Premature mortality and Freezing days
Where Premature mortality is higher, Freezing days tends to be lower. The link is weak: -0.31 across 3,038 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 39 states, the middle value of the link is -0.06.
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
Premature mortality
Deaths before age 75 per 100,000 people. It is adjusted so places with older or younger populations can be compared fairly.
County Health Rankings · County Health Rankings v127
Freezing days
The number of days in the year when the low temperature fell to 32°F or below, the point where water freezes. The low is the average over the county of NOAA's daily map of weather station readings, made of squares about 3 miles wide. A state's figure is the average of its counties, each weighted by its land area. Lower 48 states only, from 1951.
NOAA NCEI nClimGrid-Daily · NOAA NCEI nClimGrid-Daily v1.0.0 county averages (prcp/tmax/tmin, scaled), freeze_days; states, counties averaged by land area
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 premature mortality
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Buffalo County, SD | 1,949 | 175 |
| Dewey County, SD | 1,606 | 176 |
| Oglala Lakota County, SD | 1,585 | 165 |
| Corson County, SD | 1,445 | 178 |
| Sioux County, ND | 1,416 | 183 |
| Lowest | ||
| Pitkin County, CO | 140 | 204 |
| Teton County, WY | 152 | 227 |
| Summit County, CO | 156 | 230 |
| Eagle County, CO | 167 | 202 |
| San Juan County, WA | 168 | 35 |
By freezing days
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Lake County, CO | 327 | 251 |
| Sublette County, WY | 326 | 231 |
| Summit County, CO | 156 | 230 |
| Teton County, WY | 152 | 227 |
| Park County, CO | 297 | 222 |
| Lowest | ||
| Solano County, CA | 351 | 0 |
| San Mateo County, CA | 202 | 0 |
| San Francisco County, CA | 280 | 0 |
| Riverside County, CA | 393 | 0 |
| Orange County, CA | 261 | 0 |
States
By premature mortality
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Mississippi | 629 | 26.9 |
| West Virginia | 625 | 97.7 |
| Alabama | 569 | 29.6 |
| Kentucky | 565 | 65.5 |
| Louisiana | 563 | 11.8 |
| Lowest | ||
| Massachusetts | 303 | 117 |
| Minnesota | 315 | 172 |
| Utah | 317 | 160 |
| Connecticut | 323 | 112 |
| New Hampshire | 324 | 152 |
By freezing days
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Wyoming | 427 | 189 |
| North Dakota | 381 | 184 |
| Colorado | 344 | 181 |
| Montana | 403 | 176 |
| South Dakota | 401 | 175 |
| Lowest | ||
| Florida | 398 | 1.84 |
| Louisiana | 563 | 11.8 |
| Georgia | 461 | 24.3 |
| Mississippi | 629 | 26.9 |
| South Carolina | 501 | 29.1 |
Metro areas
By premature mortality
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Gallup, NM | 1,079 | 169 |
| Clarksdale, MS | 1,070 | 30 |
| Middlesborough, KY | 929 | 69 |
| Cleveland, MS | 927 | 26 |
| Greenwood, MS | 891 | 23.2 |
| Lowest | ||
| Edwards, CO | 167 | 202 |
| Los Alamos, NM | 178 | 169 |
| Jackson, WY-ID | 184 | 223 |
| Breckenridge, CO | 189 | 238 |
| Heber, UT | 202 | 194 |
By freezing days
| Premature mortality | Freezing days | |
|---|---|---|
| Highest | ||
| Breckenridge, CO | 189 | 238 |
| Jackson, WY-ID | 184 | 223 |
| Evanston, WY-UT | 479 | 204 |
| Edwards, CO | 167 | 202 |
| Alamosa, CO | 571 | 198 |
| Lowest | ||
| Zapata, TX | 532 | 0 |
| Yuma, AZ | 493 | 0 |
| Vallejo, CA | 351 | 0 |
| Sebastian-Vero Beach-West Vero Corridor, FL | 405 | 0 |
| San Francisco-Oakland-Fremont, CA | 254 | 0 |
Counties furthest from the pattern
Compared with the straight line that best fits all 3,050 counties: the five with far more freezing days than their premature mortality would suggest, and the five with far less.
| Premature mortality | Freezing days | |
|---|---|---|
| Above the line | ||
| Buffalo County, SD | 1,949 | 175 |
| Dewey County, SD | 1,606 | 176 |
| Oglala Lakota County, SD | 1,585 | 165 |
| Sioux County, ND | 1,416 | 183 |
| Corson County, SD | 1,445 | 178 |
| Below the line | ||
| Marin County, CA | 195 | 0 |
| San Mateo County, CA | 202 | 0 |
| Orange County, CA | 261 | 0 |
| Fort Bend County, TX | 261 | 0 |
| Alameda County, CA | 262 | 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 | -0.400 | 3,038 counties reporting both |
| After adjusting for density, income and education | -0.314 | the part of the link that is not just how urban, rich and educated a place is |
| Also adjusting for population size | -0.312 | size taken out as well as density |
| Also comparing only within each state | -0.068 | each county is compared only with others in its own state, so this is no longer a comparison between states |
| Also holding winter sunshine equal | -0.193 | compares counties that get the same sunlight in winter as well. If this is much weaker, part of the link is a difference between sunny and grey regions (more) |
| Giving bigger counties more weight | -0.143 | the adjusted link for the average resident rather than the average county. With this weighting, the data counts for about 279 equally weighted counties |
| Middle value inside single states | -0.064 | the link worked out inside each of 39 states with at least 20 counties reporting |
| Using ranks, after adjusting | -0.366 | 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 = 2.5e-70. But counties in the same state tend to resemble each other, so the better figure allows for that: p = 0.001. The site tested 32,131 pairs, and testing so many turns up some flukes. The q-value allows for that too. For this pair it is 2.0e-69.
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 +0.76 (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 premature mortality rose most, did freezing daysmove 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 | 3,027 | -0.11 | -0.01 | 0.865 |
| 2019–2023 a repeat check: its two five-year survey figures both include 2019, and it covers the COVID years | 3,037 | +0.13 | -0.01 | 0.939 |
Measured over the nearest years published: Premature mortality: 2013–2019 for the 2014–2019 row; Premature mortality: 2019–2022 for the 2019–2023 row.
Over the main period, any link between the changes could easily be chance. How change is measured.
Related pairs
- Freezing days and Heating degree days — +0.95
- Freezing days and Average temperature — -0.94
- Freezing days and Cooling degree days — -0.81
- Freezing days and Summer daily high — -0.76
- Freezing days and Days of 90°F or hotter — -0.74
- Premature mortality and Homicide rate, annual — +0.67
- Freezing days and Winter daily sunlight — -0.65
- Share American Indian or Alaska Native, not Hispanic and Premature mortality — +0.63
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 Premature mortality and Freezing days