Cholesterol screening and Firearm suicide rate
Across 2,070 counties, Cholesterol screening and Firearm suicide rate have a link of -0.55, on a scale from −1 (opposite) to +1 (in step). But compare places with the same population density, income and education, and a very weak link of -0.15 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.
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What each measure is
Cholesterol screening
The estimated percentage of adults who have been screened for cholesterol. 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 CHOLSCREEN, age-adjusted
Firearm suicide rate
Suicides by gun per 100,000 residents a year, 2018-2023.
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 cholesterol screening
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Howard County, MD | 90.4% | 2.67 |
| District of Columbia, DC | 90.2% | 1.55 |
| Middlesex County, MA | 90% | 1.35 |
| Norfolk County, MA | 90% | 1.35 |
| Nassau County, NY | 89.8% | 1.36 |
| Lowest | ||
| Bethel Census Area, AK | 71.7% | 28.2 |
| Northwest Arctic Borough, AK | 74.1% | 28.7 |
| Nome Census Area, AK | 74.1% | 33.7 |
| Yukon-Koyukuk Census Area, AK | 74.2% | 32 |
| Todd County, SD | 75.2% | 20.5 |
By firearm suicide rate
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Catron County, NM | 80.6% | 58.8 |
| Musselshell County, MT | 78.6% | 47.7 |
| Hot Springs County, WY | 78.9% | 44.1 |
| Custer County, ID | 78.7% | 41.8 |
| Boise County, ID | 80.4% | 41.3 |
| Lowest | ||
| New York County, NY | 89% | 0.529 |
| Queens County, NY | 88.1% | 0.69 |
| Kings County, NY | 88.1% | 0.711 |
| Bronx County, NY | 86.2% | 0.713 |
| Hudson County, NJ | 87.4% | 0.968 |
States — Cholesterol screening is not published for states
By firearm suicide rate
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Wyoming | — | 20.3 |
| Montana | — | 18.1 |
| Alaska | — | 16.7 |
| Idaho | — | 14.3 |
| New Mexico | — | 13.9 |
| Lowest | ||
| District of Columbia | — | 1.55 |
| Massachusetts | — | 2.05 |
| New Jersey | — | 2.17 |
| New York | — | 2.37 |
| Hawaii | — | 2.79 |
Metro areas
By cholesterol screening
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Barnstable Town, MA | 89.2% | 3.22 |
| Boston-Cambridge-Newton, MA-NH | 89% | 2.03 |
| Amherst Town-Northampton, MA | 88.8% | 2.89 |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 88.6% | 3.82 |
| Bridgeport-Stamford-Danbury, CT | 88.6% | 2.33 |
| Lowest | ||
| Gallup, NM | 75.8% | 8.71 |
| Vernal, UT | 77.3% | 20.2 |
| Hilo-Kailua, HI | 77.6% | 4.83 |
| Deming, NM | 77.6% | 15.5 |
| Rexburg, ID | 77.7% | 7.89 |
By firearm suicide rate
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Pahrump, NV | 81.3% | 30.9 |
| Brookings, OR | 80.5% | 29.4 |
| Payson, AZ | 80.8% | 29.3 |
| Elko, NV | 81.8% | 27.8 |
| Mountain Home, AR | 82.1% | 27.6 |
| Lowest | ||
| New York-Newark-Jersey City, NY-NJ | 88.2% | 1.32 |
| Boston-Cambridge-Newton, MA-NH | 89% | 2.03 |
| Urban Honolulu, HI | 80.7% | 2.05 |
| Trenton-Princeton, NJ | 87.9% | 2.13 |
| Bridgeport-Stamford-Danbury, CT | 88.6% | 2.33 |
Cities and towns — Firearm suicide rate is not published for cities and towns
By cholesterol screening
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Highest | ||
| Fulton CDP, MD | 91.9% | — |
| Lexington CDP, MA | 91.8% | — |
| Brock Hall CDP, MD | 91.6% | — |
| Fairwood CDP, MD | 91.6% | — |
| Lake Arbor CDP, MD | 91.6% | — |
| Lowest | ||
| Binghamton University CDP, NY | 68% | — |
| Clemson University CDP, SC | 74.8% | — |
| Anthony city, NM | 75.2% | — |
| Jerome city, ID | 75.2% | — |
| Makaha CDP, HI | 75.2% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,070 counties: the five with far more firearm suicide rate than their cholesterol screening would suggest, and the five with far less.
| Cholesterol screening | Firearm suicide rate | |
|---|---|---|
| Above the line | ||
| Catron County, NM | 80.6% | 58.8 |
| Musselshell County, MT | 78.6% | 47.7 |
| Hot Springs County, WY | 78.9% | 44.1 |
| Boise County, ID | 80.4% | 41.3 |
| Dunn County, ND | 81.3% | 39.5 |
| Below the line | ||
| Hawaii County, HI | 77.6% | 4.83 |
| Kauai County, HI | 78.9% | 4.34 |
| Madison County, ID | 77.4% | 6.36 |
| Honolulu County, HI | 80.7% | 2.05 |
| McKinley County, NM | 75.8% | 8.71 |
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 | -0.550 | 2,070 counties reporting both |
| After adjusting for density, income and education | -0.151 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.190 | size taken out as well as density |
| Also comparing only within each state | +0.033 | 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 | -0.202 | 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 | -0.437 | the link worked out inside each of 33 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.141 | 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 = 5.8e-12. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.021. 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 1.4e-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 +0.25 (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
- Suicide rate and Firearm suicide rate — +0.75
- Injury deaths and Firearm suicide rate — +0.39
- Share never married and Firearm suicide rate — -0.37
- Effective property tax rate and Firearm suicide rate — -0.31
- Cholesterol screening and Suicide rate — -0.30
- Cholesterol screening and Local income tax as a share of income — +0.30
- Hearing disability and Firearm suicide rate — +0.22
- Motor vehicle crash deaths and Firearm suicide rate — +0.19
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 Cholesterol screening and Firearm suicide rate