The Epidemiologic and Economic Impact of Improving HIV Testing, Linkage, and Retention in Care in the United States
Bibliographic record
Abstract
BACKGROUND: Recent guidelines advocate early antiretroviral therapy (ART) to decrease human immunodeficiency virus (HIV) morbidity and prevent transmission, but suboptimal engagement in care may compromise impact. We sought to determine the economic and epidemiologic impact of incomplete engagement in HIV care in the United States. METHODS: We constructed a dynamic transmission model of HIV among US adults (aged 15-65 years) and conducted a cost-effectiveness analysis of improvements along the HIV care continuum : We evaluated enhanced HIV testing (annual for high-risk groups), increased 3-month linkage to care (to 90%), and improved retention (50% relative reduction in yearly disengagement and 50% increase in reengagement). Our primary outcomes were HIV incidence, mortality, costs and quality-adjusted life-years (QALYs). RESULTS: Despite early ART initiation, a projected 1.39 million (95% uncertainty range [UR], 0.91-2.2 million) new HIV infections will occur at a (discounted) cost of $256 billion ($199-298 billion) over 2 decades at existing levels of HIV care engagement. Enhanced testing with increased linkage has modest epidemiologic benefits and could reduce incident HIV infections by 21% (95% UR, 13%-26%) at a cost of $65 700 per QALY gained ($44 500-111 000). By contrast, comprehensive improvements that couples enhanced testing and linkage with improved retention would reduce HIV incidence by 54% (95% UR, 37%-68%) and mortality rate by 64% (46%-78%), at a cost-effectiveness ratio of $45 300 per QALY gained ($27 800-72 300). CONCLUSIONS: Failure to improve engagement in HIV care in the United States leads to excess infections, treatment costs, and deaths. Interventions that improve not just HIV screening but also retention in care are needed to optimize epidemiologic impact and cost-effectiveness.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".