In 2023, Black adults had the highest obesity rates of any race or ethnicity in the United States, followed by American Indians/Alaska Natives and Hispanics. As of that time, around ** percent of all Black adults were obese. Asians/Pacific Islanders had by far the lowest obesity rates. Obesity in the United States Obesity is a present and growing problem in the United States. An astonishing ** percent of the adult population in the U.S. is now considered obese. Obesity rates can vary substantially by state, with around ** percent of the adult population in West Virginia reportedly obese, compared to ** percent of adults in Colorado. The states with the highest rates of obesity include West Virginia, Mississippi, and Arkansas. Diabetes Being overweight and obese can lead to a number of health problems, including heart disease, cancer, and diabetes. Being overweight or obese is one of the most common causes of type 2 diabetes, a condition in which the body does not use insulin properly, causing blood sugar levels to rise. It is estimated that just over ***** percent of adults in the U.S. have been diagnosed with diabetes. Diabetes is now the seventh leading cause of death in the United States, accounting for ***** percent of all deaths.

About a fifth of Hispanic high school students in the United States were overweight between 2016 and 2017, making it the ethnic group with the highest percentage of overweight high school students. Female obesity rates were considerably higher than those of male students for the black and Hispanic groups during the measured period.

Overweight and obese U.S. adults

U.S. overweight rates in adults differed slightly from those of U.S. high school students in 2017. That year, the African American population had the highest overweight and obesity rates of any race or ethnicity, closely followed by American Indians/Alaska Natives and Hispanics. Over 73 percent of all African American adults in the country were either overweight or obese. In 2018, the highest rates of obesity among African Americans could be found in states, such as Mississippi, Arkansas, and Tennessee.

Overweight youth worldwide

Many children and adolescents in other countries, such as New Zealand, Greece, and Italy, also struggle with overweight and obesity. In New Zealand, for example, over forty percent of boys and girls, up to age 19, were overweight or obese in 2016. In the same year, less than ten percent of Indian children and teenagers were overweight.

Abstract The incidence of medical costs of obesity disproportionately falls on women and racial minorities. Prior research has shown that when employers provide health insurance coverage to workers, the additional healthcare costs associated with obesity are passed through to obese workers in the form of reduced wages, relative to their non-obese counterparts. However, estimation of a population-level wage penalty of obesity obscures substantial variation in the relative impact of these wage differences by race and gender. We partition the 1979 NLSY data by race and gender, and find that while the dollar-denominated wage penalty borne by obese workers with health insurance is borne predominantly by white women, these wage offsets disproportionately impact blacks and white women when modeled as a percentage of income. Upload includes data, R code, and write-up

In 2022, over 33 percent of both men and women in the United States reported themselves as obese (BMI over 30), making it the country with the highest percentage of obese adults on this list. Other selected countries on the list with a high prevalence of obesity among adults included the United Kingdom and Australia. Obesity groups in the United States In 2022, Black adults had the highest overweight and obesity rates of any race or ethnicity in the United States. Asians and Native Hawaiians or Pacific Islanders had the lowest rates by far, with roughly 14 percent. In 2021, about 30 percent of people aged 65 and older were obese in the United States. This estimate has been steadily increasing since 2013 when roughly 27 percent of elderly Americans were obese. Leading health problems worldwide Obesity was considered one of 2023’s biggest health problems: 25 percent of adults worldwide stated that obesity was the biggest health issue for people within their country. Around 44 percent of adults stated that mental health was the most significant problem facing their country that year.

From 2017 to March 2020, the obesity prevalence among Hispanic men was around 45 percent. This statistic represents the obesity prevalence among adults aged 20 and older in the United States from 2017 to March 2020, sorted by gender and race/ethnicity.

Objective:This study is a descriptive investigation of trends in BMI in the USA over time, across race/ethnicity, gender, and socioeconomic status (SES) groups, and across different datasets. Methods: The study analyzes micro-level data from three widely used cross-sectional US health datasets: the National Health and Nutrition Examination Survey (NHANES), the National Health Interview Survey (NHIS), and the Behavioral Risk Factor Surveillance System (BRFSS), from the 1970s to 2008. Consistent race/ethnicity and SES groups are constructed for all datasets. SES is measured by education and income. Focusing on adults aged 20–74 years, the study estimates BMI time trends, distributional shifts, and incremental associations (gradients) with SES. Results: SES-BMI gradients are consistently larger for women than for men, differ across race/ethnicity groups, and are similar across datasets. Trends in mean BMI are comparable across White, Black and Hispanic males, while Hispanic females range between White and Black females. Self-reported BMI in the NHANES differs markedly from self-reports in the NHIS and BRFSS. Conclusion: The NHANES, NHIS, and BRFSS provide similar evidence regarding BMI trends over time and across race/ethnicity, gender, and SES groups. Racial disparities in BMI remain after adjusting for SES and should be studied further.

Roughly 15 percent of U.S. high school students from grades 9 to 12, across all genders and ethnicities, were obese between 2016 and 2017. On average, African and Hispanic students struggled more with obesity than white students. Over 22 percent of male students with a Hispanic background were reported to be obese.

Prevalence among adults

Obesity is a growing problem among all age groups in the United States. Among American adults aged 20 and over, obesity rates have risen considerably since 1997. By 2018, roughly 12 percent more adults were obese, compared to 21 years earlier.

Fast food industry

Unhealthy ingredients, larger portions, and lower cost are some of the reasons why fast food easily leads to obesity in the United States. Between 2004 and 2018, U.S. consumer spending in quick-service restaurants has increased by approximately 37.5 percent. Examples of leading restaurant chains within this sector, in terms of company value, are McDonald’s, KFC, and Domino’s Pizza.

This statistic shows the results of a survey among American high school students in grades 9 to 12 on obesity in 2017, by gender and ethnicity. About 22.2 percent of male student respondents with a Hispanic background stated they are obese.

Layers in this service includes: Birth, Cancer, Hospitalization Discharge, Mortality and STI Rates, as well as Demographics.

These data represent the predicted (modeled) prevalence of being Obese among adults (Age 18+) for each census tract in Colorado. Obese is defined as a BMI of 30 or greater. BMI is calculated from self-reported height and weight.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Layers in this service includes: Birth, Cancer, Hospitalization Discharge, Mortality and STI Rates, as well as Demographics.

BMI, body mass index; N, unweighted number; IQR, interquartile range.

From 2017 to March 2020, the prevalence of obesity among children and adolecents aged 2 to 19 years in the U.S. was highest among non-Hispanic black girls. This statistic shows the age-adjusted prevalence of obesity among U.S. children and adolecents from 2017 to March 2020, by gender and race/ethnicity.

This dataset provides an estimate of the percentage of adult respondents ages 18+ who had a body mass index (BMI) of 30 or above by zip code as well as an estimate of the population ages 18+ residing in that zip code. The estimates covered are from the years 2013-2014. Information like this may be useful for studying obesity rates across zip codes of different demographics.Spatial Extent: Los Angeles Spatial Unit: Zip Code Created: 2018 Updated: n/a Source: California Health Interview SurveySurvey Contact Telephone: 310-794-0909 Contact Email: dacchpr@ucla.edu Source Link: https://askchisne.ucla.edu/ask/_layouts/ne/dashboard.aspx#/API Source Link: https://www.arcgis.com/home/item.html?id=342a05f149004bb0ab43eb976682beba

ObjectiveThis study investigates the association between phenotypic age acceleration (PAA) and all-cause and cause-specific mortality in obese individuals.MethodsData were drawn from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018, including 9,925 obese adults (BMI ≥ 30 kg/m2). PAA, defined as phenotypic age exceeding chronological age, was assessed using clinical biomarkers. Kaplan-Meier survival analysis and Cox proportional hazards models were used to assess the relationship between PAA and all-cause, cardiovascular, and cancer mortality, adjusting for covariates such as age, gender, race, lifestyle, and health status. Subgroup and sensitivity analyses were performed to ensure the robustness of the findings.ResultsDuring a median follow-up of 10.6 years, 1,537 deaths were recorded, including 419 from cardiovascular disease and 357 from cancer. PAA was significantly associated with all-cause mortality (HR = 1.84, 95% CI: 1.64–2.06), cardiovascular mortality (HR = 1.86, 95% CI: 1.50–2.31), and cancer mortality (HR = 1.47, 95% CI: 1.17–1.85). These associations remained significant after adjusting for multiple variables, and sensitivity analyses confirmed the robustness of the results.ConclusionPAA is an independent predictor of all-cause, cardiovascular, and cancer mortality in obese individuals. This study highlights the importance of PAA in mortality risk assessment and health management in the obese population.

Layers in this service includes: Birth, Cancer, Hospitalization Discharge, Mortality and STI Rates, as well as Demographics.

Obesity Intervention Devices Market size was valued at USD 267.88 Million in 2024 and is projected to reach USD 417.08 Million by 2031, growing at a CAGR of 5.69% during the forecast period 2024-2031.

Global Obesity Intervention Devices Market Drivers

The market drivers for the Obesity Intervention Devices Market can be influenced by various factors. These may include:

Growing Obesity Rates: Due to changes in nutrition, lifestyle, and physical activity levels, obesity rates have been rising globally throughout time. As a result, there is an increasing need for therapies that deal with health problems associated with obesity. Enhanced Knowledge and Education: As people become more conscious of the health hazards linked to obesity, there is a greater need for efficient solutions. This awareness is further fueled by educational programs and public health campaigns. Technological Developments: Wearables, implantable devices, and minimally invasive surgery are just a few of the novel obesity intervention devices that have been made possible by advances in medical technology. These developments draw in patients looking for practical answers as well as healthcare professionals. Government Policies and Initiatives: By fostering an environment that is conducive to the adoption of obesity intervention devices, government policies and initiatives, such as taxes on sugar-filled beverages, subsidies for healthful foods, and physical activity-promoting regulations, can help drive demand for these devices. Healthcare Cost Burden: Health problems associated with obesity place a heavy financial strain on healthcare systems around the globe. Consequently, there is an increasing emphasis on interventions and preventive strategies to lower healthcare expenses connected to illnesses related to obesity. This justifies the purchase of obesity intervention technology as an affordable fix. Growing Healthcare Expenditure: Consumers and healthcare providers are eager to invest in cutting-edge therapies and equipment for managing obesity, as seen by the general increase in healthcare spending around the world. Changing Demographics: The incidence of obesity and associated health problems is influenced by aging populations and shifting demographics, especially in wealthy nations. The need for obesity intervention tools that can specifically cater to the needs of senior populations is being driven by this shift in demographics. Lifestyle Changes: The obesity epidemic is partly caused by urbanization, sedentary lifestyles, and changes in food habits. Obesity intervention devices are appealing choices for people looking for efficient weight-management techniques since they provide answers that fit with contemporary lifestyles. Growing Interest in Personalized Medicine: Thanks to developments in precision medicine and genetics, there is a growing movement in personalized healthcare. Customized obesity prevention tools that consider personal characteristics like genetics, metabolism, and behavior are becoming more and more popular. Partnerships and Collaborations: The development of comprehensive obesity management solutions, including intervention devices, is fueled by partnerships and collaborations among healthcare providers, pharmaceutical companies, research institutes, and makers of medical devices. These collaborations support the field's efforts in R&D and commercialization.

This dataset contains the estimated percentages of adults smokers (18+ years), overweight or obese adults (18+ years) and physically inactive adults (19+ years) by England regions, counties and unitary authorities, and by socioeconomic variables. Data for demographic groups (age, gender, race/ethincity, religion or sexual orientation-based) and comparisons to England and region levels, are also available in the dataset.

Characteristics of age, anthropometric traits and ALT levels by race/ethnicity and gendera.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity