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Medicare spending per enrollee and Daily sunlight

Where Medicare spending per enrollee is higher, Daily sunlight tends to be higher. The link is weak: +0.33 across 3,121 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 40 states, the middle value of the link is +0.13.

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

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

Daily sunlight

The average amount of sunlight reaching the ground each day over the year, in kilowatt-hours per square metre. NASA estimates it from weather satellites: the satellites see the clouds, and a model of the air works out how much sunlight gets through them. The figures are for squares of land about 60 miles across, so nearby counties often share a value. Each county gets the square where its people live, around its center of population. Years before 2001 come from older satellites, which NASA adjusted to match the newer ones.

NASA POWER (Prediction Of Worldwide Energy Resources) · NASA POWER daily regional API, community RE, ALLSKY_SFC_SW_DWN

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 Medicare spending per enrollee

Medicare spending per enrolleeDaily sunlight
Highest
Miller County, GA27,1294.73
Hamilton County, KS24,4635.03
Greer County, OK22,5805.07
Oglala Lakota County, SD21,7994.44
Loving County, TX21,0635.68
Lowest
Copper River Census Area, AK5,3332.67
Judith Basin County, MT5,7543.98
Denali Borough, AK5,9172.52
Yakutat City and Borough, AK6,2052.4
Arthur County, NE6,3314.64

By daily sunlight

Medicare spending per enrolleeDaily sunlight
Highest
Yuma County, AZ11,3555.97
El Paso County, TX10,7275.92
Imperial County, CA14,1505.91
Cochise County, AZ9,9195.89
Santa Cruz County, AZ8,4415.89
Lowest
Chugach Census Area, AK12,7572.12
Nome Census Area, AK12,9962.18
North Slope Borough, AK19,0642.25
Kusilvak Census Area, AK7,9602.33
Sitka City and Borough, AK14,0792.33
States

By Medicare spending per enrollee

Medicare spending per enrolleeDaily sunlight
Highest
Louisiana13,8054.77
Oklahoma13,6904.71
Florida13,6865.03
Mississippi13,4744.67
Texas13,2884.89
Lowest
Hawaii8,2035.69
Oregon9,0193.97
Alaska9,0822.46
Montana9,1983.97
Vermont9,3153.43

By daily sunlight

Medicare spending per enrolleeDaily sunlight
Highest
Arizona11,7905.81
Hawaii8,2035.69
New Mexico9,7935.54
Nevada12,3255.3
California12,1255.13
Lowest
Alaska9,0822.46
Vermont9,3153.43
Maine9,8413.53
New Hampshire10,0263.65
Michigan11,7803.69
Metro areas

By Medicare spending per enrollee

Medicare spending per enrolleeDaily sunlight
Highest
Bogalusa, LA17,9544.75
Rio Grande City-Roma, TX17,3955.08
Vernon, TX17,0935.07
Alice, TX16,7605.04
Durant, OK16,5364.73
Lowest
Fairbanks-College, AK7,0652.5
Hilo-Kailua, HI7,4745.46
Kapaa, HI7,4925.7
Sheridan, WY7,4954.3
Kahului-Wailuku, HI7,8705.86

By daily sunlight

Medicare spending per enrolleeDaily sunlight
Highest
Yuma, AZ11,3555.97
El Paso, TX10,7165.92
El Centro, CA14,1505.91
Nogales, AZ8,4415.89
Sierra Vista-Douglas, AZ9,9195.89
Lowest
Juneau, AK9,4262.42
Anchorage, AK9,0552.48
Ketchikan, AK9,8872.49
Fairbanks-College, AK7,0652.5
Barre, VT8,9763.33

Counties furthest from the pattern

Compared with the straight line that best fits all 3,143 counties: the five with far more daily sunlight than their Medicare spending per enrollee would suggest, and the five with far less.

Medicare spending per enrolleeDaily sunlight
Above the line
Maui County, HI7,8705.86
Santa Cruz County, AZ8,4415.89
Presidio County, TX8,1975.87
Greenlee County, AZ8,3785.82
Hudspeth County, TX9,2665.87
Below the line
North Slope Borough, AK19,0642.25
Northwest Arctic Borough, AK17,7092.35
Chugach Census Area, AK12,7572.12
Nome Census Area, AK12,9962.18
Dillingham Census Area, AK14,7472.37

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+0.3353,121 counties reporting both
After adjusting for density, income and education+0.333the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.343size taken out as well as density
Also comparing only within each state+0.115each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal—compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more)
Giving bigger counties more weight+0.381the 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+0.131the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting+0.394uses 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 = 1.4e-81. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 1.2e-80.

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 +0.89 (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 Medicare spending per enrollee rose most, did daily sunlightmove 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.

PeriodCountiesChange togetherWithin statesq
2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic3,141-0.10-0.000.961
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,141+0.08-0.010.874

Over the main period, any link between the changes could easily be chance. How change is measured.

Related pairs

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 Medicare spending per enrollee and Daily sunlight