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

"Explore detailed statistics on diabetes and obesity prevalence in U.S. states and counties, with a focus on both men and women. This dataset includes numeric data and percentages, shedding light on critical health indicators. The comprehensive insights derived from this dataset serve as a valuable resource for public health professionals, policymakers, and researchers to inform evidence-based interventions and strategies for addressing health disparities across regions."

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.

Now I have comprehensive information about the obesity dataset. Let me create a detailed Kaggle-style description for this dataset.

Obesity Level Estimation Dataset

This dataset contains comprehensive information for estimating obesity levels in individuals based on their eating habits and physical conditions. The data includes 2,111 records with 17 attributes collected from individuals in Mexico, Peru, and Colombia, aged between 14 and 61 years.[1][2][3][4]

Dataset Overview

The dataset comprises 2,111 observations across 17 features, with no missing values, making it ready for immediate analysis and modeling. An important characteristic of this dataset is that 77% of the data was generated synthetically using the Weka tool and the SMOTE (Synthetic Minority Over-sampling Technique) filter, while 23% was collected directly from real users through a web platform. The data is relatively balanced across seven obesity categories, ranging from insufficient weight to obesity type III.[2][4][1]

Origin and Context

This dataset was donated to the UCI Machine Learning Repository on August 26, 2019 by Fabio Mendoza Palechor and Alexis De la Hoz Manotas, and published in the journal Data in Brief. The dataset was created to support the development of intelligent computational tools for identifying obesity levels and building recommender systems to monitor obesity. The synthetic data augmentation approach has been validated and is widely recognized as an effective method for obesity detection research.[4][5][2]

Features Description

Demographic Information: - Gender: Male or Female - Age: Age of the individual (14-61 years) - Height: Height in meters (1.45-1.98m) - Weight: Weight in kilograms (39-173 kg)

Family History: - family_history_with_overweight: Family history of overweight (yes/no)

Eating Habits: - FAVC (Frequent consumption of high caloric food): yes/no - FCVC (Frequency of consumption of vegetables): Scale 1-3 - NCP (Number of main meals): 1-4 meals per day - CAEC (Consumption of food between meals): no, Sometimes, Frequently, Always - CH2O (Consumption of water daily): Scale 1-3 liters

Physical Condition and Lifestyle: - SCC (Calories consumption monitoring): yes/no - FAF (Physical activity frequency): Scale 0-3 (times per week) - TUE (Time using technology devices): Scale 0-2 hours per day - CALC (Consumption of alcohol): no, Sometimes, Frequently, Always

Habits: - SMOKE: Smoking habit (yes/no) - MTRANS (Transportation used): Public_Transportation, Automobile, Walking, Motorbike, Bike

Target Variable: - NObeyesdad (Obesity Level): Seven categories - Insufficient_Weight (272 records) - Normal_Weight (287 records) - Overweight_Level_I (290 records) - Overweight_Level_II (290 records) - Obesity_Type_I (351 records) - Obesity_Type_II (297 records) - Obesity_Type_III (324 records)

Dataset Statistics

The dataset exhibits diverse characteristics with ages averaging 24.3 years (ranging from 14 to 61), heights averaging 1.70m, and weights averaging 86.6 kg. The gender distribution is nearly balanced with 1,068 males and 1,043 females. Notably, 81.8% of individuals have a family history of overweight, and 88.4% frequently consume high-caloric food. The most common transportation method is public transportation (74.8%), and most individuals do not smoke (97.9%) or monitor their calorie consumption (95.5%).[1]

Data Characteristics

Feature Types: Mixed (continuous, categorical, ordinal, binary)[2] Subject Area: Health and Medicine[2] Associated Tasks: Multi-class Classification, Regression, Clustering[2] Data Source: 23% real survey data + 77% synthetic data using SMOTE[4][2]

Potential Use Cases

This dataset is ideal for: 1. Multi-class Classification: Predicting obesity levels (7 categories) using machine learning algorithms (Decision Trees, Random Forest, SVM, Neural Networks, XGBoost) 2. Binary Classification: Simplifying to obese vs. non-obese predictions 3. Regression Analysis: Predicting BMI based on lifestyle and eating habits 4. Feature Importance Analysis: Identifying key factors contributing to obesity 5. Clustering Analysis: Discovering natural groupings in eating habits and physical conditions 6. Health Recommender Systems: Building personalized health monitoring and intervention systems 7. Public Health Research: Understanding obesity patterns across Latin American populations 8. Synthetic Data Methodology: Studying the effectiveness of SMOTE for healthcare data augmentation

Research Applications

This dataset has been extensively used in machine learning research, with state-of-the-art models achieving accuracy rates exceeding 97% when including BMI-related features (height and weigh...

Bangladesh BD: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 1.800 % in 2022. This records a decrease from the previous number of 2.300 % for 2019. Bangladesh BD: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 1.700 % from Dec 1997 (Median) to 2022, with 10 observations. The data reached an all-time high of 2.600 % in 1997 and a record low of 0.800 % in 2000. Bangladesh BD: 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 Bangladesh – Table BD.World Bank.WDI: Social: 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 2006 Child Growth Standards.;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.;;Estimates of overweight children are 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.

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

CF: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 1.500 % in 2019. This records an increase from the previous number of 0.700 % for 2018. CF: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 1.850 % from Dec 1994 (Median) to 2019, with 8 observations. The data reached an all-time high of 11.000 % in 2000 and a record low of 0.700 % in 2018. CF: 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 Central African Republic – Table CF.World Bank.WDI: Social: 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 2006 Child Growth Standards.;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.;;Estimates of overweight children are 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.

Somalia SO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 3.100 % in 2009. This records a decrease from the previous number of 4.500 % for 2006. Somalia SO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 3.800 % from Dec 2006 (Median) to 2009, with 2 observations. The data reached an all-time high of 4.500 % in 2006 and a record low of 3.100 % in 2009. Somalia SO: 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 Somalia – Table SO.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

This table contains 14040 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Northwest Territories; Yukon; Northwest Territories including Nunavut ...), Age group (13 items: Total; 18 years and over; 18-19 years; 18-24 years; 18-34 years ...), Sex (3 items: Both sexes; Males; Females ...), Body mass index (BMI) (9 items: Underweight - BMI under 18.5; Normal weight - BMI 18.5-24.9; Total population for the variable body mass index; Overweight - BMI 25.0-29.9 ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons; Low 95% confidence interval - number of persons ...).

This trend chart shows the statewide, NYC, and NYS excluding NYC percentage of obese (95th percentile or higher) children in the Women, Infant, and Children (WIC) program. New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and are updated annually to consolidate and improve data linkages for the health indicators included in the County Health Assessment Indicators (CHAI) for all communities in New York. The CHIRS trend data table presents data for close to 300 health indicators and are provided for all 62 counties, for New York State, for New York City, and Rest of State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

This table contains 136080 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (126 items: Canada; Central Regional Integrated Health Authority; Newfoundland and Labrador; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...), Age group (5 items: Total; 18 years and over;18 to 34 years ...), Sex (3 items: Both sexes; Males; Females ...), Body mass index (BMI), self-reported (9 items: Total population for the variable body mass index; self-reported; Normal weight; body mass index; self-reported 18.5 to 24.9;Overweight; body mass index; self-reported 25.0 to 29.9;Underweight; body mass index; self-reported under 18.5 ...), Characteristics (8 items: Number of persons; Low 95% confidence interval; number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons ...).

This table contains 27456 series, with data for years 2004 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...); Age group (13 items: Total, 18 years and over; 18 to 34 years; 18 to 24 years; 18 to 19 years; ...); Sex (3 items: Both sexes; Males; Females); Measured adult body mass index (8 items: Total population for the variable measured adult body mass index; Underweight, measured adult body mass index under 18.50; Normal weight, measured adult body mass index 18.50 to 24.99; Overweight, measured adult body mass index 25.00 to 29.99; ...); Characteristics (8 items: Number of persons; Low 95% confidence interval, number of persons; High 95% confidence interval, number of persons; Coefficient of variation for number of persons; ...).

Comprehensive YouTube channel statistics for Attractive Overweight Women, featuring 330,000 subscribers and 29,819,851 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Lifestyle category and is based in US. Track 677 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Number and percentage of adults who reported being overweight or obese, by age group and sex.

BackgroundA decreased level of serum adiponectin is associated with obesity and an increased risk of breast cancer among postmenopausal women. Yet, the interplay between genetic variants associated with adiponectin phenotype, obesity, and breast cancer risk is unclear in African American (AA) women.MethodsWe examined 32 single-nucleotide polymorphisms (SNPs) previously identified in genome-wide association and replication studies of serum adiponectin levels using data from 7,991 AA postmenopausal women in the Women’s Health Initiative SNP Health Association Resource.ResultsStratifying by obesity status, we identified 18 adiponectin-related SNPs that were associated with breast cancer risk. Among women with BMI ≥ 30 kg/m2, the minor TT genotype of FER rs10447248 had an elevated breast cancer risk. Interaction was observed between obesity and the CT genotype of ADIPOQ rs6773957 on the additive scale for breast cancer risk (relative excess risk due to interaction, 0.62; 95% CI, 0.32–0.92). The joint effect of BMI ≥ 30 kg/m2 and the TC genotype of OR8S1 rs11168618 was larger than the sum of the independent effects on breast cancer risk.ConclusionsWe demonstrated that obesity plays a significant role as an effect modifier in an increased effect of the SNPs on breast cancer risk using one of the most extensive data on postmenopausal AA women.ImpactThe results suggest the potential use of adiponectin genetic variants as obesity-associated biomarkers for informing AA women who are at greater risk for breast cancer and also for promoting behavioral interventions, such as weight control, to those with risk genotypes.

Users can access data related to international women’s health as well as data on population and families, education, work, power and decision making, violence against women, poverty, and environment. Background World’s Women Reports are prepared by the Statistics Division of the United Nations Department for Economic and Social Affairs (UNDESA). Reports are produced in five year intervals and began in 1990. A major theme of the reports is comparing women’s situation globally to that of men in a variety of fields. Health data is available related to life expectancy, cause of death, chronic disease, HIV/AIDS, prenatal care, maternal morbidity, reproductive health, contraceptive use, induced abortion, mortality of children under 5, and immunization. User functionality Users can download full text or specific chapter versions of the reports in color and black and white. A limited number of graphs are available for download directly from the website. Topics include obesity and underweight children. Data Notes The report and data tables are available for download in PDF format. The next report is scheduled to be released in 2015. The most recent report was released in 2010.

Descriptive statistics and percentage overweight or obese by predictor at age 4–5 years.

This table contains 229248 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (199 items: Canada; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H); Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador ...), Age group (8 items: Total; 20-64 years; 20-24 years; 20-34 years; 25-34 years ...), Sex (3 items: Both sexes; Females; Males ...), Body mass index (BMI), International standard (6 items: Total population for the variable body mass index; Normal weight - BMI 18.5-24.9; Overweight - BMI 25.0-29.9; Underweight - BMI under 18.5 ...), Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

Spearman correlation matrix, descriptive statistics, and mean difference between bariatric and no bariatric surgery for the main continuous variables.