O1-S08.06 Variability in the numbers and types of sex partners of female sex workers and its impact on HIV prevalence in southern India
Bibliographic record
Abstract
Background Heterogeneity in the structure of sex work within and across female sex worker (FSW) populations may be an important determinant of the spread of HIV. We characterised heterogeneity in sexual structure and assessed how it influenced HIV prevalence on a population level in southern India. Methods We used data collected from FSWs and clients in three districts (A, B, C) in Karnataka state, India, as part of the Avahan AIDS Initiative, a large-scale targeted HIV prevention program. A deterministic model of HIV transmission among FSWs/clients was parameterised/fitted to the district (A) with the highest FSW/client HIV prevalence in 2005 (33.9%/6.2%). We conducted multiple sensitivity analyses to examine how sexual structure parameters (Abstract O1-S08.06 figure 1) influenced modelled HIV prevalence within district A and identified sources of heterogeneity that could explain differences in HIV prevalence between district A and districts B and C. Abstract O1-S08.06 Figure 1 Impact of varying each sexual structure parameter through a wide range (half to double the baseline value) on peak HIV prevalence in district A. 1 . 1 Red bars=a positive correlation and grey bars=a negative correlation. Bars are arranged in the order of the absolute impact of each parameter on peak HIV prevalence. Results Duration of sex work (2.6-fold), duration of the repeat client/FSW relationship (1.7-fold) and numbers of clients of FSWs (1.6-fold) exhibited the largest differences across districts. In district A, doubling the numbers of clients of FSWs, numbers of visits to FSWs by clients and frequency of sex acts with repeat clients increased HIV prevalence the most. Interestingly, doubling the duration of the repeat FSW-client relationship and fraction of repeat clients decreased HIV prevalence the most (Abstract O1-S08.06 figure 1). The observed differences in the mean numbers of clients per month between districts (A:56; B:46; C:34) could alone explain most of the lower HIV prevalence in districts B and C relative to district A. Relative to that observed in district A, the lower number of sex acts with repeat clients in district B and larger FSW population size in district C could also explain these districts' lower HIV prevalence. Conclusions The impact of each sexual structure parameter on HIV prevalence depended on the values of other parameters, since many parameters were correlated to keep the total number of FSW-client/client-FSW partnerships equal. Although the rules defining how parameters need to be empirically validated, results demonstrate how their definition were of critical importance in influencing outcomes. The heterogeneous risk faced by FSWs needs to be recognised to explain differences in HIV prevalence, and to inform population-specific interventions capable of addressing the complexities implied by this heterogeneity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".