E-cigarette use among adolescents in Latin America: A systematic review of prevalence and associated factors
Bibliographic record
Abstract
Electronic cigarettes, introduced as a safer tobacco alternative, have unintentionally exposed millions of youths to nicotine and harmful chemicals. Adolescence, a key period for forming lifelong habits, has seen rising e-cigarette use, particularly in developing regions like Latin America, warranting thorough investigation. To describe the prevalence and factors associated with e-cigarette use among adolescents in Latin America. A systematic review was conducted according to PRISMA guidelines, covering studies published between 2003 and May 2024. Database searches included PubMed/Medline, Web of Science, Scopus, Google Scholar, Scielo, and LILACS. A total of 582 studies were identified, of which 14 met the inclusion criteria. Study data were synthesized and assessed using the Newcastle-Ottawa Scale and Joanna Briggs Institute checklist. The prevalence of e-cigarette among adolescents in six Latin American countries ranged from 2.6 % to 64.2 %, with a pooled mean prevalence of 18.9 %, and lifetime use higher than current use. Key associated factors included male sex, concurrent use of tobacco and other substances, social influences, and exposure to online advertising. A widespread lack of knowledge regarding e-cigarette risks, coupled with limited regulatory oversight, may be contributing to higher usage rates. This review underscores critical gaps in data on adolescent e-cigarette use in Latin America and highlights the need for expanded research and targeted public health interventions. Nearly one-fifth of adolescents reported using e-cigarettes. Comprehensive prevention programs addressing factors associated with adolescent e-cigarette use in Latin America, involving diverse stakeholders and integrating education, school-based policies, social media campaigns, and policy restrictions, are strongly recommended. • E-cigarette use among Latin American adolescents ranges from 2.4 % to 64.2 %, with a pooled mean prevalence of 18.9 %. • Tobacco, substance use, and online ads are key factors linked to e-cigarette use in adolescents. • Knowledge gaps exist: 6 %–86 % of adolescents are unaware of e-cigarette risks. • Limited research exists in key countries, such as Brazil, Peru, Chile, and Uruguay. • Targeted prevention programs and stricter regulations are urgently needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".