placestats

Share Black, not Hispanic and Premature mortality

Where Share Black, not Hispanic is higher, Premature mortality tends to be higher. The link is weak: +0.35 across 2,319 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 35 states, the middle value of the link is +0.12.

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 Black, not Hispanic

The percentage of residents who are Black or African American alone and not Hispanic. The middle county is under 2%, and this group is one of the most concentrated in the data. A handful of counties in the Mississippi Delta and the Black Belt are above 80%.

Census ACS 5-year · ACS table B03002_004, 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 Black, not Hispanic

Share Black, not HispanicPremature mortality
Highest
Claiborne County, MS86.1%781
Holmes County, MS82.4%938
Greene County, AL81.5%767
Jefferson County, MS81%769
Tunica County, MS79.3%907
Lowest
Phillips County, CO0%435
Kiowa County, CO0%438
Huerfano County, CO0%627
Dolores County, CO0%393
Custer County, CO0%338

By premature mortality

Share Black, not HispanicPremature mortality
Highest
Buffalo County, SD0%1,949
Jackson County, SD0.177%1,152
McDowell County, WV9.02%1,106
Petersburg city, VA74.6%1,097
McKinley County, NM0.44%1,079
Lowest
Eagle County, CO0.946%167
Los Alamos County, NM1.2%178
Loudoun County, VA7.45%181
Fairfax County, VA9.6%182
Arlington County, VA9.21%195
States

By share Black, not Hispanic

Share Black, not HispanicPremature mortality
Highest
District of Columbia42.5%486
Mississippi36.9%629
Georgia31%461
Louisiana30.8%563
Maryland29.2%383
Lowest
Montana0.474%403
Idaho0.686%348
Wyoming0.815%427
Utah1.04%317
Vermont1.16%340

By premature mortality

Share Black, not HispanicPremature mortality
Highest
Mississippi36.9%629
West Virginia3.21%625
Alabama25.9%569
Kentucky7.77%565
Louisiana30.8%563
Lowest
Massachusetts6.51%303
Hawaii1.8%310
Minnesota6.73%315
Utah1.04%317
Connecticut9.88%323
Metro areas

By share Black, not Hispanic

Share Black, not HispanicPremature mortality
Highest
Clarksdale, MS75.9%1,070
Greenville, MS72%875
Selma, AL70.3%865
Greenwood, MS63.9%891
Cleveland, MS63.2%927
Lowest
Pierre, SD0%436
Mitchell, SD0%328
Jackson, WY-ID0%184
Hailey, ID0%229
Gardnerville Ranchos, NV-CA0%350

By premature mortality

Share Black, not HispanicPremature mortality
Highest
Gallup, NM0.44%1,079
Clarksdale, MS75.9%1,070
Middlesborough, KY2.23%929
Cleveland, MS63.2%927
Greenwood, MS63.9%891
Lowest
Edwards, CO0.946%167
Los Alamos, NM1.2%178
Jackson, WY-ID0%184
Breckenridge, CO0.0945%189
Nantucket, MA9.14%203
Cities and towns — Premature mortality is not published for cities and towns

By share Black, not Hispanic

Share Black, not HispanicPremature mortality
Highest
Merrydale CDP, LA97.3%—
East St. Louis city, IL94%—
Warrensville Heights city, OH92.4%—
Panthersville CDP, GA92.1%—
Silver Hill CDP, MD91.6%—
Lowest
Salida city, CO0%—
Roxborough Park CDP, CO0%—
Redlands CDP, CO0%—
Milliken town, CO0%—
Mead town, CO0%—
ZIP codes — Premature mortality is not published for ZIP codes

By share Black, not Hispanic

Share Black, not HispanicPremature mortality
Highest
ZIP 36610 (Prichard city, AL)97.2%—
ZIP 62207 (Centreville city, IL)97%—
ZIP 70812 (Merrydale CDP, LA)97%—
ZIP 36617 (Mobile city, AL)96.6%—
ZIP 62205 (East St. Louis city, IL)96.5%—
Lowest
ZIP 08867 (Hunterdon County, NJ)0%—
ZIP 08551 (Hunterdon County, NJ)0%—
ZIP 08251 (Villas CDP, NJ)0%—
ZIP 07739 (Little Silver borough, NJ)0%—
ZIP 07072 (Carlstadt borough, NJ)0%—

Counties furthest from the pattern

Compared with the straight line that best fits all 2,323 counties: the five with far more premature mortality than their share Black, not Hispanic would suggest, and the five with far less.

Share Black, not HispanicPremature mortality
Above the line
Buffalo County, SD0%1,949
Jackson County, SD0.177%1,152
McKinley County, NM0.44%1,079
Owsley County, KY0%1,074
McDowell County, WV9.02%1,106
Below the line
Prince George's County, MD59%393
Howard County, MD19.6%215
Fairfax County, VA9.6%182
Montgomery County, MD18.3%223
Loudoun County, VA7.45%181

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+0.4542,319 counties reporting both
After adjusting for density, income and education+0.348the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.338size taken out as well as density
Also comparing only within each state+0.227each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.461the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 267 equally weighted counties
Middle value inside single states+0.119the link worked out inside each of 35 states with at least 20 counties reporting
Using ranks, after adjusting+0.318uses 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 = 4.2e-67. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.9e-7. 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 2.7e-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 +0.36 (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 black, 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 pandemic2,147-0.04-0.030.303
2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years2,162-0.10-0.030.708

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 Black, not Hispanic and Premature mortality