HIV Incidence Could Rise by 68% in 11 States if Ryan White Ends: A Simulation Study
Schnure M, Forster R, Jones JL, Lesko CR, Batey DS, Butler I, Ward D, Musgrove K, Althoff KN, Jain MK, Gebo KA, Dowdy DW, Shah M, Kasaie P, Fojo AT
If the Ryan White HIV/AIDS Program ends permanently, we project 69,695 additional HIV infections by 2030—a 68% increase across 11 U.S. states representing 63% of all people diagnosed with HIV.
This state-level analysis complements our city-level study, using the same validated HIV transmission model to project impacts across entire state populations. State-level variation is substantial, ranging from a 45% increase in Texas to 126% in Missouri.
Policy Scenarios Modeled
We simulated three funding disruption scenarios starting July 2025 to understand how different policy outcomes would affect HIV transmission through 2030.
Brief Interruption
Services resume January 2027. Projected impact: +26% new infections (26,951 additional).
Prolonged Interruption
Services resume January 2029. Projected impact: +52% new infections (53,594 additional).
Complete Cessation
Program ends with no recovery. Projected impact: +68% (69,695 excess infections). State range: 45% to 126%.
States Studied
These 11 states represent 63% of all people diagnosed with HIV in the United States, selected for geographic diversity, Medicaid expansion status, and Ending the HIV Epidemic prioritization.
Highest projected impact: Missouri (+126%), Alabama (+111%), Wisconsin (+108%), Illinois (+101%)
Full Citation
Schnure M, et al. HIV Incidence Could Rise by 68% in 11 States if Ryan White Ends: A Simulation Study. AJPH. Forthcoming 2026. Preprint: doi:10.1101/2025.07.31.25332525
Research Funding
& Institutional Support
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.
Johns Hopkins Bloomberg School of Public Health
Computational Epidemiology Research Group
Advancing mathematical modeling for HIV prevention and control