Chronic obstructive pulmonary disease and Child mortality
Across 1,621 counties, Chronic obstructive pulmonary disease and Child mortality have a link of +0.63, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a weak link of +0.28 remains. Most of the first figure came from both measures following how urban, rich and educated a place is, not from a link with each other.
Mostly gone 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
Chronic obstructive pulmonary disease
The estimated percentage of adults who have been told they have COPD (chronic obstructive pulmonary disease, a long-term lung disease). 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 COPD, age-adjusted
Child mortality
Deaths of children under 18 per 100,000 children.
County Health Rankings · County Health Rankings v128
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 chronic obstructive pulmonary disease
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 15% | 208 |
| Todd County, SD | 14.3% | 153 |
| Mingo County, WV | 13% | 67.5 |
| Kusilvak Census Area, AK | 12.4% | 274 |
| Scott County, TN | 12% | 69.9 |
| Lowest | ||
| Santa Clara County, CA | 3.3% | 27.9 |
| King County, WA | 3.3% | 42 |
| Arlington County, VA | 3.4% | 35 |
| Somerset County, NJ | 3.4% | 26.8 |
| San Mateo County, CA | 3.5% | 26.1 |
By child mortality
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 12.4% | 274 |
| Oglala Lakota County, SD | 15% | 208 |
| Northwest Arctic Borough, AK | 9.1% | 202 |
| Bethel Census Area, AK | 10.4% | 200 |
| Holmes County, MS | 10.4% | 192 |
| Lowest | ||
| Putnam County, NY | 4.3% | 19 |
| Benton County, OR | 5.7% | 19.1 |
| Kendall County, IL | 4.6% | 19.3 |
| Washington County, RI | 5.1% | 20.8 |
| Norfolk County, MA | 4.2% | 21.2 |
States — Chronic obstructive pulmonary disease is not published for states
By child mortality
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| Mississippi | — | 86.5 |
| Louisiana | — | 78.6 |
| Arkansas | — | 73 |
| Alaska | — | 72.9 |
| Alabama | — | 72.7 |
| Lowest | ||
| Massachusetts | — | 30.4 |
| New Jersey | — | 33.9 |
| New Hampshire | — | 34.6 |
| Rhode Island | — | 34.7 |
| Vermont | — | 36.3 |
Metro areas
By chronic obstructive pulmonary disease
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| Gallup, NM | 10.8% | 65.4 |
| Newport, TN | 10.7% | 78.2 |
| Kennett, MO | 10.3% | 50.4 |
| Lumberton, NC | 10.3% | 103 |
| Minden, LA | 10.3% | 135 |
| Lowest | ||
| San Jose-Sunnyvale-Santa Clara, CA | 3.34% | 28.3 |
| San Francisco-Oakland-Fremont, CA | 3.77% | 30.9 |
| Heber, UT | 3.88% | 36.1 |
| Seattle-Tacoma-Bellevue, WA | 3.88% | 41.5 |
| Urban Honolulu, HI | 4.1% | 44.3 |
By child mortality
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| Greenville, MS | 9.4% | 143 |
| Uvalde, TX | 6% | 139 |
| Vernon, TX | 8.4% | 137 |
| Rockingham, NC | 9.1% | 137 |
| Minden, LA | 10.3% | 135 |
| Lowest | ||
| Corvallis, OR | 5.7% | 19.1 |
| Blacksburg-Christiansburg-Radford, VA | 6.97% | 21.2 |
| Putnam, CT | 5.7% | 21.6 |
| Amherst Town-Northampton, MA | 5.2% | 22.8 |
| Santa Cruz-Watsonville, CA | 4.7% | 27.5 |
Cities and towns — Child mortality is not published for cities and towns
By chronic obstructive pulmonary disease
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Highest | ||
| New Square village, NY | 17.1% | — |
| Kiryas Joel village, NY | 15.4% | — |
| Hamtramck city, MI | 14.5% | — |
| Kaser village, NY | 14.2% | — |
| Benton Harbor city, MI | 13.7% | — |
| Lowest | ||
| San Marino city, CA | 2.2% | — |
| Cupertino city, CA | 2.2% | — |
| Princeton Meadows CDP, NJ | 2.2% | — |
| Saratoga city, CA | 2.3% | — |
| Belle Mead CDP, NJ | 2.3% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,622 counties: the five with far more child mortality than their chronic obstructive pulmonary disease would suggest, and the five with far less.
| Chronic obstructive pulmonary disease | Child mortality | |
|---|---|---|
| Above the line | ||
| Kusilvak Census Area, AK | 12.4% | 274 |
| Northwest Arctic Borough, AK | 9.1% | 202 |
| Bethel Census Area, AK | 10.4% | 200 |
| Petersburg city, VA | 7.5% | 168 |
| Blaine County, MT | 8% | 171 |
| Below the line | ||
| Mingo County, WV | 13% | 67.5 |
| Dunklin County, MO | 10.3% | 50.4 |
| Scott County, TN | 12% | 69.9 |
| Logan County, WV | 11% | 59.6 |
| Athens County, OH | 8.3% | 34.9 |
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 | +0.627 | 1,621 counties reporting both |
| After adjusting for density, income and education | +0.278 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.234 | size taken out as well as density |
| Also comparing only within each state | +0.290 | 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 | +0.373 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 239 equally weighted counties |
| Middle value inside single states | +0.570 | the link worked out inside each of 29 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.128 | 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 = 4.8e-30. 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 1.9e-29.
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 +0.25 (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 chronic obstructive pulmonary disease rose most, did child 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.
| 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 | — | — | — | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 1,372 | +0.01 | +0.01 | 0.820 |
Measured over the nearest years published: Child mortality: 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
- Child mortality and Homicide rate, annual — +0.69
- Child mortality and Homicide rate — +0.67
- Child mortality and Years of potential life lost — +0.61
- Child mortality and Firearm fatalities — +0.57
- Share Black, not Hispanic and Child mortality — +0.56
- Stroke and Child mortality — +0.56
- Child mortality and Safety score — -0.56
- Child mortality and Life expectancy — -0.55
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 Chronic obstructive pulmonary disease and Child mortality