Cross-sectional study on the characteristics of unrecorded alcohol consumption in nine newly independent states between 2013 and 2017
Bibliographic record
Abstract
OBJECTIVES: As unrecorded alcohol use contributes to a substantial burden of disease, this study characterises this phenomenon in newly independent states (NIS) of the former Soviet Union with regard to the sources of unrecorded alcohol, and the proportion of unrecorded of total alcohol consumption. We also investigate associated sociodemographic characteristics and drinking patterns. DESIGN: Cross-sectional data on overall and unrecorded alcohol use in the past 7 days from WHO STEPwise Approach to NCD Risk Factor Surveillance (STEPS) surveys. Descriptive statistics were calculated at the country level, hierarchical logistic and linear regression models were used to investigate sociodemographic characteristics and drinking patterns associated with using unrecorded alcohol. SETTING: Nine NIS (Armenia, Azerbaijan, Belarus, Georgia, Kyrgyzstan, Republic of Moldova, Tajikistan, Turkmenistan and Uzbekistan) in the years 2013-2017. PARTICIPANTS: Nationally representative samples including a total of 36 259 participants. RESULTS: A total of 6251 participants (19.7%; 95% CI 7.9% to 31.5%) reported alcohol consumption in the past 7 days, 2185 of which (35.1%; 95% CI 8.2% to 62.0%) reported unrecorded alcohol consumption with pronounced differences between countries. The population-weighted average proportion of unrecorded consumption in nine NIS was 8.7% (95% CI 5.9% to 12.4%). The most common type of unrecorded alcohol was home-made spirits, followed by home-made beer and wine. Older (45-69 vs 25-44 years) and unemployed (vs employed) participants had higher odds of using unrecorded alcohol. More nuanced sociodemographic differences were observed for specific types of unrecorded alcohol. CONCLUSIONS: This contribution is the first to highlight both, prevalence and composition of unrecorded alcohol consumption in nine NIS. The observed proportions and sources of unrecorded alcohol are discussed in light of local challenges in policy implementation, especially in regard to the newly formed Eurasian Economic Union (EAEU), as some but not all NIS are in the EAEU.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".