Methods for calculation of per capita alcohol consumption in a Muslim majority country with a very low drinking level: Findings from the 2011 Iranian mental health survey
Bibliographic record
Abstract
INTRODUCTION AND AIMS: There is a paucity of data on volume of alcohol use from Muslim majority countries. We aimed to present estimation methods for alcohol consumption with the use of survey data for these societies and provide an estimation for age 15+ per capita consumption of pure alcohol for Iran. DESIGN AND METHODS: The Iranian Mental Health Survey was a nationally representative household survey on individuals aged 15-64 years, with a multistage, cluster sampling design. We used the 'Last Week' method and 'Quantity-Frequency' methods for gathering data on alcohol consumption and combined these to provide more complete estimates. RESULTS: The response rate was 85.7%. From the total of 7840 respondents, 5.7% and 1% reported past 12 months and past week alcohol use, respectively. The highest estimation for age 15+ per capita consumption of pure alcohol was yielded by the 'combination method' (0.108 L ethanol/person/year) followed by the Quantity-Frequency method (0.079 L). The 'Last Week' method provided the lowest estimate (0.059 L). DISCUSSION AND CONCLUSIONS: Unlike in surveys of non-Muslim countries, frequency of drinking from recent recall (last week) was much lower than from recall of usual drinking in the last year. We conclude that 0.108 L (SE = 0.03) is the best survey-based estimate of age 15+ per capita consumption, which translates to about 5 750 000 L of national consumption per year in Iran. However, this method is still likely to under-estimate per capita consumption due to evidence of under-reporting in the survey.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".