Percentage of the adult population (age 18 and older) that reports a body mass index (BMI) greater than or equal to 30 kg/m2 (age-adjusted). The 2023 County Health Rankings used data from 2020 for this measure.
Adult obesity is a chronic condition that puts individuals at increased risk of hypertension, heart disease, type 2 diabetes, breathing problems, chronic inflammation, mental illness, and some cancers.1
Obesity is a product of environmental and individual factors. Environmental factors such as accessibility and affordability of nutrient-dense foods, the prevalence of fast-food marketing, and the acceptability of stigma around weight may impact the prevalence and risk of obesity.2, 3 Communities experiencing segregation or disinvestment – the results of systemic racism and classism - are more likely to have environmental conditions, such as marketing of unhealthy foods and under-resourced areas for physical activity, that increase obesity risk.4, 5, 6
Individuals with obesity can experience stigma and discrimination in their communities. These experiences can lead to chronic psychosocial stress and increase the risk of heart disease, depression, anxiety, and stroke.7, 8 Racism and weight stigma may compound societal discrimination and contribute to poor health outcomes.4, 5, 9 Research shows that many health care providers hold negative attitudes and stereotypes about people with adult obesity and provide these individuals with a lower quality of care.7 On average, health care providers spend less time with patients with adult obesity, provide less health education, and are more likely to ignore symptoms or attribute these to patient weight; failing to account for underlying conditions.3, 8
Data and methods
Behavioral Risk Factor Surveillance System
The Behavioral Risk Factor Surveillance System (BRFSS) is a state-based random digit dial (RDD) telephone survey that is conducted annually in all states, the District of Columbia, and U.S. territories. Data obtained from the BRFSS are representative of each state’s total non-institutionalized population over 18 years of age and have included more than 400,000 annual respondents with landline telephones or cellphones since 2011. Data are weighted using iterative proportional fitting (also called "raking") methods to reflect population distributions. For the County Health Rankings, data from the BRFSS are used to measure various health behaviors and health-related quality of life (HRQoL) indicators. HRQoL measures are age-adjusted to the 2000 U.S. standard population.
Prior to the 2016 County Health Rankings, up to seven survey years of landline only BRFSS data were aggregated to produce county estimates. However, even with multiple years of data, these did not provide reliable estimates for all counties, particularly those with smaller respondent samples. For the 2016 County Health Rankings and beyond, the CDC produced county estimates using single-year BRFSS data and a multilevel modeling approach based on respondent answers and their age, sex, and race/ethnicity, combined with county-level poverty, as well as county- and state-level contextual effects.1 To produce estimates for those counties where there were no or limited data, the modeling approach borrowed information from the entire BRFSS sample as well as Census Vintage population estimates. CDC used a parametric bootstrapping method to produce standard errors and confidence intervals for those point estimates. This estimation methodology was validated for all U.S. counties, including those with no or small (< 50 respondents) samples.2 This same method was used in constructing the 500 cities study, which includes BRFSS data for the 500 largest cities in the U.S. and can be found here: https://www.cdc.gov/500cities.
For the 2021 Rankings, the CDC has updated their modeling procedure for producing small-area estimates. With the PLACES project, a multilevel statistical modeling framework using multilevel regression and poststratification (MRP) is performed for small-area estimation that links BRFSS data with high spatial resolution population demographic and socioeconomic data from the Census’ American Community Survey (ACS). The CDC has performed internal and external validation studies, which confirm strong consistency between their model-based estimates and the direct BRFSS survey estimates at both the state and county levels. For more technical information on the PLACES modeling procedure, please see their website.3
1Zhang X, Holt JB, Lu H, Wheaton AG, Ford ES, Greenlund K, Croft JB. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. American Journal of Epidemiology 2014;179(8):1025–1033.
2Zhang X, Holt JB, Yun, S, Lu H, Greenlund K, Croft JB. Validation of multilevel regression and poststratification methodology for small area estimation of health outcomes. American Journal of Epidemiology 2015;182(2):127-137.
3PLACES Project. Centers for Disease Control and Prevention. Accessed March 9, 2021. https://www.cdc.gov/places.
Website to download data
For more detailed methodological information
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 (ages 18 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.
Adult Obesity estimates are age-adjusted
Age is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. We report an age-adjusted rate in order to fairly compare counties with differing age structures.
The method for calculating Adult Obesity has changed
In the 2022 County Health Rankings, the source for this measure switched from the United States Diabetes Surveillance System to the Behavioral Risk Factor Surveillance System.
Adult Obesity is created using statistical modeling
Statistical modeling is used to obtain more informed and reliable estimates than survey data alone can provide. Modeling generates more stable estimates for places with small numbers of residents or survey responses. The Adult Obesity estimates are produced from one year of survey data and are created using complex statistical modeling. For more technical information on PLACES modeling using BRFSS data, please see their methodology.5
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 assumptions about statistical relationships that may not hold in all cases. Finally, there is no perfect model and each model generally has limitations specific to their methods.
The numerator is the number of adult respondents age 18 and older with a BMI greater than or equal to 30kg/m2.
The denominator is the number of adult respondents age 18 and older.
BMI may contain measurement error as it is calculated from self-reported weight and height.10
BMI thresholds are not a direct measure of body fat. An individual’s BMI cannot independently distinguish them as being healthy, unhealthy, or at risk for disease.11
Can This Measure Be Used to Track Progress
First, it is important to note the change in data source in the 2022 Rankings from United States Diabetes Surveillance System to Behavioral Risk Factor Surveillance System (BRFSS). This measure could be used to track progress, but only after considering its substantial limitations. Methodological changes in the BRFSS, which were implemented in the 2016 Rankings, make comparisons with estimates prior to that release year difficult. Additional changes to the methodology to create the estimates were implemented in the 2021 Rankings, further making comparisons with estimates prior to that release year difficult. Finally, current estimates are produced using sophisticated modeling techniques which make them difficult to use for tracking progress, especially 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.
Finding More Data
Disaggregation means breaking data down into smaller, meaningful subgroups. Disaggregated data are often broken down by characteristics of people or where they live. Disaggregated data can reveal inequalities that are otherwise hidden. These data can be disaggregated by:
- Subcounty Area
The PLACES Project provides county-, city-, census tract-, and zip code-level small-area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use, including Adult Obesity, across the United States.
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. In many states, you can access county level BRFSS estimates, and in some cases you can stratify those estimates by age, gender, education, income, or race. You can find BRFSS resources for most states in our Data beyond the Rankings section.
1 Centers for Disease Control and Prevention (CDC). Overweight & Obesity. 2022.
2 James, WPT. The epidemiology of obesity: the size of the problem. Journal of Internal Medicine. 2008;263(4):336-352.
3 Puhl RM, Heuer CA. Obesity stigma: important considerations for public health. American Journal of Public Health. 2010;100(6):1019-28.
4 Kumanyika S. Obesity, health disparities, and prevention paradigms: hard questions and hard choices. Preventing Chronic Disease. 2005;2(4):A02.
5 Aaron DG, Stanford FC. Is obesity a manifestation of systemic racism? A ten-point strategy for study and Intervention. Journal of Internal Medicine, 2021;290(2):416–420.
6 Cohen SA, Nash CC, Byrne EN, Mitchell LE, Greaney ML. Black/White Disparities in Obesity Widen with Increasing Rurality: Evidence from a National Survey. Health Equity. 2022;6(1):178-188.
7 Phelan SM, Burgess DJ, Yeazel MW, et al. Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obesity Reviews. 2015;16(4):319-26.
8 Monaghan L. Discussion Piece: A Critical Take on the Obesity Debate. Social Theory & Health. 2005;3:302–314.
9 Stern C. Why BMI is a flawed health standard, especially for people of color. The Washington Post. 2021.
10 Stommel M, Schoenborn CA. Accuracy and usefulness of BMI measures based on self-reported weight and height: findings from the NHANES & NHIS 2001-2006. BMC Public Health. 2009;9:421.
11 Centers for Disease Control and Prevention (CDC). Body Mass Index: Considerations for Practitioners; 2011.