Cannabis Use and Prospective Long-Term Association with Anxiety: A Systematic Review and Meta-Analysis of Longitudinal Studies: Usage du cannabis et association prospective à long terme avec l’anxiété: une revue systématique et une méta-analyse d’études longitudinales
Bibliographic record
Abstract
Objectives: Cannabis use is proposed as a risk factor for psychosis and is associated with depressive disorders. However, the relationship between recreational cannabis use and its longitudinal implications on anxiety conditions is less studied. The aim of this investigation is to systematically evaluate published literature and perform a meta-analysis of the data. Methods: A systematic search was performed of MEDLINE, Embase, and PsychINFO from inception to May 31, 2020, in addition to a hand search. Longitudinal studies that evaluated the relationship of cannabis use and development of anxiety were included. Where applicable, adjusted odds ratios ( ORs) were extracted, pooled, and evaluated using random-effects meta-analysis. Results: After screening of unique abstracts ( n = 6835), the final evaluation included 24 studies, of which 10 reported ORs that were analyzed quantitatively. Cannabis use was significantly associated with increased odds of developing any anxiety conditions ( OR = 1.25; 95% CI, 1.01 to 1.54). Cannabis use was not significantly associated with developing generalized anxiety disorder, panic disorder, or social anxiety disorder. Review of studies not reporting OR revealed mixed results but are suggestive of a link between cannabis use and increased rates/severity of anxiety. Conclusions: Published evidence suggests that cannabis use is likely associated with increased risk of anxiety in the long term but variability of study designs precludes declaration of a causal relationship. Awareness of this association is of relevance for both clinical practice and mental health policy implementation.
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| 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".