Cannabis use and suicide attempts among 86,254 adolescents aged 12–15 years from 21 low- and middle-income countries
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that cannabis use may be associated with suicidality in adolescence. Nevertheless, very few studies have assessed this association in low- and middle-income countries (LMICs). In this cross-sectional survey, we investigated the association of cannabis use and suicidal attempts in adolescents from 21 LMICs, adjusting for potential confounders. METHOD: Data from the Global school-based Student Health Survey was analyzed in 86,254 adolescents from 21 countries [mean (SD) age = 13.7 (0.9) years; 49.0% girls]. Suicide attempts during past year and cannabis during past month and lifetime were assessed. Multivariable logistic regression analyses were conducted. RESULTS: The overall prevalence of past 30-day cannabis use was 2.8% and the age-sex adjusted prevalence varied from 0.5% (Laos) to 37.6% (Samoa), while the overall prevalence of lifetime cannabis use was 3.9% (range 0.5%-44.9%). The overall prevalence of suicide attempts during the past year was 10.5%. Following multivariable adjustment to potential confounding variables, past 30-day cannabis use was significantly associated with suicide attempts (OR = 2.03; 95% CI: 1.42-2.91). Lifetime cannabis use was also independently associated with suicide attempts (OR = 2.30; 95% CI: 1.74-3.04). CONCLUSION: Our data indicate that cannabis use is associated with a greater likelihood for suicide attempts in adolescents living in LMICs. The causality of this association should be confirmed/refuted in prospective studies to further inform public health policies for suicide prevention in LMICs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".