CMAM FORUM TECHNICAL BRIEF, MARCH 2016 PUTTING...

PUTTING CHILD KWASHIORKOR ON THE MAP

CMAM FORUM TECHNICAL BRIEF, MARCH 2016

AUTHORS: Jose Luis Alvarez, Nicky Dent, Lauren Browne, Mark Myatt and André Briend

02PUTTING CHILD KWASHIORKOR ON THE MAP

Core Mapping Group: UNICEF: Julia Krasevec, Diane Holland; WHO: Monika Blöessner, Zita Weise Prinzo; ACF-UK: Saul Guerrero.

Technical Advisory Group: CDC (Carlos Colorado-Navarro, Leisel Talley, Oleg Bilukha); CRED/University Uclouvain (Chiara Altare); Jimma

University (Tsinuel Girma); KEMRI (Jay Berkley); Mwanamugimu Nutrition Unit, Mulago Hospital, Uganda (Hanifa Numusoke) ; MSF,

ALIMA (Kevin PQ Phelan); Washington University in St. Louis (Mark Manary, Indi Trehan); Valid International (Paul Binns).

Many thanks for additional review comments from ACF (Benjamin Guesdon, Victoria Sauveplane); London School of Hygiene and

Tropical Medicine (Severine Frison); MSF (Kerstin Hanson, Saskia van der Kam); SCF (Jessica Bourdaire); UNICEF regional offices (Cecile

Basquin, Christiane Rudert, Helene Schwartz); University of Copenhagen (Henrik Friis, Pernille Kæstel); University of Southampton

(Alan Jackson); University of Westminster (Nidia Huerta Uribe).

For data sharing thanks to Government health and nutrition ministries of Burkino Faso (Bertine Dowrot Ouaro), Central Africa Republic

(Gisele Molomadon), Chad (Adoum Daliam), the Democratic Republic of Congo (Damien Ahimana, Jean Pierre Banea, Nicole Mashukano),

the Gambia (Samba Ceesay), Guinea (Mamady Daffe), Guinea Bissau (Ivone Menezes Moreira), Ivory Coast (Theckly Ngoran), Kenya

(Lucy Gathigi), Liberia (Kou Baawo), Sierra Leone (Aminata Koroma), Togo (Mouawiyatou Bouraima) and Nigeria Bureau of Statistics

(Isiaka Olarewaju); Afghanistan: UNICEF/Ministry of Public Health/ Agha Khan University; Guatamala (FEWS NET and ACF-Spain,

with USAID funds); Pakistan: IRC international Maryland USA, NIPS Islamabad Pakistan; the Philippines: Philippine National Nutrition

Cluster, the National Nutrition Council, UNICEF, and ACF Philippines.

All headquarters and country offices including the following who have helped access the data:

ACF (Benjamin Guesdon, Rachel Lozano, Danka Pantchova, Damien Pereyra, Victoria Sauveplane); ACF intern (Kaiser Esquillo, Sabine

Appleby); ALIMA (Géza Harczi, Ali Ouattara, Susan Shepherd); College of Medicine, Department of Paediatrics, University of Malawi,

Blantyre, Malawi (Emmanuel Chimwezi, Wieger Voskuijl); Concern Worldwide (Kate Golden, Ros Tamming); ECHO (David Rizzi) FEWS

NET (Gilda Maria Walter Guerra, Christine McDonald); Food Security and Nutrition Analysis Unit/FAO (Nina Dodd, Rashid Mohamed);

GOAL (Amanda Agar); International Medical Corps (Caroline Abla, Suzanne Brinkman, Amelia Reese-Masterson) ; International Rescue

Committee (Bethany Marron, Casie Tesfai) ; KEMRI (Jay Berkley, Kelsey Jones); LSHTM (Severine Frison); Médecins Sans Frontières

(Kerstin Hanson, Kevin PQ Phelan, Saskia van der Kam); MRC International Nutrition Group and MRC The Gambia Unit (Helen

Nabwera); Plan International (Unni Krishnan); Save the Children (Christoph Andert, Jessica Bourdaire); Terre des Hommes (Charulatha

Banerjee); United Nations High Commissioner for Refugees (Vincent Kahi, Eugene Paik Caroline Wilkinson, Joelle Zeitouny); UNICEF

(Victor Aguayo, Bulti Assaye, Arshidy Awale, Fanceni Balde, Amina Bangana, Faraja Chiwile, Patrick Codjia, Nguyen Dinh Quang,

Martin Eklund, Katherine Faigao, Denis Garnier, Lucy Gathigi Maina, Rene Gerard Galera, Aashima Garg, Mariama Janneh, Vandana

Joshi, Angela Kangori, Wisal Khan, James Kingori, Edward Kutondo, Chirchir Langat, Anne-Sophie Le Dain, Leo Matunga, Bonaventure

Muhimfura, Grainne Moloney, Mueni Mutunga, Simeon Nanama, Mamadou Ndiaye, Mara Nyawo, Jecinter Akinyi Oketch, Lucy Oguguo,

Magali Romedenne, Christiane Rudert, Kalil Sagno, Maria Claudia Santizo, Lilian Selenje, Flora Sibanda-Mulder, Ismael Ngnie Teta, Noel

Marie Zagre); World Vision International (Sarah Carr, Colleen Emary, Simon Karanja, Tim Roberton); Zerca y Lejos (Patricia Postigo y

Mamen Segoviano). Particular thanks to Helene Schwartz and Sara Gari-Sanchis of the UNICEF West Africa Regional Office.

This report was produced by an independent expert group led by the CMAM Forum and funded by UNICEF, ACF and ECHO (via the CMAM Forum).

Jose Luis Alvarez, Nicky Dent, Lauren Browne, Mark Myatt and André Briend. Coordinated by Nicky Dent and Jose Luis Alvarez

CONTACT: please write to Jose Luis Alvarez at [email protected]

PROPOSED CITATION: Alvarez JL, Dent N, Browne L, Myatt M, and Briend A. Putting Child Kwashiorkor on the map. CMAM Forum Technical brief. March 2016.

ACKNOWLEDGEMENTS

03PUTTING

CHILD KWASHIORKOR ON THE MAP

CONTENTS

ABBREVIATIONS 05 EXECUTIVE SUMMARY 06 KEY FINDINGS 08 INTRODUCTION 09

1 BACKGROUND 10 1.1 An introduction to kwashiorkor 10

2 METHODOLOGY 13 2.1 Supervision and technical support 13 2.2 Data collection, management and analyses 13 2.3 Admission data 14

3 RESULTS FROM SURVEYS 15 3.1 Data received 15 3.2 Estimated oedema prevalence in affected areas by country 18 3.3 Proportion of oedema among SAM cases by country 20 3.4 Oedema prevalence and oedema as a proportion of SAM cases by age and sex 22 3.5 Distribution of MUAC and WHZ among oedema cases 24

4 ADMISSION AND OUTCOME DATA 26 4.1 Proportion of kwashiorkor among SAM cases admitted to therapeutic programmes 26 4.2 Mortality 29 4.3 Seasonality 32

5 COMPARISON OF SURVEY AND ADMISSION DATA 34

6 DISCUSSION 36 6.1 Limitations, data issues and biases 36 6.2 The wider context of kwashiorkor 38

7 RECOMMENDATIONS 39 7.1 Programmatic recommendations 39 7.2 Research priorities 40

8 CONCLUSION 42

REFERENCES 43 ANNEXES 44 Annex 1: Project information sheet shared with partners 44 Annex 2: Letter of agreement 46 Annex 3: Surveys by contributing agency 47 Annex 4: Additional maps 48 Annex 5: Description of data sources 49 Annex 6: Survey dataset 49 Annex 7: UNHCR admission data 50 Annex 8: Other tables 51


MAPSMAP 1 Oedema Prevalence 2006-2015 18MAP 2 Proportion of SAM cases defined by MUAC

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CAR Central African RepublicCFR Case Fatality Rate CI Confidence IntervalCMAM Community-based Management of Acute MalnutritionCSV Comma-Separated-ValueDHS Demographic and Health Surveys DRC Democratic Republic of the CongoHAZ Height-for-Age z-scoreIDP Internally Displaced PersonMAM Moderate Acute MalnutritionMICS Multiple Indicator Cluster SurveyMoH Ministry of HealthMUAC Mid Upper Arm CircumferenceNGO Non-Governmental OrganisationRAF R Analytic FlowROC Receiver Operating CharacteristicSAA Sulphur Amino AcidsSAM Severe Acute MalnutritionSMART Standardized Monitoring and Assessment of Relief and TransitionsUNHCR United Nations High Commissioner for RefugeesUNICEF United Nations Children’s FundWHO World Health OrganizationWHZ Weight-for-Height z-score

ABBREVIATIONS


EXECUTIVE SUMMARY

Putting Kwashiorkor on the Map started as a call for sharing data to give an idea of prevalence and raise the profile of kwashiorkor. In order to help fill data gaps and obtain a more comprehensive understanding of the global situation for kwashiorkor, Phase Two of the project was launched in September 2014 with funding assistance from UNICEF. A Kwashiorkor Mapping Core Group was established to manage the project outputs including data collection, interpretation and documentation.

The aims of Phase Two are:

1 To refine and update the initial kwashiorkor map, provide a broad estimate of the numbers and location of cases of kwashiorkor and identify high burden countries/areas.2 To strengthen the evidence base and support advocacy for inclusion of kwashiorkor in relevant methodology discussions at global level.

Extent of the problem

This report highlights the importance of kwashiorkor as a public health problem, as reflected by its prevalence and also by the proportion of SAM cases it represents in surveys. Kwashiorkor, is an acute condition, and standard cross-sectional surveys are not adapted to assess the real importance of this problem. The high proportion of kwashiorkor reported among SAM children admitted for treatment in some areas where its prevalence is low shows the difficulty in assessing the extent of this problem. For example, the reported prevalence of oedema during the last ten years was less than 1% in most of the countries were data was available but when examining the estimate proportion of SAM cases with kwashiorkor, figures ranged between 50% in Malawi, to 32% in the Democratic Republic of Congo and just 1.6% in Pakistan. This suggests that kwashiorkor is probably far more extensive than what cross-sectional surveys show. Certain types of studies, such as incidence studies or community studies with regular active case finding, may be better suited to more accurately describe the burden of oedema in countries.

Distribution of kwashiorkor

Despite its limitations, this report gives, for the first time, a representation of the geographic distribution of kwashiorkor, based on 2,515 datasets with information on more than 1,736,000 individual children collected from 55 countries during the time period 1992 to 2015. It shows that this form of malnutrition occurs most frequently in some parts of Africa, specifically around the equator. This is consistent with what has been reported for more than 40 years in West Africa. DRC is the highest burden country in the world with respect to oedema prevalence and surveys from a significant number of countries in Africa indicated that more than a third of SAM cases defined by MUAC

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MUAC is less sensitive to changes in hydration status and seems better for assessing the general nutritional status of children with oedema that does not extend up to the child’s upper arms (i.e., +++ oedema). This latter assumption is supported by the ROC curves in this report that describe the association between anthropometry and oedema, showing that MUAC more readily identifies children with oedema, compared to WHZ.

Mortality associated with kwashiorkor also varies across studies, with some reporting lower, identical or higher mortality compared to non-oedematous malnutrition. These discordant observations may be related to a different level of associated malnutrition. In children with SAM, the presence of oedema is considered as an aggravating factor associated with a higher risk of death as reported by a number of studies but some of the patterns analysed may be indicative of no association. However, the lack of actual and reliable data hinders the assessment and comparison of the mortality rates between the 3 types of SAM, as well as the identification of prognostic factors that could guide the treatment of these patients.

Poor association between prevalence surveys and admission data

Another important finding of this report is that standard cross-sectional surveys do not adequately reflect the clinical importance of kwashiorkor, since there appears to be a lack of a relationship between admission data and kwashiorkor prevalence obtainedd from surveys. The possible reasons for this discrepancy are many and should be explored. A possibly poorly adapted survey methodology with insufficient standardisation for collection of oedema data should be considered first. The recommendation of national protocols in terms of referral to inpatient care, the existence of community-based management of acute malnutrition in the country or the level of community mobilisation activities were not assessed in relation to each annual national admission dataset. It is therefore uncertain whether variations in the level of inpatient care for oedematous children were due to country policy or severity of cases.

It is possible that duration of oedematous malnutrition is not the same in different settings, in particular as a result of the very different degree of associated malnutrition. This may have an influence on the associated mortality and/or on the rapidity of recovery, both of which can have an influence on the probability of finding oedematous cases during a nutritional survey.

High variations of association with background malnutrition and with mortality and poor association between results of prevalence surveys and admission data are factors that could also be related to the shift in treatment with the introduction of community-based management of acute malnutrition (CMAM) around 2005, where oedema + or ++ (variation between agencies) moved from being treated only in an inpatient setting to being treated in an outpatient setting. Ideally inpatient admissions could be interpreted on a timescale in relation to the country protocols at that time, but given the historical data used, exact admission protocols at the time were not available.

Data collection and standardization

It is made clear throughout the report, that better collection methodologies on kwashiorkor data and improvements to the current survey reporting systems are needed. Additionally, a global database that includes admissions for oedema should be included in each country’s surveillance system.

The high proportion of kwashiorkor reported among SAM children admitted for treatment in some areas where its prevalence is low shows the difficulty in assessing the extent of this problem and suggest that kwashiorkor is probably far more extensive than cross-sectional surveys show.


Key Findings:

01

02

03

04

There is a need for better, standardised and routine data collection to correctly identify the burden of kwashiorkor across the world.

High rates of oedema were reported in Central and Southern Africa, as well as Haiti (DRC, Yemen and Zimbabwe reported prevalence rates between 1-2%). When examining the proportion of SAM cases with kwashiorkor, surveys from a significant number of countries indicated that more than a third of SAM cases had kwashiorkor, including Malawi, Rwanda, Zambia, Togo and Cameroon. Mortality rates are highest among children who have MUAC

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Putting child kwashiorkor on the map initiative

Putting Kwashiorkor on the Map28 started as a call for sharing data to give an idea of prevalence and to raise the profile of kwashiorkor. In Phase One of the project a short commentary and map of “kwashiorkor” based on a database of 557 surveys (from 1992-2006) compiled by Brixton Health was released in October 2013 by the CMAM Forum (http://www.cmamforum.org). The initial data outputs indicated that there is a problem of high caseloads or prevalence of oedematous malnutrition, although its distribution could not be ascertained due to significant gaps in data. Phase One also led to the establishment of an informal Technical Advisory Group (TAG) to define parameters for any future data collection and liaison with organisations that were willing to share data.

In order to help fill data gaps and obtain a more comprehensive understanding of the global situation for kwashiorkor, Phase Two of the project was launched in September 2014 with funding assistance from UNICEF, ACF and ECHO (via the CMAM Forum) to further compile existing information on kwashiorkor. A Kwashiorkor Mapping Core Group was established to manage the project outputs including data collection, interpretation and documentation. This was comprised of representatives from UNICEF and WHO nutrition departments, ACF operations department and the CMAM Forum, with tasks shared between members. The TAG group was continued and data sourcing was extended to key international non-govermental organisations (NGO), United Nations and governments involved with conducting surveys or nutritional programmes.

The overall goal of the project is to draw attention to kwashiorkor and stimulate improved data collection and further research to ultimately support better detection or management of these children. This step is to assemble the information on kwashiorkor that currently exists. The audience includes practitioners, researchers, academics and policy makers.

The aims of Phase Two were:

1 To refine and update the initial kwashiorkor map, provide a broad estimate of the numbers and location of kwashiorkor and identify high burden countries/areas.2 To strengthen the evidence base and support advocacy for inclusion of kwashiorkor in relevant methodology discussions at global level.

Specific questions to be answered using any combined datasets included: ◆ Geographic distribution of kwashiorkor with a more robust and recent dataset◆ Proportion of SAM cases that have bilateral pitting oedema◆ Comparison of kwashiorkor prevalence co-incidental to low MUAC and to low WHZ◆ Concurrence of kwashiorkor and low MUAC

While the limitations of current data collection tools to analyse the burden of an acute condition such as kwashiorkor are recognised, it was considered that collation of a large number of surveys from different areas and settings, and including recent national surveys, would contribute to learning more about the global situation and help assess where we are at the current time. The intiative also assembled admission data, where possible. It is hoped that assembling existing information could help highlight potential trends, identify countries with higher and lower burdens and encourage more rigorous and standardised data collection in the future, stimulate research and generally raise attention to child kwashiorkor.

INTRODUCTION


1 BACKGROUND

1.1 An introduction to kwashiorkor

Severe acute malnutrition (SAM) is currently defined by the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF) by a mid-upper arm circumference (MUAC) less than 115mm or by a weight-for-height Z-score (WHZ) less than -3 or by presence of bilateral pitting oedema (WHO UNICEF Joint statement 2009). This report focusses on bilateral pitting oedema and assembling information on the prevalence and outcomes of children with this sign.

The WHO ICD 10 classification1,2 defines kwashiorkor as a “form of severe malnutrition with nutritional oedema with dyspigmentation of skin and hair.” We use the term kwashiorkor and oedema for forms of SAM associated with bilateral pitting oedema (referred to as oedema in this report), without necessarily including associated dyspigmentation of skin and hair or any other clinical signs. We chose this definition because it is frequently used in-country, specifically for admission to therapeutic programmes, and it is the simplest clinical sign of kwashiorkior that can be assessed. Skin dyspigmentation, on the other hand, may be slight and remain unnoticed, and hair changes require a longer time to take place, as noted by Cicely Williams3. This approach was also adopted by the Wellcome Trust, which defines kwashiorkor based only on the presence of oedema4. It is acknowledged that this condition is also referred to as “nutritional oedema” or “oedematous malnutrition.”

The Wellcome classification also introduced the term “marasmic kwashiorkor” for children with oedema and a weight-for-age indice less than 60%. This index is difficult to interpret in the presence of oedema and is not routinely measured in most nutritional programmes.

Kwashiorkor is therefore a clinical syndrome indicated by the typical onset of bilateral pitting oedema in the lower limbs that can gradually spread to the upper sections as it becomes more severe. The grade of oedema has been classified5 as + mild: both feet; ++ moderate: both feet, plus lower legs, hands, or lower arms; +++ severe: generalised oedema including both feet, legs, hands, arms and face. Other clinical features that may be found in these patients include: apathy, irritability, fatty liver, “flaky-paint” dermatosis and scanty lustreless hair6,7. A comprehensive characterisation of the

clinical signs and laboratory test abnormalities has not been done so far.

The condition affects hundreds of thousands of children every year in the poorest countries of the world, killing many of them – and yet it seems not to attract global attention. Given the high mortality risk associated with this form of SAM, the low level of understanding of kwashiorkor and limited new research are surprising. There is no mention of kwashiorkor in the comprehensive implementation plan on maternal, infant and young child nutrition adopted by the 2012 World Health Assembly, which sets global nutrition targets for 2025. The condition is also overlooked in the 2013 “Maternal and Child Nutrition” Lancet series8, which does not acknowledge its importance in public health terms or mention the existence of effective treatment that is capable of preventing many deaths every year. Likewise it is not mentioned in the Global Nutrition Report 2014 and 20159.

Currently, there is no reliable estimate of the number of children suffering from kwashiorkor around the world. The last published global map showing the prevalence of kwashiorkor was produced in 195410 and was done at a time when the diagnostic criteria were more vague. It is not clear how this map was obtained, and in absence of reference to

Part of the problem of identifying kwashiorkor is that this condition is often transient, meaning that children usually spontaneously recover or die within a few days or weeks of onset

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community surveys, it may just be based on hospital records as was usual at that time. Oedema is not included in the Joint Child Malnutrition Estimates compiled by UNICEF, WHO and the World Bank11, despite being a key diagnostic and admission criterion to therapeutic services (MUAC as well is not included in these joint estimates, despite being an important diagnostic and admission criterion). Similarly, cases of kwashiorkor are seldom documented in standard national surveys, such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). This gap was highlighted in a letter to the Lancet in 201312 and more recently by the article: Omitting oedema measurement: how much acute malnutrition are we missing, defining prevalence, overlap between oedematous malnutrition and wasting and regional estimates13. This article, through an assessment of 852 cross-sectional surveys (conducted from 1992-2011, with 95% from 2000) estimates that “the median prevalence of edema cases was very low (0.2%) in all regions except Central and South Africa (0.6%). The mean was also fairly low in all regions (0.6% or less) except in Central and South Africa (1.2%).” It also highlights that “though these prevalences may represent a large proportion of SAM cases two-thirds of oedema cases are missed by measuring wasting only and over 80% missed by assessing severe wasting.”

Part of the problem of identifying kwashiorkor is that this condition is often transient, meaning that children usually spontaneously recover or die within a few days or weeks of onset, and due to this short duration, kwashiorkor is poorly captured by cross-sectional surveys commonly used to assess the importance of malnutrition. This problem was clearly described in 1972 by Cicely Williams14 with kwashiorkor as a form of protein calorie malnutrition (PCM): “Acute cases of P.C.M. are not often seen on surveys; they are more likely to arrive as outpatients or in hospital (The more chronic the disease the more likely it is to be identified in surveys.)”. In addition, some surveys do not include oedema as an indicator. For those that do, inexperienced surveyors, lack of training and poor follow up of oedematous cases treated elsewhere during the survey are likely to contribute to underdiagnosis of oedema.

FIGURE 1

NUMBER OF PUBLICATIONS WITH “KWASHIORKOR” OR “OEDEMATOUS MALNUTRITION” AS KEYWORD (IN ENGLISH) SINCE 1945

Nu

mb

er o

f P

ub

lica

tion

s

Year

0

20

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

40

60

80

100

120

140


While there is indirect evidence that the number of children suffering from kwashiorkor has declined over the last 30 years or more, possibly in parallel with the reduction in infectious diseases (especially the increased coverage of measles immunisation)15,16, the public health significance of kwashiorkor and its associated morbidity and mortality warrant further attention, especially since kwashiorkor has been documented as the most common form of SAM in some parts of Africa. 12,17,18, 19

Much of the research around kwashiorkor goes back some 30 or 40 years. As stated in the recent review: Kwashiorkor: still an enigma – the search must go on20, kwashiorkor was the topic of high quality research and intense debate in the 1950-1970s, but “surprisingly, it later became an ‘orphan disease,’ with very few groups actively involved in disentangling its mechanism and decreasing number of publications over the years…” (see Figure 1 above). While recent research into kwashiorkor in Malawi has been promising21, significant gaps remain, with even the aetiology and physiopathology of the condition poorly understood.

There is generally a lack of concrete up-to-date data to demonstrate whether mortality is higher or lower for children with oedema compared to acutely malnourished children with status assessed by WHZ (

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2 METHODOLOGY

2.1 Supervision and technical support

Roles and responsibilities were established within the Kwashiorkor Mapping Core Group (ACF, CMAM Forum, WHO and UNICEF) and external consultants to manage agreements with NGOs, data storage on a secure server, contractual arrangements, coordination, communication and liaison with data contributors, construction of the database, the analysis and the reporting work. Particular care was taken to ensure that raw survey data were cleaned and not duplicated. Technical consensus was generated on relevant points for data management.

The TAG guided the type of information to be collected, the database construction, the analyses and the final report. This was made up of representatives from CDC; CRED/University Uclouvain; Jimma University Ethiopia; KEMRI; Mwanamugimu Nutrition Unit, Uganda; Médecins Sans Frontières (MSF); Washington University in St. Louis and Valid International. The report was also shared with additional experts and data contributors for review.

2.2 Data collection, management and analyses

The CMAM Forum, with ACF, put out a request for sharing anthropometric surveys to NGOs, United Nations (UNICEF, WHO, UNHCR, WFP) and governments (directly or via UNICEF) involved with nutrition programmes. In addition, a request for any admission and outcome data or case studies distinguishing cases of kwashiorkor was made. Requests were accompanied by an information sheet of the project (see Annex 1) and a data sharing letter of agreement which was signed between agencies and ACF-UK (for NGOs) or a signed permission letter, facilitated by UNICEF (for government data) (see Annex 2).

From January to September 2015, all anthropometric datasets collected and deemed eligible were collated in a central database constructed by the data management team with feedback from the TAG. Datasets were added to an existing database of 557 datasets held by Brixton Health and used in Phase One.

The raw data of any nutritional survey adopting the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology or similar with population proportional to size (PPS) or exhaustive sampling, simple random sampling, or systematic sampling and including the key variables of age, sex, weight, height, MUAC and oedema were utilised for the project. MICS or DHS datasets were acceptable if they collected all of the variables required and PPS sampling was used within strata. Age was limited to 6-59 months, since only a very small number of surveys include children 0-6 months and MUAC is not usually taken on infants under 6 months old.

Raw datasets were used in preference to narrative reports to enable standardisation and avoid transcription errors. Narrative reports were also requested to provide additional metadata.

A database management system was developed. This provides a range of functions including data import, calculation of anthropometric indices, flagging of extreme values by SMART or WHO flagging criteria, checking for duplicate datasets, reporting on data quality, setting inclusion criteria for data analysis, data analysis, mapping and reporting.

The datasets received were all converted to CSV (comma-separated values text file) format, stripped of unnecessary variables, restructured to the common format and cleaned according to the pre-determined standards. Variables were re-coded as needed to ensure that the codes were consistent throughout the datasets. Where necessary, measurement units were converted (e.g. MUAC records changed from centimetres to millimetres).


Datasets with any of the key variables (data columns) missing, besides that of the cluster identifiers, were not included in the database. For each dataset eligible for inclusion, any obvious data entry errors were fixed or deleted, and extreme values were also removed.

If the validity of any survey’s data was questioned (e.g. due to a large number of repeated records), the affected dataset was removed from the database. An analysis to determine databases with abnormal results for oedema (i.e. outliers) was implemented; given that only 1 survey was classified as an outlier the authors decided to include all surveys in the analysis.

The R Analytic Flow (RAF) scientific workflow software was used to organise the database. Using the R Language and Environment for Statistical Computing, scripts were written to import, restructure, check, calculate anthropometric indices, identity duplicate datasets and analyse the data and produce maps, tables, and graphs showing the proportion of SAM cases that have bilateral pitting oedema.

In order to provide a representative figure for each country, maps and tables were generated taking into account the following considerations:

◆ SUBNATIONAL SURVEYS VS NATIONAL SURVEYS: National surveys, when stratified by regional level with the necessary information provided, were separated into datasets by region.

◆ BORDER DETERMINATION: Use of geolocation was not possible due to coordinate data not being provided by the vast majority of surveys. Where historical national and/or regional divisions differed from current divisions, the name of the survey site was utilised to help determine its current location. If the location could not be accurately determined, its location was described as “unspecified”.

◆ COUNTRIES WITH NO OR LITTLE DATA: If no data was received, the affected countries are marked on maps with grey colour to indicate uncertainty regarding the national nutrition situation with respect to oedema. The countries whose surveys did not identify at least 20 SAM cases (according to the definition of SAM employed in each analysis) were excluded from the any map analysis requiring calculation of the number of cases of SAM to avoid distorting the map, although the numbers are kept in the tables.

◆ WHO FLAGS were used to remove records with outlying indices, but only for the analyses which concerned that specific indice: height-for-age (HAZ)+6.0, WHZ+5.0. Extreme MUAC values were censored using MUAC/A Z-scores (MAZ). Records with MAZ < -5 or MAZ > +5 were removed. Records were excluded from particularly analyses bases on suspect values in the relevant anthropometric index identified using WHO flagging criteria.

2.3 Admission data

Agencies were requested to share programme admission data if available, segregated by oedematous and nonoedematous cases. These data were then combined to give total numbers of cases with oedema, total numbers of children with SAM and the percentage of oedematous admissions for each year of available data per country. Data were separated into inpatient admissions and outpatient admissions where available. Refugee information was recorded separately given the population differences (for example, in a refugee context, coverage and thus representativeness of the admission data are supposed to be much better than in the other contexts). Associated information of what national protocol was in place at the time of the programme (ie pre or post CMAM) or what level of community mobilisation and case finding was operational was not provided given the historical nature of most of the data and this may explain some of the high admission rates.

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3 RESULTS FROM SURVEYS

3.1 Data received

1,958 eligible datasets were collected from 15 national Governments/UNICEF and 11 NGOs, FEWS NET, and two United Nations bodies. These were mostly SMART surveys, with only one MICS survey included. These were added to the original 557 datasets already held by Brixton Health and used for the mapping in Phase One to give a total of 2,515 datasets, with information on more than 1,736,000 individual children for analysis (for additional information on data sources see Annexes 3, 5 and 6).

TABLE 1

DATA SOURCES FOR ELIGIBLE SURVEYS RECEIVED

Agency # datasets (total)

# reports # countries

ACF 814 363 40

Concern Worldwide 108 68 11

FEWS NET 2 2 1

FSNAU 207 0 1

GOAL 141 0 7

IMC 15 0 2

IRC 3 0 2

MSF 95 0 25

Plan International 2 0 1

Save the Children 58 2 8

Terre des Hommes 7 0 3

UNHCR 193 170 23

UNICEF/GovernmentAfghanistan, Burkina Faso, Central African Republic, Chad, the Democratic Republic of Congo, the Gambia, Guinea, Guinea Bissau, 640 220 22 Ivory Coast, Kenya, Nigeria, Pakistan, the Philippines, Sierra Leone, Togo

World Vision 11 0 5

Zerca y Lejos (local NGO) 1 0 1

TOTAL No OF UNIQUE SURVEYS 2277* 825 55 unique countries

Proportion of datasets that also shared narrative reports 36.2%

*2297 but 20 were partnerships between multiple organisations


Exclusion criteria were applied to the datasets received (see Figure 2). Most of the excluded datasets were missing MUAC data. Any duplication of the surveys received was checked for and duplicate datasets were removed from the database. WHO flags were used, with

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* includes 1 national survey that could not be divided into regions

TABLE 2

COUNTRY BREAKDOWN OF SURVEYS, DATES, AND SAMPLE SIZES (ALL YEARS)

Country # Surveys Earliestsurvey

Most recentsurvey

Total # childrenmeasured

Afghanistan 43* 1995 2013 48,878Albania 1 1999 1999 906Angola 22 1993 2002 17,361Bangladesh 26 2009 2014 13,480Benin 7* 2008 2014 7,930Bolivia 3 2011 2011 1,775Botswana 1 2013 2013 164Burkina Faso 50 2008 2014 40,446Burundi 25 1994 2013 14,742Cameroon 9 1998 2013 5,642Central African Republic 58 1993 2015 36,443Chad 201 1994 2015 124,096Congo, Democratic Republic 264 1994 2014 227,390Cote d’Ivoire 49 1994 2014 24,233Djibouti 7 2008 2014 2,516Eritrea 3 2001 2014 1,969Ethiopia 233 1994 2015 155,494The Gambia 8 2012 2012 6,769Guatemala 2 2015 2015 625 Guinea 12 1995 2012 9,603Guinea-Bissau 13 2008 2012 7,216Haiti 49 1994 2014 39,764India 8 2008 2014 5,182Indonesia 3 1999 2002 1,749Jordan 2 2014 2014 802Kenya 107 2000 2014 71,475Liberia 52 1993 2013 31,230Macedonia 1 1999 1999 865Madagascar 4 2001 2014 3,180Malawi 16 1994 2012 16,277Mali 14 2007 2011 10,968Mauritania 56 2007 2014 36,432Mozambique 11 1992 2012 3,867Myanmar 22 2000 2015 14,391Nepal 12 2006 2014 7,650Nicaragua 2 Unspecified Unspecified 1,017Niger 38 2001 2011 49,411Nigeria 107 2005 2015 66,398Pakistan 18 2000 2012 14,200Philippines 12 2010 2015 6,220Rwanda 21 1993 2012 13,534Senegal 7 2012 2012 8,445Sierra Leone 58* 1994 2014 64,028Somalia 227 1993 2015 237,498South Sudan 140 1993 2014 96,959Sri Lanka 3 1997 2002 2,586Sudan 136 1996 2015 109,099Tajikistan 5 1999 2004 4,337Tanzania 7 1995 2014 4,903Thailand 2 2004 2004 1,812Togo 18* 2009 2014 11,976Uganda 74 1996 2014 48,503Yemen 2 2013 2014 816Zambia 5 2009 2013 2,095Zimbabwe 1 2009 2009 700

55 COUNTRIES 2277 1992 2015 1,736,047


3.2 Estimated oedema prevalence in affected areas by country

The reported prevalence of oedema in these surveys during the last ten years was less than 1% in most of the countries where data was available. Some countries in Central and South Africa, as well as Haiti (the Caribbean), reported higher rates, and Yemen, Zimbabwe and the Democratic Republic of Congo (DRC) reported rates between 1% and 2% (see Map 1 and Table 3). It must be taken into account that for some countries the number of surveys and children screened was very limited in contrast to others and that surveys are usually conducted in areas with suspected nutritional problems and so are not necessarily representative of the whole country. Only one survey was available from Zimbabwe, while more than 200 were available from each of the following countries: Chad, DRC, Ethiopia and Somalia.

MAP 1

OEDEMA PREVALENCE 2006-2015

The percentage of children with oedema is highest in Zimbabwe (1.71), Yemen (1.10), The Democratic Republic of Congo (1.04), and Zambia (0.81)

19PUTTING


TABLE 3

OEDEMA PREVALENCE 2006 - 2015

Country # Children surveyed

# Oedema cases found

% children withoedema (95% CI)

Afghanistan 16,021 81 0.51 (0.40, 0.63)

Bangladesh 13,480 3 0.02 (0, 0.07)

Benin 7,930 5 0.06 (0.02, 0.15)

Bolivia 1775 3 0.17 (0.03, 0.49)

Botswana 164 0 0 (0, 2.22)

Burkina Faso 40,446 50 0.12 (0.09, 0.16)

Burundi 2,830 1 0.04 (0, 0.20)

Cameroon 4,312 33 0.77 (0.53, 1.07)

Central African Republic 36,005 153 0.42 (0.36, 0.50)

Chad 94,736 192 0.20 (0.18, 0.23)

Congo, Democratic Republic 194,334 2021 1.04 (1.00, 1.09)

Cote d’Ivoire 21,742 79 0.36 (0.29, 0.45)

Djibouti 2,516 7 0.28 (0.11, 0.57)

Eritrea 347 0 0 (0, 1.06)

Ethiopia 134,201 241 0.18 (0.16, 0.20)

Gambia 6769 3 0.04 (0.01, 0.13)

Guatemala 625 3 0.48 (0.10, 1.40)

Guinea 7,932 4 0.05 (0.01, 0.13)

Guinea-Bissau 7,216 2 0.03 (0, 0.10)

Haiti 12,012 61 0.51 (0.39, 0.65)

India 5,182 5 0.10 (0.03, 0.23)

Jordan 802 0 0 (0, 0.46)

Kenya 66,220 101 0.15 (0.12, 0.19)

Liberia 8,574 17 0.20 (0.12, 0.32)

Madagascar 2,280 0 0 (0, 0.16)

Malawi 5,645 44 0.78 (0.57, 1.04)

Mali 10,968 2 0.02 (0, 0.07)

Mauritania 36432 8 0.02 (0.01, 0.04)

Mozambique 1,085 2 0.18 (0.02, 0.66)

Myanmar 8,007 7 0.09 (0.04, 0.18)

Nepal 7,650 29 0.38 (0.25, 0.54)

Nicaragua 1,017 8 0.79 (0.34, 1.54)

Niger 40,301 14 0.03 (0.02, 0.06)

Nigeria 65,449 130 0.20 (0.17, 0.24)

Pakistan 6,545 4 0.06 (0.02, 0.16)

Philippines 6,220 0 0 (0, 0.06)

Rwanda 4,040 17 0.42 (0.25, 0.67)

Senegal 8,445 3 0.04 (0.01, 0.10)

Sierra Leone 32,755 121 0.37 (0.31, 0.44)

Somalia 218,048 692 0.32 (0.29, 0.34)

South Sudan 44,457 114 0.26 (0.21, 0.31)

Sri Lanka 293 1 0.34 (0.01, 1.89)

Sudan 40,417 117 0.29 (0.24, 0.35)

Tanzania 619 4 0.65 (0.18, 1.65)

Togo 11,976 42 0.35 (0.25, 0.47)

Uganda 26,079 52 0.20 (0.15, 0.26)

Yemen 816 9 1.10 (0.51, 2.08)

Zambia 2,095 17 0.81 (0.47, 1.30)

Zimbabwe 700 12 1.71 (0.89, 2.98)


3.3 Proportion of oedema among SAM cases by country

Map 2 shows these results when the WHO definition of SAM is used (MUAC, WHZ and oedema criteria) and map 3 shows results when SAM is defined by a MUAC cut-off (

21PUTTING


TABLE 4

PROPORTION OF SAM CASES (DEFINED BY MUAC


Surveys from a significant number of countries in Africa indicated that more than a third of SAM cases defined by MUAC

23PUTTING


When SAM was defined by WHZ


3.5 Distribution of MUAC and WHZ among oedema cases

Figure 3 shows that children with kwashiorkor tend to have a lower MUAC and a lower WHZ (median MUAC 125mm, median WHZ -1.55) than children without kwashiorkor (median MUAC 142mm, median WHZ -0.62). It also shows that the range of MUACs and WHZ among children with oedema is highly variable and that a large number are well above the 115mm MUAC or WHZ

25PUTTING


Table 8 demonstrates that there is huge variation in the median MUAC among children with oedema in different countries, varying from less than 115mm in several Sahelian West African countries and Madagascar to more than 130mm in Central and East Africa regions, suggesting that marasmic-kwashiorkor is much more common in some settings.

Figure 4 shows the association between MUAC and WHZ with kwashiorkor by means of ROC curves (receiver operating characteristic). Sensitivity in this case measures the proportion of oedema cases that fall below the cut-off shown on the curve, while specificity measures the proportion of children without oedema that are above the same cut-off. The ROC curve of MUAC is above that of WHZ, indicating that MUAC is better at identifying cases of oedema than WHZ.

FIGURE 4

ROC CURVES COMPARING THE ASSOCIATION OF MUAC AND WHZ WITH OEDEMA

TABLE 8

COUNTRY BREAKDOWN OF MEDIAN MUAC RANGES AMONG CHILDREN WITH OEDEMA

Median MUAC among children with oedema

Countries

139

Unknown

Mali

Gambia, Senegal, Madagascar

Myanmar, Niger, Burkina Faso, Nigeria, Mauritania, Central African Republic, Guinea-Bissau

Congo (Kinshasa), Eritrea, Chad, Guinea, Haiti, Cote d’Ivoire, Angola, Tajikistan

Ethiopia, Indonesia, Uganda, Afghanistan, Nepal, India, Mozambique, South Sudan

Benin, Burundi, Cameroon, Liberia, Rwanda, Djibouti, Kenya, Pakistan, Sierra Leone, Malawi, Somalia

Sudan, Tanzania, Nicaragua

Guatemala, Zimbabwe, Togo, Macedonia, Bangladesh, Zambia, Yemen, Bolivia, Sri Lanka

Albania, Botswana, Jordan, Philippines, Thailand

Please note, italicised countries were not included in Map 2 due to


4 ADMISSION AND OUTCOME DATA

Currently there is no existing global database that includes admissions for oedema in treatment programmes. In 2010, UNICEF tried to rectify this by commissioning a SAM mapping29, followed by a pilot Annual SAM Update that has been developed into a “Nutridash”30 system. However, this is not broken down by type of admission criteria. Some countries and some agencies collect separate statistics for kwashiorkor and marasmus, but outcomes for SAM tend to use a common denominator for all children discharged (as per SPHERE standards), regardless of admission criteria, so it is not possible to ascertain from routine data where the highest mortality lies. This is partly due to the simplification of reporting requirements over recent years that has led to a combining of kwashiorkor and marasmus admissions.

This section attempts to collate information from various sub-analyses of SAM across countries and specific agency routine programme data or case studies to determine the percentage of admissions that can be attributed to kwashiorkor.

4.1 Proportion of kwashiorkor among SAM cases admitted to therapeutic programmes

Table 10 is a summary of all the admission data received from different sources. It shows a high variability of kwashiorkor admissions across different countries, years and admission modality. Although this dataset is incomplete, it shows small consistent trends across agencies and countries. In almost all countries, inpatient admissions from kwashiorkor were higher than outpatient and these often comprised up to 50% of total inpatient admissions. Malawi and several Central African countries demonstrated some of the highest admission rates from kwashiorkor, especially Central African Republic and DRC. Ethiopia, Haiti, Kenya, Uganda (inpatient data available only) and Zambia also showed high proportions of admissions with kwashiorkor. Bangladesh, the Gambia and Somalia showed particularly low levels of oedematous admissions.

Similar results were retrieved from refugee camp data, provided by UNHCR. These were collated separately and not combined with the main table to avoid skewing country results and can be found in Annex 7. In addition, camps in Chad, Rwanda and Uganda showed high levels of kwashiorkor as a percentage of admissions for therapeutic care, while Nepal, Sudan and South Sudan had low levels.

TABLE 9

ADMISSION DATA PROVIDED BY AGENCY

Agency # countries Years

ACF 8 2011 - 2014

ALIMA 5 2006 - 2015

College of Medicine, Malawi 1 2014 - 2015

KEMRI, Kenya 1 2000 - 2014

Mulago Hospital,Uganda 1 2009 - 2014

MRC, Gambia 1 2010 - 2015

MSF 13 2014

Save the Children 12 2010 - 2015

Terre des Hommes 1 2011 - 2015

UNHCR 19 2008 - 2015

UNICEF/Government, Malawi 1 2010 - 2014

World Vision 17 2006 - 2015

TOTAL No OF COUNTRIES 26

27PUTTING


OV

ERA

LL S

UM

MA

RY

OF

KW

ASH

IOR

KOR

PER

CEN

TAG

ES (

TOTA

L N

UM

BER

OF

KW

ASH

IOR

KOR

AD

MIS

SIO

NS/

TOTA

L N

UM

BER

OF

SAM

AD

MIS

SIO

NS)

BY

YEA

R A

ND

BY

CO

UN

TRY

(20

06-

2015

)

TAB

LE 1

0

Cou

ntry

2006

2007

2008

2009

2010

2011

2012

2013

Afg

ha

nis

tan

I

4.1%

(143

/3,5

30)

O

0.9%

(15/

1,61

3)

1.0%

(9/9

25)

0.9

% (2

/232

)

An

go

la

O

8.0

% (2

,051

/25,

645)

Ba

ng

lad

esh

I

0%

(0/2

) 0%

(0/1

16)

0% (0

/49)

0.

3% (1

/350

)

O

0% (0

/23)

0%

(0/2

21)

0% (0

/104

)

Bu

rkin

a F

aso

I

28.0

% (1

77/6

33)

21.3

% (2

21/1

,040

)

O

2.

0% (8

8/4,

340)

1.

3% (9

5/7,

424)

Bu

run

di

O

27.2

% (3

21/1

,181

) 20

.7%

(279

/1,3

51)

CA

R

I

49.8

% (4

33/8

69)

41.7

% (3

41/8

18)

30.0

% (7

9/26

3)

37.

0% (1

,135

/3,0

71)

29.4

% (4

5/15

3)

O

38.6

% (6

32/1

,635

) 33

.7%

(973

/2,8

83)

2.6%

(257

/9,7

33)

15.6

% (6

52/4

,169

) 1

2.5%

(149

/1,1

89)

Ch

ad

I

25

.0%

(21/

84)

11.6

% (2

39/2

,060

) 37

.1%

(13/

35)

O

6.7%

(86/

1,28

0)

13.3

% (2

94/2

,215

) 2.

8% (4

35/1

5,59

4)

2.4%

(822

/34,

076)

3.

2% (3

55/1

1,16

6)

Co

ng

o (

DR

) I

25

.7%

(19/

74)

35.0

% (1

17/3

34)

38.8

% (1

87/4

82)

50.0

% (3

3/66

)

34.7

% (1

,841

/5,3

10)

62.5

% (1

0/16

)

O

4

7.3%

(370

/782

) 38

.5%

(1,1

21/2

,915

) 29

.8%

(1,5

07/5

,058

) 21

.0%

(90/

428)

21

.4%

(329

/1,5

39)

24.6

% (6

,314

/25,

668)

44

.4%

(176

/396

)

Eth

iop

ia

I 10

0% (5

2/52

) 10

0% (

318/

318)

55

.7%

(162

/291

) 58

.5%

(72/

123)

73

.9%

(329

/445

) 63

.3%

(348

/550

) 53

.4%

(20

6/38

6)

52.3

% (7

9/15

1)

3.4%

(80/

2,38

4)

O

6.

1% (7

/115

) 13

.3%

(111

/837

) 18

.2%

(814

/4,4

70) 6

.6%

(157

/2,3

62)

7.9%

(605

/7,6

43)

21.7

% (2

,115

/9,7

60)

29.1

% (2

,689

/9,2

48)

22.8

% (8

76/3

,835

) 0%

(0/9

34)

Ga

mb

ia

I

1.

7% (1

/60)

2.

9% (3

/104

) 0%

(0/1

96)

0% (0

/160

) 0%

(0/1

67)

0% (0

/70)

Ind

ia

I

2.2%

(1/4

6)

1.8%

(3/1

65)

0.5%

(1/

202)

0.

0% (0

/180

) 1.

3% (1

/78)

Ken

ya

I

40.1

% (1

42/3

54)

42.6

% (1

80/4

23)

29.0

% (8

5/29

7)

35.0

% (8

1/23

1)

59.0

% (2

3/39

) 70

.0%

(14/

20)

O

1.

3% (1

3/97

6)

1.0%

(70/

6,91

4)

1.0%

(27/

4,76

9)

0.1%

(3/3

,894

)

0.0%

(0/1

,390

)

Ma

law

i I

64.5

%

66.1

%

69.3

%

60.1

%

55.6

%

(8,2

00/1

2,71

3)

(7,1

50/1

0,81

2)

(6,6

47/9

,592

) (5

,405

/8,9

87)

(4,2

53/7

,651

)

O

57

.0%

57

.4%

61

.8%

49

.0%

44

.9%

(14,

006/

24,5

91)

(14,

040/

24,4

71)

(12,

554/

20,3

22)

(10,

835/

22,0

99)

(11,

868/

26,4

17)

2014

2015

I =

Inp

ati

ent

O =

Ou

tpa

tien

t

TABLE 10 CONTINUED OVERLEAF


Coun

try

2006

2007

2008

2009

2010

2011

2012

2013

Ma

li

I

46

.3%

(1,6

68/3

,600

)

O

4.5%

(63/

1,41

4)

3.1%

(252

/8,0

21)

4.8%

(885

/18,

353)

5.

0% (9

20/1

8,50

5)

3.9%

(212

/5,4

62)

Ma

uri

tan

ia

I

0%

(0/1

5)

0.0%

(0/2

0)

O

0.

4% (1

/267

) 0.

7% (4

/562

) 0.

3% (1

/381

) 12

.8%

(25/

195)

0%

(0/5

60)

0.0%

(0/5

72)

My

an

ma

r I

5.

2% (1

/19)

7.

2% (1

4/19

5)

2.1%

(2/9

4)

3.1%

(1/3

2)

O

0.

9% (5

/47)

1.

8% (5

0/2,

764)

1.

0% (1

7/1,

688)

0.

8% (1

1/1,

432)

1.

9% (2

/105

)

Nep

al

O

0.1%

(2/3

,013

)

Nig

er

I

27.7

% (3

04/1

,096

) 19

.5%

(585

/3,0

01)

O

0.1%

(12/

11,4

79)

2.0%

(1,0

20/5

1,23

6) 1

.3%

(1,1

51/8

7,49

3)

2.1%

(1,3

27/6

4,28

4)

2.1%

(378

/17,

673)

Nig

eria

I

20.5

% (1

8/88

) 12

.1%

(47/

387)

14

.7%

(78/

529)

15

.6%

(80/

512)

12

.8%

(37/

289)

O

0.

6% (3

4/5,

357)

0.

2% (3

6/18

,964

) 2.

1% (

392/

18,4

78)

0.0%

(0/3

0,60

9)

0.0%

(0/1

4162

)

Pa

kis

tan

I

69

.0%

(238

/345

) 84

.4%

(961

/1,1

39)

17.8

% (2

41/1

,353

)

O

0%

(0/1

,327

) 0%

(3/9

,114

) 0.

2% (1

7/7,

862)

0%

(0/1

95)

0.5%

(57/

10,5

20)

So

ma

lia

I

1.

6% (3

7/2,

300)

7.

8% (1

09/1

,384

) 1.

2% (6

/489

)

O

1.4%

(51/

3,66

3)

0.2%

(39/

18,3

34)

0.9%

(108

/12,

359)

3.

9% (1

32/3

,342

) 1.

5% (2

6/1,

680)

So

uth

Su

da

n

I

0% (0

/7)

27.3

% (3

/11)

1.

1% (5

/470

) 8.

3% (4

1/49

3)

16.3

% (8

0/49

2)

14.7

% (3

27/2

,227

) 1.

3% (1

/77)

O

0% (0

/15)

0%

(0/8

2)

1.6%

(56/

3,50

9)

0.4%

(14/

3,81

3)

0.8%

(30/

3,61

5)

0.2%

(33/

13,7

67)

0.1%

(1/1

,585

)

Su

da

n

I

21.0

% (2

2/10

5)

11.2

% (1

8/16

1)

6.5%

(5/7

7)

O

1.3%

(3/2

29)

2.0%

(21/

1,06

0)

2.5%

(259

/10,

544)

3.

5% (4

21/1

1,92

5)

1.6%

(38/

2,38

9)

Sy

ria

I

0% (0

/19)

O

0%

(0/1

7)

Ug

an

da

I

65.

5% (4

66/7

11)

60.4

% (5

98/9

90)

51.2

% (6

08/1

,188

) 44

.0%

(547

/1,2

44)

50.0

% (5

32/1

,063

) 54

.4%

(657

/1,2

08)

Yem

en

O

0.

2% (1

2/5,

848)

0.

2% (2

1/9,

545)

0.

0% (0

/444

)

Za

mb

ia

I

47

.0%

(71/

151)

41

.2%

(40/

97)

O

31.3

% (1

5/48

) 21

.2%

(84/

397)

27

.3%

(39/

143)

Zim

ba

bw

e O

10

.6%

(5/4

7)

5.8%

(12/

206)

3.

9% (4

/103

)

17.5

% (6

3/36

0)

2014

2015

I =

Inp

ati

ent

O =

Ou

tpa

tien

t

TABLE 10 CONTINUED

29PUTTING


4.2 Mortality

The question of whether there is greater mortality amongst children with oedema than amongst those with marasmus is still unclear and there is a lack of data stratifying by grade of oedema (+, ++, +++) and by inpatient versus outpatient admissions. While the following data are from small programmes, they have been included to demonstrate some of the variety found at different sites and to encourage further monitoring of oedema levels in the future. Mortality data from prevalence surveys was not available, so all the data in this section comes from admission data.

Figure 5 shows the case fatality rate (CFR) for children admitted in Kilifi County Hospital, Kenya, since 1999. The graph clearly shows that the highest mortality exists among children who have oedema, and while the case fatality rate has fallen over the years, it has remained high.

FIGURE 5

EMRI, KILIFI HOSPITAL, KENYA, INPATIENT MORTALITY DATA

2000

MUAC


Further details were collected from ALIMA N’Djamena project in Chad. A total of 1,600 children were admitted to inpatient stabilisation care and 11,900 to outpatient or ambulatory care, with kwashiorkor cases making up 19% and 1% of total admissions, respectively. The admissions to inpatient care were analysed by anthropometrics/age and outcome, with the kwashiorkor admissions broken down into two groups: those with “straightforward” kwashiorkor (defined as bilateral pitting oedema but no wasting) and those with marasmic kwashiorkor (defined as oedema and MUAC

31PUTTING


Published data from a community-based randomised trial in Malawi also gives information on the outcome of SAM cases detected in the community and treated as outpatients 26. Of 2,767 admissions to outpatient therapeutic care with uncomplicated SAM, 1,945 (70.0%) had kwashiorkor, 244 (8.8%) had marasmic-kwashiorkor (defined as oedema plus WHZ -3 1,945 65 3.3

Oedema with WHZ < -3 (marasmic kwashiorkor) 244 42 17.2

Oedema (total) 2,189 107 4.9

WHZ < -3 without oedema 578 43 7.4


In contrast to the above, a small inpatient dataset in the same area (Moyo Inpatient ward, Blantyre, Malawi, where marasmus is defined as MUAC

33PUTTING


MSF admission data (Mali, DRC, South Sudan, Niger and CAR) also suggest that the prevalence of kwashiorkor does not appear to be seasonal in nature.

The lack of seasonal pattern cannot be substantiated with only these examples. Additional analyses will be needed in areas with strong seasonal variations. For example, in the Sahel and West Africa where they have very dry and very wet seasons, with subsequent large variation in the burden of malaria, diarrhoea and respiratory infections that may affect oedema prevalence.

FIGURE 7

UGANDA MWANAMUGIMU NUTRITION UNIT, MULAGO HOSPITAL, INPATIENT ADMISSIONS: 2009-2014

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

70%

60%

50%

40%

30%

80%

2009 2010 2011 2012 2013 2014

% Kwashiorkor in Mulago Hospital from April 2009 to December 2014

50% of SAM cases diagnosed in Malawi had kwashiorkor, 32% in Democratic of Congo and 1.6% in Pakistan.


5 COMPARISON OF SURVEY AND ADMISSION DATAFigure 8 compares the proportion of kwashiorkor as per admissions in different centres and the burden of kwashiorkor as per prevalence of total SAM (data from surveys), matched by country and year. No clear pattern can be identified, suggesting a lack of relationship between admission data and kwashiorkor prevalence obtained from surveys.

When comparing the data on admissions in refugee camps provided by UNHCR from 2008-2014 with the prevalence data from camp surveys, similar results as in Figure 8 were obtained (data shown in Annex 7).

FIGURE 8

SURVEY-ADMISSION SCATTER PLOT FOR GLOBAL ADMISSION DATA (NOT INCLUDING REFUGEE CAMPS)

Data from ACF in Artibonite, Haiti, also showed a discrepancy with respect to survey and admission data taking into account that surveys occurred before admissions started. All nutritional surveys carried out in the zone by ACF or other agencies showed zero or close to zero levels of bilateral oedema (surveys in 2008 and 2009 showed global acute malnutrition levels of 4.3% (CI: 2.5-6.1%) and 4.4% (CI: 3.0-5.8%) and oedema 0.1%, 0.3% and 0.1% respectively (NCHS)); a post-earthquake survey in Artibonite conducted in January 2010 found 2 cases of oedema, or 0.3% (WHO), and a national SMART in 2009 did not use oedema as an indicator. However, when activities were extended to the rural areas, the team noted that the average caseload of kwashiorkor rose to around 40% of total admissions. A breakdown of these cases was conducted and showed that 36% of the children admitted to outpatient centres and 50% admitted to inpatient units had kwashiorkor.

% o

f SA

M c

hil

dre

n w

ith

oed

ema

(dat

a fr

om a

dm

issi

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ds)

% of SAM children with oedema (data from prevalence surveys)

20

00 20 40 60 80 100

40

60

80

100

% o

f SA

M c

hil

dre

n w

ith

oed

ema

(dat

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ds)

% of SAM children with oedema (data from prevalence surveys)

20

00 20 40 60 80 100

40

60

80

100

35PUTTING


A spatial pattern was noted according to the geographical area of residence or origin of children with kwashiorkor, but information was not available as to whether this is due to more extensive screening of bilateral oedema by community health workers or better awareness of the condition by the community in these areas.

TABLE 14

BROAD ANALYSIS OF ADMISSIONS TO HEALTH FACILITIES IN HAUT ARTIBONITE, HAITI (2009 TO 2013), DEMONSTRATING PERCENTAGE OF ADMISSIONS WITH OEDEMA

2009

Total SAM 183 570 1437 1773 186

Total with oedema 47 191 508 694 84

% with oedema 25.7% 33.5% 35.4% 39.1% 45.2%

2010 2011 2012 2013

There does not appear to be a relationship between kwashiorkor prevalence based on surveys and that determined by nutritional programme admission data. However, the data from nutritional surveys and that from programmes, when collated on a larger scale, both suggest a number of the same countries as being high burden for oedema.


6 DISCUSSION

6.1 Limitations, data issues and biases

Analyses presented in this report are based on “found data” derived from “available” information utilised in programming. Thus, the current dataset is not ideal, since it has not been directly collected by the authors for research purposes. Data was predominantly from areas experiencing a nutritional emergency or where funding was granted to conduct nutritional surveys or provide nutritional services. The maps and graphs produced in this report give an indication of the current situation for oedematous malnutrition at national level, but do not give a definitive picture. Individual country results should not be generalised to all regions, but rather should stimulate further research into the condition at a sub-regional level.

The data used in this report was collected for purposes other than mapping oedema and suffers from a number of selection biases, mainly:

◆ PLACE: A mapping sample should, ideally, be based on a reasonably even and exhaustive spatial sample. The sample that constitutes the database is spatially clustered. The data gathering process concentrated on collecting survey datasets from UN organisations and NGOs working in humanitarian contexts. Many of the surveys and clinical datasets come from locations in which there is a suspected or confirmed high prevalence of malnutrition in which UN and NGOs are present and active in the nutrition field.

◆ TIME: The database is limited with regard to analysing data as a time series and it is not possible to derive trends due to the limited number of data points in certain locations, whilst in other locations there are numerous data points, albeit temporally clustered. This temporal clustering is due to a similar selection bias as with the spatial sample. A second temporal bias is that all data comes from cross-sectional prevalence surveys, which may be problematic for conditions, such a oedema, believed to have a rapid onset & short duration.

Results should then be interpreted with care. Readers of this report are urged to focus on rankings rather that absolute values. Other limitations can be found in table 15.

TABLE 15

LIMITATIONS OF SURVEY AND ADMISSION DATA

Survey selection As cross-sectional surveys are mainly small scale and conducted when and where a nutritional problem is highlighted, or a nutritional intervention is planned, rather than always on a national scale, bias is presented. Furthermore, SMART survey sampling use population proportional sampling where small rural areas have a smaller representation given smaller populations in the dataset. Governments have shared national surveys when they exist, thus improving the dataset.

Nutritional surveys measure prevalence, and oedema is better assessed using incidence rather than prevalence due to the short duration of kwashiorkor (marasmus also might be affected too).

Surveys were received in 5 different formats (ENA software for SMART, Epi-Info, STATA, SPSS, and Excel), which required file conversions to the comma-separated values format. Some datasets were provided as raw data, while others had already been cleaned before receipt. Therefore, many surveys were cleaned based on the contributing organisation’s standards, while others were cleaned based on the project’s standards, resulting in some variability. Agencies may have used WHO and/or SMART flagging criteria, censored or deleted records, or left flagged data alone but excluded them from the analysis.

Oedema could be seasonal, and surveys are often conducted prior to and at the end of interventions, depending on funding and not necessarily based on seasonal variation.

Prevalence vs incidence

Lack of standardisation

Timing of surveys

SURVEY DATA

37PUTTING


Poor training and lack of measurement skills can lead to under or overestimation of oedema, although many surveys build in a “check” for oedematous cases by survey supervisors..

The missing variables were most often MUAC and sometimes oedema. MUAC was not consistently taken on younger but eligible children in some surveys, which may bias the age distribution of the results. Data entry errors were common in the original datasets. The MUAC variable was most often recorded incorrectly, so some errors may have been made in assuming the intended values. The duplicate code could not account for cleaning differences amongst data entry persons, so this may have prevented some duplicate surveys from being detected.

Agencies found it difficult to find all the original raw datasets, especially from older surveys, as these had not always been systematically stored at headquarter or country level.

Some countries did not provide permission for use of nutritional surveys outside of the country of origin. This limits the extent that which the map can represent the actual nutritional situation worldwide. Obtaining data permission can be a lengthy process and only limited access may be granted to some datasets.

Metadata was often not present or was coded in an opaque manner. For example, sometimes a livelihood zone was used as a region (within a country), when in fact it spanned many regions, so results for specific regions may not have been possible. This complicated interpretation as each livelihood zone may span many regions and each region may have many relevant livelihood zones. Although current classifications were used for the final analysis, some regional classifications may have been different at the time of survey, or at the time of the survey classifications may not have been clearly defined.

Definitions of livelihood zones, pastoralists, urban, peri-urban and rural varied, as did inclusion in the data collected. It would be useful to have a minimal set of metadata collected across surveys in the future.

Few surveys collect data on children aged


6.2 The wider context of kwashiorkor

The main goal of this report is to highlight the importance of kwashiorkor as a public health problem, as reflected by its prevalence and also by the proportion of SAM cases it represents in surveys. Despite its limitations, this report gives, for the first time, a representation of the geographic distribution of kwashiorkor and the main findings are consistent with what has been reported for more than 40 years in West Africa, with a higher frequency of kwashiorkor in the Guinean compared to the Sudanic Zone31. The explanation given at that time was that the proportion of energy derived from protein seemed lower near the equator, where diet had a higher proportion of roots and tubers. The hypothesis that kwashiorkor is due to protein deficiency has been challenged, as it fails to explain many of its clinical features32 Also, recent studies failed to show an association between protein intake and risk of kwashiorkor33,34. The cause of kwashiorkor is still unknown, despite all the research conducted to date, and various hypotheses are still being tested in an attempt to better explain onset of the condition.. Some of these explanations are: lower sulphur amino acid consumption, different types of soil, Enteric Environmental Disorder, gut microbiota, high HIV prevalence, climatological contexts, etc.. 21, 35, 36, 37, 38, 39, 40, 41.

MUAC is apparently less sensitive to changes in fluid retention42 and seems better for assessing the general nutritional status of children with oedema that does not extend up to the child’s upper arms (i.e., +++ oedema). This latter assumption is supported by the ROC curves in this report that describe the association between anthropometry and oedema, showing that MUAC more readily identifies children with oedema, compared to WHZ. A similar observation was made earlier in Malawi43.

Mortality associated with kwashiorkor also varies across studies. Data from Malawi suggests that the prognosis of oedematous malnutrition may be greatly influenced by an associated WHZ

39PUTTING


7 RECOMMENDATIONS

7.1 Programmatic recommendations

Improve the current data collection system for kwashiorkorFirst and foremost this report highlights the need for information on kwashiorkor to be collected in a more intensive, systematic and standardised manner.

Despite the limitations of the cross-sectional survey, given it is the main tool used to detect acute malnutrition, the assessment of bilateral pitting oedema (and MUAC) should be included in the list of key variables (age, sex, weight, height) collected, especially in standard national surveys (i.e. SMART, MICS, Demographic Health Surveys (DHS)) and those surveys conducted in areas where kwashiorkor is potentially prevalent. Ideally, context information should also be collected in order to explain high oedema rates (e.g. seasonality, emergency-related indicators, IDP setting, etc). Hopefully this will not only provide more data but also increase awareness among surveyors, communities and health practitioners of how to screen for, detect and refer cases of oedema.

Encourage moving to active community-based screening of kwashiorkor to assess its burdenA correct assessment of the importance of kwashiorkor can only be obtained by active regular screening at the community level of all at risk under five years old children in populations where it is known to be present. This cannot be achieved by cross-sectional surveys alone, but the data collection process could be integrated into treatment programmes where children are regularly screened for referral to treatment or national surveillance systems.

More training of health workers and caretakers on the recognition of oedema is needed, as well as creating awareness of kwashiorkor at the community level to better understand how to assess and how to treat this condition. Special attention could be given to the possible involvement of mothers in oedema detection. A pilot study has shown that mothers can be trained to measure MUAC45. It would be worth investigating the viability of training mothers to assess for oedema. As this condition seems transient, mothers are likely in the best position to detect it before it becomes too severe.

Review admission categories and encourage monitoring of marasmic-kwashiorkorFurther attention should be given to classification of admission categories and possible pooling of data across centres. Ideally, admission and outcome data should be broken down by type of SAM (marasmus or kwashiorkor) to help monitor where the highest caseloads are found. Standard definition or standardisation of the grading (+, ++, +++) would be useful.

To better understand the diversity of background nutritional status, a MUAC-based definition of marasmic-kwashiorkor may be more relevant. The authors recommend using the presence of bilateral pitting oedema and MUAC cut offs as a definition, given that WHZ is particularly influenced by fluid accumulation caused by oedema, thus the latter would be more difficult to interpret. Since it is suspected that children with marasmic-kwashiorkor are at greater risk of dying, there should be further study into mortality in this group. It would also be beneficial to increase awareness and monitoring of marasmic-kwashiorkor children in surveys and nutrition programmes. As mortality is higher for these children, piloting the inclusion of a marasmic-kwashiorkor category at selected centres would help determine its feasibility, a necessary step before it could be suggested as a new admission category.

Improve the current survey reporting systemFurthermore, to benefit measurement of acute malnutrition in general, there is a need for systematic collection, storage, and standardisation of nutritional survey data, software and definitions within narrative reports. The inadequacy of the current state of surveys (raw data and narrative reports) is a huge obstacle to having a global view of SAM, and more specifically, the problem of kwashiorkor.


During this project many agencies could only provide narrative reports and had lost the original raw data, which limits the interpretation of the surveys and led to a number of surveys being excluded from the analysis. Narrative reports varied hugely in definition, presentation and inclusion of metadata, and therefore could not be used in this exercise. Inconsistencies were found across surveys, including lack of a standard format, varying codes for some indicators, loss of original files (often with past employees who left or through corrupted files), no clear contact person, etc. Variation was found in the type of software used, coding/labelling and units (gender, age, grade or absence/presence of oedema, use of millimetres or centimetres etc). Many surveys did not collect oedema or MUAC as one of the key indicators, even though these are admission criteria for services managing acute malnutrition. No DHS surveys were included, as these did not have all the indicators, especially MUAC or oedema, and only one MICS survey had all the needed variables. The rest of the MICS surveys provided were missing MUAC.

There is neither a standard data entry system nor a standard code system used among organisations. The creation and adherence of such a system would facilitate reviews such as this one, so it is worth advocating for the preservation of raw data given its potential value for future research.

Grade of oedema is usually not included in surveys, even though it is important for admissions, as many countries dictate whether a child should be managed in inpatient or outpatient care. A lack of definition or standardisation of the grading (+, ++, +++) was also found.

7.2 Research priorities

It would be useful to increase research efforts around the following areas:

Examine the mortality risk of children with oedema, marasmus and marasmic-kwashiorkor More studies examining different admission categories (marasmus and kwashiorkor) and stratified by grade of kwashiorkor (+, ++, +++) and treatment category (inpatient or outpatient) would be beneficial for identifying more prognostic factors and determining specific treatment recommendations. In particular, a research priority should be to look in more detail at the outcomes of children with kwashiorkor and low MUAC (marasmic-kwashiorkor) as defined by oedema and MUAC less than 115mm.

Examine the geographical distribution of potential causal factors More epidemiological studies and careful examination of dietary patterns in different parts of Africa and their association with possible variation of intake of some key nutrients could help explain the geographical distribution of kwashiorkor, that it is more frequent in central and humid parts of Africa, as well as identify potential causal factors. The pattern of sulphur amino acid intake deserves special attention, as available data suggest it may play a role in the origin of kwashiorkor36, 46. It appears that the last attempt to examine variations of sulphur amino acid intake in Africa goes back to more than 40 years ago39. No attempt has ever been made to examine it on a global scale. This early report suggests a lower sulphur amino acid intake in humid parts of Africa, a pattern which is consistent with the geographical distribution of kwashiorkor presented in this report. This early study on sulphur amino acid availability was based on agriculture and food consumption data published in the previous 25 years. This information needs updating.

The role of a possible limiting nutrient should also be examined with modern techniques of diet modelling, using linear programming, which are powerful tools to investigate limiting nutrients based on food available in the community and semi-quantitative data on food portions 47, 48. The geographical distribution of dietary patterns and of the possible association

41PUTTING


with kwashiorkor could also be studied at the county level, at the regional level or even in smaller geographical units. In Malawi, for example, kwashiorkor prevalence seems to vary within regions with no clear explanation49. Comparing maps of diet quality or dietary diversity and distribution of kwashiorkor could show possible linkages.

The limits of the examination of the geographical distribution of kwashiorkor to unravel its causes should be acknowledged. This approach provides a very low level of evidence to assess causality, as this association can be produced by unknown confounding factors or by untested causal factors. For a disease such as kwashiorkor, which is most likely multifactorial, it has the advantage, however, of avoiding the problem of universal exposure to a risk factor. For instance, if kwashiorkor is caused by the conjunction of two different risk factors, a case control study will fail to identify one of these if present among all children in the study area. So this type of exploratory analysis is worth undertaking despite its limitations. Other factors proposed to explore are: diet (including but not limited to deficient sulphur amino acid), gut microbiota, metabolism alterations, and infections (as a potential precipitating factor) and factors that tend to coincide with geography, such as temperature, altitude, harvest patterns, infections and waterborne diseases.

Investigate why cross-sectional surveys poorly reflect the type of SAM seen in treatment facilities Many factors can explain the weak relationship observed between survey and clinical data. First, there may be a problem in data collection of oedema. Research should try to improve standardisation of the oedema assessment and test its reproducibility. The use of physical models or of photographs showing different degrees of oedema should be tested.

Measure incidence rather than prevalenceIncidence studies may be better suited to more accurately describe the burden of oedema in countries. Actual incidence of kwashiorkor was measured in Malawi, with 2.6% of children developing kwashiorkor during 20 weeks follow up (50). Studies such as this may be a better source of data on kwashiorkor than cross-sectional studies, so they could potentially supplement similar databases in the future.

The duration of each episode of oedema and different treatment-seeking behaviours deserve further investigationSpecial attention should be given to variations of these different factors in relation to the degree of associated wasting. It is quite plausible that when this level of associated malnutrition is low, oedema resolves more rapidly and is not perceived as worrisome by carers, as it would be in situations with high levels of associated malnutrition. On the other hand, in situations where oedema is associated with a high mortality, the early death of these children may explain their low prevalence in surveys but a high level of treatment-seeking behaviour. Clinical studies examining the relationship between MUAC, mortality, response to treatment and duration of oedema under treatment could shed light on these possible factors and allow practitioners to identify areas of treatment in need of improvement.

Examine the effect of the degree of oedema and of background malnutrition on the prognosis of kwashiorkor There is little data on the relationship between the degree of oedema, the associated malnutrition and the associated risk of mortality. Improved knowledge of risk associated with the degree of oedema and of background malnutrition, as assessed by MUAC (as it is less influenced by oedema), would help to better choose between different treatment options.

Other important aspects to try to gather data on are:

1 Comorbidities associated with kwashiorkor and marasmus (e.g. diarrhoea, fever, HIV status, etc.).2 Degree of chronic malnutrition (stunting, underweight) in each of these cases. Little information was found on kwashiorkor’s relationship with stunting, and this should be further explored.3 Response to treatments, stratified by malnutrition type, as well as co-morbidities.


8 CONCLUSION

As highlighted above, the data we have on kwashiorkor are limited, of poor quality and sporadic, despite this being an important disease with high mortality. The dearth of clear data suggests that there really needs to be a more systematic and standardized approach in collecting better data that is of immediate use for practitioners as well as researchers in the future.

There are likely more children with kwashiorkor than are reported in surveys, as the admission data indicates, and these children pose a huge resource burden to the health care system in many countries. Appropriate resources need to be devoted to screening, referring and treating children with kwashiorkor.

The authors would like to urge more studies to include kwashiorkor and for the health and nutrition sector to improve collection and availability of data on oedema within the SAM management programmes, community work and health systems and to identify the extent of kwashiorkor in order to generate donor and Government interest and leverage more resources for preventing kwashiorkor.

43PUTTING


REFERENCES1 ICD-10 Version: 2010 [Internet]. ICD-10. Available from: http://apps.who. int/classifications/icd10/browse/2010/en#/E40-E46

2 WHO | International Classification of Diseases (ICD) Revision [Internet]. WHO. [cited 2015 Nov 24]. Available from: http://www.who.int/ classifications/icd/revision/icd11faq/en/

3 Heikens GT, Manary M. 75 years of Kwashiorkor if Africa. Malawi Med J. 2009 Sep;21(3):96–100.

4 Classification of infantile malnutrition. Lancet Lond Engl. 1970 Aug 8;2(7667):302–3.

5 WHO | Training course on the management of severe malnutrition [Internet]. WHO. [cited 2015 Nov 24]. Available from: http://www.who.int/ nutrition/publications/severemalnutrition/training_inpatient_MSM/en/

6 Ahmed T, Rahman S, Cravioto A. Oedematous malnutrition. Indian J Med Res. 2009 Nov;130(5):651–4.

7 Jackson AA. Severe undernutrition in Jamaica. Kwashiorkor and marasmus: the disease of the weanling. Acta Paediatr Scand Suppl. 1986;323:43–51.

8 The Lancet. Maternal and Child Nutrition series [Internet]. 2013. Available from: http://www.thelancet.com/series/maternal-and-child-nutrition

9 Global nutrition report 2014: Actions and accountability to accelerate the world’s progress on nutrition. S.l; S.l: Washington, D.C; 2014.

10 Brock JF. Survey of the world situation on kwashiorkor. Ann N Y Acad Sci. 1954 May 10;57(6):696–713.

11 Joint Child Malnutrition Estimates (UNICEF/WHO/World Bank) | Data [Internet]. 2014 [cited 2015 Nov 24]. Available from: http://data.worldbank. org/child-malnutrition

12 Briend A, Collins S, Golden M, Manary M, Myatt M. Maternal and child nutrition. Lancet Lond Engl. 2013 Nov 9;382(9904):1549.

13 Frison S, Checchi F, Kerac M. Omitting edema measurement: how much acute malnutrition are we missing? Am J Clin Nutr. 2015 Sep 16;ajcn108282.

14 Williams CD. Protein-calorie malnutrition. Lancet Lond Engl. 1972 Jun 17;1(7764):1333.

15 Bhattacharyya AK, Chattopadhyay PS, Paladhi PK, Ganguli S, Bhattacharyya N. Kwashiorkor and marasmus: changing hospital incidence of syndromic presentation (1957-88). Indian Pediatr. 1990 Nov;27(11):1191–8.

16 Oyelami OA, Ogunlesi TA. Kwashiorkor--is it a dying disease? South Afr Med J Suid-Afr Tydskr Vir Geneeskd. 2007 Jan;97(1):65–8.

17 Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet Lond Engl. 2008 Jan 19;371(9608):243–60.

18 Ndekha MJ. Kwashiorkor and severe acute malnutrition in childhood. Lancet Lond Engl. 2008 May 24;371(9626):1748; author reply 1749.

19 Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M,

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