O2-S6.03 Heterogeneity of HIV risk: female sex worker clients and their non-commercial sexual partners in south India
Bibliographic record
Abstract
Introduction Clients of female sex workers (FSWs) are an important bridging population for the further transmission of HIV and other sexually transmitted infections (STIs). However, the extent of risk to non-commercial partners (NCP) of clients has rarely been quantified. This study sought to characterise the risk behaviours of clients with both non-commercial and commercial partners. Methods Data were collected from a cross-sectional integrated behavioural and biological survey of FSW clients from five districts in Karnataka state, southern India. Clients were classified into three groups: married (and thus with an NCP); unmarried with at least one NCP; and unmarried without an NCP. Bivariate and multivariable logistic regression models were constructed to examine the association between group membership and condom use patterns with regular and casual FSWs, and where applicable, non-commercial partners. Associations between the prevalences of HIV, HSV-2, syphilis, gonorrhoeal and chlamydial infections and group membership were examined in multivariable models. Normalised weights were used to account for a complex sampling design. Results The total sample size was 2328. Most respondents (61%) were married, 9% were unmarried with an NCP, and 30% were unmarried without an NCP. Married clients were, on average 9 years older (34 vs 24.8 years), and had been visiting FSWs for an average of 12 years, vs 4 years in the other two marital categories. Compared to respondents without an NCP, married clients were at higher odds of reporting never using condoms with both casual (AOR: 1.8; 95% CI:1.3% to 2.4%, p<0.0001) and regular (AOR:1.8; 95% CI 1.2% to 2.7%, p=0.009) FSWs. Among the two groups reporting an NCP, 91% of married respondents reported never using condoms with their NCP, compared to 62% of unmarried clients (AOR: 5.2; 95% CI 3.3% to 8.1%, p<0.0001). HIV prevalence was 6 %, 5% and 8% among those that were married, those unmarried without an NCP, and those unmarried with an NCP, respectively (p=0.384). For the same groups, HSV-2 prevalence was 37%, 16% and 19% (p<0.0001). In adjusted analyses, married respondents remained at highest odds of being infected with HSV-2 (AOR: 1.5; 95% CI 1.1% to 2.1%, p=0.006). Conclusion Married respondents were least likely to use condoms with both commercial and non-commercial sexual partners, while also having the highest prevalence of HSV-2. These findings illustrate the high risk posed to both commercial and non-commercial partners of married clients of FSWs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".