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Adults under 65 without health insurance and Cooling degree days

Where Adults under 65 without health insurance is higher, Cooling degree days tends to be higher. The link is moderate: +0.58 across 2,902 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 37 states, the middle value of the link is +0.10.

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

Adults under 65 without health insurance

The estimated percentage of adults who aged 18 to 64 have no health insurance. 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 ACCESS2, age-adjusted

Cooling degree days

A measure of how hot the year was, as a total over its days. A day whose average temperature is 80°F counts 15 cooling degree days, the degrees it rose above 65°F. A day at 65°F or cooler counts none. More cooling degree days mean more weather for air conditioning. NOAA estimates them from its monthly temperatures for each county. A state's figure is the average of its counties, each weighted by its land area. Alaska and Hawaii are not covered.

NOAA NCEI nClimDiv · NOAA NCEI nClimDiv climdiv-cddccy, sum of the 12 months; states, counties averaged by land area

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 adults under 65 without health insurance

Adults under 65 without health insuranceCooling degree days
Highest
Starr County, TX43.7%4,909
Zapata County, TX43.3%4,830
Zavala County, TX42.8%4,118
Dimmit County, TX41.6%4,274
Jim Hogg County, TX41.3%4,608
Lowest
Norfolk County, MA4%643
Middlesex County, MA4.1%596
Barnstable County, MA4.1%524
Dukes County, MA4.2%578
Hampshire County, MA4.5%496

By cooling degree days

Adults under 65 without health insuranceCooling degree days
Highest
Monroe County, FL14.6%4,995
Starr County, TX43.7%4,909
Hidalgo County, TX37.5%4,870
Miami-Dade County, FL26.2%4,834
Zapata County, TX43.3%4,830
Lowest
Summit County, CO9.2%0
San Juan County, CO8.9%0
Park County, CO8.2%0
Mineral County, CO7.6%0
Lake County, CO12%0
States — Adults under 65 without health insurance is not published for states

By cooling degree days

Adults under 65 without health insuranceCooling degree days
Highest
Florida—3,667
Louisiana—3,100
Texas—3,063
Mississippi—2,553
Alabama—2,172
Lowest
Wyoming—240
Maine—243
Vermont—257
New Hampshire—298
Idaho—300
Metro areas

By adults under 65 without health insurance

Adults under 65 without health insuranceCooling degree days
Highest
Rio Grande City-Roma, TX43.7%4,909
Zapata, TX43.3%4,830
Raymondville, TX40.7%4,676
Eagle Pass, TX39%4,319
McAllen-Edinburg-Mission, TX37.5%4,870
Lowest
Barnstable Town, MA4.1%524
Vineyard Haven, MA4.2%578
Amherst Town-Northampton, MA4.5%496
Nantucket, MA4.8%417
Ithaca, NY4.9%328

By cooling degree days

Adults under 65 without health insuranceCooling degree days
Highest
Key West-Key Largo, FL14.6%4,995
Rio Grande City-Roma, TX43.7%4,909
McAllen-Edinburg-Mission, TX37.5%4,870
Zapata, TX43.3%4,830
Brownsville-Harlingen, TX36.3%4,730
Lowest
Breckenridge, CO9.72%0
Jackson, WY-ID9.54%17.3
Edwards, CO11.8%24
Port Townsend, WA6.6%31
Port Angeles, WA8.1%33
Cities and towns — Cooling degree days is not published for cities and towns

By adults under 65 without health insurance

Adults under 65 without health insuranceCooling degree days
Highest
Cameron Park CDP, TX51.5%—
Doffing CDP, TX49.9%—
Doolittle CDP, TX48.4%—
Mila Doce CDP, TX48.4%—
Salida del Sol Estates CDP, TX48.3%—
Lowest
Lexington CDP, MA2.4%—
Marblehead CDP, MA2.5%—
Belmont CDP, MA2.6%—
Winchester CDP, MA2.7%—
Sharon CDP, MA2.7%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,920 counties: the five with far more cooling degree days than their adults under 65 without health insurance would suggest, and the five with far less.

Adults under 65 without health insuranceCooling degree days
Above the line
Monroe County, FL14.6%4,995
Martin County, FL13.5%4,409
Pinellas County, FL12.3%4,242
Broward County, FL17.3%4,762
Charlotte County, FL13%4,198
Below the line
Deaf Smith County, TX35.1%1,630
Costilla County, CO21.1%80
Conejos County, CO19.5%41
Clark County, ID20%150
Adams County, WA24.2%706

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+0.6082,902 counties reporting both
After adjusting for density, income and education+0.582the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.582size taken out as well as density
Also comparing only within each state+0.212each county is compared only with others in its own state, so this is no longer a comparison between states
Also holding winter sunshine equal+0.124compares 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+0.670the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 261 equally weighted counties
Middle value inside single states+0.098the link worked out inside each of 37 states with at least 20 counties reporting
Using ranks, after adjusting+0.537uses 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 = 5.5e-263. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.9e-11. 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 1.1e-261.

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 +0.78 (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 adults under 65 without health insurance rose most, did cooling degree daysmove 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 pandemic————
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years2,890-0.12+0.000.969

Not measurable over the main period, so only the second period is shown. 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 Adults under 65 without health insurance and Cooling degree days