In 2021-2022, Mississippi topped the ranking of states with the highest share of children/adolescents between 6 and 17 years of age who were obese. This statistic illustrates the obesity rates among children/adolescents between 6 and 17 years of age in the United States in 2021-2022, by state.

The prevalence of obesity in the United States has risen gradually over the past decade. As of 2023, around ** percent of the population aged 18 years and older was obese. Obesity is a growing problem in many parts of the world, but is particularly troubling in the United States. Obesity in the United States The states with the highest prevalence of obesity are West Virginia, Mississippi, and Arkansas. As of 2023, a shocking ** percent of the population in West Virginia were obese. The percentage of adults aged 65 years and older who are obese has grown in recent years, compounding health issues that develop with age. Health impacts of obesity Obesity is linked to several negative health impacts including cardiovascular disease, diabetes, and certain types of cancer. Unsurprisingly, the prevalence of diagnosed diabetes has increased in the United States over the years. As of 2022, around *** percent of the population had been diagnosed with diabetes. Some of the most common types of cancers caused by obesity include breast cancer in postmenopausal women, colon and rectum cancer, and corpus and uterus cancer.

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.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Between 2015 and 2018, obesity rates in U.S. children and adolescents stood at 19.3 and 20.9 percent, respectively. This is a noteworthy increase compared to the percentages seen between 1988 and 1994.

U.S. high school obesity rates

Roughly 18 percent of black, as well as Hispanic students in the United States, were obese between 2016 and 2017. Male obesity rates were noticeably higher than those of female students for each of the ethnicities during the measured period. For example, about 22 percent of male Hispanic high school students were obese, compared to 14 percent of female students. The American states with the highest number of obese high school students in 2019 included Mississippi, West Virginia, and Arkansas, respectively. Mississippi had a high school student obesity rate of over 23 percent that year.

Physically inactive Americans

Adults from Mississippi and Arkansas were also reported to be some of the least physically active people in the United States in 2018. When surveyed, over 30 percent of adults from Kentucky and Arkansas had not exercised within the preceding 30 days. The national physical inactivity average stood at approximately 26 percent that year.

Detection rate of overweight and obesity of 4-year-old children who attended Maternal and Child Health Centres in 2011 to 2024

Obesity rate for adults 20+ years old. Data from US Diabetes Surveillance System; www.cdc.gov/diabetes/data; Division of Diabetes Translation - Centers for Disease Control and Prevention. Obesity is defined with BMI >30.

Obesity rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States." The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the obesity rate of a demographically similar census tract to one in Allegheny County to compute an obesity rate.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Number and percentage of youth who reported being overweight or obese, by sex.

This statistical report presents information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: Obesity related hospital admissions. Prescription items for the treatment of obesity. Adult obesity prevalence. Childhood obesity prevalence. Physical activity levels among adults and children. Diet among adults and children, including trends in purchases, and consumption of food and drink and energy intake. Each section provides an overview of the key findings from these sources, as well as providing sources of further information and links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool at the link below allows users to select obesity related hospital admissions data for any Local Authority (as contained in Excel tables 3, 7 and 11 of this publication), along with time series data from 2013/14. Regional and national comparisons are also provided.

Historical dataset showing U.S. obesity rate by year from N/A to N/A.

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.

Problem Statement Obesity is a complex disease involving an excessive amount of body fat. Obesity isn't just a cosmetic concern. It is a medical problem that increases your risk of other diseases and health problems, such as heart disease, diabetes, high blood pressure and certain cancers.

Obesity is a worldwide problem which causes a lot of serious medical problems. Obesity will be increased, about 45% out of the whole population by 2035. The proportion of morbid obese and the actual costs on healthcare will be increased. Implementation of a system that could estimate the percentage of obese population for particular time duration given the age range, income range, location, high confidence level and low confidence level of obesity, education, gender, the class level, etc. of the population can help in fight against obesity.

Objective Build a machine learning model that would help us estimate the percentage of obese population.

About the Dataset This dataset includes data on adult's diet, physical activity, and weight status from the Behavioral Risk Factor Surveillance System. 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. There are 29 variables in the train dataset including target variable. Some of the variables are listed below with their description:

YearStart & YearEnd are the starting and ending year for which the obesity percentage is to be estimated Sample_Size is the sample of population LocationDesc is the location description and LocationAbbr is the location abbreviation Topic and Question are the topic and question category under which sample population falls Age(years) is the age range to which sample population belong Data_VAlue is the population obesity percentage (the target variable)

📖 About Dataset

🌎 Overview

This dataset provides cleaned and structured information from the Behavioral Risk Factor Surveillance System (BRFSS) conducted by the CDC. It focuses on nutrition, physical activity, and obesity trends across U.S. states and national averages from 2011 to 2023.

The data originates from the Division of Nutrition, Physical Activity, and Obesity (DNPAO) and has been pre-processed to remove missing values, redundant columns, and inconsistencies, making it ready for analysis.

📊 Contents

The dataset contains 29 columns and over 106,000 rows of observations, including:

• Year: Start and end years of data collection (2011–2023)
• Location: State abbreviation, state name, and geographic coordinates
• Class & Topic: High-level categories such as Obesity/Weight Status, Physical Activity, Fruits and Vegetables
• Question: Specific health behavior measured (e.g., % of adults with BMI ≥30)
• Data_Value: The main metric (percentage or proportion)
• Confidence Intervals: Statistical lower and upper bounds
• Sample Size: Number of participants
• Demographics: Age, sex, income, education, race/ethnicity

✅ Cleaning Process

• Removed fully empty columns (e.g., Total, Data_Value_Unit)
• Imputed missing numeric values using median replacement
• Categorical variables (Age, Sex, Education, Income, Race/Ethnicity) filled with Unknown
• Dropped non-essential ID columns (ClassID, TopicID, etc.) to simplify analysis
• Final dataset contains no missing values

🎯 Use Cases

This dataset is highly valuable for:

• Public Health Research: Tracking obesity and physical activity trends
• Policy Evaluation: Comparing state-level health initiatives
• Data Science & ML: Building predictive models on obesity & lifestyle behaviors
• Visualization Projects: Heatmaps, time series, and demographic comparisons

📌 Example Questions You Can Answer

• How have obesity rates changed from 2011–2023 across U.S. states?
• Which states report the highest vs lowest physical activity levels?
• What is the relationship between income, education, and obesity?
• How do dietary habits (fruit & vegetable intake) correlate with weight status?

📂 File Information

• File Name: Nutrition_Physical_Activity_Obesity_Clean.csv
• Rows: 106,260
• Columns: 29
• Format: CSV (comma-separated)

🏛 Source

• Centers for Disease Control and Prevention (CDC)
• Division of Nutrition, Physical Activity, and Obesity (DNPAO)
• Original dataset: Data.gov – BRFSS Nutrition, Physical Activity, and Obesity

💡 Citation

If you use this dataset in your work, please cite: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System (BRFSS), 2011–2023.

✨ This cleaned version was prepared for easy exploration, analysis, and machine learning applications on Kaggle.

Data on obesity among children and adolescents aged 2-19 years by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time.

SOURCE: NCHS, National Health and Nutrition Examination Survey. For more information on the National Health and Nutrition Examination Survey, see the corresponding Appendix entry at https://www.cdc.gov/nchs/data/hus/hus19-appendix-508.pdf.

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.

Overview: This dataset combines publicly available data on obesity rates, poverty rates, and median household income for all 50 U.S. states from 2019 to 2023. It also includes calculated regional averages based on U.S. Census Bureau-defined regions (Northeast, Midwest, South, and West).

Use Cases - Public health research - Data visualization projects - Socioeconomic analysis - ML models exploring health + income

Sources - CDC BRFSS – Adult Obesity Prevalence Maps (2019–2023) - U.S. Census Bureau – SAIPE Datasets (2019–2023)

Tableau Dashboard View the interactive Tableau dashboard:
https://public.tableau.com/app/profile/geo.montes/viz/ObesityPovertyandIncomeintheU_S_2019-2023/Dashboard1#2

Created by Geo Montes, Informatics major at UT Austin

Welcome to the dataset offering a comprehensive look at obesity trends in California from the years 2013 to 2014. These valuable insights are derived from three prominent surveys: the 2013 California Dietary Practices Surveys (CDPS), the 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the 2013 California Children's Healthy Eating and Exercise Practices Surveys (CalCHEEPS).

The dataset encompasses diverse age groups, with data collected from adults, adolescents, and children (with parental assistance). Participants were asked to provide their current height and weight, enabling the calculation of Body Mass Index (BMI). Obesity classifications varied according to age groups: adults were classified as obese if their BMI was 30.0 and above, while adolescents and children were considered obese if their BMI was at or above the 95th percentile, as per CDC growth charts.

Key Headers:

1. Year: Specifies the year of data collection (2013-2014).
2. Age Group: Categorizes participants into relevant age brackets.
3. Category: Represents the broad classification of the surveyed population (e.g., adults, adolescents, children).
4. Type: Provides additional categorization or subgrouping within each age category.
5. Obese (%): Indicates the percentage of participants classified as obese within each subgroup.

This dataset offers a granular understanding of obesity prevalence across different demographics within California during the specified timeframe. Researchers, policymakers, and healthcare professionals can utilize this dataset to identify trends, formulate targeted interventions, and assess the effectiveness of existing health initiatives aimed at combating obesity in the state.

Source: https://data.world/health/california-obesity-2012-2013