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en-net updates

en-net update September 2019 to March 2020

Over the past six months, 50 questions have been posted on en-net, generating 157 responses. The forum area for Assessment and Surveillance generated most discussions, followed by the Prevention and management of severe acute malnutrition and Infant and young child feeding interventions areas. Fifty-two vacancy notices and announcements have been posted, which have accumulated 24,526 views on the website.

A question was raised in the Assessment and Surveillance area as to whether KoBo Toolbox[1] for mobile data collection can include a function for SMART survey teams to be able to see a child’s z-score immediately during data collection to facilitate prompt referral of cases of severe acute malnutrition. Various technical workarounds were proposed and UNHCR shared that they are currently developing an algorithm of z-score calculation for incorporation into KoBo/Open Data Kit (ODK) tool for their Standardised Expanded Nutrition Survey (SENS) methodology[2].

A mobile app developed in the Philippines was shared:

The main purpose of the app is for reporting Violations of the Code of Marketing of Breastmilk Substitutes, but additional modules have been added, including one for growth monitoring. A growth monitoring chart intended for mothers to be able to monitor their child's growth, has proven useful for health workers to detect and monitor stunting and wasting in their communities. The growth monitoring module is a simple calculator that can compute the child's Weight for Height/Length, Weight for Age, and Height/Length for Age. Users just need to enter the child's birth date, weight in kg and height/length in cm. The app can be used offline. 

The discussion evolved to consider the use of flags in survey data collection and analysis. Flagged entries during data collection should alert the team to immediately repeat the measurements and verify the age data of the child in question. While flags are meant to highlight ‘implausible’ values, implausibility does not mean impossibility and there are many reported instances of flagged cases being valid. Excluding them automatically can bias results.  

In large surveys (e.g. MICS or DHS) that collect data from many populations, each population may have different distributions of anthropometric indices and different prevalence of anthropometric indicators. In this case the mean of the entire survey sample is unlikely to be a suitable reference mean and the assumed standard deviation (i.e. SD = 1) will usually be too narrow to set limits that define statistical outliers with the expected probabilities. This will lead to records being flagged incorrectly, likely leading to biased prevalence estimates.

It was proposed that it would be interesting to conduct an analysis of flagged data from multiple data sets and surveys to inform issues around the quality of evidence. The analysis could examine patterns in relation to the timing of measurements being taken, the survey team structure and composition or survey locale. Although participants in the discussion were aware that some analysis of these issues has been done, they were not aware of any systematic global studies. 


To read more or join this discussion, go to

en-net has seen greater uptake by francophone users over the past few months with an increasing number of posts from francophone Africa. These include requests for, and sharing of the national protocol for the management of acute malnutrition in Cameroun, discussion on Ebola Virus Disease, anthropometry and nutritional care in adults in DRC and a question on thresholds for infant and young child feeding indicators from Cameroun,

To join any discussion on en-net, share your experience or post a question, visit  or

For any feedback on the site, please write to



Anon, Kristine Atienza, Dr Kouakou Egnon, Ernest Guevarra, Heqian K, Bill Kinsey, Mark Myatt, Mija Ververs, Kemgueu Tiemo Willie



[2] SENS is an approach rooted in SMART used by UNHCR in refugee populations.


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