Estimating the epidemiological impact of reaching the objectives of the Florida integrated HIV prevention and care plan in Miami-Dade County
Bibliographic record
Abstract
Background: (EHE) initiative aims to reduce national HIV incidence 90% by 2030 and to address the disproportionate burden of HIV among different racial/ethnic populations. Florida's state-wide 2022-2026 Integrated HIV Prevention and Care Plan outlines objectives for reaching EHE goals. In Miami-Dade County, we determined the epidemiological impact of achieving the integrated plan's objectives individually and jointly. Methods: We adapted an HIV transmission model calibrated to Miami-Dade County adjusting access to HIV testing, pre-exposure prophylaxis (PrEP) and antiretroviral treatment to model the effects of each objective between 2022 and 2030. We compared two service scale-up approaches: (a) scale-up proportionally to existing racial/ethnic group access levels, and (b) scale-up according to new diagnoses across racial/ethnic groups (equity-oriented). We estimated reductions in new HIV infections by each objective and approach, compared to the EHE's incidence reduction target. Findings: The single most influential strategy was reducing new HIV diagnoses in Hispanic/Latinx men who have sex with men through increased PrEP uptake, resulting in 907/2444 (37.1%) fewer annual new HIV infections in 2030. Achieving all objectives jointly would result in 1537/2444 (62.9%) and 1553/2444 (63.5%) fewer annual new HIV infections with the proportional and equity-oriented approaches, respectively. Interpretation: Achieving the goals of Florida's integrated care plan would significantly reduce HIV incidence in Miami-Dade County; however, further efforts are required to achieve EHE targets. Structural changes in service delivery and a focus on effective implementation of available interventions to address racial/ethnic disparities will be crucial to ending the HIV epidemic. Funding: This work was supported by the National Institutes of Health/National Institute on Drug Abuse grant no. R01-DA041747.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".