Alcohol use and death by suicide: A meta‐analysis of 33 studies
Bibliographic record
Abstract
INTRODUCTION: Decades of research show an association between alcohol use and death by suicide. However, findings on the temporal link between alcohol use and death by suicide are unclear. In the most comprehensive meta-analysis on the topic to date, we analyzed data from longitudinal studies to determine if alcohol use is a risk for death by suicide. We also explored moderators to uncover conditions where the alcohol use-suicide link is strengthened/weakened. METHODS: Our literature search of six databases yielded 33 eligible studies involving 10,253,101 participants (community, psychiatric, and military samples). RESULTS: Alcohol use was associated with a 94% increase in the risk of death by suicide. Specifically, random-effects meta-analysis revealed alcohol use displayed small-to-large significant risk and odds ratios with suicide for quantity of alcohol use and alcohol use diagnosis/alcohol-related problems. Meta-regression generally indicated larger effect sizes for studies with a higher percentage of women, younger age, unadjusted estimates, longer follow-up periods, military samples, and higher frequencies and quantities of alcohol use (relative to drinker/non-drinker status). CONCLUSION: Our study highlights alcohol use as a substantive risk factor for death by suicide and underscores the importance of monitoring alcohol use among suicidal individuals and screening for suicidality among heavier alcohol users.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".