High cholesterol
The estimated percentage of adults who have been told they have high 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.
Reported for2,956areas, in 49 states — none in KY, PA, PR
Link to density0.27not mostly about how urban a place is
Nearby places alike0.83Moran’s I: strongly, neighbouring places tend to be similar
Typerate%
How it has moved
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Every state, every year
Nothing to draw here: this measure has no state figures. Its publisher reports it for counties. This site does not add county numbers up into state figures, since a home-made figure could clash with one an agency publishes itself. The chart above covers the whole country, year by year.
Where it comes from
CDC PLACES · PLACES HIGHCHOL, age-adjusted
An estimate from a statistical model, not a direct measurement. The model starts from interviews in a national health survey (BRFSS). It fills in local detail using Census population counts and figures from the American Community Survey (ACS). So the estimate partly reflects ACS measures that also appear on this site. When counties are combined, bigger ones get more weight. That gives a close figure, not an exact one. Each county rate is age-adjusted: adjusted to the age mix of the 2000 US population, so places with older or younger people can be compared fairly. That makes the rates nearly combinable. The exact figure would need counts by age group, which the CDC does not release. Where precision matters, read the county values directly.
Combining areas: an average weighted by population aged 18 and over.
What it goes with
The strongest links that have a page of their own. Unadjusted is the link before adjusting for anything, from −1 (opposite) to +1 (in step). Adjusted compares places with the same population density, income and education levels, so those differences can’t explain the link. This list leaves out the 95 pairs built from the same numbers or the same model as this measure, since they are bound to match.
| Measure | Unadjusted | Adjusted |
|---|---|---|
| High cholesterol and Evangelical Protestant adherentsWhere High cholesterol is higher, Evangelical Protestant adherents tends to be higher: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Evangelical Protestant adherents by US county | +0.46 | +0.35 |
| High cholesterol and Black-white residential segregationWhere High cholesterol is higher, Black-white residential segregation tends to be lower: a weak link that holds when only places with similar density, income and education are compared. Black-white residential segregation by US county | -0.36 | -0.35 |
| High cholesterol and Returns claiming the earned income creditWhere High cholesterol is higher, Returns claiming the earned income credit tends to be higher: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Returns claiming the earned income credit by US county | +0.54 | +0.33 |
| High cholesterol and Premature mortalityWhere High cholesterol is higher, Premature mortality tends to be higher: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Premature mortality by US county | +0.49 | +0.32 |
| High cholesterol and Material security scoreWhere High cholesterol is higher, Material security score tends to be lower: a weak link that holds when only places with similar density, income and education are compared, and inside states as well. Material security score by US county | -0.45 | -0.31 |
| High cholesterol and Economic connectednessLinked across counties, but mostly as both follow how urban, rich and educated a place is. Comparing only places with similar density, income and education, a weak link of -0.27 remains. Economic connectedness by US county | -0.54 | -0.27 |
Population makeup
How this measure varies with the racial and ethnic makeup of counties. Every group is shown, whatever its rank. These are links across counties: they describe places, not people. They say nothing about any person’s risk or behaviour based on their race. Counties with a larger share of one group also differ in much else, such as income, segregation, region and history. The adjusted figure only accounts for density, income and education.
| Share of residents | Unadjusted | Adjusted |
|---|---|---|
| High cholesterol and Share White, not HispanicNot an independent finding: one is modelled from the other. It is an estimate from a statistical model that uses the other as an input, so part of the match is built in. Share White, not Hispanic by US county | -0.24 | -0.15 |
| High cholesterol and Share Black, not HispanicNot an independent finding: one is modelled from the other. It is an estimate from a statistical model that uses the other as an input, so part of the match is built in. Share Black, not Hispanic by US county | +0.41 | +0.26 |
| High cholesterol and Share Hispanic or Latino, any raceNot an independent finding: one is modelled from the other. It is an estimate from a statistical model that uses the other as an input, so part of the match is built in. Share Hispanic or Latino, any race by US county | +0.03 | +0.06 |
| High cholesterol and Share Asian, not HispanicNot an independent finding: one is modelled from the other. It is an estimate from a statistical model that uses the other as an input, so part of the match is built in. Share Asian, not Hispanic by US county | +0.03 | +0.09 |
| High cholesterol and Share American Indian or Alaska Native, not HispanicNot an independent finding: one is modelled from the other. It is an estimate from a statistical model that uses the other as an input, so part of the match is built in. Share American Indian or Alaska Native, not Hispanic by US county | -0.15 | -0.08 |
Highest and lowest
Of 2,956 counties reporting it. These are the two ends of a national ranking. For most measures, that ranking mostly sorts places by income and how urban they are. That is why the place pages compare each place with similar places instead.
Highest
- Mingo County, WV — 37.9%
- Wyoming County, WV — 37.8%
- Franklin County, AL — 37.7%
- Chilton County, AL — 37.7%
- Fayette County, WV — 37.6%
Lowest
- Lamoille County, VT — 25%
- Twin Falls County, ID — 25.9%
- Chittenden County, VT — 25.9%
- Windsor County, VT — 25.9%
- Lake County, MT — 26%
The middle area reports 32.2%. The middle half of counties falls between 30.3% and 33.8% (the 25th to 75th percentile).
All measures · Explore its pairs · How the US county data works