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 2021 County Health Rankings used data from 2017 for this measure.
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. 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 (such as asthma), osteoarthritis, and poor health status.[2-4]
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.
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.
Years of Data Used
United States Diabetes Surveillance System
The US Diabetes Surveillance System is an interactive web application that allows the user to view diabetes surveillance data and trends at national, state, and county levels. Data from CDC's Behavioral Risk Factor Surveillance System (BRFSS) and from the US Census Bureau's Population Estimates Program were used to obtain county-level estimates of diagnosed diabetes, newly diagnosed diabetes, obesity, and physical inactivity.
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 http://www.cdc.gov/brfss/smart/smart_data.htm. 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.
 Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). American Journal of Preventive Medicine. 2001;46:S3-S42.
 Centers for Disease Control and Prevention. Overweight and obesity: Causes and consequences. Centers for Disease Control and Prevention Web Site. http://www.cdc.gov/obesity/adult/defining.html. Updated June 16, 2016. Accessed June 27, 2016.
 Mokdad AH, Ford ES, Bowman BA, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA.2003;289:76-79.
 Peters U, Dixon AE, Forno E. Obesity and Asthma. The Journal of allergy and clinical immunology: official organ of American Academy of Allerg, 2018, Vol.141(4), p.1169
See how this component fits into our model
When it comes to developing and implementing solutions to problems that affect communities, evidence matters. The strategies below give some ideas of ways communities can harness evidence to make a difference locally. You can learn more about these and other strategies in What Works for Health, which summarizes and rates evidence for policies, programs, and systems changes.