medRxiv2025Preprint

The Potential Effect of Ending CDC Funding for HIV Tests: A Modeling Study in 18 States

Balasubramanian R, Schnure M, Forster R, Hanage WP, Batey DS, Althoff KN, Gebo KA, Dowdy DW, Shah M, Kasaie P, Fojo AT

If CDC-funded HIV testing ends permanently, we project 12,719 additional HIV infections by 2030—a 10% increase across 18 U.S. states.

CDC-funded HIV testing is a cornerstone of the national HIV prevention strategy, enabling early diagnosis and linkage to care. Testing identifies infections before transmission can occur, and people who know their status can access treatment that prevents onward transmission.

Related: Ryan White Program analysis (HIV care and treatment funding)

Policy Scenarios Modeled

We simulated three funding disruption scenarios to understand how different policy outcomes would affect HIV diagnosis and transmission.

15months

Brief Interruption

Funding ends October 2025, resumes by end of 2027. Allows time for alternative funding arrangements.

39months

Prolonged Interruption

Funding ends October 2025, resumes by end of 2029. Extended gap with eventual program restoration.

permanent

Complete Cessation

Program ends with no recovery. Projected impact: +10% infections (12,719 excess). State range: 3% to 30%.

States Studied

State-specific epidemic models capturing local patterns in HIV prevalence, testing rates, and CDC-funded testing program utilization.

AlabamaArizonaCaliforniaFloridaGeorgiaIllinoisKentuckyLouisianaMarylandMississippiMissouriNew YorkOhioSouth CarolinaTennesseeTexasWashingtonWisconsin

Full Citation

Balasubramanian R, et al. The Potential Effect of Ending CDC Funding for HIV Tests: A Modeling Study in 18 States. medRxiv. 2025. doi:10.1101/2025.09.19.25336182

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.

K08MH118094
K01AI138853
P30-AI094189
R01MD018539
JH

Johns Hopkins Bloomberg School of Public Health

Computational Epidemiology Research Group

Advancing mathematical modeling for HIV prevention and control