Adult obesity

Percentage of the adult population (age 20 and older) that reports a body mass index (BMI) greater than or equal to 30 kg/m2.
The 2019 County Health Rankings used data from 2015 for this measure.

Measure Tabs


Reason for Ranking

The County Health Rankings measure of obesity serves as a proxy metric for poor diet and limited physical activity and has been shown to have very high reliability.[1]  Obesity increases the risk for health conditions such as coronary heart disease, type 2 diabetes, cancer, hypertension, dyslipidemia, stroke, liver and gallbladder disease, sleep apnea and respiratory problems, osteoarthritis, and poor health status.[2,3]

Key Measure Methods

Adult Obesity is a Percentage

Adult Obesity is based on responses to the Behavioral Risk Factor Surveillance Survey (BRFSS) and is the percentage of the adult population (age 20 and older) that reports a body mass index (BMI) greater than or equal to 30 kg/m2. Participants are asked to self-report their height and weight. From these reported values, BMIs for the participants are calculated.

The Method for Calculating Adult Obesity Changed

Data for Adult Obesity are provided by the CDC Interactive Diabetes Atlas which uses BRFSS data to provide county-level estimates. Beginning with the 2015 County Health Rankings, Adult Obesity estimates include both landline and cell phone users. Previously, only landline users were included in the data. This change was implemented in order to provide users with the most accurate estimates of health in their community as possible.

Adult Obesity is Created Using Statistical Modeling

Our Adult Obesity estimates are produced from three years of survey data and created using a complex statistical model. Modeling generates more stable estimates for places with small numbers of residents or survey responses. There are also drawbacks to using modeled data. The smaller the population or sample size of a county, the more the estimates are derived from the model itself and the less they are based on survey responses. Models make statistical assumptions about relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods. For more technical information on BRFSS modeling in the Diabetes Atlas please see their methodology.

Caution Should be Used When Comparing these Estimates Across State Lines

The model used to create these estimates includes a state-level factor that limits comparability between neighboring counties of adjacent states.

Measure Limitations

Proxy measures are strongly correlated with, but indirectly measure, the outcome of interest. Obesity is used as a proxy measure for diet and exercise because a reliable measure of diet is unavailable at the county level.


The numerator is the number of adult respondents age 20 and older with a BMI greater than or equal to 30kg/m2.


The denominator is the number of Adult respondents age 20 and older.

Can This Measure Be Used to Track Progress?

This measure could be used to measure progress, but only after considering its substantial limitations. Methodological changes in the Behavioral Risk Factor Surveillance System, which are discussed above and were implemented in the 2015 Rankings, make comparisons with estimates prior to that release year difficult. In addition, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress in small geographic areas.

Modeled estimates have specific drawbacks with regard to their usefulness in tracking progress in communities. Modeled data are not particularly good at incorporating the effects of local conditions, such as health promotion policies or unique population characteristics, into their estimates. Counties trying to measure the effects of programs and policies on the data should use great caution when using modeled estimates. In order to better understand and validate modeled estimates, confirming this data with additional sources of data at the local level is particularly valuable.

Data Source

Years of Data Used


CDC Diabetes Interactive Atlas

The National Diabetes Surveillance System provides county-level estimates of obesity, physical inactivity, and diabetes using three years of data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and data from the U.S. Census Bureau’s Population Estimates Program. The county-level estimates are based on indirect model-dependent estimates. Bayesian multilevel modeling techniques are used to obtain estimates.

Digging Deeper

Subcounty Areatrue

There are several methods to try to get more specific data than the county-level. For larger counties, you can access county- or MSA-specific data from the CDC at However, using this data requires somewhat advanced analytic capabilities.

The 500 Cities project also provides city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including adult obesity, for the largest 500 cities in the United States.

In many states, you can access county level BRFSS estimates, and in some cases, you can stratify those estimates by age, gender or race. You can find BRFSS resources for most states in our Finding More Data section.


[1] Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Soz Praventivmed. 2001;46:S3-S42.
[2] Centers for Disease Control and Prevention. Overweight and obesity: Causes and consequences. Centers for Disease Control and Prevention Web Site. Updated June 16, 2016. Accessed June 27, 2016.
[3] Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA.2003;289:76-79.

See how this measure fits into our model

To learn more, view our interactive model.
Policies and Programs Health Factors Health Outcomes Length of Life (50%) Quality of Life (50%) Health Behaviors (30%) Tobacco Use Diet & Exercise Alcohol & Drug Use Sexual Activity Clinical Care (20%) Access to Care Quality of Care Social and Economic Factors (40%) Education Employment Income Family & Social Support Community Safety Physical Environment (10%) Air & Water Quality Housing & Transit County Health Rankings model © 2014 UWPHI