Association of electronic cigarette use and suicidal behaviors: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The proliferation of electronic cigarettes (e-cigarettes) has presented new challenges in public health, particularly among adolescents and young adults. While marketed as safer than tobacco and as cessation aids, e-cigarettes have raised concerns about their long-term health and psychosocial impacts, including potential links to increased suicidal behaviors. This study aims to evaluate the relationship between e-cigarette use and suicidal behaviors by conducting a systematic review of the current literature. METHODS: We searched PubMed, Web of Science, and EMBASE for studies up to March 10, 2024, examining the relationship between e-cigarette use and suicidal behaviors. Eligible studies included cross-sectional, longitudinal, retrospective, prospective, and case-control designs. Meta-analysis was performed to calculate pooled odds ratios (ORs). Newcastle Ottawa scale was used to assess the quality of studies. R software (V 4.3) was used to perform the meta-analysis. RESULTS: Our analysis included fourteen studies, predominantly from the US and Korea, with participants ranging from 1,151 to 255,887. The meta-analysis identified a significant association between e-cigarette use and an increased risk of suicidal ideation (OR = 1.489, 95% CI: 1.357 to 1.621), suicide attempts (OR = 2.497, 95% CI: 1.999 to 3.996), and suicidal planning (OR = 2.310, 95% CI: 1.810 to 2.810). Heterogeneity was noted among the studies. CONCLUSION: E-cigarette use is significantly associated with the risk of suicidal behaviors, particularly among adolescents. The findings underscore the necessity for caution in endorsing e-cigarettes as a safer smoking alternative and call for more extensive research to understand the underlying mechanisms. Public health strategies should be developed to address and mitigate the risks of suicidal behaviors among e-cigarette users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".