P1-S2.01 Prevalence of HIV and sexually transmitted infections among clients of female sex workers in Karnataka, South India
Bibliographic record
Abstract
Introduction Several studies have demonstrated the importance of commercial sex work in the transmission of HIV and other sexually transmitted infections (STIs) in India. Clients of female sex workers (FSWs) are thought to be an important bridging population for HIV and STIs. However, there is little information on basic characteristics of sex work clients. This study sought to describe the prevalence of HIV and other STIs, as well as examine the determinants of these pathogens, among a sample of clients in south India. Methods Data were from a cross-sectional biological and behavioural survey of FSW clients from six districts in Karnataka State, India. The prevalence of HIV, syphilis, herpes simplex virus type 2 (HSV-2), chlamydia (CT) and gonorrhoea (GC) among clients was examined. Multivariable logistic regression models were used to analyse the socio-demographic, sexual behaviour and sex-work related characteristics related to the prevalence of each pathogen. Sampling weights and appropriate survey methods were utilised in regression models to account for a complex sampling design. Results The total sample size was 2745. The average age of clients was 30.4 years (SE—0.3). Across the total sample, the prevalences of HIV, HSV-2, syphilis and CT/GC were 5.6%, 28.4%, 3.6% and 2.2%, respectively. The prevalence of HIV/STIs varied substantially across districts, reaching statistical significance for HIV (p<0.0001) and CT/GC (p=0.005). In multivariable models, duration of paying for commercial sex was associated with increased risk for HIV and HSV-2 (both AORs—1.1; 95% CI 1.0 to 1.1, p<0.0001). Clients using brothels as a main FSW solicitation site were associated with increased risk of HIV (AOR—2.4; 95% CI 1.2 to 4.7, p=0.001), while those frequenting lodges were at increased risk for CT/GC (AOR—6.3; 95% CI 1.9 to 20.6, p=0.03). Clients with HSV-2 infection were at substantially higher risk of being HIV-positive (AOR—10.4; 95% CI 6.1 to 17.7, p<0.0001). Conclusions This study fills in important gaps in knowledge regarding clients of FSWs in Southern India. FSW clients clearly constitute an important bridging population between FSWs and their other sexual partners in the population. It is important to design and implement effective prevention and care programs for this well-hidden population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".