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 ...).

1. File Information

This dataset is a synthetic dataset created based on sampling statistics from the Taiwan Ministry of Health and Welfare. It includes data on height, weight, BMI, and age of individuals, making it suitable for various health-related analyses.

2. Meta Data

3. Potential Analyses

Exploratory Data Analysis (EDA):

• Distribution analysis for height, weight, and BMI.
• Age and gender-based trends.

Regression Analysis:

• Linear Regression: Predict weight based on height and BMI.
• Logistic Regression: Classify individuals by BMI categories.

Clustering and Classification:

• Group individuals into categories (e.g., underweight, healthy, overweight) based on BMI.

Time-Series/Trend Analysis:

• Investigate how health metrics (BMI) evolve over age groups.

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

Standard BMI formula: weight (kg) / height (m)²

Liberia LR: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 3.200 % in 2013. This records a decrease from the previous number of 4.200 % for 2007. Liberia LR: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 4.200 % from Dec 2000 (Median) to 2013, with 3 observations. The data reached an all-time high of 4.600 % in 2000 and a record low of 3.200 % in 2013. Liberia LR: 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 Liberia – Table LR.World Bank.WDI: 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

Description

This is the data extracted by me from this website - "https://height-weight-chart.com/"

Contents Downloaded

Images of subject with certain height( '," --> feet and inches ) and weigth('lbs') and a dataset containing the "html link" of image, "src" path of image and height and weight of the subject in the image.

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

India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 3.700 % in 2024. This records an increase from the previous number of 3.400 % for 2023. India IN: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.300 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 3.700 % in 2024 and a record low of 2.100 % in 2013. India IN: 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 India – Table IN.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.

This table provides information about the height and weight of the Dutch person and about the prevalence of underweight and overweight. The data come from Statistics Netherlands' Health Survey and can be broken down by age and gender. The Health Survey is an ongoing survey among the Dutch population in private households. Data available from: 1981 Status of the figures: Final Changes as of 29 March 2023: The figures for 2022 have been added. When will new numbers come out? The figures for the 2023 reporting year will be published in the first quarter of 2024.

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 ...).

Myanmar MM: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data was reported at 0.600 % in 2024. This records a decrease from the previous number of 0.700 % for 2023. Myanmar MM: Prevalence of Overweight: Weight for Height: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 1.800 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 4.800 % in 2000 and a record low of 0.600 % in 2024. Myanmar MM: 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 Myanmar – Table MM.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.

These data represent the predicted (modeled) prevalence of being Overweight or Obese among adults (Age 18+) for each census tract in Colorado. Overweight is defined as a BMI of 25 or greater. Obese is defined as a BMI of 30 or greater. BMI is calculated from self-reported height and weight.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

The Motivation

The real motivation behind creating this dataset was to work on a project of IOT health monitoring device

Content

There are columns heart rate , sysBP , diaBP, height, weight, BMI etc. these parameters are necessary for predicting heart condition

Acknowledgements

The height/weight tables with heart rate are taken from this website

https://www.mymathtables.com/chart/health-wellness/height-weight-table-for-all-ages.html

Methodology

The following code has been used to generate the data according t research from different resources on the web: `import numpy as np import pandas as pd

age = np.random.randint(1,70,500000) sex = np.random.randint(0,2,500000) SysBP = np.random.randint(105,147,500000) DiaBP = np.random.randint(73,120,500000) HR = np.random.randint(78,200,500000) weightKg = np.random.randint(2,120,500000) heightCm = np.random.randint(48,185,500000) BMI = weightKg / heightCm / heightCm * 10000 \data=[] for age,sex,SysBP,DiaBP,HR,weightKg,heightCm,BMI in zip(age,sex,SysBP,DiaBP,HR,weightKg,heightCm,BMI): if BMI > 40 or BMI < 10: continue elif ( age < 20): continue elif ( weightKg < 45): continue elif (1 <= age <= 10) & (17 < BMI < 31) & (104< SysBP <121) & ( 73 < DiaBP < 81) & ( 99 < HR <= 200) & ( 3 < weightKg <= 36) & ( 48 < heightCm <= 139) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif (10 < age <= 20) & (17 < BMI < 31) & (104< SysBP <121) & ( 73 < DiaBP <= 81) & ( 99 < HR <= 200) & ( 36 < weightKg < 60) & ( 139 < heightCm < 170) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif (20 < age <= 30) & (17 < BMI < 31) & (108< SysBP <=134) & ( 75 <= DiaBP <= 84) & ( 94 < HR <= 190) & ( 28 < weightKg < 80) & ( 137 <= heightCm <= 180) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif (30 < age <= 40) & (17 < BMI < 31) & (110< SysBP <=135) & ( 81 <= DiaBP <= 86) & ( 93 <= HR <= 180) & ( 50 < weightKg < 90) & ( 137 <= heightCm <= 213) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif (40 < age <= 50) & (17 < BMI < 31) & (112< SysBP <=140) & ( 79 <= DiaBP <= 89) & ( 90 <= HR <= 170) & ( 50 < weightKg < 90) & ( 137 <= heightCm <= 213) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif (50 < age <= 90) & (17 < BMI < 31) & (116< SysBP <=147) & ( 81 <= DiaBP <= 91) & ( 85 <= HR <= 160) & ( 50 < weightKg < 90) & ( 137 <= heightCm <= 213) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) elif ( 20 <= age < 90) & (17 < BMI < 31) : data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),0]))) else: data.append(dict(zip(['age','sex', 'SysBP', 'DiaBP', 'HR', 'weightKg','heightCm', 'BMI','indication'], [age,sex,SysBP,DiaBP,HR,weightKg,heightCm,np.round(BMI),1]))) df1 = pd.DataFrame(data) df1.to_csv("Health_heart_experimental.csv") `

Jordan JO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 3.900 % in 2012. This records a decrease from the previous number of 5.200 % for 2009. Jordan JO: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 4.500 % from Dec 1990 (Median) to 2012, with 5 observations. The data reached an all-time high of 7.500 % in 1990 and a record low of 3.700 % in 1997. Jordan JO: 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 Jordan – Table JO.World Bank.WDI: 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

BMI (body mass index) has long been used as an indicator to classify one's weight. It is calculated as weight (in kilograms)/(height (in meters)). The Centers for Disease Control and Prevention (CDC) website states "Growth charts are percentile curves showing the distribution of selected body measurements in children. Growth charts are used by pediatricians, nurses, and parents to track the growth of infants, children, and adolescents." For adults, one's weight classification is calculated simply by using their BMI, but for people aged 2 to 20 years of age, the weight classification is calculated differently - it uses percentiles. This is because it accounts for natural growth in children. The dataset is split into two - one for males, and one for females. This is because the percentiles for each gender are different. The weight classifications for children aged 2-20 are as follows: 1. BMI below the 5th percentile is Underweight 2. BMI falls somewhere from the 5th to 85th percentile is Normal weight 3. BMI between the 85th and 95th (inclusive) percentile is At risk of overweight 4. BMI above the 95th percentile is Overweight

The datasets' Age column is given in months, and not years. This allows for a more accurate diagnosis.

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

Body measurements of 507 physically active individuals.
Body girth measurements and skeletal diameter measurements, as well as age, weight, height and gender, are given for 507 physically active individuals - 247 men and 260 women. These data can be used to provide statistics students practice in the art of data analysis. Such analyses range from simple descriptive displays to more complicated multivariate analyses such as multiple regression and discriminant analysis.

Overview

This dataset provides measurements related to the growth of cow based on various factors, including weight, height, volume, type of feed, and sunlight intensity. It consists of data points for two different types (A and B) and aims to facilitate analysis of how these variables affect cow growth.

Data Structure

• Weight (kg): Continuous variable representing the weight of the cow in kilograms.
• Height (cm): Continuous variable indicating the height of the cow in centimeters.
• Volume (liter): Continuous variable reflecting the volume associated with the cow (e.g., space or habitat) in liters.
• Type of Feed: Categorical variable indicating the type of feed (A or B) provided to the cow.
• Sunlight Intensity: Categorical variable denoting sunlight intensity (e.g., Gt for good, Lt for low).

Sample Data

Insights and Potential Analyses

• Correlations: Analyze how weight, height, and volume correlate with each other and with the type of feed and sunlight intensity.
• Comparison of Feed Types: Examine growth metrics (weight, height) between different types of feed (A vs. B) under varying sunlight conditions.
• Impact of Environmental Factors: Investigate the role of sunlight intensity on cow growth metrics.

Usage

This dataset can be utilized for research in cow husbandry, agricultural studies, or environmental science, providing insights into how various factors influence cow growth.

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.