Identifying Key Drivers of the Impact of an HIV Cure Intervention in Sub-Saharan Africa
Bibliographic record
Abstract
BACKGROUND: It is unknown what properties would be required to make an intervention in low income countries that can eradicate or control human immunodeficiency virus (HIV) without antiretroviral therapy (ART) cost-effective. METHODS: We used a model of HIV and ART to investigate the effect of introducing an ART-free viral suppression intervention in 2022 using Zimbabwe as an example country. We assumed that the intervention (cost: $500) would be accessible for 90% of the population, be given to those receiving effective ART, have sufficient efficacy to allow ART interruption in 95%, with a rate of viral rebound of 5% per year in the first 3 months, and a 50% decline in rate with each successive year. RESULTS: An ART-free viral suppression intervention with these properties would result in >0.53 million disability-adjusted-life-years averted over 2022-2042, with a reduction in HIV program costs of $300 million (8.7% saving). An intervention of this efficacy costing anything up to $1400 is likely to be cost-effective in this setting. CONCLUSIONS: Interventions aimed at curing HIV infection have the potential to improve overall disease burden and to reduce costs. Given the effectiveness and cost of ART, such interventions would have to be inexpensive and highly effective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".