Exploring the population-level impact of antiretroviral treatment
Bibliographic record
Abstract
OBJECTIVE: To compare the potential population-level impact of expanding antiretroviral treatment (ART) in HIV epidemics concentrated among female sex workers (FSWs) and clients, with and without existing condom-based FSW interventions. DESIGN: Mathematical model of heterosexual HIV transmission in south India. METHODS: We simulated HIV epidemics in three districts to assess the 10-year impact of existing ART programs (ART eligibility at CD4 cell count ≤350) beyond that achieved with high condom use, and the incremental benefit of expanding ART by either increasing ART eligibility, improving access to care, or prioritizing ART expansion to FSWs/clients. Impact was estimated in the total population (including FSWs and clients). RESULTS: In the presence of existing condom-based interventions, existing ART programs (medium-to-good coverage) were predicted to avert 11-28% of remaining HIV infections between 2014 and 2024. Increasing eligibility to all risk groups prevented an incremental 1-15% over existing ART programs, compared with 29-53% when maximizing access to all risk groups. If there was no condom-based intervention, and only poor ART coverage, then expanding ART prevented a larger absolute number but a smaller relative fraction of HIV infections for every additional person-year of ART. Across districts and baseline interventions, for every additional person-year of treatment, prioritizing access to FSWs was most efficient (and resource saving), followed by prioritizing access to FSWs and clients. CONCLUSION: The relative and absolute benefit of ART expansion depends on baseline condom use, ART coverage, and epidemic size. In south India, maximizing FSWs' access to care, followed by maximizing clients' access are the most efficient ways to expand ART for HIV prevention, across baseline intervention context.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".