Over the past four months, and in the budget proposal for the coming fiscal year, the Trump administration has made unprecedented reductions in staffing and funding in U.S. science-related agencies, including the National Institutes of Health (NIH) and the Food and Drug Administration (FDA). By early April, the NIH had experienced $2.4 billion in canceled and frozen grants and contracts, had fired 1,200 employees, and induced retirement and resignations from a yet unspecified number. The administration’s 2026 budget proposes a 37% further cut to the agency. Meanwhile, over 3,500 jobs at the FDA have been eliminated, and the administration has hinted at further restructuring of the agency.

Together, the NIH, with its unparalleled capacity to conduct and fund health research, and the FDA, with its state-of-the-art regulatory structure, form the underpinning of the many technological and oversight advances that have generated significant improvements in the health of Americans in the post-World War II era. Changes in funding and staffing at these agencies portend serious risks to further progress. The administration argues that such concerns are misplaced because cuts aimed to eliminate initiatives focused on diversity—the new NIH Director, Dr. Jay Bhattacharya, has referred to these as “political ideology“—and on reducing excessive staff capacity at the FDA in order to eliminate waste and improve efficiency. But they are wrong, as the particular changes they have advanced will harm the rate and quality of innovations in addressing Americans’ health needs.

NIH, DEI, and Health Science: Examining the Potential Impacts of NIH and FDA Cuts

Over the past two decades, the NIH has, as the Trump administration decries, prioritized expanding the scope of populations considered in the research it funds. It did so for very good, evidence-based reasons. Historically, research aimed at discovering and assessing conditions and treatments, whether conducted by the NIH or the pharmaceutical industry, has focused on homogeneous populations. Clinical trials, for example, typically minimized diversity in age, gender, race, and ethnicity and ruled out people with comorbid conditions (for example, those with diabetes and heart disease in cancer trials). The rationale for this narrow focus was that heterogeneity within study populations reduced the statistical power of a given sample, making trials more expensive.

Read the full article about NIH and FDA cuts by Richard G. Frank and Sherry Glied at Brookings.