Estimating the impact of universal antiretroviral therapy for HIV serodiscordant couples through home HIV testing: insights from mathematical models
Bibliographic record
Abstract
Antiretroviral therapy (ART) prevents HIV transmission within HIV serodiscordant couples (SDCs), but slow implementation and low uptake has limited its impact on population-level HIV incidence. Home HIV testing and counselling (HTC) campaigns could increase ART uptake among SDCs by incorporating couples' testing and ART referral. We estimated the reduction in adult HIV incidence achieved by incorporating universal ART for SDCs into home HTC campaigns in KwaZulu-Natal (KZN), South Africa, and southwestern (SW) Uganda.We constructed dynamic, stochastic, agent-based network models for each region. We compared adult HIV incidence after 10 years under three scenarios: (1) "Current Practice," (2) "Home HTC" with linkage to ART for eligible persons (CD4 <350) and (3) "ART for SDCs" regardless of CD4, delivered alongside home HTC.ART for SDCs reduced HIV incidence by 38% versus Home HTC: from 1.12 (95% CI: 0.98-1.26) to 0.68 (0.54-0.82) cases per 100 person-years (py) in KZN, and from 0.56 (0.50-0.62) to 0.35 (0.30-0.39) cases per 100 py in SW Uganda. A quarter of incident HIV infections were averted over 10 years, and the proportion of virally suppressed HIV-positive persons increased approximately 15%.Using home HTC to identify SDCs and deliver universal ART could avert substantially more new HIV infections than home HTC alone, with a smaller number needed to treat to prevent new HIV infections. Scale-up of home HTC will not diminish the effectiveness of targeting SDCs for treatment. Increasing rates of couples' testing, disclosure, and linkage to care is an efficient way to increase the impact of home HTC interventions on HIV incidence.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".