Risk of HIV infection among adolescent girls and young women in age-disparate relationships in sub-Saharan Africa
Bibliographic record
Abstract
OBJECTIVE: To determine the association between age-disparate relationships and risk of HIV infection among adolescent girls and young women (AGYW) aged 15-24 years. DESIGN: Systematic review and meta-analysis of published studies until January 5, 2020 in sub Saharan Africa (SSA). METHODS: We searched several electronic databases, grey literature, and hand searched reference list of included studies to identify eligible studies for data abstraction. We assessed the quality of included studies using Newcastle-Ottawa Scale for nonrandomized studies. The DerSimonian-Laird random effects model was used to pool the overall results using risk ratios (RR), presented in a forest plot with 95% confidence interval (CI) and predictive interval. Heterogeneity was assessed with Cochrane's Q-test and quantified with I values. Publication bias was checked with funnel plots and Egger's test. RESULTS: We included 24 studies with an overall sample size of 33 390. Data show that age-disparate relationships were significantly associated with unprotected sexual intercourse (pooled RR, 1.57; 95% CI, 1.34-1.83; 95% predictive interval, 1.22-2.02), and higher risk for HIV infection (pooled RR, 1.39; 95 CI, 1.21-1.60; 95% predictive interval, 0.80-2.42). Studies included in pooling risk of unprotected sexual intercourse were largely homogeneous (I-value= 0.0, P = 0.79) whereas those for HIV infection were heterogeneous (I- value = 89.0%, P < 0.01). We found no publication bias and no study influenced the meta-analytic results. CONCLUSION: Age-disparate relationships among AGYW are associated with increased risk of unprotected sexual intercourse and HIV infection in SSA. HIV prevention interventions should target this sub-population.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".