Self-care disability and Homicide rate, annual
Where Self-care disability is higher, Homicide rate, annual tends to be higher. The link is strong: +0.68 across 383 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.
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.
Loading every place…
What each measure is
Self-care disability
The estimated percentage of adults who report difficulty dressing or bathing. 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 SELFCARE, age-adjusted
Homicide rate, annual
Deaths recorded as homicide in the year, per 100,000 residents, from death certificates (CDC WONDER). It is shown only for places and years with 10 or more homicides, so small counties appear rarely or never.
CDC WONDER mortality · CDC WONDER underlying cause of death, homicide by intent, by county and year
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 self-care disability
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| Oglala Lakota County, SD | 10.8% | 81.9 |
| Holmes County, MS | 9% | 95.1 |
| Coahoma County, MS | 8.2% | 69.7 |
| Leflore County, MS | 7.9% | 87.2 |
| Washington County, MS | 7.6% | 69.1 |
| Lowest | ||
| Fairfax County, VA | 2.2% | 1.75 |
| Johnson County, KS | 2.2% | 2.57 |
| King County, WA | 2.3% | 6.21 |
| Norfolk County, MA | 2.3% | 1.65 |
| Middlesex County, MA | 2.3% | 1.35 |
By homicide rate, annual
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| Holmes County, MS | 9% | 95.1 |
| Leflore County, MS | 7.9% | 87.2 |
| Oglala Lakota County, SD | 10.8% | 81.9 |
| Coahoma County, MS | 8.2% | 69.7 |
| Washington County, MS | 7.6% | 69.1 |
| Lowest | ||
| Nassau County, NY | 2.5% | 1.01 |
| DuPage County, IL | 2.4% | 1.19 |
| Westchester County, NY | 2.8% | 1.31 |
| Middlesex County, MA | 2.3% | 1.35 |
| Norfolk County, MA | 2.3% | 1.65 |
States — Self-care disability is not published for states
By homicide rate, annual
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| District of Columbia | — | 35.9 |
| Mississippi | — | 18.5 |
| Louisiana | — | 18.3 |
| Alabama | — | 14 |
| New Mexico | — | 13.7 |
| Lowest | ||
| New Hampshire | — | 2.07 |
| Utah | — | 2.22 |
| Idaho | — | 2.49 |
| Massachusetts | — | 2.6 |
| Rhode Island | — | 2.74 |
Metro areas
By self-care disability
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| Clarksdale, MS | 8.2% | 69.7 |
| Greenville, MS | 7.6% | 69.1 |
| Cleveland, MS | 7.3% | 34.5 |
| Greenwood, MS | 7.23% | 87.2 |
| Selma, AL | 7% | 66.4 |
| Lowest | ||
| Boulder, CO | 2.4% | 3.98 |
| San Jose-Sunnyvale-Santa Clara, CA | 2.44% | 2.45 |
| Seattle-Tacoma-Bellevue, WA | 2.56% | 5.81 |
| Manchester-Nashua, NH | 2.6% | 2.57 |
| Boston-Cambridge-Newton, MA-NH | 2.65% | 2.33 |
By homicide rate, annual
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| Greenwood, MS | 7.23% | 87.2 |
| Clarksdale, MS | 8.2% | 69.7 |
| Greenville, MS | 7.6% | 69.1 |
| Selma, AL | 7% | 66.4 |
| Pine Bluff, AR | 5.55% | 48.7 |
| Lowest | ||
| Worcester, MA | 3% | 1.73 |
| Boston-Cambridge-Newton, MA-NH | 2.65% | 2.33 |
| San Jose-Sunnyvale-Santa Clara, CA | 2.44% | 2.45 |
| San Diego-Chula Vista-Carlsbad, CA | 3.2% | 2.57 |
| Manchester-Nashua, NH | 2.6% | 2.57 |
Cities and towns — Homicide rate, annual is not published for cities and towns
By self-care disability
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Highest | ||
| Doffing CDP, TX | 11.3% | — |
| Benton Harbor city, MI | 11.2% | — |
| Tallulah city, LA | 11.1% | — |
| Cameron Park CDP, TX | 11.1% | — |
| East Bakersfield CDP, CA | 11.1% | — |
| Lowest | ||
| West University Place city, TX | 1.4% | — |
| Short Hills CDP, NJ | 1.4% | — |
| Saratoga city, CA | 1.5% | — |
| Los Altos city, CA | 1.5% | — |
| Hillsborough town, CA | 1.5% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 383 counties: the five with far more homicide rate, annual than their self-care disability would suggest, and the five with far less.
| Self-care disability | Homicide rate, annual | |
|---|---|---|
| Above the line | ||
| Leflore County, MS | 7.9% | 87.2 |
| Holmes County, MS | 9% | 95.1 |
| District of Columbia, DC | 3.2% | 35.9 |
| Dallas County, AL | 7% | 66.4 |
| Washington County, MS | 7.6% | 69.1 |
| Below the line | ||
| Hidalgo County, TX | 6.7% | 3.78 |
| Cameron County, TX | 6.2% | 5.86 |
| Webb County, TX | 5.9% | 3.72 |
| Bronx County, NY | 6.5% | 9.44 |
| Apache County, AZ | 6.7% | 15.4 |
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.
Loading…
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 | +0.764 | 383 counties reporting both |
| After adjusting for density, income and education | +0.683 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.685 | size taken out as well as density |
| Also comparing only within each state | +0.620 | 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 | +0.380 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 121 equally weighted counties |
| Middle value inside single states | +0.617 | the link worked out inside each of 6 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.351 | 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-53. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 5.1e-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 7.5e-53.
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
- Self-care disability and Material security score — -0.88
- Self-care disability and Returns claiming the earned income credit — +0.82
- Stroke and Homicide rate, annual — +0.80
- Threat of utility shut-off and Homicide rate, annual — +0.72
- Children in single-parent households and Homicide rate, annual — +0.71
- Share Black, not Hispanic and Homicide rate, annual — +0.69
- Child mortality and Homicide rate, annual — +0.69
- Health score and Homicide rate, annual — -0.68
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 Self-care disability and Homicide rate, annual