Land surface form (flat to mountainous) and Medicare spending per enrollee
Where Land surface form (flat to mountainous) is higher, Medicare spending per enrollee tends to be lower. The link is weak: -0.35 across 3,043 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 39 states, the middle value of the link is -0.18.
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
Land surface form (flat to mountainous)
The shape of the land, as a code from 1 (flat plains) to 21 (high mountains), taken from the 1970 National Atlas. Codes in between cover plains with hills, tablelands (high flat land cut by valleys) and open hills. A higher code means more varied land.
USDA ERS Natural Amenities Scale · USDA ERS natural amenities scale, land surface form topography code
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 land surface form (flat to mountainous)
| Land surface form (flat to mountainous) | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Adams County, ID | 21 | 7,801 |
| Benewah County, ID | 21 | 8,616 |
| Blaine County, ID | 21 | 9,618 |
| Boise County, ID | 21 | 8,840 |
| Bonner County, ID | 21 | 7,915 |
| Lowest | ||
| Sutter County, CA | 1 | 10,794 |
| Woodruff County, AR | 1 | 13,504 |
| St. Francis County, AR | 1 | 12,112 |
| Prairie County, AR | 1 | 12,138 |
| Poinsett County, AR | 1 | 12,858 |
By Medicare spending per enrollee
| Land surface form (flat to mountainous) | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Miller County, GA | 1 | 27,129 |
| Hamilton County, KS | 2 | 24,463 |
| Greer County, OK | 4 | 22,580 |
| Oglala Lakota County, SD | 14 | 21,799 |
| Loving County, TX | 4 | 21,063 |
| Lowest | ||
| Judith Basin County, MT | 17 | 5,754 |
| Arthur County, NE | 14 | 6,331 |
| Boundary County, ID | 21 | 6,576 |
| Hanson County, SD | 2 | 6,656 |
| Powder River County, MT | 15 | 6,712 |
States — Land surface form (flat to mountainous) is not published for states
By Medicare spending per enrollee
| Land surface form (flat to mountainous) | 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 — Land surface form (flat to mountainous) is not published for metro areas
By Medicare spending per enrollee
| Land surface form (flat to mountainous) | Medicare spending per enrollee | |
|---|---|---|
| Highest | ||
| Bogalusa, LA | — | 17,954 |
| Rio Grande City-Roma, TX | — | 17,395 |
| Vernon, TX | — | 17,093 |
| Alice, TX | — | 16,760 |
| Durant, OK | — | 16,536 |
| Lowest | ||
| Fairbanks-College, AK | — | 7,065 |
| Hilo-Kailua, HI | — | 7,474 |
| Kapaa, HI | — | 7,492 |
| Sheridan, WY | — | 7,495 |
| Kahului-Wailuku, HI | — | 7,870 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,062 counties: the five with far more Medicare spending per enrollee than their land surface form (flat to mountainous) would suggest, and the five with far less.
| Land surface form (flat to mountainous) | Medicare spending per enrollee | |
|---|---|---|
| Above the line | ||
| Miller County, GA | 1 | 27,129 |
| Hamilton County, KS | 2 | 24,463 |
| Oglala Lakota County, SD | 14 | 21,799 |
| Greer County, OK | 4 | 22,580 |
| Upton County, TX | 10 | 20,975 |
| Below the line | ||
| Hanson County, SD | 2 | 6,656 |
| Judith Basin County, MT | 17 | 5,754 |
| Arthur County, NE | 14 | 6,331 |
| Harding County, SD | 9 | 7,125 |
| King County, TX | 5 | 7,540 |
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 | -0.366 | 3,043 counties reporting both |
| After adjusting for density, income and education | -0.350 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.342 | size taken out as well as density |
| Also comparing only within each state | -0.172 | 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 | -0.172 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 277 equally weighted counties |
| Middle value inside single states | -0.176 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.366 | 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-88. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 4.0e-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 1.7e-87.
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 +0.48 (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
- Medicare spending per enrollee and Summer daily high — +0.51
- Medicare spending per enrollee and Average temperature — +0.48
- Land surface form (flat to mountainous) and Expected yearly inland flood loss — +0.46
- Land surface form (flat to mountainous) and Taking blood pressure medication — -0.43
- No leisure-time physical activity and Medicare spending per enrollee — +0.42
- Taking blood pressure medication and Medicare spending per enrollee — +0.39
- Land surface form (flat to mountainous) and Summer daily high — -0.39
- Preventable hospital stays and Medicare spending per enrollee — +0.36
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 Land surface form (flat to mountainous) and Medicare spending per enrollee