Menu ENN Search

Miscalculation of the Prevalence of Acute Malnutrition in Surveys with Oedematous Children

Unpublished paper

Naso-gastric feeding

Since the beginning of the nineties, software has been developed to facilitate the calculation of anthropometric indices and prevalence of malnutrition, i.e. Epi-Info 5 and Epinut 2, developed by the Center for Disease Control. The software has since been updated and currently the most widely used software for the analysis of nutrition surveys is Epi-Info 6, which includes an updated version of Epinut (Dean AG et al).

The system on the Nutrition Information in Crisis Situations (NICS) of the UN Standing Committee on Nutrition has received hundreds of nutrition survey reports from NGOs and UN agencies over the past ten years. However, calculation of the prevalence of acute malnutrition and the classification of children according to the presence of oedema and the weight-for-height index have been incorrect in some of these surveys.

Two hundred and ninety six reports of nutrition surveys conducted by UN and international NGOs between 1993 and 2004 in 17 countries received by NICS have recently been further analysed. The distribution of children surveyed according to their weight-height and presence of oedema was provided in 155 of the 296 survey reports analysed (52.4%). Out of these 155 survey reports, 149 (96.1%) were correctly calculated, i.e. oedematous children were considered severely malnourished and were not included in the distribution of the weight-for-height index (in reference to table 2: a+b+c+d = n), whilst six (3.1%) were incorrectly calculated. Of the 155 survey reports which provide the table of distribution, 30 did not state the software used for the analysis (19%). One hundred and eight surveys (70%) were analysed using Epi-Info 5 and Epinut 2 and 17 surveys (11%) were analysed using Epi-Info 6. The six surveys where the distribution of the children according to their nutrition status was incorrectly calculated had been analysed using Epi-Info 6. This means that of the 17 surveys analysed using Epi-Info 6, 6 (35%) were wrongly analysed, whilst all the surveys analysed with Epi5/Epinut 2 were correctly analysed.

In all the six surveys with miscalculations, oedematous children were counted twice in the table of the distribution of nutrition status: once as oedematous children and once in the distribution of the weight-height index (in reference to table 1: b+c+d = n and a+b+c+d = n+a).

In calculating prevalence of malnutrition, three surveys accounted for oedematous children twice. The other three surveys did not take oedematous children into account as severely malnourished but considered them only according to their weight-height status.

These errors of calculation may be explained by the fact that whilst the older version of Epinut (Epinut 2 used with Epi-Info 5) automatically classifies children with oedema as severely malnourished and excludes them from the analysis of the weight-height index, Epinut in Epi-Info 6 does not. With Epinut in Epi-Info 6 it is necessary to go to an option menu and click a box for the oedematous children to be excluded from the analysis of the weight-for-height index.

Epinut 2 also automatically gives the prevalence and 95% confidence intervals of global and severe acute malnutrition, taking into account oedematous children as severely malnourished. In contrast, with the Epinut version of Epi-Info 6, if there are some oedematous children in the survey, users need to calculate the prevalence of acute malnutrition according to the weight-forheight index and the presence of oedema, and to go through a cumbersome manipulation of creating new variables in order to calculate the 95% confidence intervals.

Table 1: Classification of children according to the weight-for-height index and oedema
Number of children surveyed Number of oedematous children Number of children with a weight-forheight <-3 Z-score, excluding oedematous children Number of children with a weight-forheight >= - 3 Z-score & < - 2 Z-score, excluding oedematous children Number of children with a weight-forheight >= - 2 Z-score, excluding oedematous children
n = a+b+c+d   b c d

 

Box 1: Calculation of the prevalence of malnutrition

Prevalence of severe acute malnutrition = (a+b)/n
Prevalence of moderate acute malnutrition = c/n
Prevalence of (global) acute malnutrition =(a+b+c)/n

 

Table 2: Recalculation of the prevalence of malnutrition
  Number of children Number of children with oedema Proportion of children with oedema < - 2 Z-scores Prevalence from the survey report Recalculated prevalence
1a 870 11 2 17.2 17.0 4.5 4.2-4.5d
2a 874 24 10 15.6 14.4 5.1 4.0-5.1d
3a 892 24 3 9.7 9.5 3.7 3.3-3.7d
4b 768 6 ? 1.8 1.8-2.6c 0.4 0.8-1.2d
5b 419 9 ? 3.1 3.1-5.2c 0.6 2.1-2.6d
6b 778 3 ? 5.1 5.1-5.5c 0.9 0.9-1.3d

aSurveys where oedema were counted twice

bSurveys where oedematous children were not taken into account as severely malnourished children

cThe lower figure is drawn from the hypothesis that all the oedematous children have a weight-height < - 2 Zscores; the upper figure is drawn from the hypothesis that none of the oedematous children have a weightheight index < - 2 Z -scores

dThe lower figure is drawn from the hypothesis that all the oedematous children (in the limitation of the total number of severely malnourished children) have a weight-height < - 3 Z - scores; the upper figure is drawn from the hypothesis that none of the oedematous children have a weight-height index < -3 Z-scores

 

Based on the sample of survey reports, prevalence of acute malnutrition was recalculated (table 2). For the three surveys where oedematous children had been double-counted, recalculated prevalence was lower than the prevalence given in the reports. The more oedematous children in the survey and the higher the proportion of oedematous children having a weight-height index less than - 2 Zscores, the greater the difference between prevalence of malnutrition calculated in the report and the recalculated prevalence.

For the three surveys where oedematous children were not classified as severely malnourished, the prevalence of severe acute malnutrition was significantly under-estimated in the survey reports.

Verification of the calculation of the prevalence of malnutrition was only possible for half of the 296 surveys which were conducted in 17 countries and made available to NICS over the last ten years. While only 17 of these were analysed using Epi-info 6, it is worrying that about one third of these surveys reported incorrect calculations.

In this study, miscalculations of the prevalence of malnutrition did not lead to results which were substantially different from the actual prevalence. However, the prevalence of severe malnutrition was significantly incorrect in three surveys and the greater the proportion of oedematous children in a survey the greater the miscalculation.

This study highlights the fact that the analysis of nutrition surveys may be challenging and that action is needed to improve the process, such as the dissemination of existing guidelines and the development of training material and of user-friendly software.

Amanual on "data processing and analysis of nutrition surveys using Epi-Info 6" was edited by Save the Children in August 2003 (SC, 2003). Workers involved in the analysis of nutrition surveys are encouraged to use this manual or alternatively, to use the older version of Epi-Info (Epi-Info 5/Epinut 2), although this version is no longer available on the internet.

Epinut in Epi-info 6 should be updated, and any updates of existing software and development of new software to analyse nutrition surveys should be made as userfriendly as possible particularly with regard to accounting for oedematous children as severely malnourished.

References
Dean AG et al. Epi Info version 6 a word processing database and statistics program for public health. Center for Disease Control and Prevention, Atlanta, GA. Epi-Info Museum, Key events in the development of Epi-Info http://www.cdc.gov/epiinfo/background.htm

SC 2004 Using Epi-info 6.04, data processing and analysis of nutrition surveys, a training manual, Save the Children, London. Sphere 2004 Handbook, Chapter 3: Minimum standards in food security, nutrition and food aid, Geneva. WHO 2000 The management of nutrition in major emergencies. WHO, Geneva.

For further information contact; Claudine Prudhon at prudhonc@who.int

More like this

en-net: Analysis of survey data

Analysis of survey data in EPI INFO 6 and SMART (both against NCHS reference) seems to give different results. Has anyone else faced with the same problem? What is the reason?...

en-net: SMART Vis-à-vis EPI info 6 on definition of Flags

Why SMART is customized very tight set within range mean -3 to +3? EPI Info 6 is within -5 to +5. SMART excludes a lot of children being considered as out of range. this is...

FEX: MUAC versus weight-for-height debate in the Philippines

By Bernardette Cichon Bernardette is a Public Health Nutritionist who at the time of the work described in this article, worked with Action Contre la Faim (ACF). She is...

FEX: Effect of nutrition survey ‘cleaning criteria’ on estimates of malnutrition prevalence and disease burden: secondary data analysis

Summary of research1 Location: Global What we know: Standardised methods for collection and reporting malnutrition prevalence data in nutrition surveys are used. What this...

FEX: Can height-adjusted cut-offs improve MUAC’s utility as an assessment tool?

By Michel Van Herp, An Verwulgen, Bérengère Leurquin, and Pascale Delchevalerie Michael Ven Herp, Bérengère Leurquin, An Verwulgen & Pascale Delchevalerie Michael Ven Herp is...

FEX: Outpatient therapeutic programme (OTP): an evaluation of a new SC UK venture in North Darfur, Sudan (2001)

Summary of internal evaluation1 by Anna Taylor (headquarters nutrition advisor for SC UK) North Darfur experienced a severe drought in 1999 and 2000. This caused widespread...

en-net: SMART flags vs WHO flags in SMART surveys

Hello, we are currently doing a SMART survey. Our results show very low levels of Malnutrition, our curve is almost the same of WHO standards with a mean value close to 0. In...

FEX: Letter on WHO 2006 Growth Standards, by Marko Kerac and Andrew Seal

This new 2006 WHO Growth standards: What will they mean for emergency nutrition programmes? Dear Editor Whilst welcoming the principles which have driven the development of...

FEX: Weight-for-height and mid-upper-arm circumference should be used independently to diagnose acute malnutrition: policy implications

Summary of research1 Location: Global What we know: Overlap between mid-upper-arm circumference (MUAC) and weight-for-height Z-scores (WHZ) when assessing acute malnutrition...

FEX: Integrating anaemia analysis in SMART surveys in Bolivia

By Susana Moreno, Brigitt Olagivel and Elisa Dominguez Susana Moreno Romero is the Nutrition Programme Manager for Acción contra el Hambre (ACH) (Action Against Hunger -...

FEX: Impact of WHO Growth Standards on SAM response to treatment

Summary of published research1 More younger admissions to therapeutic feeding programmes are one of the implications of moving to the new WHO Growth Standards. A recent study...

FEX: Effect of body shape on weight-for-height and MUAC in Ethiopia

Summary of research1 Measuring a young child's MUAC In November 2005, a survey undertaken in rural areas of Belete Weyne district of Somalia by Save the Children UK (SC UK)...

FEX: Including infants in nutrition surveys

Experiences of ACF in Kabul city Claudine Prudhon is the head of the nutrition department in ACF HQ. Claudine is interested in improving the nutritional management of the under...

FEX: Substandard discharge rules in current severe acute malnutrition management protocols: An overlooked source of ineffectiveness for programmes?

View this article as a pdf By Benjamin Guesdon and Dominique Roberfroid Benjamin Guesdon is a nutrition and health research advisor at Action Contre la Faim...

en-net: Discrepancy between EpiInfo 2000/ENA and ENA for SMART

Question regarding the discrepancy between Epi info and SMART was posted in this forum and the detail answer was given by Mark Myat, Michael Golden, and Kevin Sullivan. My...

FEX: Anthropometric predictors of mortality in undernourished adults in southern Sudan

Summary of published research1 Location: South Sudan What we know: Acute adult undernutrition tends to occur in prolonged severe famines. There is a lack of evidence on which...

FEX: Integrated Nutrition and Food Security Surveillance in Malawi

By Elena Rivero, Núria Salse and Eric Zapatero Elena Rivero is currently working for Action Against Hunger as Surveillance Advisor in the Malawi Integrated Nutrition and Food...

FEX: Putting Child Kwashiorkor on the Map

Jose Luis Alvarez, Nicky Dent, Lauren Browne, Mark Myatt and André Briend Putting Kwashiorkor on the Map started as a call for sharing data to give an idea of...

FEX: Review of nutrition and mortality indicators for Integrated Phase Classification

Summary of technical review1 The Integrated Phase Classification (IPC) Technical Working Group and the Standing Committee on Nutrition (SCN) Task Force on Assessment,...

FEX: Admission profile and discharge outcomes for infants aged less than six months admitted to inpatient therapeutic care in ten countries

Summary of research* Location: Global (Burundi, DRC, Kenya, Liberia, Myanmar, Niger, Somalia, Sudan, Tajikistan, Uganda) What we know: The burden of acute malnutrition in...

Close

Reference this page

Miscalculation of the Prevalence of Acute Malnutrition in Surveys with Oedematous Children. Field Exchange 24, March 2005. p7. www.ennonline.net/fex/24/miscalculation

(ENN_2403)

Close

Download to a citation manager

The below files can be imported into your preferred reference management tool, most tools will allow you to manually import the RIS file. Endnote may required a specific filter file to be used.