placestats

Premature mortality and Suicide rate

Where Premature mortality is higher, Suicide rate tends to be higher. The link is weak: +0.31 across 2,048 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 34 states, the middle value of the link is +0.48.

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

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

Suicide rate

Suicides per 100,000 people, adjusted so places with older or younger populations can be compared fairly.

County Health Rankings · County Health Rankings v161

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 premature mortality

Premature mortalitySuicide rate
Highest
Oglala Lakota County, SD1,58555.8
Todd County, SD1,41370.5
Roosevelt County, MT1,33736.3
Benson County, ND1,26573.4
McDowell County, WV1,10633.1
Lowest
Pitkin County, CO14015
Teton County, WY15215.3
Summit County, CO15614.8
Eagle County, CO16721
San Juan County, WA16817.2

By suicide rate

States

By premature mortality

Premature mortalitySuicide rate
Highest
Mississippi62914.4
West Virginia62519.6
Alabama56916.1
Kentucky56517.5
Louisiana56314.8
Lowest
Massachusetts3038.64
Hawaii31014.1
Minnesota31513.9
Utah31721.3
Connecticut32310.4

By suicide rate

Premature mortalitySuicide rate
Highest
Wyoming42728.6
Alaska43927.8
Montana40327.6
New Mexico53124.5
Idaho34822
Lowest
District of Columbia4866.24
New Jersey3347.63
New York3368.2
Massachusetts3038.64
Maryland3839.78
Metro areas

By premature mortality

Premature mortalitySuicide rate
Highest
Gallup, NM1,07933.7
Middlesborough, KY92919.4
Cleveland, MS92711
Greenwood, MS89117.5
Greenville, MS8759.98
Lowest
Edwards, CO16721
Jackson, WY-ID18420.4
Breckenridge, CO18914.8
Heber, UT20217.8
Bozeman, MT20721

By suicide rate

Premature mortalitySuicide rate
Highest
Payson, AZ76051
Show Low, AZ85142.3
Baker City, OR44839.1
Montrose, CO36438.7
Cañon City, CO46537.9
Lowest
El Centro, CA4725.7
McAllen-Edinburg-Mission, TX4316.42
New York-Newark-Jersey City, NY-NJ3146.67
McComb, MS7496.84
Laredo, TX4536.96

Counties furthest from the pattern

Compared with the straight line that best fits all 2,049 counties: the five with far more suicide rate than their premature mortality would suggest, and the five with far less.

Premature mortalitySuicide rate
Above the line
Kusilvak Census Area, AK800107
Nome Census Area, AK70387.9
Northwest Arctic Borough, AK75587.8
Bethel Census Area, AK62667
Benson County, ND1,26573.4
Below the line
Richmond County, NC7837.15
Pike County, MS7556.84
Washington County, MS8759.98
Bolivar County, MS92711
Mingo County, WV1,01414.8

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+0.3762,048 counties reporting both
After adjusting for density, income and education+0.306the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.306size taken out as well as density
Also comparing only within each state+0.299each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.251the 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+0.480the link worked out inside each of 34 states with at least 20 counties reporting
Using ranks, after adjusting+0.253uses 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-45. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.7e-5. 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 7.7e-45.

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 +0.20 (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 premature mortality rose most, did suicide ratemove 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 years1,923+0.20+0.209.2e-7

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

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 Premature mortality and Suicide rate