Factors Associated With Recent HIV Infections Among Newly HIV Diagnosed in Rwanda
Bibliographic record
Abstract
INTRODUCTION: Rwanda has initiated recency testing alongside existing HIV testing services of provider-initiated testing, partner notification services (PNSs), and of prevention of mother-to-child HIV transmission. We aimed to determine characteristics of the newly diagnosed population using a nationwide cohort. METHODS: We included all newly diagnosed patients with HIV aged 15 and above who consented to recency testing and assessed patient- and health center-related predictors of recent HIV infection using multivariable logistic regression models. RESULTS: We obtained data from 485 of 565 health facilities in Rwanda that introduced PNS and recency testing alongside preexisting testing services. From October 2018 to February 2024, 8940 individuals consented to HIV recency testing. Among them, 537 (6.0%) were recently infected. The odds of detecting a recent HIV infection increased by 1% for each month of experience in PNS. Patient-related factors associated with recent infections included female sex, younger age (15-24 years), residing in southern or northern provinces compared with the western province, and self-reported sex with a known HIV-infected person or unprotected sex outside of a relationship in the last 12 months. CONCLUSIONS: Only 6% of newly diagnosed HIV infections were characterized as recent. Public health interventions targeting younger females may assist in reducing new infections in this group.
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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 | 0.002 |
| 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 | 0.001 |
| 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".