Ryan White Program Disruptions: City-Level Impact Projections
What happens if Ryan White funding is interrupted or eliminated? Modeling impacts on HIV care outcomes across major US metropolitan areas.
Ann Intern Med, 2025
ExploreHIV policy & population health
JHEEM provides evidence for HIV policy decisions through calibrated mathematical modeling across US metropolitan areas and states.
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What happens if Ryan White funding is interrupted or eliminated? Modeling impacts on HIV care outcomes across major US metropolitan areas.
Ann Intern Med, 2025
ExploreStatewide projections of Ryan White funding disruption, providing jurisdictional insights for policy makers.
AJPH, 2026 · CROI, 2026
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Working paper
ExploreForster RJ, Kasaie P, Schnure MC et al.Annals of Internal Medicine
Ending the Ryan White HIV/AIDS Program could result in 75,436 additional HIV infections (95% CrI, 19,251 to 134,175) across 31 high-burden U.S. cities from 2025 to 2030—a 49% increase. Even temporary interruptions lasting 18-42 months would cause 19-38% more infections. The impact varies dramatically by city, from 9% increase in Riverside, CA to 110% in Baltimore, MD, highlighting the critical public health value of Ryan White services.
Zalesak A, Kasaie P, Schnure MC et al.medRxiv (preprint)
Proposed cuts to Ryan White Parts C/D, Minority AIDS Initiative, and Ending the HIV Epidemic programs could result in 23,883 additional HIV infections (17.6% increase) across 30 states and DC from 2025 to 2030. The impact would be largest in states with high Ryan White dependency, with rural and underserved communities experiencing disproportionate effects.
Balasubramanian R, Schnure M, Forster R et al.Clinical Infectious Diseases
Ending CDC funding for HIV testing could result in 12,719 additional HIV infections (a 10% increase) across 18 states from 2025 to 2030. Testing reductions would delay HIV diagnoses by an average of 0.5-1.5 years, increasing onward transmission and worsening health outcomes, with disproportionate impacts in rural and underserved communities.
We use mathematical modeling to understand and predict HIV transmission and the impact of interventions across local populations. The simulated population is stratified by age, race, sex, sexual behavior, and drug use, and is calibrated to real-world HIV surveillance data under the Ending the HIV Epidemic initiative—enabling projections of how policy and funding decisions may shape future transmission.
This research is supported by grants from the National Institute of Mental Health, the National Institute of Allergy and Infectious Diseases, and the National Institute on Minority Health and Health Disparities.
A note on the name: JHEEM was previously published as the “Johns Hopkins Epidemiology and Economic Model.” The acronym is retained; the name was revised in 2026 to reflect use beyond a single institution. Citations to the prior name remain equivalent for attribution, reproducibility, and continuity.