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.

US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2012. This records a decrease from the previous number of 7.800 % for 2009. US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 7.000 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 8.100 % in 2005 and a record low of 5.400 % in 1991. US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

United States Prevalence of Overweight: % of Adults data was reported at 67.900 % in 2016. This records an increase from the previous number of 67.400 % for 2015. United States Prevalence of Overweight: % of Adults data is updated yearly, averaging 55.200 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 67.900 % in 2016 and a record low of 41.000 % in 1975. United States Prevalence of Overweight: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;;

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

In 2023, it was estimated that around 37 percent of adults with an annual income of less than 15,000 U.S. dollars were obese, compared to 29 percent of those with an annual income of 75,000 dollars or more. This statistic shows the percentage of U.S. adults who were obese in 2023, by income.

This subset of the community health indicator report data will not be updated. A dataset containing all of the community health indicators is now available. To view the latest community health obesity and diabetes related indicators, see the featured content section. This Obesity and Diabetes Related Indicators dataset provides a subset of data (40 indicators) for the two topics: Obesity and Diabetes. The dataset includes percentage or rate for Cirrhosis/Diabetes and Obesity and Related Indicators, where available, for all counties, regions and state.
New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and annually updated to provide data for over 300 health indicators, organized by 15 health topic and data for all counties, regions and state are presented in table format with links to trend graphs and maps. Most recent county and state level data are provided. Multiple year combined data offers stable estimates for the burden and risk factors for these two health topics.

Adolescents Who Have Obesity - "This indicator shows the percentage of adolescent public high school students who are obese. In the last 20 years, the percentage of overweight/obese children has more than doubled and, for adolescents, it has tripled. Overweight/obese children are at increased risk of developing life-threatening chronic diseases, such as Type 2 diabetes." Link to Data Details

Obesity has become a major concern for health officials in the United States. Rates of obesity are higher than ever before and as a result, consequential medical conditions have arisen in those who suffer from obesity; while at the same time, medical expenses are skyrocketing for these same individuals. In this study, I analyze regional trends in the United States of both obesity rates and walkability in 74 cities in the United States. After analyzing the data and constructing visual representations, I found that the Northeast region of the US is most walkable, while the Southeast and Southwestern regions are the least walkable. In regards to obesity rates, I found that the West had the lowest obesity rates in both 2010 and 2013, while the Midwest and the Southeast had a high obesity rate in both 2010 and 2013. Additionally, the Northeastern US had a high obesity rate in 2013.

State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 8.200 % in 2014. This records an increase from the previous number of 5.300 % for 2010. State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 6.750 % from Dec 1996 (Median) to 2014, with 4 observations. The data reached an all-time high of 11.400 % in 2007 and a record low of 4.000 % in 1996. State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.World Bank.WDI: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

The Student Weight Status Category Reporting System (SWSCR) collects weight status category data (underweight, healthy weight, overweight or obese, based on BMI-for-age percentile). The dataset includes separate estimates of the percent of students overweight, obese and overweight or obese for all reportable grades within the county and/or region and by grade groups (elementary and middle/high). The rates of overweight and obesity reported are percentages based on counts of students in selected grades (Pre-K, K, 2, 4, 7, 10) reported to the NYSDOH. Because these rates reflect a broad range of factors that vary by school district, to make comparisons about observed differences in the rates of obesity and overweight between school districts requires the use of multivariate statistics. County, regional and statewide estimates will only be provided biennially, District estimates will be updated annually. For more information check out http://www.health.ny.gov/prevention/obesity/, see our Instruction Guide on How to Create Visualizations https://health.data.ny.gov/api/assets/6490BDA9-AE4D-406F-BA5A-703793526B9F or go to the "About" tab.

State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data was reported at 6.000 % in 2010. This records a decrease from the previous number of 13.400 % for 2007. State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data is updated yearly, averaging 9.700 % from Dec 2007 (Median) to 2010, with 2 observations. The data reached an all-time high of 13.400 % in 2007 and a record low of 6.000 % in 2010. State of Palestine (West Bank and Gaza) PS: Prevalence of Overweight: Weight for Height: % of Children Under 5: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s State of Palestine (West Bank and Gaza) – Table PS.World Bank.WDI: Health Statistics. Prevalence of overweight, male, is the percentage of boys under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

The notebook that generates this dataset is here: https://www.kaggle.com/johnjdavisiv/us-counties-weather-sociohealth-location-data

For an introduction to the data, check out this notebook: https://www.kaggle.com/johnjdavisiv/intro-to-the-us-counties-covid19-data

The 3,142 counties of the United States span a diverse range of social, economic, health, and weather conditions. Because of the COVID19 pandemic, over 2,400 of these counties have already experienced some COVID19 cases.

Combining county-level data on health, socioeconomics, and weather can help us address identify which populations are at risk for COVID19 and help prepare high-risk communities.

Temperature and humidity may affect the transmissibility of COVID19, but in the United States, warmer regions also tend to have markedly different socioeconomic and health demographics. As such, it's important to be able to control for factors like obesity, diabetes, access to healthcare, and poverty rates, since these factors themselves likely play a role in COVID19 transmission and fatality rates.

This dataset provides all of this information, formatted, cleaned, and ready for analysis. Most columns have little or no missing data. A small number have larger amounts of missing data; see the kernel that generated this dataset for details.

The crude prevalence rate of obesity is defined as the ratio of respondents that are 18 years or older who have a body mass index (BMI) of 30.0 kg/m2 or greater over the total number of respondents in the study (excluding those who refused to answer or those whose information was unknown”). Respondents were excluded if their height was less than 3 ft or equal to/greater than 8 ft; they weighed less than 50 lbs or more than 650 lbs; they had a BMI of less than 12 kg/m2 or 100 kg/m2 and greater; and/or were pregnant.Prevalence data are derived from the Behavioral Risk Factor Surveillance System (BRFSS) 2012.The 500 Cities Project seeks to provide city- and census tract-level small area estimates for chronic disease risk factors, health outcomes, and clinical preventive service use for the largest 500 cities in the United States.Data source: CDC (Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion)Date: 2015

The Student Weight Status Category Reporting System (SWSCR) collects weight status category data (underweight, healthy weight, overweight or obese, based on BMI-for-age percentile) on children and adolescents attending public school in New York State, outside New York City.

The dataset includes separate estimates of the percent of students that are overweight, obese and overweight or obese for all reportable grades within the county and/or region and by grade groups (elementary and middle/high). The rates of overweight and obesity reported are percentages based on counts of students in selected grades (Pre-K, K, 1, 3, 5, & 7, 9, 11) reported to the NYSDOH. District, county, regional and statewide estimates are provided biennially. Data can be filtered by a variety of variables within the dataset and exported into various formats for additional analyses. Half of the public schools in New York State, exclusive of New York City, are required to report Student Weight Status data each year. It takes two years to collect a full cycle of data for all eligible school districts in the state.

Beginning in 2010, all data reported through the Student Weight Status Category Reporting System have been released on the Health Data New York website. Prior to 2010, data from SWSCR were released through printable report and continue to be incorporated into reports released to the public. These reports are accessible through web-links posted on this page.

The Student Weight Status Category Reporting System (SWSCR) collects weight status category data (underweight, healthy weight, overweight or obese, based on BMI-for-age percentile). The dataset includes separate estimates of the percent of students overweight, obese and overweight or obese for all reportable grades within the county and/or region and by grade groups (elementary and middle/high). The rates of overweight and obesity reported are percentages based on counts of students in selected grades (Pre-K, K, 2, 4, 7, 10) reported to the NYSDOH. Because these rates reflect a broad range of factors that vary by school district, to make comparisons about observed differences in the rates of obesity and overweight between school districts requires the use of multivariate statistics. For more information check out http://www.health.ny.gov/prevention/obesity/, see our Instruction Guide on How to Create Visualizations https://health.data.ny.gov/api/assets/6490BDA9-AE4D-406F-BA5A-703793526B9F or go to the "About" tab.

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content

Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in all 32 federal states in Mexico. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older were selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). A sub-sample of 211 PSUs were selected from the 797 WHS PSUs. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0; The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 4 968 surveyed TSU: Individual = 5 449 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Spanish. All SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the FoxPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=59%

Individual Response rate=51%

The Student Weight Status Category Reporting System (SWSCR) collects weight status category data (underweight, healthy weight, overweight or obese, based on BMI-for-age percentile). The dataset includes separate estimates of the percent of students overweight, obese and overweight or obese for all reportable grades within the county and/or region and by grade groups (elementary and middle/high). The rates of overweight and obesity reported are percentages based on counts of students in selected grades (Pre-K, K, 2, 4, 7, 10) reported to the NYSDOH. Because these rates reflect a broad range of factors that vary by school district, to make comparisons about observed differences in the rates of obesity and overweight between school districts requires the use of multivariate statistics. For more information check out http://www.health.ny.gov/prevention/obesity/, see our Instruction Guide on How to Create Visualizations https://health.data.ny.gov/api/assets/6490BDA9-AE4D-406F-BA5A-703793526B9F or go to the "About" tab.

Total number of obese pregnant women,rate of obesity among female for the 20 high obesity burden countries.