Patterns of educational attainment vary greatly across countries, and across population groups within countries. In some countries, virtually all children complete basic education whereas in others large groups fall short. The primary purpose of this database, and the associated research program, is to document and analyze these differences using a compilation of a variety of household-based data sets: Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS); Living Standards Measurement Study Surveys (LSMS); as well as country-specific Integrated Household Surveys (IHS) such as Socio-Economic Surveys.As shown at the website associated with this database, there are dramatic differences in attainment by wealth. When households are ranked according to their wealth status (or more precisely, a proxy based on the assets owned by members of the household) there are striking differences in the attainment patterns of children from the richest 20 percent compared to the poorest 20 percent.In Mali in 2012 only 34 percent of 15 to 19 year olds in the poorest quintile have completed grade 1 whereas 80 percent of the richest quintile have done so. In many countries, for example Pakistan, Peru and Indonesia, almost all the children from the wealthiest households have completed at least one year of schooling. In some countries, like Mali and Pakistan, wealth gaps are evident from grade 1 on, in other countries, like Peru and Indonesia, wealth gaps emerge later in the school system.The EdAttain website allows a visual exploration of gaps in attainment and enrollment within and across countries, based on the international database which spans multiple years from over 120 countries and includes indicators disaggregated by wealth, gender and urban/rural location. The database underlying that site can be downloaded from here.

The Global Subnational Prevalence of Child Malnutrition dataset consists of estimates of the

percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO

International Reference Population. Data are reported for the most recent year with subnational information available at the time of

development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in

order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of

underweight children under five (the rate numerator), and a tabular dataset of the same and associated data. This dataset is produced

by the Columbia University Center for International Earth Science Information Network (CIESIN).

About this dataset

Context

The dataset tabulates the White Earth population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of White Earth. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 42 (49.41% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the White Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in White Earth is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the White Earth is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Age. You can refer the same here

Description

This dataset provides a comprehensive record of missing migrants and their tragic journeys towards international destinations , collected by the Missing Migrants Project, an initiative implemented by the International Organization for Migration (IOM) since 2014. The dataset documents deaths and disappearances, shedding light on the challenges migrants face during their journeys. Please note that due to the complexities of data collection, the figures presented are likely an undercount. The dataset serves as a tribute to the individuals who lost their lives, as well as the families and communities impacted by their absence.

Key Features:

• Incident Type: Type of migration incident
• Incident Year: Year when the incident occurred
• Reported Month: Month when the incident was reported
• Region of Origin: Geographical region where the migrants originated
• Region of Incident: Geographical region where the incident occurred
• Country of Origin: Country from which the migrants originated
• Number of Dead: Number of confirmed deceased migrants
• Minimum Estimated Number of Missing: Minimum estimated count of missing migrants
• Total Number of Dead and Missing: Total count of both deceased and missing migrants
• Number of Survivors: Number of migrants who survived the incident
• Number of Females: Number of female migrants involved
• Number of Males: Number of male migrants involved
• Number of Children: Number of children migrants involved
• Cause of Death: Cause of death for the migrants
• Migration Route: Route taken by migrants during their journey (if available)
• Location of Death: Approximate location where the incident occurred
• Information Source: Source of information about the incident
• Coordinates: Geographical coordinates of the incident location
• UNSD Geographical Grouping: Geographical grouping according to the United Nations Statistics Division

Potential Use Cases:

• Migration Patterns Analysis: Explore trends and patterns in migration incidents to understand the most affected regions and routes.
• Gender and Age Analysis: Investigate the demographics of migrants to identify gender and age-related vulnerabilities.
• Survival and Mortality Analysis: Analyze survival rates and causes of death to highlight risks and challenges migrants face.
• Temporal Analysis: Examine incidents over time to identify any temporal patterns or changes.
• Geospatial Analysis: Utilize geographical coordinates to map migration routes and incident locations.

If you find this dataset valuable, your support through votes is highly appreciated! ❤️ Thank you 🙂

Every child has the right to quality education

Description of Database:

This database presents four indicators (described in the next section) for children with and without functional difficulty: 1. ANAR (primary to upper secondary): Each education level is presented in a separate sheet. 2. OOSR (primary to upper secondary): Each education level is presented in a separate sheet. 3. Completion rate: Only primary level is presented 4. Foundational learning skills (reading and numeracy for 7 to 14 year olds) :Foundational reading and numeracy skills are presented in separate sheets

For each group, the total indicator values as well as disaggregation by sex and urban location are also provided. This database is calculated using data from the five to seventeen questionnaire. It is important to note the value of the ""total"" presented here and the survey findings report may differ due to the different weighting scheme of the questionnaires estimated using the household questionnaire. However, the choice was made to make this information available despite the discrepancy to allow for comparison of the education for children with disabilities compared to those without disabilities and also against the population of all five to seventeen year olds.

Please note, that the cut-off for the datasets were 17 year olds, and therefore ANAR upper secondary and OOS upper secondary excludes children 18 or above. Indicator values are not shown for less than 50 unweighted observations.

Glossary	Information
Countries and areas	The UNICEF Global databases contain a set of 202 countries as reported on through the State of the World's Children Statistical Annex 2017 (column A)
Subject	This database provides information on varions education indicators (ANAR, OOS, Completion rate and Foundational skills) for children with and without functional difficulty
Indicator	Specifies indicators with the level of education or age group when relevant
Category	Indicator values by category including total, sex (male and female) and location (urban and rural)
Total	Total indicator values including children with and without functional difficulties (coloumn H - coloumn J)
Children without functional difficulty	Indicator values of children without functional difficulties (coloumn K- coloumn M)
Children with functional difficulty	Indicator values of children with functional difficulties (coloumn N-coloumn P)
Point estimate	Value of the indicator (coloumn H, coloumn K and coloumn N)
Upper limit	95% upper confidence interval of the point estimate (coloumn I, coloumn L and coloumn O)
Lower Limit	95% lower confidence interval of the point estimate (coloumn J, coloumn M and coloumn P)
Data Source	The data source is the 6th round of Multiple Indicator Cluster Survey (MICS6), (column T).
Time period	Represents the year(s) in which the data collection (e.g. survey interviews) took place. (column U)
Development regions	Economies are currently divided into four income groupings: low, lower-middle, upper-middle, and high. Income is measured using gross national income (GNI) per capita, in U.S. dollars, converted from local currency using the World Bank Atlas method (column E).
ISO code	3-letter ISO code for countries

Indicators	Definition
ANAR	Adjusted net attendance rate (ANAR) – Percentage of children of a given age that are attending an education level compatible with their age or attending a higher education level.
OOSR	Out-of-school children rate (SDG4.1.4) – Percentage of children or young people in the official age range for a given level of education who are not attending either pre-primary, primary, secondary, or higher levels of education.
Completion Rate	Completion rate (SDG4.1.2) – Percentage of cohort of children or young people three to five years older than the intended age for the last grade of each level of education (primary, lower secondary, or upper secondary) who have completed that level of education.
Foundational learning skills	Foundational learning skills (SDG4.1.1.a) – Percentage of children achieving minimum proficiency in (i) reading and (ii) numeracy. If the child succeeds in 1) word recognition, 2) literal questions, and 3) inferential questions, s/he is considered to have foundational reading skills. If the child succeeds in 1) number reading, 2) number discrimination, 3) addition, and 4) pattern recognition, s/he is considered to have foundational numeracy skills.

Methodology
Unit of measure	Percentage
Time frame for survey	The sixth round of Multiple Indicator Cluster Survey (MICS6) from participating countries with data available is used. The time range of MICS6 survey included in this database is 2017 and onwards.

| Region, Sub-...

United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: 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 United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; 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; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2012. This records a decrease from the previous number of 7.800 % for 2009. US: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 7.000 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 8.100 % in 2005 and a record low of 5.400 % in 1991. US: 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 USA – Table US.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

This table contains 30 series, with data for years 1961 - 1971 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Unit of measure (1 items: Persons ...) Geography (1 items: Canada ...) Children born to ever-married women (10 items: Number of children born to ever-married women 15 years of age and over; total; Number of children born to ever-married women aged 15-19 years; Number of children born to ever-married women aged 20-24 years; Number of children born to ever-married women aged 25-29 years ...) Type of area (3 items: Total urban and rural areas; Rural; Urban ...).

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally and by region and country. Calculated each year by the International Food Policy Research Institute (IFPRI), the GHI highlights successes and failures in hunger reduction and provide insights into the drivers of hunger, and food and nutrition security. The 2014 GHI has been calculated for 120 countries for which data on the three component indicators are available and for which measuring hung er is considered most relevant. The GHI calculation excludes some higher income countries because the prevalence of hunger there is very low. The GHI is only as current as the data for its three component indicators. This year's GHI reflects the most recent available country level data for the three component indicators spanning the period 2009 to 2013. Besides the most recent GHI scores, this dataset also contains the GHI scores for four other reference periods- 1990, 1995, 2000, and 2005. A country's GHI score is calculated by averaging the percentage of the population that is undernourished, the percentage of children youn ger than five years old who are underweight, and the percentage of children dying before the age of five. This calculation results in a 100 point scale on which zero is the best score (no hunger) and 100 the worst, although neither of these extremes is reached in practice. The three component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1990, 1995, 2000, 2005, and 2014GHI scores. Undernourishment data for the 2014 GHI are for 2011-2013. 2. Child underweight: The "child underweight" component indicator of the GHI scores includes the latest additions to the World Health Organization's (WHO) Global Database on Child Growth and Malnutrition, and additional data from the joint data base by the United Nations Children's Fund (UNICEF), WHO and the World Bank; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey reports; and statistical tables from UNICEF. For the 2014 GHI, data on child underweight are for the latest year for which data are available in the period 2009-2014. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1990, 1995, 2000, and 2005, and 2014 GHI scores. For the 2014 GHI, data on child mortality are for 2012. Resources related to 2014 Global Hunger Index

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey, 2019-2020 began as the usual school-based survey (i.e. completed at school as part of lessons). From 20 March 2020, schools, colleges and nurseries were closed in the UK due to the COVID-19 pandemic and remained closed until 1 June 2020, when there was a phased reopening for reception, and Years 1 and 6. The Active Lives survey fieldwork in Spring term finished two weeks early before the end of term, in line with the school closures.

Due to the closure of schools, the survey had to be adapted for at home completion. The adaptions involved minor questionnaire changes (e.g. to ensure the wording was appropriate for both the new lockdown situation and to account for the new survey completion method at home) and communication changes. For further details on the changes, please see the accompanying technical report. The circumstances and adaptations resulted in a delay to survey fieldwork re-starting. This means that the data does not cover the full lockdown period, and instead re-starts from mid-May 2020 (when the survey was relaunched). Sample targets were also reduced as a result of the pandemic, resulting in a smaller proportion of summer term responses for 2019-20 when compared to previous years. As part of Sport England’s official publication, an additional Coronavirus report was produced, which outlines changes during the ‘easing restrictions’ phase of lockdown from mid-May to the end of July, comparing the summer term in 2020 with summer 2019. Due to the reduced summer term sample, it is recommended to analyse within term and/or school phase for academic year 2019-20.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1. Main dataset: includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).
2. Year 1-2 dataset: includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_gross.csplan files are available for SPSS users who can utilise them).
3. Teacher dataset – this .sav file includes response from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years...

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data was reported at 11.000 % in 2014. This records a decrease from the previous number of 16.400 % for 2009. Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data is updated yearly, averaging 17.500 % from Dec 1993 (Median) to 2014, with 8 observations. The data reached an all-time high of 20.100 % in 1993 and a record low of 11.000 % in 2014. Kenya KE: Prevalence of Underweight: Weight for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Health Statistics. Prevalence of underweight children is the percentage of children under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's 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; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

About this dataset

Context

The dataset tabulates the White Earth population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of White Earth. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 51 (60.71% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the White Earth population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in White Earth is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the White Earth is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for White Earth Population by Age. You can refer the same here

Childhood Obesity Levels

Trends and Correlations in U.S. Gender and Age Groups (1971-2014)

By Health [source]

About this dataset

This dataset contains data on the prevalence of childhood obesity in the United States from 1971 to 2014. It examines both the gender and age of children affected by this epidemic, providing a comprehensive look at how much this problem has grown over time. Offering key insights into how many children are overweight and obese today, this dataset is insightful for researchers, medical professionals and policy makers looking for further understanding about how childhood obesity affects America's youth. With its information about how much this issue has grown since 1971, it is a powerful tool to help determine potential solutions that can effectively reduce rates of health complications caused by obesity

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to use this dataset

• Explore the data - review the columns and familiarize yourself with any relationships within the data that could be interesting or relevant.
• Select a metric - decide what metric you would like to use (e.g., percent obese, percent overweight) when analyzing trends in the dataset.
• Choose an analysis - determine if you want to analyze trends in a single factor (i.e., gender or age) or multiple factors together (i.e., gender+age). If multiple factors, make sure there is no significant bias between them (ie weighting issues).
• Filter relevant information - drill down into your chosen metric/metrics and look for interesting/relevant subsets within it/them . Be sure to keep track of your filters! 5 .Visualize data - create graphs which accurately illustrate any relationships between chosen metrics over time (if exploring time series data). Heatmaps are also useful for understanding patterns in 2X2 datasets over time when appropriate
  6 .Interpret results' Findings should be compared with external sources and further research should be conducted where appropriate

Research Ideas

• Evaluating the effectiveness of school lunch programs for reducing childhood obesity over time, broken down by gender and age-group.
• Looking at regional trends in childhood obesity over time to identify which areas are dealing with more severe levels of this epidemic.
• Correlating socio-economic factors (such as poverty and income levels) with childhood obesity rates across different ethnicities, genders, and age groups over time to better understand health disparities among different populations in the US

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: child_ob_gender.csv | Column name | Description | |:--------------|:-------------------------------------------------------| | Time | The year the data was collected. (Integer) | | Gender | The gender of the children in the dataset. (String) | | PercentOW | The percentage of children who are overweight. (Float) | | PercentOB | The percentage of children who are obese. (Float) |

File: obesity_child_age.csv | Column name | Description | |:-----------------|:--------------------------------------------------------| | Time | The year the data was collected. (Integer) | | Gender | The gender of the children in the dataset. (String) | | Age | The age group of the children in the dataset. (Integer) | | PercentObese | The percentage of children who are obese. (Float) | | SE | The standard error of the data. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

Will all children be able to read by 2030? The ability to read with comprehension is a foundational skill that every education system around the world strives to impart by late in primary school—generally by age 10. Moreover, attaining the ambitious Sustainable Development Goals (SDGs) in education requires first achieving this basic building block, and so does improving countries’ Human Capital Index scores. Yet past evidence from many low- and middle-income countries has shown that many children are not learning to read with comprehension in primary school. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10. For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

The Identification for Development (ID4D) Global Dataset, compiled by the World Bank Group's Identification for Development (ID4D) Initiative, presents a collection of indicators that are of relevance for the estimation of adult and child ID coverage and for understanding foundational ID systems' digital capabilities. The indicators have been compiled from multiple sources, including a specialized ID module included in the Global Findex survey and officially recognized international sources such as UNICEF. Although there is no single, globally recognized measure of having a "proof of legal identity" that would cover children and adults at all ages or, of the digital capabilities of foundational ID systems, the combination of these indicators can help better understand where and what gaps in remain in accessing identification and, in turn, in accessing the services and transactions for which an official proof of identity is often required. Newly in 2022, adult ID ownership data is primarily based on survey data questions collected in partnership with the Global Findex Survey, while coverage for children is based on birth registration rates compiled by UNICEF. These data series are accessible directly from the World Bank's Databank: https://databank.worldbank.org/source/identification-for-development-(id4d)-data. Prior editions of the data from 2017 and 2018 are available for download here. Updates were released on a yearly basis until 2018; beginning in 2021-2022, the dataset will be released every three years to align with the Findex survey. For further details, please refer to https://id4d.worldbank.org/annual-reports This collection includes only a subset of indicators from the source dataset.

The Poverty Mapping Project: Global Subnational Prevalence of Child Malnutrition data set consists of estimates of the percentage of children with weight-for-age z-scores that are more than two standard deviations below the median of the NCHS/CDC/WHO International Reference Population. Data are reported for the most recent year with subnational information available at the time of development. The data products include a shapefile (vector data) of percentage rates, grids (raster data) of rates (per thousand in order to preserve precision in integer format), the number of children under five (the rate denominator), and the number of underweight children under five (the rate numerator), and a tabular data set of the same and associated data. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

This dataset is about book subjects. It has 1 row and is filtered where the books is The adventures of Raoul the owl : a story for children of all ages and parents of any age who believe the world is still a place of magic and fun. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Objective(s): The 2024 Pediatric Sepsis Data Challenge provides an opportunity to address the lack of appropriate mortality prediction models for LMICs. For this challenge, we are asking participants to develop a working, open-source algorithm to predict in-hospital mortality and length of stay using only the provided synthetic dataset. The original data used to generate the real-world data (RWD) informed synthetic training set available to participants was obtained from a prospective, multisite, observational cohort study of children with suspected sepsis aged 6 months to 60 months at the time of admission to hospitals in Uganda. For this challenge, we have created a RWD-informed synthetically generated training data set to reduce the risk of re-identification in this highly vulnerable population. The synthetic training set was generated from a random subset of the original data (full dataset A) of 2686 records (70% of the total dataset - training dataset B). All challenge solutions will be evaluated against the remaining 1235 records (30% of the total dataset - test dataset C). Data Description: Report describing the comparison of univariate and bivariate distributions between the Synthetic Dataset and Test Dataset C. Additionally, a report showing the maximum mean discrepancy (MMD) and Kullback–Leibler (KL) divergence statistics. Data dictionary for the synthetic training dataset containing 148 variables. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Child malnutrition joint country dataset (UNICEF, WHO, World Bank Group)

Definitions:
Severe Wasting: Percentage of children aged 0–59 months who are below minus three standard deviations from median weight-for-height of the WHO Child Growth Standards.
Wasting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-height of the WHO Child Growth Standards.
Overweight – Moderate and severe: Percentage of children aged 0-59 months who are above two standard deviations from median weight-for-height of the WHO Child Growth Standards.
Stunting – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median height-for-age of the WHO Child Growth Standards.
Underweight – Moderate and severe: Percentage of children aged 0–59 months who are below minus two standard deviations from median weight-for-age of the World Health Organization (WHO) Child Growth Standards.