Abstract
Background.
The e-cigarette industry has targeted low- and middle-income countries and young adults, suggesting the particular susceptibility of populations at the intersection. This study assessed e-cigarette perceptions, use, and use intention among college students in Georgia, an LMIC with high smoking rates.
Methods.
We analyzed 2023 cross-sectional survey data among 225 students (Mage=21.68, SD=3.43; 56.4% female) at a large medical university in Tbilisi, Georgia. Multivariable regression analyses assessed sociodemographics, tobacco use, and e-cigarette perceptions in relation to past-month e-cigarette use and next-year use intentions.
Results.
Past-month use of cigarettes and e-cigarettes was 44.9% and 23.3%, respectively. The most common sources of initial exposure were friends/family (72.2%), followed by media (25.6%). The most common reasons for initiation were curiosity (49.6%), friends’ use (28.5%), cigarette cessation (15.5%), and lower perceived risk versus cigarettes (2.4%). Past-month e-cigarette use was associated with past-month cigarette use (p<.001), being more informed about e-cigarettes (p=.001), and more favorable e-cigarette perceptions (p=.009). Greater next-year e-cigarette use intent was associated with past-month e-cigarette use and perceiving e-cigarettes as less of a public health problem (p=.029).
Discussion.
Enhanced regulatory and educational efforts must address e-cigarette use and related factors among young adults in Georgia and countries with similar contexts.
Keywords: tobacco control, e-cigarettes, young adults, risk factors, risk perceptions
Background
The global tobacco market has diversified, with e-cigarettes representing a particularly salient product that has emerged in the past 15 years (World Health Organization, 2022). Exposure to e-cigarette marketing may impact how people perceive, use, and intend to use e-cigarettes (Aljandaleh et al., 2020; Aly et al., 2022; Farrelly et al., 2015; Goulette et al., 2023; Trumbo, 2015). Indeed, e-cigarette use has been associated with more favorable e-cigarette perceptions (Aljandaleh et al., 2020; Cuccia et al., 2022) and lower perceived addictiveness (Jiang et al., 2019; Wiseman et al., 2019) and harm of e-cigarettes (Aly et al., 2022; Persoskie et al., 2019), potentially shaped by harm reduction claims in e-cigarette marketing (Klein et al., 2016; Lyu et al., 2022).
The marketing of novel tobacco products, such as e-cigarettes, in low- and middle-income countries (LMICs) is a particular concern. The industry has increasingly targeted LMICs, as many represent untapped markets, consumers enticed by harm reduction claims, and weaker regulatory frameworks (International Union Against Tuberculosis and Lung Disease, 2020). Georgia, one middle-income country, serves as an intriguing case study for examining the impact of e-cigarette marketing given its high male cigarette use prevalence (49.5%), albeit lower female prevalence (8.5%) (Kakutia, 2020). Since 2018, Georgia has had comprehensive bans on tobacco advertising and promotion, and these policies have subsequently been strengthened. However, products that do not contain nicotine, like some e-cigarettes, are not captured in Georgia’s legal definition of tobacco products, and compliance is limited, as is common across LMICs for various reasons (e.g., less resources for enforcement, greater tobacco industry interference) (Anderson et al., 2020; Gilmore et al., 2015; Hoe et al., 2019).
Despite studies in other countries assessing e-cigarette perceptions and use behaviors (Collins et al., 2019; Kundu et al., 2023; Wadsworth et al., 2018; Wagoner et al., 2019), few studies in Georgia (and other LMICs with high tobacco use rates) have examined sociodemographic factors in relation to these outcomes. Given that young adults are particularly likely to use e-cigarettes globally and in Georgia (World Health Organization, 2022), this study analyzed data from students at a large public university in Georgia and assessed their e-cigarette awareness, perceptions, use, and use intention, as well as factors associated with e-cigarette use and use intention. We hypothesized that certain sociodemographics (e.g., male sex, older age), current cigarette use, greater e-cigarette awareness, and more positive perceptions of e-cigarettes were associated with e-cigarette use and use intentions.
Methods
Study Design and Participants
This study analyzed cross-sectional survey data collected in April-May 2023 among students at Tbilisi State Medical University in Tbilisi, Georgia – a public university and the largest medical university in Georgia (~9,400 students; ~6,000 Georgian students). Inclusion criteria were: 1) ≥18 years old; and 2) able to read Georgian. We intentionally included both sexes, despite the lower national tobacco use prevalence among females in Georgia (Kakutia, 2020). This decision was made to enable assessment of tobacco-related perceptions in both sexes, which is particularly important as the tobacco use rates have increased among females in Georgia, particularly young, educated women in urban areas (Kakutia, 2020).
The university registrar’s office randomly selected 1,500 students to be contacted to participate in the survey. The selected students were contacted via email, were provided a study overview and consented, then continued to the online survey. In total, 225 students (127 females, 98 males) fully completed the survey (participation rate: 15.0%). Because tobacco use prevalence was expected to be lower among females versus males in this sample, a slightly greater representation of females was deemed advantageous in terms of identifying a sufficient number of females reporting current tobacco use to meaningfully interpret data on female use.
Measures
The survey was administered in Georgian, took about ~5–10 minutes to complete, and assessed the following factors. (See Supplementary Materials for survey.)
Sociodemographic characteristics.
We assessed age and sex.
E-cigarette knowledge and beliefs.
Participants were asked, “How informed are you about e-cigarettes?” (1=not at all to 4=quite). To assess perceived addictiveness of cigarettes and e-cigarettes, participants were asked, “How addictive are: 1) traditional cigarettes? 2) e-cigarettes?” (1=not at all to 10=extremely). Participants were also asked, “Are e-cigarettes a public health problem?” (1=not at all to 10=extremely).
E-cigarette perceptions index measure.
An e-cigarette perceptions index measure was based on 4 questions that assessed the extent to which participants agreed with the following items: “E-cigarettes: 1) are safer than cigarettes; 2) can help quitting smoking; 3) can lead to other tobacco use; 4) should be regulated like other tobacco (1=completely disagree to 4=completely agree). These 4 items were significantly correlated and indicated collinearity in preliminary multivariable models; furthermore, their associations with outcomes trended in the same direction (i.e., more positive perceptions were associated with e-cigarette use and use intentions). Thus, items 3 and 4 were reverse scored, then the average of the 4 items was calculated as an e-cigarette perception index score (Cronbach’s alpha=.60).
Tobacco use characteristics.
We assessed lifetime e-cigarette use and number of days of use of cigarettes and e-cigarettes, respectively, in the past 30 days. We also assessed likelihood of using cigarettes and e-cigarettes in the next year (1=definitely will not to 4=definitely will).
Initial sources of e-cigarette exposure and reasons for initial use.
To characterize initial sources of exposure and reasons for initial use, participants were asked: 1) “Where did you first hear about e-cigarettes? from the media, from a friend/relative/family member, from advertising billboards, from an educational institution”; and 3) “What was the reason you started/used e-cigarettes? I have not used them, a friend used them, a family member used them, to stop using other tobacco products such as cigarettes, they are less harmful than other tobacco products, just out of curiosity.”
Data Analysis
Descriptive and bivariate analyses were used to characterize participants overall and in relation to past-month e-cigarette use and next-year e-cigarette use intention. Next, multivariable regression was used to examine sociodemographics, tobacco use characteristics, and e-cigarette knowledge and perceptions in relation to past-month e-cigarette use (logistic regression) and e-cigarette use intentions (linear regression). Data were analyzed using SPSS v26.
Results
In this sample (N=225), participants were an average of 21.68 (SD=3.43) years old and 56.4% female (Table 1). Past-month use of cigarettes and e-cigarettes was 44.9% and 23.3%, respectively: 33.1% of females and 60.2% of males reported past-month cigarette use, and 15.0% of females and 33.7% of males reported past-month e-cigarette use. The average rating regarding how informed they were about e-cigarettes was 2.47 (SD=0.78 on a scale of 1=not at all to 4=quite). The most common sources of initial exposure to e-cigarettes were friends, relatives, and/or family (72.2%), followed by the media (25.6%). The most commonly reported reasons for starting to use e-cigarettes was curiosity (49.6%), followed by because a friend used e-cigarettes (28.5%), to quit smoking traditional cigarettes (15.5%), or they believed that e-cigarettes are less harmful than other tobacco products (2.4%).
Table 1.
Participant characteristics and bivariate findings regarding correlates of past-month e-cigarette use and next-year e-cigarette use intent (N=225)
| Total | Past-month e-cigarette use | Next-year e-cigarette use intent§ |
||||
|---|---|---|---|---|---|---|
| N=225 (100%) |
No N=171 (76.7%) |
Yes N=52 (23.3%) |
||||
| N (%) or M (SD) |
N (%) or M (SD) |
N (%) or M (SD) |
p | M (SD) or r |
p | |
| Sociodemographics | ||||||
| Age, M (SD) and r | 21.68 (3.43) | 21.28 (2.50) | 22.58 (4.35) | <.05 | 0.19 | <.01 |
| Sex, N (%) and M (SD) | <.01 | <.01 | ||||
| Female | 127 (56.4) | 107 (62.6) | 19 (36.5) | 1.70 (0.75) | ||
| Male | 98 (43.6) | 64 (37.4) | 33 (63.5) | 2.00 (0.87) | ||
| Tobacco use characteristics | ||||||
| Past-month cigarette use, N (%) and M (SD)+, F | <.001 | <.001 | ||||
| No | 124 (55.1) | 118 (69.0) | 5 (9.6) | 1.44 (0.72) | ||
| Yes | 101 (44.9) | 53 (31.0) | 47 (90.4) | 2.31 (0.66) | ||
| Next-year cigarette use intent, M (SD) and r§ | 2.15 (1.09) | 1.90 (1.02) | 2.94 (0.90) | <.001 | 0.65 | <.001 |
| Lifetime e-cigarette use, N (%) and M (SD)F | <.001 | <.001 | ||||
| No | 91 (40.8) | 91 (53.2%) | 0 (0.0%) | 1.21 (0.41) | ||
| Yes | 132 (59.2) | 80 (46.8%) | 52 (100.0%) | 2.26 (0.75) | ||
| Past-month e-cigarette use, N (%) and M (SD)& | <.001 | |||||
| No | 171 (76.7) | - | - | - | 1.53 (0.60) | |
| Yes | 52 (23.3) | - | - | 2.83 (0.62) | ||
| Next-year e-cigarette use intent, M (SD) and r§ | 1.83 (0.82) | 1.53 (0.60) | 2.83 (0.62) | <.001 | - | - |
| Knowledge and perceptions, M (SD) and r | ||||||
| How informed are you about e-cigarettes? * | 2.47 (0.78) | 2.36 (0.74) | 2.85 (0.80) | <.001 | 0.12 | NS |
| How addictive are traditional cigarettes?# | 9.13 (1.25) | 9.26 (1.07) | 8.75 (1.67) | <.05 | −0.18 | <.01 |
| How addictive are e-cigarettes?# | 7.96 (1.99) | 8.24 (1.83) | 7.04 (2.24) | <.01 | −0.24 | <.001 |
| Are e-cigarettes a public health problem?# | 8.39 (1.92) | 8.60 (1.64) | 7.69 (2.53) | <.05 | −0.29 | <.001 |
| E-cigarette perceptions index items | ||||||
| E-cigarettes: M (SD) and r ^ | ||||||
| Are safer than cigarettes | 2.01 (0.70) | 1.95 (0.67) | 2.19 (0.79) | <.05 | 0.20 | <.01 |
| Can help quitting smoking | 1.83 (0.64) | 1.80 (0.62) | 1.92 (0.71) | NS | 0.16 | <.05 |
| Can lead to other tobacco useR | 2.76 (0.71) | 2.85 (0.70) | 2.46 (0.67) | NS | −0.28 | <.001 |
| Should be regulated like other tobaccoR | 3.04 (0.73) | 3.12 (0.66) | 2.77 (0.88) | <.05 | −0.19 | <.01 |
| Index score | 2.01 (0.47) | 1.95 (0.42) | 2.22 (0.57) | <.01 | 0.31 | <.001 |
Notes:
1=definitely will not to 4=definitely will.
1=not at all to 4=quite.
1=not at all to 10=extremely.
1=completely disagree to 4=completely agree.
Reverse scored to calculate index score (Cronbach’s alpha=.60); individual item scores not reverse scored.
Past-30-day cigarette use: 0 days (n=124, 55.1%), 1–9 days (n=34, 15.1%), 10–19 days (n=27, 12.0%), 20–29 days (n=15, 6.7%), 30 days (n=25, 11.1%).
Past-30-day e-cigarette use: 0 days (n=171, 76.7%), 1–9 days (n=45, 20.2%), 10–19 days (n=1, 0.4%), 20–29 days (n=1, 0.4%), 30 days (n=5, 2.2%). First heard about e-cigarettes from: a friend or relative (n=149, 66.8%), the media (n=57, 25.6%), a family member (n=12, 5.4%), advertising billboards (n=3, 1.3%), an educational institution (n=2, 0.9%).
Fisher’s exact test used for cell size ≤5.
In bivariate analyses (Table 1), those reporting past-month use of e-cigarettes were older (p<.05), more likely male (p<.01), more likely to use cigarettes (p<.001), had greater next-year intent to use cigarettes and e-cigarettes (p’s<.001), reported being more informed about e-cigarettes (p<.001), perceived cigarettes and e-cigarettes to be less addictive (p’s<.05), reported that e-cigarettes were less of a public health problem (p<.05), and reported greater agreement with “e-cigarettes being safer than cigarettes” (p<.05) and lower agreement with “e-cigarettes should be regulated like other tobacco” (p<.05). In multivariable regression analyses (Table 2), past-month e-cigarette use was associated with past-month cigarette use (p<.001), being more informed about e-cigarettes (p<.01), and more favorable e-cigarette perceptions (p<.01).
Table 2.
Multivariable regression models examining correlates of past-month e-cigarette use and next-year e-cigarette use intent (N=225)
| Past-month e-cigarette use | Next-year e-cigarette use intent§ | |||||
|---|---|---|---|---|---|---|
| aOR | 95% CI | p | B | 95% CI | p | |
| Sociodemographics | ||||||
| Age | 1.13 | 0.99, 1.28 | NS | 0.02 | −0.01, 0.05 | NS |
| Male (ref: female) | 1.24 | 0.51, 2.98 | NS | 0.06 | −0.11, 0.23 | NS |
| Tobacco use characteristics | ||||||
| Past-month cigarette use (ref: no) | 36.79 | 9.82, 137.89 | <.001 | -- | -- | -- |
| Past-month e-cigarette use (ref: no) | -- | -- | -- | 1.19 | 0.99, 1.40 | <.001 |
| Knowledge and perceptions | ||||||
| How informed are you about e-cigarettes? * | 2.48 | 1.44, 4.28 | <.01 | −0.06 | −0.17, 0.04 | NS |
| How addictive are traditional cigarettes?# | 0.90 | 0.66, 1.22 | NS | −0.06 | −0.13, 0.01 | NS |
| How addictive are e-cigarettes?# | 0.86 | 0.68, 1.09 | NS | 0.03 | −0.02, 0.09 | NS |
| Are e-cigarettes a public health problem?# | 0.92 | 0.71, 1.18 | NS | −0.06 | −0.11, −0.01 | <.05 |
| E-cigarette perceptions index ^ | 4.06 | 1.43, 11.57 | <.01 | 0.19 | 0.00, 0.38 | NS |
Notes: Nagelkerke R-square = .486; Adjusted R-square = .584, respectively.
1=definitely will not to 4=definitely will.
1=not at all to 4=quite.
1=not at all to 10=extremely.
See items in Table 1.
In bivariate analyses (Table 1), greater next-year e-cigarette use intent was associated with being older (p<.01) and male (p<.01), past-month cigarette and e-cigarette use (p’s<.001), greater likelihood of future cigarette use (p<.001), perceiving both cigarettes and e-cigarettes to be less addictive (p’s<.01), reporting that e-cigarettes are less of a public health problem (p<.001), and reporting more favorable e-cigarette perceptions overall (p<.001) and across individual index items. In multivariable regression analyses (Table 2), greater next-year e-cigarette use intent was associated with past-month e-cigarette use (p<.001) and perceiving e-cigarettes to be less of a public health problem (p<.05).
Discussion
In this sample of young adults in Georgia, nearly half reported past-month cigarette use and nearly one-fourth reported e-cigarette use, which may reflect concerning increases in e-cigarette use prevalence among Georgian youth in the past decade (Tarasenko et al., 2022). As hypothesized, factors associated with e-cigarette use included cigarette use, greater awareness of e-cigarettes, and more favorable e-cigarette perceptions (i.e., safer than cigarettes, can aid in smoking cessation, not a gateway to other tobacco, regulated differently than other tobacco) (Aljandaleh et al., 2020; Aly et al., 2022; Cuccia et al., 2022; Jiang et al., 2019; Persoskie et al., 2019; Wiseman et al., 2019). Furthermore, factors associated with greater next-year e-cigarette use intentions included current e-cigarette use and perceiving e-cigarettes to be less of a public health problem to use cigarettes. These findings highlighting the importance of perceptions in driving use behaviors and the need to address dual use (Chapman et al., 2019; Martinelli et al., 2023).
These findings highlight the need to address e-cigarette use among young adults in Georgia. Such efforts should include greater regulation e-cigarette marketing and sales, as well as oversight of the implementation and enforcement of e-cigarette related regulations. Additionally, intervention efforts to prevent and reduce e-cigarette use (e.g., campus-based interventions) could increase awareness of the potential harms and addictiveness of e-cigarettes, and should also address the risks of dual cigarette and e-cigarette use (Hartmann-Boyce et al., 2023). Notably, findings from multivariable analysis did not support the hypothesis that certain sociodemographics (e.g., male sex, older age) were associated with e-cigarette use and use intentions. However, bivariate analyses did show that males and those who were older were more likely to use e-cigarettes, suggesting that accounting for current cigarette use in multivariable analysis likely masked these associations, given that older individuals and males are more likely to use cigarettes in Georgia (Kakutia, 2020). Furthermore, the most common sources of initial exposure to e-cigarettes were friends, relatives, and/or family, followed by the media, underscoring the important roles of social networks, media, and marketing (Collins et al., 2019; Lyu et al., 2022). Thus, intervention efforts should target older young adults, males, and those vulnerable to social influences that encourage e-cigarette use and to e-cigarette marketing (Kakutia, 2020).
Study limitations include limited generalizable to the adult population of Georgia, given the low response rate and that participants were drawn from a large public medical university. Additionally, the small sample size, use of self-reported measures, and cross-sectional nature of the study have implications for findings (e.g., modest power, potential bias, inability to investigate causal relationships). Finally, not all potentially relevant factors were assessed.
Conclusions
In this sample of Georgian young adults, nearly one-fourth reported past-month e-cigarette use. Findings underscore the need for intervention efforts to increase knowledge regarding potential risks of e-cigarette use as well as dual use of cigarettes and e-cigarettes, and to address social influences that encourage use, especially among key populations (e.g., older young adults, males). Policymakers and practitioners in Georgia may also consider increasing restrictions on e-cigarette marketing and enforcement of existing regulations.
Supplementary Material
Funding.
This work was supported by the US National Institute of Environmental Health Sciences and Fogarty International Center (D43ES030927, MPIs: Berg, Caudle, Sturua). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Competing interests. The authors report no conflicting interests.
Ethics approval. This study was approved by the Tbilisi State Medical University Institutional Review Board.
Availability of data and materials.
The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.