Declining trends in HIV and other sexually transmitted infections among female sex workers in Iran could be attributable to reduced drug injection: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: The HIV trend among female sex workers (FSWs) is understudied. We assessed the prevalence and trend of HIV and five other STIs among FSWs in Iran. METHODS: We recruited FSWs (1337 in 2015, 1005 in 2010) from 21 sites in 13 cities in two cross-sectional biobehavioural surveys. Eligible FSWs were women aged ≥18 years who reported selling sex to more than one male client in the past 12 months. Consenting FSWs were interviewed using a behavioural questionnaire and tested for HIV and five other STIs. We considered study sites as clusters in the analysis and two-sided Fisher's exact test to compare the HIV prevalence between the two survey rounds. RESULTS: HIV prevalence was 2.1% in 2015 (vs 4.0% in 2010, p=0.007). Lifetime drug injection was reported by 6.1% of participants in 2015 (vs 14.6% in 2010, p=0.003). In 2015, among FSWs with history of lifetime drug injection, HIV prevalence was 8.6% (vs 9.8% in 2010, p=0.425). The prevalence of other STIs in 2015 was 0.4% (95% CI 0.2 to 1.0) for syphilis, 1.3% (95% CI 0.8 to 2.1) for gonorrhoea, 6.0% (95% CI 4.8 to 7.4) for chlamydia, 11.9% (95% CI 8.5 to 16.5) for trichomoniasis and 41.8% (95% CI 39.2 to 44.5) for human papillomavirus. CONCLUSIONS: HIV prevalence among FSWs in Iran decreased, but remains considerably high. The decrease in HIV prevalence compared with 2010 might be explained by a decrease in drug injection. Other STIs are also high in this population. Harm reduction programmes need to be continued and scaled up among this underserved population 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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".