County Health Rankings & Roadmaps Model

We provide measures of health for counties across the nation. The annual data release is compiled from a variety of national and state data sources. Select measures, based on a conceptual model of population health, are standardized and combined using scientifically-informed weights to provide nearly all counties with overall measures of local Health Factors and Health Outcomes. 

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 Health Factor sub-areas are composed of individual measures.

To learn more about research on our methods, see our Working Paper and selected publications.

Detailed information about our current methodology is available in our downloadable Technical Document.

What is a county?

Any entity that has its own Federal Information Processing Standard (FIPS) county code is included in our data snapshots. 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.  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.

Not all states use a county system. Some examples of county equivalents include Louisiana’s parishes, Connecticut’s planning regions and Alaska’s boroughs and census areas. These different systems are reflected across our materials when possible.

County definitions can change over time. A county may merge with another county, dissolve and be distributed into other counties, or undergo a name change.

Learn more about county changes in our Technical Document.

How are Health Factor and Health Outcomes calculated?

Health Outcomes and Health Factors are constructed annually using summary composite scores calculated from the individual measures based on a conceptual model of population health. For each county with available data, we calculate two composite scores: a Health Outcomes summary and a Health Factors summary.

To calculate Health Outcome summaries, the following weights are used: 

  • Length of Life (50%)
  • Quality of Life (50%) 

To calculate Health Factor summaries, 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 assigned to one of ten groups nationally using a data-informed grouping 

method, which groups counties based on similarity and meaningful gaps in the data. Health Outcome and Health Factor composite scores are grouped into ten unequally-sized groups based on the data, ranging from the least healthy to healthiest counties across the nation. A county in the first group is among the healthiest in the nation, while a county in the tenth group is among the least healthy in the nation. States may or may not have counties that fall within each of the ten groups across the range of health nationally.

Beginning in 2024, Health Outcome and Health Factor groups are displayed in the county snapshot. These graphics provide an indication of how the county fares relative to other counties in the state and the nation.

It is important to note that these groups themselves do not necessarily represent statistically significant differences from county to county, but rather support more data-informed comparisons and a focus on meaningful similarities that can better support action.

Treatment of missing values

Many counties do not have a large enough sample size to report data for one or more select measures. In these counties, the national average is assigned in place of any missing measure value to generate the data needed to calculate Health Factor or Health Outcome summaries.

Learn more about how Health Factors and Health Outcomes are summarized in our Technical Document

How are measures selected?

We have selected 34 measures to include in our Health Factor and Health Outcome summaries. These select measures summarize the opportunity for health and how long and well people live in a county. We also provide additional measures and demographic measures, which offer a broader picture of health and wellbeing in a county. In our interactive model, these additional measures are noted with an asterisk (*). 

View a full list of measures for our most recent annual data release 

There are five main considerations in choosing select 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?

We draw upon the most reliable and valid data available to compile measures of opportunity for health and how long and well people live in a county. 

Data Reliability 

The reliability of the data used is one of the primary concerns when estimating values for relatively small areas like counties. We make every effort to provide the most reliable data available, and 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 for the associated data value.

There is varied reliability among the  select measures, however, when multiple measures are used to capture an underlying concept, the overall reliability improves. For example, each individual measure comprising the Health Outcome summary may suffer from some reliability weaknesses, but together, the Health Outcome measures provide a reliable indication of morbidity and mortality. 

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 value. 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 that have 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.

The measure of Adult Smoking is related to the age structure of the county. Research has found cigarette smoking to be highest among those ages 25-64. A county with a relatively large population of people in the 25-64 age group would be more likely to have a higher percentage of adults reporting smoking behavior because more adults in the high-risk age group live in that county.

However, age-adjustment can mask the true burden of a health need in a county – especially counties with many residents in higher-risk age groups. Measure data tables are available to communicate the absolute number of events occurring for many measures where the county value has been age-adjusted. We follow best practice to determine which measures are age-adjusted. 


Additional methodological information is available in our Technical Document