Age-disparate partnerships and HSV-2 among adolescent girls and young women in South Africa: implications for HIV infection risk
Bibliographic record
Abstract
OBJECTIVE: There is an urgent need to understand high HIV-infection rates among young women in sub-Saharan Africa. While age-disparate partnerships have been characterised with high-risk sexual behaviours, the mechanisms through which these partnerships may increase HIV-risk are not fully understood. This study assessed the association between age-disparate partnerships and herpes simplex virus type-2 (HSV-2) infection, a factor known to increase HIV-infection risk. METHODS: Cross-sectional face-to-face questionnaire data, and laboratory HSV-2 and HIV antibody data were collected among a representative sample in the 2014/2015 household survey of the HIV Incidence Provincial Surveillance System in KwaZulu-Natal, South Africa. Among 15-24-year-old women who reported having ever had sex (n=1550), the association between age-disparate partnerships (ie, male partner ≥5 years older) and HSV-2 antibody status was assessed using multivariable Poisson regression models with robust variance. Analyses were repeated among HIV-negative women. RESULTS: HSV-2 prevalence was 55% among 15-24-year-old women. Women who reported an age-disparate partnership with their most recent partner were more likely to test HSV-2 positive compared with women with age-similar partners (64% vs 51%; adjusted prevalence ratio (aPR):1.19 (95% CI 1.07 to 1.32, p<0.01)). HSV-2 prevalence was also significantly higher among HIV-negative women who reported age-disparate partnerships (51% vs 40 %; aPR:1.25 (95% CI 1.05 to 1.50, p=0.014)). CONCLUSIONS: Results indicate that age-disparate partnerships are associated with a greater risk of HSV-2 among young women. These findings point towards an additional mechanism through which age-disparate partnerships could increase HIV-infection risk. Importantly, by increasing the HSV-2 risk, age-disparate partnerships have the potential to increase the HIV-infection risk within subsequent partnerships, regardless of the partner age-difference in those relationships.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".