Bayesian spatial analysis of age differences and geographical variations in illicit-drug-related mortality in the Islamic Republic of Iran
Bibliographic record
Abstract
Background: Drug use disorders are significant social and public health concerns in the Islamic Republic of Iran; however, little is known about drug-related mortality. Aims: We quantified the spatial and age distribution of direct illicit-drug-related mortality in the Islamic Republic of Iran, to inform harm reduction policies and interventions. Methods: We modelled and mapped registered illicit-drug-related deaths from March 2016 to March 2017. Data were obtained from the Iranian Forensic Medicine Organization. Besag-York-Mollie models were fitted using Bayesian spatial analysis to estimate the relative risk of illicit-drug-related mortality across different provinces and age groups. Results: There were 2203 registered illicit-drug-related deaths during the study period, 1289 (58.5%) occurred in people aged 20-39 years and among men (n = 2013; 91.4%). The overall relative risk (95% credible interval) of illicit-drug-related mortality in the provinces of Hamadan (3.37; 2.88-3.91), Kermanshah (1.90; 1.55-2.28), Tehran (1.80; 1.67-1.94), Lorestan (1.71; 1.37-2.09), Isfahan (1.40; 1.21-1.60), and Razavi Khorasan (1.18; 1.04-1.33) was significantly higher than in the rest of the country. Conclusion: We found evidence of age differences and spatial variations in illicit-drug-related mortality across different provinces in the Islamic Republic of Iran. Our findings highlight the urgent need to revisit existing drug-use treatment and harm reduction policies and ensure that overdose prevention programmes are adequately available for different age groups and settings.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".