Share of suicides by firearm and Congregations per 10,000 people
Across 2,220 counties, Share of suicides by firearm and Congregations per 10,000 people have a link of +0.51, 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 +0.23 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.
Loading every place…
What each measure is
Share of suicides by firearm
The percentage of suicides that were by gun, 2018-2023. Where more households own guns, more suicides are by gun. So researchers use this as the standard stand-in for gun ownership, which nobody counts directly.
CDC WONDER mortality; RAND firearm ownership database · CDC WONDER underlying cause of death, suicide by intent, firearm by mechanism, 2018-2023
Congregations per 10,000 people
How many separate congregations a place has per ten thousand residents. That is a different question from how many people belong. Rural counties have many small churches, and cities have fewer large ones.
· Religion Census 2020 files; 2010 from the ARDA, RCMSCY10 and RCMSST10
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 share of suicides by firearm
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Custer County, ID | 100% | 39.8 |
| Harrison County, MO | 100% | 42.9 |
| Teton County, MT | 100% | 38.5 |
| Gates County, NC | 100% | 29.6 |
| Pendleton County, WV | 100% | 89.5 |
| Lowest | ||
| New York County, NY | 6.48% | 6.88 |
| Queens County, NY | 10.5% | 5.01 |
| Kings County, NY | 12.2% | 4.35 |
| Bronx County, NY | 12.7% | 3.96 |
| Hudson County, NJ | 14.7% | 5.61 |
By congregations per 10,000 people
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Pendleton County, WV | 100% | 89.5 |
| Yukon-Koyukuk Census Area, AK | 55.6% | 67.4 |
| Pocahontas County, WV | 64.7% | 63.5 |
| Fayette County, AL | 69.6% | 55.1 |
| Blaine County, OK | 64.7% | 55 |
| Lowest | ||
| Ocean County, NJ | 31.5% | 3.63 |
| Kendall County, IL | 39.3% | 3.64 |
| Bronx County, NY | 12.7% | 3.96 |
| Broomfield County, CO | 50% | 4.05 |
| Richmond County, NY | 20.5% | 4.05 |
States
By share of suicides by firearm
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Mississippi | 70.8% | 24.3 |
| Wyoming | 70.6% | 16.6 |
| Alabama | 70.5% | 21.4 |
| Louisiana | 65.6% | 14.2 |
| South Carolina | 64.7% | 16 |
| Lowest | ||
| Hawaii | 19% | 9.79 |
| Massachusetts | 22% | 6.2 |
| District of Columbia | 24% | 10.9 |
| Rhode Island | 27% | 6.45 |
| New Jersey | 27% | 6.54 |
By congregations per 10,000 people
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Arkansas | 62.6% | 24.7 |
| Mississippi | 70.8% | 24.3 |
| West Virginia | 63.4% | 22.7 |
| South Dakota | 50.7% | 21.9 |
| Alabama | 70.5% | 21.4 |
| Lowest | ||
| Nevada | 58.9% | 4.96 |
| California | 37.6% | 5.96 |
| Massachusetts | 22% | 6.2 |
| Rhode Island | 27% | 6.45 |
| New Jersey | 27% | 6.54 |
Metro areas
By share of suicides by firearm
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Cleveland, MS | 94.1% | 31 |
| Brenham, TX | 90% | 19.6 |
| Troy, AL | 87% | 25.4 |
| Ruidoso, NM | 86.8% | 18.7 |
| Scottsboro, AL | 86.2% | 29.7 |
| Lowest | ||
| Urban Honolulu, HI | 16.6% | 7.77 |
| New York-Newark-Jersey City, NY-NJ | 18.1% | 5.48 |
| Boston-Cambridge-Newton, MA-NH | 20.8% | 5.74 |
| Kahului-Wailuku, HI | 21.1% | 13 |
| Barnstable Town, MA | 22.5% | 7.64 |
By congregations per 10,000 people
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Highest | ||
| Arkadelphia, AR | 60.9% | 46.2 |
| Magnolia, AR | 62.5% | 43.4 |
| El Dorado, AR | 67.6% | 41.5 |
| Paris, TN | 68.5% | 38.8 |
| Union City, TN | 71.4% | 38.7 |
| Lowest | ||
| Las Vegas-Henderson-North Las Vegas, NV | 56.7% | 4.2 |
| San Jose-Sunnyvale-Santa Clara, CA | 27.6% | 4.89 |
| Reno, NV | 58.6% | 5.15 |
| San Diego-Chula Vista-Carlsbad, CA | 38.5% | 5.3 |
| Riverside-San Bernardino-Ontario, CA | 43% | 5.31 |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,220 counties: the five with far more congregations per 10,000 people than their share of suicides by firearm would suggest, and the five with far less.
| Share of suicides by firearm | Congregations per 10,000 people | |
|---|---|---|
| Above the line | ||
| Pendleton County, WV | 100% | 89.5 |
| Yukon-Koyukuk Census Area, AK | 55.6% | 67.4 |
| Pocahontas County, WV | 64.7% | 63.5 |
| Leon County, TX | 63.6% | 54.7 |
| Blaine County, OK | 64.7% | 55 |
| Below the line | ||
| Humboldt County, NV | 84.8% | 9.84 |
| Oconee County, GA | 80% | 9.33 |
| Bryan County, GA | 77.4% | 8.72 |
| Poquoson city, VA | 73.3% | 7.22 |
| Lafayette Parish, LA | 71.6% | 6.66 |
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 | +0.511 | 2,220 counties reporting both |
| After adjusting for density, income and education | +0.230 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.157 | size taken out as well as density |
| Also comparing only within each state | +0.087 | 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 | +0.129 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 274 equally weighted counties |
| Middle value inside single states | +0.419 | the link worked out inside each of 35 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.283 | 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 = 4.9e-28. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 1.1e-7. 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 1.9e-27.
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 +0.40 (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
- Congregations per 10,000 people and Homicide rate, annual — +0.49
- Median property tax bill and Share of suicides by firearm — -0.42
- Share of suicides by firearm and Returns reporting unemployment compensation — -0.39
- Child mortality and Congregations per 10,000 people — +0.39
- Share widowed and Congregations per 10,000 people — +0.37
- Infant mortality and Congregations per 10,000 people — +0.37
- Motor vehicle crash deaths and Congregations per 10,000 people — +0.36
- Taking blood pressure medication and Congregations per 10,000 people — +0.36
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 Share of suicides by firearm and Congregations per 10,000 people