Birnin-Magaji-SQUEAC-Report_Final

Semi-Quantitative Evaluation of Access and Coverage (SQUEAC)
Birnin Magaji LGA’s CMAM programme
Zamfara State, Northern Nigeria
June 2014
Adamu Abubakar Yerima, Ayobami Oyedeji, Salisu Sharif Jikamshi and Ode Okponya Ode
Save the Children International

ii
Acknowledgement
The SQUEAC assessment in Birnin Magaji LGA was accomplished through the generous support of Children
Investment Fund Foundation (CIFF). Special thank goes to the Federal Ministry of Health (FMOH), Zamfara State
Ministry of Health (SMOH), Birnin Magaji Local Government Area (LGA) and Birnin Magaji LGA Health Facilities’
Staff and of prominent note, the Permanent Secretary, Director Primary Health Care & State Nutrition Officer of
Zamfara State (SMOH), the Chairman and Director PHC of Birnin Magaji LGA for their collaboration in the
implementation of the SQUEAC1
assessment in the state.
Our profound gratitude goes to the care givers and various community leaders for sparing their precious time and
opening their doors to the SQUEAC team without which this investigation would not have been a reality. Finally
and most importantly, we wish to appreciate the technical support of Adaeze Oramalu (Nutrition Advisor) and
Lindsey Pexton (Senior Nutrition Adviser, Save the Children International), Joseph Njau of ACF International and
Coverage Monitoring Network (CMN) in analyzing the data and compilation of this report.
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Semi Quantitative Evaluation of Access and Coverage

iii
Table of contents
Abbreviations...............................................................................................................................................................vi
Executive summary....................................................................................................................................................vii
1. Introduction........................................................................................................................................................ 9
2. Objectives......................................................................................................................................................... 10
3. Methodology.................................................................................................................................................... 10
3.1. Stages of SQUEAC of Birnin Magaji LGA CMAM program ........................................................................... 11
4. Results and Findings......................................................................................................................................... 15
4.1. Stage 1 : Identification of barriers and boosters and of potential areas of high or low coverage ............ 15
4.1.1. Routine monitoring data and individual OTP cards................................................................................. 15
4.1.2. Qualitative data ........................................................................................................................................ 23
4.1.3. Boosters, Barriers and Questions (BBQ) .................................................................................................. 25
4.1.4. Concept Map............................................................................................................................................. 26
4.2. Stage 2: Confirmation areas of high and low coverage........................................................................... 26
4.2.1. Small area survey.............................................................................................................................. 26
4.2.2. Barriers of the small area survey...................................................................................................... 27
Table 4: Barriers of the small area survey......................................................................................................... 27
4.3. Stage 3: The coverage estimate (application of Bayesian Theory) ......................................................... 28
4.3.1. Development of Prior ....................................................................................................................... 28
4.3.2. Likelihood.......................................................................................................................................... 34
4.3.3. Posterior............................................................................................................................................ 36
5. Discussion......................................................................................................................................................... 37
6. Conclusion ........................................................................................................................................................ 38
7. Recommendations ........................................................................................................................................... 39
Annex 1: Itinerary of the mission............................................................................................................................. 41
Annex 2: List of participants .................................................................................................................................... 42
Annex 3: Seasonal calendar...................................................................................................................................... 43
Annex 4: Survey Questionnaire for caregivers with cases NOT in the programme ............................................... 44
Annex 5: Pictures of Concept map carried out by the two teams .......................................................................... 55
Annex 6: Barriers to program access and uptake-small area survey...................................................................... 56
Annex 7: Wide are survey results............................................................................................................................. 57
Annex 8: Summary of qualitative findings............................................................................................................... 58

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List of figures
FIGURE 1: MAP OF BIRNIN MAGAJI LGA SHOWING LOCATION OF WARDS AND OTP SITE .................................................................................9
FIGURE 2: STAGES OF SQUEAC...........................................................................................................................................................11
FIGURE 3: PROCESS OF COLLATION ENTRY AND ANALYSIS OF THE ROUTINE AND OTHER PROGRAM DATA .............................................................11
FIGURE 4: NUMBER OF ADMISSIONS BY HF FROM ROUTINE DATA EXTRACTION..............................................................................................15
FIGURE 5: NUMBER OF ADMISSION BY HF FROM LGA DATA......................................................................................................................16
FIGURE 6: PROPORTION OF ADMISSIONS BY LGA OF CASES........................................................................................................................16
FIGURE 7: ADMISSION TREND OVER TIME FOR THE LGA PERFORMANCE DATA AND ROUTINE DATA EXTRACTED ....................................................17
FIGURE 8: ADMISSION TREND AND SEASONAL CALENDAR ..........................................................................................................................17
FIGURE 9: ADMISSION TREND BY HF.....................................................................................................................................................18
FIGURE 10: THE PLOT ADMISSION MUACS SHOWING THE MEDIAN ADMISSION MUAC IN BLACK .....................................................................18
FIGURE 11: A HISTOGRAM REPRESENTATION OF THE LOS WITH MEDIAN LOS SHOWN BY ARROW IN BLACK ........................................................19
FIGURE 12: A BAR CHART REPRESENTATION OF EXIT MUAC......................................................................................................................19
FIGURE 13: PIE CHART REPRESENTATION OF PROGRAM INDICATORS FOR THE PERIOD JAN 2013 TO MAY 2014 (A) ROUTINE PROGRAM DATA AND (B)
MONTHLY LGA PERFORMANCE DATA...........................................................................................................................................20
FIGURE 14: TRENDS OF PERFORMANCE INDICATOR FROM THE ROUTINE DATA...............................................................................................20
FIGURE 15: DISAGGREGATION OF RECOVERY RATE BY HF.........................................................................................................................21
FIGURE 16: DISAGGREGATION OF NON-RECOVERED RATE BY HF................................................................................................................21
FIGURE 17: HISTOGRAM REPRESENTATION OF NUMBER OF VISITS AT DEFAULT..............................................................................................22
FIGURE 18: A BAR CHART PRESENTATION OF MUAC AT DEFAULT...............................................................................................................22
FIGURE 19: SHOWS DISTRIBUTION OF DEFAULTERS BY LGA.......................................................................................................................23
FIGURE 20: OBSERVED VS. EXPECTED TIME TO TRAVEL PLOT ....................................................................................................................23
FIGURE 21: BARRIERS TO ACCESS FOUND IN SMALL AREA SURVEY................................................................................................................28
FIGURE 22: PRIOR MODE FOR BELIEF HISTOGRAM ..................................................................................................................................29
FIGURE 23: TRIANGULATION OF DIFFERENT PRIORS TO GIVE THE PRIOR MODE..............................................................................................33
FIGURE 24: THE PLOT OF THE PRIOR MODE IN BAYES SQUEAC CALCULATOR SHOWING SUGGESTED SAMPLE SIZE CIRCLED IN RED ..........................33
FIGURE 25: BARRIERS TO PROGRAM ACCESS AND UPTAKE DURING WIDE AREA SURVEY ..................................................................................35
FIGURE 26: FINAL RESULT OF THE COVERAGE-BINOMIAL CONJUGATE ANALYSIS .............................................................................................36

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List of tables
TABLE 1: SHOWS THE DISTRIBUTION OF HEALTH FACILITIES BY WARD AND THE PROPORTION OF HF HAVING OTP SERVICES ...................................10
TABLE 2: BARRIERS AND BOOSTERS FOUND DURING THE QUALITATIVE DATA COLLECTION WITH THE SOURCE AND METHODS ...................................25
TABLE 3: RESULT OF SMALL AREA SURVEY .............................................................................................................................................27
TABLE 4: BARRIERS TO ACCESS AND UPTAKE FOUND AT WIDE AREA SURVEY .................................................................................................35
TABLE 5: WIDE AREA SURVEY RESULTS...................................................................................................................................................57

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Abbreviations
ACF Action Contre La faim/Action Against Hunger International
CIFF Children Investment Fund Foundation
CMAM Community-based Management of Acute Malnutrition
CMN Coverage Monitoring Network
CV Community Volunteers
FMOH Federal Ministry of Health
HF Health Facility
HW Health Worker
LGA Local Government Area
MUAC Mid-Upper Arm Circumference
NGO Non-Governmental Organization
NFP Nutrition Focal Person
NPHCDA National Primary Health Care Development Agency
OTP Outpatient Therapeutic Programme
RC Recovering Case
RUTF Ready to Use Therapeutic Food
SAM Severe Acute Malnutrition
SC Stabilization Centre
SCI Save the Children International
SLEAC Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage
SMART Standardized Monitoring Assessment of Relief and Transitions
SMOH State Ministry of Health
SNO State Nutrition Officer
SPHCDA State Primary Health care Development Agency
SQUEAC Semi-Quantitative Evaluation of Access and Coverage
UNICEF United Nations Children's Fund
VI Valid International

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Executive summary
Birnin Magaji Local Government Area (LGA) is one of the Local Government Areas supported by UNICEF in the
implementation of the Community Management of Acute Malnutrition program (CMAM) program since 2009.The
result of the Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) survey in Birnin
Magaji (LGA) revealed a low classification of coverage2
for the CMAM program. The SQUEAC investigation was
carried out as a follow-up to the SLEAC assessment conducted in November, 20133
by Valid International (VI) in
collaboration with National Bureau of Statistics (NBS) and the Federal Ministry of Health (FMOH). The coverage
assessment is funded by Children Investment Fund Foundation (CIFF).
In order to investigate the CMAM program in the LGA, quantitative data were extracted from the individual OTP
cards from 5 Outpatient Therapeutic Program (OTP) sites4
while the qualitative information were collected from
the communities which form the catchment population of these sites. The information obtained from this
investigation was continuously analyzed to yield the barriers and boosters5
that affect the CMAM coverage.
Qualitative information was collected from various sources6
using various methods7
. The information gathered
was triangulated for consistency and were used as evidence of the findings in this SQUEAC assessment.
The CMAM program performance indicators revealed that the recovery and the defaulter rates did not meet the
minimum SPHERE standards8
. The trend of the recovery rate was consistently below 75% while, defaulter rate
was above 15% except death rate which was within the SPHERE minimum standard. However, the LGA
performance data (January, 2013 – May, 2014) shows recovery rate that meets the SPHERE standards and a
reducing defaulter rate (mostly 0% and only once above 15%). The upward trend of the recovery and defaulter
rate in the LGA data is contradictory to data from the routine data and may be attributed to under reporting. The
analysis of the routine program data reveals that the proportion of the recovered cases was 32.87%%, defaulting
cases was 63.87% which when compared SPHERE standards are low and high respectively. It is important to note
that due to lack of sufficient information of the exit data and lack of data tools “Hidden Defaulters” had not been
identified in the exits data.
The analysis of the Mid-Upper Arm Circumference (MUAC) measurement at admission9
yielded a median MUAC
at admission of 109mm which is indicative of late treatment seeking behavior. Furthermore, the histogram
representation of the data reveals a long right tail indicative of late admissions (admission when the condition is
critical).
The SQUEAC investigation identified the following factors affecting coverage; Boosters: High number of self-
referral, peer to peer referral, Support from key community figures, Good awareness about the program, Good
client/staff relationship, RUTF is well accepted, Good interface between Health Workers and the Community.
Barriers: Alternative health practitioner preferred, Husband refusal, Distance, Long waiting times, RUTF Stock-
out/break, Poor delivery of service, Rejection (Previous), RUTF sales in the community, Poor out-reach activities,
2
Less than 20%
3
Chrissy B., Bina S., Safari B., Ernest G., Lio F. & Moussa S.; Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage
(SLEAC) Survey of Community-based Management of Acute Malnutrition program; Northern States of Nigeria-(Sokoto, Kebbi, Zamfara,
Kano, Katsina, Gombe, Jigawa, Bauchi, Adamawa, Yobe, Borno). Valid International. February 2014
4
Birnin Magaji, Gora, Nasarawa Godel, Modomawa and Gusami HFs
5
Barriers refers to the negative factors while boosters refer to the positive factors that affect the CMAM program and coverage.
6
caregivers (in the program and not in the program), community leaders, community members, health workers, and the State and LGA
nutrition officers
7
Semi-structured interview, informal group discussion and in-depth interview
8
SPHERE standards define the minimum performance of a Therapeutic Feeding Program (TFP) in emergency setting. Thus recovery rate of
>75%, death rate of <10% and defaulter rate of <15%
9
Measurement of the MUAC at admission is a strong indicator of late/early detection as well as treatment seeking behavior and the
effectiveness of community mobilization activities.

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Stigma, Seasonal barriers, Stock-out of routine drugs, Poor program monitoring, Lack of awareness about
malnutrition, Shortage of trained health workers on CMAM program, Stock-out of data tools and Insecurity.
The findings from the SQUEAC investigation reveal a coverage estimate of 29.1% (22.9%– 35.9% CI; 95%) and z-
test p-value of 0.9328 which implies that there is no Prior-Likelihood conflict.
The following actions were recommended for the program improvement:
o Improving community mobilization & sensitization to bring active community participation and
ownership
o Retraining of all health workers on CMAM and training additional health workers especially from none
OTP health facilities to make-up the numbers on OTP day
o Improve and make effective the system for the delivery of inputs (RUTF) and routine drugs from the state
level to the OTP site in the LGA
o Expanding the CMAM program to all wards (at least one OTP center per wards) and recruitment/training
of all CVs
o Retrain and provide Motivation/Incentives to Community Volunteers
o Improving CMAM data monitoring system & integrating SQUEAC methodology in Birnin Magaji LGA
(establish up to date data base for all admission at the OTP center (conduct DQA) and an effective referral
system)

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1. Introduction
Zamfara state located in Northwestern Nigeria was created from old Sokoto state on 1st
October, 1996. It shares
an international border with the Republic of Niger to the north and interstate boundaries with Katsina State to
the east, Sokoto State to the west, and Kebbi and Niger states to the south, it lies at latitude 12° 10’N and
longitude 6° 15’E and is subdivided into 14 administrative units call the LGA10
. It has an estimated population of
4,269,513 (2014 estimate)11
and covers a total area of 35,711Km2
.
Zamfara State Ministry of Health (SMOH) with support from UNICEF started implementing Community-based
Management of Acute Malnutrition (CMAM) with opening of 5 Stabilization Centers (SCs) in 2009 and then OTP
sites in Birnin Magaji LGA in 2010. This was scaled-up to 3 LGAs in 2012 with inclusion of Maradun and Tsafe, then
finally to 6 LGAs in 2012 through scaling-up in 3 LGAs of Bakura, Bungudu and Shinkafi supported by Save the
Children under WINNN program a DFID funded project. The CMAM geographic coverage is 42.86% with 6 out of
14 LGAs offering CMAM services.
Birnin Magaji LGA is the first LGA in the state to start operating an OTP site and is among the 3 UNICEF supported
LGAs. It is located at latitude 12°33′00″N longitude 6°49′00″E and has an interstate boundary to the east
bordering Katsina state, Kaura Namoda LGA to the west, Gusau LGA to the south and Zurmi LGA to the north. It
has an estimated population of 85,403 and a total of area of 1,173 km². It is subdivided into 10 distinct
administrative areas or wards namely; Birnin Magaji, Damfami/Sabon Birni, Gusami Hayi, Nasarawa Godal, Gora,
Modomawa West, Modomawa East, Nasarawa Mailayi, Nasarawa Godal East and Gusami Gari with 5 wards
having Health Facilities (HF) that serves as OTP centres and the remaining 5 with none (see Figure 1 below for
details)
Figure 1: Map of Birnin Magaji LGA showing location of Wards and OTP site
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Anka, Bakura, Birnin Magaji, Bukkuyum, Bungudu, Tsafe, Gumi, Gusau, Kaura Namoda, Maradun, Maru, Shinkafi, Talatan
Mafara and Zurmi LGAs
11
Based on 2006 census

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There are 42 HFs in the LGA out of which only 5 are offering OTP services with the remaining 37 as non-OTP HFs
thereby giving a Geographical Coverage of 11.90% based on the proportion of HF (see Table 1 below for details).
Table 1: Shows the distribution of Health Facilities by ward and the proportion of HF having OTP services
S/No Wards CMAM Services HF with OTP HF without OTP Total Proportion
1. Birnin Magaji Yes 1 2 3 33.33%
2. Gora Yes 1 5 6 16.67%
3. Gusami Hayi No 0 4 4 0.00%
4. Gusami Gari Yes 1 2 3 33.33%
5. Nasarawa Mailayi No 0 6 6 0.00%
6. Nasarawa Godel West Yes 1 2 3 33.33%
7. Nasarawa Godel East No 0 5 5 0.00%
8. Damfani/Sabon Birni No 0 6 6 0.00%
9. Modomawa East No 0 3 3 0.00%
10. Modomawa West Yes 1 2 3 33.33%
Total 5 5 37 42 11.90%
The main challenge with the investigation in the LGA is insecurity. The LGA is plagued by the activity of cattle
rustlers with 3 out of the 10 wards (Gusami Gari, Gusami Hayi and Nasarawa Mailayi) totally inaccessible to the
team and 1 ward (Nasarawa Godel East) partially accessible due to insecurity. These have hampered the
investigation in a way and the areas that are most likely going to have low coverage were not accessible.
2.Objectives
The objectives of the SQUEAC investigation were:
• Investigate in depth the barriers and boosters of the CMAM programme;
• Evaluate the spatial pattern of coverage.
• Estimate overall program coverage.
• Issue recommendations in order to reform and to improve activities of the CMAM programme; and,
• Build the capacity of SMOH, LGA staff, NBS and NPHCDA in Zamafara state
3.Methodology
SQUEAC comprise a set of tools that are used to investigate coverage as well as positive and negative factors that
affect the coverage of CMAM (OTP) program. SQUEAC uses Bayesian approach12
to estimate overall program
coverage. In SQUEAC investigation collection and collation of information to generate more information is based
on the generated level of evidence during the process of investigation13
. SQUEAC investigation, while using the
12
Refer to Myatt, Mark et al. 2012. Semi-Quantitative Evaluation of Access and Coverage (SQUEAC)/Simplified Lot Quality Assurance
Sampling Evaluation of Access and Coverage (SLEAC) Technical Reference. Washington, DC: FHI 360/FANTA for details.
13
Investigative. Involves a technique for investigating coverage and factors influencing coverage. A SQUEAC investigation will, if needed,
include surveys, but is not limited to undertaking surveys. •Iterative. The process of a SQUEAC investigation is not fixed, but is modified as
knowledge is acquired. This can be thought of as a process of ‘learning as you go’. New information is used to decide the next steps of the
investigation. •Innovative. Different tools may be used and new tools may be developed as required. •Interactive. The method collects
information through intelligent interaction with program staff, program beneficiaries, and community members using semi-structured
interviews, case histories, and informal group discussions. •Informal. The method uses informal but guided interview techniques as well
as formal survey instruments to collect information about coverage and factors influencing coverage. •In the community. Much of the
information used in SQUEAC investigations is collected in the community through interaction with community members. SQUEAC lets you
see your program as it is seen by the community. •Intelligent. Triangulation is a purposeful and intelligent process. Data from different
sources and methods are compared with each other. Discrepancies in the data are used to inform decisions about whether to collect
further data.

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principles of triangulation and sampling to redundancy ensures that the body of evidence generated in the
process gradually builds up a picture of the “truth” about program coverage whilst simultaneously indicating what
practical measures can be undertaken to improve access and coverage. It is semi-quantitative in nature and uses
a mixture of quantitative data collected from routine program monitoring, anecdotal program information, small
studies, small-area surveys and wide area surveys, as well as qualitative data collected using informal group
discussions (IGDs), semi structured interviews and case histories with a variety of respondents (involving but not
limited to community leaders, caregivers, community gatherings, religious leaders, traditional birth attendants,
traditional healers and other leaders in the community).
3.1.Stages of SQUEAC of Birnin Magaji LGA CMAM program
SQUEAC investigation was conducted in 3-stages as illustrated in the Figure 2 below;
Figure 2: Stages of SQUEAC
Stage 1
The objective at this stage was to identify possible areas of low and high coverage as well as reasons for coverage
failure. This involved analysis of quantitative and qualitative data. Quantitative data was extracted from the
existing routine program data in individual OTP cards, while qualitative data was obtained from IGD and semi
structured interviews with care-givers, village leaders, health workers, community volunteers, lay people
(majalis14
and tea-shop gathering), religious leaders, key influential people and traditional healers. The Routine
programme data were collected from the 5 OTP centers (that is, Birnin Magaji, Gora, Nasarawa Godel,
Modomawa and Gusami) in the LGA. The analysis yielded various plots that would enable the SQUEAC team to
establish whether the coverage could be homogenous or patchy. The process of collation of the OTP cards is
illustrated in the figure below:
Figure 3: Process of collation entry and analysis of the routine and other program data
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Majalis; is a gathering of peers in a specific location (be it under a tree, or a shed or simply by the roadside) and at a specified time
(depending on seasonality but mostly at the close of work)
Stage
1
Stage
2
Stage
3
SQUEAC

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Qualitative data was collected at the qualitative data phase of SQUEAC investigation and lasted for 3 days. Data
was collected from:
 Health workers working within the CMAM health facilities and in the community.
 Caregivers of the beneficiaries in the program
 Various members of the community from 20 villages across the LGA
Qualitative data collection was done from sampled communities (villages) close to the CMAM HF and also from
village(s) that were far from the CMAM HF within the same Ward (see the hypothesis below for relative distance
comparison). In another way, qualitative data were collected in a community within the ward having a CMAM HF
and also, in a community that was not within the Ward and far from the CMAM HF. This was done with an aim of
development of the hypotheses in an advanced study. It is important to note that the “thinking” of forming a
hypothesis which would be done in further study began during the analysis of routine programme data.
Qualitative data provided vital information concerning the causes of low or high coverage with an objective of
identifying principal barriers to programme access and coverage. It was essential to triangulate the information
gathered by source and method until not new information was forthcoming. The main methods of qualitative
data collection used during the SQUEAC investigations included; IGD, in-depth interview, semi-structured
interview, simple-structured interview and observations.
Stage 2
In this stage the following processes were carried out:
 Formulation of the hypothesis that would be tested in the LGA to establish spatial coverage.
 Small area survey to gather data which when analyzed will prove the hypothesis formulated and also
gather barriers to access and coverage. This stage was meant to confirm the location of areas of low and
high coverage and establish the program geographical coverage. It is worth noting that the results
confirming the hypothesis would determine the next course of action in the SQUEAC investigation. That
is, if the hypotheses were rejected in the sense that the areas that we believed were of low coverage
turned out to be areas of high coverage, more data would need to be gathered until the “certainty” of the
spatial coverage was established. This would be important at a later stage when the ‘prior’ of the program
coverage would be determined. This process outlines the iterative nature of SQUEAC investigation. The
hypothesis that was formulated (as described in the below section of the report) was tested using the
small area survey.
The small area survey aimed at verifying our beliefs about the program and confirmed our belief about program
coverage on whether it was patchy or not. In a nutshell the steps involved in analysis of the small area survey data
and then testing the formulated hypothesis were done as follows:
1. Setting a standard (p): It is often reasonable to set ‘p’ in line with the SPHERE minimum standards15
for
therapeutic feeding program. Considering that the SLEAC16
assessment that was done in November 2013
15
Minimum standards for nutrition are a practical expression of the shared beliefs and commitments of humanitarian agencies and the
common principles, rights and duties governing humanitarian action set out in the Humanitarian Charter. For CMAM programme, the
minimum standard for coverage is 90% for camp setting, 70% for urban area and 50% for rural areas.
16
Simplified lot quality assurance sampling Evaluation of Access and Coverage

13
in Birnin Magaji LGA (and that unveiled results of program classification of coverage) a 20% threshold17
was used to classify coverage as either low or high.
2. Undertaking of the small area survey
3. Use of the total number of cases found (n) and the standard (p) to calculate the decision rule (d) using the
formula for 20% coverage.
4. Application of the decision rule: if the number of cases in the program is > d then the coverage is
classified as good and if < d then it is classified as low.
5. Determination of the areas that have low and those that have high coverage. If the results do not agree
with the hypothesis formulated then more information would need to be collected.
The Process of field data collection in Birinin Magaji
Identification of villages to be visited
Eight villages were selected; 4 from a ward that has OTP (2 host/near villages and 2 far villages >1hour walk) and
4 villages (2 from OTP ward and 2 villages from non-OTP wards). The villages selected are distributed between the
survey teams.
Active and adaptive case finding
Each team used an exhaustive active & adaptive case-finding method to search for SAM cases. The process
involved:
a) Case definition of malnutrition using local terms recognized in the community;
b) Identification of key informants who were given the description of the children that are being searched.
The key informant would then direct the SQUEAC team to the households perceived by the informant to
have the described children;
c) Use the caregiver of the SAM case that has been identified to lead the team to another dwelling that
could have similar case
d) Repeat the process until the SQUEAC team is led to the dwellings they have previously visited. A simple
structured questionnaire was administered to the beneficiaries of non-covered cases identified during
the process. In some cases house to house search was done in settlements close to the urban areas to
search for SAM cases.
Stage 3
At this stage it was considered that adequate information about the program had been collected and analyzed.
The following processes were carried out:
Building of the ‘Prior’ (statistical representation of our belief about the program coverage)
The barriers, boosters and questions were re-analyzed based on the available sources and methods to confirm
that information has been exhaustively gathered. The data gathered from the small area survey was also
17
The coverage threshold used to define classes of coverage in hypothesis was adopted from the two-standard three class classification
used in SLEAC survey that is: < 20; >=20 to <50; and >=50 for low, moderate and high coverage classification respectively. In Birnin
Magaji SQUEAC stage two, results <= 20% was classified as low while that >20% was classified as high.

14
considered at this stage. Concept map was completed to show relationship of all the factors affecting the
program coverage and whether they interlink to reflect the ‘true picture’ of the program.
Belief histogram was drawn. This was done by the team whereby the team determined the minimum and
maximum probable coverage (i.e. the coverage limits that the team believes the coverage will not go below or
beyond) based on the evidence generated in stage 1 & 2 first. Then each of the team members gave his/her own
estimate of the coverage based on a preselected interval of 5% (10%, 15%, 20%...X%) and the result is tallied
according to preselected interval (5% interval), the interval with the highest tally is selected as the ‘prior mode’
and the tallied data is presented in the form of histogram.
Using the built ‘prior’, the lowest and the highest possible values of the program coverage, the Bayes SQUEAC
Calculator was used to present the prior distribution and the relevant shaping parameters. The calculator helped
to give a sample size of SAM cases required to estimate our coverage in the wide area survey (likelihood survey).
The number of villages to be visited was determined. The (MUAC) SAM prevalence of the SMART Nutrition
survey18
, the median village population and estimated percentage of Under 5 children were used in the following
formula to calculate the number of the villages to be visited.
⌈ ⌉
The actual villages to be visited were selected from a complete list of villages19
which were stratified by Wards
and the CMAM facility. The sampling interval was calculated as:
Field data collection
Seventeen villages were selected from the village list based on SAM sample size stratified by ward. The villages
selected were distributed between the survey teams. Each team used an exhaustive active & adaptive case-
finding methodology where a key informant was identified and house to house search in the absence of key
informants or in places with settings close to urban to identify cases (children between 6-59months, MUAC <11.5,
presence of edema and the child that is in the program) that are either covered or not by the programme in the
selected villages.
18
SMART nutrition survey done in Zamafara state in 2013
19
Provided by the National Bureau of Statistics, Zamafara State

15
4. Results and Findings
4.1. Stage 1 : Identification of barriers and boosters and of potential areas of high or
low coverage
The stage one commenced with the extraction of quantitative data from individual OTP cards, followed by the
analysis of qualitative information obtained from the communities. The results are as presented below;
4.1.1. Routine monitoring data and individual OTP cards
The routine data was extracted from the individual OTP cards of all the 5 HFs offering OTP services in the LGA.
The data was extracted from the OTP cards for the period of January 2013 to May 2014 (17 months period). The
major challenge with the routine data extracted is that majority of the admission and discharge data were not
captured from the cards. Only 887 cards were available with majority of them entered in exercise books and not
in OTP cards without any exit information. However, the LGA monthly program data showed admission of 4700
for the same period. The results are presented below;
4.1.1.1. Admission Data
The admission data analyzed includes admission trend, admission MUAC and facility admission.
Facility Admission
The number of admissions varies from facility to facility with Birnin Magaji and Gora HFs having the highest
number of admission followed by Nasarawa Godel HF with 190 cases with Gusami HF showing the least number
of admissions with 122 admissions per year (figure 4). However, it should be noted that this information is based
on the scanty extracted routine.
Figure 4: Number of admissions by HF from routine data extraction
However, on analyzing the LGA level data, Gusami HF which has the lowest in routine data extracted happens to
be the HF with highest number of admission of 1172 closely followed by Birnin Magaji HF with 1060 admissions.
Gora HF has the least number of admissions of 768 (Figure 5).
234
201
190
139
122
0 50 100 150 200 250
Gora
B/Magaji
Nasarawar Godel
Modomawa West
Gusami
Number of Cases
HealthFacility
Total Admission by HF

16
Figure 5: Number of admission by HF from LGA data
A further analysis of the admission by LGA where the cases are coming from reveals that only 67.14% of the cases
are from Birnin Magaji LGA with the remaining coming from Kaura Namoda, Gusau and Zurmi LGAs respectively.
The proportion of cases by LGA is as presented in the figure 6 below.
Figure 6: Proportion of admissions by LGA of cases
Admission Trend
The number of admissions was compiled for the period analysed (January, 2013 - May, 2014) from the 5 HFs
offering OTP service. A total of 887 admissions were recorded from routine data and 4700 cases from the LGA
performance data for the period. The trend of admission provides a good picture about the evolution of
admission over time. The admission trend is depicted in the figure 7 below.
67.43%
1.71%
16.00% 14.86%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
Birnin Magaji Gusau Kaura Namoda Zurmi
Admission by LGA
0 200 400 600 800 1000 1200 1400
Gusami
Birnin Magaji
Modomawa
Nasarawa Godel
Gora
Number of Cases
HealthFacility Admission by HF

17
Figure 7: Admission trend over time for the LGA performance data and routine data extracted
Smoothing was applied to the data using a moving average a span of 3 months (that is median of 3 and average
of 3 months-M3 and A3 respectively) so as to hide the random ‘noise’ component and reveal the seasonality and
trend component of the data (Figure 8). A comparison of trend of admission and seasonal calendar was presented
in the figure 8 below.
Figure 8: Admission trend and seasonal calendar
0
100
200
300
400
500
600
NumberofCases
Months
LGA Data
M3A3
Routine Data
M3A3

18
The trend of admission reveals that there is a significant drop in admission from July, 2013 through May, 2014.
This is as a result of insecurity resulting from activities of the cattle rustlers. The insecurity reached a level that
RUTF had to be transported to Gusami HF under security escort. However, disaggregation of the trend by HF
reveals that there is zero admission in Gusami HF (January – March, 2014) which is the only HF we were not able
to visit due to insecurity (figure 9).
Figure 9: Admission Trend by HF
Admission MUAC
The measurement of the MUAC at admission is a strong indicator of late/early detection as well as treatment
seeking behaviour and the effectiveness of community mobilization activities. The median MUAC at admission
was 109mm which is indicative of late treatment seeking behaviour. Further still, the histogram representation of
the data reveals a long right tail indicative of late admission (admission when the condition is critical), see Figure
10 below.
Figure 10: The plot admission MUACs showing the median admission MUAC in black
Length of Stay (LOS)
0
20
40
60
80
100
120
140
160
NumberofCases
Months
Admission Trend by HF
Birnin Magaji
Gora
Gusami
Modomawa West
Nasarawar Godel
Routine Data
0
20
40
60
80
100
120
140
160
115
114
113
112
111
110
109
108
107
106
105
104
103
102
101
100
99
98
97
96
95
94
93
92
91
90
89
88
87
86
85
84
82
80
78
75
71.4
NumberofCases
MUAC Measurement (mm)
Admission MUAC
Median MUAC
at Admission=109mm

19
The median LOS observed is 4 weeks which means that half of the cured cases are discharged from OTP in under
4 weeks (Figure 11). When interpreted, this indicates that the programme admission and discharge is good.
However, this is not consistent with the plot of admission MUAC data which shows that there is late health
seeking behaviour and that a good percentage of the cases were admitted late when their condition is critical.
This necessitates the need to further investigate the exit data to ascertain why the LOS is short.
Figure 11: A histogram representation of the LOS with median LOS shown by arrow in black
Further investigation into the LOS results was done by looking at exit MUAC for all recovered cases. And the result
reveals that 41.18% of the exits have a MUAC measurement of above 125mm with 36.33% being discharge at
exactly 125mm and the remaining 22.49% discharged at a MUAC measurement of less than 125mm which is
below the minimum discharge MUAC (Figure 12). This explains why median LOS is good when the admission data
suggest that the LOS could be longer. Hence, the LOS result should be treated with caution
Figure 12: A bar chart representation of exit MUAC
4.1.1.2. Exit Data (Discharge Outcomes)
22.49%
36.33%
41.18%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
<124 125 >125
PercentageofCases
MUAC at Exit
Exit MUAC

20
The discharge outcomes (referred as the program performance indicators) are generally classified into four;
Recovered, Death, Defaulted and Non-recovered. The analysis of all the exit outcomes is presented as key
performance indicators in figure 13 below;
Figure 13: Pie chart representation of program indicators for the period Jan 2013 to May 2014 (a) Routine program
data and (b) monthly LGA performance data
Performance Indicator Trends
The trend of the indicators from the routine data revealed that all the indicators except the death rate were
below SPHERE minimum standard with the defaulter rate consistently above 30% (Figure 14). However, analysis
of the LGA performance data reveals an entirely unreliable result. The sum of outcomes in some months is
greater than the total exit of the same month and less in other months. Hence the result was not presented
here.
Figure 14: Trends of Performance Indicator from the routine data
Recovery Rate
The analysis of the routine programme data reveals that the Recovery rate for the entire duration is below
the SPHERE minimum standard of 75% except for January – March, 2014 (see figure 14 above for
details). However, further disaggregation by HF reveals that none of the 5 OTP sites has reached the
SPHERE standard of >75% recovery rate (see figure 15 below for details).

21
Figure 15: Disaggregation of Recovery Rate by HF
Non-recovered rate
The overall non-recovered rate for the period in question is 2.91% and the breakdown of the non-
recovered rate by HF is shown in figure 16 below. All the HFs have Non-recovered rate of below 5%
with the exception of Gora HF which returns a rate of 8.12% (Figure 16 below)
Figure 16: Disaggregation of Non-recovered rate by HF
Death rate
As previously indicated in figure 13, the overall death rate stands at 0.35% for routine data and 0.98% for LGA
performance data which are far below the SPHERE minimum standard of 10%. However, since this program is a
high default program, the death rate might have been greatly under reported as a good number of defaulters
may end up dying without being reported.
Defaulter rate
The routine data analysis reveals an overall Defaulter proportion of 63.8% for routine data which is above the
SPHERE standard of <15%. However, LGA performance data reveals a defaulter rate of 3.65% (Figure 13). This
discrepancy can be attributed to gross under reporting of defaulters in the LGA performance data. Even though
the LGA performance data is much higher than the routine data extracted (4700 as against 886), the absolute
number of defaulters in the LGA performance data is much less than the number reported in the routine data
extracted (160 as against 545). Further still, aggregating the KPIs for the LGA performance data reveals a total of
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
B/Magaji
Nasarawar Godel
Gusami
Gora
Modomawa West
Recovery Rate
1.03%
0.53%
0.00%
8.12%
2.16%
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10%
B/Magaji
Nasarawar Godel
Gusami
Gora
Modomawa West
Non-Recovered Rate

22
80.09% with 19.91% unaccounted for. These further reaffirm our belief that the defaulter rate is being grossly
under reported. Although it is evident that the default rate is high, both routine data and LGA performance data
are incomplete, hence it should be treated with caution.
Analysis of default by time of defaulting or number of visit at default reveals that the median number of visit at
default is 2 weeks (Figure 17 below). This implies that majority of the defaulters are defaulting early with only
about 19% defaulting late as recovering (after 4th
visit).
Figure 17: Histogram representation of number of visits at Default
Further analysis of defaulters by MUAC at default reveals that only 37.88% of the defaulters defaulted with a
MUAC of above 115mm (outside the admission MUAC) with the remaining 62.12% having a MUAC of below or
within the admission criteria (defaulted while still being SAM cases), see figure 18 below
Figure 18: A bar chart presentation of MUAC at default
Disaggregation of defaulters by LGA reveals that only 58.79% of the default cases are from Birnin Magaji LGA with
the remaining 41.29% from neighboring LGAs of Zurmi, Gusau, and Kaura Namoda and or with no data to classify
the LGA of origin (Figure 19 below). The breakdown of the default cases by LGA is presented in the figure 19
below.
31.01%
24.59%
15.60%
9.17%
19.63%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
1 2 3 4 ≥5
NumberofDefaulters
Number of Visits
Number of Visit at Default
62.12%
37.88%
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
MUAC <115 MUAC ≥115
ProportionofCases
MUAC Measurement'
MUAC at Default

23
Figure 19: Shows distribution of defaulters by LGA
Time to Travel to CMAM site
In order to plot the time-travel plots, we disaggregated the total admission by LGA. The result reveals that only
58.79% of the total admissions are from Birnin Magaji LGA with rest coming from neighboring LGAs (see Figure 19
above). The observed time to travel plot shows that greater than 50% of the cases came to the OTP from within
<2hours travel from the OTP; with very few cases observed from beyond 2 hours travel time from the HF see
figure 20 below. This is not in tandem with the expected time to travel. Even though the majority of cases
are expected to come from near villages, nonetheless the observed number of cases is below the expected
cases as per the plot. No cases were observed to come from 3hours and between 3-4hours villages and
very few cases came from beyond 4 hours.
Figure 20: Observed Vs. Expected Time to Travel Plot
4.1.2. Qualitative data
During the qualitative data collection phase which lasted for 2 days, a total of 13 villages were visited in the LGA.
The qualitative data collection was organized in the following visits:
 2 villages per ward (one hosting or near the HF and one far away from the HF within the wards with OTP
services)
 Villages in OTP ward and villages in non-OTP wards.
58.79%
0.19%
3.78%
3.97%
33.27%
Defaulter by LGA
Birnin Magaji
Gusau
Kaura Namoda
Zurmi

24
The results are explained below;
Understanding of Malnutrition
Informal group discussions were conducted with Majalis, semi structured interviews with care-givers (in the
program and not in the program), community leaders, religious leaders and CVs from across the 15 villages, 2
each from the HF’s catchment area; one hosting the HF and the 2nd village is chosen based on the distance from
HF and the other ten 6 each from non-OTP wards. The result reveals that the community does not have a
complete understanding of malnutrition as only cases resulting from sicknesses such as diarrhea, malaria etc. are
recognized as SAM. Cases resulting in wasting such as those from lack of sufficient breast feeding or breast
feeding while pregnant shanciki20
, marasmic kwashiorkor and Oedema were not recognized as SAM. In general,
SAM is refer to with various local names with Tamowa being the most common and simply means wasting, others
are kwamazo, kwamjinje and Tamuka. Oedema is refer to in many different ways such as Ciwon Kaba21
, Ciwon
iska22
, Ciwon Sanyi23
etc and all are not associated with community’s understanding of SAM cases hence not
recognized as such, see Annex II for details of local terms used in describing SAM in the community.
Treatment Seeking Behaviour
The communities visited all have a late treatment seeking behavior as they visit a chemist, traditional healer and
or use herbal remedies before visiting an OTP site as a last resort. Some of the traditional healers interviewed
revealed that they have some special herbs which they give to children with SAM (wasting) in which they drink
and bath with.
Awareness about the Program
Generally, all the community members interviewed were fully aware about the program. They know the OTP site,
the OTP day and duration of treatment. However, there is huge gap as there are no CVs in wards that do not have
an OTP and those in OTP wards are not active thus the majority of respondents have never seen a MUAC tape
within the community. Furthermore, except for the community leaders who were sensitized at the LGA level, no
sensitization were done in the community, the only sensitization going on in the communities are announcement
by town criers.
Perception about program
The community’s perception about the program is very good mainly due to the visible physical response of SAM
cases to the treatment. The people in the community all love the program. Generally, the community members
said that almost all SAM cases that received treatment recovered which is good for the program. Furthermore,
majority of the referrals are community referral especially peer-to-peer referral. Although there is little or no
mobilization activity in the community with the exception of announcement by town criers about the program,
the communities had fully accepted the program and are full of praises about the miracle of RUTF in SAM
treatment.
Skills of OTP staff
During the investigation, the SQUEAC teams had the opportunity to observe the process of admission of SAM
cases into the CMAM program in 2 out of 5 OTP sites in the LGA. The main challenge at the OTP is lack of enough
health workers trained in CMAM. But the SMOH had come up with a brilliant idea in which one HW each from
neighboring non-OTP health facilities were trained in CMAM and they come to the OTP site to help out on OTP
20
A local term used to describe breastfeeding while pregnant
21
A local term for diseases manifesting in the form of tumour or a generalized tumour
22
A local term for disease resulting from evil spirits
23
A local term for diseases associated with too much exposure to cold temperatures

25
days. The skill of the HWs is below average and this is due to the fact that because of RUTF shortage in the state,
the SMOH is constantly asking them to admit a limited number of cases and or severe cases (cases with MUAC
<10cm). The discharge criteria are also subjected to the same treatment as all those that reached a MUAC
measurement of 12cm were discharged. Thus, the admission and discharge criteria are not strictly following the
national guidelines. However, the official position presently is that restriction on number of admission had been
lifted and discharges should follow the national guideline. But most of the HWs interviewed are not aware of the
new instruction and still did some admissions using the criteria communicated earlier. There are few and critical
cases admitted and discharged at 120mm MUAC.
The qualitative information was analyzed in the sections below.
4.1.3. Boosters, Barriers and Questions (BBQ)
The result of the qualitative data collected from the community was analyzed using the BBQ tool and presented in
the table below;
Table 2: Barriers and boosters found during the qualitative data collection with the source and methods
S/N BOOSTERS SOURCE CODES
1. Peer to peer, cv, health workers, husband referrals 1A
,2A
,4A
,9D
2. Community mobilization and sensitization 1A
,2A
,9D
,5C
,4A
,6A
3. Good awareness about the program 5C
,1A
,9D
,8C
,4A
,6A
,7C
4. Good interface between health workers and the communities 2A
,9D
5. Good client/staff relationship 1A
,2A
,3B
,
6. Support from key community figures 1A
,2A
,9D
,5C
,4A
,6A
S/N BARRIERS SOURCE CODES
1. Alternative health practitioner preferred 1A
,2A
,8C
,9D
2. Husband refusal 1A
,5C
,7C
3. Distance 1A
,9D
,2A
,4A
,7C
4. Long waiting times 1A
5. RUTF Stock-out/break 1A
,9D
,5C
,6A
,4A
,7C
6. Poor delivery of service 1A
,5C
,2A
,4A
,6A
,7C
7. Rejection (Previous) 1A
,9D
,7C
8. RUTF sales in the community 5C
,4A
,7C
9. Poor out-reach activities 1A
,6A
,5C
,4A
,2A
10. Stigma 1A
,7C
11. Seasonal barriers 1A
12. Stock-out of routine drugs 9D
13. Poor program monitoring 10E
,11D
,3B
14. Lack of awareness about malnutrition 1A
,8C
,7C
15. Shortage of trained health workers on CMAM program 3B
,9D
,1A
16. Stock-out of data tools 9D
,11D
,10E
,3B
,
17. Insecurity 9D
,11D
S/N Source Method Code
1. Care-givers SSI A
2. Community Volunteer SSI A
3. Checklist Observation B
4. Community Leaders SSI A
5. Majalisa IGD C
6. Religious Leaders SSI A
7. Care-givers not in Program IGD C
8. Traditional Healers IGD C

26
9. Health Workers IDI D
10. Routine Data Extraction E
11. Program staff IDI D
The analysis reveals that there are 6 boosters and 17 barriers to access and coverage
4.1.4. Concept Map
After completing the BBQ phase, the SQUEAC investigators were divided into two teams. Each team did its own
concept map in which they presented how different barriers and boosters interact with each other and how they
affect the coverage. The finding from the two teams is low community mobilization; distance, shortage of RUTF,
shortage of data tools; long-waiting time and absence of CVs increases defaulter which negatively affect
coverage. Seasonal flooding which impair assess to the CMAM site whereby increasing defaulter also affect
coverage. Early treatment seeking behavior, short stay in the program, self-referral and good opinion about the
program increases coverage. The details of the 2 concept maps are presented in Annex
4.2. Stage 2: Confirmation areas of high and low coverage
4.2.1. Small area survey
At the end of Stage 1 and 2, the location of areas of high and low coverage and the reasons for coverage failure
identified were tested in a small-area survey. In this stage, we rely on active case finding methodology, meaning
we have to look for the SAM cases actively rather than expecting to find them in the sample. It was hypothesized
that;
Hypothesis
i. Coverage will be higher in villages hosting the OTP centres and nearby villages (<2hour walk) than far
villages (>2hour walk) from the health facility.
ii. Coverage will be different between wards hosting the OTP centre and the wards without OTP
Based on the results of SLEAC survey conducted in November, 2013 a threshold of 20% was chosen for Birinin
Magaji LGA because of its low coverage classification of <20%.
The result was presented in the table 3 below which disapprove our hypothesis of areas of low and high coverage;

27
Table 3: Result of Small Area Survey
Hypothesis Village
Total
SAM = n
SAM
Covered
(C)
SAM not
Covered
(NC)
Recovering
(RC)
d =
n/5
Coverage
Classification
OTPWard
Near
Modomawa 21 6 15 0
4
C = 6 > d = 4
High
Saran
Galadima
3 0 3 0
Total 24 6 18 0
Far
Kirifada 14 7 7 0
3
C = 8 > d = 3
High
Dadin Marafa 2 1 1 0
Total 16 8 8 0
Ward
OTP
Gora 8 3 5 7
2
C = 5 > d = 2
High
Yautabaki 6 2 4 1
Total 14 5 9 8
Non
OTP
Garin Kaka 16 8 8 0
14
C = 24 > d =
14 High
Chigama 55 16 39 0
Total 71 24 47 0
Grand Total 125 43 82 8
Hypothesis I: The table above show that a total number of 24 SAM cases were found in the settlements that are
near the OTP sites in which out of these 6 cases are covered while the remaining 18 were not covered. Also 16
SAM cases were found in the settlements that are far away from the OTP Wards out of which 8 cases are covered
and the remaining 9 cases found were not covered. This implies that the coverage is high in the communities. This
is because even though Kirifada is in Modomawa ward and >2hours from the HF, it is <20minutes walk from Gora
HF located in Gora ward hence the high coverage. This implies that, our hypothesis of having a high coverage in
villages hosting/near OTP site than villages far from the OTP does not holds true due to geographical convenience
for the beneficiaries .
Hypothesis II: A total number of 14 SAM cases were found in the OTP ward out of which 5 cases were covered
while 9 were not covered with 8 of the cases found as recovering cases. A total number of 71 SAM cases were
found in the Non-OTP ward out of which 24 are covered and 47 not covered. This also disproves our hypothesis
of having a higher coverage in wards hosting OTP than those not hosting an OTP.
In view of the result above, it could be concluded that the coverage in the LGA is somewhat homogeneous rather
than ‘patchy’ or heterogeneous as coverage is high in villages and wards with or without OTP. However, it should
be noted that 3 wards and a portion of the 4th
ward were not accessible due to insecurity.
4.2.2. Barriers of the small area survey
Barriers to coverage brought forth by caregivers during this small area survey were presented in the table below;
Table 4: Barriers of the small area survey
Barrier Value
1. Lack of knowledge about malnutrition 15
Don’t know the child is malnourished 15
2. Lack of knowledge about Programme 1
Not aware about the program 1
3. Access Issues 11

28
Distance/Too far 7
Don’t have money 4
4. Service delivery problems 3
Relapse 3
5. Rejection or fear of rejection at the site 13
The child has been rejected by the programme already 7
Others have been rejected 6
6. Misconceptions about the programme or how it works 4
Thought it was necessary to enrol at the hospital first 3
They take the child to the chemist first 1
7. Challenges / constraints faced by mother 35
Husband refusal 7
There is no one else who can take care of the other siblings 2
The mother cannot carry more than one child 1
No time / too busy 4
Mother is sick 2
No reason 15
The amount of RUTF was too little to justify the journey 1
The mother feel ashamed or shy 1
Mother travelled 2
The graphical representation of the barriers is depicted by the diagram below;
Figure 21: Barriers to access found in small area survey
4.3. Stage 3: The coverage estimate (application of Bayesian Theory)
4.3.1. Development of Prior
SQUEAC methodology utilizes Bayesian technique in which existing information such as routine
monitoring data were extracted, additional qualitative information collected and the result of small area
survey analyzed to give us a fair idea about the program coverage in the LGA. This fair idea or our belief
about the program is known as the ‘Prior’ which is aimed at giving us a fair estimate of the program
coverage.
0 5 10 15 20 25 30 35 40
Challenges / constraints faced by mother
Lack of knowledge about malnutrition
Rejection or fear of rejection at the site
Access Issues
Misconceptions about the programme or how it…
Service delivery problems
Lack of knowledge about Programme
Barriers to Programm access and uptake

29
Relevant information collected from the routine data i.e. qualitative information, the quantitative data
and small area survey were used to inform and develop the prior. Informed guess was done in the
process of investigation of what the coverage value is most likely to be, that is the mode of probability
density.
The factors affecting the coverage positively are the booster and those affecting the coverage negatively
are the barriers. Information collected was separated into the boosters and the barriers of CMAM
coverage. A procedure was employed to weigh the barriers and booster in four ways; weighted barriers
and boosters, Un-weighted barriers and boosters, concept map and belief histogram.
The result of the various prior modes for belief histogram, weighted and un-weighted BBQ prior modes
and the prior mode from the concept map are presented below;
The belief histogram
Prior 1: Histogram of belief = 30%
Figure 22: Prior Mode for Belief Histogram
The belief histogram was based on probability of occurrence of a certain coverage percentage by
individual team members of the coverage assessment. The agreement was based on their knowledge
about the coverage gotten from the result of routine data analysis and qualitative information from the
first stage and the small area survey of the SQUEAC investigation. The value of the belief histogram
coverage varied between 10% and 45% for each participant and minimum values of 5% with a maximum
value of 50% were agreed. Six of the participants gave a potential coverage of between 30 % which gave
us a prior mode of 30% whereas all the other participants gave a potential coverage of 15, 20, 25, 35, 40
and 45% respectively. The result is as presented in figure 22 above.
Estimation of prior mode from the BBQ was calculated based on the data in the table 5 below as;
Prior from the weighted barriers and boosters
( ) ( )

30
Prior from the un-weighted barriers and boosters
The barriers and booster in the BBQ were given equal score of 5 to assume that each of the barriers and booster
affects the coverage in the same manner. The result is presented in the table 5 below
( ) ( )
.

31
Table 5: Tabular representation of weighed and un-weighted barriers and boosters obtained in Stage 1 by the 2 groups
S/No
Barriers Boosters
Weighted Un-
Weighted
Weighted Un-
WeightedGP1 GP2 Average GP1 GP2 Average
1
Alternative health practitioner
preferred
3 3 3 5
High number of self-referral, peer to peer
referral
4 4 4 5
2 Husband refusal 2 2 2 5 Support from key community figures 3 5 4 5
3 Distance 3 4 3.5 5 Good awareness about the program 5 5 5 5
4 Long waiting times 2 3 2.5 5 Good client/staff relationship 4 5 4.5 5
5 RUTF Stock-out/break 4 5 4.5 5 RUTF Well accepted 3 3 3 5
6 Poor delivery of service 4 4 4 5
Interface between Health Workers and the
Community
3 4 3.5 5
7 Rejection (Previous) 2 3 2.5 5
8 RUTF sales in the community 3 2 2.5 5
9 Poor out-reach activities 2 4 3 5
10 Stigma 1 4 2.5 5
11 Seasonal barriers 2 2 2 5
12 Stock-out of routine drugs 1 3 2 5
13 Poor program monitoring 4 5 4.5 5
14
Lack of awareness about
malnutrition
2 4 3 5
15
Shortage of trained health
workers on CMAM program
4 3 3.5 5
16 Stock-out of data tools 3 4 3.5 5
17 Insecurity 5 5 5 5
Total 47 60 53.5 85 22 26 24 30

32
Prior with Concept map
Table 6: Prior from the Concept Map
Group Boosters Barriers Prior
ModeArrows # Weight Total Arrows # Weight Total
Group 1 6 3.5 21 21 3.5 73.5 23.75
Group 2 12 3.5 42 26 3.5 91 25.5
Prior Mode (Concept Map) = Average 24.625
( ) ( )
( ) ( )
( ) ( )
Triangulation of 4 methods
The different prior mode estimations were used to give a final Prior mode of 26.4% as depicted by the equation
and figure 25 below;
The final Prior Mode is 28.1%. See Figure 23 below

33
Figure 23: Triangulation of different Priors to give the Prior Mode
Therefore, the Prior Mode is 28.1% which was plotted using the Bayes SQUEAC Calculator and presented in the
figure 24 below;
Figure 24: The plot of the Prior Mode in Bayes SQUEAC calculator showing suggested sample size circled in red

34
4.3.2. Likelihood
After plotting the prior, the suggested sample size for SAM cases was obtained from the Bayes SQUEAC
calculator, the number of villages to be visited was calculated to get the required number of SAM cases for the
likelihood survey. The number of villages was then calculated from the number of SAM cases, median village
population, percentage of children 6-59 months and the prevalence of SAM in the state.
Therefore:
⌈ ⌉
⌈ ⌉
⌈ ⌉
This meant that to obtain 51 SAM cases approximately 17 villages needed to be visited and find the cases using
active and adaptive case finding methodology.
Stratified spatial systematic sampling
Since there was no reliable map for Birnin Magaji LGA, systematic stratified spatial sampling of villages was done.
A total of 17 villages were selected for the wide area survey.
Therefore, an active and adaptive case finding method was carried out in the 17 villages. The case definition was
a child who:
 Had MUAC less than 11.5 cm
 Had bilateral pitting oedema
 Was aged 6-59 months
The result of the wide area survey is presented in table 7 below.
Table 7: The result of Wide Area Survey
Village Total SAM = n
SAM
Covered (C)
SAM not
Covered (NC)
Recovering
(RC)
Bukut 8 1 7 0
Kadage 4 1 3 0
Tungar Janbuzu 9 1 8 0
Baichi 2 1 1 0
Garin Halidu 7 2 5 0
Ballaka 6 0 6 0
Tsalle 3 0 3 0
Shiyar Daudu 21 9 12 1
Garka 8 4 4 0
Jargaba 8 5 3 0
Kokiya 15 4 11 0
Shiyar Mai Dawa 5 2 3 0
Gidan Kyabda 9 3 6 0
Gidan Dan Jumma 22 3 19 0
Damfami 14 4 10 0

35
Danwala 12 6 6 0
Dan Dambo 9 1 8 0
162 47 115 1
Barriers found during the wide area survey are presented in the figure 25 and table 8 below
Figure 25: Barriers to program access and uptake during Wide Area Survey
Table 4: Barriers to access and uptake found at Wide Area Survey
Barrier Value
1. Lack of knowledge about malnutrition 8
Don’t know the child is malnourished 8
2. Lack of knowledge about Programme 27
Not aware about the program 27
2. Access Issues 6
Insecurity 1
Distance/Too far 3
Don’t have money 2
3. Service delivery problems 3
No new adminission 1
Discharged 2
4. Rejection or fear of rejection at the site 19
The child has been rejected by the programme already 8
Others have been rejected 11
5. Misconceptions about the programme or how it works 6
Thought it was necessary to enrol at the hospital first 5
Child Recovered 1
6. Challenges / constraints faced by mother 47
Husband refusal 11
There is no one else who can take care of the other siblings 2
The child refused to consume the RUTF 1
No time / too busy 2
0 5 10 15 20 25 30 35 40 45 50
Challenges / constraints faced by mother
Lack of knowledge about Programme
Rejection or fear of rejection at the site
Lack of knowledge about malnutrition
Access Issues
Misconceptions about the programme or how it
works
Service delivery problems
Barriers to Access/Coverage in WAS

36
Mother is sick 2
Mother feel ashamed or shy 2
No reason 21
The amount of RUTF was too little to justify the journey 3
The mother feel ashamed or shy 3
4.3.3. Posterior
Using the Binomial conjugate analysis, the result of stage 1 (Prior) and the stage 3 (Likelihood) were combined to
ascertain the final coverage estimate in the Bayes SQUEAC Calculator (Figure 26)
Figure 26: Final result of the coverage-Binomial conjugate analysis
The final coverage was estimated as 29.1% (22.9 – 35.9% CI24
: 95%). The satisfactory overlap of the prior and the
likelihood distributions coupled with z-test p-value of 0.9328 which is greater than 0.05 implies that there is no
Prior-Likelihood conflict and the results are valid.
24
Credible interval

37
5. Discussion
The total coverage of CMAM programme in the Birnin Magaji LGA was estimated at 29.1% (22.9 – 35.9%; 95%CI)
which is below the SPHERE standard of 50% coverage for rural area. Although the coverage is below the SPHERE
minimum standard, there is a significant improvement when compared to 2013 SLEAC result which returns a
crude estimate of 10.19% coverage for the LGA. The increase in coverage could be attributed to availability of
RUTF and data tools in the LGA since when the SLEAC was conducted there was a nationwide shortage of RUTF
and data tools were not available which makes them to initially stop admission before improvising to use exercise
books as OTP cards. Furthermore, the sum of N102,000.00 is currently being provided monthly to the LGAs
through the basket fund for the distribution of RUTF, printing of data tools as well as provision of incentives in the
form of transportation to the CVs.
Although funding is available and consistent, RUTF/routine drugs shortage still remain an issue in the LGA. This is
as a result of improper supply and distribution, because the distribution is not based on demand but rather a flat
rate was used where each HF receives the same amount of RUTF/routine drugs irrespective of admission burden.
This created a situation; where some HFs has more than enough RUTF and routine drugs and on the hand others
have shortages.
The late treatment seeking observed in the communities could be attributed to the poor understanding of
malnutrition among community members. Generally, all the community members recognized only wasting as
SAM as other manifestations such as oedema were not recognized. This together with poor community
mobilization and outreach activities by the stakeholders in the LGA is by far the biggest challenge of the CMAM
program. The community mobilization was done in such a way that only a select numbers of village heads were
invited to the Birnin Magaji for sensitization with the community members left out. None of the community
members were ever sensitized in the community except for announcement by town criers and very limited
activities of CVs.
Another big challenge in the LGA is insecurity due cattle rustling. During the SQUEAC investigation, the team was
not able to visit 3 wards and a portion of the 4th
ward. Although we may not be able to ascertain the exact extent
of the insecurity on the coverage, it is worthy to note that in some instances RUTF had to be transported under
armed escort to the OTP site in the area. Hence, there was period of RUTF shortage in the ward before the
commencement of arm escort and people in the area were not able to move freely. These have made supervision
impossible in the area.
The most challenging thing to mothers is distance and long waiting times. Mothers reported having to wait for an
upward of 4hours in the HF before being attended. This is a direct effect of shortage of health workers in the HFs,
none of the HFs has up to 3 HWs trained in CMAM and one HF has no trained HW at all including the officer in
charge. This is due to transfer of HWs within the LGA. Trained staffs were constantly being transferred from
CMAM offering HFs to none OTP sites. Although stigma and husband refusal were noted as barriers in Stage 1,
their effect on coverage was minute as very small portion of the communities reported that, Furthermore, due to
good opinion about the program and acceptability of RUTF, the effect of both on coverage was limited.
The key boosters observed are good awareness and opinion about the program, excellent community referral by
especially the mothers through self and peer to peer referrals; almost all the communities we visited knows the
OTP site, OTP day and whom the service is meant for. The only exception is the exclusion of cases not recognized
by the communities as SAM (eg Oedema cases). There is a very good relationship between the different actors
and key community figures, this is what led to allocating a constant funding for treatment of malnutrition to the
tune of N102,000.00 monthly through the basket fund. However, even though funding is available there is still the
need to utilize the funding in a more efficient manner so as to eliminate the intermittent shortages being
experienced by some HFs especially those with higher case loads.

38
6. Conclusion
In conclusion, the program was designed by UNICEF in such a way that it will cater for at least 50% of SAM cases
in the LGA (meaning the CMAM program expected target is 50% of SAM cases in the LGA). Thus if the program
was able to have a Met Need of 50%, then it must have reached its intended target. In our effort to investigate
this, the Met Need of the LGA was calculated using parameters for cases that are from the LGA only. However,
the routine data was scanty with few outcomes or exit information and the LGA performance data was limited by
the fact that it has incomplete exit information (the total outcome or KPIs sums up to 80.89%). In the routine data
which was for only 887 cases, there are more defaulters than in the LGA performance data (548 vs 20 absolute
number of defaulters in 887 vs 1410 admission). This know brings out the flaws in the data as deliberate under
reporting especially of defaulters is evident and or wrong discharge criteria are being used in all the HFs in which
cases that are yet to meet the discharge criteria are discharged and the team consider them as having defaulted
from the program.
Even though the data has lots of short comings and the result in conclusive, we decided to still go ahead and
calculate the Met Need for both the routine data and LGA performance data. The calculation was as follows;
for routine data
for LGA performance data
Thus the Met Need of the program is somewhere between 9.57% from the routine data and 21.96% for the LGA
performance data. These are far below the intended target of 50% set in the initial set up of the program in the
LGA. This implies that there is much that is needed to be done in order to reach the minimum of target of 50%
Met Need.

39
7.Recommendations
According to the barriers found by SQUEAC investigation, key recommendations perceived to strengthen the CMAM program were identified and presented for further
deliberations in a meeting with SNO, director of primary health and other key officials in the state and LGA. These are tabulated below:
SHORT TERM MEASURES
TARGET ISSUE PROCESS OF IMPLEMENTATION RESPONSIBLE PARTY EXPECTED OUTPUT TIME LINE
Improvement of
community mobilization
& sensitization to bring
active community
participation and
ownership
Advocacy visit to the traditional leaders
and policy makers to further disseminate
the SQUEAC findings
DPH (LGA), SNO,LNO and
OTP I/c
Increase community participation
and ownership
July – August, 2014
Community dialogue with Husbands ,
traditional leaders, religious leader and
traditional healers
SNO,LNO and OTP I/c Increase early detection of cases
and reduced defaulters
August – September,
2014
Sensitization of the community members
through focus group discussion
DPH(LGA), SNO,LNO and
OTP I/c
Increase community participation August, 2014
Use the IPDs strategies to assist in active
case finding and mobilizing the Caregivers
about the program through coopting CVs.
DPHC(SMOH), DPHC(LGA),
SIO, SNO, LIO, LNO,
SHE,LGHE, WHO, UNICEF
Massive recruitment of SAM cases
and increase awareness
August – October,
2014
Display of social mobilization posters in
local language
UNICEF, SNO, LNO and
OTP I/c
Increase awareness in the
communities
September, 2014
Health Education Weekly Health talk on Nutrition LNO, ALNO and OTP I/c Increase awareness and reduction in
SAM prevalence in the communities
July, 2014
Procurement and display of IEC material in
the health facilities
UNICEF, SNO, LNO and
OTP I/c
Increase awareness
Frequent home visit and follow-up by
CVs
LNO, ALNO and OTP I/cs Reduced Defaulter and Non-
recovered Rates
August, 2014
Program Monitoring
and Capacity Building
Conduct regular integrated supportive
supervision
SNO and LNO Provision of top level support to
Health workers
Monthly, starting July
2014
Health facilities visits for on the job
coaching and mentoring by SNO, LNO
SNO and LNO Capacity of Health Workers build Monthly, starting
August, 2014
Capacity building of ICs and LNOs to
request for RUTF based on demand
SNO and LNO RUTF break/shortage eliminated in
OTP sites
Monthly, starting
August, 2014
Financial Support Incentives to CVs SNO Improvement in active case finding

40
and follow-up by CVs
Provision of support to SNO, LNOs for
supportive supervision and on the job
coaching and mentoring
Perm Sec and DPHC
(SMOH),SNO
Regular Supportive supervision to
HFs
August, 2014
Improve transportation of RUTF from
state to LGA and OTP sites
DPHC(SMOH),
DPHC(MOLG) and SNO
Regular supply of RUTF to HFs August, 2014
LONG TERM MEASURES
TARGET ISSUE TARGET ISSUE TARGET ISSUE TARGET ISSUE TARGET ISSUE
Improvement of
community mobilization
& sensitization to bring
active community
participation and
ownership
Advocate for Printing and distribution of
posters translated in local languages, in
Arabic text (Ajami)
DPHC(SMOH),SNO,
UNICEF, SCI
Posters using Arabic text in local
languages produced & displayed in
communities
October – December,
2014
Incorporating CMAM awareness into
existing TV/Radio Health Programs and
DPHC(SMOH),SNO Nutrition/CMAM talks conducted on
Radio
July – December, 2014
Production and airing of Radio jingles
Drama and documentary on CMAM
DPHC(SMOH),SNO,
UNICEF
Drama to be performed in
communities
November, 2014
Program Monitoring
and Capacity Building
Re-training of HWs DPHC(SMOH),SNO,
UNICEF
Health workers in low performing
LGAs update their knowledge of
CMAM
July-August, 2014
Training of new CVs and refresher training
of existing CVs
DPHC(SMOH),SNO,
UNICEF, LNO, Community
Leaders,
CVs recruited and trained where
absent and existing CVs retrained
August – September,
2014
Routine data quality assessment(DQA) to
be conducted in all OTP site to bring the
data up to date
SNO, UNICEF CMAM Database updated and gaps
identify for targeted training to HWs
on data management
September – October,
2014
Transferring a trained health worker only
between OTP sites
LNO, DPHC(LGA), LGA
Secretary
All trained HWs transferred only
from an OTP to OTP
July – December, 2014
Financial Support Printing of OTP cards and procurement of
routine drugs
Perm Sec and DPHC
(SMOH),SNO
OTP cards became available in all
HFs
December, 2014
Provision of ambulance at all OTP sites
for conveying referral cases to SC
Perm Sec and DPHC
(SMOH),SNO
Transportation of referred cases to
SC improved
December, 2014
Full SQUEAC assessment Advocate for allocation of funding for a
low scale follow-up SQUEAC study
Perm Sec and DPHC
(SMOH),SNO, UNICEF
SQUEAC report November 2014

41
Annex 1: Itinerary of the mission
Days Date Activity
Tuesday –
Friday
22nd
- 24th
April
Meeting with SCI Zamfara field office, SMOH, LGA
and Routine Data extraction
Saturday 25th
April Routine Data extraction
Sunday 26th
April
Monday 27th
April Training of Enumerators and Government
counterparts
Tuesday 28th
April Training continues
Wednesday 29th
April Training finalized, field testing and MUAC
standardization
Thursday 30th
April Data Extraction
Friday 1st
May Data Extraction
Saturday 2nd
May
Sunday 3rd
May
Monday 4th
May Routine Data Analysis
Tuesday 5th
May Qualitative data gathering; visit Hirishi, Badariya
Wuro Gauri, Shadadi and Unguwar Wa
Wednesday 6th
May Qualitative data gathering; visit Ruga Dan Bagga,
Diggi, Etene, Keta
Thursday 7th
May Qualitative data gathering; visits Zuguru, Kwimi,
Nayelwa
Friday 8th
May Qualitative data analysis, Development of BBQ and
Mind Mapping
Saturday 9th
May Qualitative data analysis, Development of BBQ and
Mind Mapping finalized
Sunday 10th
May Hypothesis setting, Concept map and Mind mapping
finalized
Monday 11th
May Small Area Survey
Tuesday 12th
May Small Area Survey
Wednesday 13th
May Development of Prior and Sample size for likelihood
survey
Thursday 14th
May Wide Area Survey
Friday 15th
Saturday 16th
Sunday 17th
Monday 18th
May Binomial conjugate analysis; Posterior
Tuesday 19th
May Debriefing of Stakeholders/Dissemination Meeting

42
Annex 2: List of participants
Participants list for Zamafara SQUEAC Training
S/No. Name Position
1 Maryam Ibrahim Enumerator
2 Rukaiya Musa Nawani Enumerator
3 Elizabeth Awaritoma Enumerator
4 Zainab Lawl Enumerator
5 Bello Umaru Jabaka Enumerator
6 Karima Idris Enumerator
7 Abdullahi B. Suleiman LNO
8 Namakka Abuda Enumerator
9 Bello Ibrahim Enumerator
10 Hafsat Halilu Enumerator
11 Salisu Sharif CCO/SCI
12 Oyedeji Ayobami CCO/SCI
13 Ode O. Ode CCO/SCI
14 Rupert Ossai Driver/Log Asst. SCI
15 Abdullahi Matazu Driver/Log Asst. SCI
16 Saratu Ibrahim Enumerator
17 Sama’ila Bakwai SNO
18 Ibrahim S. Fulani DPHC
19 Niima Umar Enumerator
20 Esther Anthony Enumerator
21 Moh’d Hassan G. Enumerator
22 Suleiman Aliyu B DDPHC
23 Arc Lawal U. Perm Sec MOHZ
24 Yusuf A. Musa DPHC
25 Saratu Absdullahi
26 Ayo Ogunjobi M&E SCI
27 Babatunde Lawani STA
28 Abubakar Aji SSA/SCI
29 Hassan Dan Gwaggo Driver SCI
30 Muhd Mas’ud Liman Enumerator
31 Adamu Abubakar Yarima CCC/SCI

43
Annex 3: Seasonal calendar
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
CROP
production
Land
preparation
Planting
Green harvest
Processing
Weeding
Rainy season
Dry season
Hunger
season peak
Staple food
price peak
Live stock sale
Employment
Farm casual
labour

44
Annex 4: Survey Questionnaire for caregivers with cases NOT in the programme
State: ________________ LGA: ______________ WARD: ______________
Village: _____________ Team No: ____________
Child Name: __________________________________
1a. DO YOU THINK YOUR CHILD IS SICK? IF YES, WHAT IS HE/SHE SUFFERING FROM? ___________
__________________________________________________________________________________
1. DO YOU THINK YOUR CHILD IS MALNOURISHED?
YES NO
2. DO YOU KNOW IF THERE IS A TREATMENT FOR MALNOURISHED CHILDREN AT THE HEALTH CENTRE?
YES NO (stop)
3. WHY DID YOU NOT TAKE YOUR CHILD TO THE HEALTH CENTRE?
Too far (How long to walk? ……..hours)
No time / too busy
Specify the activity that makes them busy this season __________________________
The mother is sick
The mother cannot carry more than one child
The mother feels ashamed or shy about coming
No other person who can take care of the other siblings
Service delivery issues (specify ………………………………………………….)
The amount of food was too little to justify coming
The child has been rejected. When? (This week, last month etc)________________
The children of the others have been rejected
My husband refused
The mother thought it was necessary to be enrolled at the hospital first
The mother does not think the programme can help her child (prefers traditional healer, etc.)
Other reasons: ___________________________________________________
4. WAS YOUR CHILD PREVIOUSLY TREATED FOR MALNUTRITION AT THE HC (OTP/SC)?
YES NO (=> stop!)
If yes, why is he/she not treated now?
Defaulted, When?.................Why?..................
Discharged cured (when? ............)
Discharged non-cured (when? .............)
Other:___________________________________________
(Thank the mother/carer)

45
Carer Interview Form
Name of Health facility/CMAM Site: __________________________
LGA:________________________
Ward Name: __________________________Interviewer Name/ Team#: __________________
State: _____________________________
Date: ______/______/ 2014 Village name:
________________________
*********************************************************************
Description of Questions: BOLD fonts define questions while ITALIC fonts declare guidance notes.
Question# 1: How did this child get to be in this program?
Guidance notes:
Take history
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Explore local terms used for SAM children
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Where did you take the child the first time you noticed he/she was malnourished? Find out its treatment seeking behavior
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Who told/referred you to visit the CMAM site? Program case findings and referrals methods
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Question#2 Do you know of any children in your village that are like your child and are not
attending this program? Yes No
Guidance Notes: (Then ask about index child specific history from above, common SAM etiologies(with specific local
names for severe malnutrition) like e.g description of illeness with specific signs e.g thin arms, swollen feet, kwashiorkor signs etc,
Treatment seeking behavior/pathways to care).
if YES please follow Part-A if NO please follow Part-B
Part-A why do you think child is not attending this program? (How do you know this, any other reasons, any other children, record
name and home location of the informant for follow up).
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Part-B: if there were children like your child that are not attending this program why do you think they would not attend the
program? (Explore of any other reason that stops peoples coming to health facility?)

46
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Question#3 if I wanted to find children like your child and the children we have spoken about, how would I best describe them to
other people? (discover local terms used to describe SAM in community e.g Kangi in sindhi and soori in Pashtu, sookha pan in Urdu, any
other names used to describe sever malnourished children’s, are these the same things as malnutrition).
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
_______________________________________________________________________________________________
________________________________________________________________________________________________________
Question# 4 if I wanted to find a children like your child and the children we have spoken about who would best be able to help me
to find them? (Ask directly about midwives, traditional birth attendants, traditional healer’s, LHW, Community volunteer’s, the people
mentioned in history when exploring treatment seeking behaviors and the peoples use by the programs for case findings/ referrals, Ask
“why” and “why not” e.g confirm as an example “that you saying that I should ask PERSON LHW Miss. Robina etc to take me to see
children with severe malnutrition called kangi in this community” is that right).
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#5 where do you come from, how do you reach here?
Guidance Notes: Is CMAM sites nearby/ Far/or very far from your home, walk by feet, time to travel to site, if there is an need for transport
how much does it cost?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#6 What are some challenges you face accessing this programme?
Guidance Notes: Is the queue unusually long? Do you usually wait for a long time before being attended to? Are you asked to pay for some
services? Are the health workers friendly to you?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#7 What do you think we can do to improve this programme?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#8 Is there any other place you/other carers can get/buy RUTF in your village or nearby community?
Chemist shop, tea shop, market, other mothers/carers, Okada.
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#9. Is RUTF being tasted, eaten or shared by adult or siblings in your village or nearby community?
Guidance Note; probe further if sharing/consumption of RUTF is being done by non-SAM children & adults.
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Q#10. How do you give RUTF to your child and how many sachets per day?
Guidance Note; probe further on number of sachet given to the mother at the OTP site
________________________________________________________________________________________________________
________________________________________________________________________________________________________

47
Community Questionnaire
community elders opinion leaders, religious leaders
tea shop gatherings/majalisa, (Age- range) Gender
State:________________________
LGA:__________________________ Ward:_____________________________
Village:__________________________________ Distance from the CMAM Site:________________
Date:______/______/ 2014 Interviewer Name/ Team#: __________________
Instruction: ask all the question and probe where necessary.
1. What does the community refer “malnutrition” (probe for local terms used in describing malnutrition and
explain the local meanings e.g. ‘Tamowa’ means wasted while ‘Kumburi’ means swollen body)
2. How are the cases who have the condition mentioned above treated/where are the sick cases currently
treated in the community?
3. Have you heard about the CMAM program/program treating malnutrition?
4. If there is a program/health facility treating malnutrition (use local term for malnutrition), what is the name
of the facility giving treatment?
5. Do you see malnourished children going to this program/facility that treats malnutrition (CMAM
programme)?
6. Do you know of any carer/mother taking their child for treatment? Give the name of the Child and the parent
and the village where they come from?
7. Has anyone come to the community to talk to you about the malnutrition (use local terms) and how it is
treated?
8. Have you seen a MUAC tape (show the MUAC strip to the interviewees)?

48
9. Have you seen RUTF (Show sachets of RUTF in use in the program e.g plumpy nut)?
10. Have you ever seen adults eating RUTF (Plumpy nut)?
11. Apart from the CMAM site, is there any other place in this community where I can obtain/buy RUTF?
12. What is your involvement/contribution to the programme?
13. What do you think about the program and how can we improve it?(keep on probing)

49
Health Facility Staff (clinic staff) Interview Form
LGA:________________________
Ward Name:__________________________Interviewer Name/ Team#: __________________
State:_____________________________
Date:______/______/ 2014
*********************************************************************
Question# 1
a. Have you being trained on CMAM? If yes, how many times and when did you receive the last
training?
b. How many beneficiaries do you see on daily basis (average flow of admission)?
a)OTP ___________________________________________________________________________________
b) Other Patients________________________________________________
Question#2: How do you get new OTP beneficiaries who come here (SAM- children)?
Question#3: From where do your OTP beneficiaries (SAM- children) come from?
Nearest villages
a) What is/are the name(s) of the nearest village(s)_______________________________
b) What do the carers mostly use to come to facility _______________________ eg: motor cycle, walking, bus etc
c) If walking how long (in estimated minutes/hours) does it take carer to walk to facility________________
Furthest villages
a) What is/are the name(s) of the farthest village(s)________________________________
b) What do the carers mostly use to come to facility _______________________ eg: motor cycle, walking, bus etc
c) If walking how long (in estimated minutes/hours) does it take carer to walk to facility________________
Question#4 what is community impression about OTP program (that is part of this CMAM
program)?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Question# 5 What are the main challenges that you think are related to OTP program here?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________
Question# 6 How do you deal with OTP defaulters?
________________________________________________________________________________________________________
________________________________________________________________________________________________________
________________________________________________________________________________________________________

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