Dual HIV risk and vulnerabilities among people who inject drugs in Iran: Findings from a nationwide study in 2020
Bibliographic record
Abstract
INTRODUCTION: People who inject drugs (PWID) are a key population at risk of HIV in Iran. We measured the prevalence and covariates of HIV-related risk behaviours among PWID in Iran. METHODS: We conducted a respondent-driven bio-behavioural surveillance survey among PWID from July 2019 to March 2020 in 11 major cities. We assessed PWID's recent (i.e., last three months) HIV-related risk behaviours using a four-level categorical variable: Only unsafe injection (i.e., sharing needles/syringes or injecting equipment), only unsafe sex (i.e., unprotected sex), dual HIV risk (i.e., both unsafe injection and unprotected sex), and safe injection and sex. Data were summarized using RDS-weighted analysis. Multinomial logistic regression models were built to characterize HIV-related risk behaviours and relative risk ratio (RRR) with 95% confidence interval (CI) were reported. RESULTS: Overall, 2562 men who inject drugs (MWID) were included in the regression analysis. The RDS-weighted prevalence of dual HIV risk was 1.3% (95% CI: 0.8, 1.9), only unsafe injection was 4.5%, and only unsafe sex was 11.8%. Compared to the safe injection and sex group, dual HIV risk was significantly and positively associated with multiple partnership (RRR = 15.06; 3.30, 68.73). Only unsafe injection was significantly associated with homelessness in the last 12 months (RRR: 3.02; 95% CI: 1.34, 6.80). Only unsafe sex was significantly associated with multiple partnership (RRR = 6.66; 4.27, 10.38), receiving free condoms (RRR = 1.71; 1.01, 2.89), receiving free needles (RRR = 2.18; 1.22, 3.90), and self-received risk for HIV (RRR = 2.51; 1.36, 4.66). Moreover, history of HIV-testing in the last three months was significantly associated with only unsafe injection (RRR = 2.71; 1.84, 3.80). Among the 90 women who injected drugs, none reported dual HIV risk behaviours. DISCUSSION AND CONCLUSIONS: While the low prevalence of dual HIV risk among PWID is encouraging, unprotected sexual practices among PWID is concerning. Expanding sexual health education and care services as well as tailored interventions aimed at reducing high-risk sexual activities among PWID are warranted. Additionally, tackling potential misperceptions about risk of HIV transmission among PWID in Iran is warranted.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".