The International Rescue Committee (IRC) in Partnership with the Alliance for International Medical Action (ALIMA), Cooperazione Internazionale (COOPI), Life Helpers Initiative (LHI) and Grassroot Initiative for Strengthening Community Resilience (GISCOR) has been implementing the Integrated Emergency, Recovery and Resilience Response for Crisis-Affected Persons in Zamfara, Katsina and Sokoto States. The project is funded by USAID/BHA.[1] The overall purpose of the project under Nutrition sector is to contribute to the reduction of child morbidity and mortality and build resilience by improving access to safe, quality lifesaving nutrition services for crisis-affected communities in Sokoto, Katsina, and Zamfara (SoKaZa) states. The project aims to enhance community mobilization and sensitization, ensuring families are aware of and can access essential nutrition services, thus improving health-seeking behaviors and timely treatment of acute malnutrition. This will help communities meet their household dietary needs while seeking sustainable solutions to malnutrition.
The Community Management of Acute Malnutrition (CMAM) approach used by IRC and the partners is a methodology[2] for treating acute malnutrition in young children using a case-finding and triage approach. Using the CMAM method, malnourished children receive treatment suited to their nutritional and medical needs. Most malnourished children can be rehabilitated at home with only a small number needing to travel for in-patient care. The CMAM model was developed by Valid International[3] and has been endorsed by World Health Organization (WHO) and United Nation’s Children Fund (UNICEF)[4]. CMAM was originally designed for the emergency context, as an alternative to the traditional model of rehabilitating all severely malnourished children through in-patient care at Therapeutic Feeding Centers. However, it is increasingly being implemented in the context of long-term development programming through integrated approaches, with several Ministries of Health including components of CMAM in their routine services. Through the IMAM (Integrated Management of Acute Malnutrition) program, children who are severely malnourished are managed through the outpatient therapeutic care (OTP), while children with complication are treated through the in-patient program (Stabilization Centers-SC). Coverage surveys (in this case, Semi Quantitative Evaluation of Access and Coverage- SQUEAC survey) are therefore an approach to identifying the uptake of the program among the communities being served by the existing CMAM activities. This will inform the CMAM programming in Sokoto, Katsina and Zamfara (SoKaZa) States which hosts one of the largest IDPS in the northwest and experiencing frequent and ravaging banditry activities. This puts additional pressure on already insufficient and over-stretched nutrition services in the SoKaZa states. As per the IPC analysis published by UNICEF in November 2024, In the northwest, 24 LGAs were classified in Phase 4 (Critical) and 29 LGAs in Phase 3 (Serious). The remaining 18 LGAs were all classified in Phase 2 (Alert).
The primary contributing factors to acute malnutrition in these regions include poor food consumption in both quantity and quality, inadequate feeding practices, poor health services, prevalence of diseases, and low health-seeking behaviors. Moreover, the current economic situation, coupled with food insecurity, limited access to water, sanitation, and hygiene (WASH) services, and persistent issues like banditry, protracted conflict, population displacement, flooding, and general insecurity, exacerbates malnutrition by restricting access to vulnerable populations.
The specific objective on nutrition activities is to reduce the prevalence of acute malnutrition, improve coverage of and access to malnutrition treatment services. As part of the sustainability plan, IRC would like to assess the nutrition situation, barriers to access to malnutrition treatment and ad hoc coverage of nutrition programs. IRC plans to conduct a SQUEAC survey. Below are the details of this methodology.
This SQUEAC survey will be carried out by the IRC and partners including UNICEF and state primary health agency, the nutrition clusters through the steering of an International consultant.
PURPOSE
The overall purpose of the assessment is to estimate the coverage of the CMAM program; to strengthen the routine program monitoring with the aim to increase the program coverage in future; and finally, to allow the IRC, the SoKaZa states and other implementing partners to practice lessons learned from the survey.
OBJECTIVES OF THE SQUEAC SURVEY
The main objective of this assignment is to evaluate access and build skills of key nutrition staff in IRC and at different levels of government and community level local institutions (medical college, community medicine departments) in conducting access and coverage survey using SQUEAC methodology, develop an institutional mechanism and training methodology to ensure continuity of its use by IRC staff, local institutions with minimum supervision and support coverage and factors influencing access of Integrated Management of Acute Malnutrition (IMAM) program using SQUEAC methodology in 10 LGAs across Zamfara, Katsina and Sokoto States.
SPECIFIC OBJECTIVES
To identify the barriers and boosters to program access and coverage.develop in collaboration with the SMoH/SPHCDA in SoKaZa actionable recommendations/action plan to improve acceptance and coverage of IMAM progTo evaluate the spatial pattern of program coverage.To estimate overall program coverage.To make relevant recommendations to reform and to improve the IMAM program.To build the capacity of SMoH/SPHCDA in the SoKaZa States to conduct a SQUEAC assessment in the Future.SCOPE OF WORK
To achieve the above-mentioned objectives, the survey team will be led by the 2 Consultant(s) and will undertake the following:
Design the survey protocol, develop comprehensive tools for data collection and present it to the Nutrition Cluster working group for validation.Before conducting training, develop an appropriate contextual training package for SQUEAC assessment for enumerator and other stakeholder orientation.Conduct training for IRC and other partners nutrition staff on SQUEAC methodology and thereafter guide and supervise them as they take part in the assessment.Organize adequate supervision and coordination of the survey teams in the field; the consultant(s) would conduct field data collection with the team.Analyze data and compile a comprehensive coverage survey report.coordination with the different technical working groups in design and implementation on the coverage surveyPresent investigation results to the nutrition technical working group for validation.SURVEY METHODOLOGY.
This coverage assessment will use Semi Quantitative Evaluation of Access & Coverage (SQUEAC) methodology which is specifically designed to evaluate the coverage of selective feeding programs and focuses on a detailed investigation of factors influencing coverage. The assessment will apply all three stages of SQUEAC methodology.
Stage 1: Routine Program Data Collection and Analysis
This stage entails the collection and analysis of quantitative (routine program monitoring data) and qualitative (contextual) data to understand the trends in admission, defaulting from the OTP sites. Further investigation will be conducted through deep discussions (FGD session) with health facility in-charges, and relevant LGA PHC staff such as Nutrition focal person and State-MEAL officer on contextual analysis of the OTP program to identify boosters and barriers, which later led to set the hypotheses of the second stage.
Case Definition
During this SQUAEC investigation, a case will be defined as “a child from 6-59 months matching the admission criteria of OTP “programme” as stated by the government of Nigeria which is MUAC <11.5 cm & Bilateral pitting oedema(+ or ++)
Quantitative data collection and analysis
The assessment target period from February to April 2025 retrospective quantitative data will be collected and analyzed from OTP treatment cards, facility sites in SokaZa States. Data like OTP performance indicators (cure rate, death rate, defaulter rate, average length of stay and none-response rate), admission and MUAC at admission data, defaulter and MUAC at defaulter data, exit and MUAC at exit data and others such as origin of home/villages where they come from, as well as distance and time to travel to health facilities will be collected and analyzed.
Qualitative data collection
As to the qualitative component, community assessment will be conducted in the OTP site catchment populations where data will be collected through observations, focused group discussions, key informant interviews and informal and group discussions. This is aimed at understanding the socio-cultural factors, which the malnourished children live using different methods of qualitative data collection techniques.
Primary data are collected from caregivers of SAM children in program, caregivers of SAM children not in program, caregivers of defaulted children, religious leaders and community leaders (Mai-anguwa/Hakimi), nutrition program staff, nurse/mid-wife, community health/nutrition volunteers, traditional birth attendants, mother to mother support groups and men groups.
The output of qualitative data is a list of barriers that caregivers of malnourished children face in accessing treatment at OTP sites. This is also an opportunity to identify positive factors, or “boosters”, which encourage caregivers to take their children to health centers for treatment.
The qualitative data will be collected through FGD, and observations mainly to identify boosters and barriers to OTP access and services. Interviews will be conducted with OTP service providers, religious leaders, community leaders, community volunteers, beneficiaries and other program staff and key persons of the OTP program. FGDs will be conducted with women and men groups separately. The qualitative data will be triangulated by various sources and methods with quantitative data.
Stage 2: Small area survey
The small area survey will focus on potentially high and low either coverage areas or any other scenario that might be identified during the stage one (Routine Program Data Collection and Analysis) process of SQUEAC. At this stage a null hypothesis will be set and tested whether to reject or accept it. To test the hypothesis, a number of areas will be selected based on the information gathered and analyzed at stage one in general and based on the number of admissions and defaulter records in particular. The areas selected will be distributed between the survey teams. Each team will use an active/adaptive case-finding methodology to identify cases (as per the case definition) that are either covered or not by the program.
The steps that will be used for testing a hypothesis/making a classification using SQUEAC small area survey data are:
Set the standard (p):
The standard (p) is generally set according to SPHERE minimum standards for IMAM programs (50% for rural areas, 70% for urban areas and 90% for refugee camps/IDPs)
(b) Carry out the small area survey
(c) Use the total number of cases found (n) and the standard (p) to calculate the decision rule. For example, if n = 9 and p = 50% then: d = ⌊n ×p /100⌋=⌊9 × 50 /100⌋=⌊4.5⌋= 5
(d) Apply decision rule: if the number of cases in the program is > d then the coverage will be classified as good otherwise it will be classified as poor.
Stage 3: Formulating the prior mode and wide area survey
Forming a Prior
The Prior is the expression of beliefs about coverage based on qualitative and quantitative data that will be generated by the Mind Map exercise. The mode will be calculated as the mid-point between the “built-up” and “built-down” results. It will be estimated by combining the results of stages 1 and 2 which are the routine program, quantitative and qualitative data analysis as well as the results of the small-area survey. These elements together will generate a probability density—the prior probability distribution or prior. The prior will be calculated from the average of the three coverage estimates from the following three SQUEAC tools.
Simple BBQ(Barrier, Booster Questions) Tool: the simple BBQ tool is the most basic approach to calculate the prior. A uniform weight of 5 points will be attributed to each element (either barrier or booster). The corresponding booster point-sum will be added to the minimum possible coverage (0%) while the barrier point-sum will be subtracted from the maximum possible coverage (100%). The average of these two values will then be calculated to obtain a prior mode.The Weighted BBQ Tool: for the weighted BBQ approach, scores or weights will be attributed to each element that reflects the relative likely effect on coverage. Scores will range on a scale from (0 to 100/ the number of barriers) and denote the importance of each element under the category of barrier and booster. The same point-sum average method will be used like the simple BBQ tool to obtain a prior mode as well.Concept Map: Factors which have both positive and negative relationships with the OTP program will be sketched. The sum of the number of positive relations will be added to zero whereas the sum of the negative relations will be subtracted from 100. Then, the average of the two will be taken as a prior mode.The shape parameters of α prior and β prior will be calculated from the prior mode with a degree of uncertainty oscillating between + 25 percentage points for a first SQUEAC investigation and will deem consistent with prior information.
Sampling Technique of the Wide Area Survey
In order to enhance the reliability of the belief mode, both quantitative and qualitative data will be collected during the wide area survey.
Sample Size Estimation for the Wide Area Survey
The sample size for the wide area survey will be calculated through simulation of the Bayesian-SQUEAC software based on the ‘Prior’ (belief mode) coverage value. The Bayesian-SQUEAC software basically estimates sample size by using a formula,
n=((Mode(1-Mode))/〖(Precision÷1.96)〗^2 -(α+β-2))
In which mode is the prior mode, α and β are shape parameters which will be generated by the Bayesian SQUEAC software and precision is the ideal for the posterior coverage estimate. The wide area sample size will be typically calculated to attain a precision of + 12% around the posterior coverage estimate. The sample size that will be estimated in this way will be then used to estimate in turn the number of areas needed to visit based on the below formula:
n_areas=⌈n/(〖((Average village population〗_(all age))X 〖Percentage of population〗_(6 to 59 months )/100 X((Prevalence of SAM)/100)⌉
Areas in the different OTP service sites will be randomly selected to undertake an exhaustive Active Case Finding survey either by simple random stratified sampling technique or by Central Systematic Area Sampling (CSAS) method.
The wide-area survey will be conducted by using the following two-stage sampling method:
First Stage Sampling Method: a systematic, stratified sampling framework or CSAS will be applied to randomly select villages from a complete list of villages sorted by OTP sites.
Within-community Sampling Method: active and adaptive case-finding technique will be employed as sketched below to identify, find and bring potential SAM cases to a congregation area to be screened. Simple-structured interviews with caregivers will be conducted in each sampled area.
In general, the survey will employ a two stage sampling design. In the first stage the areas will be sampled (by using spatial method i.e. systematic random sampling technique) and the second stage will employ sampling of areas by using the Active and Adaptive Case Finding method.
Single Coverage Estimation (Posterior Generation by Conjugation)
Single OTP Coverage is an OTP coverage estimator that includes both recovering cases that are in the program and recovering cases that are not in the programme and could give an unbiased estimate of overall program performance. By using the Bayes statistical method of beta-binomial conjugate analysis, the prior probability density will be combined with the results of the wide-area survey to calculate the final posterior probability density which is the global coverage estimate. Both point and period coverage could take into account recovery cases. Thus, in this survey the single coverage will be estimated and reported by using the below formula which indicates coverage over a period of twelve months:
Where R_out will be calculated with the formula; R_out ≈ 1/k × (R_in ×(C_in+C_out+1)/(C_in+1)-R_in ) K=3
Cin = Current SAM cases in the programme
Cout= Current SAM cases not in the programme
Rin= Recovering SAM cases in the programme
Rout = Recovering SAM cases not in the programme
K= A Constant Given =3
Data Quality Control and Assurance
EXPECTED DELIVERABLES FROM THE SURVEY
Prepare survey training slides and a pre-survey presentation.Submission of concept note/inception report SQUEAC assessment methodology, tools and guidelines.Submission of training package and completion of the recruitment and training of the enumerators.Submission of the report of Field level data collection, review, and its analysis and power point presentation.Submission final report (after validation and with hard and soft copy of the report and access to raw data).LOCATION
Three states of Sokoto, Katsina and Zamfara, one Local Government authority area for each state. The survey will cover a total of 10 wards across the three states.
S/No
State
LGA
Ward
Community
Population 2023
Number of children 6-59 months
SAM Prevalence
Prevalence of GAM (2024)
Average Pop by Village
Date of the last malnutrition coverage survey
1
Zamfara
Gusau
Tudun Wada
Jauri
124,649
2,240
1.9%
11.1%
11,250
unknown
2
Zamfara
Gusau
Rijiya
Tsunami
9,186
1,837
1.9%
11.1%
7,808
unknown
3
Zamfara
Gusau
Madawaki
Gada Biyu
2,949
590
1.9%
11.1%
2,949
unknown
4
Zamfara
Gusau
Tudun Wada
Samaru
42,436
8,487
1.9%
11.1%
30,000
unknown
5
Sokoto
Sabon Birni
Lajinge
Lajinge
13,319
2,397
4.1%
12.9%
1,203
unknown
6
Sokoto
Sabon Birni
Makuwana
Makuwana
12,635
2,274
4.1%
12.9%
1,575
unknown
7
Katsina
Batagarawa
Ajiwa
Ajiwa
44,230
6,635
4.2%
17.1%
4,423
unknown
8
Katsina
Batagarawa
Dandagoro
Dandagoro
51,349
7,702
4.2%
17.1%
5,135
unknown
9
Katsina
Batagarawa
Jino
Jino
25,857
3,879
4.2%
17.1%
2,586
unknown
10
Katsina
Batagarawa
Batagarawa
Batagarawa
48,972
7,345
4.2%
17.1%
4,897
unknown
DATES AND DURATION OF THE INVESTIGATION
This survey is planned to start from February to April 2025.
DATA QUALITY CONTROL AND ASSURANCE
Data quality will be ensured through:
Intensive training coupled with practical field test (pilot test).Close supportive supervision from the supervisors.Daily meetings during data collection to address key challenges experienced from a typical data collection dayDaily data entry/uploading of data and regular cross checks will be done.Feedback will be given daily prior to data collection for a typical dayKEY PERSONNEL AND FUNCTIONS
Technical Team Lead/SQUEAC Expert:
Coordinate with the IRC, UNICEF and relevant stakeholders for the assessments, • Develop assessment design as per SQUEAC assessment methodology,Develop appropriate and all necessary research tools for data collection consistent with the objectives of the study,Ensure the quality of the study,Technically supervise and monitor roll out of the assessments at the field level and the human resource, andReview and finalize the assessment report in consultation with relevant cluster.Assessment Manager (Consultant needs to identify 3 National Managers for each of the three states):
Perform a rigorous desk review of all pertinent documents related to the IMAM programme,Gather essential information and materials necessary for a robust assessment,Develop a comprehensive SQUEAC training package and train all the field enumerators,Collect both quantitative and qualitative data using SQUEAC methodology and ensure the data quality,Analyse both the quantitative and qualitative data,Provide supportive supervision and manage the enumerators, andProduce a draft final assessment report in consultation with the working groups.Enumerators- Consultants needs to identify local enumerators in each state
Participate in the training and learn about the data collection following SQUEAC methodology, Introduce self to the community, take/fill the consent form before the data collection, Collect/enter the field level data e.g., age, sex, anthropometric assessment etc.,Identify SAM children un/covered by the IMAM program, and Support in data verification and its interpretation, if needed. • Support assessment manager as required.[1] IRC technical Narrative - BHA, 2024
[2] CMAM Model; https://www.wvi.org/nutrition/project-models/cmam
[3] https://www.validnutrition.org/cmam/
[4] https://www.unicef.org/media/96981/file/Statement-WHO-WFP-SCN-and-UNICEF-on-Community-Based-Management-of-SAM.pdf