Alcohol consumption in Iran: A systematic review and meta‐analysis of the literature
Bibliographic record
Abstract
ISSUES: Alcohol production, marketing and consumption are illegal in Iran. This systematic review examines the lifetime and last 12-month prevalence of alcohol consumption among the general and young population in Iran. APPROACH: We searched Web of Science, PubMed, Embase, Scopus and Iranian scientific databases (i.e. Scientific Information Database and Magiran) for relevant publications in English and Persian from inception to 12 May 2019. Following a random-effects meta-analysis, we estimated the pooled prevalence of alcohol consumption among the general population and young people (<30 years old). Meta-regression was used to identify potential sources of heterogeneity. KEY FINDINGS: Of the 2400 identified records, 62 met the inclusion criteria. The overall pooled prevalence of lifetime alcohol consumption among the general population and young people was 13.0% [95% confidence intervals (CI) 10.0, 16.0]. The overall pooled prevalence of last 12-month alcohol consumption was 12.0% (95% CI 7.0, 18.0) for the general population and 15.0% (95% CI 9.0, 22.0) for young people. The prevalence of alcohol consumption varied from 0.03% to 68.0% in different regions, 0.3% to 66.6% among males and 0.2% to 21.0% among females. IMPLICATIONS: Our findings highlight the need for public health surveillance of alcohol use in Iran. CONCLUSION: These estimates show that, on average, one in eight people in the general population have ever consumed alcohol in Iran, indicating that alcohol consumption is not an uncommon practice in the country.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.000 | 0.002 |
| 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".