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.

These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS). These surveys have been discontinued. Adults, adolescents, and children (with parental assistance) were asked for their current height and weight, from which, body mass index (BMI) was calculated. For adults, a BMI of 30.0 and above is considered obese. For adolescents and children, obesity is defined as having a BMI at or above the 95th percentile, according to CDC growth charts.

The California Dietary Practices Surveys (CDPS), the California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS) (now discontinued) were the most extensive dietary and physical activity assessments of adults 18 years and older, adolescents 12 to 17, and children 6 to 11, respectively, in the state of California. CDPS and CalCHEEPS were administered biennially in odd years up through 2013 and CalTEENS was administered biennially in even years through 2014. The surveys were designed to monitor dietary trends, especially fruit and vegetable consumption, among Californias for evaluating their progress toward meeting the Dietary Guidelines for Americans and the Healthy People 2020 Objectives. All three surveys were conducted via telephone. Adult and adolescent data were collected using a list of participating CalFresh households and random digit dial, and child data were collected using only the list of CalFresh households. Older children (9-11) were the primary respondents with some parental assistance. For younger children (6-8), the primary respondent was parents. Data were oversampled for low-income and African American to provide greater sensitivity for analyzing trends among the target population. Wording of the question used for these analyses varied by survey (age group). The questions were worded are as follows: Adult:1) How tall are you without shoes?2) How much do you weigh?Adolescent:1) About how much do you weigh without shoes?2) About how tall are you without shoes? Child:1) How tall is [child's name] now without shoes on?2) How much does [child's name] weigh now without shoes on?

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.

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.

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.

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

Data for cities, communities, and City of Los Angeles Council Districts were generated using a small area estimation method which combined the survey data with population benchmark data (2022 population estimates for Los Angeles County) and neighborhood characteristics data (e.g., U.S. Census Bureau, 2017-2021 American Community Survey 5-Year Estimates). Data for this indicator are based on self-reported height and weight. Body Mass Index (BMI) is calculated by dividing a person’s weight in kilograms by the square of their height in meters. Individuals with a BMI ≥ 30 are considered to have obesity. Note, while BMI can be helpful in screening for individuals with obesity or overweight, it does not measure how much body fat an individual has or provide any diagnostic information about their overall health.Obesity is associated with increased risk for heart disease, diabetes, and cancer. Cities and communities can help curb the current obesity epidemic by adopting policies that support healthy food retail and physical activity and improve access to preventive care services.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Context

The Behavioral Risk Factor Surveillance System (BRFSS) is the Unites States’s premier system of health-related telephone surveys that collect state data about U.S. residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.

By collecting behavioral health risk data at the state and local level, BRFSS has become a powerful tool for targeting and building health promotion activities.

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss).

Dataset

2,289,902 rows by 27 columns

Each entry contains the number and percent of responses to a survey question for a given year and demographic category sub-group.

Demographic categories:

• Overall
• Gender
• Age Group
• Race/Ethnicity
• Education Attained
• Household Income

Question categories:

• Alcohol Consumption
• Cholesterol Awareness
• Chronic Health Indicators
• Colorectal Cancer Screening
• Demographics
• Fruits and Vegetables
• Health Care Access/Coverage
• Health Status
• HIV-AIDS
• Hypertension Awareness
• Immunization
• Injury
• Oral Health
• Overweight and Obesity (BMI)
• Physical Activity
• Prostate Cancer
• Tobacco Use
• Women's Health
• E-Cigarette Use
• Days of Poor Health

Source

Methodology Glossary Original data source Date Created: June 4, 2015 Last Updated: October 21, 2022

This data comes under public domain licensing. Please use it responsibly and ethically. Thank you :)

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For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1

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  Dataset Card for Estimation of Obesity Levels Based on Eating Habits and Physical Condition

This dataset contains survey data collected from individuals in Mexico, Peru, and Colombia to estimate obesity levels based on eating habits and… See the full description on the dataset page: https://huggingface.co/datasets/Steffen-H-S/Obesity_level.

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.

Proportion of children aged 10 to 11 years classified as overweight or living with obesity. For population monitoring purposes, a child’s body mass index (BMI) is classed as overweight or obese where it is on or above the 85th centile or 95th centile, respectively, based on the British 1990 (UK90) growth reference data. The population monitoring cut offs for overweight and obesity are lower than the clinical cut offs (91st and 98th centiles for overweight and obesity) used to assess individual children; this is to capture children in the population in the clinical overweight or obesity BMI categories and those who are at high risk of moving into the clinical overweight or clinical obesity categories. This helps ensure that adequate services are planned and delivered for the whole population.

Rationale There is concern about the rise of childhood obesity and the implications of obesity persisting into adulthood. The risk of obesity in adulthood and risk of future obesity-related ill health are greater as children get older. Studies tracking child obesity into adulthood have found that the probability of children who are overweight or living with obesity becoming overweight or obese adults increases with age[1,2,3]. The health consequences of childhood obesity include: increased blood lipids, glucose intolerance, Type 2 diabetes, hypertension, increases in liver enzymes associated with fatty liver, exacerbation of conditions such as asthma and psychological problems such as social isolation, low self-esteem, teasing and bullying.

It is important to look at the prevalence of weight status across all weight/BMI categories to understand the whole picture and the movement of the population between categories over time.

The National Institute of Health and Clinical Excellence have produced guidelines to tackle obesity in adults and children - http://guidance.nice.org.uk/CG43.

1 Guo SS, Chumlea WC. Tracking of body mass index in children in relation to overweight in adulthood. The American Journal of Clinical Nutrition 1999;70(suppl): 145S-8S.

2 Serdula MK, Ivery D, Coates RJ, Freedman DS, Williamson DF, Byers T. Do obese children become obese adults? A review of the literature. Preventative Medicine 1993;22:167-77.

3 Starc G, Strel J. Tracking excess weight and obesity from childhood to young adulthood: a 12-year prospective cohort study in Slovenia. Public Health Nutrition 2011;14:49-55.

Definition of numerator Number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP with a BMI classified as overweight or living with obesity, including severe obesity (BMI on or above the 85th centile of the UK90 growth reference).

Definition of denominator The number of children in year 6 (aged 10 to 11 years) with a valid height and weight measured by the NCMP.

Caveats Data for local authorities may not match that published by NHS England which are based on the local authority of the school attended by the child or based on the local authority that submitted the data. There is a strong correlation between deprivation and child obesity prevalence and users of these data may wish to examine the pattern in their local area. Users may wish to produce thematic maps and charts showing local child obesity prevalence. When presenting data in charts or maps it is important, where possible, to consider the confidence intervals (CIs) around the figures. This analysis supersedes previously published data for small area geographies and historically published data should not be compared to the latest publication. Estimated data published in this fingertips tool is not comparable with previously published data due to changes in methods over the different years of production. These methods changes include; moving from estimated numbers at ward level to actual numbers; revision of geographical boundaries (including ward boundary changes and conversion from 2001 MSOA boundaries to 2011 boundaries); disclosure control methodology changes. The most recently published data applies the same methods across all years of data. There is the potential for error in the collection, collation and interpretation of the data (bias may be introduced due to poor response rates and selective opt out of children with a high BMI for age/sex which it is not possible to control for). There is not a good measure of response bias and the degree of selective opt out, but participation rates (the proportion of eligible school children who were measured) may provide a reasonable proxy; the higher the participation rate, the less chance there is for selective opt out, though this is not a perfect method of assessment. Participation rates for each local authority are available in the https://fingertips.phe.org.uk/profile/national-child-measurement-programme/data#page/4/gid/8000022/ of this profile.

Community Health Status Indicators (CHSI) to combat obesity, heart disease, and cancer are major components of the Community Health Data Initiative. This dataset provides key health indicators for local communities and encourages dialogue about actions that can be taken to improve community health (e.g., obesity, heart disease, cancer). The CHSI report and dataset was designed not only for public health professionals but also for members of the community who are interested in the health of their community. The CHSI report contains over 200 measures for each of the 3,141 United States counties. Although CHSI presents indicators like deaths due to heart disease and cancer, it is imperative to understand that behavioral factors such as obesity, tobacco use, diet, physical activity, alcohol and drug use, sexual behavior and others substantially contribute to these deaths.

Estimation of Obesity Levels Based on Eating Habits and Physical Condition

  Overview

This dataset is designed to estimate obesity levels based on several parameters including eating habits, physical condition, and lifestyle. It includes data from diverse individuals across different demographics, offering insights for research in healthcare, nutrition, and public health.

  Dataset Characteristics

Type: Multivariate Number of Instances: 2111 Number of Attributes:… See the full description on the dataset page: https://huggingface.co/datasets/naabiil/Obesity_Levels_Estimation.

A growing body of research suggests that obesity can be understood as a complex and biobehavioral condition influenced by social relationships ─social networks. Social network analysis allows us to examine how an individual’s network characteristics (e.g., popularity) are associated with obesity and obesity-related behaviors. The objectives of the study were to (a) examine whether network members in African American churches are similar in body mass index (BMI) and obesity-related behaviors (physical activity, eating, alcohol consumption) and (b) examine whether an individual’s network characteristics, such as popularity (i.e., receiving nominations from peers) and expansiveness (i.e., sending nominations to peers) are associated with BMI and obesity-related behaviors. We used a cross-sectional study design and conducted social network analysis using Exponential random graph models with three African American church-based social networks (network A, B, and C, n = 281). There were no significant network members’ similarities on BMI in the three church-based networks. One out of three networks showed similarities in fruit and vegetable consumption (network B), fast food consumption (network C), physical activity, sedentary behaviors, and alcohol consumption (network A). African Americans with a high BMI were more popular, as were individuals with greater fat intake and alcohol consumption. Our findings support the perspective that we need to improve obesity-related behaviors by targeting influential individuals and existing ties and to develop obesity interventions using social networks. The degree to which our findings varied across churches also suggests that the relationship among an individual’s obesity-related behaviors and network characteristics should be understood in the unique social context.

BackgroundUnderstanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data.PurposeThis article explores the relationship between online social environment via web-based social networks and population obesity prevalence.MethodsWe performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook.ResultsHigher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set.ConclusionsActivity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

Context

This dataset is taken from a large National Health and Nutrition Survey conducted by the National Center for Health Statistics. From the original Body Measures Set (P_BMX) we selected only adult observations: Height (BMXHT), Weight (BMXWT) and Index body weights (BMXBMI). To select only adults, the BMDBMIC trait was used, which was determined only for children from 2 to 19 years old.

Full materials are available on the agency's website free. Use of the Materials, including any links to Materials on the CDC, ATSDR, or HHS Web Sites, does not imply endorsement by the CDC, ATSDR, HHS, or the US Government of you, your company, product, facility, service, or enterprise.

Content

Columns contain data for males and females 20 years - 150 years Weight (kg) Standing Height (cm) BMI(kg/m**2)

Detailed description: https://wwwn.cdc.gov/Nchs/Nhanes/2017-2018/P_BMX.htm

Inspiration

This dataset allows you to study the relationship between height and weight.

This is the Google Search interest data that powers the Visualisation Searching For Health. Google Trends data allows us to see what people are searching for at a very local level. This visualization tracks the top searches for common health issues in the United States, from Cancer to Diabetes, and compares them with the actual location of occurrences for those same health conditions to understand how search data reflects life for millions of Americans.

How does search interest for top health issues change over time? From 2004–2017, the data shows that search interest gradually increased over the past few years. Certain regions show a more significant increase in search interest than others. The increase in search activity is greatest in the Midwest and Northeast, while the changes are noticeably less dramatic in California, Texas, and Idaho. Are people generally becoming more aware of health conditions and health risks?

The search interest data was collected using the Google Trends API. The visualisation also brings in incidences of each condition so they can be compared. The health conditions were hand-selected from the Community Health Status Indicators (CHSI) which provides key indicators for local communities in the United States. The CHSI dataset includes more than 200 measures for each of the 3,141 United States counties. More information about the CHSI can be found on healthdata.gov.

Many striking similarities exist between searches and actual conditions—but the relationship between the Obesity and Diabetes maps stands out the most. “There are many risk factors for type 2 diabetes such as age, race, pregnancy, stress, certain medications, genetics or family history, high cholesterol and obesity. However, the single best predictor of type 2 diabetes is overweight or obesity. Almost 90% of people living with type 2 diabetes are overweight or have obesity. People who are overweight or have obesity have added pressure on their body's ability to use insulin to properly control blood sugar levels, and are therefore more likely to develop diabetes.” —Obesity Society via obesity.org

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.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

The dataset, "Heart Attack in Youth vs. Adults in America", contains 500,000 synthetic records detailing health, lifestyle, and demographic factors contributing to heart attack risks among youth and adults in the United States. This dataset can help researchers and data enthusiasts analyze patterns, predict risk levels, and understand disparities between age groups and regions in terms of heart health.

Insights Beginners, Intermediate, and Advanced Users Can Derive:

For Beginners:

Descriptive Statistics:

Calculate average cholesterol levels or blood pressure for youth vs. adults. Determine the distribution of heart attack risk levels across different states or demographics.

Data Visualization:

Visualize the distribution of obesity indices across age groups. Plot the survival rates based on risk levels.

For Intermediate Users:

Exploratory Data Analysis (EDA):

Investigate the correlation between lifestyle factors (e.g., dietary habits, smoking history) and heart attack risk levels. Compare access to healthcare between low-income and high-income groups.

Predictive Modeling:

Build a logistic regression or decision tree model to predict high-risk individuals. Use clustering techniques to group individuals based on heart attack risks.

For Advanced Users:

Deep Analysis and Insights:

Perform a time series analysis on hospital visits and prior heart attacks. Use advanced ML algorithms (e.g., Gradient Boosting, Neural Networks) for risk prediction and survival rate forecasting.

Feature Engineering:

Create new features, such as BMI categories or healthcare accessibility indices. Analyze the interaction effects between physical activity, obesity index, and smoking history.

Explainable AI:

Use SHAP (SHapley Additive exPlanations) to understand model predictions. Identify biases in predictions related to ethnicity or access to healthcare.