Drug overdose deaths and Membership organisations
Counting counties equally, Drug overdose deaths and Membership organisations have a link of -0.12 across 1,774 counties, on a scale from −1 to +1. Giving bigger places more weight asks about the average resident rather than the average place, and it flips the link to +0.19. Neither figure is likely to be chance. So this is a real disagreement about who is being described.
Flips when bigger places count more
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
Drug overdose deaths
Deaths from drug overdose per 100,000 people, from any drug.
County Health Rankings · County Health Rankings v138
Membership organisations
Membership groups per 10,000 people, such as churches, clubs, unions and sports leagues. They are counted from the government's list of businesses and organizations.
County Health Rankings · County Health Rankings v140
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 drug overdose deaths
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| McDowell County, WV | 181 | 5.04 |
| Logan County, WV | 164 | 6.71 |
| Baltimore city, MD | 144 | 10.7 |
| Raleigh County, WV | 138 | 9.88 |
| Cabell County, WV | 132 | 14.3 |
| Lowest | ||
| Hidalgo County, TX | 5.37 | 3.53 |
| Cameron County, TX | 6.05 | 4.99 |
| Limestone County, AL | 6.55 | 7.66 |
| Gallatin County, MT | 7.14 | 11.8 |
| Lincoln County, SD | 7.43 | 11.6 |
By membership organisations
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| District of Columbia, DC | 64.5 | 29.4 |
| Martinsville city, VA | 68.2 | 26.2 |
| Charlottesville city, VA | 25.4 | 24.9 |
| Bertie County, NC | 39.3 | 24.4 |
| Perry County, IL | 22.5 | 23.8 |
| Lowest | ||
| Clay County, WV | 83.2 | 0 |
| Sanpete County, UT | 17.7 | 0 |
| Wolfe County, KY | 99.9 | 0 |
| Jackson County, KY | 35.6 | 0 |
| Gallatin County, KY | 91.2 | 0 |
States
By drug overdose deaths
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| West Virginia | 78 | 12.6 |
| District of Columbia | 64.5 | 29.4 |
| Tennessee | 51 | 11 |
| Delaware | 50.1 | 10.1 |
| Kentucky | 49.9 | 10.2 |
| Lowest | ||
| South Dakota | 10.5 | 15.6 |
| Nebraska | 11.1 | 13.7 |
| Iowa | 14.4 | 14.2 |
| Texas | 16.5 | 7.38 |
| North Dakota | 16.6 | 15.6 |
By membership organisations
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| District of Columbia | 64.5 | 29.4 |
| North Dakota | 16.6 | 15.6 |
| South Dakota | 10.5 | 15.6 |
| Iowa | 14.4 | 14.2 |
| Montana | 17.2 | 14.1 |
| Lowest | ||
| Utah | 19.2 | 3.48 |
| Nevada | 29.4 | 4.38 |
| Arizona | 36 | 5.65 |
| California | 26.1 | 6.17 |
| Hawaii | 19.3 | 6.49 |
Metro areas
By drug overdose deaths
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| Portsmouth, OH | 126 | 9.83 |
| Beckley, WV | 125 | 10.3 |
| Española, NM | 111 | 7.24 |
| Charleston, WV | 104 | 15.7 |
| Bluefield, WV-VA | 97 | 11.7 |
| Lowest | ||
| McAllen-Edinburg-Mission, TX | 5.37 | 3.53 |
| Brownsville-Harlingen, TX | 6.05 | 4.99 |
| Bozeman, MT | 7.14 | 11.8 |
| Ames, IA | 7.73 | 11.6 |
| Grand Island, NE | 8.1 | 13.8 |
By membership organisations
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Highest | ||
| Oil City, PA | 34.7 | 21.3 |
| El Dorado, AR | 24.5 | 21.2 |
| Somerset, PA | 35.6 | 21 |
| Centralia, IL | 36.8 | 20.9 |
| Alexandria, MN | 15.4 | 19.9 |
| Lowest | ||
| Blackfoot, ID | 15.8 | 1.2 |
| Provo-Orem-Lehi, UT | 11.9 | 1.75 |
| Brigham City, UT-ID | 22.4 | 2.26 |
| Logan, UT-ID | 9.81 | 2.38 |
| Evanston, WY-UT | 29.2 | 2.57 |
Counties furthest from the pattern
Compared with the straight line that best fits all 1,774 counties: the five with far more membership organisations than their drug overdose deaths would suggest, and the five with far less.
| Drug overdose deaths | Membership organisations | |
|---|---|---|
| Above the line | ||
| District of Columbia, DC | 64.5 | 29.4 |
| Martinsville city, VA | 68.2 | 26.2 |
| Charlottesville city, VA | 25.4 | 24.9 |
| Bertie County, NC | 39.3 | 24.4 |
| Perry County, IL | 22.5 | 23.8 |
| Below the line | ||
| Sanpete County, UT | 17.7 | 0 |
| Jackson County, KY | 35.6 | 0 |
| Clay County, WV | 83.2 | 0 |
| Gallatin County, KY | 91.2 | 0 |
| Wolfe County, KY | 99.9 | 0 |
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 | -0.060 | 1,774 counties reporting both |
| After adjusting for density, income and education | -0.123 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.167 | size taken out as well as density |
| Also comparing only within each state | -0.147 | 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 | +0.190 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 260 equally weighted counties |
| Middle value inside single states | +0.000 | the link worked out inside each of 30 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.097 | 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 = 2.2e-7. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.033. 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 4.4e-7.
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 +0.42 (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?
The figures above compare counties at one moment. This asks a different question: where drug overdose deaths rose most, did membership organisationsmove too? Each county’s change is compared with the average change in its own state. That takes out ups and downs that hit a whole region at once. Each change is the gap between two estimates that both contain some error. That error pulls these links towards zero, so a weak one is weak partly for that reason. The q column shows whether a link could be a fluke, allowing for the many pairs tested: 0.05 or less means it is unlikely to be chance.
| Period | Counties | Change together | Within states | q |
|---|---|---|---|---|
| 2014–2019 the main test: each Census survey figure averages five years, and these two do not overlap; before the pandemic | — | — | — | — |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 1,431 | +0.04 | +0.02 | 0.811 |
Measured over the nearest years published: Drug overdose deaths: 2019–2022 for the 2019–2023 row; Membership organisations: 2019–2022 for the 2019–2023 row.
Not measurable over the main period, so only the second period is shown. How change is measured.
Related pairs
- Membership organisations and Mainline Protestant adherents — +0.54
- Drug overdose deaths and Years of potential life lost — +0.53
- Drug overdose deaths and Premature mortality — +0.44
- Life expectancy and Drug overdose deaths — -0.42
- Frequent mental distress and Drug overdose deaths — +0.42
- Frequent physical distress and Membership organisations — -0.36
- Arthritis and Drug overdose deaths — +0.36
- Depression and Drug overdose deaths — +0.34
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 Drug overdose deaths and Membership organisations