High cholesterol and Hospital stays per 1,000 Medicare enrollees
Where High cholesterol is higher, Hospital stays per 1,000 Medicare enrollees tends to be higher. The link is weak: +0.31 across 2,932 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 +0.17.
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
High cholesterol
The estimated percentage of adults who have been told they have high cholesterol. 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 HIGHCHOL, age-adjusted
Hospital stays per 1,000 Medicare enrollees
Hospital stays, counted from admission to discharge, in a year for every 1,000 people in traditional Medicare. It includes all kinds of stays, not only those that better care might have prevented. 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, IP_CVRD_STAYS_PER_1000_BENES
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 high cholesterol
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Mingo County, WV | 37.9% | 303 |
| Wyoming County, WV | 37.8% | 309 |
| Chilton County, AL | 37.7% | 233 |
| Franklin County, AL | 37.7% | 298 |
| Fayette County, WV | 37.6% | 229 |
| Lowest | ||
| Lamoille County, VT | 25% | 174 |
| Windsor County, VT | 25.9% | 169 |
| Chittenden County, VT | 25.9% | 180 |
| Twin Falls County, ID | 25.9% | 171 |
| Lake County, MT | 26% | 137 |
By hospital stays per 1,000 Medicare enrollees
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 29.1% | 537 |
| North Slope Borough, AK | 29.9% | 425 |
| Red River Parish, LA | 34% | 408 |
| Caldwell Parish, LA | 35.1% | 401 |
| Todd County, SD | 29.7% | 388 |
| Lowest | ||
| Rich County, UT | 28.8% | 68.5 |
| Ouray County, CO | 28.5% | 78.6 |
| San Miguel County, CO | 28.6% | 85.9 |
| Harding County, SD | 28.3% | 90.5 |
| Gilpin County, CO | 27.8% | 92.9 |
States — High cholesterol is not published for states
By hospital stays per 1,000 Medicare enrollees
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Massachusetts | — | 259 |
| Florida | — | 259 |
| Illinois | — | 252 |
| Connecticut | — | 251 |
| West Virginia | — | 249 |
| Lowest | ||
| Hawaii | — | 144 |
| Idaho | — | 153 |
| Montana | — | 158 |
| Puerto Rico | — | 159 |
| Oregon | — | 160 |
Metro areas
By high cholesterol
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Russellville, AL | 37.7% | 298 |
| Beckley, WV | 37.5% | 276 |
| Arcadia, FL | 37.4% | 248 |
| Seaford, DE | 37.4% | 176 |
| Tuscaloosa, AL | 36.9% | 243 |
| Lowest | ||
| Brattleboro, VT | 26.3% | 181 |
| Burlington-South Burlington, VT | 26.3% | 180 |
| Bennington, VT | 26.4% | 189 |
| Sheridan, WY | 26.5% | 145 |
| Rutland, VT | 26.5% | 204 |
By hospital stays per 1,000 Medicare enrollees
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Durant, OK | 35.4% | 371 |
| Moultrie, GA | 34.8% | 370 |
| Rio Grande City-Roma, TX | 35.2% | 349 |
| Okeechobee, FL | 34.5% | 329 |
| Parkersburg-Vienna, WV | 35.8% | 314 |
| Lowest | ||
| Sandpoint, ID | 28.4% | 106 |
| Breckenridge, CO | 27.1% | 110 |
| Ellensburg, WA | 28.9% | 119 |
| Kahului-Wailuku, HI | 28.5% | 119 |
| Steamboat Springs, CO | 28.9% | 121 |
Cities and towns — Hospital stays per 1,000 Medicare enrollees is not published for cities and towns
By high cholesterol
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Georgetown town, DE | 39% | — |
| Southeast Arcadia CDP, FL | 38.2% | — |
| Palisades Park borough, NJ | 38.2% | — |
| Russellville city, AL | 38.1% | — |
| University CDP (Hillsborough County), FL | 37.9% | — |
| Lowest | ||
| Binghamton University CDP, NY | 23.2% | — |
| Shelburne CDP, VT | 25.4% | — |
| South Burlington city, VT | 25.7% | — |
| Redlands CDP, CO | 25.8% | — |
| Polson city, MT | 25.8% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,950 counties: the five with far more hospital stays per 1,000 Medicare enrollees than their high cholesterol would suggest, and the five with far less.
| High cholesterol | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Above the line | ||
| Oglala Lakota County, SD | 29.1% | 537 |
| North Slope Borough, AK | 29.9% | 425 |
| Todd County, SD | 29.7% | 388 |
| Red River Parish, LA | 34% | 408 |
| Caldwell Parish, LA | 35.1% | 401 |
| Below the line | ||
| Rich County, UT | 28.8% | 68.5 |
| Ouray County, CO | 28.5% | 78.6 |
| Santa Cruz County, AZ | 33.3% | 128 |
| McCormick County, SC | 34.2% | 138 |
| San Miguel County, CO | 28.6% | 85.9 |
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 | +0.504 | 2,932 counties reporting both |
| After adjusting for density, income and education | +0.313 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.305 | size taken out as well as density |
| Also comparing only within each state | +0.065 | 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 | +0.211 | 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 | +0.170 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.295 | 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 = 2.2e-67. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.4e-7. 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-66.
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 +0.38 (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?
The figures above compare counties at one moment. This asks a different question: where high cholesterol rose most, did hospital stays per 1,000 Medicare enrolleesmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | — | — | — | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 2,917 | +0.08 | +0.02 | 0.382 |
Not measurable over the main period, so only the second period is shown. How change is measured.
Related pairs
- High cholesterol and Winter nightly low — +0.55
- High cholesterol and Average temperature — +0.52
- High cholesterol and Evangelical Protestant adherents — +0.35
- High cholesterol and Black-white residential segregation — -0.35
- High cholesterol and Summer daily high — +0.34
- No leisure-time physical activity and Hospital stays per 1,000 Medicare enrollees — +0.33
- High cholesterol and Returns claiming the earned income credit — +0.33
- Taking blood pressure medication and Hospital stays per 1,000 Medicare enrollees — +0.33
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 High cholesterol and Hospital stays per 1,000 Medicare enrollees