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Associated Indicators
Short name:
Stunting among children under 5 years of age Data type:
Percent/Numbers
Topic:
Risk factors
Sustainable Development Goals
General Programme of Work
Nutrition, Child Malnutrition
Rationale:
Child growth is an internationally accepted outcome reflecting child nutritional status. Child stunting refers to a child who is too short for his or her age and is the result of chronic or recurrent malnutrition. Stunting is a contributing risk factor to child mortality and is also a marker of inequalities in human development. Stunted children fail to reach their physical and cognitive potential. Child stunting is one of the World Health Assembly nutrition target indicators. Child stunting is one of the indicators under Sustainable Development Goal (SDG) indicators target 2.2 Definition:
Prevalence of stunting (height-for-age <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age Disaggregation:
Country, regional and worldwide JME global estimates refer to the age group of children under 5 years, sexes combined. Sex-specific disaggregates are available for the JME global estimates. The survey-based estimates are compiled in a disaggregated dataset of national primary sources with sub national and stratified estimates (e.g. sex, age groups, wealth, mothers' education, residence). Method of measurement
Methods and Guidance:
WHO and UNICEF provide recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. (Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. )
Analysis Tool
To facilitate re-running of nutritional survey data based on standardized approach, WHO has developed an online tool to analyse child anthropometric data. The WHO Anthro Survey Analyzer aims to promote best practices on data collection, analyses and reporting of anthropometric indicators. It offers analysis for four indicators: length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age. (WHO Anthro Survey Analyser and other tools)
Global Reporting
The modelled estimates are the official source used for global reporting on this indicator. (Metadata: SDG 2.2.1) M&E Framework:
Impact Method of estimation:
Data collection method
UNICEF and WHO employ their existing networks to obtain data. WHO relies on the organization’s structure and an expanding network developed following the creation of the WHO Global Database on Child Growth and Malnutrition. For UNICEF, the cadre of dedicated data and monitoring specialists working at national, regional and international levels in 190 countries routinely provides technical support to produce child malnutrition estimates through surveys and administrative systems and analyses for improved programme planning. The World Bank Group provides estimates available through the Living Standard Measurement Surveys (LSMS).
Method of computation
National estimates from primary sources (e.g., from household surveys) used to generate the JME global estimates are based on standardized methodology using the WHO Child Growth Standards as described in “The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology” (The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children’s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO. )and WHO Anthro Survey Analyser.
The JME global estimates are generated using smoothing techniques and covariates applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME global estimates for 205 countries. (The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children’s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO. ) However, estimates are only presented in the cases where a country has input data.
In line with WHO’s data principle to use transparent models and methods, all codes used to generate estimates for the latest round and prior rounds are openly available on WHO’s GitHub repository. (UNICEF-WHO-World Bank Joint Child Malnutrition Estimates- Stunting and Overweight Global Health Estimates. ) Method of estimation of global and regional aggregates:
Annual regional and global aggregates are based on the weighted average of the constituent countries. The under-five population of these countries from the United Nations World Population Prospects serve as the weights. These estimates also used countries with modelled estimates generated for development of regional and global aggregates but for which country modelled estimates are not shown because they did not have any data sources included in the JME country database. The confidence intervals around the global and regional estimates were generated based on bootstrapping methodology. (The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children’s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO. )
Preferred data sources:
Household surveys
Specific population surveys
Surveillance systems; All references to Kosovo should be understood to be in the context of the United Nations Security Council resolution 1244 (1999)
Expected frequency of data dissemination:
Data sources are updated in the WHO Global Database for Child Growth and Malnutrition (WHO Global Database on Child Growth and Malnutrition) a continuous basis to feed into the annual production of global and regional estimates and updated country level dataset released every March of uneven years.
For more information about contributing to the WHO Global Database for Child Growth and Malnutrition, please review the “Identification of data sources section” on the Methodology Page (WHO Department of Nutrition and Food Safety – Joint Child Malnutrition Estimates) Expected frequency of data collection:
The UNICEF-WHO-WB Joint Child Malnutrition (JME) group releases country, regional and worldwide estimates at the end of March of uneven years so that data are available for reporting on the Sustainable Development Goals (SDGs) (The UNICEF-WHO-World Bank Joint Child Malnutrition Estimates (JME) standard methodology New York: the United Nations Children’s Fund (UNICEF), the World Health Organization and the World Bank, 2024. Licence: CC BY-NC-SA 3.0 IGO. ; Metadata: SDG 2.2.1) Contact person email:
nfsdata@who.int Name:
Department of Nutrition and Food Safety Limitations:
Survey estimates come have uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error). The JME modelled estimates consider estimates of sampling error around survey estimates.
While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age data from household surveys supports compilation of a time series that is comparable across countries and over time.
The JME working group carefully utilizes all available national data sources, and documents all the steps taken to infer about country trends based on the national data sources. The estimation method (McLain AC, Frongillo EA, Feng J, Borghi E. Prediction intervals for penalized longitudinal models with multisource summary measures: An application to childhood malnutrition. Stat Med. 2019 Mar 15;38(6):1002-1012. doi: 10.1002/sim.8024. Epub 2018 Nov 14. PMID: 30430613.) is based on and closely aligned to country data. The approach smooths and fits a trend line across the national data points.
The basis of the estimates are nationally representative household surveys as well as data from administrative and surveillance systems. However, as surveys are conducted infrequently (e.g., less frequently than every 3 years) in some countries, models produce a complete time series with estimates available in the same years for all countries. This allows for comparable assessment of progress; for example, all countries can be assessed using the same baseline year. For any individual country, an increase in the availability of primary data points can result in more robust and accurate modelled estimates. Links: