The association between cannabis use and depression: a systematic review and meta-analysis of longitudinal studies
Bibliographic record
Abstract
BACKGROUND: Longitudinal studies reporting the association between cannabis use and developing depression provide mixed results. The objective of this study was to establish the extent to which different patterns of use of cannabis are associated with the development of depression using meta-analysis of longitudinal studies. METHOD: Peer-reviewed publications reporting the risk of developing depression in cannabis users were located using searches of EMBASE, Medline, PsychINFO and ISI Web of Science. Only longitudinal studies that controlled for depression at baseline were included. Data on several study characteristics, including measures of cannabis use, measures of depression and control variables, were extracted. Odds ratios (ORs) were extracted by age and length of follow-up. RESULTS: After screening for 4764 articles, 57 articles were selected for full-text review, of which 14 were included in the quantitative analysis (total number of subjects = 76058). The OR for cannabis users developing depression compared with controls was 1.17 [95% confidence interval (CI) 1.05-1.30]. The OR for heavy cannabis users developing depression was 1.62 (95% CI 1.21-2.16), compared with non-users or light users. Meta-regression revealed no significant differences in effect based on age of subjects and marginal difference in effect based on length of follow-up in the individual studies. There was large heterogeneity in the number and type of control variables in the different studies. CONCLUSIONS: Cannabis use, and particularly heavy cannabis use, may be associated with an increased risk for developing depressive disorders. There is need for further longitudinal exploration of the association between cannabis use and developing depression, particularly taking into account cumulative exposure to cannabis and potentially significant confounding factors.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".