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.

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

From 2017 to March 2020, the prevalence of obesity among adults in the United States with a family income 130% or less the federal poverty level was nearly ** percent. This statistic shows the age-adjusted prevalence of obesity among adults 20 years and older in the United States from 2017 to March 2020, by family income relative to federal poverty level.

BackgroundIn high-income countries, obesity prevalence (body mass index greater than or equal to 30 kg/m2) is highest among the poor, while overweight (body mass index greater than or equal to 25 kg/m2) is prevalent across all wealth groups. In contrast, in low-income countries, the prevalence of overweight and obesity is higher among wealthier individuals than among poorer individuals. We characterize the transition of overweight and obesity from wealthier to poorer populations as countries develop, and project the burden of overweight and obesity among the poor for 103 countries.Methods and findingsOur sample used 182 Demographic and Health Surveys and World Health Surveys (n = 2.24 million respondents) from 1995 to 2016. We created a standard wealth index using household assets common among all surveys and linked national wealth by country and year identifiers. We then estimated the changing probability of overweight and obesity across every wealth decile as countries’ per capita gross domestic product (GDP) rises using logistic and linear fixed-effect regression models. We found that obesity rates among the wealthiest decile were relatively stable with increasing national wealth, and the changing gradient was largely due to increasing obesity prevalence among poorer populations (3.5% [95% uncertainty interval: 0.0%–8.3%] to 14.3% [9.7%–19.0%]). Overweight prevalence among the richest (45.0% [35.6%–54.4%]) and the poorest (45.5% [35.9%–55.0%]) were roughly equal in high-income settings. At $8,000 GDP per capita, the adjusted probability of being obese was no longer highest in the richest decile, and the same was true of overweight at $10,000. Above $25,000, individuals in the richest decile were less likely than those in the poorest decile to be obese, and the same was true of overweight at $50,000. We then projected overweight and obesity rates by wealth decile to 2040 for all countries to quantify the expected rise in prevalence in the relatively poor. Our projections indicated that, if past trends continued, the number of people who are poor and overweight will increase in our study countries by a median 84.4% (range 3.54%–383.4%), most prominently in low-income countries. The main limitations of this study included the inclusion of cross-sectional, self-reported data, possible reverse causality of overweight and obesity on wealth, and the lack of physical activity and food price data.ConclusionsOur findings indicate that as countries develop economically, overweight prevalence increased substantially among the poorest and stayed mostly unchanged among the wealthiest. The relative poor in upper- and lower-middle income countries may have the greatest burden, indicating important planning and targeting needs for national health programs.

In the United States, the rate of obesity is lower among college graduates compared to those who did not graduate from college. For example, in 2023, around 27 percent of college graduates were obese, while 36 percent of those with some college or technical school were obese. At that time, rates of obesity were highest among those with less than a high school education, at around 37 percent. Income and obesity As with education level, there are also differences in rates of obesity in the United States based on income. Adults in the U.S. with an annual income of 75,000 U.S. dollars or more have the lowest rates of obesity, with around 29 percent of this population obese in 2023. On the other hand, those earning less than 15,000 U.S. dollars per year had the highest rates of obesity at that time, at 37 percent. One reason for this disparity may be a lack of access to fresh food among those earning less, as cheap food in the United States tends to be unhealthier. What is the most obese state? As of 2023, the states with the highest rates of obesity were West Virginia, Mississippi, and Arkansas. At that time, around 41 percent of adults in West Virginia were obese. The states with the lowest rates of obesity were Colorado, Hawaii, and Massachusetts. Still, around a quarter of adults in Colorado were obese in 2023. West Virginia and Mississippi are also the states with the highest rates of obesity among high school students. Children with obesity are more likely to be obese as adults and are at increased risk of health conditions such as asthma, type 2 diabetes, and sleep apnea.

State-level and county-level data, all from public sources described in "Recent origin and evolution of obesity-income correlation across the United States" Note rates are age-adjusted and that household income statistics are in 2009 U.S. dollars.

This dataset includes data on adult's diet, physical activity, and weight status from 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. I was particularly curious on whether socioeconomic status has an impact on obesity. In my analysis, I compare the obesity rate in each state, and then perform a linear regression on the obesity rate for each educational status and the income bracket.

From 2017 to March 2020, the prevalence of obesity among children and adolecents in the United States with a family income ***% or less the federal poverty level was nearly ** percent. This statistic shows the age-adjusted prevalence of obesity among children and adolecents aged 2 to 19 years in the United States from 2017 to March 2020, by family income relative to federal poverty level.

ObjectivesTo describe the relationship between minimum wage and overweight and obesity across countries at different levels of development.MethodsA cross-sectional analysis of 27 countries with data on the legislated minimum wage level linked to socio-demographic and anthropometry data of non-pregnant 190,892 adult women (24–49 y) from the Demographic and Health Survey. We used multilevel logistic regression models to condition on country- and individual-level potential confounders, and post-estimation of average marginal effects to calculate the adjusted prevalence difference.ResultsWe found the association between minimum wage and overweight/obesity was independent of individual-level SES and confounders, and showed a reversed pattern by country development stage. The adjusted overweight/obesity prevalence difference in low-income countries was an average increase of about 0.1 percentage points (PD 0.075 [0.065, 0.084]), and an average decrease of 0.01 percentage points in middle-income countries (PD -0.014 [-0.019, -0.009]). The adjusted obesity prevalence difference in low-income countries was an average increase of 0.03 percentage points (PD 0.032 [0.021, 0.042]) and an average decrease of 0.03 percentage points in middle-income countries (PD -0.032 [-0.036, -0.027]).ConclusionThis is among the first studies to examine the potential impact of improved wages on an important precursor of non-communicable diseases globally. Among countries with a modest level of economic development, higher minimum wage was associated with lower levels of obesity.

📖 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.

In 2020, around ** percent of men aged 20 years and older in high income countries were considered obese. This share is expected to increase to around ** percent by the year 2035. This statistic shows the percentage of men aged 20 years and older who were obese worldwide in 2020 and forecasts for the years 2025, 2030, and 2035, by country income level.

Abstract Overweight and obesity in childhood and adolescence have reached alarming rates in Brazil. The aim of this study was to analyze the prevalence rates of overweight and obesity according to socioeconomic status, gender and age in schoolchildren from 11 towns around the Itaipu Lake, western Paraná. The sample consisted of 5,962 subjects (3,024 girls) aged 6 to 17 years. Overweight and obesity were identified based on body mass index, adopting the cut-off values for age and gender suggested by the International Obesity Task Force. The prevalence rates according to different strata (gender, age and socioeconomic status) were compared by means of logistic regression using odds ratios. The prevalence of overweight and obesity was 14.8% and 5.8% among boys, respectively, and 15.2% and 4.5% among girls. The prevalence rates decreased with increasing age. Considering socioeconomic status, the prevalence of overweight and obesity was significantly higher in schoolchildren from high-income families. The prevalence rates indicate a trend similar to that observed for the highest prevalence reported in surveys conducted in other cities and regions of Brazil. The present results suggest the need for interventions of the public health system and of society, seeking alternatives to alleviate this problem and its consequences.

Context: Currently it is not well understood to what extent there are obesity inequalities by socioeconomic status (SES) in urban Latin America. Objective: This study reviewed the literature assessing associations between overweight, obesity and SES in adults. Data sources: Pubmed and Scielo databases. Data extraction: Data extraction was conducted using the PRISMA guidelines. We extracted data on the direction of the association between SES (e.g. education and income), overweight (BMI ≥25 and <30 kg/m2) and obesity (BMI≥30 kg/m2) in Latin American urban regions. Relative differences between low and high SES groups were assessed and defined a priori as significant at p<0.05. Data analysis: Thirty-one studies met our inclusion criteria and most were conducted in Brazil (22) and Mexico. Only one study presented just non-significant associations. Fifty percent of associations between education or income and overweight were negative/inverse. Regarding obesity, 80% were negative and 20% positive. Most negative associations were found in women. Associations between BMI and SES usually followed the same pattern, except in men where they varied depending on the indicator used. Conclusion: Low SES individuals in urban Latin America, especially women, have higher BMI levels highlighting the need for interventions.

Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 10.900 % in 2024. This records a decrease from the previous number of 11.100 % for 2023. Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 10.900 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 11.500 % in 2019 and a record low of 8.300 % in 2000. Panama PA: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Panama – Table PA.World Bank.WDI: Social: 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 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;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. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

1Model  =  education (three levels)*wealth quintile (continuous) + age group + urban/rural residence + parity.2ORs represent the effect of an increase in one wealth quintile on the odds of obesity within each education level.3Estimates for education not shown.4Test of whether the wealth effects differ by education level.

El Salvador SV: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.400 % in 2014. This records an increase from the previous number of 5.700 % for 2008. El Salvador SV: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 4.800 % from Dec 1988 (Median) to 2014, with 6 observations. The data reached an all-time high of 6.400 % in 2014 and a record low of 3.000 % in 1988. El Salvador SV: 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 El Salvador – Table SV.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

Context This dataset makes use of several different datasets which range from macro-sources as well as primary-sources. Macro-sources include data from ‘DataUSA’ regarding adult obesity rates, average age, average income, and poverty rates for each state. Primary-sources include data from both the Federal Reserve as well as the U.S. Bureau of Economic Analysis regarding Real GDP by state and state region. There are four years used in this dataset: 2014, 2015, 2016, 2017.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: 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 Mali – Table ML.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

In 2020, around 14 percent of girls aged 5-19 years in high income countries were considered obese. This share is expected to increase to around 22 percent by the year 2035. This statistic shows the percentage of girls aged 5-19 years who were obese worldwide in 2020 and forecasts for the years 2025, 2030, and 2035, by country income level.

The double burden of malnutrition has assumed severer forms in Low and Middle Income Countries (LMICs) arising from sharper increases in prevalence rates of overweight and obesity in these countries compared to higher income countries. Considering that LMICs already have fragile health systems, the rising obesity levels may just be a ticking time bomb requiring expeditious implementation of priority actions by all global and national actors to prevent an explosion of cardiovascular disease related deaths. The aim of this systematic review and meta-analysis was to provide a current estimate of obesity and overweight prevalence among Ghanaian adults and assess socio-demographic disparities following the PRISMA guidelines. We searched Pubmed with Medline, Embase, Science direct and African Journals Online (AJOL) for studies on overweight and obesity published between 2013 and January 2023. Applying a quality effects model, pooled mean Body Mass Index (BMI) and prevalence of overweight and obesity were obtained from 42 studies conducted across all three geographical locations of Ghana with a combined sample size of 29137. From the analysis, the mean BMI of adults in Ghana was 24.7 kgm-2 while overweight and obesity prevalence was estimated as 23.1% and 13.3% respectively. Temporal analysis showed sharper increases in overweight and obesity prevalence from 2017/2018. Mean BMI (Females: 25.3kgm-2 vrs Males: 23.1 kgm-2), overweight (Females: 25.9% vrs Males: 16.5%) and obesity (Females: 17.4% vrs Males: 5.5%) prevalence were higher among females than males. Gender differences in mean BMI and obesity prevalence were both significant at p