Opioid agonist therapy uptake among people who inject drugs: The findings of two consecutive bio-behavioral surveillance surveys in Iran
Bibliographic record
Abstract
Abstract Background Opioid agonist therapy (OAT) uptake has been associated with multiple positive health outcomes among people who inject drugs (PWID). This study evaluated the pattern of OAT uptake among PWID in two consecutive national bio-behavioral surveillance surveys (2010 and 2014) in Iran.Methods Data were obtained from two national bio-behavioral surveillance surveys (N2010 = 1,783 and N2014 = 2,166) implemented using convenience sampling at the harm reduction facilities and street venues in 10 geographically diverse urban centers across Iran. Multivariable logistic regression models were built to determine the correlates of OAT uptake for the 2014 survey and adjusted odds ratios (AORs) and 95% confidence intervals (CI) were reported.Results The prevalence of OAT uptake decreased from 49.2% in 2010 to 45.8% in 2014 (P-value = 0.033). OAT uptake varied across the studied cities ranging from 0.0%-69.3% in the 2010 survey and 3.2%-75.5% in the 2014 survey. Ever being married (AOR = 1.40; 95% CI: 1.12, 1.75), having a history of incarceration (AOR = 1.56; 95% CI: 1.16, 2.09), and human immunodeficiency virus (HIV) sero-positivity (AOR = 1.63; 95% CI: 1.08, 2.5) were associated with OAT uptake. Conversely, PWID who reported using only non-opioid drugs (AOR = 0.43; 95% CI: 0.26, 0.71) and those who reported concurrent use of opioid and non-opioid drugs (AOR = 0.66; 95% CI: 0. 0.51, 0.86) were less likely to uptake OAT. Conclusions Although OAT uptake among PWID in Iran is above the 40% threshold defined by the World Health Organization, there remains significant disparities across urban settings in Iran. Importantly, the OAT services appear to be serving high-risk PWID including those living with HIV and those with a history of incarceration. Evaluating service integration including mental health, HIV and hepatitis C virus care, and other harm reduction services may support the optimization of health outcomes of OAT across 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".