Ending the HIV Epidemic in Metropolitan Atlanta: a mixed‐methods study to support the local HIV/AIDS response
Bibliographic record
Abstract
INTRODUCTION: Four counties within the Atlanta, Georgia 20-county eligible metropolitan area (EMA) are currently prioritized by the US "Ending the HIV Epidemic" (EHE) initiative which aims for a 90% reduction in HIV incidence by 2030. Disparities driving Atlanta's HIV epidemic warrant an examination of local service availability, unmet needs and organizational capacity to reach EHE targets. We conducted a mixed-methods evaluation of the Atlanta EMA to examine geographic HIV epidemiology and distribution of services, service needs and organization infrastructure for each pillar of the EHE initiative. METHODS: We collected 2021 county-level data (during June 2022), from multiple sources including: AIDSVu (HIV prevalence and new diagnoses), the Centers for Disease Control and Prevention web-based tools (HIV testing and pre-exposure prophylaxis [PrEP] locations) and the Georgia Department of Public Health (HIV testing, PrEP screenings, viral suppression and partner service interviews). We additionally distributed an online survey to key local stakeholders working at major HIV care agencies across the EMA to assess the availability of services, unmet needs and organization infrastructure (June-December 2022). The Organizational Readiness for Implementing Change questionnaire assessed the organization climate for services in need of scale-up or implementation. RESULTS: We found racial/ethnic and geographic disparities in HIV disease burden and service availability across the EMA-particularly for HIV testing and PrEP in the EMA's southern counties. Five counties not currently prioritized by EHE (Clayton, Douglas, Henry, Newton and Rockdale) accounted for 16% of the EMA's new diagnoses, but <9% of its 177 testing sites and <7% of its 130 PrEP sites. Survey respondents (N = 48; 42% health agency managers/directors) reported high unmet need for HIV self-testing kits, mobile clinic testing, HIV case management, peer outreach and navigation, integrated care, housing support and transportation services. Respondents highlighted insufficient existing staffing and infrastructure to facilitate the necessary expansion of services, and the need to reduce inequities and address intersectional stigma. CONCLUSIONS: Service delivery across all EHE pillars must substantially expand to reach national goals and address HIV disparities in metro Atlanta. High-resolution geographic data on HIV epidemiology and service delivery with community input can provide targeted guidance to support local EHE efforts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".