This map shows where obesity and diabetes are happening in the US, by county. It shows each component of the map as its own layer, and also shows the patterns overlapping. Diabetes prevalence (% of adults)Obesity prevalence (% of adults)This data can be used to assess the health factors, and answer questions such as:Are certain counties more/less at risk in regards to diabetes and obesity?Are diabetes, obesity, and physical inactivity happening within the same areas of the US?According to the CDC: "These data can help the public to better use existing resources for diabetes management and prevention efforts." The data comes from the Behavioral Risk Factor Surveillance System (BRFSS) through the Centers for Disease Control and Prevention (CDC), and the data vintage is 2013. To explore other county indicators, different vintages, or the original data, click here. To view the interactive map through the CDC website, click here. To learn more about the methodology of how county-level estimates are calculated, see this PDF.

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Data for this indicator are based on self-reported height and weight. Body Mass Index (BMI) is calculated by dividing a person’s weight in kilograms by the square of their height in meters. Individuals with a BMI ≥ 30 are considered to have obesity. Note, while BMI can be helpful in screening for individuals with obesity or overweight, it does not measure how much body fat an individual has or provide any diagnostic information about their overall health.Obesity is associated with increased risk for heart disease, diabetes, and cancer. Cities and communities can help curb the current obesity epidemic by adopting policies that support healthy food retail and physical activity and improve access to preventive care services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset shows the levels of overweight and obese people by country. Data is provided for 2002 and 2010 as a percentage of the total population and is also broken down by sex. Rates of change between 2002 and 2010 are also provided. The data was collated by the World Health Organisation (WHO)(http://www.who.int/gho/ncd/risk_factors/overweight/en/index.html) and was downloaded via the Guardian website (http://www.theguardian.com/news/datablog/interactive/2013/feb/19/obesity-map-of-world-weight). GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-01-03 and migrated to Edinburgh DataShare on 2017-02-22.

Obesity percentages for Lake County, Illinois. Explanation of field attributes: Pct_Obese – The percent of people in the zip code who are considered obese, defined as having a BMI greater than or equal to 30. ObsOrOvrwt –The percent of people in the zip code who are considered overweight (defined as having a BMI greater than or equal to 25 but less than 30) or obese (defined as having a BMI greater than or equal to 30).

The prevalence of obesity in the United States has risen gradually over the past decade. As of 2023, around ** percent of the population aged 18 years and older was obese. Obesity is a growing problem in many parts of the world, but is particularly troubling in the United States. Obesity in the United States The states with the highest prevalence of obesity are West Virginia, Mississippi, and Arkansas. As of 2023, a shocking ** percent of the population in West Virginia were obese. The percentage of adults aged 65 years and older who are obese has grown in recent years, compounding health issues that develop with age. Health impacts of obesity Obesity is linked to several negative health impacts including cardiovascular disease, diabetes, and certain types of cancer. Unsurprisingly, the prevalence of diagnosed diabetes has increased in the United States over the years. As of 2022, around *** percent of the population had been diagnosed with diabetes. Some of the most common types of cancers caused by obesity include breast cancer in postmenopausal women, colon and rectum cancer, and corpus and uterus cancer.

This layered map shows the locations of Creating Healthy Places interventions that are targeted towards children and the percentage of students (elementary, middle, and high school) who are obese (95th percentile or higher) by county (source: Student Weight Status Category Reporting System). The purpose of the Creating Healthy Places initiative is to implement community level interventions to promote healthy lifestyles to prevent obesity and type 2 diabetes. The lighter shaded counties have a lower percentage of obese students. The darker shaded counties have a higher percentage of obese students. This map can help identify areas that could benefit from more community level and school level interventions like the ones implemented through the Creating Healthy Places Initiative. The "About" tab contains additional details concerning this dataset.

Combined Hospital Fund area data depicting obesity rates

West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

Note: This data was created by the Center for Disease Control, not the City of Rochester. This map is zoomed in to show the CDC data at the census tract level. You can zoom out to see data for all 500 cities in the data set. This map has been built to symbolize the percentage of adults who, in 2017, had a body mass index (BMI) at/above 30.0, classifying them as obese according to self-reported data on their height on weight. However, if you click on a census tract, you can see statistics for the other public health statistics mentioned below in the "Overview of the Data" section.Overview of the Data: This service provides the 2019 release for the 500 Cities Project, based on data from 2017 or 2016 model-based small area estimates for 27 measures of chronic disease related to unhealthy behaviors (5), health outcomes (13), and use of preventive services (9). Twenty measures are based on 2017 Behavioral Risk Factor Surveillance System (BRFSS) model estimates. Seven measures (all teeth lost, dental visits, mammograms, Pap tests, colorectal cancer screening, core preventive services among older adults, and sleep less than 7 hours) kept 2016 model estimates, since those questions are only asked in even years. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. It represents a first-of-its kind effort to release information on a large scale for cities and for small areas within those cities. It includes estimates for the 500 largest US cities and approximately 28,000 census tracts within these cities. These estimates can be used to identify emerging health problems and to inform development and implementation of effective, targeted public health prevention activities. Because the small area model cannot detect effects due to local interventions, users are cautioned against using these estimates for program or policy evaluations.Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. Data sources used to generate these measures include BRFSS data (2017 or 2016), Census Bureau 2010 census population data, and American Community Survey (ACS) 2013-2017 or 2012-2016 estimates. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

IntroductionAddressing health inequities across chronic diseases is a critical public health objective, and policy, systems, and environmental (PSE) change approaches are integral to achieving this goal. However, assumptions about mechanisms of effect or population salience of PSE approaches do not necessarily generalize to inequitable social and economic contexts, partially due to limited ability to operationalize the dynamic complexity of such contexts. Systems thinking applications have the potential to characterize this complexity and improve understanding of where and how to intervene.MethodsThe Getting to Equity in Obesity Prevention Framework (GTE) posits a theory of change involving PSE-related considerations for achieving equity grouped into four categories with a general systems feedback structure. We used systems mapping with a case study to explore the anticipated synergy across categories of the GTE. Data were extracted from a narrative account of childhood obesity prevention initiatives in a predominantly African American and Hispanic, urban public-school district: the Philadelphia Childhood Obesity Declines Project. Project documentation described PSE strategies and contextual influences thought to have contributed to concurrently observed declines in child obesity prevalence and disparities in this population.ResultsOur final dynamic framework, which was anchored by Philadelphia's Universal Feeding Pilot for school meals, identified synergies among intervention strategies. The systems map revealed how planned and unplanned processes accumulated to align with the observed disparities reductions in the participating school district, consistent with the GTE theory of change. Community context dynamics, which evolved over time, were prominent features of the map.DiscussionThis case study enhances the utility of the GTE framework when paired with systems mapping enabled by detailed documentation of PSE initiatives and relevant contextual influences. This suggests that prospective mapping of considerations prompted by the GTE could improve anticipation of unplanned pathways, intervention design, and implementation and supports a need for greater priority for using systems mapping or other systems science tools and methodologies in obesity-prevention research and practice.

Overview: This dataset combines publicly available data on obesity rates, poverty rates, and median household income for all 50 U.S. states from 2019 to 2023. It also includes calculated regional averages based on U.S. Census Bureau-defined regions (Northeast, Midwest, South, and West).

Use Cases - Public health research - Data visualization projects - Socioeconomic analysis - ML models exploring health + income

Sources - CDC BRFSS – Adult Obesity Prevalence Maps (2019–2023) - U.S. Census Bureau – SAIPE Datasets (2019–2023)

Tableau Dashboard View the interactive Tableau dashboard:
https://public.tableau.com/app/profile/geo.montes/viz/ObesityPovertyandIncomeintheU_S_2019-2023/Dashboard1#2

Created by Geo Montes, Informatics major at UT Austin

BMI data is obtained from each systems’ electronic health record and combined into one database managed by the Colorado Department of Public Health and Environment. These data represent individuals who presented for routine care at one of the participating health care organizations, and had a valid height and weight measured. Overweight and obesity prevalence estimates are available for the 7 metro Denver counties, and for rural Prowers County. Estimates generated from the Colorado BMI Monitoring System may be linked with other data sources to identify contributory social and environmental factors.This feature layer represents childhood/youth obesity estimates only.DefinitionsCoverage: The total number of individuals in the BMI Monitoring System with a valid BMI divided by the total estimated population from the American Community Survey Population and Demographic Estimates produced by the US Census Bureau in the specified geographic area and age group.Obesity Children/Youth: BMI is calculated from height and weight and plotted on the Centers for Disease Control and Prevention (CDC) male or female BMI-for-age growth chart to determine a percentile. Obesity is defined as a BMI at the 95th percentile or higher.Obesity Prevalence Estimates: Percentage of individuals with obesity based upon the total number of individuals with obesity in the specified geographic area and age group divided by the total number of valid BMI measurements in the same specified geographic area and age group.

A collection of 19 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

Collection description

T-maps of contrasts derived from the 'olfactory perception in obesity' study

State of Illinois Obesity Percentages by County. Explanation of field attributes: Obesity - The percent of each Illinois county’s population that is considered obese from the 2015 CDC BRFSS Survey.

This dataset includes data on policy and environmental supports for physical activity, diet, and breastfeeding. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding.

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in “bow-tie” architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

In 2023, the distribution of body-mass-index (BMI) across Italy varied greatly by region. According to the data, southern regions had a higher share of overweight and obese people compared to the national average. Overall, the overweight population in Italy is projected to reach 69.4 percent by 2029. The Italian regions with the highest share of people considered as having a normal weight in 2023 were Trentino-South Tyrol, Tuscany, and Marche. Conversely, the region of Aosta Valley hosted the most underweight people in the country, in relative terms, with 5.7 percent.

Diabetes The number of individuals suffering from diabetes in Italy amounted to 3,888 in 2022. Although the risk factors related to type one diabetes are not fully known, among the risk factors for diabetes type 2, being overweight or obese are among the most common. Indeed, in 2021, almost 17 percent of obese women were also diabetic. This rate lowers to 14.1 percent for men. Obesity among children and adolescents Childhood obesity is becoming an issue in the country, with the share of overweight and obese children growing every year. Indeed, Italy has become one of the European countries with the highest obesity rate among children. This tendency is more prevalent among young boys, with 29.8 percent of male minors overweight between 2020 and 2021, compared to 24 percent of females.

This map includes layers from ACS and CDC. ACS updates poverty status annually. The CDC tract-level data are represented as location points. They are part of the "500 cities project."

This dataset includes data on adolescent's diet, physical activity, and weight status from Youth Risk Behavior Surveillance System (YRBSS). This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding. For more information about YRBSS visit https://www.cdc.gov/healthyyouth/data/yrbs/index.htm.

Between 2015 and 2018, obesity rates in U.S. children and adolescents stood at 19.3 and 20.9 percent, respectively. This is a noteworthy increase compared to the percentages seen between 1988 and 1994.

U.S. high school obesity rates

Roughly 18 percent of black, as well as Hispanic students in the United States, were obese between 2016 and 2017. Male obesity rates were noticeably higher than those of female students for each of the ethnicities during the measured period. For example, about 22 percent of male Hispanic high school students were obese, compared to 14 percent of female students. The American states with the highest number of obese high school students in 2019 included Mississippi, West Virginia, and Arkansas, respectively. Mississippi had a high school student obesity rate of over 23 percent that year.

Physically inactive Americans

Adults from Mississippi and Arkansas were also reported to be some of the least physically active people in the United States in 2018. When surveyed, over 30 percent of adults from Kentucky and Arkansas had not exercised within the preceding 30 days. The national physical inactivity average stood at approximately 26 percent that year.