Mapping the costs of mental health- and substance use-related grant cancellations
Beccia, A. L.; Liu, L.; Delaney, S.; Zubizarreta, D.; Ross, N.; Austin, S. B.
Show abstract
BackgroundSince the 2025 Presidential Inauguration, the Trump Administration has terminated billions of dollars in federal funding for science; however, the impacts of these grant terminations on the mental health and substance use fields have not yet been examined. We thus aimed to quantify and map the costs associated with federally funded mental health- and substance use-related grants that have been prematurely terminated. MethodsWe used a comprehensive dataset of grants terminated by the National Institutes of Health (NIH), National Science Foundation (NSF), and Substance Abuse and Mental Health Services Administration (SAMHSA) compiled from multiple sources. After identifying terminated mental health- and substance use-related grants from this database via a two-step screening process, we quantified their number and associated lost funding for each congressional district, which we visualized using a series of maps to examine trends and regional variations. OutcomesWe identified 474 mental health- and/or substance use-related grants that were terminated by the NIH, NSF, or SAMHSA from February 28, 2025, through April 11, 2025, totaling $2,098,731,548 in lost funds. Congressional districts corresponding to urban centers with large academic and research institutions (e.g., New York City, Boston) experienced the most pronounced losses from NIH and NSF grants, whereas districts located throughout the Mid-Atlantic, Midwest, Southeast, and Southwest were the hardest hit by the termination of SAMHSA block grants (i.e., those used to pay for community mental health and substance use services). InterpretationAgainst a backdrop of ongoing and intersecting mental health and substance use crises, the Trump Administration has slashed research dollars on these topics, creating a chilling effect on the field. Such cuts are likely to destabilize existing mental health and substance use services and exacerbate inequities between and within U.S. states, ultimately intensifying the challenges faced by local communities. FundingNone to report.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Geospatial disparities in federal COVID-19 test-to-treat program 91%
- Associations between governor political affiliation and COVID-19 cases and deaths in the United States 89%
- Time trends between vaccination coverage and voting patterns before and during the COVID-19 pandemic: analysis of COVID-19 and flu surveys in the United States 88%
Similar papers in this journal
- Intimate partner violence and its correlates in middle-aged and older adults during the COVID-19 pandemic: A multi-country secondary analysis 88%
- Identifying and preventing fraudulent responses in online public health surveys: Lessons learned during the COVID-19 pandemic 87%
- Predicting Mobile Health Clinic Utilization for COVID-19 Vaccination in South Carolina: A Statistical Framework for Strategic Resource Allocation 86%
Similar papers in this journal
- The Impact of the “Muslim Ban” Executive Order on Healthcare Utilization in Minneapolis-St. Paul, Minnesota 90%
- COVID-19 cases and hospitalizations averted by case investigation and contact tracing in the United States 89%
- Geographic and Temporal Patterns in Covid-19 Mortality by Race and Ethnicity in the United States from March 2020 to February 2022 88%
Similar papers in this journal
- COVID-19 vaccine hesitancy January-March 2021 among 18-64 year old US adults by employment and occupation 88%
- COVID-19 booster vaccine attitudes and behaviors among university students and staff: the USC Trojan Pandemic Research Initiative 87%
- Factors associated with COVID-19 vaccine acceptance and hesitancy among residents of Northern California jails 87%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.