placestats

Share White, not Hispanic and Premature mortality

Where Share White, not Hispanic is higher, Premature mortality tends to be lower. The link is weak: -0.35 across 3,064 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 -0.14.

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

Share White, not Hispanic

The percentage of residents who are White alone and not Hispanic or Latino. It comes from a table that sorts people by both race and Hispanic origin, so nobody is counted twice. The eight race and ethnicity shares here add up to exactly 100% in every county.

Census ACS 5-year · ACS table B03002_003, divided by total population

Premature mortality

Deaths before age 75 per 100,000 people. It is adjusted so places with older or younger populations can be compared fairly.

County Health Rankings · County Health Rankings v127

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 share White, not Hispanic

Share White, not HispanicPremature mortality
Highest
Magoffin County, KY97.6%829
Dickenson County, VA97.6%696
Holmes County, OH97.4%333
Boone County, WV97.2%794
Jasper County, IL97%367
Lowest
Starr County, TX2.15%535
Maverick County, TX2.84%602
Kusilvak Census Area, AK3.07%800
Webb County, TX3.57%453
Zapata County, TX4.57%532

By premature mortality

Share White, not HispanicPremature mortality
Highest
Buffalo County, SD21.2%1,949
Dewey County, SD20.2%1,606
Oglala Lakota County, SD6.63%1,585
Corson County, SD30.1%1,445
Sioux County, ND12.2%1,416
Lowest
Pitkin County, CO80.5%140
Teton County, WY78.9%152
Summit County, CO74.9%156
Eagle County, CO63.1%167
San Juan County, WA86.1%168
States

By share White, not Hispanic

Share White, not HispanicPremature mortality
Highest
Maine90.6%392
Vermont90.6%340
West Virginia90.2%625
New Hampshire87.5%324
Montana84.1%403
Lowest
Hawaii21.1%310
California34.6%334
New Mexico36.5%531
District of Columbia36.6%486
Texas39.9%419

By premature mortality

Share White, not HispanicPremature mortality
Highest
Mississippi55.4%629
West Virginia90.2%625
Alabama63.8%569
Kentucky82.3%565
Louisiana56.2%563
Lowest
Massachusetts67.8%303
Hawaii21.1%310
Minnesota76.7%315
Utah75.7%317
Connecticut63%323
Metro areas

By share White, not Hispanic

Share White, not HispanicPremature mortality
Highest
Pikeville, KY95.3%857
Greenville, OH95.1%457
Effingham, IL94.9%362
Warren, PA94.9%452
Wapakoneta, OH94.8%389
Lowest
Rio Grande City-Roma, TX2.15%535
Eagle Pass, TX2.84%602
Laredo, TX3.57%453
Zapata, TX4.57%532
McAllen-Edinburg-Mission, TX6.08%431

By premature mortality

Share White, not HispanicPremature mortality
Highest
Gallup, NM8.01%1,079
Clarksdale, MS19.3%1,070
Middlesborough, KY93.2%929
Cleveland, MS30.6%927
Greenwood, MS32.1%891
Lowest
Edwards, CO63.1%167
Los Alamos, NM70.3%178
Jackson, WY-ID79.2%184
Breckenridge, CO71.5%189
Heber, UT82.5%202
Cities and towns — Premature mortality is not published for cities and towns

By share White, not Hispanic

Share White, not HispanicPremature mortality
Highest
Treasure Lake CDP, PA99.2%—
Litchfield city, IL98.8%—
Cicero town, IN98.2%—
East Harwich CDP, MA98.1%—
Wellston city, OH98.1%—
Lowest
Mecca CDP, CA0%—
Earlimart CDP, CA0%—
Yabucoa zona urbana, PR0%—
San Lorenzo zona urbana, PR0%—
San Isidro comunidad, PR0%—
ZIP codes — Premature mortality is not published for ZIP codes

By share White, not Hispanic

Share White, not HispanicPremature mortality
Highest
ZIP 62056 (Montgomery County, IL)99%—
ZIP 47558 (Daviess County, IN)98.9%—
ZIP 47452 (Orange County, IN)98.7%—
ZIP 44627 (Holmes County, OH)98.6%—
ZIP 24609 (Tazewell County, VA)98.5%—
Lowest
ZIP 00952 (Sabana Seca comunidad, PR)0%—
ZIP 00782 (Comerío Municipio, PR)0%—
ZIP 00780 (Ponce Municipio, PR)0%—
ZIP 00731 (Ponce Municipio, PR)0%—
ZIP 00723 (Patillas Municipio, PR)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 3,078 counties: the five with far more premature mortality than their share White, not Hispanic would suggest, and the five with far less.

Share White, not HispanicPremature mortality
Above the line
Buffalo County, SD21.2%1,949
Dewey County, SD20.2%1,606
Oglala Lakota County, SD6.63%1,585
Corson County, SD30.1%1,445
Kingman County, KS90.6%1,289
Below the line
Aleutians East Borough, AK15.4%198
Santa Clara County, CA28.2%211
Aleutians West Census Area, AK23.7%229
San Mateo County, CA35.8%202
Manassas Park city, VA27.4%225

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-0.2973,064 counties reporting both
After adjusting for density, income and education-0.354the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size-0.378size taken out as well as density
Also comparing only within each state-0.281each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight-0.049the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 282 equally weighted counties
Middle value inside single states-0.138the link worked out inside each of 40 states with at least 20 counties reporting
Using ranks, after adjusting-0.305uses 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 = 3.7e-91. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.000. The site tested 20,910 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 2.9e-90.

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 +0.30 (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 share white, not hispanic rose most, did premature mortalitymove 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 pandemic3,049+0.03+0.010.788
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years3,062+0.20+0.150.011

Measured over the nearest years published: Premature mortality: 2013–2019 for the 2014–2019 row; Premature mortality: 2019–2022 for the 2019–2023 row.

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 Share White, not Hispanic and Premature mortality