south darfur emergency assistance program (nutrition) · international, switzerland (cis) in kass...

CARE International Foundation, Switzerland (CIS)

South Darfur Emergency Assistance & Recovery Program (SDEARP) Nutrition CMAM Programme Coverage and Nutrition

Causal Analysis December 2013

Dr. Hamed Elneel Yousif Maryoud

Dr. Khalid Yousif Ahmed Algaali

Nutrition CMAM Programme Coverage and Nutrition Causal Analysis Report – December 2013

Hamed Maryoud, Khalid Algaali i

ACKNOWLEDGEMENT

Consultant team would like to acknowledge the help and support from the following people,

without whom this study could not have been conducted.

Thanks to CARE International, Switzerland for funding the survey.

Thanks to the enumerators who formed the survey teams for their hard work and

commitment. Thanks to the State Ministry of Health, for their willingness to help and

permission to conduct our study, and the village and camps chiefs (Sheikhs) who gave up their

time to show the teams around the villages.

Thanks to CIS for their support and collaboration on this study, and especially for their staff

who helped a lot. Thanks also to all the team supervisors who ensured that data collected was

accurate, and that correct sampling methodologies were followed.

Finally, thanks go to all the mothers/caretakers and household leaders who gave up their time

to answer our questions and participate in the discussions.

Dr Hamed Elneel Yousif Maryoud, MBBS, MPH, MD

Dr Khalid Yousif Ahmed Algaali, MBBS, EMDM

December 2013


Hamed Maryoud, Khalid Algaali ii

ACRONYMS & ABBREVIATIONS

ARIs Acute Respiratory Infections

CBV Community-based Volunteer

C.I. Confidence interval (at 95%)

CIS Care International, Switzerland

CMAM Community Management of Acute Malnutrition

EPI Extended Programme of Immunization

GAM Global Acute Malnutrition

HAC Humanitarian Aid Commission

IDP Internally Displaced Person

IMCI Integrated Management of Child Illness

LOS Length of Stay

MUAC Mid Upper Arm Circumference

NGO Non-governmental Organisation

OTC/P Out-patient Therapeutic Centre / Programme

RUTF Ready to Use Therapeutic Food

SAM Severe Acute Malnutrition

SC Stabilisation Centre

SFC/P Supplementary Feeding Centre / Programme

SMOH State Ministry of Health

SQUEAC Semi-Quantitative Evaluation of Access and Coverage

TBA Traditional Birth Attendant

TFP Therapeutic Feeding Programme

TH Traditional Healer

Unicef United Nations Children Fund

VMW Village Midwife

WFP World Food Programme

WHO World Health Organisation

WHZ Weight for height z-score


Hamed Maryoud, Khalid Algaali iii

TABLE OF CONTENTS

ACKNOWLEDGEMENT .......................................................................................................................... i

ACRONYMS & ABBREVIATIONS ........................................................................................................... ii

TABLE OF CONTENTS ......................................................................................................................... iii

List of Tables ....................................................................................................................................... v

List of Figures...................................................................................................................................... v

EXECUTIVE SUMMARY ....................................................................................................................... vi

1. INTRODUCTION............................................................................................................................... 1

1.1. Background ................................................................................................................................1

1.2. South Darfur Emergency Assistance and Recovery Program ......................................................1

2. OBJECTIVES ..................................................................................................................................... 3

2.1. General Objective ......................................................................................................................3

2.2. Specific Objectives .....................................................................................................................3

3. METHODOLOGY .............................................................................................................................. 4

3.1. STAGE 1: Semi-quantitative assessment ................................................................................4

3.2. STAGE 2: Small area survey..................................................................................................4

3.3. STAGE 3: SQUEAC likelihood survey ...................................................................................5

3.4. Data collection ......................................................................................................................6

3.5. Nutrition Causal Analysis ......................................................................................................7

4. FINDINGS ...................................................................................................................................... 10

4.1. Findings from Carers ............................................................................................................... 10

4.2. Findings from Community Key Informants ............................................................................... 12

4.3. Findings from Community Based Volunteers (CBVs) ................................................................ 14

4.4. Findings from Clinic Staffs ....................................................................................................... 14

4.5. Findings from Observations ..................................................................................................... 14

4.6. Findings from Beneficiaries’ Records ....................................................................................... 15

4.7. Small Study & Small Area Surveys ........................................................................................... 23

4.8. TFP Prior Estimation ............................................................................................................... 24

4.9. SQUEAC Likelihood Survey ..................................................................................................... 26

4.10. SFP coverage ......................................................................................................................... 27

4.11. Barriers and Boosters Affecting CMAM Program Coverage ................................................... 31

4.12. Nutrition Causal Analysis ....................................................................................................... 32


Hamed Maryoud, Khalid Algaali iv

5. CONCLUSION AND RECOMMENDATIONS ...................................................................................... 34

5.1. Conclusion ............................................................................................................................... 34

5.2. Recommendations .................................................................................................................... 34

APPENDICES ..................................................................................................................................... 36

Appendix 1 MAP of Kass CMAM Programme Geographical Coverage ...................................... 36

Appendix 2 Mind Map ................................................................................................................ 37

Appendix 3 Causal Analysis Questionnaire ................................................................................. 38


Hamed Maryoud, Khalid Algaali v

List of Tables

Table 1: local names for the malnourished child ................................................................................................ 10 Table 2: Percentages of Referral Sources among Interviewed Carers .................................................................. 11 Table 3: A story from the community ................................................................................................................. 13 Table 4: Interface and referrals in-between different programme centres (December 2012 – November 2013) ... 22 Table 5: Barriers and boosters to CMAM coverage ............................................................................................ 32 Table 6: Risk factors and association with severe malnutrition ........................................................................... 32

List of Figures

Figure 1: GAM & SAM rates from previous local nutrition surveys in the programme area (2011-2013) ................ 2 Figure 2 Program performance indicators in Kass SC over time (December 2012 – November 2013) ................... 16 Figure 3: Program performance indicators in three CIS CMAM OTP centres in Kass over time (December 2012 – November 2013) ............................................................................................................................................... 16 Figure 4: Defaulters (OTPs and SC) and labour demand calendar ....................................................................... 17 Figure 5: Home locations of Defaulters (OTPs and SC). ....................................................................................... 18 Figure 6: Admissions Trend and Disease Calendar ............................................................................................. 19 Figure 7: MUAC on admission............................................................................................................................ 20 Figure 8: home location of program beneficiaries .............................................................................................. 21 Figure 9: Duration of Treatment Episode ........................................................................................................... 22 Figure 10: Proportion of > 8 weeks LOS .............................................................................................................. 22 Figure 11: Prior was described using the probability density Alpha = 19.0 and Beta = 10.7 ................................. 25 Figure 12: Accessible areas during assessment period ........................................................................................ 26 Figure 13: TFP Coverage estimation in Bayes SQUEAC....................................................................................... 27 Figure 14: SFP programme indicator and stock-out ............................................................................................ 28 Figure 15: MAM Admission (SFP) over time with disease calendar (December 2012 – November 2013) .............. 28 Figure 16: Prior was described using the probability density Alpha = 22.6 and Beta = 12.7 ................................. 30 Figure 17: SFP Coverage estimation in Bayes SQUEAC........................................................................................ 31 Figure 18: CIS CMAM program coverage barriers .............................................................................................. 32


Hamed Maryoud, Khalid Algaali vi

EXECUTIVE SUMMARY

Introduction: CARE International Switzerland (CIS) is implementing emergency nutrition interventions as part of the South Darfur Emergency Assistance and Recovery Program. The nutrition program, which adopts CMAM protocols, aims to improving the nutrition status of 72,000 beneficiaries in Kass locality, through five facilities; Kass Hospital SC, Dawagin OTP, Abdel Jabar OTP/SFP, EPI OTP/SFP, Rohall SFP; in addition to a community outreach component. Programme coverage assessment accompanied by nutrition causal analysis was carried by external consultants in December 2013.

Methodology:

The SQUEAC methodology was followed, which uses routine programme data (such as admissions over time, MUAC on admission, proportion of discharges of defaulters, non-response and cures, length of stay and location of admissions). Qualitative data collected from beneficiary carers, programme delivery staff and various community key informants (including community leaders, traditional healers, TBAs, Imams and other community members) were used to make an estimate of programme coverage. A small study and small area surveys were then carried out to quantify the proportion of severely malnourished children (current and recovering cases) reached by the programme. These 2 sets of information were then combined using conjugate analysis (done with Bayes SQUEAC software) to give an overall estimation of coverage with a ±11% precision.

The causal analysis study was conducted in two phases: (a) Identification of possible risk/protective factors of SAM in the programme target area and the generation of testable and falsifiable hypotheses regarding causation of severe acute malnutrition, and (b) Case control study in which a matched case-control design was used. Cases and controls were matched based on age and location.

Key Findings:

Point coverage of CIS Nutrition CMAM programme coverage in Kass Locality was found to be 69.3% (95% C.I. 58.0% - 78.7%) for TFP and 65.8% (95% C.I. 57.7% - 73.1%) for SFP. The coverage was found to be higher in areas closer to CMAM sites and low elsewhere in the programme area (patchy coverage).

In Kass, malnutrition (SAM) was found to be related to few meals, fever illnesses, and acute respiratory tract infections. Based on the results of causal analysis data, it showed all different groups of samples from IDPs and host rural community took less meals and even the frequency is too long between them. Furthermore fever illnesses are common among children under 5 years, due to different causes (e.g. malaria measles, etc.) apart from ARI which was investigated separately with other related symptoms. Programme activities (e.g. health promotion and nutrition messages) can be reoriented to address these risk factors to help reducing the incidence of SAM.


Hamed Maryoud, Khalid Algaali vii

Recommendations:

Recommendation Responsibility Time frame

Strengthening absentees and defaulter tracing activities to address high defaulter rates especially during rainy seasons

CIS/SMOH/ Community Immediately

Strengthening management and adherence to CMAM protocol (esp. regarding interface and referral pathways) at SC level CIS/SMOH Immediately

Involvement and training of the THAs, TBAs, VMWs to be active and committed community members. CIS/SMOH Immediately

Increasing geographic coverage of CMAM program in the short term by expanding to the remote uncovered villages where SAM cases were found (e.g. Singita)

CIS/SMOH As soon as possible

Coordinate with different Governmental authorities and UNICEF & WFP to sort out CMAM supplies stock out, transport, and security stocks in Kass level, etc.

CIS/SMOH As soon as possible

Continued orientations, awareness raising and community mobilization of the communities regarding the CMAM program and involve the community leaders, Sheiks, Imams and other key community informants

CIS/SMOH Continuously

Carry out regular joint monitoring, and supportive supervision visits using CMAM and SQUEAC tools and including interviews with beneficiaries.

CIS/SMOH Monthly

Address root causes of fewer meals, fever illnesses and acute respiratory tract infections, and advocate with other relevant sectors and actors (health and food security) to minimize its burden

CIS As soon as possible


Hamed Maryoud, Khalid Algaali 1

1. INTRODUCTION

This report covers analysis and findings of nutrition CMAM programme coverage assessment for the South Darfur Emergency Assistance & Recovery Program (SDEARP – NUTRITION) being carried by Care International, Switzerland (CIS) in Kass Locality, South Darfur State, Sudan. The coverage assessment is also accompanied by nutrition causal analysis.

1.1. Background

Severe acute malnutrition (SAM) is a major killer of under 5 year children in humanitarian crises. It contributes globally to 1 million deaths of child per year; from estimated 20 million under-five years suffering from severe acute malnutrition (WHO, 2007). In all over the Sudan, the wasting prevalence (global acute malnutrition-GAM) is 14.8% and severe wasting alone is 3.6% (MOH 2010). Emergency and conflicts increase the risk of SAM. Darfur, which is the in western part of the Sudan, experiences a long lasting, low intensity complex humanitarian emergency with frequent exacerbation of acute conflicts since 2003. This has markedly increased the risk of SAM in this region. The Darfur emergency has resulted in wide displacement estimated by United Nations (UN) at 2.4 million people. The consequences of this have led to massive deterioration of the health situation and food security status which result in high mortality among under 5 children from malnutrition in Darfur (MOH, 2010).

Kass IDPs camps have a population of approximately 115,000 IDPs, and are located in close proximity to Kass town (population 25,000) with surrounding rural communities (population in the region of 20,000). Most IDPs have been in the camps since 2004 and seems voluntarily return to their original homes is unlikely in the near future due to safety challenges. The survival of the IDPs depend on the continued receipt of humanitarian assistance in the form of food rations, occasional non -food items and basic services in health care, nutrition, water, sanitation and hygiene. As one of the largest concentrations of IDPs in Darfur, Kass camps have limited land space and are integrated in the host community. The camps are therefore overcrowded and prone to public health and nutrition hazards that put children below five years, pregnant and lactating women as most vulnerable. The nutrition status of Kass IDPs and residents in the surrounding rural areas before the project intervention was reported to be relatively critical. Figure 1 shows results from previous nutrition surveys conducted in Kass Locality.

1.2. South Darfur Emergency Assistance and Recovery Program

CARE International Switzerland (CIS) is a registered NGO in Sudan since July 2009, and has been actively involved in South Darfur since then, with emergency and early recovery interventions in WASH, health, nutrition, food distribution, livelihoods and community development (collectively known as the South Darfur Emergency Assistance and Recovery Program ‘SDEARP’). The Netherlands Ministry of Foreign Affairs (MoFA) and the Common Humanitarian Fund (CHF) are among the largest contributors to CIS’s humanitarian interventions in Darfur.



CIS implements emergency nutrition interventions as one of the key component of the SDEARP. The nutrition program, which adopts CMAM protocols, aims to improving the nutrition status of 72,000 beneficiaries in Kass locality, specifically targeting children under five years and women of reproductive age; through improving access to acceptable quality of care and treatment of acute malnutrition, strengthening of MoH and partners capacity, and promoting child survival, growth and development through high impact, effective and low cost nutrition interventions.

CIS implements the nutrition programme with five facilities in Kass Locality providing comprehensive nutrition intervention services. These include Kass Hospital SC, Dawagin OTP, Abdel Jabar OTP/SFP, EPI OTP/SFP, Rohall SFP. This is in addition to a community outreach component managed by the national NGO Mobadiroon Organization.

Figure 1: GAM & SAM rates from previous local nutrition surveys in the programme area (2011-2013)

19% 18.8%

14.8%

2.80% 2.5% 2.2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Nov

-11

Dec-

11

Jan-

12

Feb-

12

Mar

-12

Apr-

12

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-12

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12

Jul-1

2

Aug-

12

Sep-

12

Oct

-12

Nov

-12

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-13

GAM

SAM



2. OBJECTIVES

2.1. General Objective

The objective was to conduct a contextually appropriate nutrition coverage assessment and causal analysis, designed to cover a representative sample population in Kass IDPs camps, Kass town, and rural Kass villages for the nutrition programme implemented by CARE International, Switzerland (CIS).

2.2. Specific Objectives

Specific objectives were to:

• Evaluate the nutrition coverage of the SFP, OTP, and SC programmes in Kass IDPs camp, Kass town, and rural Kass villages.

• To identify barriers and boosters to coverage of CMAM services in Kass • Identify determinant causes and risk factors of malnutrition in Kass locality that should be

taken into consideration in programme planning and implementation. • Present recommendations based on the survey to improve access to the SFP, OTP, and SC and

increase programme coverage in the locality. • Make recommendations on preventative measures for malnutrition in Kass.



3. METHODOLOGY

SQUEAC (Semi-Quantitative Evaluation of Access and Coverage) method was used to investigate the CMAM program in the project locations. A multi-stage screening test model was used as follows:

3.1. STAGE 1: Semi-quantitative assessment

Areas of low and high coverage as well as reasons for coverage failure were identified using routine program data, already available data, quantitative data, and qualitative data collected by the teams from five CMAM program sites (Kass Hospital SC, Dawagin OTP, Abdel Jabar OTP/SFP, EPI OTP/SFP, Rohall SFP) and villages.

Verification was guaranteed through triangulation by source (extraction of the specific information from various sources through asking each source separately - e.g. caretaker, clinic staff and traditional healers about treatment seeking behaviours) and by method (different methods were used to verify that specific information e.g. semi structured questionnaires, natural focus group discussions and direct interviews were used to establish e.g. disease and labour demand calendars).

The approach of sampling to redundancy was used during this stage. Data collection continued until no new information was gained and the answers were repeated each time. At this point, collecting of that piece of information was stopped.

A Mind-Map approach was used to store, review, and present the findings (quantitative and/or qualitative data) gathered time-by-time and to analyse the relationships between them. It was done by hand initially at the filed level and later presented in a computerized format using Xmind software.

3.2. STAGE 2: Small area survey

The locations of areas of high and low coverage and the reasons for coverage failure identified in stage one (above), were confirmed using small-area surveys. Hypotheses about coverage were formulated based on findings in stage 1 and then tested.

For the small-area surveys, communities and sub-communities were selected purposefully (directed by the hypothesis being tested). Sample sizes were not calculated in advance. These surveys were carried out for a short period of time over a small area (as per SQUEAC guidelines). Three survey

Semi-quantitative assessment

STAGE 1

•Small area surveys & small studies

•Building the Prior

STAGE 2

Likelihood survey

STAGE 3

Overall coverage



teams collected samples of SAM from 6 neighbouring villages. The survey sample size was the number of SAM children found by the surveyors. Surveyed villages in this stage were Baitary, Kifah, and Taringa suburbs inside the catchment area (within 6 Km from OTPs sites); and Limo, Fadwa, and Tabaldia and Um Ashosh villages outside the catchment area.

The within-community case-finding methods used were house to house (exhaustive screening) except in very big villages where active and adaptive case finding was used as follows:

ACTIVE: The method actively searched for cases rather than just expecting cases to be found in a sample.

ADAPTIVE: The method used information found during case-finding to inform and improve the search for cases.

An overall coverage was estimated for each program (TFPs and SFPs) separately using Bayesian techniques. This technique included estimation of the prior (estimate of coverage before conducting a survey) and the likelihood (estimation of coverage using a small sample wide area survey) and the use of the two estimates (prior and likelihood) to calculate the overall coverage (the posterior) using a beta-binomial conjugate analysis technique with BayesSQUEAC software.

Therapeutic Feeding Programmes (TFPs) prior information was used to make an informed guess about the most likely coverage value and express it as a probability density. Several estimates of the prior estimate were calculated using the following information and findings:

Positive and negative findings in the mind-map to “build up” from zero (i.e. lowest possible) coverage and to “knock down” from 100% (i.e. highest possible) coverage respectively.

Scores or weights of mind-map findings. Proportion of villages located within the catchment of the 5 CMAM sites. Proportion of health centres covered with CMAM services out of all HCs.

The average from all these estimates and upper and lower expected values around coverage were then estimated. The Prior estimate was then described using the probability density Alpha prior = 19.0 and Beta prior = 10.7 using Bayes SQUEAC software.

SFP prior was estimated using the Bayesian approach, an informed guess about the most likely coverage value and express it as a probability density (Prior) was estimated informed by MAM prevalence from a recent SMART survey, population figures and SFPs beneficiaries’ numbers in addition to quantitative and qualitative information collected during stage 1.

3.3. STAGE 3: SQUEAC likelihood survey

A wide-area survey of the entire program catchment area was carried out.



Methodology: For both likelihood surveys (TFP and SFP), a two-stage sampling procedure was conducted. The first stage was the sampling method that was used to select the villages to be included. Quadrat stratified systematic random sampling method was used as follows:

A map of Kass locality showing all 71 villages, 41 were considered (28 villages were excluded because of displacement and 2 because of security); a numbered grid of 22 Km was laid on the map yielding 6 squares. Villages in accessible areas (3 squares ) each square (Quadrat) were listed in the order of the quadrants and 6 villages were selected using systematic spatial sampling.

The second stage was the within-community sampling method. This was done using house to house (exhaustive screening), active and adaptive case finding methods. This method allowed inclusion of all, or nearly all, current and recovering SAM cases and most MAM cases in a sampled village.

Sample size requirements were calculated (using simulation with the Bayes SQUEAC calculator) to provide a coverage estimate with a 95% credible interval of about ± 11% and to ensure that all geographical parts of the locality covered. This precision was decided based on the available time - which was 2 days for the likelihood survey - and logistics (three vehicles and 10 persons). The minimum sample size required was calculated to be n = 41 current or recovering SAM cases cases and 37 MAM cases.

It was estimated (from the work conducted for the prior estimation) that 6 villages would be required to find 41 current or recovering SAM cases and 37 MAM cases. Surveyed villages in the likelihood survey were Gemiza, Erly IDPs camp, Rohall IDPs camp, El Naseem, Kideda, and Singita.

3.4. Data collection

Prior to the start of data collection, a 3-day trainings on the SQUEAC survey methods was conducted to 10 data collectors by consultants using a SQUEAC training package based on the SQUEAC guidelines published by FANTA in 2012. The package presents the basic steps of conducting a SQUEAC assessment and teaches the required skills. The SQUEAC training was held at CIS office in Kass from 2–4 December 2013.



Qualitative and quantitative data were collected from different informants using different methodologies in line with SQUEAC guidelines (triangulation by source and method) as follows:

1. Data from patients' records: • Admissions • Cured • Defaulted • Died • Non responded Admissions MUAC • Defaulters • Transferred cases to inpatient care

2. Case histories from caretakers in OTPs, SFPs and SC: • Case detection • Local terms • Program uptake • Local informants (other HH members, THs, TBAs ) • Program messages • Distance estimation

3. Interviews and informal group discussions (IGDs) with clinic staff, program staff,: • Case detection • Geographical coverage • Disease calendar • Labour demand calendar • Local aetiologies • Local terms

4. Natural focus group discussions (NFGDs) with community members (in coffee shops), community leaders: • Knowledge about the program • Disease calendar • Labour demand calendar • Local aetiologies • Local terms

5. Observation: Data were summarized using mind-mapping which is a graphical way of storing and organizing data and ideas in tree structures organized around a central theme (see annex 2).

3.5. Nutrition Causal Analysis

The causal analysis study was conducted in two phases:

Phase One: Identification of possible risk/protective factors of SAM



The SQUEAC tools box was used to identify possible risk/protective factors of SAM in the programme target area and the generation of testable and falsifiable hypotheses regarding causation of severe acute malnutrition. This was done through in-depth interviewing of caretakers of SAM cases in the program and caretakers of non-covered SAM cases found in the community, interviewing medical assistants and CMAM program staff using semi structured questionnaires, and informal group discussions with traditional birth attendants (TBAs), traditional healers (THs) and village midwives (VMWs) who stated that SAM is possibly caused by diseases like diarrhoea, fever and ARIs, poor dietary diversity and poor IYCF practices and the female sex of the child. Accordingly, relevant indicators to be assessed were identified (see below) and previously tested data collection tools were used.

Phase two: Case Control Study

A matched case-control design was used as follows:

Sampling:

The sample was nested in the likelihood survey sample (see likelihood survey methodology above) in two stages: villages’ selection in stage one and a within-community sampling using active and adaptive case finding method in the second stage. This method allowed finding of all, or nearly all, current and recovering SAM cases in a sampled village out of which SAM cases and controls were included according to case and control definitions:

SAM case definition: a child 6 – 59 months of age with MUAC measurement <115mm and/or bilateral pitting oedema who is either not enrolled in the CMAM program or enrolled for less than 2 weeks and has lived in the selected village of the study for at least 2 months.

SAM control definition: a child of the same age that lives in the neighbourhood to the case child with a MUAC of ≥ 115mm and no bilateral pitting oedema.

Matching: Cases and controls were matched based on age and location (i.e. controls were of similar age and were neighbours of cases). Once a case was found, measured and his/her carer interviewed, two controls of similar age that were living in the neighbourhood were selected.

The sample size was thus all active and recovering SAM cases and 2 controls per case in the selected villages. 48 cases and 96 controls were found (2 controls per case).

Steps:

1. Collection of causal data using the SQUEAC toolbox: i. Case-histories from the carers of SAM cases in the program and from carers of non-covered

SAM cases found in the community.



ii. Other causal information from a variety of informants (e.g. medical assistants, TBAs, THPs, village leaders, &c.) using informal group discussions, in-depth interviews, and semi structured interviews.

2. Expressing findings as testable hypotheses: Based on the previous exercise hypotheses about causality of malnutrition were developed stating the possible causal relation between SAM and:

i. Diseases (particularly fever , diarrhoea and ARI) ii. Household dietary diversity (poor diversity probably related to SAM). iii. Poor infant and young child feeding practices (early introduction of fluids possibly before 6

months of age and time when foods were introduced). iv. Meal frequency for children. v. Sex of the child (females more prone to SAM)

3. Data collection, entry and analysis: Then data were collected from 6 villages and entered using Epi-Info software. Data were analyzed using R software and the MSDOS version of Epi-Info (v6.04d) and the cLogistic add-in software.



4. FINDINGS

4.1. Findings from Carers

Semi-structured interviews (including case histories and informal group discussions) with 72 carers of children attending all program OTP and SFP sites from 5–8 December 2013 were used to obtain detailed information on the mother's awareness and local knowledge on malnutrition (e.g. local terms), CMAM program (especially community outreach component), childhood diseases associated with malnutrition, treatment seeking behaviour, and other information. Simple structured interview questionnaires were administered to 14 carers of SAM cases outside the program and 5 carers of defaulted SAM cases.

A. Local Names for Malnourished Child

In this aspect, the information was obtained from carers and records, then cross-checked with other community members, clinic staff, TBAs, THs and SMOH. It was shown that the common local names for malnourished children varied according to location and their meanings are shown in table 1. These names are used in case finding.

Table 1: local names for the malnourished child

Name in Arabic

Name in English

Meaning Usage

Sou’ Taghzya Poor nourished Younger mothers سوء تغذية

Daeef Small/Thin Almost all ضعيف

Hobal Four Child identified by traditional healer as حبال فورmalnourished and being treated by fastening some tree leaves around his/her

Used rarely in camps, old women and men, THs &



neck, arms, and trunk. TBAs

Ma Endo Dam Anaemic THs & TBAs ما عندو دم

B. Location

The majority of children carers interviewed at the nutrition centres came from close villages (within a 2 hour walking distance which is approximately equal to 6–7 kilometres). Few children and carers came from more distant villages in rural Kass. This reflects the pattern of patchiness in coverage and access, and one reason beyond high defaulting.

C. Treatment Seeking Behaviour

Findings from carers’ interviews revealed that the most common source of healthcare was from the health clinics (staffed with Medical Doctors and Medical Assistant). The second common source is traditional healers especially in rural Kass with some degree of community belief in their ability to treat some diseases but not malnutrition. Cutting uvula is one measure done by THs towards malnourished child. The last used source of treatment is home-based remedies. The above findings show that there is some success on the community component to educate the community on where and how to seek healthcare.

D. Referral Sources

As shown in table 2, most of interviewed carers were referred by CBVs to the nutrition centres (57%), followed by self-referrals (18%). The remaining was referred by clinics staff and old nutrition beneficiaries. None were referred by TBAs or THs. These findings reflect a good pattern of referrals pathways. They also reflect that the community outreach component of CMAM is functioning well and the community is well sensitized due to relatively high percentages of self-referral and referral by old beneficiaries what is reflecting good community opinion about the programme. However the programme needs advocating to involve the THs and TBAs in the future for more detection and management of malnutrition cases.

Table 2: Percentages of Referral Sources among Interviewed Carers

Frequency Percentage

Self-referral 13 18%

Referral by old beneficiaries 7 10%

CBVs 41 57%

Clinic staff 11 15%

TBAs 0 0%

Traditional healers 0 0%



E. Waiting Time and Staff Behaviours

Feedback obtained from carers and also by observation during sessions showed obviously that the service time in the centres was acceptable to them. They reported being treated by staff with respect and in a friendly manner. This is more obvious in OTP & SFP sites. Waiting time is filled by education messages. These findings favoured positive opinion and supporting better coverage of the program.

F. RUTF Sharing

Carers of malnourished children totally denied behaviours of RUTF sharing with other children in the HH nor selling them in the market. It is acknowledged by SMOH representative in Kass that marketing was a widespread phenomenon previously; but they undertook several measures to minimize this (e.g. returning empty RUTF bags in the next session before giving the new bags). According to the representative, these measures got some success however it is still present according to feedback from clinics and program staffs and observation from the consultant who managed to buy some from some sellers for 3 SDG per bag. These findings sharing and marketing are supported by longer average length of stay (See Figure 9) because the malnourished child is not receiving the required amount of RUTF. These resulted in decreasing the coverage value by around 3 to 5% based on SQUEAC guidelines.

G. Barrier to Access and Rejection

Semi-structured interviews and structured questionnaires were administered to the mothers of malnutrition cases in OTP & SFP centres and in the communities to determine the reasons for non-attendance and beliefs of mothers on CMAM services. The vast majority of caretakers in the community reported that they didn’t know that their children are malnourished. However, all caretakers gave minor possible reasons for non-attendance as mother unaware of the CMAM Program and distance factor (in rural Kass). The intermittent RUTF stock-outs gave negative opinion within the community. Stock out of plumpy-nut (OTP) occurred 3 times in the period from July to September each 1-2 weeks. For WFP CSB-based mixtures (SFP) the supply interrupted several times during period of April to August 2013.

Malnourished children below 6 months can be treated only in the inpatient (SC) according to the CMAM protocol. Accordingly children were found to have been rejected from the OTP, and then referred back to the programme after the child had completed 6 months. Similarly, other children were rejected because they do not match criteria for admission, and referred back later after their malnutrition deteriorated. These are creating distrust among carers on admission criteria and staff capacity, what lead to negative opinion about the program. The problem is that the program staffs were not giving appropriate explanation to such carers when rejected or referred to other program component.

4.2. Findings from Community Key Informants

Community Key informants (9 community leaders, 4 TBAs, 3 traditional healers and 7 community members) were interviewed from around all OTP and SFP sites as individuals and in groups. General



knowledge about malnutrition and CMAM programme, treatment seeking behaviours and disease and labour demand calendars was assessed.

Regarding awareness about CMAM programme, the majority of informants (totally apart from TBAs & THs) know the program very well and are involved in joint committees and also in activities such as defaulter tracing. CIS, through Mobadiroon Organization helped in organizing these committees and has conducted advocacy awareness activities on this regard. The informants knew features of malnutrition using local terms (see Table 2 above). Most of key community members thought that the main causes of malnutrition were childhood diseases and poor breastfeeding.

Table 3: A story from the community

Just few roads from Kifah (Humedica) Health centre and Dawagin OTP, Shiekh Abdelwahid Ahmed Mohamed (traditional healer) and his wife Kaltam Basher Osman (traditional birth attendant) live in their simple house. Two women attending the ANC in the HC accompanied the consultants to their house and left before we knock the door. The Shiekh and his wife received the consultants in welcome and accepted to be interviewed. Both of them have never heard about CMAM programme and never received any kind of formal training. They identified malnourished children using local names. They see sick people from outside Kass town (Singita, Kalmo, and Southern Kass in general) and from inside Kass (Janobya and Baitary mainly). Interestingly, both of them are willing to be trained and interact with both formal health and nutrition structures if they offered the chance.

Regarding treatment seeking behaviour, the mothers seek treatment from health clinics and nutrition centres, and they seek treatment behaviours from THs only when formal health care fails to treat the child.

Regarding seasonal labour demand calendar, household members (esp. women) are recruited to work in agriculture activities (from land preparation until harvest) during rainy season (July – November). Often, these restrict the time available for activities such as clinic attendance (see figure 6 and 14).



4.3. Findings from Community Based Volunteers (CBVs)

Informal group discussions were conducted with the community based volunteers. There are 50 CBVs involved in the programme attached to the centres as follows; 21 to Dawagin, 15 to Abdel Jabar, and 14 to EPI. They were asked about referrals from the community. The discussion revealed that they are aware of their roles, and are working only in the closest villages due to distance and security situation especially in rural Kass.

Regarding CBVs locations, they are active in Kass town, Kass camps, and closest villages to the health facilities. This finding is supported by carers’ feedback that most of them are from the closest areas and referred by CBVs (57%). CBVs conduct community awareness sessions bi-weekly and active case finding monthly using MUAC screening through home visits as well as defaulter tracing. CBVs were found to work and incentivised for 5 days per week.

4.4. Findings from Clinic Staffs

Staff from Kifah (Humedica) & Abdelgabar health centers and from Kass Hospital was interviewed. All of them received CMAM training (OTP/inpatient). Coordination and collaboration between the health centers and the nutrition centers is good especially in mutual referral of potential malnourished children with common diseases. All staff confirmed availability of routine medicines as well as using CMAM registry books.

Concerning seasonal disease calendar, information was obtained from Medical Assistants, Nurses, other program staff, triangulated by source and method from mothers and key informants (Sheiks, THAs and TBAs). They stated that most common illnesses among children under the age of 5 years were diarrhea, acute respiratory tract infections, fever, and eye infections. Seasonal patterns of these illnesses are shown in figure 6 and 14 below.

4.5. Findings from Observations

The CMAM protocol was well followed in the programme implementation considering all 4 components. In general the quality of documentation (registry books, cards) is good. Routine medicines and nutrition supplies were present during period of evaluation. Nutrition messages and appetite test are carried on to attending children and carers.

The personal hygiene of carers attending the centres was not very good. The environment inside the centres was clean but not in the near surroundings. Furthermore the latrine facilities in the centres were not very clean however water was supplied in all centres adequately. Waiting time was acceptable, and the centres staffs were trying to fill with educational messages.



4.6. Findings from Beneficiaries’ Records

Information was extracted by the data collection teams during the period 4-8 December 2013 when all available beneficiary follow up cards and registry books (a total of 4,168) in 1SC, 3 OTP and 3 SFP in Kass Locality were thoroughly examined and reviewed by the assessment teams to calculate CMAM performance indicators and to investigate program uptake by the community, program response to need and efficiency in treating malnourished children. The review of the records covered the period of 12 months (December 2012 – November 2013). Monthly Reports from CIS were availed and also examined. For the sake of this SQUEAC investigation and to maintain neutrality, we have used the data from beneficiary cards and registry books in this assessment.

4.1.1 Program Performance Indicators

As a first measure of the effectiveness of the CMAM program, standard program monitoring data was analysed and compared against SPHERE Standards for Therapeutic Feeding programs in rural areas. The analysis included three indicators: cured, default, and death rates for OTPs and the SC

4.1.1.1. Stabilization Centre:

One Stabilization Centre (SC) is run by CIS in the program area to treat severely malnourished children who have complications and/or poor appetite.

Registration books from Kass hospital SC were examined by assessment teams for the period of 1 year (December 2012 – November 2013). Reviewing performance indicators for this SC has shown good performance with all performance indicators reaching minimum SPHERE standards for SCs (see figure 2). Good compliance to the inpatient treatment is expected based on the high recovery rates and low default rates as follows:

Cured: 91.7 % (319 children): Within SPHERE minimum standards (> 80%).

Default: 1.7% (6 children): Within SPHERE minimum standards (< 15%). Defaulting was found to be affected by seasonal trends / patterns / behaviours.

Deaths: 5.7 % (20 children): Within SPHERE minimum standards (< 10%)



Non-response: 0.9% (3 children)

Figure 2 Program performance indicators in Kass SC over time (December 2012 – November 2013)

4.1.1.2. Outpatient therapeutic centres:

Treatment cards and registration books from all three OTPs were reviewed by assessment teams for the period of 1 year (December 2012 – November 2013) except for EPI OTP where the program started since August 2013.

Figure 3: Program performance indicators in three CIS CMAM OTP centres in Kass over time (December 2012 – November 2013)

A. Cured Rate The review of the routine program data in all 3 OTP centres revealed that cured rate for the year preceding this SQUEAC investigation i.e. from December 2012 to November 2013 for all centres



together was found to be 77.1% (1019 children) which is considered above the minimum Sphere standards for nutrition programs of 75% (Figure 3).

B. Defaulter Rate High defaulted rates were observed across OTPs with an overall defaulter rate for the period (December 2012 to November 2013) of 21.3% (281 children) which is well above SPHERE minimum standards (< 15%). Defaulting was found to be affected by:

• Frequent RUTF stock-outs starting in July, August, and September 2013 each for 1 or 2 weeks. • Seasonal trends or patterns with increased labour demand during agriculture season (Figure 4). • Distance was also among factors thought to have affected return of beneficiaries from admission

to cure (see home location of defaulters map below (figure 5).

Figure 4: Defaulters (OTPs and SC) and labour demand calendar



Figure 5: Home locations of Defaulters (OTPs and SC).

C. Death Rate

Low deaths rate of 0.2 % (3 children) was reported across the 3 centers during the period of December 2012 to November 2013. It is within SPHERE minimum standards (< 10%)

D. Non-Response Rate Reported non-response cases were 19 children across OTP centres at rate of 1.4%.

4.1.2. Admissions



Admissions in the OTP programs were analysed. Admissions were plotted against time to determine whether they reflected seasonal changes, the context and whether they revealed an increasing, decreasing, or flat trend.

Both MUAC and weight for height were used for admitting malnourished children into the CMAM program. Admissions based on weight for height constituted one half out of the examined new admissions 50.1% (384 children) recorded in the 3 clinics visited. That proportion of admission using (WHZ), although widely used in CMAM programs, is considered as a negative sign for program coverage. A considerable number of SAM children were admitted by oedema 3.7% (28 children), those children were not picked up by program outreach until late stage of the disease manifested by the development of oedema, pointing towards improper case finding.

i. Response to needs

CMAM program response to need was investigated by developing a seasonal calendar for diseases and looking at admissions and defaulter trends over time. As shown in figures 4 & 6, program admissions has increased during the hunger gap (April – August) and when Diarrhea and malaria peaked in the rainy season when the number of SAM cases were expected to rise, on the other hand admissions decreased at and post-harvest time and in the dry season (less diarrhea and malaria) when number of SAM cases decrease, this implies good program response to need.

SAM causality study has been carried out concomitantly with this SQUEAC investigation, and it was found that SAM development was associated with fever, ARIs and - to less extent - with diarrhea (see causality study results below).

Figure 6: Admissions Trend and Disease Calendar



ii. Program Recruitment (Admissions/MUAC) Plots of 341 MUAC readings at admission during the period from December 2012 to November 2013 – out of 1559 admissions in this period, admissions information of 753 children was found; 341 admissions by MUAC, 384 admission by WHZ and 28 admissions by Oedema - showed that half of the admissions at 109 – 110 mm and above, presence of middle (less than 105 mm), late (less than 90 mm), and critically late (less than 85 mm) admission (Figure 7) indicates poor case finding and recruitment which is linked to the absence of the CBVs in some villages/suburbs. Median admissions’ MUAC was found to be 109 – 110 mm.

Figure 7: MUAC on admission

iii. Admission by Location

The investigating team found that most of the SAM cases in all three centers came from Kass town/camps where most of the population of locality (around 70%) reside (see small study below). This gives Kass OTPs the chance for good performance and coverage with above 70% of population living within 6 Km from OTP sites. Beneficiaries have also come from villages located beyond 6 km from CMAM centre locations. Accordingly, although geographical coverage was poor with all three OTPs placed inside Kass town/camps, population coverage was high. See home location of program beneficiaries map below (figure 8)



Figure 8: home location of program beneficiaries

4.1.3. Duration of Treatment Episode The review of 658 records of the planned discharges in all three OTP centers indicated that the median duration of the treatment episode is 8 weeks (see Figure 9 and 10). The plotting of the LOS of the planned discharges showed that many children stayed for long periods before being cured what can lead to higher defaulting (see defaulters’ rates above).

Figure 10 below shows the proportion of above 8 weeks LOS among planned discharges in the three CIS OTPs, high proportions were observed in Abdegabar and EPI and lower in Dwagin.



Figure 9: Duration of Treatment Episode

Figure 10: Proportion of > 8 weeks LOS

4.1.4. Interface: Table 4: Interface and referrals in-between different programme centres (December 2012 – November 2013)

0

5

10

15

20

25

30

35

40

45

50

EPI Dwagin Abdelgabar

Prop

ortio

n of

LO

S >

8 w

eeks



Transferred children

Percentages (out of all discharges)

SC 7 2.0% OTPs 43 3.4% SFPs 216 8% i. Clinic to Outpatient Program: Interviews with health clinic staffs, CMAM program staffs and beneficiaries showed that malnourished children picked up in clinic were referred to the OTP/SFP. It was noted in all OTP centres that referred cases were not properly followed up until admitted in the OTP centre. This area needs more strengthening and followup. ii. Outpatient to Inpatient Program: SAM cases were referred where required from OTP sites to the SC centre, the followup in this area is better however there was poor followup of discharged cases from SC back to OTP. It is noteworthy to mention that the SC sometimes recieves cases from other OTP centres e.g Nertiti.

4.7. Small Study & Small Area Surveys

A. Small Study

Small study was performed to test the hypothesis of presence and distribution of population in the locality. Secondary data review (population from EPI data, WFP beneficiaries, and OCHA figures) and interviews with community leaders, executive administrator of Kass Locality, Government HAC representative in Kass, and Director of MOH-Nutrition Department revealed that 65 – 70% of the locality population are residing in Kass town and its IDPs camps. CBVs activities were also assessed and found to be highly active inside the catchment areas but not in remote villages.

B. Small Area Surveys:

The small area survey focused on potentially high and low coverage areas. A number of villages were selected according to the location in relation to program catchment areas of 6 Km around Kass town and presence of CBVs.

Three hypotheses were generated:

• Areas within 6 Km from program sites have high coverage. • Areas beyond 6 Km from program sites where there are CBVs have high coverage. • Areas beyond 6 Km from program sites where no CBVs are there have low coverage.

Three villages where selected in each category of 1 and three and 2 for category 2 and were distributed between three different survey teams.

Each team used an active / adaptive case-finding methodology to identify cases (MUAC < 115mm) that were either covered or not covered by the program.

The steps for testing a hypotheses / making a classification using SQUEAC small area survey data are:



(a) Set the standard (p): The standard (p) was set at 50% (SPHERE minimum standard for TFPs in rural settings) for the surveys reported here.

(b) Carry out the small area survey.

(c) Use the total number of cases found (n) and the standard (p) to calculate the decision rule. For example, if n = 9 and p = 50% then:

d = �𝑛 ∗ 𝑝100

� = �9 ∗ 50100

� = ⌊4.5⌋ = 4

(d) Apply decision rule: If the number of cases in the program is > d then coverage is classified as good (otherwise it is classified as bad).

Scenario 1: A total of 32 cases were identified out of which 30 were found to be in the program. Coverage was classified as > 50%.

Scenario 2: A total of 10 cases were identified out of which 8 were found to be in the program. Coverage was classified as > 50%.

Scenario 3: A total of 6 cases were identified out of which no one was found to be in the program. Coverage was classified as < 50%.

The small area surveys confirmed all three hypotheses about spatial coverage, coverage high in close areas and in beyond 6 Km where there are CBVs and low elsewhere. It was concluded that close distances and presence of active CBVs positively contributed to Kass CMAM program coverage, were as big distances and absence of CBVs were among factors negatively affecting it.

4.8. TFP Prior Estimation

Using the Bayesian approach, all previous information was used to make an informed guess about the

most likely coverage value and express it as a probability density (Prior). Several estimates of the prior

were calculated using the following information and findings:

• Positive and negative findings in the mind-map to “build up” from zero (i.e. lowest possible) and to “knock down” from 100% (i.e. highest possible) coverage respectively.

• Scores or weights of mind-map findings. • Proportion of population living within the catchment of the CMAM sites. • Proportion of health centres covered with CMAM services out of all HCs.



Average from all these estimates and upper and lower expected values around coverage were then

calculated. Prior coverage of 27 % (not lower than 10% and not higher than 50 %) was suggested. The

Prior was described using the probability density Alpha prior = 19.0 and Beta prior = 10.7 in

BayesSQUEAC software (Figure 11).

Figure 11: Prior was described using the probability density Alpha = 19.0 and Beta = 10.7



4.9. SQUEAC Likelihood Survey

A quantitative wide area survey was conducted in the accessible parts of the intended program area (Figure 12) during the period 10-11 December 2013 to estimate the likelihood coverage. Sample size was 41 villages (see the methodology)

Figure 12: Accessible areas during assessment period

Final Coverage Estimation

Point estimation of the program was used in light of the information availed from SQUEAC investigation phases 1 and 2, where we found long lengths of stay (see LOS and figure 9 above) and below average case finding and recruitment (see admissions and figures 2 and 3 above).



Using a Bayesian Beta-Binomial Conjugate Analysis performed using the SQUEAC Coverage Estimate Calculator (BayesSQUEAC.app) that combines the prior information about coverage and the likelihood results from the wide area survey conducted in the third stage of this investigation (Figure 13), overall CMAM point program coverage was estimated to be 69.3% (95% Credible Interval from 58.0% - 78.7% z-test: z = -0.63, p = 0.5269).

Figure 13: TFP Coverage estimation in Bayes SQUEAC

4.10. SFP coverage

4.10.1. SFP routine data Survey teams checked treatment cards and registration books from all three SFPs n for the period of 1 year (December 2012 – November 2013) Cured: 80.3 % (2005 children). It is above SPHERE minimum standards (> 75%). Default: 18.7% (466 children). It is well above SPHERE minimum standards (< 15%). In addition to the above mentioned factors affecting CIS CMAM program retention of cases from admission to discharge, it was found that ration stock-out that took place from April to August 2013 had caused steady rise in



defaulter rate of SFP beneficiaries (figure 14). This negatively impacted program performance hence program coverage. Deaths: 0 % which is within SPHERE minimum standards (< 3%) Non-response: 1.1% (27 children).

Figure 14: SFP programme indicator and stock-out

4.10.2. SFP program response to need:

Similar to TFPs, Plots of SFPs’ admissions over time against disease calendar has shown good program response to need with more admissions during the hunger gap and with elevated levels of illnesses (diarrhoeas and fever).

Figure 15: MAM Admission (SFP) over time with disease calendar (December 2012 – November 2013)

4.10.3. SFP Prior Estimation



Using the Bayesian approach, an informed guess about the most likely coverage value and express it as a probability density (Prior) was estimated as follows:

Total Kass locality population estimate : 275483

Proportion of children (6-59 months) : 16%

MAM : 5.1% (95% CI = 3% - 8.7%)

SFP beneficiaries : 3208

Expected cases:

N : 275483 * 0.16 * 0.03 * 2.6 = 5845

Lower C.I. : 275483 * 0.16 * 0.051 * 2.6 = 3438

Upper C.I. : 275483 * 0.16 * 0.087 * 2.6 = 9970

An indirect coverage estimate is:

P value : 3208 / 5845 = 55%

Lower C.I. : 3208 / 9970 = 32%

Upper C.I. : 3208 / 3438 = 93%

The prior was further informed by population coverage (around 75%) and an average from tow and upper and lower expected values around coverage were then calculated. Prior coverage of 65% ± 20% was suggested. The Prior was described using the probability density Alpha prior = 22.6 and Beta prior = 12.7 in BayesSQUEAC software (Figure 16).



Figure 16: Prior was described using the probability density Alpha = 22.6 and Beta = 12.7

4.10.4. SQUEAC Likelihood Survey

A quantitative wide area survey was conducted in the accessible parts of the intended program area (Kass locality) using the same sampling frame for TFP likelihood survey during the period 16-17 December 2013 to estimate the likelihood coverage (see the methodology).

Sample size of 37 cases was enough, however it was guaranteed that more MAM children will be found (the prevalence of MAM is higher than SAM prevalence). Actually 106 MAM cases were found of which 70 were covered.

4.10.5. Final SFP Coverage Estimation



Using a Bayesian Beta-Binomial Conjugate Analysis performed using the SQUEAC Coverage Estimate Calculator (BayesSQUEAC.app) that combines the prior information about coverage and the likelihood results from the wide area survey conducted in the third stage of this investigation (Figure 17), overall SFP point coverage was estimated to be 65.8% (95% Credible Interval from 57.7% - 73.1% ).

z-test: z = -0.12, p = 0.901 .

Figure 17: SFP Coverage estimation in Bayes SQUEAC

4.11. Barriers and Boosters Affecting CMAM Program Coverage

Upon questioning caretakers of non-covered children, major barriers and boosters affecting CMAM program coverage are shown in table 5 below. The barriers are weighted according to caretakers responses are shown in the figure 18 below:



Table 5: Barriers and boosters to CMAM coverage

Barriers Boosters • Mother did not recognize her child was

malnourished • Distance (program site too far away) • Mother did not know about the program

• CMAM services are free • Availability of supplies (RUTF) and medicines • Mothers’ recognition when a child is faring

well because of the treatment • Healthy and supportive staff relationships

with patients at centres and home visit

Figure 18: CIS CMAM program coverage barriers

4.12. Nutrition Causal Analysis

The independent association between risk factors and SAM was tested using conditional logistic regression with backward elimination models. Few meals, fever and acute respiratory tract infections were found to be strongly associated with SAM causation in Kass area (Table 6). Odd’s ratios were 0.2, 11.4 and 10.7 respectively. Weak association between diarrhoea and SAM was also observed with Odd’s ratios of 3.6 (p-values 0.0544). Table 6: Risk factors and association with severe malnutrition

Factor Odds ratio

95% Confidence limits P-value

Lower Upper

Meal Frequency 0.1604 0.0553 0.4651 0.0000

0 5 10 15 20 25

Mother did not know about the program

Distance (program site too far away)

Mother did not recognize her child wasmalnourished



Fever 11.3886 1.5791 82.1372 0.0015

ARIs 10.7049 1.4254 80.3936 0.0053

Diarrhoea 3.6211 0.8474 15.4745 0.0544

Odd’s Ratios: Meal frequency: Increasing Meal frequency has a protective effect against SAM. An increase of one meal/day reduces the risk of SAM 0.16 times. Fever: Children who have fever are 11 times more prone to SAM compared to the others who didn’t have fever in the prior 2 weeks. ARIs: Children who have ARI in the prior 2 weeks are almost 10 times more prone to SAM compared to the others who didn’t have ARI. Diarrhoea: Children who have diarrhoea in the previous 2 weeks are 3 times more prone to SAM compared to the others who didn’t have the disease. Time of introduction of fluids and solid foods, sex of the child, household dietary diversity score and breastfeeding practices were found to have no independent effect on the causation of SAM.



5. CONCLUSION AND RECOMMENDATIONS

5.1. Conclusion

The CIS SDEARP (Nutrition) is supporting SMOH in implementing CMAM protocol through 1 SC, 3 OTPs, and 3 SFPs in Kass Locality, South Darfur to serve severely malnourished children.

A thorough investigation of the existing CMAM program in Kass using SQUEAC tools showed point coverage of 69.3% (95% Credible Interval from 58.0% - 78.7%) for TFP and 65.8% (95% Credible Interval from 57.7% - 73.1%) for SFP.

Since the community in Kass is a mixed community (IDPs and residents), these figures cannot be compared objectively with SPHERE Minimum Standards, which are 50% for rural areas and 90% for IDPs camps. However, they lie midway between these cut-offs and also the performance indicators are within the SPHERE Minimum Standards with the exception of defaulter rates.

As mentioned, the high defaulter rate requires attention. It is highly related to labour demand season and also to coinciding stock out.

Other negative aspects of the program include lower coverage in remote rural villages, high defaulter rates, long length of stay among children who were retained in the program until recovery, and late detection of cases (low admission MUAC figures) and also high percentage of admission by Z-score which are against higher coverage.

The community outreach CMAM programme component (CBVs) is functioning in inside Kass town but low in remote villages. The use of key community figures such as traditional healers, Imams and TBAs is ignored in this programme and should be addressed.

Experience from other CMAM programmes in the region has proven that it is possible to deliver the service at high coverage especially if well integrated within the health system. CIS has achieved to a great extent to do this integration, what explains that the current coverage figures are higher than similar programmes in the region although of the high default rate.

In Kass, malnutrition (SAM) was found to be related to few meals, fever illnesses, and acute respiratory tract infections. Programme activities (e.g. health promotion and nutrition messages) can be reoriented to address these risk factors to help reducing the incidence of SAM.

5.2. Recommendations


Strengthening absentees and defaulter tracing activities to address high defaulter rates especially during rainy seasons

CIS/SMOH/Community Immediately

Strengthening management and adherence to CMAM protocol (esp. regarding interface and referral pathways) at SC level

CIS/SMOH Immediately

Involvement and training of the THAs, TBAs, VMWs to be active and committed community members.

CIS/SMOH Immediately




Increasing geographic coverage of CMAM program in the short term by expanding to the remote uncovered villages where SAM cases were found (e.g. Singita)

CIS/SMOH As soon as

possible

Coordinate with different Governmental authorities and UNICEF & WFP to sort out CMAM supplies stock out, transport, and security stocks in Kass level, etc.

CIS/SMOH As soon as

possible

Continued orientations, awareness raising and community mobilization of the communities regarding the CMAM program and involve the community leaders, Sheiks, Imams and other key community informants

CIS/SMOH Continuously

Carry out regular joint monitoring, and supportive supervision visits using CMAM and SQUEAC tools and including interviews with beneficiaries.

CIS/SMOH Monthly

Address root causes of fewer meals, fever illnesses and acute respiratory tract infections, and advocate with other relevant sectors and actors (health and food security) to minimize its burden

CIS As soon as

possible



APPENDICES

Appendix 1 MAP of Kass CMAM Programme Geographical Coverage



Appendix 2 Mind Map



Appendix 3 Causal Analysis Questionnaire

الحاد الشديد دراسة أسباب سوء التغذيةاستبيان تمأل لألطفال المصابين بسوء التغذية الشديد الحاد خارج البرنامج أو داخل البرنامج لمدة التزيد عن زيارتين. • .مقابل كل حالة سوء تغذية controlsأطفال معياريين 2تمأل استمارة ل •

_________التاريخ_______ _______ فريقال م______ رق_______:_ /المعسكرقريةال

__________بالشهور العمر ) M / F ( النوع ____________________اسم الطفل

= نعم 1= ال 0 :الورم ________________________(ملم ) المواك

control معيارية حالة = SAM case 2 حالة= 1 نوع الحالة:

:استبيان االمراض -القسم األول 4إذا ( ال ) انتقل للسؤال رقم نعم/ال هل اصيب طفلك باالسهال خالل االسبوعين الماضيين ؟ .1

لو كانت االجابة بنعم ماهى الفترة الزمنية التى استمر بها االسهال ؟ .2

يوم 7> يوم 7-4 يوم 1-3

االسهال ؟ هالسوائل لطفلك خالل فترة اصابتية وذهل استمريتى بتقديم االغ .3

(سوائل فقط) ال نعم (أطعمة فقط)نعم (أطعمة و سوائل) نعم

نعم/ال هل اصيب طفلك بالحمى خالل االسبوعين الماضيين ؟ .4 نعم/ال ) خالل االسبوعين الماضيين ؟التنفسى (كحة مع صعوبة التنفسهل اصيب طفلك بااللتهاب .5

الفطام -ثانيالقسم ال سوائل ؟ ____________ شهر الفي أي سن بدأت ادخال .6 ______ شهر (أي صلبة أو شبه صلبة) مع الرضاعة ؟ في أي سن بدأت ادخال األطعمة التكميلية .7

12إذا (نعم) انتقل للسؤال رقم نعم/ال هل الطفل يرضع حاليا؟ .8 ___________ شهر رضعت طفلك ؟ألمدة كم شهر .9

؟أوقفت رضاعة هذا الطفل لماذا .10 ...................... الطفل اكمل الرضاعة ............الحمل ....................................

رفض الطفل ................................ .........لبن االم .............ال يوجد ما يكفي من

عمل االم .................................... ..........................................مرض األم

ال أعرف ................................... أخري (حدد) ........................................

(أستمع لألم ثم اختار من أسفله) كيف فطمت طفلك ؟ .11 ال أعرف تدريجي مفاجئ

ID:



التغذية – ثالثالقسم ال ساعة الماضية (ال تشمل الرضاعة الطبيعية)؟ 24كم عدد الوجبات التي تناولها هذا الطفل خالل ال .12

________ وجبة

ة الماضية:ساع 24خالل الاالسرة لألصناف التالية عن استهالك ةتمال هذه االستمار .13 امس يوم ده الزمن نفس مناسرتك في االن اريد ان اسئلك عن انواع االكل اللذي اكلتيهو اواى واحد

)الحبوب أو (أسأل عن األسماء المحلية ألنواع األكلاالن المقابلة زمن حتي ، لحومبسكويت، الذرة أو القمحمكرونة، الرغيف، األرز، ، العصيدة

A................................................................................ |___| البطاطس، البامبي، الجزر أو أي أكل يصنع من الجزور أو الدرنات

B................................................................................|___| البامية الطماطم، البصل األخضر، الجرجير، الخدرة؟ أي نوع من الخضروات

C................................................................................|___| الموز، البرتقال ، المانجو ، الجوافة و البطيخ؟ الفواكهأي نوع من

D................................................................................|___| البقر، الضان ، الغنم، الدجاج، الطيور ، األرانب أو أي لحم بري؟ أنواع اللحوم

؟لحوم جافة أو طازجةE ................................................................................|___|

؟البيضF................................................................................|___|

؟االسماك طازجة أو جافةG................................................................................ |___|

، دكوة مثل الفاصوليا، العدس ، البسلة و الفول ؟أي أكل مصنوع من البقوليات H................................................................................|___|

الزبادي، الجبنة ،الروب أو اللبن الحليب I ................................................................................|___|

الزيت أو السمنJ ................................................................................|___|

السكر و العسلK................................................................................|___|

أي أكل أو شراب اخر مثل الشاي و القهوة L ................................................................................|___|

الويكة، الصلصة، البصل الجاف و الخدرة الجافةM ................................................................................|___|

south darfur emergency assistance program (nutrition) · international, switzerland (cis) in kass...

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