Unrecorded alcohol use: a global modelling study based on nominal group assessments and survey data
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol use is among the most important risk factors for burden of disease globally. An estimated quarter of the total alcohol consumed globally is unrecorded. However, due partly to the challenges associated with its assessment, evidence concerning the magnitude of unrecorded alcohol use is sparse. This study estimated country-specific proportions of unrecorded alcohol used in 2015. DESIGN: A statistical model was developed for data prediction using data on the country-specific proportion of unrecorded alcohol use from nominal group expert assessments and secondary, nationally representative survey data and country-level covariates. SETTING: Estimates were calculated for the country level, for four income groups and globally. PARTICIPANTS: A total of 129 participants from 49 countries were included in the nominal group expert assessments. The survey data comprised 66 538 participants from 16 countries. MEASUREMENTS: Experts completed a standardized questionnaire assessing the country-specific proportion of unrecorded alcohol. In the national surveys, the number of standard drinks of total and unrecorded alcohol use was assessed for the past 7 days. FINDINGS: Based on predictions for 167 countries, a population-weighted average of 27.9% [95% confidence interval (CI) = 10.4-44.9%] of the total alcohol consumed in 2015 was unrecorded. The proportion of unrecorded alcohol was lower in high (9.4%, 95% CI = 2.4-16.4%) and upper middle-income countries (18.3%, 95% CI = 9.0-27.6%) and higher in low (43.1%, 95% CI = 26.5-59.7%) and lower middle-income countries (54.4%, 95% CI = 38.1-70.8%). This corresponded to 0.9 (high-income), 1.2 (upper middle-income), 3.2 (lower middle-income) and 1.8 (low-income) litres of unrecorded alcohol per capita. CONCLUSIONS: A new method for modelling the country-level proportion of unrecorded alcohol use globally showed strong variation among geographical regions and income groups. Lower-income countries were associated with a higher proportion of unrecorded alcohol than higher-income countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".