Attributing health benefits to preventing HIV infections versus improving health outcomes among people living with HIV: an analysis in six US cities
Bibliographic record
Abstract
OBJECTIVE: Combination strategies generate health benefits through improved health outcomes among people living with HIV (PLHIV) and prevention of new infections. We aimed to determine health benefits attributable to improved health among PLHIV versus HIV prevention for a set of combination strategies in six US cities. DESIGN: A dynamic HIV transmission model. METHODS: Using a model calibrated for Atlanta, Baltimore, Los Angeles, Miami, New York City (NYC) and Seattle, we assessed the health benefits of city-specific optimal combinations of evidence-based interventions implemented at publicly documented levels and at ideal (90% coverage) scale-up (2020-2030 implementation, 20-year study period). We calculated the proportion of health benefit gains (measured as quality-adjusted life-years) resulting from averted and delayed HIV infections; improved health outcomes among PLHIV; and improved health outcomes due to medication for opioid use disorder (MOUD). RESULTS: The HIV-specific proportion of total benefits ranged from 68.3% (95% credible interval: 55.3-80.0) in Seattle to 98.5% (97.5-99.3) in Miami, with the rest attributable to MOUD. The majority of HIV-specific health benefits in five of six cities were attributable HIV prevention, and ranged from 33.1% (26.1-41.1) in NYC to 83.1% (79.6-86.6) in Atlanta. Scaling up to ideal service levels resulted in three to seven-fold increases in additional health benefits, mostly from MOUD, with HIV-specific health gains primarily driven by HIV prevention. CONCLUSION: Optimal combination strategies generated a larger proportion of health benefits attributable to HIV prevention in five of six cities, underlining the substantial benefits of antiretroviral therapy engagement for the prevention of HIV transmission through viral suppression. Understanding to whom benefits accrue may be important in assessing the equity and impact of HIV investments.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".