placestats

Housing insecurity and Child mortality

Where Housing insecurity is higher, Child mortality tends to be higher. The link is moderate: +0.46 across 1,277 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 23 states, the middle value of the link is +0.57.

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

Housing insecurity

The estimated percentage of adults who were unable to pay rent or mortgage on time 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 HOUSINSECU, 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 housing insecurity

By child mortality

States — Housing insecurity is not published for states

By child mortality

Housing insecurityChild 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 housing insecurity

Housing insecurityChild mortality
Highest
Gallup, NM30.2%65.4
Clarksdale, MS29.8%90
Greenville, MS28.1%143
Deming, NM26.5%77.2
Selma, AL26.5%73.4
Lowest
Bozeman, MT7.5%40.2
Heber, UT7.6%36.1
Bismarck, ND8.31%39.4
Pella, IA8.4%53.8
Helena, MT8.44%52.1

By child mortality

Housing insecurityChild mortality
Highest
Greenville, MS28.1%143
Rockingham, NC20%137
Minden, LA22.6%135
Blytheville, AR20.3%126
Forrest City, AR25.2%119
Lowest
Blacksburg-Christiansburg-Radford, VA11%21.2
Putnam, CT11%21.6
Amherst Town-Northampton, MA10.3%22.8
Santa Cruz-Watsonville, CA12.9%27.5
Keene, NH9.4%27.5
Cities and towns — Child mortality is not published for cities and towns

By housing insecurity

Housing insecurityChild mortality
Highest
Benton Harbor city, MI38.8%—
Lamont CDP, CA38.8%—
East Bakersfield CDP, CA38.7%—
Glasgow Village CDP, MO38.3%—
Tallulah city, LA37.4%—
Lowest
Great Falls CDP, VA4.7%—
Difficult Run CDP, VA4.8%—
Mountain Brook city, AL4.9%—
Huntington Woods city, MI5%—
Whitefish Bay village, WI5.1%—

Counties furthest from the pattern

Compared with the straight line that best fits all 1,278 counties: the five with far more child mortality than their housing insecurity would suggest, and the five with far less.

Housing insecurityChild mortality
Above the line
Kusilvak Census Area, AK31.6%274
Switzerland County, IN12.8%177
Northwest Arctic Borough, AK24.4%202
Blaine County, MT17.5%171
Sanders County, MT12%148
Below the line
Bronx County, NY31.4%45
Imperial County, CA25.5%36
McKinley County, NM30.2%65.4
Merced County, CA23%42
Tulare County, CA22.4%45.9

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+0.6491,277 counties reporting both
After adjusting for density, income and education+0.456the part of the link that is not just how urban, rich and educated a place is
Also adjusting for population size+0.492size taken out as well as density
Also comparing only within each state+0.439each county is compared only with others in its own state, so this is no longer a comparison between states
Giving bigger counties more weight+0.153the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 173 equally weighted counties
Middle value inside single states+0.569the link worked out inside each of 23 states with at least 20 counties reporting
Using ranks, after adjusting+0.273uses 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 = 2.4e-66. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.7e-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-65.

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 +0.21 (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

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 Housing insecurity and Child mortality