Association Between Electronic Cigarette Use and Marijuana Use Among Adolescents and Young Adults
Bibliographic record
Abstract
IMPORTANCE: Use of electronic cigarettes (often called e-cigarettes) has increased considerably among young people in the past 5 years. Use of e-cigarettes has been associated with higher rates of marijuana use, which is associated with several adverse health outcomes in youth. OBJECTIVE: To characterize and quantify the association between e-cigarette and marijuana use among youth using a meta-analysis. DATA SOURCES: PubMed, Embase, and Web of Science & ProQuest Dissertations and Theses were searched from inception to October 2018. A gray-literature search was also conducted on conference abstracts, government reports, and other sources. STUDY SELECTION: Included studies compared rates of marijuana use among youth aged 10 to 24 years who had used e-cigarettes vs those who had not used e-cigarettes. Two reviewers independently assessed studies for inclusion; disagreements were discussed with a third reviewer and resolved by consensus. DATA EXTRACTION AND SYNTHESIS: Data were extracted by 2 independent reviewers following Meta-analyses of Observational Studies in Epidemiology (MOOSE) reporting guidelines and pooled using a random-effects analysis. The Newcastle-Ottawa Scale was used to assess data quality and validity of individual studies. MAIN OUTCOMES AND MEASURES: Adjusted odds ratios (AORs) of self-reported past or current marijuana use by youth with vs without past or current e-cigarette use. RESULTS: Twenty-one of 835 initially identified studies (2.5%) met selection criteria. The meta-analysis included 3 longitudinal and 18 cross-sectional studies that included 128 227 participants. Odds of marijuana use were higher in youth who had an e-cigarette use history vs those who did not (AOR, 3.47 [95% CI, 2.63-4.59]; I2, 94%). Odds of marijuana use were significantly increased in youth who used e-cigarettes in both longitudinal studies (3 studies; AOR, 2.43 [95% CI, 1.51-3.90]; I2, 74%) and cross-sectional studies (18 studies; AOR, 3.70 [95% CI, 2.76-4.96]; I2, 94%). Odds of using marijuana in youth with e-cigarette use were higher in adolescents aged 12 to 17 years (AOR, 4.29 [95% CI, 3.14-5.87]; I2, 94%) than young adults aged 18 to 24 years (AOR, 2.30 [95% CI, 1.40-3.79]; I2, 91%). CONCLUSIONS AND RELEVANCE: This meta-analysis found a significant increase in the odds of past or current and subsequent marijuana use in adolescents and young adults who used e-cigarettes. These findings highlight the importance of addressing the rapid increases in e-cigarette use among youths as a means to help limit marijuana use in this population.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.016 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".