Prevalence of drug use, alcohol consumption, cigarette smoking and measure of socioeconomic-related inequalities of drug use among Iranian people: findings from a national survey
Bibliographic record
Abstract
BACKGROUND: Drug use can lead to several psychological, medical and social complications. The current study aimed to measure and decomposes socioeconomic-related inequalities in drug use among adults in Iran. METHODS: This was a cross-sectional study The PERSIAN Cohort is the largest and most important cohort among 18 distinct areas of Iran. This study was conducted on 130,570 adults 35 years and older. A structured questionnaire was applied to collect data. The concentration index (C) was used to quantify and decompose socioeconomic inequalities in drug use. RESULTS: The prevalence experience of drug use was 11.9%. The estimated C for drug use was - 0.021. The corresponding value of the C for women and men were - 0.171 and - 0.134, respectively. The negative values of the C suggest that drug use is more concentrated among the population with low socioeconomic status in Iran (p < 0.001). For women, socioeconomic status (SES) (26.37%), province residence (- 22.38%) and age (9.76%) had the most significant contribution to socioeconomic inequality in drug use, respectively. For men, SES (80.04%), smoking (32.04%) and alcohol consumption (- 12.37%) were the main contributors to socioeconomic inequality in drug use. CONCLUSIONS: Our study indicated that drug use prevention programs in Iran should focus on socioeconomically disadvantaged population. Our finding could be useful for health policy maker to design and implement effective preventative programs to protect Iranian population against the drug use.
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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 | 0.000 | 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) | 0.001 | 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".