Bayesian spatial analysis of age differences and geographical variations in illicit-drug-related mortality in the Islamic Republic of Iran
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it