Obesity and Expected yearly tornado loss
Where Obesity is higher, Expected yearly tornado loss tends to be higher. The link is weak: +0.35 across 2,934 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 38 states, the middle value of the link is +0.09.
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
Obesity
The estimated percentage of adults who have a body mass index (a measure of weight for height) of 30 or above. 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 OBESITY, age-adjusted
Expected yearly tornado loss
The damage tornadoes are expected to do to buildings in an average year, in dollars for every $100,000 that the buildings are worth. FEMA works it out from the tornadoes recorded from 1986 to 2023 and the damage they did.
FEMA National Risk Index · FEMA National Risk Index v1.20, NRI_Table_Counties / NRI_Table_States, TRND_ALRB
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 obesity
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Highest | ||
| Perry County, AL | 54% | 22.5 |
| Greene County, AL | 53.4% | 25.1 |
| Humphreys County, MS | 53% | 30.3 |
| Wilcox County, AL | 51.9% | 16.2 |
| East Carroll Parish, LA | 51.7% | 26.3 |
| Lowest | ||
| San Francisco County, CA | 17.1% | 0.257 |
| Boulder County, CO | 17.4% | 12.3 |
| Summit County, UT | 19.3% | 0.705 |
| New York County, NY | 19.5% | 0.907 |
| Pitkin County, CO | 20.3% | 0.344 |
By expected yearly tornado loss
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Highest | ||
| Johnson County, IA | 32.8% | 62.9 |
| Logan County, OK | 40.2% | 60.3 |
| York County, NE | 41.8% | 47.5 |
| Platte County, NE | 42.1% | 44.7 |
| Sumter County, AL | 50% | 44.5 |
| Lowest | ||
| Anchorage Municipality, AK | 35% | 0.000001 |
| Sitka City and Borough, AK | 29.7% | 0.000003 |
| Fairbanks North Star Borough, AK | 35.8% | 0.000005 |
| Skagway Municipality, AK | 35% | 0.000005 |
| Ketchikan Gateway Borough, AK | 37.4% | 0.000006 |
States — Obesity is not published for states
By expected yearly tornado loss
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Highest | ||
| Nebraska | — | 27.4 |
| Iowa | — | 22.6 |
| Mississippi | — | 19.2 |
| Oklahoma | — | 18.4 |
| Alabama | — | 18.1 |
| Lowest | ||
| Alaska | — | 0.000238 |
| Nevada | — | 0.0317 |
| Washington | — | 0.183 |
| Hawaii | — | 0.196 |
| Oregon | — | 0.219 |
Metro areas — Expected yearly tornado loss is not published for metro areas
By obesity
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Highest | ||
| Lumberton, NC | 48.1% | — |
| Forrest City, AR | 47.9% | — |
| Logansport, IN | 47.3% | — |
| Clarksdale, MS | 47.1% | — |
| Cleveland, MS | 46.8% | — |
| Lowest | ||
| Boulder, CO | 17.4% | — |
| Durango, CO | 20.8% | — |
| Heber, UT | 21.4% | — |
| San Jose-Sunnyvale-Santa Clara, CA | 22.2% | — |
| San Francisco-Oakland-Fremont, CA | 22.6% | — |
Cities and towns — Expected yearly tornado loss is not published for cities and towns
By obesity
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Highest | ||
| Muskegon Heights city, MI | 58.4% | — |
| Benton Harbor city, MI | 56% | — |
| Grambling city, LA | 55.7% | — |
| Yazoo City city, MS | 55.4% | — |
| Tallulah city, LA | 53.6% | — |
| Lowest | ||
| Superior town, CO | 14.5% | — |
| San Marino city, CA | 14.7% | — |
| Louisville city, CO | 15.8% | — |
| Cupertino city, CA | 15.9% | — |
| Gunbarrel CDP, CO | 16.1% | — |
Counties furthest from the pattern
Compared with the straight line that best fits all 2,956 counties: the five with far more expected yearly tornado loss than their obesity would suggest, and the five with far less.
| Obesity | Expected yearly tornado loss | |
|---|---|---|
| Above the line | ||
| Johnson County, IA | 32.8% | 62.9 |
| Logan County, OK | 40.2% | 60.3 |
| York County, NE | 41.8% | 47.5 |
| Platte County, NE | 42.1% | 44.7 |
| Union County, SD | 31% | 34.4 |
| Below the line | ||
| Kusilvak Census Area, AK | 47% | 0.00164 |
| Boone County, WV | 46.2% | 0.674 |
| Randolph County, WV | 46.2% | 0.724 |
| McDowell County, WV | 46.1% | 0.683 |
| Rolette County, ND | 50.2% | 4.01 |
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 | +0.385 | 2,934 counties reporting both |
| After adjusting for density, income and education | +0.347 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | +0.317 | size taken out as well as density |
| Also comparing only within each state | +0.044 | 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 | +0.412 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 264 equally weighted counties |
| Middle value inside single states | +0.095 | the link worked out inside each of 38 states with at least 20 counties reporting |
| Using ranks, after adjusting | +0.370 | 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 = 6.4e-84. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 6.4e-6. The site tested 29,403 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 5.7e-83.
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 +0.77 (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
- Natural amenities scale and Obesity — -0.45
- Natural amenities scale and Expected yearly tornado loss — -0.42
- Expected yearly tornado loss and Area in drought — +0.41
- Expected yearly tornado loss and Weeks with half the area in drought — +0.41
- Obesity and House price index (2000 = 100) — -0.40
- Taking blood pressure medication and Expected yearly tornado loss — +0.40
- Unemployment rate and Expected yearly tornado loss — -0.35
- Median home value and Expected yearly tornado loss — -0.35
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 Obesity and Expected yearly tornado loss