Association between alcohol consumption per capita and suicide mortality rate across 30 European countries
Bibliographic record
Abstract
BACKGROUND: Suicide was the cause of 86 000 deaths in Europe in 2021. Accordingly, suicide prevention has been designated as a serious public health concern by the World Health Organization (WHO). Alcohol use is a causal risk factor for suicide and may be a cost-effective target for prevention efforts. Globally, the United Nations' Sustainable Development Goals have set a target of a 20% reduction in alcohol consumption per capita (APC). However, evidence on the utility of APC as a metric in relation to the prevention of alcohol-attributable suicides is currently limited. OBJECTIVE: To evaluate the association between APC and suicide mortality rate across 30 European countries. METHODS: Suicide mortality rates were obtained for 27 European Union countries as well as the UK, Switzerland and Norway for 1991-2021 from the Institute of Health Metrics and Evaluation. APC data was obtained from the WHO Global Information System on Alcohol and Health. Linear mixed effects models with random intercepts for country and first-order autoregressive errors were used to test the association between APC and suicide mortality rate. Tests of interaction were conducted to examine the impact of sex, country-level drinking patterns and the 2008 global economic recession. All models were adjusted for relevant population-level confounders. FINDINGS: On average, a 1 L increase in total APC was associated with a 0.39% increase in the suicide mortality rate (95% CI 0.03% to 0.76%; p value=0.03) among males, and a 0.59% increase in the suicide mortality rate (95% CI 0.12% to 1.06%; p value=0.01) among females. There was no evidence of a significant sex difference, nor was there evidence of a modifying effect of country-level drinking patterns or 2008 global economic recession on the respective association. CONCLUSIONS: National suicide prevention strategies should consider incorporating APC as an indicator for monitoring and assessing the impact of interventions targeting alcohol use on suicide.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".