Estimating the potential value of MSM‐focused evidence‐based implementation interventions in three Ending the HIV Epidemic jurisdictions in the United States: a model‐based analysis
Bibliographic record
Abstract
INTRODUCTION: Improving the delivery of existing evidence-based interventions to prevent and diagnose HIV is key to Ending the HIV Epidemic in the United States. Structural barriers in the access and delivery of related health services require municipal or state-level policy changes; however, suboptimal implementation can be addressed directly through interventions designed to improve the reach, effectiveness, adoption or maintenance of available interventions. Our objective was to estimate the cost-effectiveness and potential epidemiological impact of six real-world implementation interventions designed to address these barriers and increase the scale of delivery of interventions for HIV testing and pre-exposure prophylaxis (PrEP) in three US metropolitan areas. METHODS: We used a dynamic HIV transmission model calibrated to replicate HIV microepidemics in Atlanta, Los Angeles (LA) and Miami. We identified six implementation interventions designed to improve HIV testing uptake ("Academic detailing for HIV testing," "CyBER/testing," "All About Me") and PrEP uptake/persistence ("Project SLIP," "PrEPmate," "PrEP patient navigation"). Our comparator scenario reflected a scale-up of interventions with no additional efforts to mitigate implementation and structural barriers. We accounted for potential heterogeneity in population-level effectiveness across jurisdictions. We sustained implementation interventions over a 10-year period and evaluated HIV acquisitions averted, costs, quality-adjusted life years and incremental cost-effectiveness ratios over a 20-year time horizon (2023-2042). RESULTS: Across jurisdictions, implementation interventions to improve the scale of HIV testing were most cost-effective in Atlanta and LA (CyBER/testing cost-saving and All About Me cost-effective), while interventions for PrEP were most cost-effective in Miami (two of three were cost-saving). We estimated that the most impactful HIV testing intervention, CyBER/testing, was projected to avert 111 (95% credible interval: 110-111), 230 (228-233) and 101 (101-103) acquisitions over 20 years in Atlanta, LA and Miami, respectively. The most impactful implementation intervention to improve PrEP engagement, PrEPmate, averted an estimated 936 (929-943), 860 (853-867) and 2152 (2127-2178) acquisitions over 20 years, in Atlanta, LA and Miami, respectively. CONCLUSIONS: Our results highlight the potential impact of interventions to enhance the implementation of existing evidence-based interventions for the prevention and diagnosis of HIV.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".