Diagnosed diabetes and Economy score
Across 2,934 counties, Diagnosed diabetes and Economy score have a link of -0.71, 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 -0.30 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
Diagnosed diabetes
The estimated percentage of adults who have been told they have diabetes, excluding during pregnancy. 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 DIABETES, age-adjusted
Economy score
Four income measures from four agencies: income per person, middle household income (an estimate from a statistical model), personal income per person, and average adjusted gross income on tax returns. Read it as four ways of measuring income, not as a broad economic summary. It moves very closely with middle household income (a link of 0.91 on a scale where 1 is perfect), and the site already adjusts for that. So a link with this score and a link with income are close to the same question.
· derived from 4 measures (ACS, BEA, IRS, SAIPE)
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 diagnosed diabetes
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 23.8% | -1.19 |
| Todd County, SD | 22.9% | -0.846 |
| Dimmit County, TX | 21.5% | 0.0678 |
| Zavala County, TX | 21.4% | -0.505 |
| East Carroll Parish, LA | 21.3% | -1.03 |
| Lowest | ||
| Douglas County, CO | 6% | 3.34 |
| Chittenden County, VT | 6.2% | 2.16 |
| Hinsdale County, CO | 6.4% | 1.63 |
| Pitkin County, CO | 6.5% | 4.81 |
| Broomfield County, CO | 6.5% | 2.75 |
By economy score
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Highest | ||
| Teton County, WY | 7.3% | 5.78 |
| Pitkin County, CO | 6.5% | 4.81 |
| Summit County, UT | 7.2% | 4.73 |
| Marin County, CA | 7.5% | 4.44 |
| San Mateo County, CA | 9% | 4.38 |
| Lowest | ||
| Kusilvak Census Area, AK | 18% | -1.28 |
| Oglala Lakota County, SD | 23.8% | -1.19 |
| Buffalo County, SD | 18.7% | -1.09 |
| Issaquena County, MS | 17.8% | -1.08 |
| Wheeler County, GA | 15.1% | -1.07 |
Metro areas
By diagnosed diabetes
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | 20.5% | -2.7 |
| Zapata, TX | 20.1% | -2.58 |
| Clarksdale, MS | 19.5% | -1.36 |
| Raymondville, TX | 19% | -2.08 |
| Eagle Pass, TX | 18.1% | -1.85 |
| Lowest | ||
| Burlington-South Burlington, VT | 6.59% | 1.24 |
| Los Alamos, NM | 6.7% | 2.77 |
| Boulder, CO | 6.7% | 2.6 |
| Fort Collins-Loveland, CO | 6.9% | 1.28 |
| Barre, VT | 7.1% | 0.938 |
By economy score
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Highest | ||
| Jackson, WY-ID | 7.54% | 6.15 |
| Heber, UT | 7.29% | 4.36 |
| San Jose-Sunnyvale-Santa Clara, CA | 9.82% | 4.15 |
| Naples-Marco Island, FL | 9.5% | 3.86 |
| Bridgeport-Stamford-Danbury, CT | 8.02% | 3.85 |
| Lowest | ||
| Rio Grande City-Roma, TX | 20.5% | -2.7 |
| Zapata, TX | 20.1% | -2.58 |
| Gallup, NM | 17.9% | -2.29 |
| Deming, NM | 15% | -2.08 |
| Raymondville, TX | 19% | -2.08 |
Cities and towns — Economy score is not published for cities and towns
By diagnosed diabetes
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Highest | ||
| Cameron Park CDP, TX | 23.8% | — |
| Doffing CDP, TX | 23.6% | — |
| Mila Doce CDP, TX | 22.9% | — |
| Prichard city, AL | 22.7% | — |
| Benton Harbor city, MI | 22.6% | — |
| Lowest | ||
| Shelburne CDP, VT | 5.2% | — |
| Old Greenwich CDP, CT | 5.4% | — |
| Cherry Hills Village city, CO | 5.4% | — |
| Shorewood city, MN | 5.4% | — |
| Staples CDP, CT | 5.5% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,955 counties: the five with far more economy score than their diagnosed diabetes would suggest, and the five with far less.
| Diagnosed diabetes | Economy score | |
|---|---|---|
| Above the line | ||
| Teton County, WY | 7.3% | 5.78 |
| San Mateo County, CA | 9% | 4.38 |
| Santa Clara County, CA | 9.8% | 4.16 |
| Summit County, UT | 7.2% | 4.73 |
| New York County, NY | 8.4% | 4.38 |
| Below the line | ||
| Crowley County, CO | 11.3% | -0.983 |
| Petroleum County, MT | 8.2% | 0.165 |
| Schuyler County, MO | 9.8% | -0.0257 |
| Madison County, ID | 10.7% | -0.226 |
| Wheatland County, MT | 9.6% | 0.0548 |
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 | -0.709 | 2,934 counties reporting both |
| After adjusting for density, income and education | -0.305 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.305 | size taken out as well as density |
| Also comparing only within each state | -0.347 | 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 | -0.278 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 264 equally weighted counties |
| Middle value inside single states | -0.747 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.197 | 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-64. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.4e-6. 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 1.6e-63.
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?
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
- Diagnosed diabetes and Material security score — -0.79
- Diagnosed diabetes and Returns claiming the earned income credit — +0.78
- Median household income and Economy score — +0.78
- Diagnosed diabetes and Children in single-parent households — +0.65
- Diagnosed diabetes and Homicide rate, annual — +0.62
- Diagnosed diabetes and Homicide rate — +0.62
- Diagnosed diabetes and Average test score — -0.57
- Diagnosed diabetes and Social connection score — -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 Diagnosed diabetes and Economy score