Vision disability and Infant mortality
Across 1,102 counties, Vision disability and Infant mortality have a link of +0.57, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and what is left could easily be chance. 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
Vision disability
The estimated percentage of adults who report serious difficulty seeing. 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 VISION, age-adjusted
Infant mortality
Deaths of babies before their first birthday per 1,000 babies born alive.
County Health Rankings · County Health Rankings v129
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 vision disability
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 17.5% | 16.9 |
| Todd County, SD | 16.3% | 15.4 |
| Starr County, TX | 14.7% | 4.84 |
| Kusilvak Census Area, AK | 13.8% | 20.2 |
| McKinley County, NM | 12.9% | 6.02 |
| Lowest | ||
| Douglas County, CO | 2.7% | 3.34 |
| Delaware County, OH | 2.7% | 3.55 |
| Hamilton County, IN | 2.8% | 4.9 |
| Broomfield County, CO | 2.9% | 5.23 |
| Arlington County, VA | 2.9% | 2.8 |
By infant mortality
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Kusilvak Census Area, AK | 13.8% | 20.2 |
| Oglala Lakota County, SD | 17.5% | 16.9 |
| Todd County, SD | 16.3% | 15.4 |
| Big Horn County, MT | 9.2% | 15.3 |
| Cheboygan County, MI | 4.8% | 14.6 |
| Lowest | ||
| Marin County, CA | 3.4% | 1.57 |
| San Francisco County, CA | 3.8% | 2.65 |
| Placer County, CA | 3.5% | 2.71 |
| Rockingham County, NH | 3% | 2.74 |
| Sherburne County, MN | 3.4% | 2.76 |
States — Vision disability is not published for states
By infant mortality
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Mississippi | — | 8.7 |
| Arkansas | — | 7.78 |
| Louisiana | — | 7.53 |
| Alabama | — | 7.49 |
| West Virginia | — | 7.09 |
| Lowest | ||
| Massachusetts | — | 3.72 |
| New Hampshire | — | 3.82 |
| New Jersey | — | 3.98 |
| Vermont | — | 4.08 |
| California | — | 4.12 |
Metro areas
By vision disability
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Rio Grande City-Roma, TX | 14.7% | 4.84 |
| Gallup, NM | 12.9% | 6.02 |
| Eagle Pass, TX | 11.5% | 6.82 |
| McAllen-Edinburg-Mission, TX | 11.3% | 4.63 |
| Clarksdale, MS | 11% | 10.2 |
| Lowest | ||
| Barnstable Town, MA | 3.2% | 3.32 |
| Portland-South Portland, ME | 3.29% | 5.42 |
| Burlington-South Burlington, VT | 3.38% | 4.3 |
| Bozeman, MT | 3.4% | 3.38 |
| Madison, WI | 3.46% | 5.03 |
By infant mortality
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Forrest City, AR | 9.1% | 13.3 |
| Greenville, MS | 10.1% | 13.1 |
| Minden, LA | 8.5% | 12.9 |
| Natchitoches, LA | 7.4% | 12.2 |
| Kinston, NC | 8% | 12 |
| Lowest | ||
| Santa Fe, NM | 5.1% | 2.8 |
| Rexburg, ID | 5.82% | 2.96 |
| San Jose-Sunnyvale-Santa Clara, CA | 3.77% | 3.01 |
| Bend, OR | 3.83% | 3.11 |
| Santa Rosa-Petaluma, CA | 4.3% | 3.14 |
Cities and towns — Infant mortality is not published for cities and towns
By vision disability
| Vision disability | Infant mortality | |
|---|---|---|
| Highest | ||
| Cameron Park CDP, TX | 19.2% | — |
| Doffing CDP, TX | 19% | — |
| Mila Doce CDP, TX | 17.9% | — |
| East Bakersfield CDP, CA | 17.6% | — |
| Lamont CDP, CA | 17.6% | — |
| Lowest | ||
| Huntington Woods city, MI | 2% | — |
| Winnetka village, IL | 2% | — |
| Western Springs village, IL | 2% | — |
| Glencoe village, IL | 2% | — |
| Difficult Run CDP, VA | 2.1% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,103 counties: the five with far more infant mortality than their vision disability would suggest, and the five with far less.
| Vision disability | Infant mortality | |
|---|---|---|
| Above the line | ||
| Cheboygan County, MI | 4.8% | 14.6 |
| Banks County, GA | 5.4% | 14.5 |
| McKenzie County, ND | 4.5% | 13 |
| Kusilvak Census Area, AK | 13.8% | 20.2 |
| Dallas County, MO | 6% | 13.2 |
| Below the line | ||
| Starr County, TX | 14.7% | 4.84 |
| Hidalgo County, TX | 11.3% | 4.63 |
| Imperial County, CA | 9.9% | 3.56 |
| McKinley County, NM | 12.9% | 6.02 |
| Webb County, TX | 10.3% | 4.89 |
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 | +0.567 | 1,102 counties reporting both |
| After adjusting for density, income and education | +0.199 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.249 | size taken out as well as density |
| Also comparing only within each state | +0.219 | 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 | -0.102 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 210 equally weighted counties |
| Middle value inside single states | +0.605 | the link worked out inside each of 22 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.021 | 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 = 2.9e-11. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.100. 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 6.9e-11.
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
- Vision disability and Material security score — -0.83
- Vision disability and Returns claiming the earned income credit — +0.81
- Vision disability and Social connection score — -0.62
- Vision disability and Children in single-parent households — +0.61
- Vision disability and Average test score — -0.61
- Vision disability and Economic connectedness — -0.60
- Share Black, not Hispanic and Infant mortality — +0.54
- Vision disability and Republican share of the two-party vote, 2016 — -0.53
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 Vision disability and Infant mortality