Lack of reliable transportation and Premature mortality
Where Lack of reliable transportation is higher, Premature mortality tends to be higher. The link is moderate: +0.57 across 2,245 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 30 states, the middle value of the link is +0.77.
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
Lack of reliable transportation
The estimated percentage of adults who lacked reliable transport for daily life in the past year. 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 LACKTRPT, age-adjusted
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 lack of reliable transportation
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 26.8% | 800 |
| McKinley County, NM | 24% | 1,079 |
| Sioux County, ND | 24% | 1,416 |
| East Carroll Parish, LA | 23.5% | 741 |
| Humphreys County, MS | 23% | 993 |
| Lowest | ||
| Falls Church city, VA | 4.1% | 281 |
| Arlington County, VA | 4.7% | 195 |
| Los Alamos County, NM | 4.7% | 178 |
| Delaware County, OH | 4.8% | 230 |
| Hamilton County, IN | 4.8% | 236 |
By premature mortality
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Sioux County, ND | 24% | 1,416 |
| Roosevelt County, MT | 17.4% | 1,337 |
| Kingman County, KS | 7.2% | 1,289 |
| Benson County, ND | 14.1% | 1,265 |
| Thurston County, NE | 13% | 1,119 |
| Lowest | ||
| Los Alamos County, NM | 4.7% | 178 |
| Loudoun County, VA | 5.1% | 181 |
| Fairfax County, VA | 5.5% | 182 |
| Summit County, UT | 4.9% | 192 |
| Arlington County, VA | 4.7% | 195 |
States — Lack of reliable transportation is not published for states
By premature mortality
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Mississippi | — | 629 |
| West Virginia | — | 625 |
| Alabama | — | 569 |
| Kentucky | — | 565 |
| Louisiana | — | 563 |
| Lowest | ||
| Massachusetts | — | 303 |
| Hawaii | — | 310 |
| Minnesota | — | 315 |
| Utah | — | 317 |
| Connecticut | — | 323 |
Metro areas
By lack of reliable transportation
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Gallup, NM | 24% | 1,079 |
| Clarksdale, MS | 20.9% | 1,070 |
| Greenville, MS | 19.5% | 875 |
| Greenwood, MS | 18.2% | 891 |
| Cleveland, MS | 18% | 927 |
| Lowest | ||
| Los Alamos, NM | 4.7% | 178 |
| Heber, UT | 5.12% | 202 |
| Madison, WI | 5.81% | 273 |
| Bismarck, ND | 5.83% | 337 |
| Pella, IA | 5.9% | 351 |
By premature mortality
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Gallup, NM | 24% | 1,079 |
| Clarksdale, MS | 20.9% | 1,070 |
| Cleveland, MS | 18% | 927 |
| Greenwood, MS | 18.2% | 891 |
| Greenville, MS | 19.5% | 875 |
| Lowest | ||
| Los Alamos, NM | 4.7% | 178 |
| Jackson, WY-ID | 6.4% | 184 |
| Heber, UT | 5.12% | 202 |
| Nantucket, MA | 6% | 203 |
| Bozeman, MT | 6.2% | 207 |
Cities and towns — Premature mortality is not published for cities and towns
By lack of reliable transportation
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Highest | ||
| Benton Harbor city, MI | 27% | — |
| Tallulah city, LA | 26.6% | — |
| East Bakersfield CDP, CA | 26.2% | — |
| Buena Vista CDP, MI | 26% | — |
| Yazoo City city, MS | 25.9% | — |
| Lowest | ||
| Difficult Run CDP, VA | 3.4% | — |
| Great Falls CDP, VA | 3.5% | — |
| Little Silver borough, NJ | 3.5% | — |
| Scarsdale village, NY | 3.6% | — |
| Fair Haven borough, NJ | 3.6% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,253 counties: the five with far more premature mortality than their lack of reliable transportation would suggest, and the five with far less.
| Lack of reliable transportation | Premature mortality | |
|---|---|---|
| Above the line | ||
| Kingman County, KS | 7.2% | 1,289 |
| Alpine County, CA | 8% | 1,053 |
| Edwards County, KS | 9% | 1,086 |
| Benson County, ND | 14.1% | 1,265 |
| Roosevelt County, MT | 17.4% | 1,337 |
| Below the line | ||
| Kusilvak Census Area, AK | 26.8% | 800 |
| Issaquena County, MS | 18.8% | 475 |
| Bethel Census Area, AK | 21.9% | 626 |
| Claiborne Parish, LA | 19.8% | 580 |
| Aleutians East Borough, AK | 10.8% | 198 |
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 | +0.775 | 2,245 counties reporting both |
| After adjusting for density, income and education | +0.569 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.578 | size taken out as well as density |
| Also comparing only within each state | +0.475 | 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 | +0.397 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 187 equally weighted counties |
| Middle value inside single states | +0.766 | the link worked out inside each of 30 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.412 | 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 = 2.5e-192. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 8.2e-12. 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-191.
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?
Not measurable: at least one of these is published for too few of the years a change needs (2014–2019 or 2019–2023), or for too few counties in both.
Related pairs
- Lack of reliable transportation and Returns claiming the earned income credit — +0.84
- Lack of reliable transportation and Children in single-parent households — +0.71
- Lack of reliable transportation and Average test score — -0.69
- Lack of reliable transportation and Social connection score — -0.69
- Lack of reliable transportation and Homicide rate — +0.68
- Premature mortality and Safety score — -0.67
- Premature mortality and Homicide rate, annual — +0.67
- Lack of reliable transportation and Homicide rate, annual — +0.66
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 Lack of reliable transportation and Premature mortality