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

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.

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.

"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."

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.

Forecast percentage of overweight and obese in the population to 2010–2040 (women).

Series Name: Proportion of children moderately or severely overweight (percent)Series Code: SH_STA_OVRWGTRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 2.2.2: +2 or Target 2.2: By 2030, end all forms of malnutrition, including achieving, by 2025, the internationally agreed targets on stunting and wasting in children under 5 years of age, and address the nutritional needs of adolescent girls, pregnant and lactating women and older personsGoal 2: End hunger, achieve food security and improved nutrition and promote sustainable agricultureFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

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

CI = confidence interval; BMI = body mass index.*BMI: Overweight 25.0–29.9 kg/m2; obese ≥30.0 kg/m2; obesity grade I 30.0–34.9 kg/m2; grade II 35.0–39.9 kg/m2; grade III ≥40 kg/m2.¥Waist circumference overweight 94.0–101.9 cm for men and 80.0–87.9 cm for women; obesity ≥102.0 cm for men and ≥88.0 cm for women.

This map shows the percentage of pregnant women in WIC who were pre-pregnancy obese with a BMI 30 or higher by county. Counties are shaded based on quartile distribution. The lighter shaded counties have lower percentages of pregnant women in WIC who were pre-pregnancy obese. The darker shaded counties have higher percentages of pregnant women in WIC who were pre-pregnancy obese. 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 present data for more than 300 health indicators that are organized by 15 different health topics. Data if provided for all 62 New York State counties,11 regions (including New York City), the State excluding New York City, and New York State. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/. The "About" tab contains additional details concerning this dataset.

This dataset contains information related to diabetes among females. Here's a breakdown of each column:

1. Pregnancies (numeric):

   • This column likely represents the number of pregnancies the individual has had.

2. Glucose (numeric):

   • This column typically represents the glucose (blood sugar) level measured in the individual, often in mg/dL.

3. Diastolic (numeric):

   • This could represent the diastolic blood pressure of the individual, measured in mmHg (millimeters of mercury).

4. Triceps (numeric):

   • This might refer to the thickness of the skinfold at the triceps (back of the upper arm), often used as a measure of body fat percentage.

5. Insulin (numeric):

   • This column likely represents insulin levels measured in the individual, often in µU/mL (micro-units per milliliter).

6. BMI (numeric):

   • BMI stands for Body Mass Index, a calculated value derived from an individual's weight and height (weight in kilograms divided by the square of height in meters).

7. DPF (numeric):

   • DPF could refer to Diabetes Pedigree Function, a measure estimating diabetes heredity based on family history.

8. Age (numeric):

   • This column likely represents the age of the individual.

9. Diabetes (categorical or binary):

   • This column likely indicates the presence or absence of diabetes, possibly coded as 0 (absence) and 1 (presence) or using specific labels (e.g., "yes" or "no").

• This dataset appears to be a collection of health-related measurements and demographic information for females, particularly focusing on factors associated with diabetes. The dataset likely serves as a valuable resource for analyzing risk factors, building predictive models, or studying correlations between various health indicators and diabetes outcomes among females.

• To work effectively with this dataset, you could perform descriptive statistics, exploratory data analysis, feature engineering, and possibly build machine learning models to predict diabetes status based on the provided features. It's important to ensure the dataset is properly cleaned, checked for missing values or outliers, and processed appropriately based on the specific analytical goals.

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 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 ...).

Context

Body Fat measurement for 436 people

Source

Starting from this dataset with 252 males measures I added the 184 samples (females) found here. Both datasets use body density via undewater weighing and then Siri equation for estimating body fat.

Content

1. BodyFat (percent body fat)
2. Original (Y/N if sample comes from the original dataset)
3. Sex (M/F)
4. Age (years)
5. Weight (kg)
6. Height (mt)
7. Neck (circumference in cm)
8. Chest (circumference in cm)
9. Abdomen (circumference in cm)
10. Hip (circumference in cm)
11. Thigh (middle part, circumference in cm)
12. Knee (circumference in cm)
13. Ankle (circumference in cm)
14. Biceps (circumference in cm)
15. Forearm (circumference in cm)
16. Wrist (circumference in cm)

Source

The data were generously supplied by Dr. A. Garth Fisher who gave permission to freely distribute the data and use for non-commercial purposes.

Roger W. Johnson Department of Mathematics & Computer Science South Dakota School of Mines & Technology 501 East St. Joseph Street Rapid City, SD 57701

email address: rwjohnso@silver.sdsmt.edu web address: http://silver.sdsmt.edu/~rwjohnso

References

Bailey, Covert (1994). Smart Exercise: Burning Fat, Getting Fit, Houghton-Mifflin Co., Boston, pp. 179-186.

Behnke, A.R. and Wilmore, J.H. (1974). Evaluation and Regulation of Body Build and Composition, Prentice-Hall, Englewood Cliffs, N.J.

Siri, W.E. (1956), "Gross composition of the body", in Advances in Biological and Medical Physics, vol. IV, edited by J.H. Lawrence and C.A. Tobias, Academic Press, Inc., New York.

Katch, Frank and McArdle, William (1977). Nutrition, Weight Control, and Exercise, Houghton Mifflin Co., Boston.

Wilmore, Jack (1976). Athletic Training and Physical Fitness: Physiological Principles of the Conditioning Process, Allyn and Bacon, Inc., Boston.

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; ...).

B-MMR total score and mood-regulation strategies predicting fat percentage in women.

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.

This table contains 558792 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 Eastern Region; Newfoundland and Labrador (Peer group D); Newfoundland and Labrador; Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H) ...), Age group (13 items: Total; 18 years and over; 18-34 years; 18-24 years; 18-19 years ...), Sex (3 items: Both sexes; Males; Females ...), Body mass index (BMI) (9 items: Total population for the variable body mass index; Overweight - BMI 25.0-29.9; Underweight - BMI under 18.5; Normal weight - BMI 18.5-24.9 ...), Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons ...).

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.