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.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.

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.

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.

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 **** 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 *** percent.

Diabetes The number of individuals suffering from diabetes in Italy amounted to ***** 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 ** percent of obese women were also diabetic. This rate lowers to **** 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 **** percent of male minors overweight between 2020 and 2021, compared to ** percent of females.

For more recent aggregated data reports on childhood obesity in NM, visit NM Healthy Kids Healthy Communities Program, NMDOH: https://www.nmhealth.org/about/phd/pchb/hknm/TitleChildhood Obese and Overweight Estimates, NM Counties 2016 - NMCHILDOBESITY2017SummaryCounty level childhood overweight and obese estimates for 2016 in New Mexico. *Most recent data known to be available on childhood obesity*NotesThis map shows NM County estimated rates of childhood overweight and obesity. US data is available upon request. Published in May, 2022. Data is most recent known sub-national obesity data set. If you know of another resource or more recent, please reach out. emcrae@chi-phi.orgSourceData set produced from the American Journal of Epidemiology and with authors and contributors out of the University of South Carolina, using data from the National Survey of Children's Health. Journal SourceZgodic, A., Eberth, J. M., Breneman, C. B., Wende, M. E., Kaczynski, A. T., Liese, A. D., & McLain, A. C. (2021). Estimates of childhood overweight and obesity at the region, state, and county levels: A multilevel small-area estimation approach. American Journal of Epidemiology, 190(12), 2618–2629. https://doi.org/10.1093/aje/kwab176 Journal article uses data fromThe United States Census Bureau, Associate Director of Demographic Programs, National Survey of Children’s Health 2020 National Survey of Children's Health Frequently Asked Questions. October 2021. Available from:https://www.census.gov/programs-surveys/nsch/data/datasets.htmlGIS Data Layer prepared byEMcRae_NMCDCFeature Servicehttps://nmcdc.maps.arcgis.com/home/item.html?id=80da398a71c14539bfb7810b5d9d5a99AliasDefinitionregionRegion NationallystateState (data set is NM only but national data is available upon request)fips_numCounty FIPScountyCounty NamerateRate of Obesitylower_ciLower Confidence Intervalupper_ciUpper Confidence IntervalfipstxtCounty FIPS text

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This dataset includes select data from the U.S. Census Bureau's American Community Survey (ACS) on the percent of adults who bike or walk to work. 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 ACS visit https://www.census.gov/programs-surveys/acs/.

This dataset includes select data from the U.S. Census Bureau's American Community Survey (ACS) on the percent of adults who bike or walk to work. 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 ACS visit https://www.census.gov/programs-surveys/acs/.

ObjectiveThis scoping review evaluates the breadth of research on avocado intake and health, considering all populations and health outcomes (registered on Open Science Foundation at https://osf.io/nq5hk).DesignAny human intervention or observational study where effects could be isolated to consumption of avocado were included. A systematic literature search through April 2024 was conducted (PubMed, Web of Science, Scopus, and CENTRAL) and supplemented by backwards citation screening. Dual screening, data extraction, and conflict resolution were performed by three reviewers and an interactive evidence map was created.ResultsAfter deduplication, 8,823 unique records were retrieved; 58 articles met inclusion criteria, comprising 45 unique studies (28 interventions, 17 observational studies). Studies were largely conducted in the United States or Latin America and generally included adults, with overweight/obesity, frequently with elevated lipid concentrations. Interventions assessed the impact of diets enriched in monounsaturated fatty acids, diets higher/lower in carbohydrates, or in free-feeding conditions. Larger amounts of avocados were used in interventions than commonly consumed in observational studies (60–300 vs. 0–10 g/d, respectively). Blood lipids, nutrient bioavailability, cardiovascular risk, glycemia, and anthropometric variables were the most common outcomes reported across all studies.ConclusionFuture recommendations for novel research include the study of: European, Asian, adolescent or younger, and senior populations; dose–response designs and longer length interventions; dietary compensation; and the need for greater replication. The results have been made public and freely available, and a visual, interactive map was created to aid in science translation. This evidence map should enable future meta-analyses, enhance communication and transparency in avocado research, and serve as a resource for policy guidance.

"The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs. " Source: U.S. Census Bureau.The map shows the poverty ratio for states, counties, tracts and block groups, with the data source from the U.S. Census Bureau's American Community Survey (ACS) for 2013 for the previous 12 months. The percent of each Illinois county’s population that is considered obese from the 2015 CDC BRFSS Survey (Source, Lake County Illinois).

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 adult 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 Adults: Obesity is defined as a BMI, calculated from height and weight, of 30 kilograms per meter squared (kg/m2) or greater.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.

Data are presented as mean (standard deviation), BMI – Body Mass Index; DXA - Dual-energy X-ray absorptiometry; BP – Blood Pressure.

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.

Australia's Health Tracker by Area presents data via interactive maps and graphs on a range of chronic diseases, conditions and their risk factors. In addition to the maps and graphs, data can be downloaded as spreadsheets.

Information is available on: obesity, high blood pressure, risky alcohol consumption, smoking, high cholesterol, bowel cancer screening, diabetes, death rates from various diseases, and suicide rates.

The tracker shows both the latest national data and how it compares with the 2025 Australian chronic disease targets.

Notes on the data are available from each download page and contain information on the indicators and data sources. Related publications mentioned below: 'Australia's Health Tracker 2016' , 'Australia's Health Tracker: Technical Appendix', and 'Getting Australia's Health on Track' are useful companion reports

Except where otherwise stated, all age-standardised rates and ratios presented in the maps, data or graphics are based on the Australian standard.

Data can be reported on by Population Health Area, Local Government Area, Primary Health Network, and at State and Territory level.

Population Health Areas (PHAs) are comprised of a combination of whole Statistical Area Level 2s (SA2s) and multiple (aggregates of) SA2s, where the SA2 is an area in the ABS structure:the Australian Statistical Geographical Standard (ASGS), July 2011, Cat No 1270.0.55.001

Local Government Areas (LGAs) are an Australian Bureau of Statistics (ABS) approximation of officially gazetted LGAs as defined by each State and Territory Local Government Department. LGAs cover incorporated areas of Australia. For further information regarding the LGA structure, refer to the ABS information at: Australian Statistical Geography Standard (ASGS): Volume 3 - Non ABS Structures, July 2015, Cat No 1270.0.55.003

Primary Health Networks (PHNs) comprise 31 primary health care organisations across Australia. For further information, including digital boundary and concordance files, refer to the Department of Health Primary Health Networks http://www.health.gov.au/internet/main/publishing.nsf/Content/PHN-Home