Disability Equity Requires Accurate Data
Scott D. Landes
October 30, 2026
Disabled adults face nearly twice the mortality risk of nondisabled adults, and in 2023 the NIH designated disabled people a health disparities population. Closing that gap depends on measuring it, yet the data behind health policy and funding often miscount disabled people or leave them out. As a disabled scholar, that gap keeps pulling at my attention.
My work has followed two threads. The first is death records. Among adults with intellectual and developmental disabilities, nearly half of death certificates list the disability itself as the cause of death. A disability is not a disease. That error hides what is actually killing people, such as pneumonia and choking, and obscures disparities.
The second thread is how national surveys decide who is disabled. When the nation’s flagship health survey switched question sets, estimated disability prevalence artificially fell from 17% to 8%, and more than a third of blind adults and over four in ten deaf adults were no longer counted. The Census Bureau paused a similar change in 2024 after pushback from disability researchers and advocates, including our team.
You cannot reduce a disparity you cannot measure. Disability equity requires accurate and inclusive disability data.
Scott D. Landes
Professor of Sociology
We are now moving the conversation from critique to solutions. Our team of disabled scholars and allies is developing a more comprehensive way to measure disability. In a national pilot, our new question identified more disabled adults than the six federal questions, including people with chronic, episodic, or invisible conditions that the law recognizes but federal surveys miss. We are refining the question with disabled people themselves and building the evidence federal and state data stewards need to adopt it.
The stakes are rising. The CDC proposed cutting its national health survey by more than half, including many disability questions. Health equity for disabled people is only as strong as the data behind it.