Motor vehicle crash deaths and Years of potential life lost
Where Motor vehicle crash deaths is higher, Years of potential life lost tends to be higher. The link is moderate: +0.56 across 2,480 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 36 states, the middle value of the link is +0.56.
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
Motor vehicle crash deaths
Deaths in car and other motor vehicle crashes per 100,000 people.
County Health Rankings · County Health Rankings v039
Years of potential life lost
Years of life lost before age 75, per 100,000 people. A death at 25 counts as fifty years lost and a death at 70 as five, so earlier deaths count for more.
County Health Rankings · County Health Rankings v001
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 motor vehicle crash deaths
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Big Horn County, MT | 90 | 25,709 |
| Oglala Lakota County, SD | 86.4 | 40,417 |
| Todd County, SD | 80.6 | 34,850 |
| Jackson County, SD | 76.3 | 24,380 |
| Dewey County, SD | 68.3 | 41,916 |
| Lowest | ||
| New York County, NY | 2.71 | 4,960 |
| Arlington County, VA | 2.73 | 3,513 |
| Loudoun County, VA | 3.46 | 3,355 |
| Kings County, NY | 3.66 | 6,345 |
| Richmond County, NY | 3.83 | 6,376 |
By years of potential life lost
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Dewey County, SD | 68.3 | 41,916 |
| Oglala Lakota County, SD | 86.4 | 40,417 |
| Sioux County, ND | 48.2 | 35,129 |
| Benson County, ND | 45.8 | 35,011 |
| Todd County, SD | 80.6 | 34,850 |
| Lowest | ||
| Loudoun County, VA | 3.46 | 3,355 |
| Arlington County, VA | 2.73 | 3,513 |
| Fairfax County, VA | 4.35 | 3,772 |
| Sioux County, IA | 12.6 | 3,914 |
| Carver County, MN | 5.57 | 3,990 |
States
By motor vehicle crash deaths
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Mississippi | 25.5 | 13,328 |
| South Carolina | 20.8 | 10,824 |
| Alabama | 20.7 | 11,853 |
| Arkansas | 19.8 | 11,384 |
| New Mexico | 19.8 | 12,363 |
| Lowest | ||
| New York | 5.9 | 6,637 |
| Massachusetts | 6.01 | 5,975 |
| District of Columbia | 6.39 | 9,241 |
| Rhode Island | 6.89 | 6,529 |
| New Jersey | 6.98 | 6,562 |
By years of potential life lost
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Mississippi | 25.5 | 13,328 |
| West Virginia | 15.9 | 13,324 |
| New Mexico | 19.8 | 12,363 |
| Louisiana | 18 | 12,185 |
| Alabama | 20.7 | 11,853 |
| Lowest | ||
| Massachusetts | 6.01 | 5,975 |
| Hawaii | 7.56 | 6,299 |
| Utah | 8.72 | 6,429 |
| Minnesota | 8.2 | 6,462 |
| Rhode Island | 6.89 | 6,529 |
Metro areas
By motor vehicle crash deaths
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Palatka, FL | 45.5 | 15,843 |
| Gallup, NM | 42.3 | 27,593 |
| Lumberton, NC | 41.4 | 18,938 |
| Show Low, AZ | 40.2 | 21,728 |
| Selma, AL | 39.2 | 20,427 |
| Lowest | ||
| Amherst Town-Northampton, MA | 5.04 | 5,587 |
| New York-Newark-Jersey City, NY-NJ | 5.04 | 6,070 |
| Boston-Cambridge-Newton, MA-NH | 5.14 | 5,300 |
| Pullman, WA | 5.54 | 5,805 |
| Provo-Orem-Lehi, UT | 5.83 | 5,350 |
By years of potential life lost
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Highest | ||
| Gallup, NM | 42.3 | 27,593 |
| Clarksdale, MS | 27.9 | 22,826 |
| Show Low, AZ | 40.2 | 21,728 |
| Española, NM | 29.7 | 21,299 |
| Selma, AL | 39.2 | 20,427 |
| Lowest | ||
| Corvallis, OR | 7.97 | 4,158 |
| San Jose-Sunnyvale-Santa Clara, CA | 7.35 | 4,202 |
| Bozeman, MT | 9.84 | 4,276 |
| Edwards, CO | 6.24 | 4,321 |
| State College, PA | 7.63 | 4,450 |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,482 counties: the five with far more years of potential life lost than their motor vehicle crash deaths would suggest, and the five with far less.
| Motor vehicle crash deaths | Years of potential life lost | |
|---|---|---|
| Above the line | ||
| Dewey County, SD | 68.3 | 41,916 |
| Roosevelt County, MT | 33.8 | 32,695 |
| Benson County, ND | 45.8 | 35,011 |
| Sioux County, ND | 48.2 | 35,129 |
| Petersburg city, VA | 22.3 | 24,086 |
| Below the line | ||
| Meade County, KS | 62.8 | 10,432 |
| Polk County, NE | 46.5 | 7,162 |
| Audubon County, IA | 38.5 | 5,136 |
| Norton County, KS | 42.4 | 7,015 |
| Beaver County, OK | 52 | 10,015 |
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 | +0.700 | 2,480 counties reporting both |
| After adjusting for density, income and education | +0.561 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.562 | size taken out as well as density |
| Also comparing only within each state | +0.496 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Giving bigger counties more weight | +0.581 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 276 equally weighted counties |
| Middle value inside single states | +0.564 | the link worked out inside each of 36 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.523 | uses 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 = 9.3e-206. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.0e-10. 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 1.1e-204.
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 +0.28 (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 motor vehicle crash deaths rose most, did years of potential life lostmove 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.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | 2,379 | +0.11 | +0.12 | 0.000 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 2,408 | +0.23 | +0.19 | 0.000 |
Measured over the nearest years published: Motor vehicle crash deaths: 2013–2019 for the 2014–2019 row; Motor vehicle crash deaths: 2019–2022 for the 2019–2023 row; Years of potential life lost: 2019–2022 for the 2019–2023 row.
In counties where one changed more, the other tended to move the same way, compared with other counties in the same state (r = +0.12). The same holds in the second period. How change is measured.
Related pairs
- Years of potential life lost and Safety score — -0.75
- Share American Indian or Alaska Native, not Hispanic and Years of potential life lost — +0.70
- Years of potential life lost and Homicide rate, annual — +0.67
- Injury deaths and Years of potential life lost — +0.65
- Child mortality and Years of potential life lost — +0.61
- Homicide rate and Years of potential life lost — +0.61
- Threat of utility shut-off and Years of potential life lost — +0.59
- Lack of reliable transportation and Years of potential life lost — +0.59
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 Motor vehicle crash deaths and Years of potential life lost