Abstract
Background:
Understanding factors influencing electronic cigarette (e-cigarette) trial in adolescents is crucial for shaping policies and interventions to reduce consumption and potentially prevent addictive tendencies, particularly in countries with weak regulations like Guatemala.
Objective:
We aimed to longitudinally assess predictors of e-cigarette trial among Guatemalan adolescents surveyed in 2019, 2020, and 2021.
Methods:
Students (13 to 18 years old) from nine private schools completed self-administered questionnaires about e-cigarette use and associated risk factors. Data from those who had at least one follow-up survey after reporting that they had never tried e-cigarettes at either the 2019 or 2020 survey (N=838) was analyzed. We used a mixed-effects logistic regression clustered by student and school to assess predictors of ever e-cigarette use.
Results:
Nearly one-quarter (21.0%) of unique students tried e-cigarettes at follow-up. Risk factors for e-cigarette trial included cigarette or heated tobacco products use (adjusted odds ratio [AOR]=2.95, 95%CI = 1.24–7.04), frequent exposure to online e-cigarette marketing (AOR=2.46, 95%CI = 1.39–4.35), alcohol or marihuana use (AOR=1.74, 95%CI = 1.17–2.58), and parental approval of e-cigarette use (AOR=1.65, 95%CI = 1.14–2.40). The perception that serious illness from life-long e-cigarette use is likely or certain (AOR=0.57, 95%CI = 0.36–0.91, and AOR=0.37, 95%CI = 0.19–0.72, respectively) appeared as a protective factor for e-cigarette trial.
Conclusion:
Our findings align with international research, indicating shared risk factors across youth worldwide. The incorporation of these factors into policies and interventions targeting the reduction of e-cigarette trial is imperative for optimizing their efficacy.
Keywords: Electronic cigarettes, Adolescents, Predictors, Policy, Low- or Middle-income countries (LMIC)
1. INTRODUCTION
Globally, there has been widespread adoption of electronic cigarettes (e-cigarettes) among adolescents, including never-smokers (1). Analysis from the Global Youth Tobacco Surveys (GYTS) conducted between 2014 and 2019 across 75 countries revealed that 20.2%, 10.9%, and 4.6% of school-age adolescents were ever, current, or dual users (concurrently used e-cigarettes and conventional cigarettes), respectively (1). Additional studies utilizing GYTS data to assess longitudinal trends have reported substantial rises in current e-cigarette use among adolescents surveyed from 2013 to 2019 (2) and from 2012 to 2020 (3). These data were mostly collected before the recent spike in e-cigarette use associated with the introduction of nicotine salt and disposable products.
The use of nicotine products stimulates neurotransmitters associated with the brain’s reward system, thereby predisposing users to the potential development of substance abuse and dependence (4). Studies have revealed a potential association between ever e-cigarette use and future cigarette smoking (5), along with the possibility of experimentation with other substances, such as marihuana (6), which supports the gateway hypothesis (7). Understanding the risk factors associated with ever e-cigarette use is crucial to detect susceptible groups, as their use may foster addictive tendencies among adolescents. However, most studies on this topic feature cross-sectional designs and are from high income countries with strong tobacco control policies (1,8,9).
E-cigarette use has been shown to vary by sex, with male adolescents having higher odds of use (1). Both prevalence and odds of e-cigarette use has been shown to increase with age (1). Higher family affluence (10) and lower academic performance (11,12) have also been identified as predictors of e-cigarette use. E-cigarette use among adolescents has been associated with social learning influences, particularly in conjunction with diminished self-control capacities (13). Use of other substances, particularly conventional cigarettes (10,12) and alcohol (11,14,15), has also been associated with e-cigarette use among adolescents. A positive correlation has been found between awareness of e-cigarettes and its use (2). In addition to social networks, significant sources of information regarding e-cigarettes include social media platforms and the internet (16). Tobacco companies engage in robust social media campaigns promoting e-cigarettes as a harm reduction strategy for cigarette smokers (2), and a less harmful (16,17) as well as less addictive (18) alternative to traditional cigarettes. Exposure to such ads has been associated with experimentation with these products (17).
Adolescents in countries where e-cigarettes are banned have lower odds of being current users compared to their counterparts in countries without regulatory policies (1). Even if policies are in place, enforcement and effectiveness remain a challenge for some countries. This is evidenced by India and Mexico, where e-cigarettes are easily accessible despite having bans on importation, distribution, marketing and sales (19,20). In Guatemala, a middle income country with weak tobacco control (21), e-cigarettes are unregulated and readily available in a wide array of device types, flavors, and nicotine concentrations (22–25). E-cigarettes can be purchased, without age verification in grocery stores, pharmacies, wholesale distributors, and gas stations with convenience stores (22). They are also highly accessible through social media and vending machines placed in key locations, such as shopping centers (25). In 2015, 5.6% of a sample of Guatemalan middle school students ages 13 to 15 years currently used e-cigarettes (26). In 2019, this percentage had reached 27.7% among Guatemalan adolescents aged 13 to 18 from schools located in middle to high socioeconomic urban areas (27). This population was also more likely to first try and continuously use e-cigarettes compared to traditional cigarettes (24) and to perceive them as more attractive, less harmful, and more appealing to try than either Heated Tobacco Products (HTPs) or conventional cigarettes (23). However, previous studies conducted in Guatemala have not examined the longitudinal predictors influencing e-cigarette trial. Thus, our aim was to assess these predictors among adolescents to determine whether they differ from those identified in other countries. By identifying how Guatemala’s unique context shapes patterns of behavior among youth, we can design targeted strategies that address the specific needs of the population.
2. MATERIALS AND METHODS
2.1. Study population and design
We used data from a student cohort recruited from nine private schools in Guatemala City, each of which serves all grade levels within a single institution, rather than having separate middle and high schools. Students ages 13 to 18 were invited to participate and were surveyed in person at their schools from May to September 2019 (wave 1). Due to the COVID-19 pandemic, they were surveyed online from their homes from June to November 2020 (wave 2) and from August to October of 2021 (wave 3). Online surveys were conducted via Zoom during one of their classes without the teacher being present and they were encouraged to keep their cameras on. Students did not receive any incentives for completing the survey.
At wave 1, nine schools were enrolled, however, due to the pandemic, one school dropped out at wave 2 (excluded from the analysis). Two additional schools were lost at wave 3, however, their data from waves 1 and 2 were incorporated into the analysis. The survey was adapted from the Population Assessment of Tobacco and Health study (PATH) (28) and the International Tobacco Control Youth Smoking and Vaping Survey (ITC-Youth) (29). The survey was validated for Mexican adolescents (30) and then adapted and pilot tested in Guatemalan Spanish. Self-administered questionnaires included students’ prior and current substance use (e-cigarettes, cigarettes, HTPs, alcohol, and marihuana) and potential risk factors for nicotine product use based on prior research in other countries.
Passive parental consent and students’ active assent were requested before each wave. In wave 1, 13.2% (n=436) of invited students were absent, lacked parental consent, or declined participation; in wave 2, this figure rose to 40.4% (n=1262); and in wave 3, 4.3% (n=92) of invited students did not participate due to the same reasons. The final analytic sample included students who had at least one follow-up survey and had never tried e-cigarettes at wave 1 or wave 2 (for those were newly recruited in wave 2 and those who participated in all three waves but reported that they had not tried e-cigarettes at wave 2; this latter group contributed two observations). The wave 3 responses from those who completed all three waves but tried e-cigarettes in wave 2, were not included in the analysis. A complete case analysis was conducted, including only participants with no missing data for the variables of interest.
Out of 3,845 unique students who completed the survey, 3763 provided data on ever e-cigarette use. After excluding those with missing data on key variables (n=328), those with incongruencies in their responses (n=176), students without follow-up surveys (n=1899), ever and current e-cigarette users at their baseline (n=604), and the wave 3 responses from those who completed all three waves but tried e-cigarettes in wave 2 (n=44 observations), 1086 observations from 838 unique students were analyzed (248 students contributed two observations each, as they participated in all three waves but did not try e-cigarettes at wave 2). Around 34.8% (n=292) of unique students participated in all three waves, 42.2% (n=354) just in waves 1 and 2, 9.9% (n=83) just in waves 1 and 3, and 13.0% (n=109) just in waves 2 and 3 (See supplemental material, Table 1).
2.2. Dependent variable
We assessed participants’ history of ever e-cigarette use with the question “have you ever tried an e-cigarette?” (Yes/No). To create the dependent variable, we used a “lead” approach, which assigns the value of ever e-cigarette use from the next wave (time t+1) to the prior wave (time t), allowing us to evaluate how student characteristics from the prior wave were associated with later e-cigarette trial.
2.3. Independent variables
Our independent variables reflect the wave when students had not yet tried e-cigarettes (time t). Sociodemographic covariates considered were sex (female/male), age group (13 or younger/14/15/16 or older), and average grade (below 79/80 to 89/90 to 100). Family affluence was assessed with a validated scale (31,32) using four questions that were summed (range=0–9), where higher numbers mean more wealth.
We also assessed use of substances other than e-cigarettes. Ever and past 30-day frequency of conventional cigarette and HTPs use were assessed (“Have you ever tried regular cigarettes/HTPs?”/”In the past 30 days, on how many days did you smoke cigarettes/HTPs”?). Due to small sample of HTP users, we created a joint variable that considered use of both substances (Has never smoked cigarettes or HTPs/Has smoked or currently smokes cigarettes or HTPs). Ever alcohol use was defined as having ever had an alcoholic beverage (“Have you ever had an alcoholic drink?”), and current use as having had at least one alcoholic beverage in the last 30 days (“In the past 30 days, on how many days did you have at least one glass or drink of an alcoholic beverage?”). The study also evaluated ever, past year, and past month marihuana use (“When was the last time you smoked marihuana (weed)?”). Due to the small sample size of ever and current marihuana users, we derived a variable from the use of both substances (Has never used alcohol or marihuana/Has used or currently uses alcohol or marihuana).
To estimate the influence of social networks on ever e-cigarette use we assessed friends’, parents’, siblings’, and other family members’ use of e-cigarettes, cigarettes, and HTPs, separately (“Do your parents/siblings/other family members who live in your home use e-cigarettes/cigarettes/HTPs?”/”Do any of your 5 closest friends use e-cigarettes/cigarettes/HPTs?”). Due to small sample sizes, we dichotomized the variables to indicate use of any of these nicotine products for friends (Yes/No) and a single variable for any family member (Yes/No). Parental and friends’ approval or disapproval of e-cigarette use were assessed separately using a five-level Likert scale (“Do you think your parents/friends approve or disapprove of other people using electronic cigarettes?”). For each question, responses were dichotomized to indicate disapproval (Completely disapprove/Disapprove) or not (Neither approve nor disapprove/Approve/Approve completely).
Perception of e-cigarettes’ harms was gauged with the question “assuming you used electronic cigarettes for the rest of your life, how likely is it that you would develop a serious illness from using them?”. Perceived addictiveness of e-cigarettes was assessed with the question “how likely is it that a person would become addicted to electronic cigarettes?”. For each of these questions, responses were collapsed into “Don’t know/Will not happen/Unlikely”, “Likely/Very likely”, and “Will surely happen”.
Frequency of exposure to online e-cigarette advertising while browsing the internet or using social media (Instagram, Facebook, Snapchat, Twitter, and YouTube) in the last 30 days was also assessed, with response options being trichotomized (Never or rarely/Sometimes/Frequently or very frequently). Potential exposure to e-cigarette advertising at the point of sale, where e-cigarettes are prominently displayed (22), was assessed by asking about the frequency of visiting stores, supermarkets, or convenience stores within the last 30 days (Never/Sometimes/Frequently or very frequently).
We also evaluated the effect of the follow-up period between waves (1-year vs. 2-year interval), categorizing students based on their participation in waves 1 and 2, waves 2 and 3, or waves 1 and 3.
2.4. Analysis
Descriptive analysis explored participants’ characteristics (time t) in relation to whether they tried e-cigarettes at follow-up (time t+1) or not. We estimated frequencies and percentages for categorical variables (Chi-square tests) and mean and standard deviation for continuous variables (T-test).
We used a mixed-effects logistic regression model to estimate odds of e-cigarette trial based on independent variables measured at waves when students had not yet tried e-cigarettes (time t). This approach allowed us to use student characteristics from time t to predict e-cigarette trial in a subsequent wave (time t+1). The model included students who were followed from wave 1 to wave 2 (1-year interval), from wave 2 to wave 3 (1-year interval), as well as those who participated in waves 1 and 3 without data for wave 2 (2-year interval). If a student participated in all three waves but hadn’t tried e-cigarettes by wave 2, the model included two observations from that student (one from wave 1 and another from wave 2). We calculated the intraclass correlation (ICC) at both student (ICC=0.99) and school levels (ICC=0.05), which suggested strong clustering among students and moderate clustering by school. Thus, the model adjusted for clustering both by school and students, in the cases where they provided two observations.
To assess the robustness of our findings, we evaluated alternative models, including random effects logistic regressions, and Generalized Estimating Equations models and compared the results with those obtained (See supplemental material, Tables 2, 3 and 4). The comparison revealed only minimal changes in the coefficients, but statistical significance and interpretation were consistent across model specifications. Results are summarized as adjusted odds ratios (AOR) and all analyses were conducted using Stata v.17.
2.5. Ethical considerations
The protocol was approved by the Institutional Ethics Committee of the Institute of Nutrition of Central America and Panama.
3. RESULTS
Overall, 21.0% (n=176/838) of unique students tried e-cigarettes at a follow-up wave. E-cigarette trial was associated with follow-up period (p<0.001) average grade (p=0.001), use of alcohol or marihuana (p<0.001), and the use of cigarette or HTPs (p=0.001) (Table 1). Significant associations were also observed between ever e-cigarette use and approval of e-cigarette use by friends (p=0.003) and parents (p<0.001), as well as with friends’ (p=0.001) or parents’ (p=0.022) use of cigarettes, e-cigarettes, or HTPs. Furthermore, the perceived likelihood of experiencing a serious illness (p<0.001) from lifelong e-cigarette use was significantly associated with ever e-cigarette use. Lastly, we found a significant association between e-cigarette trial and the frequency of exposure to online e-cigarette advertisements in the past 30 days (p<0.001).
Table 1.
Characteristics of Guatemalan adolescents who had never tried e-cigarettes at time t, by whether they tried e-cigarettes at follow-up (time t+1) (N=1086*)
| E-cigarette trial at time t+1 | ||||
|---|---|---|---|---|
| No | Yes | Total | ||
| [N = 881] | [N = 205] | [N = 1086] | p-value*** | |
| Characteristic at time t | n (%**) | n (%*) | n (%*) | |
|
| ||||
| Follow-up period | ||||
| Wave 1 to wave 2 | 510 (78.95) | 136 (21.05) | 646 | <0.001 |
| Wave 2 to wave 3 | 315 (88.24) | 42 (11.76) | 357 | |
| Wave 1 to wave 3 | 56 (67.47) | 27 (32.53) | 83 | |
|
| ||||
| Sex | ||||
| Female | 490 (81.40) | 112 (18.60) | 602 | 0.798 |
| Male | 391 (80.79) | 93 (19.21) | 484 | |
|
| ||||
| Age group | ||||
| 13 or younger | 159 (77.18) | 47 (22.82) | 206 | 0.445 |
| 14 | 260 (82.28) | 56 (17.72) | 316 | |
| 15 | 260 (81.50) | 59 (18.50) | 319 | |
| 16 or older | 202 (82.45) | 43 (17.55) | 245 | |
|
| ||||
| Average grade | ||||
| Below 79 | 132 (79.52) | 34 (20.48) | 166 | 0.001 |
| 80 to 89 | 378 (76.99) | 113 (23.01) | 491 | |
| 90 to 100 | 371 (86.48) | 58 (13.52) | 429 | |
|
| ||||
| Family affluence scale (0–9), mean (SD) | 7 (2) | 7 (2) | 7 (2) | 0.277 |
|
| ||||
| Alcohol or marihuana use | ||||
| Never user | 435 (86.83) | 66 (13.17) | 501 | <0.001 |
| Ever or current user | 446 (76.24) | 139 (23.76) | 585 | |
| Cigarette or HTP use | ||||
| Never user | 862 (81.78) | 192 (18.22) | 1,054 | 0.001 |
| Ever or current user | 19 (59.38) | 13 (40.63) | 32 | |
|
| ||||
| Friends’ approval of e-cigarette use | ||||
| Completely disapprove / Disapprove | 248 (87.02) | 37 (12.98) | 285 | 0.003 |
| Neither approve nor disapprove / Approve / Approve completely | 633 (79.03) | 168 (20.97) | 801 | |
|
| ||||
| Parents’ approval of e-cigarette use | ||||
| Completely disapprove / Disapprove | 604 (84.48) | 111 (15.52) | 715 | <0.001 |
| Neither approve nor disapprove / Approve / Approve completely | 277 (74.66) | 94 (25.34) | 371 | |
| Friends’ use of cigarette, e-cigarette, or HT | ||||
| No | 518 (84.64) | 94 (15.36) | 612 | 0.001 |
| Yes | 363 (76.58) | 111 (23.42) | 474 | |
|
| ||||
| Family member’s use of cigarette, e-cigarette, or HTP | ||||
| No | 549 (83.31) | 110 (16.69) | 659 | 0.022 |
| Yes | 332 (77.75) | 95 (22.25) | 427 | |
|
| ||||
| Assuming you were to use e-cigarettes for the rest of your life, what is the likelihood that you will suffer a serious illness from using e-cigarettes? | ||||
| Don’t know / Will not happen / Unlikely | 128 (68.45) | 59 (31.55) | 187 | <0.001 |
| Likely / Very likely | 564 (82.58) | 119 (17.42) | 683 | |
| Will surely happen | 189 (87.50) | 27 (12.50) | 216 | |
|
| ||||
| What is the likelihood that a person becomes addicted to e-cigarettes? | ||||
| Don’t know / Will not happen / Unlikely | 60 (72.29) | 23 (27.71) | 83 | 0.101 |
| Likely / Very likely | 691 (81.87) | 153 (18.13) | 844 | |
| Will surely happen | 130 (81.76) | 29 (18.24) | 159 | |
|
| ||||
| Frequency of noticing online e-cigarette advertisements in the last 30 days | ||||
| Never / rarely | 691 (84.17) | 130 (15.83) | 821 | <0.001 |
| Sometimes | 121 (78.06) | 34 (21.94) | 155 | |
| Frequently / Very frequently | 69 (62.73) | 41 (37.27) | 110 | |
|
| ||||
| Frequency of visiting supermarkets or convenience stores in the last 30 days | ||||
| Never | 202 (85.23) | 35 (14.77) | 237 | 0.141 |
| Sometimes | 483 (80.63) | 116 (19.37) | 599 | |
| Frequently / Very frequently | 196 (78.40) | 54 (21.60) | 250 | |
A total of 838 students contributed one observation each, while 248 contributed two observations each, having participated in all three waves without trying e-cigarettes by wave 2, totaling 1086 observations
Denominator is the row total
Chi-square or T-test result (Significance at p<0.05)
Predictors for e-cigarette trial (Table 2) included being an ever/current cigarette or HTP user (AOR=2.95, 95%CI = 1.24–7.04); being exposed to e-cigarette ads frequently/very frequently (AOR=2.46, 95%CI = 1.39–4.35); being an ever/current alcohol or marihuana user (AOR=1.74, 95%CI = 1.17–2.58); and the perception that parents either approved or had neutral opinions of e-cigarette use (AOR=1.65, 95%CI = 1.14–2.40). Perceiving that life-long e-cigarette use was likely/very likely (AOR=0.57, 95%CI = 0.36–0.91) or definitely (AOR=0.37, 95%CI = 0.19–0.72) going to cause a serious illness was inversely associated with ever e-cigarette use.
Table 2.
Adjusted odds of e-cigarette trial among adolescents in Guatemala (N=1086 observations from 838 unique students)
| Covariate | *AOR (95% CI) | p-value |
|---|---|---|
|
| ||
| Follow-up period | ||
| Wave 1 to wave 2 | Ref | |
| Wave 2 to wave 3 | 0.64 (0.41–1.01) | 0.058 |
| Wave 1 to wave 3 | 1.61 (0.90–2.86) | 0.106 |
| Sex | ||
| Female | Ref | |
| Male | 1.09 (0.77–1.55) | 0.637 |
| Age group | ||
| 13 or younger | Ref | |
| 14 | 0.64 (0.39–1.04) | 0.070 |
| 15 | 0.86 (0.53–1.40) | 0.546 |
| 16 or older | 0.80 (0.47–1.39) | 0.435 |
| Average grade | ||
| Below 79 | Ref | |
| 80 to 89 | 1.18 (0.72–1.93) | 0.512 |
| 90 to 100 | 0.76 (0.44–1.32) | 0.338 |
| Family affluence scale | 1.05 (0.94–1.17) | 0.419 |
| Alcohol or marihuana use | ||
| Never user | Ref | |
| Ever or current user | 1.74 (1.17–2.58) | 0.006 |
| Cigarette or HTP use | ||
| Never user | Ref | |
| Ever or current user | 2.95 (1.24–7.04) | 0.014 |
| Friends’ approval of e-cigarette use | ||
| Completely disapprove / Disapprove | Ref | |
| Neither approve nor disapprove / Approve / Approve completely | 1.33 (0.85–2.07) | 0.213 |
| Parents’ approval of e-cigarette use | ||
| Completely disapprove / Disapprove | Ref | |
| Neither approve nor disapprove / Approve / Approve completely | 1.65 (1.14–2.40) | 0.008 |
| Friends’ use of cigarette, e-cigarette, or HTP | ||
| No | Ref | |
| Yes | 1.23 (0.85–1.79) | 0.280 |
| Family member’s use of cigarette, e-cigarette, or HTP | ||
| No | Ref | |
| Yes | 1.03 (0.72–1.46) | 0.888 |
| Assuming you were to use e-cigarettes for the rest of your life, what is the likelihood that you will suffer a serious illness from vaping? | ||
| Don’t know / Will not happen / Unlikely | Ref | |
| Likely / Very likely | 0.57 (0.36–0.91) | 0.017 |
| It will definitely happen | 0.37 (0.19–0.72) | 0.004 |
| What is the likelihood that a person becomes addicted to e-cigarettes? | ||
| Don’t know / Will not happen / Unlikely | Ref | |
| Likely / Very likely | 0.71 (0.39–1.30) | 0.270 |
| It will definitely happen | 0.97 (0.45–2.07) | 0.935 |
| Frequency of noticing online e-cigarette advertisements in the last 30 days | ||
| Never / rarely | Ref | |
| Sometimes | 1.16 (0.73–1.86) | 0.522 |
| Frequently / Very frequently | 2.46 (1.39–4.35) | 0.002 |
| Frequency of visiting supermarkets or convenience stores in the last 30 days | ||
| Never | Ref | |
| Sometimes | 0.90 (0.57–1.43) | 0.656 |
| Frequently / Very frequently | 0.90 (0.52–1.53) | 0.687 |
AOR = Adjusted odds ratio
Significance at p<0.05
4. DISCUSSION
The findings of this study contribute to a growing body of literature on predictors of e-cigarette trial amongst adolescents.We found that the use of other nicotine products predicted e-cigarette trial, as has been found in other longitudinal studies of adolescents, including in Finland (11), and Argentina (33). We also found that other substance use (i.e., alcohol or marihuana) predicted e-cigarette trial, similar to the longitudinal association with alcohol among Finnish adolescents (11). Exposure to e-cigarette advertising predicted e-cigarette trial in our study, as was the case in Argentina (33). Parental approval also has been associated with current e-cigarette use in Indonesia (10), though that study was cross-sectional. A longitudinal study from Canada (15) also found a negative association between current e-cigarette use and high perceived health risks from e-cigarette use.
Some of our null findings have been associated with ever e-cigarette use among adolescents in other countries. For instance, prior longitudinal studies have found that friends’ use of nicotine products predicts ever e-cigarette among cohorts in Germany (34), the USA (35), and Argentina (33), as did peer approval of e-cigarettes in the US (35). However, these associations were not found in our study, perhaps because our follow-up surveys took place during the pandemic, when in-person social contact with peers was limited. Likewise, parental use of nicotine products has been identified as a significant predictor of e-cigarette use in longitudinal studies from Finland (11) and Germany (34), but not in our study. Due to the low prevalence of e-cigarette use among parents in our sample, our measure of parental use combined e-cigarette use with use of other nicotine products, which likely diluted any association with student e-cigarette trial. A prior cross-sectional study from Indonesia found an association between higher family affluence and current e-cigarette use (10). Our study did not find this association perhaps because we recruited students from private schools whose student populations come from relatively high socioeconomic status groups; as such, relatively limited variability in family affluence may explain our null findings for this variable. A comprehensive literature review (8) and a secondary analysis of data from 75 countries (1) identified older age and male sex as predictors of current e-cigarette use. Our null findings for these variables may be due to our assessment of e-cigarette trial rather than current use. Finally, longitudinal findings from Finland (11) report an inverse association between ever e-cigarette use and higher academic performance. Although our results were consistent in the direction of the associations mentioned above, they did not reach statistical significance, perhaps due to limited statistical power from our smaller sample size.
To our knowledge, this is the first study evaluating the predictors of e-cigarette trial among adolescents in Guatemala. The longitudinal design and context of a middle-income country without e-cigarette regulations enhance its contributions to the knowledge gap around adolescents e-cigarette use in low- or middle-income countries. However, our results should be interpreted in light of some limitations. Given that some schools and students were lost to follow up, our findings might be subject to attrition bias (See supplemental material, Table 5). The study sample was followed for different periods of time, and these varying lengths of time may be subject to different intervening, unmeasured variables that confound the associations we found. Perhaps the most glaring, unmeasured variable is around factors related to the COVID-19 pandemic, during which the prevalence of e-cigarette and other substance use declined among youth worldwide (36), potentially due to the limited opportunities for in-person social interactions that promote use. The study is susceptible to reporting bias given the self-reported nature of the survey, potentially leading to underreporting of compromising behaviors. Nevertheless, student confidentiality was emphasized during all survey administrations to limit this potential bias. The study was conducted in the metropolitan area of Guatemala with a convenience sample of private schools and, therefore, our findings might not be generalizable to the rest of the country. Future research should assess regional differences and examine long-term use trends of e-cigarettes among adolescents as they progress into adulthood.
5. CONCLUSION
Our findings highlight distinctive characteristics and behavioral traits linked to e-cigarette trial, some of which are consistent with studies conducted in high-income countries, suggesting common predictors among youth worldwide.
Research is sorely needed on interventions – including those that may target susceptible groups – to decrease adolescent e-cigarette use. Our findings, along with those from other studies, underscore the importance of developing comprehensive prevention and intervention strategies that: 1) encompass a broader spectrum of risk factors associated with abuse across different substances; 2) take into account the impact of social networks on substance use behaviors; 3) advocate for the prohibition of online advertisement, promotion, and sponsorship on the internet and social media platforms commonly accessed by adolescents, and 4) incorporate educational content to dispel misconceptions that e-cigarettes are less harmful than other nicotine products.
6. DECLARATION OF GENERATIVE AI AND AI-ASSISTED TECHNOLOGIES IN THE WRITING PROCESS
During the preparation of this work the authors used ChatGPT in order improve readability and language. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Supplementary Material
Highlights.
Nearly one-quarter of students tried e-cigarettes at follow-up.
The main predictor of e-cigarette trial was the use of other substances.
Social network influences and exposure to e-cigarette ads also predicted trial.
Perceived health risks from using e-cigarettes was a protective factor for trial.
Results align with global research, indicating shared predictors across contexts.
ACKNOWLEDGEMENTS
We extend our sincere gratitude to the administrators, teachers, and staff of the participating schools for their invaluable support in facilitating the data collection process.
ROLE OF FUNDING SOURCES
This research was supported by Fogarty International Center of the National Institutes of Health under award number R01 TW010652. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Additionally, the National Institutes of Health had no role in the study design, collection, analysis and interpretation of data, writing of the report or in the decision to submit the article for publication.
Footnotes
DECLARATION OF COMPETING INTERESTS
The authors declare that they have no competing interests that may influence the validity of the research.
DECLARATIONS OF INTEREST: None
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7. REFERENCES
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