Partner concurrency and HIV infection risk in South Africa
Bibliographic record
Abstract
BACKGROUND: The relationship between concurrent sexual partnerships and HIV risk is not fully understood. Evidence on the relationship between partner concurrency (one's sexual partner has another partner) and individual HIV risk is limited. In this study, the relationship between reported sexual partner concurrency and the risk of HIV infection was explored among South Africans. METHODS: Data from the third South African national HIV survey were used. In this survey, performed in 2008, questionnaires and HIV tests were administered to a nationally representative sample of 15031 persons. Bivariate analysis and multiple logistic regression were used to evaluate the relationship between partner concurrency and HIV serostatus. Spearman's correlation was used to test the association between the prevalence of HIV and partner concurrency by race in women. RESULTS: The relationship between HIV prevalence and partner concurrency varied by race. At a cross-racial level there was a positive association between HIV prevalence and partner concurrency for women (rho=0.95, p=0.05). Among coloured, white, and Indian persons, HIV prevalence and partner concurrency rates were too low to allow further statistical testing. In the bivariate analysis, black African women who reported partner concurrency had a higher prevalence of HIV (36% (95% confidence interval (CI) 29.7-42.0) vs. 23% (95% CI 19.6-26.1), p<0.001). After controlling for demographic, social, biological, and behavioural variables, the association remained statistically significant (adjusted odds ratio (aOR) 1.4, p=0.04). The association was stronger among 15-29-year-old black African women (aOR 1.8, p=0.03) than among women aged 30 years and older (aOR 1.3, p=0.36). CONCLUSIONS: These results suggest that partner concurrency may increase the HIV infection risk for black South African women, and in particular, for younger women.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".