What is the best indicator of the harmful use of alcohol? A narrative review
Bibliographic record
Abstract
ISSUES: The monitoring of the harmful use of alcohol is a key focus of global health efforts, including the Sustainable Development Goals. The current indicator of harmful alcohol use for Sustainable Development Goals is the national adult (15+ years) alcohol per capita consumption (APC) in litres of pure alcohol. Recently, the age-standardised prevalence of heavy episodic drinking (HED) has been advanced as an alternative indicator. APPROACH: This narrative review is composed of a review of advantages and disadvantages of both indicators and an empirical analysis of their associations with alcohol-attributable health harm. KEY FINDINGS: APC is greatly associated with harm and is available for almost all countries on a yearly basis as it is largely derived from routinely collected statistics. HED is based on responses to population surveys not routinely performed for most countries. These surveys commonly exclude heavy drinking populations (e.g. army personnel, institutionalised, homeless). Even when included within the sampling frame, heavy drinkers are less likely to participate than other groups. The questions used to measure HED are susceptible to biases due to issues with respondents' comprehension, recall and misreporting. Furthermore, in a regression analysis of 182 countries, APC was better at predicting alcohol-attributable harm than HED. APC was also correlated with changes in the alcohol-attributable burden of disease (from 2010 to 2016), while HED was not. IMPLICATIONS: Based on these factors, APC was found to be the preferred indicator. CONCLUSIONS: APC should be retained as the main indicator of the harmful use of alcohol.
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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".