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/.

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.

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.

1. Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. This is a filtered subset of the 500 Cities data that provides 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). 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 sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data 2015, Census Bureau 2010 census population data, and American Community Survey (ACS) 2011-2015 estimates. More information about the methodology can be found at www.cdc.gov/500cities.

This dataset contains model-based census tract level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 29 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

This data represents the age-adjusted prevalence of high total cholesterol, hypertension, and obesity among US adults aged 20 and over between 1999-2000 to 2017-2018. Notes: All estimates are age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and over. Definitions Hypertension: Systolic blood pressure greater than or equal to 130 mmHg or diastolic blood pressure greater than or equal to 80 mmHg, or currently taking medication to lower high blood pressure High total cholesterol: Serum total cholesterol greater than or equal to 240 mg/dL. Obesity: Body mass index (BMI, weight in kilograms divided by height in meters squared) greater than or equal to 30. Data Source and Methods Data from the National Health and Nutrition Examination Surveys (NHANES) for the years 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used for these analyses. NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the civilian noninstitutionalized U.S. population. The survey consists of interviews conducted in participants’ homes and standardized physical examinations, including a blood draw, conducted in mobile examination centers.

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

This is the complete dataset for the 500 Cities project 2016 release. This dataset includes 2013, 2014 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). Data were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. 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 sources used to generate these measures include Behavioral Risk Factor Surveillance System (BRFSS) data (2013, 2014), Census Bureau 2010 census population data, and American Community Survey (ACS) 2009-2013, 2010-2014 estimates. More information about the methodology can be found at www.cdc.gov/500cities. Note: During the process of uploading the 2015 estimates, CDC found a data discrepancy in the published 500 Cities data for the 2014 city-level obesity crude prevalence estimates caused when reformatting the SAS data file to the open data format. . The small area estimation model and code were correct. This data discrepancy only affected the 2014 city-level obesity crude prevalence estimates on the Socrata open data file, the GIS-friendly data file, and the 500 Cities online application. The other obesity estimates (city-level age-adjusted and tract-level) and the Mapbooks were not affected. No other measures were affected. The correct estimates are update in this dataset on October 25, 2017.

Analysis of ‘Nutrition, Physical Activity, and Obesity - American Community Survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/2230a9ce-c7a0-4c07-b22c-4e1140e65a10 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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/.

--- Original source retains full ownership of the source dataset ---

Release Date: 2023-09-28.Release Schedule:.The data in this file was released in September 2023...Key Table Information:.The estimates presented are based on data from the 2021 Vehicle Inventory and Use Survey (VIUS)..These estimates only cover vehicles registered during 2021 in one of the fifty United States (except New Hampshire) or the District of Columbia that are classified by vehicle manufacturers as trucks, minivans, vans, or sports utility vehicles. Additionally, vehicles owned by federal, state, and local governments, ambulances, buses, motor homes, farm tractors, unpowered trailer units, and any vehicle reported to have been disposed prior to January 1, 2021, are considered out of scope for the VIUS..Additionally, estimates on this table are restricted to in-scope vehicles (other than pickups, minivans, sport utility vehicles, or light vans) identified to have been used at some point in 2021..Estimates may not be additive due to rounding..The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. P-7527235, Disclosure Review Board (DRB) approval number: CBDRB-FY23-032)...Data Items and Other Identifying Records:.Primary characteristics that appear in this table:..Annual overweight permits.Single trip overweight permits...Estimates on this table:..Number of vehicles (thousands).Vehicle miles (millions).Average miles per vehicle (thousands).Coefficients of variation for all of the above estimates (percentages)...Data Item Notes:..Trailer Configuration.Estimates of 'Single Trailer Pulled', 'Double Trailer Pulled', 'Triple Trailer Pulled', and 'Trailer Pulled' exclude vehicles that pulled a trailer for less than half of all miles driven. Estimates of 'No Trailer Pulled or Vehicle Not Used' include vehicles that pulled a trailer for less than half of all miles driven....Geography Coverage:.On this table, geography refers to the address on a given vehicle's registration..Data are shown for the United States, 49 states (every state except New Hampshire), and the District of Columbia..Note that estimates at the 'United States' level also do not include vehicles with registration addresses in New Hampshire because the state did not consent to sharing registrant data for this survey. See https://www.census.gov/programs-surveys/vius/data.html for model-based estimates at the United States level that do include New Hampshire...Industry Coverage:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/vius/data/2021/VIUS211D.zip..API Information:.Vehicle Inventory and Use Survey data are housed in the Census Bureau API. For more information, see https://api.census.gov/data/2021/viusd.html..Methodology:.Estimates are based on a sample of in-scope vehicles and are subject to both sampling and nonsampling error. Estimated measures of sampling variability are provided in the tables. For information on sampling or nonsampling error or and other design and methodological details, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology...Symbols:.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see Vehicle Inventory and Use Survey (VIUS): Technical Documentation: Vehicle Inventory and Use Survey Methodology..Z - Round to Zero..X - Not Applicable..For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary. ..Source:.Suggested Citation: U.S. Department of Transportation, Bureau of Transportation Statistics; and, U.S. Department of Commerce, U.S. Census Bureau. (9/28/23). Overweight Permits by Registration State, Vehicle Type, and Trailer Configuration: 2021 [VIUSD2021]. 2021 Vehicle Inventory and Use Survey. U.S. Department of Transportation, Bureau of Transportation Statistics; U.S. Department of Commerce, U.S. Census Bureau; U.S. Department of Transportation, Federal Highway Administration; U.S. Department of Energy. Accessed [enter date you accessed/downloaded this table here] from [enter URL of the table page here]...For information about VIUS, see Vehicle Inventory and Use Survey (VIUS)...Contact Information:.U.S. Census Bureau.Vehicle Inventory and Use Survey.Tel. (301) 763-6901.Email: erd.vius@census.gov

From the CDC Places page on ArcGIS:

PLACES (Population Level Analysis and Community Estimates) is an expansion of the original 500 Cities project and is a collaboration between the Centers for Disease Control and Prevention (CDC), the Robert Wood Johnson Foundation, and the CDC Foundation. The original 500 Cities Project provided city- and census tract-level estimates for the 500 largest US cities. PLACES extends these estimates to all counties, places (incorporated and census designated places), census tracts, and ZIP Code Tabulation Areas (ZCTA) across the United States.

Title Childhood Obese and Overweight Estimates, NM Counties 2016 - NMCHILDOBESITY2017

Summary County level childhood overweight and obese estimates for 2016 in New Mexico. Most recent data known to be available on childhood obesity

Notes This 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.org

Source Data 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 Source Zgodic, 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 from The 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.html

GIS Data Layer prepared by EMcRae_NMCDC

Feature Service https://nmcdc.maps.arcgis.com/home/item.html?id=80da398a71c14539bfb7810b5d9d5a99

Alias Definition

region Region Nationally

state State (data set is NM only but national data is available upon request)

fips_num County FIPS

county County Name

rate Rate of Obesity

lower_ci Lower Confidence Interval

upper_ci Upper Confidence Interval

fipstxt County FIPS text

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). The current Physical Activity Guidelines for Americans is issued by the US Department of Health and Human Services. To meet physical activity guidelines, adults must meet aerobic physical activity guidelines (vigorous activity for at least 75 minutes a week, or moderate activity for at least 150 minutes a week, or a combination of vigorous and moderate activity for at least 150 minutes a week) and muscle-strengthening physical activity guidelines (exercise all major muscle groups on 2 or more days a week).Physical inactivity contributes to our current obesity epidemic and is a major risk factor for heart disease, diabetes, cancer, and many other chronic health conditions. It can be difficult for people to be physically active if their communities do not have available and safe places for recreation.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

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). Households experiencing food insecurity are defined as those with low food security or very low food security in the last 12 months. Food insecurity is assessed by a scaled variable created from a series of five questions.Food insecurity, or the inability to reliably afford or access sufficient quantities of healthy food, affects hundreds of thousands of low-income households in Los Angeles County. Food insecure adults are at increased risk for poor dietary intake and developing chronic conditions, such as type 2 diabetes, hypertension, hyperlipidemia, obesity, and psychological distress or depression. Increasing enrollment in food assistance programs, such as the Supplemental Nutrition Assistance Program (known as CalFresh in California) or the Special Supplemental Nutrition Program for Women, Infants, and Children (better known as WIC) is an important measure that cities and communities can take to combat food insecurity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Healthcare expenditure and (standard error) per capita by year, category of service and BMI; US population 2000–2025.

Healthcare service use and (standard error) per 1000 persons by year, category of service and BMI; US population 2000–2025.

Obesity rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States." The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the obesity rate of a demographically similar census tract to one in Allegheny County to compute an obesity rate.

This table contains data on the percent of population residing within ½ mile of a major transit stop for four California regions and the counties, cities/towns, and census tracts within the regions. The percent was calculated using data from four metropolitan planning organizations (San Diego Association of Governments, Southern California Association of Governments, Metropolitan Transportation Commission, and Sacramento Council of Governments) and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. A strong and sustainable transportation system supports safe, reliable, and affordable transportation opportunities for walking, bicycling, and public transit, and helps reduce health inequities by providing more opportunities for access to healthy food, jobs, health care, education, and other essential services. Active and public transportation promote health by enabling individuals to increase their level of physical activity, potentially reducing the risk of heart disease and obesity, improving mental health, and lowering blood pressure. More information about the data table and a data dictionary can be found in the About/Attachments section.

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). Households experiencing food insecurity are defined as those with low food security or very low food security in the last 12 months. Food insecurity is assessed by a scaled variable created from a series of five questions.Food insecurity, or the inability to reliably afford or access sufficient quantities of healthy food, affects hundreds of thousands of low-income households in Los Angeles County. Food insecurity during childhood is associated with delayed development, inability to concentrate in school, diminished academic performance, anxiety, depression, and early-onset obesity. Increasing enrollment in food assistance programs, such as the Supplemental Nutrition Assistance Program (known as CalFresh in California) or the Special Supplemental Nutrition Program for Women, Infants, and Children (better known as WIC) is an important measure that cities and communities can take to combat food insecurity.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This table contains data on the average cost of a market basket of nutritious food items relative to income for female-headed households with children, for California, its regions, counties, and cities/towns. The ratio uses data from the U.S. Department of Agriculture and the U.S. Census Bureau. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. An adequate, nutritious diet is a necessity at all stages of life. Inadequate diets can impair intellectual performance and have been linked to more frequent school absence and poorer educational achievement in children. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type 2 diabetes, and anemia. At least two factors influence the affordability of food and the dietary choices of families – the cost of food and family income. The inability to afford food is a major factor in food insecurity, which has a spectrum of effects including anxiety over food sufficiency or food shortages; reduced quality or desirability of diet; and disrupted eating patterns and reduced food intake. More information about the data table and a data dictionary can be found in the Attachments.