Prevalence and correlates of anal intercourse among female sex workers in eSwatini
Bibliographic record
Abstract
INTRODUCTION: As HIV is very effectively acquired during condomless receptive anal intercourse (AI) with serodiscordant and viremic partners, the practice could contribute to the high prevalence among female sex workers (FSW) in eSwatini (formerly known as Swaziland). We aim to estimate the proportion reporting AI (AI prevalence) among Swazi FSW and to identify the correlates of AI practice in order to better inform HIV prevention interventions among this population. METHODS: Using respondent-driven sampling (RDS), 325 Swazi FSW were recruited in 2011. We estimated the prevalence of AI and AI with inconsistent condom use in the past month with any partner type, and inconsistent condom use during AI and vaginal intercourse (VI) by partner type. Univariate and multivariable logistic regression models were used to identify behavioural and structural correlates associated with AI and AI with inconsistent condom use. RESULTS: RDS-adjusted prevalence of AI and AI with inconsistent condom use was high, at 44%[95% confidence interval (95%CI):35-53%]) and 34%[95%CI:26-42%], respectively and did not vary by partner type. HIV prevalence was high in this sample of FSW (70%), but knowledge that AI increases HIV acquisition risk low, with only 10% identifying AI as the riskiest sex act. Those who reported AI were more likely to be better educated (adjusted odds ratio(aOR) = 1.92[95%CI:1.03-3.57]), to have grown up in rural areas (aOR = 1.90[95%CI:1.09-3.32]), have fewer new clients in the past month (aOR = 0.33[95%CI:0.16-0.68]), and for last sex with clients to be condomless (aOR = 2.09[95%CI:1.07-4.08]). Although FSW reporting AI in past month were more likely to have been raped (aOR = 1.95[95%CI:1.05-3.65]) and harassed because of being a sex worker (aOR = 2.09[95%CI:1.16-3.74]), they were also less likely to have ever been blackmailed (aOR = 0.50[95%CI:0.25-0.98]) or been afraid to walk in public places (aOR = 0.46[95%CI:0.25-0.87]). Correlates of AI with inconsistent condom use were similar to those of AI. CONCLUSIONS: AI is commonly practised and condom use is inconsistent among Swazi FSW. Sex act data are needed to determine how frequently AI is practiced. Interventions to address barriers to condom use are needed, as are biomedical interventions that reduce acquisition risk during AI.
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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 | 0.000 | 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) | 0.001 | 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".