Median age and Hospital stays per 1,000 Medicare enrollees
Counting counties equally, Median age and Hospital stays per 1,000 Medicare enrollees have a link of -0.16 across 3,119 counties, on a scale from −1 to +1. Giving bigger places more weight asks about the average resident rather than the average place, and it flips the link to +0.11. Neither figure is likely to be chance. So this is a real disagreement about who is being described.
Flips when bigger places count more
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
Median age
The middle age of the county: half the residents are older and half are younger. The typical county is 41.5, and four in five are between 36 and 49. The extremes come from one big group of residents, such as retirees or students: 68.9 in Loving County, Texas and 68.4 in Sumter County, Florida, against 21.4 in Madison County, Idaho, where a university sets the middle age. Since it is a middle value, county figures cannot be averaged into a state figure.
Census ACS 5-year · ACS table B01002_001
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 median age
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Sumter County, FL | 68.4 years | 212 |
| Catron County, NM | 62.6 years | 144 |
| Terrell County, TX | 60.9 years | 240 |
| Charlotte County, FL | 60 years | 249 |
| Meagher County, MT | 59.9 years | 129 |
| Lowest | ||
| Madison County, ID | 21.4 years | 127 |
| Lexington city, VA | 22.5 years | 229 |
| Radford city, VA | 23 years | 265 |
| Kusilvak Census Area, AK | 23.6 years | 173 |
| Chattahoochee County, GA | 23.9 years | 177 |
By hospital stays per 1,000 Medicare enrollees
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 27 years | 537 |
| North Slope Borough, AK | 35.2 years | 425 |
| Red River Parish, LA | 40.4 years | 408 |
| Caldwell Parish, LA | 40.7 years | 401 |
| Todd County, SD | 25.4 years | 388 |
| Lowest | ||
| Rich County, UT | 37.2 years | 68.5 |
| Ouray County, CO | 55.6 years | 78.6 |
| San Miguel County, CO | 42.7 years | 85.9 |
| Harding County, SD | 48.9 years | 90.5 |
| Gilpin County, CO | 48.7 years | 92.9 |
States
By median age
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Maine | 44.8 years | 178 |
| Puerto Rico | 44.2 years | 159 |
| New Hampshire | 43.2 years | 197 |
| Vermont | 43 years | 181 |
| West Virginia | 42.7 years | 249 |
| Lowest | ||
| Utah | 31.7 years | 167 |
| District of Columbia | 34.9 years | 233 |
| Texas | 35.5 years | 225 |
| Alaska | 35.6 years | 168 |
| North Dakota | 35.7 years | 197 |
By hospital stays per 1,000 Medicare enrollees
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Massachusetts | 40 years | 259 |
| Florida | 42.6 years | 259 |
| Illinois | 38.9 years | 252 |
| Connecticut | 41.2 years | 251 |
| West Virginia | 42.7 years | 249 |
| Lowest | ||
| Hawaii | 40.6 years | 144 |
| Idaho | 37.1 years | 153 |
| Montana | 40.2 years | 158 |
| Puerto Rico | 44.2 years | 159 |
| Oregon | 40.1 years | 160 |
Metro areas — Median age is not published for metro areas
By hospital stays per 1,000 Medicare enrollees
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Durant, OK | — | 371 |
| Moultrie, GA | — | 370 |
| Rio Grande City-Roma, TX | — | 349 |
| Frankfort, KY | — | 345 |
| Okeechobee, FL | — | 329 |
| Lowest | ||
| Sandpoint, ID | — | 106 |
| Breckenridge, CO | — | 110 |
| Ellensburg, WA | — | 119 |
| Kahului-Wailuku, HI | — | 119 |
| Steamboat Springs, CO | — | 121 |
Cities and towns — Hospital stays per 1,000 Medicare enrollees is not published for cities and towns
By median age
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| Leisure World CDP, MD | 76.9 years | — |
| Desert Palms CDP, CA | 76.2 years | — |
| Laguna Woods city, CA | 75 years | — |
| Sun City West CDP, AZ | 74.9 years | — |
| Pelican Bay CDP, FL | 73.8 years | — |
| Lowest | ||
| Kiryas Joel village, NY | 15.1 years | — |
| Monsey CDP, NY | 15.6 years | — |
| New Square village, NY | 15.8 years | — |
| Kaser village, NY | 15.9 years | — |
| Lakewood CDP, NJ | 17.3 years | — |
ZIP codes — Hospital stays per 1,000 Medicare enrollees is not published for ZIP codes
By median age
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Highest | ||
| ZIP 92637 (Laguna Woods city, CA) | 75 years | — |
| ZIP 85375 (Sun City West CDP, AZ) | 74.8 years | — |
| ZIP 32162 (The Villages CDP, FL) | 74.2 years | — |
| ZIP 85622 (Pima County, AZ) | 73.1 years | — |
| ZIP 85351 (Sun City CDP, AZ) | 72.6 years | — |
| Lowest | ||
| ZIP 08701 (Ocean County, NJ) | 18.2 years | — |
| ZIP 55455 (Minneapolis city, MN) | 18.9 years | — |
| ZIP 22807 (Harrisonburg city, VA) | 18.9 years | — |
| ZIP 48109 (Ann Arbor city, MI) | 19.1 years | — |
| ZIP 93410 (California Polytechnic State University CDP, CA) | 19.2 years | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,137 counties: the five with far more hospital stays per 1,000 Medicare enrollees than their median age would suggest, and the five with far less.
| Median age | Hospital stays per 1,000 Medicare enrollees | |
|---|---|---|
| Above the line | ||
| Oglala Lakota County, SD | 27 years | 537 |
| North Slope Borough, AK | 35.2 years | 425 |
| Red River Parish, LA | 40.4 years | 408 |
| Caldwell Parish, LA | 40.7 years | 401 |
| Franklin County, KY | 40.6 years | 386 |
| Below the line | ||
| Rich County, UT | 37.2 years | 68.5 |
| San Miguel County, CO | 42.7 years | 85.9 |
| Madison County, ID | 21.4 years | 127 |
| Boundary County, ID | 42.8 years | 98.9 |
| Summit County, CO | 39.2 years | 105 |
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 | -0.210 | 3,119 counties reporting both |
| After adjusting for density, income and education | -0.157 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.176 | size taken out as well as density |
| Also comparing only within each state | -0.176 | 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 | +0.113 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 287 equally weighted counties |
| Middle value inside single states | -0.269 | the link worked out inside each of 40 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.131 | 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 = 1.5e-18. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. 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 5.1e-18.
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 +0.41 (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 median age 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 | 3,126 | -0.01 | -0.01 | 0.935 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,125 | +0.02 | +0.00 | 0.943 |
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Median age and Share aged 65 and over — +0.93
- Median age and Share never married — -0.59
- Homeownership rate and Median age — +0.58
- Median age and Share widowed — +0.53
- Median age and People per tax return — -0.49
- Median age and Per capita income — +0.47
- Median age and Mean income of arriving households — +0.44
- Median age and Returns reporting capital gains — +0.43
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 Median age and Hospital stays per 1,000 Medicare enrollees