The Medical Debt Belt: where hospital bills become household debt

Medical debt is often described as a national problem. But the data tells a sharper story: in some parts of the country, medical debt is not just common. It is concentrated, persistent, and geographically predictable.

Across the counties and states in this dataset, one pattern stands out above everything else: medical debt in collections varies dramatically depending on where someone lives. At the state level, the gap between the highest and lowest rates is roughly 14 times wider. In some counties, nearly one in three adults has medical debt in collections. In others, the rate is under 1%.

The result is a clear regional pattern: a "Medical Debt Belt" stretching across much of the South and parts of the Great Plains.

According to the Urban Institute's Debt in America 2024 data analyzed here, the states with the highest shares of adults with medical debt in collections are heavily clustered in states that either did not expand Medicaid or expanded it late.

The highest state-level rates include:

At the other end of the spectrum, Minnesota registers just 0.7%, while Massachusetts is at 1.4%. Colorado appears at very low rates, but only a handful of counties returned data, so that figure should be treated as a likely data coverage issue rather than a real statewide estimate.

Even with that caveat, the broader pattern is hard to miss. Oklahoma's rate is around 14 times higher than Minnesota's. These are not minor differences. They suggest that medical debt is shaped not only by individual circumstances, but also by state policy, insurance coverage, hospital billing practices, and regional economic conditions.

The county-level data makes the pattern even clearer.

Of the 50 counties with the highest medical-debt rates in the country, 49 are located in just eight states:

Texas accounts for 21. North Carolina accounts for 8. Oklahoma accounts for 8. Georgia accounts for 5. The remainder is split between South Carolina, Arkansas, Mississippi, and Missouri.

That is an extraordinary concentration. Medical debt is not randomly scattered across the map. It clusters in a set of states where coverage gaps have historically been larger and where many lower-income adults have had fewer options for affordable care.

The most striking county-level figure is Anson, NC, North Carolina.

Anson, NC has a population of around 22,000 people, and 32.5% of adults there have medical debt in collections. That means roughly one in three adults is carrying a medical bill serious enough to have reached collections.

The next highest counties show the same regional pattern:

These are not just statistics. A county-level rate above 25% means medical debt is part of everyday life. It affects credit scores, housing applications, car loans, household budgets, and the ability to recover from future emergencies.

In a place like Anson, NC, medical debt is not an edge case. It is a community-wide financial condition.

One of the clearest ways to understand the divide is to compare high-debt counties in North Carolina with counties in Minnesota.

Cleveland, NC, North Carolina, with a population of about 101,000, has a medical-debt rate of 22.6%. That is large enough to avoid treating the county as a small-population anomaly.

By contrast, many counties in Minnesota and Massachusetts sit below 1%.

That comparison is powerful because it shows the same country, the same credit bureau system, and the same broad medical economy producing radically different household outcomes.

The difference is not simply that people in one place get sick and people in another do not. The difference is that people in some states are far more likely to have medical bills turn into collections debt.

The Medicaid-expansion overlay is real at the broad regional level: the cluster of highest-medical-debt states is overwhelmingly states that never expanded Medicaid or expanded late. But the within-region pattern doesn't cleanly support "expansion explains the gradient."

Three counter-examples in the Deep South make that clear:

• Arkansas expanded Medicaid in 2014 and reports a medical-debt rate of 7.1%.
• Louisiana expanded in 2016 and reports 8.1%.
• Mississippi never expanded and reports 6.1%.

If expansion alone drove the gradient, Mississippi should sit highest in this group. It doesn't. Two things explain the inversion:

Credit-invisibility variation. Mississippi has the highest credit-invisible adult rate in the country (~14%). Those adults are excluded from the denominator entirely. AR and LA have better credit coverage, so more of their low-income adults end up in the numerator. The MS figure understates real medical-debt exposure relative to its neighbours — it's not that Mississippians carry less medical debt, it's that fewer of them appear in the data at all.

Charity-care policy + hospital market structure. Louisiana and Arkansas both saw rural hospital closures and changes to charity-care rules in the years after expansion. Expansion changes who has coverage going forward; it doesn't retire pre-existing collections accounts that the credit bureau is still reporting, and it doesn't repair gaps in how hospitals handle uninsured emergencies.

What survives: medical debt has a strong geographic concentration in the South + Plains; that concentration overlaps with non-expansion states more than chance would predict; but the within-region order is shaped by credit-bureau coverage, state charity-care law, and hospital market structure as much as by expansion timing.

The North Carolina prediction still holds. NC expanded Medicaid in December 2023, after this dataset was collected. If expansion has any effect at the margin, the next Urban Institute release should show NC's medical-debt rate falling — especially in counties like Anson, Cleveland, and Onslow that currently top the rankings. That's still a clear, falsifiable prediction tied to a concrete future data refresh.

Three caveats matter for interpreting the figures above.

1. Credit-active adults only. The Urban Institute draws its sample from a major credit bureau. Adults without a credit file — often the lowest-income households, undocumented residents, and many young adults — do not appear in the data at all. The rates shown are the share of credit-active adults with medical debt in collections, not the share of the full adult population. In states where credit invisibility is more common, the true population-wide medical-debt burden is higher than what's reported here.

2. Reporting changes depressed the numbers. In July 2022 the three national credit bureaus stopped reporting paid medical-collections accounts. In April 2023 they raised the minimum reportable balance to $500. Both changes were the right policy. Both also removed real medical debts from the dataset. A household with $400 in unpaid hospital bills experiences that debt the same way it did before — it just doesn't show up here. So these figures undercount actual medical-debt exposure, especially among households whose bills cluster below the $500 threshold.

3. County estimates have wider uncertainty than state ones. The Urban Institute model produces stable estimates at the state level. At the county level — particularly for small counties with thin credit-bureau samples — confidence intervals widen. The top-county figures (Anson NC, Presidio TX, Roger Mills OK) are real signals but should be read alongside the state-level pattern, not in isolation. Cleveland County NC (~101k population) is the cleanest single-county anchor for this reason.

None of these caveats change the headline finding — the state-level gap is too large and the geographic concentration too tight for measurement error to explain it. They do mean the absolute numbers are conservative.

Medical debt is different from many other forms of debt.

People usually do not choose to get sick, need surgery, visit an emergency room, or manage a chronic condition. Medical debt often comes from events that are urgent, confusing, and hard to price in advance.

Once a bill goes to collections, the consequences can spread far beyond the original medical visit. A collections account can damage a credit report, make borrowing more expensive, and limit access to housing or transportation. It can also discourage people from seeking future care, which can make health problems worse and more expensive later.

That is why the geography of medical debt matters. It shows where the health system is turning illness into financial damage most often.

This dataset contains other important signals: financially stressed urban areas, rent-burdened coastal counties, and political shifts in high-stress places. But the medical-debt pattern is the strongest headline because it is simple, measurable, and deeply human.

A 14-fold gap between states is not a rounding error. A county where one in three adults has medical debt in collections is not a marginal problem. A top-50 list dominated by a handful of non-expansion or late-expansion states is not random noise.

Together, the data points to one conclusion: medical debt in America has a geography.

It is highest in a Medical Debt Belt running through Texas, Oklahoma, the Carolinas, Georgia, and parts of the South. It is lowest in states like Minnesota and Massachusetts, where broader coverage and stronger health-policy infrastructure appear to leave fewer households exposed to medical bills becoming collections debt.

The next major test will be North Carolina. Because the state expanded Medicaid after this data was collected, the next Urban Institute release should show whether expansion begins to reduce medical debt in the counties hit hardest.

For now, the map is clear. In America, the odds that a medical bill becomes a financial scar depend heavily on the state — and sometimes the county — where a person happens to live.