Wasting among children under 5 years of age

Percent

Child growth is an internationally accepted outcome reflecting child nutritional status. Child wasting refers to a child who is too thin for his or her height and is the result of recent rapid weight loss or the failure to gain weight. A child who is moderately or severely wasted has an increased risk of death, but treatment is possible. Child wasting is one of the World Health Assembly nutrition target indicators.

Prevalence of wasting (weight for height <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age. Numbers affected (thousands) of wasting (weight for height <-2 standard deviation from the median of the World Health Organization (WHO) Child Growth Standards) among children under 5 years of age.

Country, regional and worldwide JME global estimates refer to the age group of children under 5 years, sexes combined. Disaggregates are currently not available for the JME global estimates. However, 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).

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)

Impact

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” 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. 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.

A well-established methodology for deriving global and regional trends and forecasting future trends, have been published. (de Onis M, Blössner M, Borghi E, Morris R, Frongillo EA. Methodology for estimating regional and global trends of child malnutrition. Int J Epidemiol. 2004 Dec;33(6):1260-70. doi: 10.1093/ije/dyh202. Epub 2004 Nov 12. PMID: 15542535. https://academic.oup.com/ije/article-abstract/33/6/1260/866406; de Onis M, Blössner M, Borghi E, Frongillo EA, Morris R. Estimates of global prevalence of childhood underweight in 1990 and 2015. JAMA. 2004 Jun 2;291(21):2600-6. doi: 10.1001/jama.291.21.2600. PMID: 15173151. https://jamanetwork.com/journals/jama/fullarticle/198842 ). The trend estimation uses a linear mixed-effect model allowing for random effects at country level and for heterogeneous covariance structures. The basic model contains the factors sub-region, year, and the interaction between year and the sub-region as fixed effects with country as a random effect. (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. )

Data sources are updated in the WHO Global Database for 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.

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)

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.)

Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g. measurement technical error, recording error etc.,). None of the two sources of errrors have been fully taken into account for deriving estimates neither at country nor at regional and global levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g. pre-harvest periods), disease (e.g. rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.

nfsdata@who.int

Department of Nutrition and Food Safety

Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully considered for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g., rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts.