Prevalence of alcohol use disorders in primary health‐care facilities in Russia in 2019
Bibliographic record
Abstract
AIMS: To estimate prevalence of alcohol use disorders (AUD) and alcohol dependence (AD) for Russia in 2019, based on clients in primary health-care facilities. DESIGN: Cross-sectional assessment of AUD and AD. Prevalence estimates were cross-validated using a treatment multiplier methodology. SETTING: A total of 21 primary health-care facilities, including dispanserization units (population health preventive care settings). PARTICIPANTS: A total of 2022 participants (986 women and 1036 men) 18 years of age and older. MEASUREMENTS: Composite International Diagnostic Interview. FINDINGS: The prevalence of AD and AUD was 7.0% [95% confidence interval (CI) = 5.9-8.1%] and 12.2% (95% CI = 10.8-13.6%), respectively. Marked sex differences were observed for the prevalence of AD (women: 2.8%; 95% CI = 1.7-3.8%; men: 12.2%; 95% CI = 10.3-14.1%) and AUD (women: 6.1%; 95% CI = 4.6-7.7%; men: 19.5%; 95% CI = 17.2-21.8%). Age patterns of AD and AUD prevalence were sex-specific. Among women, the prevalence of AUD and AD was highest in the youngest age group and decreased with age. Among men, the prevalence of AUD and AD was highest among men aged 45-59 years. Sensitivity analyses indicated that the prevalence of AD as estimated using a treatment multiplier (6.5%; 95% CI = 5.0-8.9%) was similar to the estimates of the main analysis. CONCLUSIONS: Even though alcohol use has declined since 2003 in Russia, the prevalence of alcohol use disorders and alcohol dependence remains high at approximately 12 and 7%, respectively.
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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".