Obesity by County: CDC PLACES Data and Access Patterns

The National Picture Masks Enormous Local Variation

The national obesity rate sits around 33-34% of US adults. That number is useful for trending and population-level discussions. But it obscures something important: where you live matters. Enormously.

Some US counties have obesity rates below 25%. Others exceed 50%. That is not a small difference. When you understand the geography of obesity prevalence, you start to see that the problem is not evenly distributed. Neither is access to treatment.

The Data Source: CDC PLACES

The Centers for Disease Control publishes a program called PLACES: Population Level Analysis and Community Estimates^[1]. This program provides health estimates at the county, census tract, and ZIP code level across the United States.

PLACES was expanded from an earlier initiative called “500 Cities.” Today it covers all approximately 3,000 counties and county-equivalents in the US. The data includes age-adjusted prevalence estimates for obesity (defined as BMI 30 or higher) in adults.

The underlying data comes from the Behavioral Risk Factor Surveillance System, or BRFSS^[2]. This is a national telephone survey conducted by the CDC and state health departments. Because not every county has enough survey respondents, the CDC uses statistical modeling to produce estimates for all counties, even those with small populations. The most recent PLACES release is from 2023.

These estimates are the best publicly available picture of obesity prevalence by geography. They are not perfect, but they are comprehensive and methodologically sound.

The Geography of High Obesity Prevalence

The Mississippi Delta

The Mississippi Delta is the region with the highest concentration of high-obesity counties in America. Counties in Mississippi, Arkansas, and Louisiana in and near the Delta show some of the nation’s highest obesity rates. Several counties in Bolivar County, Mississippi, for example, report obesity prevalence above 50%. Similar patterns appear throughout the Delta region.

These are not outlier counties. This is a consistent geographic pattern driven by deep structural factors: poverty, food environment, healthcare access, and historical employment patterns.

Appalachia

Rural Appalachian counties, particularly in Kentucky, West Virginia, and eastern Tennessee, consistently show obesity rates in the 40-50% range. West Virginia as a state has one of the highest obesity rates in the nation, and the county-level data shows this is concentrated in rural areas with limited healthcare infrastructure and limited access to full-service grocery stores.

Counties with Large Native American Populations

Several counties with substantial Native American populations show obesity rates above 45%. This reflects documented disparities in food access, healthcare access, and the legacy of economic disruption in reservation communities.

The Deep South

Beyond the Delta, rural counties throughout the Deep South, particularly in Alabama and Georgia, show elevated obesity rates. Many rural Georgia counties exceed 40% obesity prevalence. These patterns reflect similar structural barriers: poverty, food deserts, and limited healthcare access.

Selected Rural Midwest

Parts of rural Missouri, Oklahoma, and Kansas show elevated obesity rates, though not at the level of the Delta or Appalachia. These tend to be counties where agricultural employment has declined and has been replaced by sedentary service or manufacturing work, without accompanying changes in food environment or physical activity infrastructure.

The Geography of Lower Obesity Prevalence

Colorado Front Range

The Denver-Boulder area and surrounding counties in Colorado’s Front Range show some of the nation’s lowest obesity rates. Counties like Boulder, Eagle, and Routt County are among the lowest-prevalence counties in the US, with obesity rates often 20-25%.

Mountain West

Several counties in Utah, Montana, and Wyoming show relatively low obesity rates. These tend to be areas with higher incomes, more access to recreational infrastructure, and populations with higher educational attainment.

Upper New England

Vermont and New Hampshire counties show lower obesity rates, particularly the counties that include or are near major cities and college towns.

Pacific Coast

King County, Washington (Seattle area), several Bay Area counties, and Marin County, California are among the lowest-prevalence counties in the nation. These are high-income, urban areas with robust food infrastructure and access to fitness and wellness resources.

What Drives the 2.5x Difference Between Lowest and Highest

The range from 20-25% obesity prevalence to 50%+ is not random. It reflects differences in^[3]:

Food environment. Counties with robust access to full-service grocery stores, farmers’ markets, and affordable fresh produce show lower obesity rates. Counties that are food deserts, where the primary food retail is convenience stores and fast-food chains, show higher rates.

Built environment. Walkable neighborhoods with parks, trails, and recreation infrastructure support more physical activity. Car-dependent sprawl and neighborhoods without safe walking or cycling infrastructure make movement harder.

Poverty and income. Counties with higher poverty rates and lower median household income show higher obesity prevalence. This is not because poverty causes obesity through individual behavior, but because poverty limits access to healthy food, time for movement, and healthcare.

Healthcare access. Counties with more healthcare providers, lower uninsurance rates, and better preventive care infrastructure show modestly lower obesity rates. Specialist availability, particularly obesity medicine specialists, is extremely limited in high-prevalence rural counties.

Race and ethnicity composition. At the population level, Black adults, Hispanic adults, and Native American adults have higher obesity prevalence than White adults^[4]. This is driven by documented historical and ongoing disparities in food access, healthcare quality, and economic opportunity. These disparities are not inherent or biological, they are the result of structural inequality.

Employment type and history. Counties where employment transitioned from agricultural or physical labor to sedentary work, without accompanying changes in food environment or recreation access, show higher obesity rates. Areas that maintained or built recreational and fitness infrastructure alongside this employment transition show lower rates.

Healthcare infrastructure. Counties with more comprehensive preventive care, including obesity medicine specialists and lifestyle medicine programs, show lower obesity rates. But these are exactly the counties that already have higher incomes and better healthcare access.

The Access Equity Problem: Highest-Need, Least-Served

Here is the core problem. The counties with the highest obesity prevalence are often the exact same counties with the least healthcare access. A resident of Bolivar County, Mississippi, not only faces a higher-prevalence food environment and lower access to recreation, they also face a shortage of obesity medicine specialists, limited primary care availability, and often long travel times to reach specialized care.

This is the access equity gap. The places with the greatest medical need have the least capacity to meet that need. Rural primary care providers often lack training in GLP-1 medications and weight loss medicine. Obesity medicine specialists are concentrated in urban areas and wealthy suburbs. Rural Mississippi and Appalachia have almost none.

This is why telehealth is structurally important. It reaches all counties equally. It bypasses geography.

How to Find Your County’s Data

The CDC PLACES tool is at cdc.gov/places. You can search by county or ZIP code and view obesity prevalence, physical inactivity, diabetes prevalence, and other health estimates. The data is updated annually.

County Health Rankings, at countyhealthrankings.org, provides obesity prevalence alongside contextual data on food environment, physical inactivity, poverty, income, uninsurance rates, and other social determinants. This tool helps you understand not just what your county’s obesity rate is, but what structural factors contribute to it.

Both are free, publicly available resources. Neither requires registration or payment.

Telehealth Removes the Geography Barrier

GLP-1 treatment historically required traveling to an obesity medicine specialist. Most Americans did not have one nearby. Residents of rural counties faced travel times of hours or days to reach a provider trained in GLP-1 medications.

Telehealth removes that barrier. An intake assessment takes about 10 minutes and is completed online. A licensed provider reviews your health history, labs, and goals asynchronously, typically within 24 hours. If a prescription is medically appropriate, your medication is prepared by a licensed US compounding pharmacy and shipped to your address. Lab work is done at your nearest Quest Diagnostics or Labcorp. Coaching and follow-up consultations are conducted via secure messaging or video, on your schedule.

Residents of AR, DC, DE, MS, NM, RI, and WV are required by state law to complete a live video consultation, but this does not require travel. The consultation takes about 30 minutes and happens on a scheduled video call.

A resident of a high-prevalence rural county now has the same access to GLP-1 evaluation and treatment as a resident of a major metropolitan area. The structural inequality in access can be addressed, at least for this intervention.

Citations

[1] Centers for Disease Control and Prevention. “PLACES: Population Level Analysis and Community Estimates.” https://www.cdc.gov/places/

[2] Centers for Disease Control and Prevention. “Behavioral Risk Factor Surveillance System (BRFSS).” https://www.cdc.gov/brfss/

[3] Swinburn BA et al. “The Global Obesity Pandemic: Shaped by Global Forces and Local Environments.” Lancet. 2011;378(9793):804-814.

[4] Powell LM et al. “Food Store Availability, Neighborhood Deprivation, and Obesity Among U.S. Adults.” Public Health Nutrition. 2017;20(12):2196-2205.