Non-injection drug use among incarcerated people in Iran: Findings from three consecutive national bio-behavioral surveys
Bibliographic record
Abstract
BACKGROUND: Prisons often serve as high-risk environments for drug use, and incarcerated people are at a high risk for substance use-related mental and physical harms. This study aimed to determine the prevalence of non-injection drug use inside the prison and its related factors among incarcerated people in Iran. METHODS: We utilized data from three national bio-behavioral surveillance surveys conducted among incarcerated people in Iran in 2009, 2013, and 2017. Eligibility criteria were being ≥ 18 years old, providing informed consent, and being incarcerated for over a week. Overall, 17,228 participants across all surveys were recruited through a multi-stage random sampling approach. Each participant underwent a face-to-face interview and HIV test. The primary objective of the study was to assess self-reported non-injection drug use within the prison environment within the last month. A multivariable logistic regression model was built to determine associated covariates with drug use inside prison and an adjusted odds ratio (aOR) with 95% confidence intervals (CI) were reported. RESULT: The prevalence of non-injection drug use inside the prison was 24.1% (95% CI 23.5, 24.7) with a significant decreasing trend (39.7% in 2009, 17.8% in 2013, 14.0% in 2017; p-value < 0.001). Overall, 44.0% of those who used drugs were also receiving opioid agonist therapy (OAT) and we noted that in 2017, 75.1% of those on OAT used stimulants. In the multivariable logistic regression model, the year of interview (2013: aOR = 1.43 and 2009: aOR = 5.60), younger age (19-29: aOR = 1.14 and 30-40: aOR = 1.37), male sex (aOR = 3.35), < high school education (aOR = 1.31), having a history of previous incarceration (aOR = 1.26), and having a history of lifetime HIV testing (aOR = 1.76) were significantly and positively associated with recent non-injection drug use inside the prison. CONCLUSIONS: Approximately one in four incarcerated people in Iran reported drug use within the last month inside prisons. While a declining trend in non-injection drug use was noted, substantial gaps persist in harm reduction programs within Iranian prisons. In particular, there is a pressing need for improvements in drug treatment programs, focusing on the integration of initiatives specifically designed for people who use stimulants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".