Socioeconomic inequalities in tobacco, alcohol and illicit drug use: evidence from Iranian Kurds
Bibliographic record
Abstract
BACKGROUND: The associations between socioeconomic status (SES) and tobacco use, alcohol consumption and drug use are poorly understood in the Islamic Republic of Iran. AIMS: To measure education- and wealth-related inequalities in cigarette smoking, hookah smoking, illicit drug use and alcohol consumption in Kermanshah Province, Islamic Republic of Iran. METHODS: We used baseline data from the Ravansar Noncommunicable Disease (RaNCD) study. The study collected information on socioeconomic and demographic characteristics, cigarette and hookah smoking, alcohol consumption and illicit drug use of 10 015 adults aged ≥ 35 years between 2014 and 2016. The relative concentration index and absolute concentration index were used to measure education- and wealth-related inequalities in cigarette smoking, hookah smoking, illicit drug use and alcohol consumption. RESULTS: Cigarette smoking was concentrated among less-educated and less-wealthy men and women. Similarly, illicit drug use was concentrated among lower-SES men. In contrast, hookah smoking and alcohol consumption were more prevalent among higher-SES men. CONCLUSIONS: There were education- and wealth-related inequalities in tobacco, alcohol and illicit drug use in the west of the Republic of Iran. Future studies should aim to identify the main socioeconomic determinants of these inequalities in Kermanshah Province and generally in the Islamic Republic of Iran.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".