Chronic obstructive pulmonary disease and Economic connectedness in high school
Across 2,556 counties, Chronic obstructive pulmonary disease and Economic connectedness in high school have a link of -0.74, 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 -0.26 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
Economic connectedness in high school
How many of a low-income teenager's high school friends are high-income, compared with an even mix. It is counted in the county of the school, not where the person lives as an adult, so it describes where people grew up.
Social Capital Atlas · Opportunity Insights child_ec_county
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 | Economic connectedness in high school | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 15% | 0.384 |
| McDowell County, WV | 13.6% | 0.282 |
| Mingo County, WV | 13% | 0.304 |
| Scott County, TN | 12% | 0.507 |
| Claiborne Parish, LA | 11.6% | 0.502 |
| Lowest | ||
| Los Alamos County, NM | 3.2% | 1.41 |
| Santa Clara County, CA | 3.3% | 1.06 |
| King County, WA | 3.3% | 1.06 |
| Arlington County, VA | 3.4% | 1.39 |
| Somerset County, NJ | 3.4% | 1.47 |
By economic connectedness in high school
| Chronic obstructive pulmonary disease | Economic connectedness in high school | |
|---|---|---|
| Highest | ||
| Calvert County, MD | 5% | 1.61 |
| Hunterdon County, NJ | 3.8% | 1.58 |
| Howard County, MD | 3.5% | 1.56 |
| Loudoun County, VA | 3.6% | 1.55 |
| Stafford County, VA | 4.6% | 1.53 |
| Lowest | ||
| Holmes County, MS | 10.4% | 0.273 |
| McDowell County, WV | 13.6% | 0.282 |
| Mingo County, WV | 13% | 0.304 |
| Lincoln County, WV | 11.2% | 0.305 |
| Summers County, WV | 10.6% | 0.309 |
Metro areas
By chronic obstructive pulmonary disease
| Chronic obstructive pulmonary disease | Economic connectedness in high school | |
|---|---|---|
| Highest | ||
| Gallup, NM | 10.8% | 0.572 |
| Newport, TN | 10.7% | 0.474 |
| Kennett, MO | 10.3% | 0.45 |
| Lumberton, NC | 10.3% | 0.495 |
| Minden, LA | 10.3% | 0.56 |
| Lowest | ||
| Los Alamos, NM | 3.2% | 1.41 |
| San Jose-Sunnyvale-Santa Clara, CA | 3.34% | 1.07 |
| San Francisco-Oakland-Fremont, CA | 3.77% | 1.01 |
| Heber, UT | 3.88% | 1.26 |
| Seattle-Tacoma-Bellevue, WA | 3.88% | 1.1 |
By economic connectedness in high school
| Chronic obstructive pulmonary disease | Economic connectedness in high school | |
|---|---|---|
| Highest | ||
| Lexington Park, MD | 5.22% | 1.54 |
| Los Alamos, NM | 3.2% | 1.41 |
| Williston, ND | 5.6% | 1.39 |
| Fairbanks-College, AK | 5% | 1.38 |
| Provo-Orem-Lehi, UT | 4.42% | 1.38 |
| Lowest | ||
| Deming, NM | 7.8% | 0.35 |
| Clarksdale, MS | 9.8% | 0.362 |
| Forrest City, AR | 9.5% | 0.409 |
| Beckley, WV | 9.83% | 0.414 |
| Elkins, WV | 8.9% | 0.441 |
Cities and towns — Economic connectedness in high school is not published for cities and towns
By chronic obstructive pulmonary disease
| Chronic obstructive pulmonary disease | Economic connectedness in high school | |
|---|---|---|
| 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 2,558 counties: the five with far more economic connectedness in high school than their chronic obstructive pulmonary disease would suggest, and the five with far less.
| Chronic obstructive pulmonary disease | Economic connectedness in high school | |
|---|---|---|
| Above the line | ||
| Madison County, ID | 7.9% | 1.52 |
| Holmes County, OH | 9.9% | 1.12 |
| Calvert County, MD | 5% | 1.61 |
| Bethel Census Area, AK | 10.4% | 1.03 |
| St. Mary's County, MD | 5.4% | 1.5 |
| Below the line | ||
| Taos County, NM | 4.9% | 0.481 |
| Greene County, GA | 5.4% | 0.442 |
| Hudson County, NJ | 4.2% | 0.576 |
| Denver County, CO | 4.4% | 0.583 |
| New York County, NY | 4.1% | 0.652 |
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 | -0.740 | 2,556 counties reporting both |
| After adjusting for density, income and education | -0.263 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.255 | size taken out as well as density |
| Also comparing only within each state | -0.119 | 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 | -0.077 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 260 equally weighted counties |
| Middle value inside single states | -0.723 | the link worked out inside each of 37 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.168 | 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 = 1.3e-41. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.6e-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 6.2e-41.
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 +0.31 (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
- Median household income and Economic connectedness in high school — +0.62
- Chronic obstructive pulmonary disease and Homicide rate, annual — +0.44
- Chronic obstructive pulmonary disease and Premature mortality — +0.42
- Chronic obstructive pulmonary disease and Years of potential life lost — +0.40
- Chronic obstructive pulmonary disease and Life expectancy — -0.38
- Chronic obstructive pulmonary disease and Material security score — -0.37
- Chronic obstructive pulmonary disease and Infant mortality — +0.33
- Chronic obstructive pulmonary disease and Safety score — -0.33
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 Economic connectedness in high school