sequence which automatically assigns participants without revealing allocation to study investigators. Participants are assigned to either the Instagram intervention or the control condition, which is a referral to free support administered by the California quitline via the website kickitca.org. Approximately two weeks after the first participant is enrolled in an intervention group, the group will begin to receive the intervention program. Groups may start slightly sooner or later than 2 weeks based on the pace of recruitment, aiming for a group size between 5 – 15 participants. Depending on the pace of enrollment, multiple groups may run simultaneously.

Development of the intervention

This intervention was adapted from the TSP described above. The TSP is a smoking cessation intervention for young adults that was delivered through private Facebook groups and utilized the US Clinical Practice Guidelines for smoking cessation [ 43 ] and the TTM of behavior change [ 44 , 45 ], with counseling messages tailored to participants’ readiness to quit delivered in 90 Facebook posts over 90 days along with weekly live counseling chat sessions with a trained smoking cessation counselor. The first adaptation of the TSP by our team was to tailor intervention content for sexual and gender minority (SGM) young adult smokers and had preliminary evidence for the effectiveness: an SGM-tailored Facebook smoking cessation intervention increased reported abstinence from smoking, compared to a non-tailored intervention [ 46 ]. This study followed the similar adaptation approach as the SGM project to tailor the intervention content for adolescents and young adults who vape: revising the intervention based on extensive iterative formative work. We have an established partnership with HopeLab (a social innovation lab focusing on designing science-based technologies to improve the health of teens and young adults) and with Rescue (a marketing agency providing behavior change marketing services) for intervention adaptation. Before our study, Rescue conducted 18 focus groups and 66 interviews with 269 adolescents to develop adolescent vaping messaging for cessation and interviewed 380 adolescents with vaping experience. Our partners worked intensively in 2021 to further develop the Instagram intervention for adolescents who vape, including co-creation sessions working with 9 adolescents. Surface-level tailoring [ 47 ] was accomplished using pictures of adolescents who vape, as well as symbols and terms that were meaningful to the group. Deep-level tailoring [ 47 ] involved discussion of adolescent issues relevant to vaping. Examples included increasing awareness of predatory marketing targeting young people, awareness and experiences of addiction, and education about the gateway effect of vaping on smoking initiation. Two design sprints and development of the websites and infrastructure were completed prior to launching a test version of the intervention. 90 adolescents who vape participated in the pilot tests to address the feasibility, acceptability, usability, and helpfulness of the test version. Participant surveys included ratings of different elements of the intervention, and interviews addressed their experience participating in different aspects of the intervention, what they liked most and least about the experience, and identified which aspects of the intervention or procedures to retain or change. The partner team met virtually on a weekly basis throughout the development process until January 2022.

Intervention group

The vaping cessation intervention will be implemented on Instagram. Participants in the intervention group will be assigned to groups on Instagram, where they will receive up to 3 posts per weekday for 25 days (5 weekdays, no weekends) over 5 weeks. Posts incorporate skills from cognitive behavioral therapy, found effective for long-term smoking cessation [ 48 ], as well as the TTM processes of self-liberation (e.g., making a commitment to quit), stimulus control (e.g., removing vaping paraphernalia from the home), and counter conditioning (e.g., engaging in alternative behaviors). Posts also encourage setting a quit date and making a detailed quit plan. Posts include a combination of images, videos, and text to elicit a response from participants. Posts may suggest that participants use their social media or real social networks for support with vaping cessation. However, they are not required to share any information about substance use on social media.

Groups are facilitated by a trained Guide, a certified cessation counselor with over 4 years of experience facilitating smoking cessation groups on Facebook, working with the Principal Investigator and Sub-Investigators. The Guide will send the daily posts in groups. In addition, the study employs a pediatrician on demand if additional expertise or clinical advice is needed. Participants will be educated about signs of nicotine dependence and if they express interest in pharmacotherapy, we will encourage them to access this through their personal healthcare providers.

Control group

Participants in the control condition will be directed to kickitca.org, a website offering links to chatline and texting cessation services operated by the California Smokers' Helpline, the free state quitline program that supports smoking, vaping, and smokeless tobacco cessation strategies.

Baseline measures

Baseline measures will consist of sociodemographic characteristics, nicotine vaping, quitting experience, motivations to quit (such as readiness to quit vaping, confidence in ability to quit, desire to quit, and commitment to abstinence), other tobacco use, marijuana/cannabis use, social media use integration, and symptoms of depression or anxiety.

Outcome measures

Primary outcome.

7-day point prevalence abstinence will be assessed immediately, 3 months, and 6 months after the treatment. Participants reporting no nicotine vaping in the past 7 days will be coded as abstinent. At each follow-up assessment, those reporting 7-day abstinence and not using NRT will be mailed a saliva cotinine test kit for biochemical verification. Participants will be provided both written instruction and a video demonstrating how to correctly perform the saliva cotinine test, and take photos of themselves doing the test and a photo of the result, and how to send the photos to our research staff. Participants with a salivary cotinine level < 20 ng/ml will be considered to have achieved biochemically verified nicotine vaping abstinence.

Secondary outcomes

We will measure seven secondary outcomes as below:

• Vaping reduction by 50% or more between baseline and each follow-up will be calculated at each time point by multiplying the number of vaping days in the past 7 days and the number of puffs or hits they took on an average day.

• Vaping quit attempt will be measured by questions asking the presence and the number of quit attempts lasting at least 1 day since the last assessment.

• Readiness to quit vaping will be assessed using the Stages of Change Questionnaire [ 42 ] to classify participants as in Contemplation or Preparation phases [ 45 ]. Outcome will be measured as proportion change in Stage at assessments conducted immediately, 3 months, and 6 months after the treatment.

• Confidence in ability to quit will be measured by asking how confident the participants are about avoiding vaping nicotine in the next 6 months.

• Desire to quit will be measured by asking how much the participants want to quit vaping.

• Commitment to abstinence will be assessed with the Thoughts About Abstinence Form [ 49 ], categorizing their goal as no goal, intermediary goal (e.g., reduced vaping), or total abstinence. Outcome will be measured as proportion endorsing a goal of abstinence at each time point.

• Use of evidence-based cessation strategies will be measured by asking the supports participants used when they tried to quit in the past (30 days/3 months).

Data collection and management

The three follow-up assessments conducted immediately, 3 months, and 6 months after the treatment will be administered online using Qualtrics through UCSF MyAccess, which includes Secure Sockets Layer encryption, and adheres to HIPAA standards, allowing encrypted transmission of all survey data. Research staff will send emails, text messages, and messages in Instagram with the survey links to notify participants of assessments. At most three emails will be sent to remind participants of the assessments. Those who don't respond after the three contact attempts are counted as dropout at each time point. Those who don't respond in the previous assessments will still be contacted for later assessments. For instance, a participant who did not do the assessment at 3 months post-treatment will still be notified of the assessment at 6 months post-treatment up to three times. Survey data will be securely transferred to password-protected Excel or SPSS files that will be securely stored online using the UCSF Research Analysis Environment, a secure, HIPAA-compliant desktop environment. Files with identifying information will be stored separately from files with other forms of data. Data will only be accessed by the PI and a research assistant until it is de-identified. Study investigators have completed required UCSF training and certification in the conduct of research with human subjects. All collected data will be analyzed and reported in aggregate form for publications. All survey data collection and storage procedures are consistent with current HIPAA guidelines.

In order to ensure and maintain the scientific integrity of this human subject research project, and to protect the safety of its research participants, we will assemble a Data Safety Monitoring Board (DSMB). The DSMB will meet every six months via video conferencing to review the study protocol and procedures. The DSMB will have the responsibility of assuring that participants are not being exposed to unnecessary or unreasonable risks as a result of the pursuit of the study’s scientific objectives.

Data analysis

Descriptive statistics will summarize sample characteristics and intervention delivery in each group. We will examine treatment condition and baseline descriptive characteristics as predictors of attrition at intervention end and will control for predictors of attrition as covariates in model testing. Missing data will be minimized through online assessment, and subjects will be re-contacted through social media or email to obtain missing information. Two sets of outcome analyses will be conducted: one self-reported by all participants who are maintained in the study, and another based on biochemically verified abstinence rates.

Primary analysis

We will use a generalized linear mixed model (GLMM) using the time points immediately, 3 months, and 6 months after the treatment. The model will account for dependence of responses within individuals attributable to repeated measures. GLMMs will include random effects person ID and will be fitted using SAS PROC GLIMMIX with maximum likelihood estimation via adaptive quadrature with a minimum of 15 integration points. Nicotine vaping abstinence will be measured by 7-day point prevalence. Covariates will be sex as a biological variable and other variables that are found to be related to abstinence. If missing data is not negligible, we will use multiple imputation procedures to impute missing data. Data about biochemically verified abstinence will be analyzed using the same method as self-reported 7-day point prevalence of abstinence.

Secondary analysis

We will estimate and test mixed effects logistic and multinomial regression models for longitudinal ordinal response data to model secondary outcomes for vaping over time: 1) vaping reduction by 50% or more; 2) vaping quit attempts; 3) readiness to quit vaping; 4) confidence in ability to quit; 5) desire to quit; 6) commitment to abstinence (i.e. endorsing the personal goal of wanting to quit nicotine completely); and 7) use of evidence-based cessation strategies.

Ethics approval

The study protocol was submitted for ethics approval to WCG IRB, a trusted partner to more than 3,300 research institutions ranging from small community hospitals and research sites to large academic medical centers and universities in the United States. WCG IRB approved this study on August 27, 2021. The protocol is registered with ClinicalTrials.gov (protocol #NCT04707911).

Research dissemination

Findings of this study will be disseminated to educators and researchers through publications in peer-reviewed journals, presentations at national or international conferences, and sharing in seminars. In addition, we will share results with community partners for this trial including but not limited to reports and meetings with the California Department of Education Tobacco-Use Prevention Education Program and associated programs, the Stanford Tobacco Prevention Toolkit, and partners at the San Francisco Department of Public Health Tobacco Free Project and the California Tobacco Control Program.

This is the first clinical trial of a social media-based vaping cessation intervention for adolescents and young adults. As vaping among young people has become an urgent problem in tobacco control, our study is well timed to provide a rigorously evaluated evidence-based social media intervention to address the epidemic of adolescent and young adult vaping. Using social media platforms to implement the vaping cessation intervention for adolescents and young adults is pioneering. Given the nature of social media, including high use rates among young people and lack of geographic restrictions, the intervention has high potential to reach a large number of young people who vape, provide accessible support, and achieve large scalability. However, though a systematic review of social media interventions for smoking cessation observed greater abstinence, reduction in relapse, and increase in quit attempts [ 29 ], social media interventions reported high dropout rates and low engagement over time, which significantly reduced the intervention efficacy [ 28 , 29 ]. To address this problem, the intervention has been developed with experts in human centered design and co-created by adolescents to maximize acceptability and engagement. We will also utilize state of the art strategies from prior longitudinal studies of adolescents and young adults to track study participants [ 50 , 51 ].

While social media may be an ideal platform for behavior change interventions targeting adolescents and young adults, the open nature of the social media may comprise a potential threat to individual privacy and unintended information sharing. To protect the participant privacy to the fullest extent possible, the Principal Investigator (Pamela Ling) has obtained a Federal Certificate of Confidentiality to protect the data from subpoena, will make sure that the identifying information (e.g., IP address, email address) will be separated from survey responses, and will give assurances that participants’ e-mail addresses will not be sold or disseminated to any company or individual (participants have been informed of this in the consent form). In addition, group sharing and membership can be limited through privacy settings and individual users can choose who has access to their information. However, the privacy settings and security of material posted on Instagram is under control of Instagram, and thus subject to change should Instagram decide to do so. Therefore, the study investigators cannot guarantee complete privacy of material posted on Instagram. Moreover, at some point during the intervention a participant may share personal information (e.g., vaping quit date, ask for support with reduction or cessation from friends) with their larger social networks. When participants enter the study, they will be informed that the intervention may ask that they share information on social media, but they can choose whether to do so and which people see this information. If participants choose not to share information publicly on social media, it will be made explicit that there will be no consequences whatsoever. In our prior work with Facebook smoking cessation groups, we have not had any privacy concerns among our participants. Any concerns about privacy will be immediately addressed with participants privately.

This intervention is designed for adolescents and young adults, populations in the midst of a stage in life in which they are more susceptible to peer influence [ 52 , 53 , 54 ]. Previous studies found that 25% of teenagers aged 16–17 years old posted references to alcohol on their social media profiles [ 55 ]; in addition, substance use, violence, sexual behavior, and even suicidality are also commonly displayed on social media platforms [ 55 , 56 , 57 , 58 ]. This calls for more serious monitoring of daily interaction among the participants in our Instagram groups. First, before starting the groups, we will remind the participants that the focus of the group is on vaping experience and quit vaping efforts, and that interactions should be respectful. Second, we have a trained Guide in the groups, who will monitor the everyday conversation of the participants. If problematic content is spotted, the guide will remind the involved participants privately and report serious cases to the Principal Investigator for resolution and further actions, including study disqualification. One of our inclusion criteria is using social media ≥ 4 days per week. Previous studies have found that depression may be common among adolescents who spend a significant amount of time on online social networks [ 59 ]. Another study found that more than half of secondary school students experienced a need for mental health support [ 60 ]. Our team is ready to deal with mental health issues and/or with serious withdrawal symptoms such as sleep problems, irritability, or in the unlikely event that they become emotionally distressed as a result of answering assessment questions. The Principal Investigator, a Sub-Investigator, and the Study Physician in this team are all practicing clinicians with experience working with vulnerable populations, young people, and substance use. They can counsel study participants privately over email, or on the phone if necessary and make appropriate referrals to medical providers or mental health resources should participants reveal a need for such support.

This study is the first to implement a vaping cessation intervention for adolescents and young adults entirely through a social media platform and rigorously evaluate it. It is based on a previously tested social media-based intervention for young adult smokers and was developed with thoughtful, iterative, and participant-engaged adaptation. If effective, this study will provide one of the very first evidence-based social media interventions to address the urgent issue of vaping among adolescents and young adults. The program has already been adopted by health departments and other youth serving partners in South Carolina, Minnesota, the San Diego school districts, and Oklahoma, demonstrating it is feasible to deliver this accessible evidence-based support to a large number of adolescents and young adults interested in quitting vaping.

Availability of data and materials

The datasets generated from the study will be available from the corresponding author upon reasonable request.

Mays D, Arrazola RA, Tworek C, Rolle IV, Neff LJ, Portnoy DB. Openness to using non-cigarette tobacco products among U.S. young adults. Am J Prev Med. 2016;50(4):528–34. https://doi.org/10.1016/j.amepre.2015.08.015 .

Rath JM, Villanti AC, Abrams DB, Vallone DM. Patterns of tobacco use and dual use in US young adults: the missing link between youth prevention and adult cessation. J Environ Public Health. 2012;2012:679134.

Article   Google Scholar  

Green MP, McCausland KL, Xiao H, Duke JC, Vallone DM, Healton CG. A closer look at smoking among young adults: where tobacco control should focus its attention. Am J Public Health. 2007;97:1427–33.

Foldes SS, An LC, Rode P, Schillo BA, Davern M, Alesci NL, et al. The prevalence of unrecognized tobacco use among young adults. Am J Health Behav. 2010;34:309–21.

Fix BV, O’Connor RJ, Vogl L, Smith D, Bansal-Travers M, Conway KP, et al. Patterns and correlates of polytobacco use in the United States over a decade: NSDUH 2002–2011. Addict Behav. 2014;39:768–81.

Lee YO, Hebert CJ, Nonnemaker JM, Kim AE. Multiple tobacco product use among adults in the United States: cigarettes, cigars, electronic cigarettes, hookah, smokeless tobacco, and snus. Prev Med. 2014;62:14–9.

Arrazola RA, Neff LJ, Kennedy SM, Holder-Hayes E, Jones CD. Tobacco use among middle and high school students–United States, 2013. MMWR Morb Mortal Wkly Rep. 2014;63:1021–6.

Google Scholar  

Gentzke AS, Creamer M, Cullen KA, Ambrose BK, Willis G, Jamal A, et al. Vital signs: tobacco product use among middle and high school students - United States, 2011–2018. MMWR Morb Mortal Wkly Rep. 2019;68:157–64.

Vallone DM, Bennett M, Xiao H, Pitzer L, Hair EC. Prevalence and correlates of JUUL use among a national sample of youth and young adults. Tob Control. 2019;28:603–9.

Barrington-Trimis JL, Urman R, Leventhal AM, Gauderman WJ, Cruz TB, Gilreath TD, et al. E-cigarettes, cigarettes, and the prevalence of adolescent tobacco use. Pediatr. 2016;138:e20153983.

Dutra LM, Glantz SA. E-cigarettes and national adolescent cigarette use. Pediatr. 2017;139:20162450.

Cullen KA, Ambrose BK, Gentzke AS, Apelberg BJ, Jamal A, King BA. Notes from the field: use of electronic cigarettes and any tobacco product among middle and high school students - United States, 2011–2018. MMWR Morb Mortal Wkly Rep. 2018;67:1276–7.

Cullen KA, Gentzke AS, Sawdey MD, Chang JT, Anic GM, Wang TW, et al. E-cigarette use among youth in the United States, 2019. Jama. 2019;322:2095.

Gaiha SM, Lempert LK, Halpern-Felsher B. Underage youth and young adult e-cigarette use and access before and during the coronavirus disease 2019 pandemic. JAMA Network Open. 2020;3:e2027572-e.

Gentzke AS, Wang TW, Cornelius M, Park-Lee E, Ren C, Sawdey MD, et al. Tobacco product use and associated factors among middle and high school students - national youth tobacco survey, United States, 2021. MMWR Surveill Summ. 2022;71:1–29.

National Center for Chronic Disease Prevention and Health Promotion (US) Office on Smoking and Health. E-Cigarette use among youth and young adults: a report of the Surgeon General. Atlanta: Centers for Disease Control and Prevention (US); 2016. Available from: https://www.ncbi.nlm.nih.gov/books/NBK538680/ .

Centers for Disease Control and Prevention (CDC). Talking to Youth and Young Adults to Prevent E-cigarette Use. [updated August 20, 2021; cited 2022 June 17]; Available from: https://www.cdc.gov/tobacco/features/back-to-school/index.html .

Voos N, Goniewicz ML, Eissenberg T. What is the nicotine delivery profile of electronic cigarettes? Expert Opin Drug Deliv. 2019;16:1193–203.

Article   CAS   Google Scholar  

Stanford Medicine. Tobacco Prevention Toolkit. 2020 [cited 2020 January 22]; Available from: https://med.stanford.edu/tobaccopreventiontoolkit.html .

Catch Global Foundation. Be Vape Free - A new initiative from CATCH, Discover Education, and the CVS Health Foundation 2019 [updated Dec 17, 2019; cited 2020 January 22]; Available from: https://catchinfo.org/be-vape-free-announcement/ .

National Jewish Health. National Jewish Health Launches Vaping and Tobacco Cessation Program to Help with Youth Vaping Epidemic. 2019 [cited 2022 March 7]; Available from: https://www.nationaljewish.org/about/news/press-releases/2019/njh-launches-vaping-and-tobacco-cessation-program-to-help-vaping-epidemic#:~:text=On%20July%201%2C%202019%2C%20National,including%20electronic%20cigarettes%20and%20vapes .

U. S. Department of Health and Human Services. Smokefree Teen. Quit Vaping. 2019; Available from: https://teen.smokefree.gov/quit-vaping .

Graham AL, Amato MS, Cha S, Jacobs MA, Bottcher MM, Papandonatos GD. Effectiveness of a vaping cessation text message program among young adult e-cigarette users: a randomized clinical trial. JAMA Intern Med. 2021;181:923.

Auxier B, Anderson M. Social media use in 2021: Pew Research Center; 2021. Available from: https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/ .

Vogel EA, Gelles-Watnick R, Mssarat N. Teens, Social Media and Technology 2022. Pew Research Center; 2022 [cited 2022 September 5]; Available from: https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/ .

Hansen J, Hanewinkel R, Morgenstern M. Electronic cigarette advertising and teen smoking initiation. Addict Behav. 2020;103:106243.

Truth Initiative. Behind the explosive growth of JUUL. 2019 [updated January 3; cited 2022 January 24]; Available from: https://truthinitiative.org/research-resources/emerging-tobacco-products/behind-explosive-growth-juul .

Thrul J, Tormohlen KN, Meacham MC. Social media for tobacco smoking cessation intervention: a review of the literature. Curr Addict Rep. 2019;6:126–38.

Naslund JA, Kim SJ, Aschbrenner KA, McCulloch LJ, Brunette MF, Dallery J, et al. Systematic review of social media interventions for smoking cessation. Addict Behav. 2017;73:81–93.

Fanshawe TR, Halliwell W, Lindson N, Aveyard P, Livingstone-Banks J, Hartmann-Boyce J. Tobacco cessation interventions for young people. Cochrane Database Syst Rev. 2017;11:Cd003289.

Ramo DE, Thrul J, Delucchi KL, Hall S, Ling PM, Belohlavek A, et al. A randomized controlled evaluation of the tobacco status project, a Facebook intervention for young adults. Addiction. 2018. https://doi.org/10.1111/add.14245 .

Sussman S, Sun P, Dent CW. A meta-analysis of teen cigarette smoking cessation. Health Psychol. 2006;25:549–57.

Ramo DE, Thrul J, Chavez K, Delucchi KL, Prochaska JJ. Feasibility and quit rates of the tobacco status project: a facebook smoking cessation intervention for young adults. J Med Internet Res. 2015;17:e291.

Ramo DE, Thrul J, Delucchi KL, Ling PM, Hall SM, Prochaska JJ. The Tobacco Status Project (TSP): Study protocol for a randomized controlled trial of a Facebook smoking cessation intervention for young adults. BMC Public Health. 2015;15:897.

Meacham MC, Lang OS, Zhao M, Yang CC, Thrul J, Ramo DE. Connectedness based on shared engagement predicts remote biochemically verified quit status within smoking cessation treatment groups on Facebook. Nicotine Tob Res. 2021;23(1):71–6.

Anderson M, Jiang J. Teens, social media & technology 2018: Pew Research Center; 2018. Available from: https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/ .

Chan AW, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Krleža-Jerić K, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med. 2013;158:200–7.

Chan A-W, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. British Med J. 2013;346:e7586.

Federal Trade Commission. Children's Online Privacy Protection Rule. 2013; Available from: https://www.ftc.gov/legal-library/browse/rules/childrens-online-privacy-protection-rule-coppa .

Hintze J. NCSS PASS 2008. 2008 ed. Kaysville, Utah: NCSS Statistical Software; 2008.

Ramo DE, Rodriguez TM, Chavez K, Sommer MJ, Prochaska JJ. Facebook recruitment of young adult smokers for a cessation trial: methods, metrics, and lessons learned. Internet Interv. 2014;1:58–64.

Teen Health Law. California Minor Consent and Confidentiality Laws. 2019 [cited 2022 March 19]; Available from: http://teenhealthlaw.org/consent/ .

Tobacco Use and Dependence Guideline Panel. Treating tobacco use and dependence: 2008 update. Rockville: US Department of Health and Human Services; 2008. Available from: https://www.ncbi.nlm.nih.gov/books/NBK63952/ .

Prochaska JO, DiClemente CC. Stages and processes of self-change of smoking: toward an integrative model of change. J Consult Clin Psychol. 1983;51:390–5.

DiClemente CC, Prochaska JO, Fairhurst SK, Velicer WF, Velasquez MM, Rossi JS. The process of smoking cessation: an analysis of precontemplation, contemplation, and preparation stages of change. J Consult Clin Psychol. 1991;59:295–304.

Vogel EA, Ramo DE, Meacham MC, Prochaska JJ, Delucchi KL, Humfleet GL. The Put It Out Project (POP) facebook intervention for young sexual and gender minority smokers: outcomes of a pilot, randomized, controlled trial. Nicotine & Tob Res. 2020;22(9):1614–21.

Resnicow K, Baranowski T, Ahluwalia JS, Braithwaite RL. Cultural sensitivity in public health: defined and demystified. Ethn Dis. 1999;9:10–21.

CAS   Google Scholar  

Hall SM, Humfleet GL, Muñoz RF, Reus VI, Prochaska JJ, Robbins JA. Using extended cognitive behavioral treatment and medication to treat dependent smokers. Am J Public Health. 2011;101:2349–56.

Hall SM, Havassy BE, Wasserman DA. Commitment to abstinence and acute stress in relapse to alcohol, opiates, and nicotine. J Consult Clin Psychol. 1990;58:175–81.

Scott CK. A replicable model for achieving over 90% follow-up rates in longitudinal studies of substance abusers. Drug Alcohol Depend. 2004;74:21–36.

Cantrell J, Bennett M, Thomas RK, Rath J, Hair E, Vallone D. It’s getting late: improving completion rates in a hard-to-reach sample. Surv Pract. 2018;11:(2).  https://www.surveypractice.org/article/2711-it-s-getting-late-improving-completion-rates-in-a-hard-to-reach-sample .

Crosnoe R, Johnson MK. Research on adolescence in the twenty-first century. Ann Rev Sociol. 2011;37:439–60.

Ary DV, Tildesley E, Hops H, Andrews J. The influence of parent, sibling, and peer modeling and attitudes on adolescent use of alcohol. Int J Addict. 1993;28:853–80.

Pentz MA, Shin H, Riggs N, Unger JB, Collison KL, Chou CP. Parent, peer, and executive function relationships to early adolescent e-cigarette use: a substance use pathway? Addict Behav. 2015;42:73–8.

Moreno MA, Parks M, Richardson LP. What are adolescents showing the world about their health risk behaviors on MySpace? MedGenMed. 2007;9:9.

Dunlop SM, More E, Romer D. Where do youth learn about suicides on the Internet, and what influence does this have on suicidal ideation? J Child Psychol Psychiatry. 2011;52:1073–80.

Ybarra ML, Mitchell KJ, Wolak J, Finkelhor D. Examining characteristics and associated distress related to internet harassment: findings from the second youth internet safety survey. Pediatr. 2006;118:e1169–77.

Ybarra ML, Mitchell KJ. How risky are social networking sites? A comparison of places online where youth sexual solicitation and harassment occurs. Pediatrics. 2008;121:e350–7.

Pantic I, Damjanovic A, Todorovic J, Topalovic D, Bojovic-Jovic D, Ristic S, et al. Association between online social networking and depression in high school students: behavioral physiology viewpoint. Psychiatr Danub. 2012;24:90–3.

O’Dea B, Campbell A. Healthy connections: online social networks and their potential for peer support. Stud Health Technol Inform. 2011;168:133–40.

Download references

Acknowledgements

Not applicable.

This project has been funded by California Tobacco Related Diseases Research Program (T31 IR1910) https://www.trdrp.org/ and the UCSF Helen Diller Comprehensive Cancer Center SF CAN program. JCL was supported by the National Cancer Institute (F32CA265059). The funders had no role in the design of this study and will not have any role during its execution, analyses, interpretation of data, or submission of outcomes.

Author information

Authors and affiliations.

Center for Tobacco Control Research and Education, University of California, San Francisco, San Francisco, CA, USA

Joanne Chen Lyu & Pamela M. Ling

Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

Sarah S. Olson

HopeLab, 100 California St #1150, San Francisco, CA, 94111, USA

Danielle E. Ramo

You can also search for this author in PubMed   Google Scholar

Contributions

PML is the lead researcher of this trial. PML and DER conceptualized and designed the trial. JCL, PML, and SSO drafted the manuscript. SSO has been responsible for the execution of the study procedures. DER critically reviewed the first draft of the manuscript. All authors revised subsequent drafts of the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Pamela M. Ling .

Ethics declarations

Ethics approval and consent to participate.

This project has been approved by WIRB-Copernicus Group Institutional Review Board (WCG IRB) (IRB Tracking Number: 20204627). The participants will be asked to provide electronic informed consent before they are enrolled in the study. The state of California allows adolescents to consent to substance use treatment, and parental consent for participation in the study could reveal the adolescent’s vaping behavior to parents; therefore, consent will not be obtained from parents or guardians, consistent with standard care for adolescents in treatment.

Consent for publication

Competing interests.

The authors declare no conflict of interest.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents.*

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Lyu, J.C., Olson, S.S., Ramo, D.E. et al. Delivering vaping cessation interventions to adolescents and young adults on Instagram: protocol for a randomized controlled trial. BMC Public Health 22 , 2311 (2022). https://doi.org/10.1186/s12889-022-14606-7

Download citation

Received : 20 April 2022

Accepted : 13 November 2022

Published : 10 December 2022

DOI : https://doi.org/10.1186/s12889-022-14606-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Adolescents and young adults
• Electronic nicotine delivery system (vaping) cessation intervention
• Social media
• Randomized controlled trial

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Vaping versus Smoking: A Quest for Efficacy and Safety of E-cigarette

Affiliations.

• 1 Department of Pharmacology, Lady Hardinge Medical College, New Delhi, India.
• 2 Department of Primary Care and General Practice, Centre of Integrated Health Care Research, School of Medicine, Pharmacy and Health, Durham University, Durham, United Kingdom.
• PMID: 29485005
• DOI: 10.2174/1574886313666180227110556

Background: Electronic Cigarettes (ECIGs) are devices with a heating element which produces aerosol for inhalation. They have been propagated as a healthier alternative to tobacco smoking and a potential device for smoking cessation, despite non-documentation of their long-term adverse health effects.

Objectives: With the glorification of ECIG, its use has increased even among non-tobacco users. This makes it critical to understand and discuss a true picture of safety and utility of ECIGs by reviewing the literature.

Methods: Literature search for narrative review was done on PubMed, Scopus and Web of Science databases using the keywords viz electronic cigarette, e-cigarette, electronic nicotine delivery systems, NRT, vaping and electronic nicotine delivery device. The review was sub-categorized into four themes (potential role in smoking cessation, chemicals in the smoke of traditional cigarette and ECIGs, pharmacology of nicotine delivery via ECIG and current regulatory status across the globe).

Results: Search revealed a total of 40 articles out of which 29 were included in the review. ECIGs achieved modest cessation rates with benefits of behavioral and sensory gratification. On the contrary, in many studies where ECIGs were introduced as an intervention, participants continued to use them to maintain their habit instead of quitting. A total of 22 toxic substances apart from nicotine were reported in liquid of ECIG cartridges and its emissions. Many compounds had lower concentrations in ECIG compared to tobacco smoke. There existed a wide variation in the content of ECIG cartridges and strengths of nicotine in refill solutions. It has been observed that the second generation ECIGs delivered nicotine with a similar kinetic profile as conventional cigarettes. In 2013, US FDA gave market authorization to ECIG as substitutes for quitting smoking and cigarette substitutes. The United Kingdom also advocates ECIGs as a medicinal quit aid but bans it from workplaces and other public spaces. India along with many other countries still need to come up with a formal regulatory stand regarding ECIGs.

Conclusion: There is a need to conduct large long-term global clinical trials in real life settings to ascertain the potential uses, adverse effects of ECIG and achieve harmonization of nicotine solution concentration.

Keywords: E-cigarette; ECIG; Electronic nicotine delivery device; NRT; smoking cessation; vaping..

Copyright© Bentham Science Publishers; For any queries, please email at [email protected].

Publication types

• Comparative Study
• Electronic Nicotine Delivery Systems*
• Nicotine / administration & dosage
• Nicotine / adverse effects
• Smoking / adverse effects*
• Smoking Cessation / methods
• Tobacco Products / adverse effects
• Vaping / adverse effects*

Featured Topics

Featured series.

A series of random questions answered by Harvard experts.

Explore the Gazette

Read the latest.

How do you read organization’s silence over rise of Nazism?

Got milk? Does it give you problems?

Cancer risk, wine preference, and your genes

Studies show that about 9 percent of the population and nearly 28 percent of high school students are e-cigarette users.

Diego Cervo/iStock by Getty Images

Restricted airways, scarred lung tissue found among vapers

MGH News and Public Affairs

Small study looks at chronic e-cigarette users, seeing partial improvement once they stop

Chronic use of e-cigarettes, commonly known as vaping, can result in small airway obstruction and asthma-like symptoms, according to researchers at Harvard-affiliated Massachusetts General Hospital.

More like this

Teen vaping rising fast, research says

Survey of oncologists finds knowledge gap on medical marijuana

Chemical flavorings found in e-cigarettes linked to respiratory disease

In the first study to microscopically evaluate the pulmonary tissue of e-cigarette users for chronic disease, the team found in a small sample of patients fibrosis and damage in the small airways, similar to the chemical inhalation damage to the lungs typically seen in soldiers returning from overseas conflicts who had inhaled mustard or similar types of noxious gases. The study was published in  New England Journal of Medicine Evidence .

“All four individuals we studied had injury localized to the same anatomic location within the lung, manifesting as small airway-centered fibrosis with constrictive bronchiolitis, which was attributed to vaping after thorough clinical evaluations excluded other possible causes,” says lead author Lida Hariri, an associate professor of pathology at Harvard Medical School and a pathologist and physician investigator at MGH. “We also observed that when patients ceased vaping, they had a partial reversal of the condition over one to four years, though not complete due to residual scarring in the lung tissue.”

A huge increase in vaping, particularly among young adults and adolescents, has occurred in the United States, with studies showing about 9 percent of the population and nearly 28 percent of high school students are e-cigarette users. Unlike cigarette smoking, however, the long-term health risks of chronic vaping are largely unknown.

In order to determine the underlying pathophysiology of vaping-related symptoms, the MGH team examined a cohort of four patients, each with a three- to eight-year history of e-cigarette use and chronic lung disease. All patients underwent detailed clinical evaluation, including pulmonary function tests, high resolution chest imaging, and surgical lung biopsy. Constrictive bronchiolitis, or narrowing of the small airways due to fibrosis within the bronchiolar wall, was observed in each patient. So was significant overexpression of MUC5AC, a gel-forming protein in the mucus layer of the airway that has been seen in airway cell and sputum samples of individuals who vape. In addition, three of the four patients had evidence of mild emphysema consistent with their former combustible cigarette smoking history, though researchers concluded this was distinct from the findings of constrictive bronchiolitis seen in the patient cohort.

Because the same type of lung damage was observed in all patients, as well as partial improvement in symptoms after e-cigarette usage was stopped, researchers concluded that vaping was the most likely cause after thorough evaluation and exclusion of other possible causes. “Our investigation shows that chronic pathological abnormalities can occur in vaping exposure,” says senior author David Christiani, a professor of medicine at HMS and a physician investigator at Mass General Research Institute. “Physicians need to be informed by scientific evidence when advising patients about the potential harm of long-term vaping, and this work adds to a growing body of toxicological evidence that nicotine vaping exposures can harm the lung.”

A hopeful sign from the study was that three of the four patients showed improvements in their pulmonary function tests and high-resolution computed tomography (HRCT) chest imaging after they ceased vaping. “While there is growing evidence to show that vaping is a risky behavior with potential long-term health consequences for users,” says Hariri, “our research also suggests that quitting can be beneficial and help to reverse some of the disease.”

The study was funded by the National Institutes of Health.

Share this article

You might like.

Medical historians look to cultural context, work of peer publications in wrestling with case of New England Journal of Medicine

Biomolecular archaeologist looks at why most of world’s population has trouble digesting beverage that helped shape civilization

Biologist separates reality of science from the claims of profiling firms

Epic science inside a cubic millimeter of brain

Researchers publish largest-ever dataset of neural connections

Finding right mix on campus speech policies

Legal, political scholars discuss balancing personal safety, constitutional rights, academic freedom amid roiling protests, cultural shifts

Good genes are nice, but joy is better

Harvard study, almost 80 years old, has proved that embracing community helps us live longer, and be happier

• International edition
• Australia edition
• Europe edition

Chemicals in vapes could be highly toxic when heated, research finds

AI analysis of 180 vape flavours finds that products contain 127 ‘acutely toxic’ chemicals, 153 ‘health hazards’ and 225 ‘irritants’

Chemicals used to produce vapes could be acutely toxic when heated and inhaled, according to research .

Vaping devices heat the liquid flavouring to high temperatures to form an aerosol that is then inhaled. They contain chemicals including vegetable glycerin, propylene glycol, nicotine and flavourings, blended in various amounts.

Previous experiments have shown that some fruit-flavoured vapes – such as strawberry, melon and blueberry – produce dangerous compounds called volatile carbonyls due to this heating process.

These compounds are known to have health implications for chronic obstructive pulmonary disease (COPD), cardiovascular disease and cancers.

With so many chemicals used in tens of thousands of different vape products, conducting experiments to test every brand and flavour for toxicity could take decades of research.

Instead, the study used AI to analyse the chemical composition of 180 vape flavours and simulate how they decompose when heated. The research, published in Scientific Reports , predicted that vapes produce 127 “acutely toxic” chemicals, 153 “health hazards” and 225 “irritants”.

Nearly every flavour put through the AI predictor showed at least one product that was classified as a health hazard, with many predicting several. The toxins were associated with vapes containing no nicotine, as well as those with.

The research team at RCSI University of Medicine and Health Sciences, Dublin , conclude there is a “potential public health threat facing the 4.5 million vapers in the UK” and an urgent need for “enhanced restrictions” on flavours and regulations that are reflective of the health risks of vaping, especially for young people.

In January, the government announced that it would ban disposable vapes and restrict sweet and fruity flavours . Lead author Donal O’Shea, professor of chemistry at RCSI, said that the UK government should go further and remove all flavours from vapes.

It is crucial to understand the impact of flavoured vapes on health “before it’s too late”, he added.

“It is plausible that we are on the cusp of a new wave of chronic diseases that will emerge 15 to 20 years from now due to these exposures.”

Given the popularity of flavoured vapes among non-smoking teenagers and young adults, understanding the long-term effects of these products on public health, morbidity and mortality is crucial, the study concludes.

“Without comprehensive regulation, as we try to treat the nicotine addictions of older tobacco smokers, there is a substantial risk of transferring new health issues to younger generations.”

Responding to the findings, a Department of Health and Social Care spokesperson said: “The health advice is clear – if you don’t smoke, don’t vape and children should never vape.

“That’s why we are banning disposable vapes and our tobacco and vapes bill includes powers to limit flavours, packaging and displays of vapes to reduce the appeal to children.

“It is clear that flavours like cotton candy and cherry cola are deliberately being targeted at children, not adult smokers trying to quit, which is completely unacceptable. That is why we are taking decisive action and will be restricting vape flavours.”

Prof Sanjay Agrawal, the Royal College of Physicians’ special adviser on tobacco, said that while vaping can be a very effective way to break the addiction to tobacco, it should only be used for this purpose.

“Vaping is not risk-free, so those who don’t smoke, including children and young people, should not vape either,” he said.

John Dunne, director general at the trade body the UK Vaping Industry Association, said: “The science on vaping is very clear, it is the most effective way for smokers to quit and is at least 95% less harmful than smoking. Every chemical used in vaping e-liquid in the UK is stringently tested, including analysing chemicals when heated, and is only approved for use by the UK government if it is deemed safe.”

Most viewed

• Alzheimer's disease & dementia
• Arthritis & Rheumatism
• Attention deficit disorders
• Autism spectrum disorders
• Biomedical technology
• Diseases, Conditions, Syndromes
• Endocrinology & Metabolism
• Gastroenterology
• Gerontology & Geriatrics
• Health informatics
• Inflammatory disorders
• Medical economics
• Medical research
• Medications
• Neuroscience
• Obstetrics & gynaecology
• Oncology & Cancer
• Ophthalmology
• Overweight & Obesity
• Parkinson's & Movement disorders
• Psychology & Psychiatry
• Radiology & Imaging
• Sleep disorders
• Sports medicine & Kinesiology
• Vaccination
• Breast cancer
• Cardiovascular disease
• Chronic obstructive pulmonary disease
• Colon cancer
• Coronary artery disease
• Heart attack
• Heart disease
• High blood pressure
• Kidney disease
• Lung cancer
• Multiple sclerosis
• Myocardial infarction
• Ovarian cancer
• Post traumatic stress disorder
• Rheumatoid arthritis
• Schizophrenia
• Skin cancer
• Type 2 diabetes
• Full List »

share this!

May 16, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

peer-reviewed publication

trusted source

Likelihood of kids and young people smoking and vaping linked to social media use

by British Medical Journal

The more time spent on social media, the greater the likelihood that children and young people will both smoke and/or vape, suggests research published online in the respiratory journal Thorax .

Clocking up a weekday tally of seven or more hours was associated with a more than a doubling in risk among 10 to 25-year-olds, the findings indicate, reinforcing concerns about the marketing clout of these platforms, say the researchers.

The existing body of research on social media use and smoking and vaping mostly concerns the U.S., so to better assess the situation in the UK, the researchers drew on data from 10 to 25-year-olds taking part in the UK Household Longitudinal Study 2015–21.

Participants were asked to report their normal weekday social media use as well as current cigarette smoking and vaping activity.

Among 10,808 participants with a total of 27,962 reported observations, just over 8.5% reported current cigarette smoking in at least one survey, and 2.5% reported current vaping. Just over 1% reported dual use.

Analysis of the responses showed that cigarette smoking, vaping, and dual use were all more common among participants reporting heavier social media use.

Just 2% of those who said they didn't use social media reported current cigarette smoking compared with nearly 16% of those who said they spent seven or more hours/weekday on it.

Similarly, current vaping ranged from less than 1% among non-users of social media to 2.5% among those spending seven or more hours on it every weekday.

The likelihood of smoking, vaping, and dual use also rose in tandem with the amount of time spent on social media.

Those who said they spent less than one hour/day on social media were 92% more likely to be current smokers than those who said they spent no time on it, while those clocking up seven or more hours/day were more than 3.5 times as likely to be current smokers.

And those who said they spent one to three hours a day on social media were 92% more likely to report current vaping than those who said they spent no time on it.

And those spending seven or more hours/day on social media were nearly three times as likely to report current vaping than those who said they didn't spend any time on these platforms.

Heavier social media use was associated with a greater likelihood of dual use. Those reporting spending one to three hours/day on it were more than three times as likely to be dual users as those who said they didn't spend any time on social media.

But those spending seven or more hours/day on social media were nearly five times as likely to both smoke and vape.

The findings were independent of other factors associated with a heightened risk of smoking and vaping, including age, sex, household income , and parental smoking and vaping.

When the analysis was broken down by sex and household income, similar associations emerged for smoking, but not for vaping. Males, those under the legal age of sale, and those from higher income households were more likely to vape.

This is an observational study , and as such, no firm conclusions can be drawn about causal factors. The researchers also acknowledge that the study relied on self reported data, and that they didn't have any information on the social media platforms used, or how they were being used. But they proffer some explanations for their findings.

"First, and most straightforwardly, there is evidence that the corporations behind cigarette smoking and vaping make use of social media to advertise and promote their products," write the researchers.

"This includes direct advertising which is algorithmically targeted and the use of paid social media influencers who present smoking and vaping as a fashionable and desirable activity. Greater time spent on social media is likely to increase exposure to these forms of influence," they explain.

"Second, social media use has been shown to have features in common with reward-seeking addictive behavior . High social media use may increase susceptibility to other addictive behaviors like smoking," they add.

"Third, as a space that is largely unsupervised by parents/caregivers, social media use may encourage behaviors that are transgressive, including cigarette smoking and vaping."

They conclude, "The companies that own social media platforms have substantial power to modify exposure to material that promotes smoking and vaping if they choose to or are compelled to. Voluntary codes seem unlikely to achieve this, and the introduction and enforcement on bans on material that promote this should be considered.

"In general, we think that algorithms should not be promoting products to individuals that they cannot legally buy. Legislation and enforcement around this and other corporate determinants of health concerns should be considered a core part of online safety and child protection."

In a linked editorial, Dr. Kim Lavoie of the University of Montreal, voices concerns about the popularity of e-cigarettes and vaping products among young people .

Aside from the addictive nature of nicotine and the relative affordability and accessibility of these products, "the answer may lie in the subtle and creative ways e-cigarette manufacturers have managed to reach, and entice, youth into taking up vaping ," which include social media, she suggests.

"The policy implications of this paper are important, particularly as they pertain to regulation of advertising and algorithms targeting under-age users," she writes.

Explore further

Feedback to editors

New analysis estimates the effects of race-neutral lung function testing on patients, hospitals, and beyond

2 hours ago

World-first trial shows benefits of finding and treating undiagnosed asthma and COPD

6 hours ago

Acetaminophen shows promise in warding off acute respiratory distress syndrome, organ injury in patients with sepsis

9 hours ago

Modular communicative leadless ICD found to be safe and exceeds performance expectations

May 18, 2024

Creativity and humor shown to promote well-being in older adults via similar mechanisms

Sweet taste receptor affects how glucose is handled metabolically by humans

Better medical record-keeping needed to fight antibiotic overuse, studies suggest

Repeat COVID-19 vaccinations elicit antibodies that neutralize variants, other viruses

A long-term ketogenic diet accumulates aged cells in normal tissues, new study shows

May 17, 2024

Gut bacteria enhance cancer immunotherapy in mouse study

Related stories.

To vape or not to vape: When an e-cigarette tax has an impact

Mar 20, 2024

Study finds most young people have been exposed to vaping ads, despite restrictions

Oct 25, 2023

Social media adverts impact vaping behaviors among young people, study shows

Feb 8, 2024

Could messages from social media influencers stop young people vaping? A look at the government's new campaign

Mar 1, 2024

Want to keep Gen Z off vaping? Teach them about the industry's marketing tactics, study says

Dec 20, 2023

Study suggests vaping can reduce cigarette smoking, but also leads to higher relapse

Jul 16, 2019

Recommended for you

Link between e-cigarette use and early age of asthma onset in US adults discovered

Study opens the door to designing therapies to improve lung development in growth-restricted fetuses

Primary health coverage found to have prevented more than 300,000 child deaths in four Latin American countries

Simple learning test may be used to diagnose autism at just six months of age

Genetics, environment and health disparities linked to increased stress and mental health challenges during adolescence

First US trial of varenicline for e-cigarette cessation shows positive results

Let us know if there is a problem with our content.

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

A .gov website belongs to an official government organization in the United States.

A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

• About Health Effects of Cigarette Smoking
• Secondhand Smoke
• E-cigarettes (Vapes)
• Menthol Tobacco Products
• Morbidity and Mortality Weekly Reports (MMWR)
• About Surveys
• Other Tobacco Products
• Smoking and Tobacco Use Features
• Patient Care Settings and Smoking Cessation
• Patient Care
• Funding Opportunity Announcements
• State and Community Work
• National and State Tobacco Control Program
• Multimedia and Tools
• Tobacco - Health Equity
• Tobacco - Surgeon General's Reports
• State Tobacco Activities Tracking and Evaluation (STATE) System
• Global Tobacco Control

Health Effects of Vaping

At a glance.

Learn more about the health effects of vaping.

• No tobacco products, including e-cigarettes, are safe.
• Most e-cigarettes contain nicotine, which is highly addictive and is a health danger for pregnant people, developing fetuses, and youth. 1
• Aerosol from e-cigarettes can also contain harmful and potentially harmful substances. These include cancer-causing chemicals and tiny particles that can be inhaled deep into lungs. 1
• E-cigarettes should not be used by youth, young adults, or people who are pregnant. E-cigarettes may have the potential to benefit adults who smoke and are not pregnant if used as a complete substitute for all smoked tobacco products. 2 3 4
• Scientists still have a lot to learn about the short- and long-term health effects of using e-cigarettes.

Most e-cigarettes, or vapes, contain nicotine, which has known adverse health effects. 1

• Nicotine is highly addictive. 1
• Nicotine is toxic to developing fetuses and is a health danger for pregnant people. 1
• Acute nicotine exposure can be toxic. Children and adults have been poisoned by swallowing, breathing, or absorbing vaping liquid through their skin or eyes. More than 80% of calls to U.S. poison control centers for e-cigarettes are for children less than 5 years old. 5

Nicotine poses unique dangers to youth because their brains are still developing.

• Nicotine can harm brain development which continues until about age 25. 1
• Youth can start showing signs of nicotine addiction quickly, sometimes before the start of regular or daily use. 1
• Using nicotine during adolescence can harm the parts of the brain that control attention, learning, mood, and impulse control. 1
• Adolescents who use nicotine may be at increased risk for future addiction to other drugs. 1 6
• Youth who vape may also be more likely to smoke cigarettes in the future. 7 8 9 10 11 12

Other potential harms of e-cigarettes

E-cigarette aerosol can contain substances that can be harmful or potentially harmful to the body. These include: 1

• Nicotine, a highly addictive chemical that can harm adolescent brain development
• Cancer-causing chemicals
• Heavy metals such as nickel, tin, and lead
• Tiny particles that can be inhaled deep into the lungs
• Volatile organic compounds
• Flavorings such as diacetyl, a chemical linked to a serious lung disease. Some flavorings used in e-cigarettes may be safe to eat but not to inhale because the lungs process substances differently than the gut.

E-cigarette aerosol generally contains fewer harmful chemicals than the deadly mix of 7,000 chemicals in smoke from cigarettes. 7 13 14 However, this does not make e-cigarettes safe. Scientists are still learning about the immediate and long-term health effects of using e-cigarettes.

Dual use refers to the use of both e-cigarettes and regular cigarettes. Dual use is not an effective way to safeguard health. It may result in greater exposure to toxins and worse respiratory health outcomes than using either product alone. 2 3 4 15

Some people who use e-cigarettes have experienced seizures. Most reports to the Food and Drug Administration (FDA ) have involved youth or young adults. 16 17

E-cigarettes can cause unintended injuries. Defective e-cigarette batteries have caused fires and explosions, some of which have resulted in serious injuries. Most explosions happened when the batteries were being charged.

Anyone can report health or safety issues with tobacco products, including e-cigarettes, through the FDA Safety Reporting Portal .

Health effects of vaping for pregnant people

The use of any tobacco product, including e-cigarettes, is not safe during pregnancy. 1 14 Scientists are still learning about the health effects of vaping on pregnancy and pregnancy outcomes. Here's what we know now:

• Most e-cigarettes, or vapes, contain nicotine—the addictive substance in cigarettes, cigars, and other tobacco products. 18
• Nicotine is a health danger for pregnant people and is toxic to developing fetuses. 1 14
• Nicotine can damage a fetus's developing brain and lungs. 13
• E-cigarette use during pregnancy has been associated with low birth weight and pre-term birth. 19 20

Nicotine addiction and withdrawal

Nicotine is the main addictive substance in tobacco products, including e-cigarettes. With repeated use, a person's brain gets used to having nicotine. This can make them think they need nicotine just to feel okay. This is part of nicotine addiction.

Signs of nicotine addiction include craving nicotine, being unable to stop using it, and developing a tolerance (needing to use more to feel the same). Nicotine addiction can also affect relationships with family and friends and performance in school, at work, or other activities.

When someone addicted to nicotine stops using it, their body and brain have to adjust. This can result in temporary symptoms of nicotine withdrawal which may include:

• Feeling irritable, jumpy, restless, or anxious
• Feeling sad or down
• Having trouble sleeping
• Having a hard time concentrating
• Feeling hungry
• Craving nicotine

Withdrawal symptoms fade over time as the brain gets used to not having nicotine.

Nicotine addiction and mental health

Nicotine addiction can harm mental health and be a source of stress. 21 22 23 24 More research is needed to understand the connection between vaping and mental health, but studies show people who quit smoking cigarettes experience: 25

• Lower levels of anxiety, depression, and stress
• Improved positive mood and quality of life

Mental health is a growing concern among youth. 26 27 Youth vaping and cigarette use are associated with mental health symptoms such as depression. 22 28

The most common reason middle and high school students give for currently using e-cigarettes is, "I am feeling anxious, stressed, or depressed." 29 Nicotine addiction or withdrawal can contribute to these feelings or make them worse. Youth may use tobacco products to relieve their symptoms, which can lead to a cycle of nicotine addiction.

• U.S. Department of Health and Human Services. E-Cigarette Use Among Youth and Young Adults: A Report of the Surgeon General . Centers for Disease Control and Prevention; 2016. Accessed Feb 14, 2024.
• Goniewicz ML, Smith DM, Edwards KC, et al. Comparison of nicotine and toxicant exposure in users of electronic cigarettes and combustible cigarettes . JAMA Netw Open. 2018;1(8):e185937.
• Reddy KP, Schwamm E, Kalkhoran S, et al. Respiratory symptom incidence among people using electronic cigarettes, combustible tobacco, or both . Am J Respir Crit Care Med. 2021;204(2):231–234.
• Smith DM, Christensen C, van Bemmel D, et al. Exposure to nicotine and toxicants among dual users of tobacco cigarettes and e-cigarettes: Population Assessment of Tobacco and Health (PATH) Study, 2013-2014 . Nicotine Tob Res. 2021;23(5):790–797.
• Tashakkori NA, Rostron BL, Christensen CH, Cullen KA. Notes from the field: e-cigarette–associated cases reported to poison centers — United States, April 1, 2022–March 31, 2023 . MMWR Morb Mortal Wkly Rep. 2023;72:694–695.
• Yuan M, Cross SJ, Loughlin SE, Leslie FM. Nicotine and the adolescent brain . J Physiol. 2015;593(16):3397–3412.
• National Academies of Sciences, Engineering, and Medicine. Public Health Consequences of E-Cigarettes . The National Academies Press; 2018.
• Barrington-Trimis JL, Kong G, Leventhal AM, et al. E-cigarette use and subsequent smoking frequency among adolescents . Pediatrics. 2018;142(6):e20180486.
• Barrington-Trimis JL, Urman R, Berhane K, et al. E-cigarettes and future cigarette use . Pediatrics. 2016;138(1):e20160379.
• Bunnell RE, Agaku IT, Arrazola RA, et al. Intentions to smoke cigarettes among never-smoking US middle and high school electronic cigarette users: National Youth Tobacco Survey, 2011-2013 . Nicotine Tob Res. 2015;17(2):228–235.
• Soneji S, Barrington-Trimis JL, Wills TA, et al. Association between initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis . JAMA Pediatr. 2017;171(8):788–797.
• Sun R, Méndez D, Warner KE. Association of electronic cigarette use by U.S. adolescents with subsequent persistent cigarette smoking . JAMA Netw Open. 2023;6(3):e234885.
• U.S. Department of Health and Human Services. How Tobacco Smoke Causes Disease: The Biology and Behavioral Basis for Smoking-Attributable Disease . Centers for Disease Control and Prevention; 2010. Accessed Feb 13, 2024.
• U.S. Department of Health and Human Services. The Health Consequences of Smoking: 50 Years of Progress. A Report of the Surgeon General . Centers for Disease Control and Prevention; 2014. Accessed Feb 12, 2024.
• Mukerjee R, Hirschtick JL, LZ Arciniega, et al. ENDS, cigarettes, and respiratory illness: longitudinal associations among U.S. youth . AJPM. Published online Dec 2023.
• Faulcon LM, Rudy S, Limpert J, Wang B, Murphy I. Adverse experience reports of seizures in youth and young adult electronic nicotine delivery systems users . J Adolesc Health . 2020;66(1):15–17.
• U.S. Food and Drug Administration. E-cigarette: Safety Communication - Related to Seizures Reported Following E-cigarette Use, Particularly in Youth and Young Adults . U.S. Department of Health and Human Services; 2019. Accessed Feb 14, 2024.
• Marynak KL, Gammon DG, Rogers T, et al. Sales of nicotine-containing electronic cigarette products: United States, 2015 . Am J Public Health . 2017;107(5):702-705.
• Regan AK, Bombard JM, O'Hegarty MM, Smith RA, Tong VT. Adverse birth outcomes associated with prepregnancy and prenatal electronic cigarette use . Obstet Gynecol. 2021;138(1):85–94.
• Regan AK, Pereira G. Patterns of combustible and electronic cigarette use during pregnancy and associated pregnancy outcomes . Sci Rep. 2021;11(1):13508.
• Kutlu MG, Parikh V, Gould TJ. Nicotine addiction and psychiatric disorders . Int Rev Neurobiol. 2015;124:171–208.
• Obisesan OH, Mirbolouk M, Osei AD, et al. Association between e-cigarette use and depression in the Behavioral Risk Factor Surveillance System, 2016-2017 . JAMA Netw Open. 2019;2(12):e1916800.
• Prochaska JJ, Das S, Young-Wolff KC. Smoking, mental illness, and public health . Annu Rev Public Health. 2017;38:165–185.
• Wootton RE, Richmond RC, Stuijfzand BG, et al. Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study . Psychol Med. 2020;50(14):2435–2443.
• Taylor G, McNeill A, Girling A, Farley A, Lindson-Hawley N, Aveyard P. Change in mental health after smoking cessation: systematic review and meta-analysis . BMJ. 2014;348:g1151.
• Centers for Disease Control and Prevention.   Youth Risk Behavior Survey Data Summary & Trends Report: 2011–2021 . U.S. Department of Health and Human Services; 2023. Accessed Dec 15, 2023.
• U.S. Department of Health and Human Services. Protecting Youth Mental Health: The U.S. Surgeon General's Advisory . Office of the Surgeon General; 2021. Accessed Jan 5, 2024.
• Lechner WV, Janssen T, Kahler CW, Audrain-McGovern J, Leventhal AM. Bi-directional associations of electronic and combustible cigarette use onset patterns with depressive symptoms in adolescents . Prev Med. 2017;96:73–78.
• Gentzke AS, Wang TW, Cornelius M, et al. Tobacco product use and associated factors among middle and high school students—National Youth Tobacco Survey, United States, 2021 . MMWR Surveill Summ. 2022;71(No. SS-5):1–29.

Smoking and Tobacco Use

Commercial tobacco use is the leading cause of preventable disease, disability, and death in the United States.

For Everyone

Health care providers, public health.

Social Media Use Tied to Higher Odds of Smoking, Vaping in Youth

S ocial media use among adolescents and young adults was associated with a higher likelihood of smoking and vaping, a national longitudinal study from the U.K. found.

In individuals ages 10 to 25 years, even those who spent less than an hour each day on social media were more likely to smoke cigarettes than those who did not use social media at all (adjusted odds ratio [aOR] 1.92 95% CI 1.43-2.58). And at least 1 to 3 hours a day on social media was linked with increased use of vaping (aOR 1.92, 95% CI 1.07-3.46).

But the odds more than doubled for youth who spent 7 or more hours on social media each day versus those who did not use social media at all:

• Cigarette smoking: aOR 3.60 (95% CI 2.61-4.96)
• Vaping: aOR 2.73 (95% CI 1.40-5.29)
• Dual use: aOR 4.96 (95% CI 1.71-14.34)

"This association was independent of other factors associated with increasing smoking and vaping including age, gender, socioeconomic status, and parental smoking and vaping," wrote Anthony Laverty, PhD, of Imperial College London School of Public Health, and coauthors in Thorax .

The researchers pointed to a number of possible explanations for their findings.

First, evidence shows that cigarette and vaping companies use social media to promote their products , including via "direct advertising which is algorithmically targeted and the use of paid social media influencers who present smoking and vaping as a fashionable and desirable activity," they noted.

"Second, social media use has been shown to have features in common with reward-seeking addictive behaviour. High social media use may increase susceptibility to other addictive behaviors like smoking," they continued. "Third, as a space that is largely unsupervised by parents/caregivers, social media use may encourage behaviours that are transgressive, including cigarette smoking and vaping."

Despite the potential health risks , recent research has indicated that social media is more likely to promote a positive view of vaping. Furthermore, social media influencers often do not disclose relationships with e-cigarette companies when they post content featuring these products.

"The policy implications of this paper are important, particularly as they pertain to regulation of advertising and algorithms targeting under-age users," wrote Kim Lavoie, PhD, of the University of Quebec in Montreal, in an editorial accompanying the study .

"The fact that previous research indicates that youth are indeed targeted by this content, that this content is reaching youth (32% of youth report having seen vape products advertised online) and their attitudes are impacted, is further evidence of the need to strengthen regulation," according to Lavoie.

In a linked podcast , study co-author Nicholas Hopkinson, MD, PhD, also of Imperial College London, said that cigarette and vaping promotion on social media often takes different forms than traditional advertising, making it more difficult to regulate.

"Because it's done in a more sort of organic way, where it's with influencers -- it's harder to pin down where the laws are being broken," he said. "But it's important to come back to the fact that what we see on social media is curated for us by algorithms, so social media companies can control this if they choose to. It may be costly for them, it may be something they need to be compelled to do."

Data for the study was taken from the U.K. Household Longitudinal Study and included a total of 10,808 participants ages 10 to 25 years from 27,962 surveys taken from 2015 to 2021. Participants were asked about their social media use as well as current cigarette or vaping activity.

Of the respondents, 8.6% reported current cigarette smoking, 2.5% reported current e-cigarette use, and 1.1% reported current dual use during at least one time point. Current cigarette and vaping use, respectively, was consistently higher among those who logged more hours each day on social media:

• 0 hours: 2% and 0.8%
• <1 hour: 6.3% and 2%
• 1-3 hours: 9.2% and 2.4%
• 4-6 hours: 12.2% and 3.8%
• ≥7 hours: 15.7% and 4%

Dual use followed a similar pattern, ranging from 0.3% to 2%.

Males were less likely to be in higher groups of social media use, and social media use was more frequent among the older individuals in the study. Parental cigarette smoking or e-cigarette use was more commonly reported among the adolescents and young adults who used social media more frequently.

Laverty and colleagues noted that the analysis relied on self-reported data, which may potentially limit the findings. Additionally, there was no information about how participants used social media, whether users were exposed to cigarette or vaping ads, and the researchers were unable to assess how the various platforms might be associated with cigarette or vaping use.

This study was supported by Cancer Research U.K.

Laverty is a trustee of Action on Smoking and Health. Hopkinson is chair of Action on Smoking and Health and medical director of Asthma and Lung U.K.

Lavoie reported no disclosures.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.12(6); 2020 Jun

An Observational Study of Vaping Knowledge and Perceptions in a Sample of U.S. Adults

Alexandra bellisario.

1 Physician Assistant Program, Wagner College, Staten Island, USA

Karissa Bourbeau

Danielle a crespo, nicole deluzio, alexandra ferro, alexandra sanchez, tracy jackson, gail kunath-tiburzi, anthony v d'antoni.

2 Radiology, Weill Cornell Medicine, New York, USA

Vaping is the use of e-cigarettes that contain inhalants such as nicotine, tetrahydrocannabinol, and cannabidiol. Vaping is associated with e-cigarette or vaping product use associated lung injury (EVALI) and is a recognized public health crisis. Despite rising numbers of hospitalizations due to EVALI, public knowledge and perceptions of the dangers of vaping require further investigation.

This exploratory study assessed knowledge and perceptions of vaping in U.S. adults.

This study was approved by an ethical board, and informed consent was obtained from all participants. A cohort of U.S. adults was recruited by shared links on social media. Participants completed an anonymous online survey that contained vaping knowledge and perceptions items. An a priori power analysis was conducted at 95% power and alpha = 0.05. Statistics were calculated using IBM SPSS Statistics Version 26 (IBM Corp., Armonk, NY, USA).

A sample of 413 (N = 413) U.S. adults participated in the survey. The majority of participants (79.18%) were females, and 65.62% were between 18 and 24 years of age. Over half (62.71%) of participants were never asked about vaping use by a clinician at any visit, and 56.51% agreed that vaping can reduce stress. Of all participants, 70.91% agreed that drinking alcohol makes someone more inclined to vape. Significant positive Spearman’s rho correlations were found between vaping and the use of cannabis, cocaine, ecstasy, hallucinogens, and inhalants (p < 0.05).

Conclusions

We found a significant correlation between vaping and drug use. We also found that if the dangers of vaping are discussed by their health care providers, participants are more inclined to quit vaping. Unfortunately, many physicians report that they avoid discussing vaping with their patients due to lack of vaping knowledge. Our results illuminate the communication gap between patients and physicians. All clinicians need to counsel patients on the dangers of vaping, which might help prevent EVALI and related conditions.

Introduction

Vaping is inhaling smoke from electronic cigarettes (e-cigarettes) that may contain nicotine, tetrahydrocannabinol, and cannabidiol [ 1 ]. Vaping is now recognized as a global public health crisis [ 2 ]. Vaping is associated with harmful conditions that include e-cigarette or vaping product use associated lung injury (EVALI) [ 1 , 3 ]. Despite the rising numbers of hospitalizations due to EVALI [ 1 ], public knowledge and perceptions of the dangers of vaping are still not clear as the incidence of vaping continues to rise in children and young adults [ 4 ].

Using a murine model, pulmonary responses to e-cigarettes were assessed and it was found that mice exposed to e-cigarettes over only a two-week period produced significant increases in pulmonary oxidative stress and moderate macrophage-mediated inflammation compared to placebo (p < 0.05) [ 5 ]. These authors concluded that e-cigarette vapor is a source of free radicals in which exposure can cause airway inflammation, oxidative stress, and suppresses bacterial clearance by alveolar macrophages [ 5 ]. Other researchers analyzed the tumorigenicity of e-cigarette smoke on lung and bladder tissue in mice [ 6 ]. They found that 22.5% of mice exposed to e-cigarette smoke developed lung tumors (adenocarcinomas) and 57.5% developed urothelial hyperplasia in their urinary bladders [ 6 ]. These data from basic science studies correlate with recent clinical findings. In the final analysis of their originally published case series, researchers stated that 98 patients (N = 98) in Wisconsin and Illinois were reported to their respective public health departments due to EVALI [ 7 ]. The patients had bilateral infiltrates on chest imaging as a result of vaping. A total of 95% of the patients were hospitalized, 26% underwent intubation and mechanical ventilation, and two deaths were reported [ 7 ]. A total of 89% of the patients reported having used tetrahydrocannabinol products in e-cigarette devices, although a wide variety of products and devices was reported [ 7 ]. Using a cross-sectional survey of 8,087 participants (N = 8,087), Wills et al. [ 8 ] found a significant association of e-cigarette use with chronic pulmonary disorder (p < 0.01). Others recently analyzed bronchoalveolar lavage fluid from a convenient sample of 51 patients (N = 51) with EVALI to quantify the degree of toxicants and their chemical effects on lung tissue [ 3 ]. These researchers found that vitamin E acetate was associated with EVALI [ 3 ]. Clearly, vaping is not an innocuous activity, and there exists a continued need to ascertain the perceptions of people who vape. Such data can help drive evidence-based public health initiatives.

Vape products come in a variety of styles, and there are over 7,000 available flavors in the market [ 9 - 11 ]. A cross-sectional survey in a large cohort (N = 728) was carried out to examine the relationship between product characteristics and e-cigarette appeal [ 12 ]. Of participants that exclusively vaped, 68.9% reported that the option of different flavors was the most attractive characteristic of using vapes that influenced their decision to begin vaping [ 12 ]. These results suggest that people who have never vaped or smoked cigarettes may be vulnerable to e-cigarette flavor marketing strategies. Allen et al. [ 9 ] analyzed the contents of 51 types of flavored e-cigarettes and found that diacetyl was detected above the laboratory limit of detection in 39 of 51 flavors (up to 239 µg/e-cigarette), 2,3-pentanedione was detected in 23 of 51 flavors (up to 64 µg/e-cigarette), and acetoin was detected in 46 of 51 flavors (up to 529 µg/e-cigarette). These data have driven lawmakers in some countries to ban flavored e-cigarettes or restrict them from being sold to adolescents. Rates of e-cigarette use among high school students in the United States have strikingly increased from 1.5% in 2011 to 20.8% in 2018 [ 13 ], and these data have been corroborated in more recent studies [ 4 ]. In a qualitative study of young adults (N = 49), researchers [ 14 ] conducted focus groups and four main themes emerged: positive reinforcement, social benefits, negative effect reduction, and negative consequences. They found that many young adults were unsure of the negative consequences of vaping [ 14 ].

Vaping research is in its infancy, and there exist large gaps in the literature related to knowledge and perceptions of vaping among people of all ages. Therefore, the purpose of this exploratory study is to assess public knowledge and perceptions of vaping by surveying a cohort of U.S. adults. The results can help clinicians provide effective vaping cessation strategies for their patients and drive evidence-based public health interventions. Our three hypotheses are as follows:

1. There exists an association between knowledge of the chemicals found in vape pods and vape usage.

2. There exists a relationship between vaping and concomitant drug use.

3. There exists an inverse association between knowledge of the dangers of vaping and vape usage.

Materials and methods

The study protocol was fully approved by the Wagner College, Staten Island, NY, USA. Informed consent was obtained by all participants prior to their participation in the study. The design was an exploratory, observational study with a sample size of 413 (N = 413) participants. An a priori power analysis using G-power version 3.1.9.6 revealed that the minimum sample size need to achieve significance was 317 participants at 95% power, effect size of 0.25, at an alpha level of 0.05 [ 15 , 16 ]. Because we did not find a published survey instrument that specifically aligned with the purpose of our study, we developed our own. The complexity of measuring perceptions related to vaping has been discussed in the literature [ 17 ]. Researchers have suggested that survey instruments be developed as e-cigarette products evolve [ 17 ]. They summarized 371 e-cigarette perception items from seven research groups, and we adapted some of our items from their summary [ 17 ]. The survey instrument was first piloted on 235 (N = 235) participants so that the items and responses could be analyzed for inconsistencies and revised, if necessary. Inconsistencies included the use of ambiguous terms or the lack of operational definitions for others. The wording of any items that appeared vague were was changed by consensus agreement among the authors. None of the data from these piloted participants were included in the final total sample. The final survey instrument included demographic items, as well as, vaping knowledge and perception items (see Appendix). A Likert scale was used for the knowledge and perception items. These items were paired (both positively and negatively worded items) but spaced from each other on the survey instrument. The purpose of these items was to evaluate acquiescence bias, which we did not find. The variables measured by the survey instrument are shown in Table  1 . The inclusion criteria were participants 18 years or older, participants who vape or do not vape, and completed surveys. The exclusion criteria were participants less than 18 years of age and incomplete surveys. All statistics were calculated using IBM SPSS Statistics Version 26 (IBM Corp., Armonk, NY, USA).

We distributed our electronic survey on a variety of social media websites using SurveyMonkey Ⓡ . These websites included Facebook as a primary source due to its popularity and number of users. Others included Reddit, YouTube, and Instagram.

A total sample of 413 (N = 413) U.S. adults participated. The internal consistency of our survey instrument was found to be moderately reliable (Cronbach’s alpha = 0.537). The gender and educational level of the sample are shown in Table  2 . Most participants were females (79.18%) between the ages of 18 to 24 years (65.62%) and white/Caucasian (79.42%). Figures  1  and 2 depict these data. Table  3  includes the medical and psychiatric diagnoses of the sample.

a Operational definition of gender variant/nonconforming: exhibiting behavioral, cultural, or psychological traits that do not correspond with the traits typically associated with one's sex; having a gender expression that does not conform to gender norms.

GED, general educational development

Data are shown as percentages (numbers).

Less than half the sample (46.49%) had never vaped, and the rest of the participants reported different frequencies of vaping (Figure  3 ). Data for current vape use among all participants can be found in Figure  4 . Data for frequency of drug use among all participants can be found in Table  4 . Figure  5  includes data related to whether or not a participant has ever been asked about vaping usage by a health care provider. Data for vaping perceptions and knowledge among all participants can be found in Tables  5  and 6, respectively.

Data are shown as percentages.

All data reported as n (%).

In order to explore relationships between variables, Spearman’s rho correlation coefficient tests were used for all categorical data at an alpha level of 0.05. Table  7  displays all the significant (p < 0.05) Spearman’s rho correlations found in this study.

a p-Value less than 0.05 is significant.

b Vaping perception.

c Vaping knowledge.

This exploratory study helped fill the gap in the literature related to knowledge and perceptions of vaping among young U.S. adults. More significant correlations with perception statements were found than with knowledge statements. This suggests that perceptions of vaping risk play a critical role in the decision to engage in vaping. This finding lends support to our first hypothesis that an association exists between knowledge of the chemicals found in vape pods and vaping. Whether or not such a perception changes as a person ages is unknown. Over 80% of our sample fell between 10 and 34 years of age (Figure  2 ). Some reasons that incline adults to vape include (1) belief that vaping reduces stress, (2) belief that drinking alcohol makes people more inclined to vape, (3) belief that the ingredients in a vape pod are safe to consume, and (4) belief that smoking cigarettes is more dangerous than vaping. The lack of a significant finding between educational level and knowledge and perceptions of the dangers of vaping suggests that all adults need sound education regarding the dangers of vaping, irrespective of their educational backgrounds. The incidence of EVALI has increased and patients, with this acute condition acutely most often present with severe pulmonary consolidation with respiratory failure [ 1 ]. Based on our participants’ responses, we found that if the dangers of vaping were discussed with them by their health care providers, they would be more inclined to quit vaping. This underscores how clinicians can influence vaping behavior changes in patients. Such changes begin with candid conversations about the dangers of vaping between clinicians and patients. Unfortunately, this may be easier said than done. Hurst and Conway [ 18 ] conducted a qualitative study on physician attitudes about discussing vaping with patients and documenting vaping usage in the electronic medical record. Many physicians believe that they lack medical knowledge needed to discuss vaping with patients and they rarely screen patients for vaping [ 18 ]. In fact, one-third of the physicians in their sample did not hold strong objections to vaping [ 18 ]. These data are sobering because they provide reasons why many clinicians avoid vaping conversations with patients.

We found significant positive correlations between vaping and concomitant drug use that support our second hypothesis. There was a moderately strong positive correlation (0.572) between vaping and cannabis use (p = 0.000). Weak positive correlations were found between vaping and cocaine use (0.245; p = 0.000), vaping and ecstasy use (0.143; p = 0.004), and vaping and inhalants (0.140; p = 0.004). Our results align with other studies that have found an association between e-cigarettes and marijuana use in young adults [ 19 ]. Researchers found that youth who had used an e-cigarette were 3.5 times more likely to use marijuana compared to youth who had not used an e-cigarette [ 19 ]. In a Dutch cross-sectional survey, it was found that access to a variety of flavors is one of the most attractive characteristics prompting initial vape use [ 12 ]. In our study, flavor was not found to be a significant factor influencing vape use. This suggests that recent legislation banning the sale of flavored cartridges may not be as effective as intended in deterring vaping [ 20 ].

In our cohort, we found that participants who lack knowledge of the content and dangers of vaping are not only more likely to engage in vaping, but they also vape more frequently. This finding supports our third hypothesis that an inverse association exists between knowledge of the dangers of vaping and vape usage. We found a weak positive correlation between vape use and the belief that vaping reduces stress (0.389, p = 0.000). Our data support those of others [ 14 ] who also reported that e-cigarette users believe that vaping reduces stress.

This study provides a unique snapshot of the vaping landscape in a cohort of young U.S. adults. Although unknown to us at the time, the data reported in this study were collected during the COVID-19 pandemic [ 21 ]. A future study during a non-pandemic time could be conducted and the data compared to ours. We were forced to close the study prematurely as New York City began to shut down. However, our sample size (N = 413) exceeded the minimum identified by our a priori power analysis. A larger sample size could have resulted in more robust results. We do believe that our sample is representative of young, computer-literate U.S. adults. The fact that we were not permitted by the ethical review board to query respondents on their places of residence prevented us from generalizing our results to specific areas both within and outside the United States. Our design was not immune to response bias inherent in survey instruments. Furthermore, we were unable to answer any queries related to unfamiliar terminology on our survey because it was electronically distributed. However, as a result of piloting our survey, we did include operational definitions in simple language to help participants. We believe the internal validity of our study is robust. The homogeneity of our sample may weaken the external validity because the ethics board did not permit us to ask for the geographic locations of participants or their IP addresses. Despite these limitations, our data can provide better direction for future studies on vaping knowledge and perceptions in adults.

Future studies can be designed to evaluate the efficacy of a vaping cessation “conversation protocol” for clinicians to help them engage in conversations about vaping with patients. Analyzing factors that are most predictive of vaping cessation success would be useful in providing much needed patient education. Future studies can also investigate the associations between vaping and drug use to see which drugs most influence a person’s decision to vape. Whether there exists a synergistic mechanism between the chemicals in vaping products and other drugs that make them more addictive in combination is currently unknown.

Undergraduate medical education should include comprehensive information on the pathophysiology and psychosocial factors of vaping. Such a topic could be included within the neurology, psychiatry, and behavior courses. Such a strategy would expose medical students to the fundamentals of vaping addiction. We also recommend screening for e-cigarettes use during every clinical encounter.

Acknowledgments

We thank the anonymous participants who responded to our survey. A special thanks to Michael J. Flory, PhD, who provided us with invaluable guidance.

Complete survey instrument (demographic items, vaping knowledge items, and vaping perception items) used in the study protocol.

●      To which gender do you most closely identify with?

○      Male 

○      Female 

○      Transgender female 

○      Transgender male 

○      Gender variant/nonconforming a

○      Not listed 

○      Prefer not to answer

a Operational definition of gender variant/nonconforming: exhibiting behavioral, cultural, or psychological traits that do not correspond with the traits typically associated with one's sex; having a gender expression that does not conform to gender norms.

●      What is your age range? 

○      18-24 years

○      25-34 years

○      35-44 years

○      45-54 years

○      55-64 years

○      65-74 years

○      75 years or older 

●      Please specify your ethnicity.

○      White/Caucasian

○      African American

○      Asian American

○      Pacific Islander

○      Hispanic/Latino

○      Other

●      What is your highest degree or level of education that you have completed?

○      Some high school, no diploma 

○      High school graduate, diploma or equivalent (example: GED [General Education Diploma]) 

○      Some college credits, no degree

○      Trade school

○      Associate’s degree

○      Bachelor’s degree 

○      Master’s degree

○      Academic doctorate degree (PhD, MD, DO, etc.)

●      Which of the following best describes your current employment status?

○      Employed for wages

○      Self-employed 

○      Out of work and looking for work 

○      Out of work but not currently looking for work 

○      A homemaker 

○      A student 

○      Military 

○      Retired 

○      Unable to work 

●      Your yearly income falls within which range?

○      Less than $25,000

○      $25,000-$50,000

○      $50,000-$100,000

○      $100,000-$200,000

○      More than $200,000

○      Prefer not to say 

●      Are you legally married? 

○      Yes 

○      No 

●      How many children do you have?

○      None 

○      1

○      2-4

○      More than 4

●      Which languages do you speak fluently? (Check all that apply.) 

○      English 

○      Spanish 

○      Portuguese

○      French 

○      Mandarin

○      Arabic 

○      Other 

●      Which of the following statements do you most closely agree with? (Vaping frequency.)

○      I do not know what vaping is.

○      I have never vaped.

○      I have tried vaping, vaped less than 1-5 times in my life, and stopped.

○      I have tried vaping, vaped more than 5 times in my life, and stopped.

○      I vape 1-5 times a week.

○      I vape nearly every day.

●      Which of the following statements do you most closely agree with? (Current vape use.)

○      Never

○      Less than monthly (less than once a month per 12 months)

○      Monthly (once a month per 12 months)

○      Weekly (1-6 days per week)

○      Daily (7 days per week)

●      Which of the following statements do you most closely agree with? Click all that apply.

○      I have used cannabis (marijuana)

○      I have used cocaine

○      I have used ecstasy (MDMA) 

○      I have used hallucinogens

○      I have used heroin

○      I have used inhalants (ex. Poppers, “Huffing”) 

○      I have used ketamine

○      I have used methamphetamines

○      I have never tried using the substances listed above before

○      I have used other substances

●      Have you ever been diagnosed with any of the following medical illnesses?

○      Asthma 

○      Reactive airway disease

○      Chronic bronchitis 

○      Chronic obstructive pulmonary disease (COPD)

○      Recurrent pneumonia 

○      Lung cancer 

○      None of the above 

●      Have you ever been diagnosed with any of the following?

○      Generalized anxiety or panic disorder

○      Major depressive disorder or seasonal depressive disorder

○      Bipolar 1 or Bipolar 2 disorder

○      Substance use disorder 

○      Schizophrenia or schizophreniform or schizoaffective disorder

○      Anorexia nervosa or bulimia 

○      Never been diagnosed with a psychiatric medical illness/condition 

●      Have you ever been questioned by a health care provider about vaping?

○      Yes

○      No

Paired knowledge statements:

I can list all the ingredients in a vape pod / I cannot list all the ingredients in a vape pod 

The ingredients in a vape pod are safe to consume / The ingredients in a vape pod are not safe to consume

Vaping can cause lung damage / Vaping cannot cause lung damage

Vaping is addictive / Vaping is not addictive

Vaping is less harmful than smoking cigarettes / Vaping is more harmful than smoking cigarettes

Vaping will negatively affect a person's health over time / Vaping will not negatively affect a person's health over time

Paired perception statements:

The news has affected my impression of vaping / The news has not affected my impression of vaping

Vaping is a health concern / Vaping is not a health concern

Drinking alcohol makes a person more inclined to vape / Drinking alcohol does not make a person more inclined to vape

Vaping makes one more socially acceptable to their friends / Vaping does not make one more socially acceptable to their friends

Vaping can reduce stress / Vaping does not reduce stress

If my doctor or other health care provider advised me to stop vaping, I would quit / If my doctor or other healthcare provider advised me to stop vaping, I would not quit

During any visit to a health care provider in the last 12 months were you questioned about vaping? / During any visit to a healthcare provider in the last 12 months were you NOT questioned about vaping? 

If tobacco was the only flavor offered, people would not vape / People would vape if tobacco was the only flavor offered

I have noticed that vaping increases difficulty breathing and coughing / I have noticed that vaping decreases difficulty breathing and coughing

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Human Ethics

Consent was obtained by all participants in this study. Human Experimentation Review Board (Wagner College) issued approval S20-2. The study protocol was fully approved by the Wagner College (Staten Island, NY) Human Experimentation Review Board in January of 2020.

Animal Ethics

Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Over 115 million pills containing illicit fentanyl seized by law enforcement in 2023

NIH-supported study highlights increasingly dangerous illicit drug supply, risk of pills not coming from a pharmacy

Law enforcement seizures of illicit fentanyl increased dramatically in number and size between 2017 to 2023 in the U.S., especially in pill form, according to a new study funded by the National Institutes of Health’s (NIH) National Institute on Drug Abuse (NIDA). The number of individual pills containing fentanyl seized by law enforcement was 2,300 times greater in 2023 compared to 2017, with 115,562,603 pills seized in 2023 vs. 49,657 in 2017. The proportion of fentanyl pill seizures to the total number of fentanyl seizures more than quadrupled, with pills representing 49% of illicit fentanyl seizures in 2023 compared to 10% in 2017. The study also found a significant increase in the number and weight of fentanyl-containing powder seizures during this time.

“Fentanyl has continued to infiltrate the drug supply in communities across the United States and it is a very dangerous time to use drugs, even just occasionally,” said NIDA Director Nora D. Volkow, M.D. “Illicit pills are made to look identical to real prescription pills, but can actually contain fentanyl. It is urgently important that people know that any pills given to someone by a friend, purchased on social media, or received from any source other than a pharmacy could be potentially deadly – even after a single ingestion.”

Although fentanyl seizures were historically less common in the Western U.S., this analysis found that this region now accounts for most of law enforcement seizures of fentanyl overall, as well as total weight of fentanyl seized. The proportion of fentanyl pill seizures compared to the overall number of fentanyl seizures was also highest in the West, with 77.8% of all law enforcement seizures of fentanyl in the West being in pill form in 2023. These data emphasize the need for continued monitoring of regional shifts in the fentanyl supply, to help inform targeted prevention and public health responses.

In 2022, over 107,000 people died of a drug overdose , with 75% of those deaths involving an opioid. The overall rise in overdose deaths is largely attributable to the proliferation of illicit fentanyl, a synthetic opioid. Illicit fentanyl is highly potent, cheaply made, and easily transported, making it extremely profitable. Fentanyl is about 50 times more potent than heroin and a lethal dose may be as small as two milligrams.

While some people knowingly consume fentanyl, many people do not know if the drugs they plan to use contain fentanyl. This is especially true of illicit counterfeit pills, which are often made to resemble prescription medications such as oxycodone or benzodiazepines, but really contain fentanyl. Recent studies have reported a dramatic rise in overdose deaths among teens between 2010 to 2021 , which remained elevated well into 2022 according to a NIDA analysis of CDC and Census data . This increase in deaths has been largely attributed to widespread availability of illicit fentanyl, the proliferation of counterfeit pills containing fentanyl, and the ease of purchasing pills through social media.

“Availability of illicit fentanyl is continuing to skyrocket in the U.S., and the influx of fentanyl-containing pills is particularly alarming,” said Joseph J. Palamar, Ph.D., M.P.H., associate professor in the Department of Population Health at NYU Grossman School of Medicine, New York City, and lead author on the paper. “Public health efforts are needed to help prevent these pills from falling into the hands of young people, and to help prevent overdose among people taking pills that unsuspectingly contain fentanyl.”

The data used for this analysis were collected through the High Intensity Drug Trafficking Areas (HIDTA) program, a grant program aimed at reducing drug trafficking and misuse administered by the Office of National Drug Control Policy . Though law enforcement seizures do not necessarily reflect prevalence of use, they represent an indicator of the availability of illicit drugs.

Unlike most survey data and surveillance systems which can be lagged for a year or more, HIDTA data are made available quarterly, allowing evaluation in almost real time. HIDTA data also distinguish between the presence of fentanyl in pill or powder form. Analyzing these data can therefore help identify trends in availability of illicit substances and act as a type of early warning system to shift public health education or interventional resources more quickly.

HIDTA data does not differentiate between fentanyl and its analogs, nor estimate the amount of fentanyl present in seized substances. However, given the small amount necessary for an overdose, the authors note that the presence of any fentanyl is an important indicator of overdose risk.

This analysis, published in the International Journal of Drug Policy , was led by researchers from the NIDA-funded National Drug Early Warning System (NDEWS). It builds on a previous NDEWS study ( press release ) of trends in seizures of powders and pills containing illicit fentanyl in the U.S. between 2018 through 2021.

If you or someone you know is struggling or in crisis, help is available. Call or text 988  or chat at   988lifeline.org . To learn how to get support for mental health, drug or alcohol issues, visit  FindSupport.gov . If you are ready to locate a treatment facility or provider, you can go directly to  FindTreatment.gov or call  800-662-HELP (4357) .

• J Palamar, et al. National and Regional Trends in Fentanyl Seizures in the United States, 2017-2023 . International Journal of Drug Policy . DOI: 10.1016/j.drugpo.2024.104417 (2024).

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

About substance use disorders: Substance use disorders are chronic, treatable conditions from which people can recover. In 2022, nearly 49 million people in the United States had at least one substance use disorder. Substance use disorders are defined in part by continued use of substances despite negative consequences. They are also relapsing conditions, in which periods of abstinence (not using substances) can be followed by a return to use. Stigma can make individuals with substance use disorders less likely to seek treatment. Using preferred language can help accurately report on substance use and addiction. View NIDA’s online guide .

NIH…Turning Discovery Into Health®

Related Articles

More than 321,000 U.S. children lost a parent to drug overdose from 2011 to 2021

Analysis of social media language using AI models predicts depression severity for white Americans, but not Black Americans

Law enforcement seizures of psilocybin mushrooms rose dramatically between 2017-2022