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.

From 2017 to March 2020, the prevalence of severe obesity in the U.S. was highest among non-Hispanic blacks. This statistic shows the age-adjusted prevalence of severe obesity among U.S. adults aged 20 and over from 2017 to March 2020, by race/ethnicity.

BackgroundFew studies have examined weight transitions in contemporary multi-ethnic populations spanning early childhood through adulthood despite the ability of such research to inform obesity prevention, control, and disparities reduction.Methods and ResultsWe characterized the ages at which African American, Caucasian, and Mexican American populations transitioned to overweight and obesity using contemporary and nationally representative cross-sectional National Health and Nutrition Examination Survey data (n = 21,220; aged 2–80 years). Age-, sex-, and race/ethnic-specific one-year net transition probabilities between body mass index-classified normal weight, overweight, and obesity were estimated using calibrated and validated Markov-type models that accommodated complex sampling. At age two, the obesity prevalence ranged from 7.3% in Caucasian males to 16.1% in Mexican American males. For all populations, estimated one-year overweight to obesity net transition probabilities peaked at age two and were highest for Mexican American males and African American females, for whom a net 12.3% (95% CI: 7.6%-17.0%) and 11.9% (95% CI: 8.5%-15.3%) of the overweight populations transitioned to obesity by age three, respectively. However, extrapolation to the 2010 U.S. population demonstrated that Mexican American males were the only population for whom net increases in obesity peaked during early childhood; age-specific net increases in obesity were approximately constant through the second decade of life for African Americans and Mexican American females and peaked at age 20 for Caucasians.ConclusionsAfrican American and Mexican American populations shoulder elevated rates of many obesity-associated chronic diseases and disparities in early transitions to obesity could further increase these inequalities if left unaddressed.

From 2017 to March 2020, the obesity prevalence among Hispanic men was around ** 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.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

In 2023, around ** percent of all adult Hispanics in Texas were obese. In the United States, processed foods are often cheaper than fresh foods, which can impact those with lower income and lead to more weight gain. This statistic depicts the obesity rates for adults in Texas in 2023, by race/ethnicity.

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

Background Investigating the validity of the self-reported values of weight allows for the proper assessment of studies using questionnaire-derived data. The study examined the accuracy of gender-specific self-reported weight in a sample of adults. The effects of age, education, race and ethnicity, income, general health and medical status on the degree of discrepancy (the difference between self-reported weight and measured weight) are similarly considered. Methods The analysis used data from the US Third National Health and Nutrition Examination Survey. Self-reported and measured weights were abstracted and analyzed according to sex, age, measured weight, self-reported weight, and body mass index (BMI). A proportional odds model was applied. Results The weight discrepancy was positively associated with age, and negatively associated with measured weight and BMI. Ordered logistic regression modeling showed age, race-ethnicity, education, and BMI to be associated with the degree of discrepancy in both sexes. In men, additional predictors were consumption of more than 100 cigarettes and the desire to change weight. In women, marital status, income, activity level, and the number of months since the last doctor's visit were important. Conclusions Predictors of the degree of weight discrepancy are gender-specific, and require careful consideration when examined.

From 2019 to 2021, obesity among pregnant women in the United States was highest among American Indian and Alaska Native women and Black women. This statistic depicts the percentage of pregnant women in the United States from 2019 to 2021 who were obese, overweight, normal weight, or underweight, by race/ethnicity.

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

BackgroundPrenatal and early life risk factors are associated with childhood obesity. Alaska Native children have one of the highest prevalences of childhood obesity of all US racial/ethnic groups.MethodsUsing the Pregnancy Risk Assessment Monitoring System (PRAMS) and the follow-up survey at 3 years of age (CUBS), we evaluated health, behavioral, lifestyle and nutritional variables in relation to obesity (95th percentile for body mass index (BMI)) at 3 years of age. Multivariate logistic regression modeling was conducted using Stata 12.0 to evaluate independent risk factors for obesity in non-Native and Alaska Native children.ResultsWe found an obesity prevalence of 24.9% in all Alaskan and 42.2% in Alaska Native 3 year olds. Among Alaska Native children, obesity prevalence was highest in the Northern/Southwest part of the state (51.6%, 95%CI (42.6-60.5)). Independent predictive factors for obesity at age 3 years in Alaska non-Native children were low income (

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 data is derived from the Maternity Indicators dataset which is provided to the Welsh Government by Digital Health and Care Wales (DHCW). The Maternity Indicators dataset was established in 2016. It combines records from a mother’s initial assessment with a child’s birth record and enabled Welsh Government to monitor its initial set of outcome indicators and performance measures (Maternity Indicators) which were established to measure the effectiveness and quality of Welsh maternity services. The Maternity Indicators dataset allows us to analyse characteristics of the mother’s pregnancy and birth process. The process for producing this data extract is complex largely because there can be multiple initial assessment data and records for both initial assessments and births are not always complete. Full details of every data item available in the Maternity Indicators dataset are available through the NHS Wales Data Dictionary: http://www.datadictionary.wales.nhs.uk/#!WordDocuments/datasetstructure20.htm The data dictionary also defines how ethnic groups are classified, namely: White (any white background); Asian (Pakistani, Bangladeshi, Chinese, Indian, any other Asian background); Mixed/multiple (white and Asian, white and black African, white and black Caribbean, any other mixed background); Other (any other ethnic group); Black (African, Caribbean, any other black background).

This statistic depicts the average body mass index (BMI) of U.S. males aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average male BMI for those that identified as white was **** in ********* and increased to **** as of *********.

Some racial and ethnic categories are suppressed for privacy and to avoid misleading estimates when the relative standard error exceeds 30% or the unweighted sample size is less than 50 respondents.

Data Source: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey (BRFSS) Data

Why This Matters

Obesity is a national and local public health emergency. Today, 4 in 10 adults are living with obesity in the United States, and the obesity rate is on the rise.

Obesity has multiple and complex causes, including genetics, health issues, and the social environment. Obesity can harm quality of life, increase the risk of heart disease, type 2 diabetes, and other health problems, and lead to discrimination in education, work, and healthcare.

Communities of color are disproportionately impacted by obesity nationally and locally. Racial and ethnic disparities in obesity are linked to policies and systemic barriers that can make it harder to access healthy foods, exercise opportunities, healthcare, and more.

The District Response

The Produce Plus Program provides financial support for residents with low access to fresh foods to spend at local farmers markets. The Food Access Fund (FAF) Grant increases equitable access to fresh, healthy, and affordable food by supporting the opening of new grocery stores in areas with low food access, with priority given to locations in Ward 7 or Ward 8.

Promoting health through free DPR fitness centers, wellness classes, the MoveDC Plan for active transportation, and health and PE classes in public schools to encourage lifelong exercise habits.

Created the Living Well DC portal for District residents, health professionals, and community-based organizations, which provides information and resources to help improve health outcomes.

Many alternative indices for insulin resistance are under investigation for their ability to assess insulin resistance and for their association with specific pathologies or their predictive value for such conditions. Among these, two newer indices—the triglyceride-glucose (TyG) index and the TyG-BMI index—are particularly promising because triglycerides and glucose can be measured at lower cost than insulin. However, three key demographic factors—biological sex, age, and ethnicity—that are known to influence the predictive performance of other indices have not yet been examined for these measures. This study provides data to replicate research aimed at determining optimal cutoffs for the TyG and TyG-BMI indices and at evaluating whether these cutoffs vary by age, biological sex, and ethnicity. Finally, the findings show that using demographic-specific cutoffs, rather than a single general cutoff, improves the predictive accuracy of these indices for all-cause mortality.

ObjectiveTo investigate the association of dynamic weight change in adulthood with leukocyte telomere length among U.S. adults.MethodsThis study included 3,886 subjects aged 36-75 years from the National Health and Nutrition Examination Survey (NHANES) 1999-2002 cycle. Survey-weighted multivariable linear regression with adjustments for potential confounders was utilized.Results3,386 individuals were finally included. People with stable obesity had a 0.130 kbp (95% CI: 0.061-0.198, P=1.97E-04) shorter leukocyte telomere length than those with stable normal weight (reference group) during the 10-year period, corresponding to approximately 8.7 years of aging. Weight gain from non-obesity to obesity shortened the leukocyte telomere length by 0.094 kbp (95% CI: 0.012-0.177, P=0.026), while normal weight to overweight or remaining overweight shortened the leukocyte telomere length by 0.074 kbp (95% CI: 0.014-0.134, P=0.016). The leukocyte telomere length has 0.003 kbp attrition on average for every 1 kg increase in weight from a mean age of 41 years to 51 years. Further stratified analysis showed that the associations generally varied across sex and race/ethnicity.ConclusionsThis study found that weight changes during a 10-year period was associated with leukocyte telomere length and supports the theory that weight gain promotes aging across adulthood.

In 2023, some ** percent of all Black adults in Georgia were obese. Childhood obesity in the United States is also on the rise, with affected children more likely to remain obese as adults. This statistic depicts the obesity rates for adults in Georgia in 2023, by race/ethnicity.

The coronavirus disease (COVID-19) has revealed existing health inequalities in racial and ethnic minority groups in the US. This work investigates and quantifies the non-uniform effects of geographical location and other known risk factors on various ethnic groups during the COVID-19 pandemic at a national level. To quantify the geographical impact on various ethnic groups, we grouped all the states of the US. into four different regions (Northeast, Midwest, South, and West) and considered Non-Hispanic White (NHW), Non-Hispanic Black (NHB), Hispanic, Non-Hispanic Asian (NHA) as ethnic groups of our interest. Our analysis showed that infection and mortality among NHB and Hispanics are considerably higher than NHW. In particular, the COVID-19 infection rate in the Hispanic community was significantly higher than their population share, a phenomenon we observed across all regions in the US but is most prominent in the West. To gauge the differential impact of comorbidities on different ethnicities, we performed cross-sectional regression analyses of statewide data for COVID-19 infection and mortality for each ethnic group using advanced age, poverty, obesity, hypertension, cardiovascular disease, and diabetes as risk factors. After removing the risk factors causing multicollinearity, poverty emerged as one of the independent risk factors in explaining mortality rates in NHW, NHB, and Hispanic communities. Moreover, for NHW and NHB groups, we found that obesity encapsulated the effect of several other comorbidities such as advanced age, hypertension, and cardiovascular disease. At the same time, advanced age was the most robust predictor of mortality in the Hispanic group. Our study quantifies the unique impact of various risk factors on different ethnic groups, explaining the ethnicity-specific differences observed in the COVID-19 pandemic. The findings could provide insight into focused public health strategies and interventions.