The PrEP Pharmacy Reach Study: Protocol for the Creation of Maps to Visualize the Impact of Expanding Access to HIV Prevention Services Through Pharmacies
Bibliographic record
Abstract
Background: Despite the proven efficacy of preexposure prophylaxis (PrEP) in reducing the risk of HIV transmission, uptake remains suboptimal among populations with limited access and availability to PrEP-prescribing locations, particularly in the Southern United States. The accessibility of pharmacies positions them as a promising resource for expanding PrEP delivery and access and supporting uptake and adherence through HIV prevention programs to reduce geographic disparities. Objective: This study outlines a geospatial protocol to assess disparities in national PrEP availability. The aim of this study is to develop a protocol to identify, map, and describe the potential impact of expanding PrEP access and HIV prevention services to pharmacies as alternative PrEP delivery sites across the United States. We propose a reproducible mapping and analytic framework to visualize gaps and inform implementation strategies at the state and local levels. Methods: We create local and state maps to help visualize the impact of expanding access to HIV prevention services through pharmacies. We obtain data from three main data sources: (1) pharmacy locations from the National Council for Prescription Drug Programs (NCPDP), (2) PrEP-prescribing facility locations from the CDC's National Prevention Information Network (NPIN), and (3) HIV case data per 100,000 persons from AIDSVu. We geocode pharmacies and PrEP-prescribing locations with Google application programming interfaces (APIs) via the ggmap package in R software. Maps are created by overlaying several different layers of general maps and aggregated data including base maps, choropleth maps, dot density maps. To quantitatively examine the potential impact of expanding HIV prevention services, we calculate a PrEP facility-to-need ratio (PfnR) as the total number of facilities (PrEP-prescribing or pharmacies) divided by the number of HIV cases per 100,000 persons. Fold-change estimates are computed to quantify the increase in service reach if pharmacies were incorporated into PrEP delivery. Results: Overall, maps depicted far greater availability of local pharmacies compared to PrEP-prescribing facilities. When considered in the context of HIV cases per 100,000 persons, pharmacies were more prevalent than PrEP-prescribing facilities in areas with higher HIV caseloads. Mean PfnRs for pharmacies ranged from 0.04 (Mississippi) to 1.3 (Alaska), while PrEP-prescribing facility PfnRs were as low as 0.0004 (Puerto Rico). Estimated fold-change increases ranged from 6.4 in Idaho to 120.3 in Puerto Rico, with the greatest increases in the Southern and Midwestern United States. Conclusions: This protocol provides a scalable and reproducible framework to assess PrEP service distribution and to identify areas which may benefit most from pharmacy integration. Our findings suggest that incorporating pharmacies into PrEP-delivery could substantially reduce geographic access barriers, especially in underserved regions. This work has critical implications for state and national policies focused on avenues to increase PrEP access and uptake and subsequently reduce HIV transmission in their regions.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.085 | 0.020 |
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".