HIV Risk Among Adolescent Girls and Young Women in Age-Disparate Partnerships: Evidence From KwaZulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Evidence on the role of age-disparate partnerships in high HIV-infection rates among young women in sub-Saharan Africa remains inconclusive. This study examined the HIV-infection risk associated with age-disparate partnerships among 15- to 24-year-old women in a hyperendemic setting in South Africa. METHODS: Face-to-face questionnaire, and laboratory HIV and viral load data were collected during 2014-2015 among a representative sample (15-49 years old) in KwaZulu-Natal. The association between age-disparate partnerships (age difference ≥5 years) and HIV status among 15- to 24-year-old women (N = 1459) was assessed using multiple logistic regression analyses. Data from the male sample on all on-going partnerships (N = 1229) involving 15- to 24-year-old women were used to assess whether young women's age-disparate male partners were more likely to have a viral load ≥1000 copies per milliliter, a marker of HIV-infection risk. RESULTS: Women reporting an age disparity in any of their 3 most recent partnerships were more likely to test HIV positive compared to women with only age-similar partners [adjusted odds ratio (aOR): 1.58, 95% confidence interval (CI): 1.20 to 2.09, P < 0.01]. Among partnerships men reported with 15- to 24-year-old women, the age-disparate male partners were more likely to be HIV positive and have a viral load ≥1000 copies per milliliter (aOR: 2.05, 95% CI: 1.30 to 3.24, P < 0.01) compared with age-similar partners. Results were similar for each category of age disparity: partners 5-9 years older (aOR: 2.01, 95% CI: 1.18 to 3.43, P = 0.010) and those ≥10 years older (aOR: 2.17, 95% CI: 1.01-4.66, P = 0.048). CONCLUSIONS: Results indicate that age-disparate partnerships increase young women's HIV risk, although conclusive evidence was not ascertained. Interventions addressing risk from age-disparate sexual partnering, including expanding antiretroviral treatment among older partners, may help to reduce HIV incidence among young women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".