Menu ENN Search

Causal analysis and the SQUEAC toolbox

By Mara Nwayo and Mark Myatt

Mara Nyawo is a nutritionist specialising in nutrition surveys and surveillance. She has nine years experience working in emergency and chronic emergency settings in Africa and is currently working for UNICEF in Sudan.

Mark Myatt is a consultant epidemiologist. His areas of expertise include surveillance of communicable diseases, epidemiology of communicable diseases, nutritional epidemiology, spatial epidemiology, and survey design. He is currently based in the UK.

The authors wish to thank the Sudan Federal Ministry of Health, Kassala State Ministry of Health, GOAL, and UNICEF's Kassala Office for help with organisation, facilities, accommodation, and logistics.

In this article we report our experiences using the SQUEAC1 toolbox to undertake a causal analysis of severe wasting (SAM) in a rural area of Eastern Sudan. The work reported here took place during a trainers-of-trainers course in SQUEAC and SLEAC2 coverage assessment methods. The course was organised by UNICEF and held in the city of Kassala in Eastern Sudan in September 2011. Course participants were drawn from United Nations (UN) organisations, non-governmental organisations (NGOs), and state and federal ministries of health. None of the course participants had prior experience with SQUEAC, SLEAC, or the CSAS3 coverage assessment method.

A semi-quantitative model of causal analysis was proposed and tested. The elements of this model are outlined in Figure 1. It is important to note that many of the activities required to undertake the causal analysis are existing SQUEAC activities. The approach uses SQUEAC tools to identify risk factors and risk markers for subsequent investigation by case-control study. A matched case-control design was proposed and tested as this requires a smaller sample size than an unmatched design for the same statistical power. Matching was done on location and age. Cases were children aged between six and fifty-nine months with a mid-upper-arm-circumference (MUAC) below 115 mm and/or bilateral pitting oedema. Controls were nearby neighbours of cases and of similar age (i.e. within ± three months) with a MUAC greater than 124 mm without bilateral pitting oedema. Data were collected on 35 sets of matched cases (n = 35) and controls (n = 78). The overall sample size for the study was, therefore, n = 113.

Collection of causal data using the SQUEAC toolbox

Trainees had no difficulty collecting case-histories from the carers of SAM cases in the programme and from carers of non-covered SAM cases found in the community during SQUEAC small-area surveys. Trainees also had no difficulty collecting causal information from a variety of informants (e.g. medical assistants, community based volunteers (CBV), traditional birth attendants, traditional health practitioners, village leaders, etc.) using informal group discussions, in-depth interviews, and semi-structured interviews. They also had no difficulty in collating and analysing the collected data using concept-maps and mind-maps (see Figure 2). Trainees had little difficulty expressing findings as testable hypotheses. These are all core SQUEAC activities. Trainees selected potential risk factors and risk markers for further investigation with minimal intervention from the trainer.

Translation of findings to data collection instruments

Some trainees had difficulty in designing instruments (i.e. question sets) to test stated hypotheses. The problem appeared to be in formulating unambiguous questions and in breaking down complex questions into small sets of simple linked questions. Future development work should explore whether role-playing might help with this activity. Trainees found little problem identifying, adapting, and using predefined question sets (e.g. for a household dietary diversity score and for infant and young child feeding (IYCF) practices) when these were available. Future development work should focus on building a library of pre-tested and ready-to-use questionnaire components likely to be of use. Trainees had little difficulty fieldtesting their data collection instruments and adaptations were made and tested in the field and again at the survey office.

Case-finding and questionnaire management

Trainees quickly developed the skills required for active and adaptive casefinding (this was expected from previous SQUEAC trainings). Identification of matched controls was performed well under minimal supervision. The management of questionnaires for a matched case-control study was also performed well under minimal supervision.

Applying the case-control questionnaire to cases, identifying appropriately matched controls for each case, applying the case-control questionnaire to controls, and the management of study paperwork added a considerable datacollection overhead above that already required by the SQUEAC likelihood survey4. It is estimated that surveyor workload for the likelihood survey may increase by 50% or more.

Data-entry and data-checking

Great difficulty was experienced and much time wasted working with EpiInfo for Windows. This software proved both difficult to use and unreliable. Data were lost on two occasions. Switching to EpiData proved necessary. This software proved much easier to learn and use. Future development work should use a simple and reliable data-entry system such as EpiData. This software can be run from a USB flash drive and does not require software to be installed.


No attempts were made to teach the details of the techniques required for data management and data analysis. This component was not tested because the computers available were configured so as to prevent the installation of software (the intention had been to test this activity using a free student version of a major commercial statistics package). Data were analysed using the MSDOS version of EpiInfo (v6.04d) and the cLogistic add-in software. This command-line driven software may not be suitable for use by workers used to using more graphical software.

The process of data analysis (i.e. conditional logistic regression with backwards elimination of non-significant variables) was demonstrated to a local supervisor with some experience with the analysis of cross-sectional survey data (e.g. SMART5, IYCF, MICS (Multiple Indicator Cluster Survey)). He managed to replicate the demonstrated analysis using EpiInfo and cLogistic. He later demonstrated the analysis to the trainee group and independently reproduced the analysis using STATA. The results of the analysis (from cLogistic) are shown in Figure 3.

Further work is required to identify useful software and to develop a practical manual including worked examples. The manual could be a self-paced programmed learning course. This would allow both self-teaching and classroom-based teaching. The manual should cover data-entry and checking, data-management, data-analysis, and reporting.


The data collected in this exercise were sufficient to identify risk factors and risk markers (i.e. diarrhoea, fever, early introduction of fluids other than breastmilk – a marker for poor IYCF practices) that were significantly associated with SAM. This suggests that it is possible to use the SQUEAC toolbox to collect causal data using the level of staff selected for training as SQUEAC supervisors and trainers. Data analysis may, however, require staff with a stronger background in data-analysis.

Consideration should be given as to whether a case-series or set of case-reports collected from carers of cases in a community based management of acute malnutrition (CMAM) programme and non-covered cases found in the community during SQUEAC small-area surveys could provide a useful causal analysis. Collected data could be organised and presented using a mind-map (as in Figure 2). This would be simpler and cheaper than a case-control study and would probably be more robust than currently utilised methods which tend to use a single round of focus groups (typically excluding carers of SAM cases) and a ‘problem-tree’ analysis.

The work reported here supports the further development and testing of the proposed model for a causal analysis add-in to SQUEAC. This article is intended to inform the emergency and development nutrition community of our experiences with this model so as to allow us to judge the level of interest in further development of the method.

For further information, contact:

Show footnotes

1Semi-quantitative Evaluation of Access and Coverage

2Simplified LQAS Evaluation of Access and Coverage (LQAS: Lot Quality Assurance Sampling)

3Centric systematic area sample

4The survey conducted in the (optional) third stage of a SQUEAC investigation which, when combined with other data, provides an estimate of overall programme coverage

5Standardised Monitoring and Assessment of Relief and Transitions.

More like this

FEX: Determining predictors for severe acute malnutrition: Causal analysis within a SQUEAC assessment in Chad

By Ruwan Ratnayake, Casie Tesfai and Mark Myatt Ruwan Ratnayake is the Epidemiology Technical Advisor with the International Rescue Committee based in New York. He supports...

en-net: sample size estimation

we are planning to do a causality study on acute malnutrition. the study intends to look at both quantative and qualitative information, we are skeeing expert advise on sample...

en-net: Causality Analysis

Does someone know where I can get resources or information on experiences and standard design for conducting causal analysis of malnutrition? Hello, as far as I know, a good...

FEX: Use of a two-stage approach to identify intervention priorities for reduction of acute undernutrition in Abaya district of Ethiopia

By Katja Siling, Asrat Dibaba and Mark Myatt Katja Siling is an independent consultant helping organisations increase their learning capability and fulfil their mandate...

en-net: Field investigation of acute malnutrition cases in a village

Dear Forum, greetings from Lao. A recent outreach in Lao uncovered increased number of SAM in three villages. An epidemiological field based investigation would be...

FEX: Study of the Risk Factors for the Development of Nutritional Oedema in North Kivu, DRC

By Mark Myatt and Frances Mason Mark Myatt is a consultant epidemiologist and senior research fellow at the Division of Epidemiology, Institute of Opthalmology, University...

en-net: Causal Analysis in SQUEAC

We are planning on conducting causal analysis on SAM cases during SQUEAC assessment. However, we are facing challenges in having a standardized/validated questionnaire to work...

en-net: Malnutrition & Disability, indicator and age

We are planning a cross sectional survey (with nested case-control study with 2 controls: sibling+neighbour) looking at malnutrition & disability. I am wondering if there is...

FEX: Boosters, Barriers, Questions: an approach to organising and analysing SQUEAC data

By Andrew Prentice (VALID), Balegamire Safari Joseph (VALID), Esther Ogonda McOyoo (Concern), Faith Manee Nzidka (ACF), Hassan Ali Ahmed (Mercy USA), Jackson N Chege (Islamic...

FEX: SQUEAC: Low resource method to evaluate access and coverage of programmes

By Mark Myatt Mark Myatt is a consultant epidemiologist and senior research fellow at the Division of Ophthalmology, Institute of Ophthalmology, University College London....

en-net: Nutrition Causal Analysis - inputs for the development of a methodology

Dear friends of en-net ACF (Action contre la Faim), together with consultants from Tufts University and IRD-Nutripass, has started a research project on Nutrition Causal...

en-net: Nutrition Causal Analysis: usefulness and application

I am considering the use of a nutrition causal analysis to find out more about how malnutrition prevalence remains high, yet mortality is low and the population is receiving...

FEX: Kenya and Malawi: Intestinal disturbances and mortality in complex malnutrition cases

View this article as a pdf Lisez cet article en français ici This is a summary of the following paper: Wen B, Farooqui A, Bourden C et al. (2023) Intestinal...

FEX: Using SLEAC as a wide-area survey method

By Ernest Guevarra, Saul Guerrero, and Mark Myatt Ernest Guevarra leads Valid International's coverage assessment team. He has formal training as a physician and a public...

en-net: Coverage estimate calculation - Final stage - Help again

Dear all, I hope you are doing fine. Well, I still have an issue and need a bit of guidance. We are in the very final stage of the SQUEAC investigation. I have just compiled...

FEX: Mortality risk factors in severelymalnourished children hospitalised with diarrhoea

Summary of published research1 Acase-control study conducted in the Dhaka Hospital of the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B) to identify...

FEX: Protein-energy malnutrition and chromosome changes

Summary of published research1 The relationship between proteinenergy malnutrition and genetic damage has been studied in human beings and laboratory animals, but results are...

en-net: UNHCR looking for a Nutrition SMART SENS Survey Specialist for Eastern Chad - Consultancy

CHAD COUNTRY OFFICE Nutrition SMART SENS Survey Specialist for Chad - Consultancy Location: Chad TERMS OF REFERENCE Duration and dates of the assignment From:...

FEX: From the editor

Successful homestead gardening in Satkhira, Bangladesh The year 2013 promises to be an important year for the nutrition aid community and those whom it serves. Since...

FEX: From the editor

This issue of Field Exchange gives extended coverage to a briefing paper just released by Oxfam and SC UK on the 2011 response to the Horn of Africa crisis. This paper argues...


Reference this page

Mara Nwayo and Mark Myatt (). Causal analysis and the SQUEAC toolbox. Field Exchange 42, January 2012. p37.



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.