Mind the gap: rethinking global alcohol metrics in high-abstention low-income and middle-income countries
Bibliographic record
Abstract
Alcohol per capita consumption (APC; total pure alcohol consumed per person 15 years or older per year) is the primary indicator used to track global progress in reducing harms associated with alcohol use. However, in many low-income and middle-income countries (LMICs), where most of the population abstain from alcohol and risk of alcohol-associated harm is concentrated in a heavy-drinking minority, APC can misrepresent both exposure and risk. This Viewpoint argues for the routine inclusion of drinker-adjusted metrics, specifically litres of alcohol consumed per drinker (alcohol per drinker), alongside the standard APC indicator. By use of data from WHO's Global Information System on Alcohol and Health, we show how alcohol per drinker reveals patterns hidden by population averages, particularly in high-abstention LMICs. For example, South Africa and the UK have similar APC but starkly different alcohol-attributable harm profiles, which are better explained by differences in alcohol per drinker. Although APC remains valuable, relying on this metric alone risks misinterpreting progress and misdirecting policy in contexts where drinking is concentrated among a minority of the population who drink heavily. As global monitoring evolves, we call for the inclusion of additional metrics that better reflect risk in diverse contexts.
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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 | 0.027 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".