The County Health Rankings measures the health of nearly all counties in the nation and ranks them within states. The Rankings are compiled using county-level measures from a variety of national and state data sources. These measures are standardized and combined using scientifically-informed weights.
The County Health Rankings are based on a conceptual model of population health that includes both Health Outcomes (length and quality of life) and Health Factors (determinants of health).
Health Outcomes include two sub-areas:
- Length of Life
- Quality of Life
Health Factors include four sub-areas:
- Health Behaviors
- Clinical Care
- Social and Economic Factors
- Physical Environment.
Each of these health outcome and factor areas are composed of individual measures.
To learn more about research on our methods, see our selected publications.
Detailed methodological information is available in our downloadable Technical Document.
What do we rank?
The County Health Rankings measures the health of nearly all counties in the nation and ranks them within states.
We do not calculate national ranks such as the healthiest or least healthy county in the nation, nor do we encourage others to do this. Challenges of data comparability and availability across states makes these kinds of analyses inaccurate.
Learn more about which measures can be compared across states in our Technical Document.
What’s a county?
Any entity that has its own Federal Information Processing Standard (FIPS) county code is included in the County Health Rankings. The FIPS county code is a five-digit code in which the first 2 digits designate the state, and the last 3 digits designate county or county equivalent.
Not all states use a county system. For example, Louisiana uses parishes and Alaska is organized into boroughs and census areas. These differences are reflected across our materials when possible. However, County Health Rankings is currently unable to alter the data to reflect the existing governance structures in all states.
Certain major cities, such as Baltimore and St. Louis, are considered county equivalents and have their own FIPS county code. Other cities, such as Chicago, do not have a FIPS county code and are not individually ranked.
Some county definitions have changed over time due to merging with another county, being dissolved and distributed into other counties, or undergoing a name change.
Learn more about county changes over time in our Technical Document.
Why are some counties not ranked?
County Health Rankings is unable to rank a county due to insufficient data.
Counties are not ranked if any of the following is true:
- The county had a missing value for Premature Death (this happens when there are less than 20 deaths and the data is suppressed for privacy reasons).
- The county had an unreliable value for Premature Death and no other measures of morbidity were available.
- The county had an unreliable Premature Death value, an unreliable Low Birthweight value, and no other morbidity measures.
Learn more about how we determine if a county can be ranked in our Technical Document.
How are ranks calculated?
The County Health Rankings are constructed annually using summary composite scores calculated from the individual measures within the health outcome and factor areas. We calculate eight different composite scores:
- Overall Health Outcomes
- Health Outcomes – Length of Life
- Health Outcomes – Quality of Life
- Overall Health Factors
- Health Factors – Health Behaviors
- Health Factors – Clinical Care
- Health Factors – Social and Economic Factors
- Health Factors – Physical Environment
To calculate the overall Health Outcomes rank, following weights are used:
- Length of Life (50%)
- Quality of Life (50%)
To calculate the overall Health Factors rank, the following weights are used:
- Health Behaviors (30%)
- Clinical Care (20%)
- Social and Economic Factors (40%)
- Physical Environment (10%).
After composite scores are calculated, they are sorted from lowest to highest within each state. The lowest score (best health) gets a rank of #1 for that state and the highest score (worst health) gets the rank which corresponds to the number of county or county equivalents ranked in that state.
For example, in Wisconsin there are 72 counties. If all counties are able to be ranked, then the healthiest county will have a rank of #1, while the least healthy county will be ranked #72.
It is important to note that the rankings themselves do not necessarily represent statistically significant differences from county to county. The top ranked county in a state (#1) is not necessarily significantly healthier than the second ranked county (#2) and so on.
Learn more about the calculation methods for summary scores and ranks in our Technical Document.
Beginning in 2021, Health Outcome and Health Factor quartiles are displayed in the county snapshot. These quartile graphics provide an indication of how the county fares relative to other counties in the state without direct comparison of individual county ranks. Looking at the underlying data and how those data have changed over time provides a better picture of a county's progress.
Health Outcome and Health Factor rankings are grouped into four equally sized groups (quartiles), ranging from the least healthy to healthiest counties within each state. A county with a rank of #1 lies in the healthiest quartile.
How are measures selected?
The Rankings analyze 34 measures that help communities understand how healthy they are. County Health Rankings offers data for additional unranked measures and demographic measures, which do not impact a county's rank but can help communities get a broader picture of the health and wellbeing of its residents. In our interactive model, these unranked measures are noted with an asterisk (*).
View a full list of measures for our most recent County Health Rankings
There are five main considerations in the process of selecting ranked measures:
- Alignment with CHR&R program goals
- Connection of the measure to health and equity
- Assessment of data sources and their methodology
- Feasibility of quantitative and qualitative analysis for evaluation and production
- Ability to meaningfully communicate and apply the measure to improve health and equity
Learn more about specific criteria for selecting new measures in our Technical Document.
How do we address data reliability and comparability?
The County Health Rankings team draws upon the most reliable and valid measures available to compile the Rankings.
The reliability of the data used is one of the primary concerns when estimating values for relatively small areas like counties. The County Health Rankings makes every effort to provide the most reliable data available, but users should be aware that reliability can vary by place and by measure. An easy estimate of reliability is the error margin for a measure. Larger error margins suggest lower reliability.
Although the reliability of some County Health Rankings’ measures varies, when multiple measures are used to capture an underlying concept, reliability improves. For example, each individual measure comprising the Quality of Life Health Outcome sub-area may suffer from some reliability weaknesses, but together, the Quality of Life measures provide a reliable indication of morbidity.
Statistical Uncertainty and Error Margins
Where possible, we provide the margins of error (95% confidence intervals) for our measure values. In many cases, the values of specific measures are not statistically different between counties.
Error margins represent a range within which the true population experience may differ from our measure. However, we are 95% certain that the true population experience falls within that range. When the error margin ranges for a given measure overlap between two places, we can be less confident that the true population experiences (in those places) are different from each other.
Age-adjustment is a useful strategy to increase the comparability of measure values between counties having different age structures, or within county comparisons over time if the county age structure changes This is especially important for measures of factors related to age. We adjust county values for factors known to differ by age groups (or change with aging) so that all counties reflect a standard age distribution.
Adult Smoking is related to aging into adulthood. A county with a comparatively older population would be more likely to have a higher percentage of adults reporting smoking behavior because more adults live in that county.
However, age-adjustment can mask the true burden of a health need in a county – especially counties with many older residents. Measure data tables are available to communicate the absolute number of events occurring for many measures where the county value has been age-adjusted. County Health Rankings follows best practice to determine which measures are age-adjusted.
Treatment of missing values
In many ranked counties, some individual measures do not have a large enough sample size to report data for that measure. In these counties, the state average is assigned in place of any missing measure value to generate the data needed to calculate a county rank. On the state map, counties with a missing value for a given measure are identified with N/A (not available).
Additional methodological information is available in our Technical Document.