The global proportion and volume of unrecorded alcohol in 2015
Bibliographic record
Abstract
BACKGROUND: (APC) consumption among adults which comprises recorded consumption and unrecorded consumption. While recorded consumption can be assessed with small measurement bias via taxation or other governmental records, unrecorded consumption is more difficult to assess. The objectives of this study were to estimate the country-specific proportion and volume of unrecorded APC in 2015, to identify main sources of unrecorded alcohol and to assess to what extent experts perceive unrecorded alcohol as a public health, social, and financial problem. METHODS: Estimates of unrecorded APC were based on a multilevel fractional response regression model using data from World Health Organization's (WHO) STEPwise approach to surveillance surveys (16 countries, 66 188 participants), estimates from the routine WHO reporting on key indicators of alcohol use (189 countries), and a nominal group expert assessment (42 countries, 129 experts). Expert assessments also included data on the sources of unrecorded alcohol and the perception of unrecorded alcohol as a public health, social, and financial problem. RESULTS: ), while the proportion of unrecorded APC was highest in the WHO Eastern Mediterranean region (57% of the total alcohol). In countries with available data, homemade alcohol was identified as a major source of unrecorded alcohol. The majority of experts considered unrecorded alcohol to be a public health (62%), social (60%), and financial problem (54%). CONCLUSIONS: High volumes of unrecorded alcohol are consumed globally; however, the volumes consumed and the sources of the unrecorded alcohol exhibit large geographical variation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".