Opioid agonist therapy uptake among people who inject drugs: the findings of two consecutive bio-behavioral surveillance surveys in Iran
Bibliographic record
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: = 2166) 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) along with 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 to 69.3% in the 2010 survey and 3.2 to 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, 2.50) 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 0.51, 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 remain 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 associated with OAT across Iran.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".