Estimation of HIV incidence and its trend in three key populations in Iran
Bibliographic record
Abstract
In Iran, People Who Inject Drugs (PWID), Female Sex Workers (FSW), and prisoners are the main key populations at risk of HIV infection. This study aimed to evaluate the trend of HIV incidence among PWID, FSW and prisoners as an impact measure of HIV harm reduction and prevention efforts in Iran. Data were obtained from the two rounds of national bio-behavioral surveillance surveys among FSW (2010 (n = 872), 2015 (n = 1339)), PWID (2010 (n = 2417), 2014 (n = 2307)), and prisoners (2009 (n = 4536), 2013 (n = 5390)) through facility-based (FSW and PWID surveys) and cluster sampling (prisoner surveys). Time-at-risk was calculated assuming the age at first sex or drug injection as the beginning of the at-risk period and the age at the time of the interview or date when they received a positive HIV test result as the end of this period, adjusted for interval censoring. HIV incidence among PWID in 2014 was 5.39 (95% CI 4.71, 6.16) per 1,000 person-years (PY), significantly lower than in 2009 (17.07, 95% CI 15.34, 19.34). Similarly, HIV incidence was 1.12 (95% CI 0.77, 1.64) per 1,000 PY among FSW in 2015, a significant drop from 2010 (2.38, 95% CI 1.66, 3.40). Also, HIV incidence decreased among prisoners from 1.34 (95% CI: 1.08, 1.67) in 2009 to 0.49 (95% CI: 0.39, 0.61) per 1,000 PY in 2013. Our findings suggest that after an increase in the 2000s, the HIV incidence may have been decreased and stabilized among key populations in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".