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Prevalence of Acute Malnutrition (WfH, oedema)

Indicator Phrasing

% of children aged 6-59 months with a weight for height < –2 Z scores (and/or bilateral oedema)
See indicator in other languages

Indicator Phrasing

English: % of children aged 6-59 months with a weight for height < –2 Z scores (and/or bilateral oedema)

French: % d'enfants âgés de 6 à 59 mois avec un poids pour la taille < -2 Z-scores (et / ou un œdème bilatéral)

Spanish: % de niños de 6 a 59 meses con un peso para la estatura < -2 puntuaciones Z (y/o edema bilateral)

Portuguese: % de crianças com idade entre 6-59 meses com um rácio peso por altura < -2 Z pontos (e/ou edema bilateral)

Czech: % dětí ve věku 6-59 měsíců s hmotnostně-výškovým poměrem < -2 Z-skóre (anebo oboustranným edémem)

What is its purpose?

The indicator measures the prevalence of moderate and severe acute malnutrition (wasting). It assesses to what degree (so-called "Z-score") a child's weight for height (WfH) deviates from the weight of a child of the same height and sex in the 2006 WHO Growth Standards.

How to Collect and Analyse the Required Data

Children's weight and height are (alongside with other data) collected by anthropometric surveys using the SMART methodology. SMART's website provides all the required guidance, forms, training modules as well as Emergency Nutrition Assessment software used for data analysis and reporting.


According to WHO, the prevalence of wasting (lower than –2 SD) shall be interpreted as

   lower than 5%: acceptable

   5-9%: poor 

   10-14%: serious 

   ≥ 15%: critical

Disaggregate by

Disaggregate the data by gender and age groups (such disaggregation is automatically produced by ENA software).

Important Comments

1) The cut-off points for moderate acute malnutrition (MAM) are lower than -2 but higher than -3 SD; for severe acute malnutrition (SAM) lower than -3 SD; and for global acute malnutrition (GAM) lower than -2 SD. 


2) This indicator relies on an accurate age assessment. Since people often do not remember the exact dates of their children’s birth, the data collectors should always verify the child’s age. This can be done by reviewing the child’s birth certificate, vaccination card or another document; however, since many caregivers do not have such documents (and since they can include mistakes), it is essential that your data collectors are able to verify the child’s age by using local events calendars. Read FAO’s Guidelines (see below) to learn how to prepare local events calendars and how to train data collectors in their correct use.


3) Always make sure that you understand and follow the local Ministry of Health's official guidance for conducting anthropometric surveys (e.g. regarding submitting a survey proposal for approval; reporting formats; use of 1977 NCHS versus 2006 WHO growth standards/ MUAC cut-off values; etc.).


4) Prevention-oriented projects should use this indicator only if their strategy is likely to have an impact on the nutritional status of the target population. If your project is too short or focuses, for example, primarily on improving agricultural production, use less ambitious indicators measuring, for example, nutritional intake (such as Individual Dietary Diversity Score) or specific nutritional practices.


5) In many countries, acute malnutrition is prone to significant seasonal differences (e.g. ranging from 5% in the months following the harvest to 11% prior to the harvest). Therefore, if you need to compare your baseline and endline data to assess the result of your work, ensure that the data is collected at the same time of a year, otherwise you will receive two sets of data which say very little about the change your project has (not) achieved.


6) With a larger team of enumerators (8-10 people), data collection usually takes about 10 - 15 working days. Training takes 6 days (incl. piloting and standardisation test); further time is required for preparing the methodology, hiring enumerators, arranging logistics and reporting.


7) Since the differences in the prevalence of acute malnutrition are often very small (e.g. from 7% to 5%), SMART surveys need to be implemented to maximum quality and precision. Always use a small margin of error (2-2.5%). If your team does not have sufficient experience with conducting SMART surveys, contract an in-country or headquarters-based advisor to design methodology, train your team and supervise the survey quality (using quality control checklist, such as the one below, is recommended). Always make maximum use of the guidance available in the resources below.


This guidance was prepared by People in Need ©

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