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.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.

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

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.

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.

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

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/).;;

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.

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

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.

To prevent obesity and diabetes environmental interventions such as eliminating food deserts, restricting proliferation of food swamps, and improving park access are essential. In the United States, however, studies that examine the food and park access relationship with obesity and diabetes using both global and local regression are lacking. To guide county, state, and federal policy in combating obesity and diabetes, there is a need for cross-scale analyses to identify that relationship at national and local levels. This study applied spatial regression and geographically weighted regression to the 3,108 counties in the contiguous United States. Global regression show food deserts exposure and density of fast-food restaurants have non-significant association with obesity and diabetes while park access has a significant inverse association with both diseases. Geographically weighted regression that takes into account spatial heterogeneity shows that, among southern states that show high prevalence of obesity and diabetes, Alabama and Mississippi stand out as having opportunity to improve park access. Results suggest food deserts exposure are positively associated with obesity and diabetes in counties close to Alabama, Georgia, and Tennessee while density of fast-food restaurants show positive association with two diseases in counties of western New York and northwestern Pennsylvania. These findings will help policymakers and public health agencies in determining which geographic areas need to be prioritized when implementing public interventions such as promoting healthy food access, limiting unhealthy food options, and increasing park access.

The present dataset is the basis of the results presented in "Modest weight loss improves leptin to adiponectin ratio and induces insulin and leptin resensitivization in individuals with obesity." (manuscript submitted 2019, December). Please read the accompanying ReadMe file for a further description of variables. Abstract: Background: Identifying patients at the highest risk of adverse consequences of obesity is of great importance in order to better monitor the effects of treatment. This study aims to investigate whether dysregulated adipokines and postprandial triglycerides (TG) improve with modest weight loss. Methods: Individuals with obesity were recruited by posters, among patients at the University Hospital of North Norway and the Stamina Health weight loss program. We calculated the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), leptin to adiponectin (L:A) ratio, indirect leptin sensitivity (REE:leptin ratio), postprandial TG clearance at 6 h and TG response from samples collected at visits before and after weight loss. The weight loss goal was ≥5% of initial total weight. Results: Twenty-eight participants attended both assessments, of which 13 lost ≥5% body weight. Of these, five lost ≥10% body weight. HOMA-IR (-23.1%), REE:leptin ratio (+80.1%) and L:A ratio (-45.7%) significantly improved with a weight loss ≥5%, whereas there was no improvement of postprandial TG response or clearance. Participants with ≥5% weight loss improved their L:A ratio over cut-off values ≥1.88 and ≥2.2 significantly, and participants with ≥10% weight loss improved their L:A ratio over the cut-off value ≥3.65 significantly. Participants with ≥10% weight loss also improved their HOMA-IR over cut-off value ≥2.3 significantly. Conclusion: Metabolic dysregulation measured by the surrogate biomarkers HOMA-IR, REE:leptin ratio and L:A ratio, but not postprandial TG, improve with a modest weight loss of ≥ 5%. Further improvements in these biomarkers are seen in weight loss of ≥10%. To measure postprandial TG response (TGR) and TG clearance, we performed an Oral Fat Tolerance Test (OFTT). Participants had their regular diet and abstained from vigorous exercise three days before the test, and showed up at 08:00 am after a 12 h overnight fast. Fasting blood samples were drawn, and a meal of sour cream porridge was served, containing 1 g of fat per kg of body weight (28). The participants ingested the meal within 30 minutes, and blood samples were drawn from the antecubital vein in a seated position at baseline and 2, 4, 6 and 8 h postprandially. We calculated TG clearance in per cent of the peak value at 6 and 8 h, in addition to the TGR, defined as the average of the two highest postprandial TG concentrations, minus the baseline concentration. The formula for calculating TG clearances (28) at time X was as follows: Clearance Xh =100×(1- ([TG]X -[TG]0h)/([TG]max -[TG]0h )) We performed a 2 h Oral Glucose Tolerance Test (OGTT) on a separate day. Preparations for the OGTT were the same as for the OFTT and was conducted by oral intake of 75 g glucose, dissolved in water. We collected blood samples in both the fasting state and 30, 60, 90 and 120 minutes after glucose intake, in which serum glucose and serum insulin were measured using ELISA kits (DRG Insulin Elisa kit, DRG Instruments GmbH, Germany). We determined insulin sensitivity by calculation of the HOMA1-IR. Both s-leptin and free s-adiponectin were analysed from frozen serum drawn at all sample times, both during OFTT and OGTT, using ELISA kits (DRG Diagnostics, Marburg, Germany) for s-leptin (sandwich ref. EIA-2395) and s-adiponectin (human, ref. EIA-4574), respectively. Leptin sensitivity was calculated as the ratio of Resting Energy Expenditure (REE) to fasting serum leptin. We performed REE measurements by a canopy test with an indirect calorimetry device from Medical Graphics CPX metabolic cart (St Paul, MN, USA). The test protocol is described by Larsen et al. Leptin to adiponectin ratio – A surrogate biomarker for early detection of metabolic disturbances in obesity. Nutrition, Metabolism and Cardiovascular Diseases. 2018. After the completion of REE measurement, the OGTT was performed. We used parametric tests on raw or transformed variables that resembled a normal distribution visually or by skewness/kurtosis. Otherwise, non-parametric tests were performed.

Childhood obesity has risen and is one of the most important global problems of our time, and school physical education programs are the key to ameliorating it. In American schools, physical fitness scores have declined; yet, global, national, state, and local concerns for the overall health, physical fitness, and wellbeing of children are at an all-time high. The lack of safe and affordable options for physical activity coupled with the significant decrease in physical activity rates among most American children underscores the need for programs, data, and research on physical fitness in schools, where children spend a significant amount of their time. The purpose of this brief research report is to call the federal government and states to mandate physical fitness programs and to increase data collection capacity on physical fitness in schools. Subsequently, this study asks researchers to study physical fitness in schools in the U.S. to increase its importance to policy makers and educational stakeholders and advance our understanding of educational inequities in school physical fitness. As an example, using descriptive analyses, we have provided policymakers, educational stakeholders, and researchers with a first look at California’s physical fitness data which shows how our findings complement prior literature as well as extend them. Implications for the research and practice are discussed.

BackgroundGlobally, the incidence rates of obesity and its related diseases, such as cardiovascular diseases and type 2 diabetes, are continuously rising, posing a significant public health challenge. Studies have indicated a potential correlation between vitamin D deficiency and obesity. However, a quantitative analysis of the studies related vitamin D and obesity is lacking. This investigation aims to fill this gap by providing a comprehensive bibliometric analysis to uncover the collaborative networks, research hotspots, and evolutionary trends within the field of vitamin D and obesity research.MethodsThis study retrieved literature related to vitamin D and obesity from the Web of Science database spanning from 2000 to 2023. Bibliometric analysis was conducted using tools such as HistCite, VOSviewer, and CiteSpace to excavate multi-dimensional information including countries, institutions, authors, journals, citations, and keywords.ResultsA total of 6,144 records were retrieved, involving 123 countries, 6,726 institutions, and 28,156 authors, published in 1,551 journals. The number of published papers and citations showed a generally increasing trend. The United States led in terms of publication volume and influence, with journals such as Nutrients and Obesity Surgery having the highest publication counts. Nasser M. Al-Daghri was the most prolific and influential author. Keyword clustering revealed that research topics covered metabolic health, nutrition, immunity, and bariatric surgery. Citation burst analysis indicated a shift in research focus from the relationship between dietary calcium and obesity to the preventive effects of vitamin D supplementation on metabolic diseases.ConclusionThe application of bibliometric methods to analyze the research literature in the fields of obesity and vitamin D has provided a comprehensive understanding of the collaborative networks, key research focus, and evolutionary trends in this field, offering insights for guiding future research directions.

S1 Table. Prevalence of HPF within Food Main Categories across Countries. S2 Table. Summary of the Prevalence of HPF across Countries.S3 Table. Logistic regression results for the HPF prevalence compared to United States. S4 Table. Logistic regression results for the FSOD prevalence compared to United States. S5 Table. Logistic regression results for the FS prevalence compared to United States. S6 Table. Logistic regression results for the CSOD prevalence compared to United States. S7 Table. Descriptive Statistics of Nutritional Compositions of FSOD across Countries.S8 Table. Descriptive Statistics of Nutritional Compositions of FS across Countries. S9 Table. Descriptive Statistics of Nutritional Compositions of CSOD across Countries. S10 Table. Ordered Beta Regression Results for nutritional compositions of FSOD across countries compared to United States.S11 Table. Ordered Beta Regression Results for nutritional compositions of FS across countries compared to United States. S12 Table. Ordered Beta Regression Results for nutritional compositions of CSOD across countries compared to United States. S13 Table. Distinct and Overlapping percentage of HPF and UPF across countries. (ZIP)

More than 60% of domestic cats in the United States are either overweight or obese (OW). High-protein low-carbohydrate (HPLC) diets have been recommended for weight management for humans and pets. Gut microbes can influence the host’s health and metabolism. Less is known about feline gut microbiomes compared to other species. Thirty-nine lean (LN) and OW domestic short-haired cats (median age, 7.2 years) with median body fat of 15.8 and 32.5%, respectively, were enrolled in a two-phase study. All cats were fed the control diet (CON) with 32.4% protein and 32.3% carbohydrate for 8 weeks followed by another 8 weeks of intervention where half of the cats continued the CON diet while the other half were switched to a HPLC diet with 51.4% protein and 11.6% carbohydrate. The goal was to understand how the HPLC diet influenced gut microbiota in obese vs. lean cats. The 16S rRNA gene profiling study revealed a significant impact on gut microbiome by dietary protein and carbohydrate ratio. The effect was more pronounced in OW cats than LN cats. While no microbial taxon was different between groups in LN cats, compositional changes occurred at different taxonomical ranks in OW cats. At the phylum level, Fusobacteria became more abundant in HPLC-fed cats than in CON-fed cats. At the genus level, five short-chain fatty acid (SCFA) producers had altered compositions in response to the diets: Faecalibacterium and Fusobacterium are more abundant in HPLC-fed cats while the abundances of Megasphaera, Bifidobacterium, and Veillonella increased in CON-fed cats. Predicted microbial gene networks showed changes in energy metabolism and one-carbon metabolism pathways. Our study demonstrated differential responses to HPLC diet between obese vs. lean cats and opportunities to explore these SCFA-producers for weight management in cats.

BackgroundThe COVID-19 pandemic caused significant global mortality. Obesity is associated with worse COVID-19 outcomes. This study examined the relationship between BMI, clinical interventions, and outcomes in hospitalized COVID-19 patients using pre-vaccine national data.MethodsWe conducted a retrospective cohort study using de-identified electronic health records from the HCA Healthcare database, comprising 149 hospitals across 18 U.S. states. Adults (≥18 years) hospitalized with confirmed SARS-CoV-2 infection between March 1 and December 31, 2022, were included. The primary outcome was a composite of in-hospital mortality or discharge to hospice, analyzed by BMI category. Secondary outcomes included inpatient mortality, need for mechanical ventilation or tracheostomy, duration of mechanical ventilation, and ICU (Intensive Care Unit) length of stay.ResultsOut of 38,321 hospital encounters, 21,996 met the inclusion criteria. Unadjusted analyses showed no significant differences in rates of all-cause mortality or hospice discharge across BMI categories. However, obese patients had higher rates of mechanical ventilation (7.8% vs. 4.6%, p 

Indicators also include behavioral risk factors (tobacco use, alcohol consumption, physical inactivity, overweight/obesity, cholesterol) and hypertension awareness, treatment, and control estimates. This table provides the quantitative foundation for regional comparisons of cardiometabolic disease burden and risk profiles across Caribbean and North American countries. (XLSX)

Summary statistics of ZCTA-level potential predictors of COVID-19 risk in the Greater St. Louis Area, Missouri (USA).

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