How measures of community need misread rural and urban areas—and why the differences matter

August 6, 2026

Across the United States, government agencies, health systems, and researchers rely on disadvantage indices, or composite scores identifying communities with the greatest socioeconomic needs, to guide the allocation of billions of dollars in health funding, public health interventions, infrastructure upgrades, and healthcare resources. But a recent study published in Health Affairs Scholar finds that these indices don’t always agree on which communities need help most—and that disagreement follows a consistent pattern across rural and urban areas.

Here, the authors of the study explain the findings and why understanding how these measurements work matters for addressing health disparities.

Hannah Lang is a PhD student at the University of Michigan School of Public Health.

Kimberly Rollings is a Research Investigator at the University of Michigan Institute for Social Research and a member of the University of Michigan Institute for Healthcare Policy & Innovation.

Can you give a brief, plain language overview of what your study found?

Our study compared five popular indices and found that they are not interchangeable. In particular, two of the most widely used measures frequently disagree on which communities should be prioritized to receive resources: the Neighborhood Atlas Area Deprivation Index (NA-ADI) and the Social Vulnerability Index (SVI).

When we looked closely at these two measures, we found a distinct, rural-urban bias.

The NA-ADI masks vulnerability in urban, cost-burdened, and predominantly minority communities with expensive housing, while simultaneously inflating rural disadvantage scores in predominantly white, rural tracts due to inherently lower housing costs in these areas.

A closer look showed this disagreement was driven by a well-known NA-ADI math flaw: it doesn’t standardize its inputs, mixing unadjusted dollar amounts—like home values—with percentages—like poverty rates. Standardizing NA-ADI inputs reduced disagreement from 13.1% to just 3.0%.

Our article exposes this hidden bias and explains why improved, mathematically sound measures are needed to equitably distribute millions to billions in healthcare and related funding across the national rural-urban landscape.

How are these indices used and what types of decisions are made from them? What happens when a community is mismeasured, in either direction?

These indices essentially create maps for resource distribution. Government agencies use them to decide which hospitals get extra funding for treating higher volumes of more vulnerable patients, or which communities need infrastructure upgrades. Health systems use them to target community interventions, such as mobile clinics, and to inform health services research by embedding indices into electronic health record software such as Epic.

When an urban neighborhood is mismeasured and appears "too advantaged" on paper, safety-net hospitals and local clinics miss out on critical dollars they need to serve highly cost-burdened patients. Conversely, when a rural area's disadvantage score is artificially inflated due to lower-cost real estate, funding might be directed there for the wrong reasons, rather than addressing the actual, unique challenges that specific community faces.

Rural and urban communities can face different, often unique, challenges. Does this mean we need better tools tailored to each, rather than a one-size-fits-all approach?

Absolutely. A one-size-fits-all national measure ignores lived experiences of need across the rural-urban landscape. For example, lacking a car in a rural area might indicate isolation and an inability to reach a doctor, but in a dense city, not having a car may just mean the community relies on a robust public transit system. Existing national tools also miss specific rural health drivers, such as the concentration of high-risk occupational facilities or the impacts of lacking broadband internet for telehealth. We urgently need context-specific tools that measure the actual assets and limitations unique to rural environments.

If you’re a policymaker or health system leader, what is the practical takeaway from this research?

The biggest takeaway is that selecting a disadvantage index is a critical decision. Using a flawed index can inadvertently worsen the very health disparities these indices aim to alleviate. Decision-makers should transition away from unstandardized measures and adopt mathematically sound, standardized alternatives. Before rolling out a new program or funding model, decision-makers can also compare different indices to see if their chosen index is, for example, accidentally restricting eligibility for highly vulnerable populations.

Anything else you think is important for people to know?

It is encouraging to see large federal entities, such as the Centers for Medicare and Medicaid Services (CMS), address these issues. CMS recently transitioned away from the NA-ADI for payment adjustments and pivoted to a standardized alternative. However, many local health departments, state agencies, and researchers are still using outdated measures. We hope our research sparks a faster, broader transition to improved metrics across the entire healthcare and rural-urban continuum.

Study: Rural-urban comparisons of area-based socioeconomic disadvantage and vulnerability indices. Health Affairs Scholar. https://doi.org/10.1093/haschl/qxag173