High blood pressure and Firearm share of suicides, standardised
Where High blood pressure is higher, Firearm share of suicides, standardised tends to be higher. The link is weak: +0.35 across 2,070 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 33 states, the middle value of the link is +0.18.
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
High blood pressure
The estimated percentage of adults who have been told they have high blood pressure. 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 BPHIGH, age-adjusted
Firearm share of suicides, standardised
The gun share of suicides compared with what you would expect if each sex, age and race group here used guns at its national rate. 100 means it matches what the local population mix would predict. Above 100 means more gun suicide than that. It is the best county measure of gun access available, and it closely tracks RAND's state estimates of gun ownership.
CDC WONDER mortality; RAND firearm ownership database · CDC WONDER underlying cause of death, suicide by intent, firearm by mechanism, 2018-2023
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 high blood pressure
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Coahoma County, MS | 50.7% | 146 |
| Washington County, MS | 49.4% | 134 |
| Leflore County, MS | 49.2% | 161 |
| Dallas County, AL | 49.2% | 129 |
| Phillips County, AR | 49% | 160 |
| Lowest | ||
| Boulder County, CO | 21% | 85.3 |
| Broomfield County, CO | 22.9% | 95.7 |
| Eagle County, CO | 23% | 106 |
| Denver County, CO | 23.2% | 78.1 |
| Douglas County, CO | 23.7% | 95.4 |
By firearm share of suicides, standardised
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Gates County, NC | 36.5% | 181 |
| Hale County, AL | 46% | 178 |
| Harrison County, MO | 34.2% | 177 |
| Ritchie County, WV | 39.1% | 176 |
| Teton County, MT | 28.1% | 175 |
| Lowest | ||
| New York County, NY | 25.8% | 12.9 |
| Queens County, NY | 29.8% | 22.5 |
| Kings County, NY | 28.8% | 24.6 |
| Bronx County, NY | 34.6% | 26.6 |
| Norfolk County, MA | 26.3% | 29.7 |
States — High blood pressure is not published for states
By firearm share of suicides, standardised
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Wyoming | — | 130 |
| Mississippi | — | 129 |
| Alabama | — | 129 |
| Louisiana | — | 121 |
| Georgia | — | 120 |
| Lowest | ||
| Massachusetts | — | 41.3 |
| Hawaii | — | 45 |
| District of Columbia | — | 46.9 |
| Rhode Island | — | 50.3 |
| New Jersey | — | 51.7 |
Metro areas
By high blood pressure
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Clarksdale, MS | 50.7% | 146 |
| Greenville, MS | 49.4% | 134 |
| Selma, AL | 49.2% | 129 |
| Cleveland, MS | 48.5% | 173 |
| Greenwood, MS | 47.2% | 152 |
| Lowest | ||
| Boulder, CO | 21% | 85.3 |
| Edwards, CO | 23% | 106 |
| Durango, CO | 24.3% | 133 |
| Heber, UT | 24.5% | 105 |
| Fort Collins-Loveland, CO | 24.6% | 94.5 |
By firearm share of suicides, standardised
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Cleveland, MS | 48.5% | 173 |
| Rio Grande City-Roma, TX | 37.5% | 167 |
| Winnemucca, NV | 29% | 161 |
| Snyder, TX | 33% | 160 |
| Brenham, TX | 32.9% | 159 |
| Lowest | ||
| New York-Newark-Jersey City, NY-NJ | 28.7% | 35.2 |
| Barnstable Town, MA | 27.5% | 38.8 |
| Boston-Cambridge-Newton, MA-NH | 27.2% | 39.2 |
| Pittsfield, MA | 27.7% | 40.7 |
| Urban Honolulu, HI | 27.9% | 40.9 |
Cities and towns — Firearm share of suicides, standardised is not published for cities and towns
By high blood pressure
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Highest | ||
| Bastrop city, LA | 53.4% | — |
| Yazoo City city, MS | 53.4% | — |
| Tallulah city, LA | 52.8% | — |
| Grambling city, LA | 52.3% | — |
| Prichard city, AL | 51.9% | — |
| Lowest | ||
| Superior town, CO | 18.7% | — |
| Louisville city, CO | 19.2% | — |
| Gunbarrel CDP, CO | 19.7% | — |
| Lafayette city, CO | 20.1% | — |
| Short Hills CDP, NJ | 20.4% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,070 counties: the five with far more firearm share of suicides, standardised than their high blood pressure would suggest, and the five with far less.
| High blood pressure | Firearm share of suicides, standardised | |
|---|---|---|
| Above the line | ||
| Teton County, MT | 28.1% | 175 |
| Harrison County, MO | 34.2% | 177 |
| Custer County, ID | 29.5% | 166 |
| Gates County, NC | 36.5% | 181 |
| Dunn County, ND | 30.3% | 166 |
| Below the line | ||
| Bronx County, NY | 34.6% | 26.6 |
| New York County, NY | 25.8% | 12.9 |
| Queens County, NY | 29.8% | 22.5 |
| Kings County, NY | 28.8% | 24.6 |
| Hudson County, NJ | 29.2% | 31.5 |
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 | +0.424 | 2,070 counties reporting both |
| After adjusting for density, income and education | +0.352 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.334 | size taken out as well as density |
| Also comparing only within each state | +0.035 | 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 | +0.423 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 252 equally weighted counties |
| Middle value inside single states | +0.183 | the link worked out inside each of 33 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.362 | 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 = 3.2e-61. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 2.6e-8. 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 2.0e-60.
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 +0.31 (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
- High blood pressure and Returns claiming the earned income credit — +0.63
- High blood pressure and Homicide rate, annual — +0.61
- High blood pressure and Material security score — -0.61
- High blood pressure and Homicide rate — +0.60
- High blood pressure and Children in single-parent households — +0.54
- High blood pressure and Black Protestant adherents — +0.53
- High blood pressure and Premature mortality — +0.49
- High blood pressure and Infant mortality — +0.48
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 High blood pressure and Firearm share of suicides, standardised