Mobility disability and Medicare spending per enrollee
Where Mobility disability is higher, Medicare spending per enrollee tends to be higher. The link is weak: +0.32 across 2,934 counties, on a scale from −1 (opposite) to +1 (in step). That figure compares places with the same population density, income and education, so those differences can't explain it. It holds inside states too. Measured separately within each of 38 states, the middle value of the link is +0.19.
Holds after adjusting
Every place, plotted
One dot for each place reporting both measures. Hover over or tap a dot to see its name and values. The thin line is the straight line that best fits the dots. The bold line, shown for counties, is the link after adjusting for density, income and education. A dot far from the lines is a place that does not follow the pattern.
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What each measure is
Mobility disability
The estimated percentage of adults who report serious difficulty walking or climbing stairs. It is adjusted so places with older or younger populations can be compared fairly. It is an estimate from a statistical model, not a direct count from a local survey.
CDC PLACES · PLACES MOBILITY, age-adjusted
Medicare spending per enrollee
What Medicare spent per person in a year, for people in traditional Medicare, where Medicare pays each doctor and hospital bill itself. Medicare pays more for the same care where wages and rents are high. This figure takes those price gaps out, so it shows how much care people used, not what it cost locally. It is not adjusted for how sick people are. It leaves out people in private Medicare Advantage plans.
CMS Medicare Geographic Variation Public Use File · CMS Medicare Geographic Variation PUF, national/state/county, all ages, TOT_MDCR_STDZD_PYMT_PC
Highest and lowest
Where each measure is highest and lowest among the places reporting both (2023), with the other measure beside it. This is shown for every size of place where both are published. Metro-area values are added up from their counties. A measure that cannot be added up across counties, such as a middle value (median), has no metro figure. Counts are shown per 1,000 residents, since raw totals would only list the largest places. Cities and towns and ZIP codes are shown where the Census survey or the CDC publishes them; most other sources stop at the county. They are shown for places of 5,000 residents or more, and only where the survey’s margin of error is small enough for the figure to be usable. The CDC publishes its health estimates for cities in a version adjusted for age, so places with older or younger people can be compared fairly, but not for ZIP codes.
Counties
By mobility disability
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 28.8% | 21,799 |
| Todd County, SD | 27.2% | 17,221 |
| East Carroll Parish, LA | 26.3% | 17,420 |
| Humphreys County, MS | 25.4% | 15,319 |
| Dimmit County, TX | 25.2% | 14,722 |
| Lowest | ||
| Los Alamos County, NM | 7% | 8,527 |
| Douglas County, CO | 7.1% | 11,986 |
| King County, WA | 7.2% | 9,826 |
| Falls Church city, VA | 7.4% | 9,651 |
| Morris County, NJ | 7.4% | 12,228 |
By Medicare spending per enrollee
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Miller County, GA | 16.3% | 27,129 |
| Hamilton County, KS | 12.7% | 24,463 |
| Greer County, OK | 15.9% | 22,580 |
| Oglala Lakota County, SD | 28.8% | 21,799 |
| Upton County, TX | 17.7% | 20,975 |
| Lowest | ||
| Copper River Census Area, AK | 12.1% | 5,333 |
| Judith Basin County, MT | 12% | 5,754 |
| Denali Borough, AK | 10.1% | 5,917 |
| Yakutat City and Borough, AK | 13.7% | 6,205 |
| Arthur County, NE | 10.9% | 6,331 |
States — Mobility disability is not published for states
By Medicare spending per enrollee
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Louisiana | — | 13,805 |
| Oklahoma | — | 13,690 |
| Florida | — | 13,686 |
| Mississippi | — | 13,474 |
| Texas | — | 13,288 |
| Lowest | ||
| Puerto Rico | — | 6,359 |
| Hawaii | — | 8,203 |
| Oregon | — | 9,019 |
| Alaska | — | 9,082 |
| Montana | — | 9,198 |
Metro areas
By mobility disability
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | 24.1% | 17,395 |
| Clarksdale, MS | 23.6% | 13,141 |
| Zapata, TX | 23.5% | 13,095 |
| Gallup, NM | 21.9% | 10,937 |
| Greenville, MS | 21.9% | 12,960 |
| Lowest | ||
| Los Alamos, NM | 7% | 8,527 |
| Boulder, CO | 8% | 10,836 |
| Nantucket, MA | 8.1% | 10,218 |
| Barre, VT | 8.3% | 8,976 |
| Burlington-South Burlington, VT | 8.36% | 9,001 |
By Medicare spending per enrollee
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Bogalusa, LA | 18.5% | 17,954 |
| Rio Grande City-Roma, TX | 24.1% | 17,395 |
| Vernon, TX | 17.9% | 17,093 |
| Alice, TX | 18.9% | 16,760 |
| Durant, OK | 17% | 16,536 |
| Lowest | ||
| Fairbanks-College, AK | 10.1% | 7,065 |
| Hilo-Kailua, HI | 11.7% | 7,474 |
| Kapaa, HI | 10.3% | 7,492 |
| Sheridan, WY | 10.1% | 7,495 |
| Kahului-Wailuku, HI | 11.5% | 7,870 |
Cities and towns — Medicare spending per enrollee is not published for cities and towns
By mobility disability
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| East Bakersfield CDP, CA | 28.9% | — |
| Lamont CDP, CA | 28.5% | — |
| Doffing CDP, TX | 28.2% | — |
| Tallulah city, LA | 28% | — |
| Benton Harbor city, MI | 27.5% | — |
| Lowest | ||
| Short Hills CDP, NJ | 4.9% | — |
| Old Greenwich CDP, CT | 5.3% | — |
| Sammamish city, WA | 5.3% | — |
| Glen Ridge borough, NJ | 5.3% | — |
| Riverside CDP, CT | 5.4% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,955 counties: the five with far more Medicare spending per enrollee than their mobility disability would suggest, and the five with far less.
| Mobility disability | Medicare spending per enrollee | |
|---|---|---|
| Above the line | ||
| Miller County, GA | 16.3% | 27,129 |
| Hamilton County, KS | 12.7% | 24,463 |
| Greer County, OK | 15.9% | 22,580 |
| Ness County, KS | 12.4% | 19,986 |
| Stafford County, KS | 12.8% | 19,970 |
| Below the line | ||
| Kusilvak Census Area, AK | 25.1% | 7,960 |
| Copper River Census Area, AK | 12.1% | 5,333 |
| Judith Basin County, MT | 12% | 5,754 |
| Yakutat City and Borough, AK | 13.7% | 6,205 |
| Boundary County, ID | 14.9% | 6,576 |
Two series, one county
Both measures over time for a county you pick. The two charts are stacked, so they share only the years along the bottom. You can pick from the thirty largest counties reporting both. This panel covers 2010–2024, and a measure published only once shows as a single dot.
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Every version of the link
Each row measures the same link, from −1 to +1, in a different way. If the rows mostly agree, the link does not depend on one choice of method.
| Before adjusting for anything | +0.400 | 2,934 counties reporting both |
| After adjusting for density, income and education | +0.324 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.330 | size taken out as well as density |
| Also comparing only within each state | +0.200 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.245 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 264 equally weighted counties |
| Middle value inside single states | +0.188 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.256 | uses each county’s rank, not its value, so a few extreme counties cannot create the link |
How much to believe it
Could it be chance?
The p-value is the chance of seeing a link this strong if there were really none. A value of 0.05 or less means the link is unlikely to be chance. Very small values are written in short form: 3.1e-16 means 0.00000000000000031, and the bigger the number after “e-”, the smaller the value. Treating every county as a separate case gives p = 1.8e-72. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 8.7e-6. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.5e-71.
Do nearby places look alike?
Moran’s I measures how much nearby places look alike: 0 means no map pattern, and higher means neighbours are more similar. For the part of each county that the adjustment leaves unexplained, it is +0.50 (p = 0.001). That unexplained part still clusters on the map. So some regional factor shared by both measures is not accounted for.
Did they change together?
Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.
Related pairs
- Mobility disability and Material security score — -0.80
- Mobility disability and Returns claiming the earned income credit — +0.78
- Mobility disability and Homicide rate, annual — +0.66
- Mobility disability and Homicide rate — +0.59
- Mobility disability and Average test score — -0.58
- Mobility disability and Premature mortality — +0.57
- Mobility disability and Social connection score — -0.56
- Mobility disability and Children in single-parent households — +0.56
Association only. Nothing here shows that either measure changes the other. This describes counties, not the people in them. Two measures going together across places does not mean the same people have both. How the US county data works · Explore Mobility disability and Medicare spending per enrollee