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.

In Mississippi, over ***** out of ten adults were reported to be either overweight or obese in 2018, making it the leading U.S. state that year. Other prominent states, in terms of overweight and obesity, included Arkansas in ******, Oklahoma in *******, and Louisiana in ***** place.

Corpulence per state

When it comes to obesity, specifically, percentages were still very high for certain states. Almost forty percent of West Virginia’s population was obese in 2018. Colorado, Hawaii, and California were some of the healthier states that year, with obesity rates between ** and ** percent. The average for the country itself stood at just over ** percent.

Obesity-related health problems

Being obese can lead to various health-related complications, such as diabetes and diseases of the heart. In 2017, almost ** people per 100,000 died of diabetes mellitus in the United States. In the same year, roughly *** per 100,000 Americans died of heart disease. While the number of deaths caused by heart disease has decreased significantly over the past sixty to seventy years, it is still one of the leading causes of death in the country.

From 2021 to 2023, the obesity prevalence among the total U.S. population aged 20 and older was around ** percent. This statistic shows the prevalence of obesity among adults aged 20 and older in the United States from 2021 to 2023, by gender and age group.

National Obesity Percentages by State. Explanation of Field Attributes:Obesity - The percent of the state population that is considered obese from the 2015 CDC BRFSS Survey.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: Prevalence of Overweight: Weight for Height: Female: % 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, female, is the percentage of girls 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

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/).;;

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. United States 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. United States 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

Analysis of ‘🧑 Childhood Obesity in the US’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/childhood-obesity-in-the-use on 13 February 2022.

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

About this dataset

Childhood Obesity in the United States (1971-2014)

data source: http://www.cdc.gov/nchs/data/hestat/obesity_child_13_14/obesity_child_13_14.htm

Data Files

1. child_ob_gender.csv
2. obesity_child_age

Visualizations

Historical Childhood Obesity Rate by Gender

Boys tended to suffer from obesity at a higher rate than girls during 2000 through 2010. More recently however, between 2011 and 2014, boys' and girls' obesity rates converged as a result of an increase for girls and decrease for boys.

For both genders, obesity rates grew rapidly during the last two decades of the 20th century, but thankfully growth rates have lessened in recent years.

http://i.imgur.com/oyWAjys.png" alt="Imgur" style="">

Historical Childhood Obesity Rate by Age

The data show that older children have been afflicted by the obesity epidemic at a higher rate than very young children.

http://i.imgur.com/7W2Bsz3.png" alt="Imgur" style="">

This dataset was created by Health and contains around 100 samples along with Se, Percent Obese, technical information and other features such as: - Gender - Time - and more.

How to use this dataset

• Analyze Age in relation to Se
• Study the influence of Percent Obese on Gender
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Health

Start A New Notebook!

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

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.

Proportion of children aged 10 to 11 years classified as overweight or living with obesity. For population monitoring purposes, a child’s body mass index (BMI) is classed as overweight or obese where it is on or above the 85th centile or 95th centile, respectively, based on the British 1990 (UK90) growth reference data. The population monitoring cut offs for overweight and obesity are lower than the clinical cut offs (91st and 98th centiles for overweight and obesity) used to assess individual children; this is to capture children in the population in the clinical overweight or obesity BMI categories and those who are at high risk of moving into the clinical overweight or clinical obesity categories. This helps ensure that adequate services are planned and delivered for the whole population.

Rationale There is concern about the rise of childhood obesity and the implications of obesity persisting into adulthood. The risk of obesity in adulthood and risk of future obesity-related ill health are greater as children get older. Studies tracking child obesity into adulthood have found that the probability of children who are overweight or living with obesity becoming overweight or obese adults increases with age[1,2,3]. The health consequences of childhood obesity include: increased blood lipids, glucose intolerance, Type 2 diabetes, hypertension, increases in liver enzymes associated with fatty liver, exacerbation of conditions such as asthma and psychological problems such as social isolation, low self-esteem, teasing and bullying.

It is important to look at the prevalence of weight status across all weight/BMI categories to understand the whole picture and the movement of the population between categories over time.

The National Institute of Health and Clinical Excellence have produced guidelines to tackle obesity in adults and children - http://guidance.nice.org.uk/CG43.

1 Guo SS, Chumlea WC. Tracking of body mass index in children in relation to overweight in adulthood. The American Journal of Clinical Nutrition 1999;70(suppl): 145S-8S.

2 Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Preventative Medicine 1993;22:167-77.

3 Starc G, Strel J. Tracking excess weight and obesity from childhood to young adulthood: a 12-year prospective cohort study in Slovenia. Public Health Nutrition 2011;14:49-55.

Definition of numerator Number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP with a BMI classified as overweight or living with obesity, including severe obesity (BMI on or above the 85th centile of the UK90 growth reference).

Definition of denominator The number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP.

Caveats Data for local authorities may not match that published by NHS England which are based on the local authority of the school attended by the child or based on the local authority that submitted the data. There is a strong correlation between deprivation and child obesity prevalence and users of these data may wish to examine the pattern in their local area. Users may wish to produce thematic maps and charts showing local child obesity prevalence. When presenting data in charts or maps it is important, where possible, to consider the confidence intervals (CIs) around the figures. This analysis supersedes previously published data for small area geographies and historically published data should not be compared to the latest publication. Estimated data published in this fingertips tool is not comparable with previously published data due to changes in methods over the different years of production. These methods changes include; moving from estimated numbers at ward level to actual numbers; revision of geographical boundaries (including ward boundary changes and conversion from 2001 MSOA boundaries to 2011 boundaries); disclosure control methodology changes. The most recently published data applies the same methods across all years of data. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of children with a high BMI for age/sex which it is not possible to control for). There is not a good measure of response bias and the degree of selective opt out, but participation rates (the proportion of eligible school children who were measured) may provide a reasonable proxy; the higher the participation rate, the less chance there is for selective opt out, though this is not a perfect method of assessment. Participation rates for each local authority are available in the https://fingertips.phe.org.uk/profile/national-child-measurement-programme/data#page/4/gid/8000022/ of this profile.

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 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.

BackgroundUnderstanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data.PurposeThis article explores the relationship between online social environment via web-based social networks and population obesity prevalence.MethodsWe performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook.ResultsHigher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set.ConclusionsActivity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

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 ---

Financial overview and grant giving statistics of American Board of Obesity Medicine Foundation

ObjectiveTo estimate the global and country-level burden of overweight and obesity among pregnant women from 2005 to 2014.MethodsPublicly accessible country-level data were collected from the World Health Organization, the World Bank and the Food and Agricultural Organization. We estimated the number of overweight and obese pregnant women among 184 countries and determined the time-related trend from 2005 to 2014. Based on panel data model, we determined the effects of food energy supply, urbanization, gross national income and female employment on the number of overweight and obese pregnant women.ResultsWe estimated that 38.9 million overweight and obese pregnant women and 14.6 million obese pregnant women existed globally in 2014. In upper middle income countries and lower middle income countries, there were sharp increases in the number of overweight and obese pregnant women. In 2014, the percentage of female with overweight and obesity in India was 21.7%, and India had the largest number of overweight and obese pregnant women (4.3 million), which accounted for 11.1% in the world. In the United States of America, a third of women were obese, and the number of obese pregnant women was 1.1 million. In high income countries, caloric supply and urbanization were positively associated with the number of overweight and obese pregnant women. The percentage of employment in agriculture was inversely associated with the number of overweight and obese pregnant women, but only in upper middle income countries and lower middle income countries.ConclusionThe number of overweight and obese pregnant women has increased in high income and middle income countries. Environmental changes could lead to increased caloric supply and decreased energy expenditure among women. National and local governments should work together to create a healthy food environment.

The Global Pet Obesity Management Market is Segmented by Product (Drugs and Food Supplements), Animal Type (Dogs, Cats, and Other Animals), End User (Pet Specialty Stores, E-commerce, and Other End Users), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The report offers the value (USD million) for the above segments.

The global electrode body fat scale market is experiencing robust growth, driven by increasing health consciousness, rising obesity rates, and the demand for accurate and convenient body composition monitoring. The market's expansion is fueled by technological advancements leading to more precise and feature-rich scales, along with the rising adoption of wearable fitness trackers that integrate with these scales. The segment encompassing eight-electrode scales is projected to witness significant growth due to their superior accuracy compared to four-electrode models. Personal use currently dominates the application segment, reflecting the increasing focus on individual health management. However, growth in the gym and hospital segments is anticipated, driven by the need for professional-grade body composition analysis in these settings. Geographic expansion is also a key factor; North America and Europe are currently leading markets, but significant growth potential exists in Asia-Pacific, particularly in China and India, due to rising disposable incomes and increased health awareness. While pricing and the potential for consumer confusion regarding the accuracy and functionality of various models present challenges, the market's overall trajectory remains positive, indicating sustained growth throughout the forecast period. The competitive landscape is characterized by a mix of established electronics companies and specialized health-tech firms. Key players are focused on innovation, incorporating advanced features like Bluetooth connectivity, smartphone app integration, and improved data analytics to enhance user experience and market appeal. Strategic partnerships and product diversification are also key strategies employed by market leaders to gain a competitive edge. Maintaining accuracy and ensuring data privacy will be crucial for companies to build consumer trust and drive long-term market success. Future market developments will likely include greater integration with broader health and wellness platforms, personalized fitness recommendations based on body composition data, and the potential expansion into related areas like impedance-based body water measurement.

In 2017-2020, almost ** percent of adults aged 20 years and older were considered obese. This is a significant increase from a rate of **** percent in the period 1999-2000. This statistic shows the percentage of children and adults in the U.S. who were obese in 1999-2000 and 2017-2020.

Table S1–S3: Considered model specifications and parametric assumptions; Estimation results: final model; Predicted life years lost associated with obesity-related diseases for U.S. non-smoking adults, 1997–2000: sensitivity analysis. (DOCX)