Guri, Birnin Kudu & Gwiwa LGAs, Jigawa State, Nigeria 21st Nov – 20th Dec 2013 Joseph Njau, Ifeanyi Maduanusi & Emmanuel Bimba
Funded by
.I. ACKNOWLEDGEMENTS
The SQUEAC survey in Jigawa state has been completed with funding by UK Government through Department for International Development (DFID) that is funding the project ‘Working to Improve Nutrition in Northern Nigeria ‘(WINNN) under which SQUEAC is a deliverable.
Valuable guidance and support was extended by the HQ technical team comprising Oscar Serrano (Health & Nutrition Advisor), Saul Guerrero (Head of Technical Development-ACF-UK) and Jose Luis Alvarez (Coverage Monitoring Network- CMN Project Coordinator). Tamanna Ferdous (Nutrition coordinator-ACF Nigeria) was instrumental in setting the pace for the SQUEAC implementation process in Jigawa.
Joseph Njau (CMAM Program Coverage Manager) trained the coverage teams and supervised the implementation process remotely. Ifeanyi Maduanusi (CMAM Program Coverage Officer) and Emmanuel Bimba (M&E Technical Advisor-Jigawa) cascaded the knowledge and supervision of the SQUEAC survey to the nutrition focal persons (NFPs) and survey enumerators at the local government areas (LGAs) of Guri, Birnin Kudu and Gwiwa.
Abdulahi Magama (State Technical Advisor-Jigawa) offered valuable support in communication to the State and LGA authorities, recruitment and organization of the program staff and survey teams for successful completion of the SQUEAC survey. The program staff in individual LGAs are appreciated in a special way for availing themselves and the needed information.
The State Ministry of Health (SMoH) and Gunduma Health System Board (GHSB) were key in granting permission for the SQUEAC team to gain access to the LGA and implement the SQUEAC survey. In addition, they commissioned the LGA based staff to participate in the learning process of implementing a standard SQUEAC survey. The LGA NFPs – Muhammad Garba (Guri LGA), Murja Nasiru (Gwiwa LGA) and Mairo Isah Idris - are commended for participating fully until the entire exercise was completed.
Last but not least, special gratitude goes to the mothers and caregivers of the CMAM beneficiaries who allowed the SQUEAC teams to interview them and shared needed information freely.
.II. ACRONYMS
ACF Action Contre la Faim/Action Against Hunger CV Community Volunteers CI Confidence Interval CHEWs Community Health Extension Workers CMAM Community based Management of Acute Malnutrition CM Community Mobilizers CMN Coverage Monitoring Network DFID Department For International Development DNA Did Not Attend (refers to cases that did not attend CMAM upon being referred) ECHO European Commission Humanitarian aid Office HF Health Facility IYCF Infant and Young Child Feeding LoS Length of Stay MAM Moderate Acute Malnutrition MUAC Middle Upper Arm Circumference OTP Outpatient Therapeutic Programme RUTF Ready to Use Therapeutic Food SAM Severe Acute Malnutrition SC Stabilization Centre SLEAC Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage. SMoH State Ministry of Health SQUEAC Semi Quantitative Evaluation of Access and Coverage TBAs Traditional Birth Attendants UNICEF United Nations Children’s Fund WINNN Working to Improve Nutrition in Northern Nigeria
.III. EXECUTIVE SUMMARY
The Semi Quantitative Evaluation of Access and Coverage (SQUEAC) survey in Jigawa state was conducted in the 3 Local Government Areas (LGAs) of Guri, Birnin Kudu and Gwiwa. Community based Management of Acute Malnutrition (CMAM) services are integrated in five health facilities in each of these LGAs.
The SQUEAC survey was contextualized for each LGA due to the diversity in demographic and socio economic characteristics of these areas and as such contextualized SQUEAC survey would be more specific in identifying factors that affect the program negatively and positively expressed as barriers and boosters1 respectively.
The SQUEAC investigations were carried out independently for the 3 LGAs of Guri, Birnin Kudu and Gwiwa LGAs. The barriers and boosters were therefore unique to each LGA under investigation. .
The processes leading to mode prior building for each LGA was done independently. The four major barriers and boosters are summarized in Table 1 for each of the LGAs:
Table 1: Summary of Barriers and Boosters-Jigawa State’s LGAs LGAs Jigawa Barriers Boosters state Guri The health seeking behaviour that High CMAM program awareness in the prefers traditional healers and program area chemists to treatment at health facility. Sharing of RUTF between siblings Positive opinion about the program result to low compliance. among the community is evident. Absenteeism and defaulting due to Active case finding and community competing activities by mobilization by CVs evident mothers/caregivers. Also mothers/caregivers withdraw the SAM child from the program when they feel child got better. Swamps and distance limits access There is referral of SAM children by
1 Barriers are defined as “anything that restrains, obstructs, or delays access to a program or restrains coverage”. Boosters are defined as “anything that encourages or enables access to a program or leads to an increase in coverage” (Taken from 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, p.212
to CMAM sites TBAs, religious leaders and traditional healers. Birnin Kudu Defaulter cards get mixed up with There is good working relationship discharge as recovered cards between health workers and the CVs making it difficult to do proper follow-up on defaulters. The CVs are not evenly distributed Communities have strong awareness across the CMAM catchment about the CMAM program population.
Inadequate number of health Some caregivers are supported by workers in some CMAM health spouses/husbands to bring SAM cases facilities (sick children) to CMAM sites Uneven distribution of CMAM sites There is strong support to the CMAM (most are located in the southern of program by the community/community Birnin Kudu) provides CVs freely to the CMAM program Gwiwa Stock out of routine drugs especially Good opinion of the program by the amoxicillin (5 months) community Some CVs do not have MUAC tapes Good health seeking behaviour of the to do active case finding in the community in seeking treatment community/Poor active case finding services from health facilities/there is in some communities. evidence of self-referral CVs do not know/or adhere to the Strong awareness of the CMAM program modalities in referral of program in the community SAM cases from the community/(IYCF CVs do not know CMAM information/not integrated)
Therefore if: x=current cases attending the program y=current cases not attending the program n=total current cases t=recovering cases attending the program
Then the results are summarized below:
Table 2: The wide area survey coverage estimates for Jigawa State and its 3 LGAs 2 LGAs in Jigawa State Current Current Recoveri Total Point coverage . cases in SAM ng cases current the cases not (t) SAM
program in the cases (n) (x) program CI 95% (y) Guri 26 24 7 50 50.4%(39.9-61.0) Gwiwa 28 28 11 56 48.1%(38.0-58.7) Birnin Kudu 12 74 10 86 14.0%(6.6-20.6)3 Total cases 66 126 28 192 (3 LGAs)
The coverage estimates for the LGAs in Jigawa State are below the recommended minimum SPHERE standard (50%)4 with exception of Guri LGA
The identified barriers in this SQUEAC assessment should be addressed and another assessment repeated as appropriate in each of the LGAs.
2 Point coverage gives overall accurate measure of this program because generally: there was evidence high default, erroneous discharge of SAM cases as recovered. Most carers were sent home without RUTF. 3 The likelihood results are used to estimate coverage. There is a strong evidence of prior-likelihood conflict in conjugate analysis for Birnin Kudu wide areas survey and therefore, the reason for adopting the likelihood results. 4 SPHERE: Program implemented in rural areas
TABLE OF CONTENTS .I. ACKNOWLEDGEMENTS ...... 2 .II. ACRONYMS ...... 3 .III. EXECUTIVE SUMMARY ...... 4 .IV. INTRODUCTION ...... 10 .V. OBJECTIVES ...... 12 .VI. METHODOLOGY ...... 12 .VI.1. SQUEAC approach and screening model ...... 12 .VI.2. SQUEAC investigation in Jigawa State LGAs ...... 13 .VII. RESULTS...... 14 .VII.1. Stage 1 investigation-Routine program data ...... 14 .VII.1.1. Guri LGA ...... 16 .VII.1.2. Birnin Kudu LGA ...... 21 .VII.1.3. Gwiwa LGA ...... 27 .VII.2. Stage 1: investigation-qualitative data analysis ...... 31 .VII.2.1. Guri LGA ...... 33 .VII.2.2. Birnin Kudu LGA ...... 35 .VII.2.3. Gwiwa LGA ...... 37 .VII.3. Stage 2: confirmation of high and low coverage areas using small area survey and small studies ...... 40 .VII.3.1. Guri LGA ...... 40 .VII.3.2. Birnin Kudu LGA ...... 43 .VII.3.3. Gwiwa LGA ...... 45 .VII.3.4. Prior setting for Guri, Birnin Kudu and Gwiwa LGAs ...... 48 .VII.4. Stage 3: Wide area survey-overall estimate of program coverage ...... 49 .VII.4.1. Guri LGA ...... 53 .VII.4.2. Birnin Kudu LGA ...... 55 .VII.4.3. Gwiwa LGA ...... 56 .VIII. DISCUSSIONS ...... 58 .VIII.1.1. Guri LGA ...... 59 .VIII.1.2. Birnin Kudu LGA ...... 59 .VIII.1.3. Gwiwa LGA ...... 60 .IX. RECOMMENDATIONS ...... 60 .X. ANNEXES ...... 65 .X.1.1. Parameters used in the report ...... 65 .X.1.2. Locations visited in the SQUEAC survey in Jigawa State ...... 66 .X.1.3. Active and adaptive case finding procedure ...... 68 .X.1.4. Sources and Methods-Guri, Birnin Kudu and Gwiwa ...... 69 .X.1.5. Summary of qualitative findings ...... 70 .X.1.6. List of the participants-Jigawa SQUEAC ...... 75 .X.1.7. Birnin Kudu concept map ...... - 76 - .X.1.8. Guri LGA-Concept map ...... - 77 - .X.1.9. Gwiwa LGA-Concept map ...... - 78 -
List of figures FIGURE 1: JIGAWA STATE MAP ...... 10 FIGURE2: PLOT OF ADMISSIONS OVER TIME-GURI LGA ...... 16 FIGURE3: EXITS OVER TIME-GURI LGA CMAM PROGRAM ...... 17 FIGURE4: PLOT OF TIME TO TRAVEL TO SITE BY CARERS-GURI LGA ...... 18 FIGURE5: DISTRIBUTION OF VILLAGES AND CURRENT CASES OVER DISTANCE TO TRAVEL TO CMAM SITE GURI LGA ...... 18 FIGURE5: LENGTH OF STAY GURI LGA...... 19 FIGURE5: ADMISSION MUACS GURI LGA ...... 20 FIGURE7: WEEKS IN PROGRAM BEFORE DEFAULT-GURI LGA ...... 20 FIGURE 9: ADMISSION OVER TIME-BIRNIN KUDU ...... 21 FIGURE10: EXIT OVER TIME BIRNIN KUDU LGA ...... 23 FIGURE11: PLOT OF TIME TO TRAVEL TO SITE-BIRNIN KUDU LGA ...... 23 FIGURE11: LENGTH OF STAY-BIRNIN KUDU LGA ...... 24 FIGURE13: MUAC ON ADMISSION TALLY-BIRNIN KUDU ...... 24 FIGURE14: WEEK IN PROGRAM BEFORE DEFAULT-BIRNIN KUDU ...... 25 FIGURE15: CASES BEING TREATED IN THE PROGRAM THAT SHOULD ARE ELIGIBLE DEFAULTERS ...... 26 FIGURE16: ADMISSION TRENDS- GWIWA LGA ...... 27 FIGURE21: EXITS OVER TIME GWIWA LGA ...... 28 FIGURE17: TIME TO TRAVEL TO SITE PLOT-GWIWA ...... 29 FIGURE18: TALLY OF LENGTH OF STAY FROM ADMISSION TO RECOVERED ...... 29 FIGURE19: TALLY OF ADMISSION MUACS- GWIWA LGA ...... 30 FIGURE21: WEEK IN PROGRAM BEFORE DEFAULT ...... 31 FIGURE22: BARRIERS AND BOOSTERS OF PROGRAM ACCESS AND UPTAKE SMALL AREA SURVEY IN GURI LGA ...... 43 FIGURE23: REASONS OF DEFAULT BY PROGRAM BENEFICIARIES IN SMALL AREA SURVEY IN GURI LGA .. 43 FIGURE24: BARRIERS TO PROGRAM ACCESS AND UPTAKE-BIRNIN KUDU ...... 45 FIGURE25: REASONS FOR DEFAULT-BIRNIN KUDU LGA ...... 45 FIGURE26: BARRIERS TO ACCESS AND UPTAKE OF THE PROGRAM-SMALL AREA SURVEY IN GWIWA LGA ...... 48 FIGURE 27: EFFECT OF BOOSTER AND BARRIERS ON COVERAGE ...... 49 FIGURE28: STRATIFIED VILLAGES FOR EACH LGA-GURI, BIRNIN AND GWIWA ...... 50 FIGURE29: CONJUGATE ANALYSIS-GURI LGA ...... 54 FIGURE30: ESTIMATION OF COVERAGE USING BAYESSQUEAC CALCULATOR-GURI LGA ...... 54 FIGURE31: BARRIER TO PROGRAM ACCESS AND UPTAKE-GURI LGA ...... 55 FIGURE32: BARRIER TO PROGRAM ACCESS AND UPTAKE-BIRNIN KUDU LGA ...... 56 FIGURE33: CONJUGATE ANALYSIS-GWIWA LGA ...... 57 FIGURE34: ESTIMATION OF COVERAGE USING BAYESSQUEAC CALCULATOR-GWIWA LGA ...... 57 FIGURE35: BARRIERS TO PROGRAM ACCESS AND UPTAKE OF WIDE ARE SURVEY IN GWIWA LGA...... 58
LIST OF TABLES TABLE 1: SUMMARY OF BARRIERS AND BOOSTERS-JIGAWA STATE’S LGAS ...... 4 TABLE 2: THE WIDE AREA SURVEY COVERAGE ESTIMATES FOR JIGAWA STATE AND ITS 3 LGAS ...... 6 TABLE 3: SUMMARY OF ADMISSION AND PERFORMANCE INDICATORS OF GURI, BIRNIN KUDU AND GWIWA LGAS ...... 11 TABLE 4: BARRIERS AND BOOSTERS-GURI LGA ...... 33 TABLE5: BARRIERS AND BOOSTER-BIRNIN KUDU LGA ...... 35 TABLE6: BARRIERS AND BOOSTERS-GWIWA LGA ...... 37 TABLE7: FINDINGS OF THE SMALL AREA SURVEY IN GURI LGA ...... 41 TABLE8: RESULTS OF THE ANALYSIS OF SMALL AREA SURVEY FOR GURI LGA ...... 42 TABLE9: RESULTS OF THE SMALL ARE SURVEY ANALYSIS-BIRNIN KUDU LGA ...... 44 TABLE10: FINDINGS OF THE SMALL AREA SURVEY IN GWIWA LGA ...... 46 TABLE11: RESULTS OF THE SMALL AREA SURVEY IN GWIWA LGA ...... 47 TABLE12: SUMMARY OF THE SAMPLE SIZE CALCULATION FOR GURI, BIRNIN KUDU AND GWIWA LGAS . 50 TABLE 13: CALCULATION OF NUMBER OF VILLAGE’S PER LGA FOR THE WIDE AREA SURVEY...... 52 TABLE14: PARAMETERS USED IN CALCULATION OF COVERAGE-GURI LGA ...... 53 TABLE15: PARAMETERS USED IN CALCULATION OF COVERAGE IN BIRNIN KUDU ...... 55 TABLE16: PARAMETERS USED IN CALCULATION OF COVERAGE IN GWIWA LGA ...... 57 TABLE 17: COMMON RECOMMENDATIONS TO CMAM PROGRAM IN GURI, BIRNIN KUDU AND GWIWA ...... 61 TABLE 18: DISAGGREGATED RECOMMENDATIONS TO CMAM PROGRAM IN GURI, BIRNIN KUDU AND GWIWA ...... 63 TABLE19: PARAMETERS USED IN PRIOR SETTING, SAMPLE SIZE CALCULATION AND CONJUGATE ANALYSES ...... 65 TABLE 20: LOCATIONS, SOURCES AND METHODS IN SQUEAC-GURI, BIRNIN KUDU AND GWIWA LGAS .. 66 TABLE 21: SOURCES AND METHODS-GURI, BIRNIN KUDU AND GWIWA LGAS, ...... 69 TABLE 22: LIST OF PARTICIPANTS IN THE JIGAWA SQUEAC SURVEY ...... 75
.IV. INTRODUCTION
Jigawa State is one of the states of Nigeria that constitute Federal Republic of Nigeria. It is situated in the north-western part of the country bordering Kano and Katsina States to the west, Bauchi to the east and Yobe State to the northeast. To the north, Jigawa shares an international border with Zinder region in The Republic of Niger.
Jigawa state has a total land area of approximately 22,410 square kilometers. The state has 27 Local Government Areas (LGAs)5.
Socio-culturally, Jigawa State can be described as homogeneous: it is mostly populated by Hausa People/Fulani, who can be found in all parts of the State. Kanuri People are largely found in Hadejia Emirate, while the tribe of Badawa is mainly found in the north eastern parts. Jigawa has approximately 3.6 million people. The average household size is about 6.7 almost all of which are headed by males6. The map of Jigawa state is shown in figure 1 below.
Figure 1: Jigawa State map
5 Auyo, Babura, Biriniwa, Birnin Kudu, Buji, Dutse, Gagarawa, Garki , Gumel, Guri, Gwaram, Gwiwa, Hadejia, Jahun, Kafin Hausa, Kaugama, Kazaure, Kiri Kasama, Kiyawa, Maigatari, Malam Madori, Miga, Ringim, and Roni 6 http://en.wikipedia.org/wiki/Jigawa_State
Jigawa state has CMAM programs distributed in 12 LGAs of Guri, Gwiwa, Birnin Kudu, Kaugama, Kazaure, Roni, Maigatari, Yaukwasha, Babura, Bikudu, Jahun, and Birnniwa
ACF, as part of WINNN (Working to Improve Nutrition in Northern Nigeria) lead the start-up of the project in Jigawa State in September 2011 and is supporting CMAM in 15 health facilities in 3 LGAs: Guri, Birnin Kudu and Gwiwa. In May 2013, ACF scaled up in 30 more HFs (10 per LGA) to provide IYCF (Infant and Young Child Feeding) services. CMAM has yet to be implemented in these 30 HFs.
The CMAM programs have been implemented within the governments’ existing facilities including the general structure of the Service Delivery Unit (SDU) and Human Resource (HR). Different SDUs within the LGA levels, such as Mother and Child Health Centres, Dispensaries and Health Posts have been considered as the CMAM sites. Each HFs have specific day for the CMAM service delivery, usually once a week. Currently there are not satellite sites and all the CMAM services are provided within the HFs.
The capacity of the HWs working in these facilities is built through training and on-the-job coaching to provide quality services.
ACF provides technical support and build capacity of the staff of state and LGA levels, conducts regular monitoring and provides supervision to ensure the quality of the program. Under WINNN structure, there are 6 key program staff: 1 state technical advisor (STA), 1 Infant and Young child feeding (IYCF) Technical advisor (TA), 1 M&E Technical advisor and 3 LGA technical advisors (LTAs).
The total admissions in all the LGAs and the performance indicators from January to December 2013 are summarized in the table below: Table 3: Summary of admission and performance indicators of Guri, Birnin Kudu and Gwiwa LGAs LGA Admissions December 2013 (Jan-Dec 13) Cure rate (%) Death Defaulter rate Non- rate (%) (%) respondent (%) Guri 1913 52.5 2.2 35.6 3.0 Birnin Kudu 7181 51.3 0.4 40.8 7.4 Gwiwa 2941 89.4 1.8 5.8 3.0
The SQUEAC survey investigation extracted routine data dating 6 months back from the period of SQUEAC implementation (that is the period of June to December 2013). The SQUEAC investigation was undertaken between 25th November and 20th December 2013.
The SQUEAC assessment was guided by the objectives below.
.V. OBJECTIVES
The SQUEAC that was conducted in Jigawa State LGAs of Guri, Birnin Kundu and Gwiwa was guided by the following objectives: 1. To establish barriers and boosters to CMAM program coverage and uptake by the community; 2. To evaluate the spatial pattern of coverage; 3. To estimate overall program coverage for the purpose of measuring its performance; 4. To provide informed recommendations for improvement the CMAM program 5. To build the capacity of the staff of the Ministry of health (MoH)7.
.VI. METHODOLOGY
.VI.1. SQUEAC approach and screening model
SQUEAC approach consists of a set of tools designed to identify and investigate CMAM program coverage and factors influencing it. SQUEAC uses Semi-Quantitative data which is a mixture of quantitative (numerical) and qualitative data. This approach that was employed in the SQUEAC survey in Jigawa State is described below:
Routine monitoring data such as charts of trend of admissions, exits, recovery, as well as in-program data for defaulting were collected. Also, data that were already on beneficiary record cards such as MUAC at admission and residential (home) villages of the program beneficiaries were included. Calendar of events data was obtained from the informal group discussions with variety of informants to inform the trends in seasons, labour demands, climate conditions, diseases and hunger gaps. The information was collated from informal group discussions, case studies, and simple structured interviews with care givers, qualitative data collected using informal group discussions and interviews with caregivers, community elders, religious leaders,
7 The SQUEAC survey incorporated the ministry of health officers at LGA level who supervise the health facilities offering CMAM services. Importantly, this assessment is implemented in an integrated CMAM program delivered in the government owned health facilities using government health officers.
Traditional Birth Attendants (TBAs), Community Volunteers (CVs), Community Health Extension Workers (CHEWs), and community gatherings8. Quantitative and qualitative information was organized into a mind mapping tool (x- mind) and analysed into barriers and booster. Small studies and small area surveys were conducted to confirm or deny hypothesis on areas of high or low coverage that arose from analysis of program and qualitative data. The barriers and boosters were used to calculate the prior mode of the program. Prior mode estimate the coverage of the program using the collected and analysed semi- quantitative data. Data from the nutritional anthropometric surveys conducted in the year 2011 and 2012 were used to estimate SAM cases in villages. Bayesian techniques were employed to estimate overall program coverage with a small sample survey.
The process of SQUEAC is described as investigative, iterative, innovative, interactive, intelligent, informal and in the community process9. Information collected is done continuously from various sources and methods using technique of triangulation by source and method and sampling to redundancy10.
.VI.2. SQUEAC investigation in Jigawa State LGAs
The LGAs of Guri, Birnin Kudu, and Gwiwa have unique characteristics despite the fact that they belong to the same State. Some of the diverse features marking individual LGA can be described in terms of:
Diverse population settlement patterns Geographical features unique to each LGA Local management of the CMAM program sites
To be able to understand the factors affecting CMAM coverage for each LGA, the SQUEAC investigation was designed so that each LGA was investigated independently. The findings of each LGA are reported as such in this report.
For each of the LGAs of Guri, Birnin Kudu and Gwiwa, the SQUEAC method was broken down into a three stage screening test model described as below:
8 Here the community gatherings refer to social groupings of people in market place, near tea shops to share social events or other discussions 9 Semi Quantitative Evaluation of Access and Coverage (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Technical reference. Mark Myatt et al. October 2012 10 The term is used to refer to the process where information is sought repetitively using variety of methods till it yields consistent information exhaustively.
Stage 1:
Areas of low and high coverage were identified. The reasons for coverage failure were identified by thorough analysis of the routine program data and the qualitative information that was collected through informal group discussions and simple structured interviews. Routine data was collected mainly from CMAM site data while other information was collected from ACF program staff11, carer givers, CVs and community members12. The sources and methods used in the SQUEAC survey are annexed in table 4 of this report.
All the information obtained was analysed into barriers, boosters and questions. Furthermore, the information obtained from the respondents was triangulated by source and method to validate the findings.
Stage 2: Confirmation of the areas of high and low coverage and the reasons for coverage failure identified in stage 1 was done using small studies, small area surveys and case studies. In SQUEAC investigation the barriers and boosters as well as reasons for coverage failure were identified. Barriers and boosters developed in stage 1 were also weighed to determine their possible impact on program access and uptake. Utilising the information obtained in this process, analysis is done and further developed into an “informed guess” or a “belief” of what the program coverage is likely to be13.
Prior probability was determined at this stage.
Stage 3: Bayesian techniques were used to estimate overall program coverage with a wide area survey using spatial sample survey14. Statistical analysis was done using the BayesSQUEAC software. Likelihood survey was implemented in the wide area survey where a census of all or nearly the SAM cases in the sampled villages were found. The procedure used in finding the SAM cases in the sample villages is illustrated in annex X.1.3 of this report.
.VII. RESULTS
.VII.1. Stage 1 investigation-Routine program data
The routine data that was analysed was obtained from the selected CMAM sites in Guri, Birnin Kudu and Gwiwa LGAs.
11 ACF staff who give technical support to the MoH staff at LGA and State level 12 Community members are people who live in the community and have mush information on its social systems and events. Some of them are community elders, local farmers, men, other caregivers, shopkeepers, teachers and opinion leaders 13 Reference is made to SQUEAC technical manual in developing prior mode. 14 Semi Quantitative Evaluation of Access and Coverage (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Technical reference. Mark Myatt et al. October 2012
The routine program information was used to plot: Plot of program admissions over time (Program inception to December 2013) Exits over time-Standard indicator graph showing recovered, defaulter, defaulter and death trend. Time to travel to site Length of stay from admission to discharge as recovered. MUAC at Admission for current cases. SAM cases week stayed in program before defaulting. Current cases in program who are eligible15 defaulters Expected versus the observed pattern for time to travel for current cases16.
The investigations on routine data are discussed under each LGA as follows:
15 Eligible defaulters are the children who have attained a criteria to be declared defaulters in the program. However, they continue treatment as current cases as shown in the OTP cards. That is, proper procedures of declaring their defaulter status has not been adhered to. When SAM cases default and are not noted as such, they would not be effectively treated for severe malnutrition. 16 Expected groups villages according to distances from CMAM site while observed groups the attending cases according to the distance from CMAM sites comparisons of the distribution in the plot gives an idea of geographical coverage of a catchment area
.VII.1.1. Guri LGA
Plot of admissions over time
Figure2: Plot of admissions over time-Guri LGA
CMAM services in Guri started in May 2012. The resultant high admission figures noticed for May/June 2012 were due to the high case load of malnourished children that previously had no place to access care. The exceedingly high rate of admissions during this period indicates that many came from outside of the LGA population catchment area.
Admissions dropped sharply after this initial case load as is characteristic of a new programme, and demonstrates a more stabilised admission trend.
In August 2012 the original data plot shows rise and fall of admissions intermittently. The trend data shows decline in admissions. At the beginning of August 2012 flood waters invaded farm lands and left households submerged. This had an effect in causing less food to be available in the household, poor sanitation & living conditions and an upsurge in disease incidences such as respiratory tract infections and diarrhoea. In addition, inaccessible routes may have resulted to most communities not able to access the health facilities especially in areas barricaded by swamps resulting to a drop in admissions (see more analysis in the small area survey in this report).
The flood waters began to recede in the month of December 2012 resulting to improved access to health facilities. Nevertheless, the accumulated effect of this flood on household food security could have resulted into rising trend of admissions between January and June 2013.
The nutrition program also scaled up IYCF services in the same period and more referrals of SAM cases would have resulted in increased admissions at HFs offering CMAM services. Program constraints caused by RUTF pipeline break between August and September 2013 is attributed to drop in admissions. The situation was contained in the late September 2013 and admissions resumed. The overall effect of flooding and RUTF supply break depicts low potential coverage of the CMAM services to the target communities.
Exits over time-Guri LGA
Figure3: Exits over time-Guri LGA CMAM program The CMAM program in Guri LGA has the following characteristics: The recovery rate has been below the SPHERE standards thus less than 75% of the exits are discharged as recovered. Defaulter rates have been significantly higher than the maximum rate of 15% recommended in SPHERE standards. The non-respondent has been markedly high. Ideally it should be kept at less than 5%. The death rate is remarkably low (within SPHERE recommendation of maximum of 10%).
The programme has very low performance rates with a nearly equal ratio of discharged as recovered cases to defaulter cases.
Plot of time to travel to CMAM site
Figure4: Plot of time to travel to site by carers-Guri LGA In the four CMAM sites that were investigated, a high proportion of the caregivers interviewed walked to the health facilities. The maximum walking distance for a caregiver in Guri LGA is approximately 2 hours. However, many caregivers also used motorized transport such as motorcycles (Okanda) and the tuk tuk17 to access services at the health facilities Considering that there is an even spread of admissions from a spread of distances it can be concluded that distance is not a major factor affecting geographical coverage in Guri LGA.
Similarly, the plot of the expected versus the observed pattern for time to travel for current cases (figure 5 below) depicts that distance is not attributed as a factor affecting coverage.
Figure5: distribution of villages and current cases over distance to travel to CMAM site Guri LGA
17 Reference made to 3 wheeled mode of transport common in the community
The distribution of attending current cases compared to the number of villages is generally even indicating that as the number of villages increases or decreases with distance so are the number of attending beneficiaries. In Guri, the population settlement is more or less evenly distributed over the CMAM area and the distribution of incidence cases of SAM is assumed to be homogenous.
Length of stay from admission to discharged as recovered
Figure6: Length of Stay Guri LGA The median length from admission to recovered, 6 weeks, was satisfactory for this program.
MUAC on Admission for the current cases in program
Figure7: Admission MUACs Guri LGA The median MUAC at admission falls between 107-108 mm, which indicated late health seeking behaviour. Inaccessibility of CMAM services by communities in flooded sections or the areas that are susceptible to flooding in Guri LGA is an important contributor to the admission of children with low MUACs.
Weeks stayed in the program before default
Figure8: Weeks in program before default-Guri LGA
Defaulting tends to happen in the beginning weeks of SAM treatment and more so in the first visit. It is important to note that the default rate in the performance of the program is very high. Defaulting is one of the factors affecting effectiveness coverage of the CMAM program. This is because the defaulting SAM cases are not retained in the program as seen in an effective CMAM program. Qualitative information reveals that most of the defaulters were observed to come from the neighbouring State (Yobe) and some nearby communities under Adiyani CMAM catchment that are separated by a River, which during the rainy season reduces access to CMAM site as water level rises. The communities separated by rivers include Kassaga Sabowa and Kassaga Tsohowa
Another factor contributing to high defaulter rates is that caregivers stop attending the program when they perceive that the child is better rather than being discharged by health staff.
Finally population movement, especially Fulani’s, affects the defaulter rates. They move away and therefore carers are not able to attend CMAM services.
.VII.1.2. Birnin Kudu LGA Plot of admissions over time
Figure 9: Admission over time-Birnin Kudu The admissions at the initial phase of the program (attack phase) rose sharply followed by a decline as the program stabilized.
Factors that describe the admission trends are:
Birnin Kudu has lots of river tributaries running across communities making access difficult during the rainy season. Dry season farming takes place between December and May. The drop in admission figures for June through September 2012 could be attrbuted to availability of food (dry season farmed food) within households. In October 2012 there was an RUTF supply chain break in Birnin Kudu LGA which had direct effect on admissions. At the eginning of 2013 there was increased awareness about the program among the communities in Birnin Kudu thus carerss residing in far villages from the CMAM sites were able to attend servces. However, the increased demand for these services during the inception period was not coupled with increased points of service delivery such that it was diffucult for the carers who are in far villages to sustain regular travel and therefore, significant numbers defaulted. There are carers who come from outside Jigawa State for CMAM services in Birnin Kudu. This could explain the increase in admissions in the initial period of 2013, although with many defaults.
There was RUTF pipeline break acoss Jigawa State from August to October 2013. Thus the decreased admissions in this period.
Exits over time-Guri LGA
Figure10: Exit over time Birnin Kudu LGA The CMAM program in Birnin Kudu LGA has consistently had defaulter rates that are significantly higher than 15% and recovery rates that are much lower than the SPHERE minimum standard of 75%. Thus the CMAM program is likely to have low effectiveness.
Plot of time to travel to CMAM site
Figure11: Plot of time to travel to site-Birnin Kudu LGA A significant proportion of the carers walk to the CMAM site. Nevertheless, there is general use of motorized transport also reported to attend the health facility by carers. Some carers mentioned that they could not afford the transport to be able to attend CMAM services regularly. Some cares also attend the CMAM sites from far locations outside the Birnin Kudu LGA. Despite that most carers try to bring the sick children to the CMAM sites, they do not
attend adequately from their child’s admission to discharge due to various factors discussed later in this report. Thus, the defaulter rates in Birnin Kudu are high.
Length of stay
Figure12: Length of Stay-Birnin Kudu LGA The median length from admission to recovered, 6 weeks, was satisfactory for this program. However, this is decreased as a result of the high default rate effect in Birnin Kudu CMAM services.
MUAC on Admission for the current cases in program
Figure13: MUAC on admission tally-Birnin Kudu The median MUAC at admission is very low at 105-106 mm. Thus SAM cases are admitted late into the program.
Week in program before default
Figure14: Week in program before default-Birnin Kudu There is a large default rate that affects program performance in Birnin Kudu. Distance from CMAM sites to the villages where carers come from is a contributor to defaulting. Carers are not able to come regularly to the CMAM site due to increased costs associated with motorized transport to the CMAM sites. There is evidence of ethnic preference18 that affect delivery of services in some facilities in Birnin Kudu. This not only causes defaults but erodes opinion on the CMAM program.
18 In Bamaina CMAM site the Hausa community which is usually migratory is not received well.
Cases in program who are eligible defaulters
Figure15: Cases being treated in the program that should are eligible defaulters The CMAM program in Birnin Kudu LGA had exceptionally high number of current cases who absented until they became defaulters but continued to be treated in the program ordinarily as if they were well followed up SAM cases as they returned to treatment after three weeks. The routine data reveals the following characteristics of the Birnin Kudu CMAM program:
Monitoring of the current cases in the CMAM program is poor and is not able to detect cases that have absented until they became defaulters. This leads to poor program outcomes in terms of retention from admission to discharged recovered and adherence to treatment protocol. Most cases were well into the program being treated as current cases who should have been defaulters.
.VII.1.3. Gwiwa LGA Plot of admissions over time
Figure16: Admission trends- Gwiwa LGA There is a decline in admissions beginning September 2012 and picks up again beginning March 2013. The factors that describe the admission trends are as follows:
Crop harvest and resultant food availability in households commence from September, reaching its peak in December 2012. This could explain may explain the downward trend of SAM admissions in this period. A supply chain break of RUTF occurred in Gwiwa LGA in December 2012 and therefore, there was no active case finding during this period. The slight increase in SAM admissions in January 2013 occurred as backload of cases that could not access care in December and additional new SAM cases when RUTF supply was restored. The rising cases from February 2013 to August 2013 typifies the dry season where food availability in households and household purchasing power diminishes. These are periods of land clearing and preparation that culminate in crop planting between July and August. The trend also shows a slight decrease in admission in September 2013. This was due to another RUTF supply chain break in the entire State of Jigawa in this period.
Exits over time-Gwiwa LGA
Figure17: Exits over time Gwiwa LGA The program in Gwiwa can be described as effective in the major period it has been operating. Defaulter rates have remained relatively low compared to the CMAM services in the other LGAs.
Plot of time to travel to CMAM site
Figure18: Time to travel to site Plot-Gwiwa There is evidence of motorized transport by high proportion of interviewed carers when they attend CMAM services. Most carers are able to attend the CMAM services due to easy access to cheap motorized transport.
Length of stay (LOS)
Figure19: Tally of Length of stay from admission to recovered The CMAM program in Gwiwa routine data shows most cases being discharged as recovered mostly at 6-8 weeks in the program. This LoS is within the SPHERE standards recommendation for TFP19 program (8 weeks).
19 Therapeutic Feeding Program (TFP)
MUAC on Admission for the current cases in program
Figure20: Tally of admission MUACs- Gwiwa LGA SAM cases are admitted into the program when they have a MUAC of less than 115mm. In early admission of SAM cases children would ideally have a MUAC of 114mm, 113 or at least 112mm. Lower MUACs than this on admission indicate late admission into the program. There is evidence that the program case definition is not well understood in the communities even in cases which are readily accessible or close or near to CMAM sites. There are some incidence cases that have remained in the community until they present late with SAM. The admission MUAC is relatively low and indicates late admission into the CMAM program.
Week in program before default
Figure21: Week in program before default There is a high number of default cases in Gwiwa particularly for SAM case that have presented for the first time in the CMAM site.
Significant number of carers would discharge their children before they are formally discharged by the health worker. This resulted in the reported defaulting. .VII.2. Stage 1: investigation-qualitative data analysis Qualitative data was collected from various sources using various methods. The detailed interviews and findings are summarized under the following headings and annexed to this report: Community perception/knowledge of the program and malnutrition, interviews with health workers and community persons20, observations, referral mechanisms and health seeking behaviour & access to CMAM services. Cases studies have also been annexed. The sources of the information and the methods employed for qualitative data are further described in detail in annex to this report. The information was continuously entered into the mind mapping tool (the x-mind21 software). The tools in which the information is presented in are: Barriers and boosters as presented in the tables below. Concept maps per LGA
20 Community members are people who live in the community and have mush information on its social systems and events. Some of them are community elders, local farmers, men, other caregivers, shopkeepers, teachers and opinion leaders 21 The trial version of the mind-mapping tool (x-mind) is free and available at http://www.xmind.net/download/
The analysis and the presentation is summarized below for each LGA’s investigation.
.VII.2.1. Guri LGA The barriers and boosters analysed and presented in the table below. Guri LGA’s Table 4: Barriers and Boosters-Guri LGA
S/n Barriers Un- Weighted Sources Label S/ Boosters Un- Weighte Sources Label weigh n weight d ted ed 1 The health seeking behaviour that -3 -5 3,4,5,6, 1 High CMAM program +3 +5 2,3,4,5,6,7,8, prefers traditional healers and 7,9 awareness in the 9,11 chemists to treatment at health program area facility by health worker 2 Sharing of RUTF between siblings -3 -5 2,3,4,7, 2 Positive opinion about +3 +5 1,2,3,4,5,6,7, result to low compliance. 14,15,1 the program among the 8,9,11 6 community is evident. 3 Absenteeism and defaulting due to -3 -5 2,3,4,14 3 Active case finding and +3 +5 2,3,4,5,6,7 competing activities by carers/carers community mobilization feel their children have recovered and by CVs evident do not continue with program. 4 Swamps and distance limits access to -3 -5 2,3,4,8, 4 There is referral of SAM +3 +5 2,3,4,6,7,9 CMAM sites 14 children by TBAs, religious leaders and traditional healers. 5 Wrong admission criteria/discharge of -3 -4 1,10, 2, 1 5 Health facility well +3 +3 1,2 1 children before recovery i.e. Program 14 organized to allow modalities not adhered to. smooth flow of carers seeking treatment at the CMAM site. 6 Stock-outs routine drugs supplies in -3 -4 1,2.14,1 2 6 Friendly attitude of +3 +3 1,2,4,9,11 2 some facilities 2 health care workers to beneficiaries. 7 Repeated relapse of children is -3 -3 2,3,11,1 3 7 Good working +3 +4 1,2,3,4 3 evident. 4 relationship between the health care workers and the community volunteer are evident. 8 Inadequate trained human resource in -3 -4 1,2,12 4 8 Highly motivated +3 +3 3,16 4 health facilities community volunteers
9 OTP cards have incomplete -3 -3 10 5 9 Follow-up of referred +3 +3 2,3 5 information thus poor follow-up of SAM cases and SAM recovery absentees by CVs to ensure attendance is existent. 10 Lack of support or restriction of carers -3 -4 4,9,11,1 6 10 There is evidence of +3 +4 2,11,14 6 by their spouses to attend the CMAM 4 mother to mother routine visits. referral 11 Lack of distinction between CMAM -3 -2 2,12,15 7 11 There is continuous on- +3 +3 2,3,12,16 7 and other health programs e.g Polio the-job training for the immunization by community results CVs. to CMAM program rejection 12 Distance to the Stabilization centre -3 -4 2,14 8 12 The health workers are +3 +4 1,2,4,7 8 (SC) discourages carers to have their motivated in executing SAM children admitted in SC CMAM services. 13 There are no community volunteers -3 -2 2,14,15, 9 13 Existent of linkage +3 +2 2,3,4,15 9 (CVs) working in areas susceptible to 12 between community flooding or barricaded by swamps. leaders, program staff and community volunteers ensures identification and referral of SAM cases to the CMAM sites. 14 Interruption of program attendance -3 -3 14,12,2, 10 14 IYCF and child +3 +4 12,2 10 by weather conditions / inaccessible 3,4 immunization program terrain especially when it rains in IYCF health facilities resulting to defaults strengthens referral of SAM cases from community to CMAM sites/CMAM integrated into Health facilities. 15 Consumption of RUTF by adults -3 -3 2,3,4,7, 11 (carers, who feel it adds breast milk, 14,15,1 sharing/selling of RUTF to neighbours 6 of the carer, consumption of RUTF by health workers. 16 Stock-outs of RUTF supplies in some -3 +2 1,2,14 health facilities Sum 48% 58% Sum 42% 53%
Upper value anchor 100% 100% Lowest value anchor 0% 0%
Totals 52% 42% Total 42% 53%
.VII.2.2. Birnin Kudu LGA Barriers and Boosters-Birnin Kudu Table5: Barriers and booster-Birnin Kudu LGA S/n Barriers-Birnin Kudu Un- Weight sources Lab S/ Boosters-Birnin Un- Weighted Sources Label weigh ed el n weighte ted d 1 Defaulter cards get mixed up with -3 -5 1,9 1 1 There is good working relationship +3 +5 1,2,3 discharge recovered cards between health workers and the inappropriately making it difficult in CVs following up defaulters/confusion between defaulters and discharged recovered. 2 Some communities within catchment -3 -5 4,5,13, 2 Communities have strong +3 +5 1,2,3,4,5,6 area do not have CVs/CVs not well awareness about the CMAM ,7,8,9,10,1 distributed across the CMAM catchment program 1,13 communities such that some communities do not have CVs 3 Inadequate number of health workers in -3 -5 1,2,4,9,1 3 Some carers are supported by +3 +5 3,5,9 some CMAM health facilities 4,16 spouses/husbands to bring SAM cases to OTP sites 4 Most of the CMAM sites are located in -3 -5 1,3,15, 4 There is strong support to the +3 +5 4,5,6,7,8,9 the southern of Birnin Kudu CMAM program by the ,10,13, community/community provides CVs freely to the CMAM program 5 Stock outs of RUTF and routine drugs -3 -4 1,2,3, 1 5 Some facilities provide carers with +3 +2 1, 1 9,14,16 adequate sitting arrangements as they wait for their SAM case to be served at the CMAM sites 6 Some health facilities are over-whelmed -3 -4 1,2,3,4,9, 2 6 Active case finding in some +3 +4 2,3,4,9, 2 by the huge crowds which constricts 14 communities by CVs client flow during the CMAM
distribution day.
7 Health workers do not adhere to the -3 -3 1,9,12 3 7 There is adequate follow-up of +3 +1 2, 3 CMAM protocols/some children who defaulters by CVs in some the are eligible for discharge continue to be communities who have CVs kept in the program or children are admitted with MUAC of more than 11.5cm 8 Distance/long walking time to CMAM -3 -4 2,3,5,6,9, 4 8 Health workers work for long +3 +4 1,2,3,9 4 sites 14 hours to attend to all the SAM cases in some facilities/some health workers are motivated 9 Most Health workers are not trained in -3 -4 1,2,12,16 5 9 Mother to mother referrals are +3 +4 2,3,9,14 5 CMAM evident 10 Some health workers close health -3 -2 4,9, 6 10 Self-referrals by carers is evident +3 +4 9,13,14 6 facility early preventing carers who have walked long distance from accessing CMAM services 11 Some heath workers have a poor -3 -2 4,14 7 11 All CMAM sites are located within +3 +4 16,1 7 attitude towards the carers of SAM the government health cases facilities//integrated with other health services 12 Tribal Bias towards particular -3 -3 4,5,9,14, 8 Sum 33 43 community (Hausa) compared to Fulani 13 at CMAM sites/Hausa not given adequate CMAM services because of their tribal belonging. 13 Poor support of Carers by -3 -2 9,14 9 Lowest value anchor 0 0 spouses/Husbands 14 RUTF shared amongst adults and -3 -4 4,5,7,8,9, 10 Totals 33 43 between other non-malnourished 14,13 children/some adults believe RUTF treats Ulcers 15 "Suspected" sale of RUTF within the -3 -1 5,9 11 community
Sum 45 53
Highest value anchor 100 100 Totals 55 47
.VII.2.3. Gwiwa LGA
Table6: Barriers and boosters-Gwiwa LGA S/n Barriers-Gwiwa LGA Un- Weighted sources Label S/ Boosters-Gwiwa Un- Weighted Sources Lab weighte n weighte el d d 1 Stock out of routine drugs especially -3 -5 1,3,12 1 Good opinion of the program by the +3 +5 1,3,4,6,7,5,8,9, amoxicillin (5 months) community 10,12,13,14
2 Some CVs do not have MUAC tapes to do -3 -5 1,4,17,18,12 2 Good health seeking behavior of the +3 +5 4,3,6,7,8,9,10,1 active case finding in the community in seeking treatment 3,14 community/Poor active case finding in services from health facilities/there is some communities. evidence of self- referral 3 CVs do not know/or adhere to the -3 -5 1,3,4,5,18,12 3 Strong awareness of the CMAM +3 +5 6,7,8,9,10,12,1 program modalities in referral of SAM program in the community 3,14,15 cases from the community/(IYCF CVs do not know CMAM information/not integrated) 4 The health workers do not adhere to the -3 -3 1,2,3,12 1 4 Active case finding in some villages +3 +3 2,3,4 1 program modalities (e.g. non responders released to the community without follow-up, inappropriate translation of defaulters cards into non-recovered exits and with discharge recovered with intent to "mask" the defaulters, some clients have more than 1 follow-up OTP cards at the site, SAM cases are discharged as recovered without attaining 12.5cm MUAC
5 Health workers newly transferred to -3 -3 1,2,3,12 2 5 Early admission of SAM cases into the +3 +3 1,2 2 CMAM sites are not able to take MUAC program appropriately/Malnutrition screening not effective on admission of SAM case in some CMAM sites 6 One Health facility is poorly organized -3 -2 1,19,12 3 6 Good working relationship between +3 +3 1,3,4,12 3 resulting to poor flow of beneficiaries health workers and the CVs during the process of treatment/carers wait for long times under the sun creating discontent among them on the services in one Health facility-Firji (Firji CMAM site) 7 Withdrawals of some beneficiaries by -3 -2 3,11 4 7 Good community sensitization and +3 +4 3,5,6,7,9,12,13, 4 carers before “proof of cure22” by health mobilization has been done by health 14 workers workers and the CVs 8 Far distance limits access to the CMAM -3 -4 3,4,11,12 5 8 Mother to Mother referral of SAM cases +3 +4 3,5,11 5 centres to health facility 9 Some carers have been rejected in their -3 -2 1,3,5,20 6 9 Integration of CMAM services into +3 +4 1,3,12 6 first visit at the health facilities health services in government health facilities 10 Sharing of the beneficiaries RUTF among -3 -4 6,9,10,13 7 10 The CVs are highly motivated +3 +2 3,4 7 siblings and consumption by adults (evidence of old men eating RUTF in the community shared by CMAM clients. 11 Migration of carers immediately after the -3 -3 3,12 8 11 Referral of suspected SAM cases from +3 +2 3,12 8 rainy season or harvest to search for health facilities within Gwiwa not water and arable land elsewhere offering CMAM services resulting to increased defaults 12 Stock outs of RUTF in one facility making -3 -1 3,11,4, 9 CMAM clients go home without RUTF (3 times)-Dabi 13 Places very far from CMAM sites have no -3 -4 4,12,17 10 CVs/Wards where CMAM sites are not present have no CVs
22 Refers to the status at which the health workers discharges the formerly admitted SAM case as recovered after period of treatment in a nutrition CMAM program. The assessment forming the basis of “proof of cure” is done in reference to IMCI and CMAM guidelines
14 Shortage of trained health -3 -3 3,12 11 workers/transfer of Health workers trained in CMAM out of the designated CMAM health facilities 15 Low awareness of the program especially -3 -2 14,7,6 12 within the communities far from the CMAM sites 16 Long waiting time at the CMAM sites due -3 -3 11,14,3,19,1 13 to large crowds 2
17 Stigmatization of Malnutrition (Tamuwa) -3 -1 18, 14 in the community Sum 51 52 Sum 33 40
Highest value anchor 100 100 Lowest value anchor 0 0
Totals 49 48 Totals 33 40
.VII.3. Stage 2: confirmation of high and low coverage areas using small area survey and small studies
Analysis of data was done using simplified Lot Quality Assurance Sampling (LQAS) classification technique. This was done by examining the number of cases found (n) and the number of cases in the program (covered cases) found. In simplified LQAS, the threshold value (d) was used to determine if the coverage would be classified as satisfactory or not. Value (p) denotes a standard used as a measure of coverage that is recommended for rural, urban, or camp settlements. The standard (p) for rural settlements is 50% coverage and it was used to gauge coverage for all the three LGAs23 in Jigawa state.
If the number of covered cases exceeded value (d), then the coverage was classified as being satisfactory. However, if the number of covered cases found did not exceed value (d) then the coverage was classified as being unsatisfactory. The combination of the (n) and (d) was used as the sampling plan for the LQAS technique used.
In depth group discussions (IGDs) were also done with carers of identified defaulters to establish the reasons why they defaulted. The interview with carers of few defaulters was conducted to establish the reasons that caused the SAM cases to stop attending treatment. The defaulters were identified from the health facility where CMAM service was available within the LGA and traced to the communities where they lived.
Active and adaptive case finding was used to take the teams to the SAM children. A short semi structured questionnaire was administered to the carer if the child was not covered in the program.
The findings and the results of the small area surveys, barriers to access and coverage and reasons for default are presented per LGA in the tables and figures below.
.VII.3.1. Guri LGA The quantitative information and qualitative information indicated patchy coverage in Guri LGA. As such a small area survey was conducted to determine spatial coverage of Guri CMAM program.
The hypothesis that was adopted to guide the small area survey is outlined below.
Hypothesis: ‘The areas that are swampy or prone to flooding were less likely to receive services of CMAM, had low coverage of <50% and high number of SAM cases that could not access
23 Guri, Birnin Kundu and Gwiwa LGAs
CMAM services compared to areas that were not swampy and that were not prone to flooding’.
Therefore, a few villages in areas that were swampy or were prone to flooding and also from areas that were not swampy or prone to flooding were identified. A small area study was done and simplified LQAS used to analyse the data. The barriers to program access and uptake were also analysed.
Focused IGD (in-depth-group discussion) was used as part of the small study on the defaulters at the time of the study to establish the reasons for defaults. The analysis is shown in figure 22 below.
The summary of the findings in Guri LGA are summarized in the table below. Table7: Findings of the Small area survey in Guri LGA Guri villages SAM cases Reasons: not covered Areas barricaded by Total SAM Found 16 swamps or prone to SAM Cases in the 4 flooding: Programme Gaduwa (15km), Dribda SAM cases not in the 8 Distance/Inaccessibility due to flood (10km), Takazza (3km), Programme (12) Kassaga Sabowa (11km), Karidu (10km), 3 Lack of awareness about malnutrition Ajibukarram (7km). 1 Other (Spouse refusal) Areas not barricaded by Total SAM Found 11 swamp or prone to flooding: SAM Cases in the 4 Adiyani Tsangaya Tsakiyar Programme (1km), Abunabo (9km), SAM Cases not in the 2 Distance/Inaccessibility due to flood. Zorio (4km). Programme (7) 3 Lack of awareness about malnutrition 2 Other (Carer was mourning and would not attend & carer had overwhelming Household chores and would not attend)
Analysis of the small area results are analysed as follows: Table8: results of the analysis of small area survey for Guri LGA Areas of small are Parameters Calculation of Conclusion. survey decision rule Areas which are Coverage standard 50% Number of cases covered barricaded by floods (p) (4) is less that decision or prone to flooding Cases covered. 1 rule (8). Therefore: served by the CMAM Decision Rule (d) [n x 50/100]= 24 25 sites. ( n = (4+12) ) Coverage is <50% =8 Gaduwa (15km), Dribda (10km), Takazza (3km), Kassaga Sabowa (11km), Karidu d 8 (10km), Ajibukarram Proportion of non- (7km). covered SAM cases
to total SAM cases
(x)
Areas not barricaded Coverage standard 50% Number of cases covered by swamp or prone to (p) (4) is less that decision flooding: Cases covered 4 rule (5). Therefore:
Adiyani Tsangaya Decision Rule (d) [n x 50/100]=
Tsakiyar (1km), (n= (4+7) Coverage is <50% =⌊ ⌋=5 Abunabo (9km), Zorio (4km).
d 5
The coverage in areas that are swampy and prone to flooding and areas that are not swampy and not prone to flooding is below 50% standard coverage for rural areas. Though the coverage in all the areas is likely to be below 50% the coverage of Guri LGA can be described to be generally homogenous.
24 Number of SAM cases found in the small area investigations 25 Sum of SAM children in villages barricaded by swamps or prone to flooding
Barriers to program access and uptake-Guri LGA
Figure22: Barriers and boosters of program access and uptake small area survey in Guri LGA
Reasons for default by program beneficiaries-Guri LGA
Figure23: Reasons of default by program beneficiaries in small area survey in Guri LGA
.VII.3.2. Birnin Kudu LGA To be able to confirm heterogeneity or homogeneity of program coverage in Birnin Kudu LGA a small are survey was undertaken to test the hypothesis below.
Hypothesis: The program coverage is in areas that are far (more than 3 hours walk) from the CMAM sites is less than 50% while the that of areas which are near the CMAM site (less than 3 hours) is more than 50%.
Focused IGD (in-depth-group discussion) was used in small study on the defaulters at the time of the investigation to establish the reasons for defaults which were also analysed and presented in figure 24 below.
The summary of the findings in Birnin Kudu LGA are summarized in Table 9. Table9: Results of the small are survey analysis-Birnin Kudu LGA Areas of small are Parameters Calculation of Conclusion. survey decision rule Areas that are far Coverage standard 50% Number of cases covered from the CMAM sites (p) (5) is less that decision (>3 hours walk): Total SAM current 29 rule (14). Therefore: Sundimina (6-8hrs), cases Hirin (8-10hrs), Cases covered. 5 Coverage is classified as
Kadangare (2-3hrs) Decision Rule (d) [n x 50/100]= <50% 26 ( n= (5+24) ) =⌊ ⌋=14
d 14
Areas not far from Coverage standard 50% Number of cases covered the CMAM sites (< 3 (p) (4) is less that decision hours walk): Total SAM current 13 rule (6). Therefore: Babaldu (1hr), cases Zaraina (2hrs), Cases covered 4 Coverage is classified as
Malamawa (2hrs) Decision Rule (d) [n x 50/100]= <50%
Masaya (2hrs), (n= (4+9) =⌊ ⌋=6 Samamiya (3hrs)
d 6
26 Sum of SAM children in villages barricaded by swamps or prone to flooding
The he facilities with CMAM are mainly accessible from the southern part of Birnin Kudu LGA. The analyses show that the coverage can be classified as significantly low in both the southern and the northern part of the LGA. In the catchment areas that are close to CMAM services the full potential accorded by the proximity of the CMAM sites to the communities is not fully utilized as observed in a good CMAM program. Thus, there are SAM cases not in the program although they are close to the CMAM site.
The barriers to the program access and uptake indicate knowledge gap in the program case definition that of the community awareness about malnutrition.
Barriers to program access and uptake-Birnin Kudu LGA
Figure24: Barriers to program access and uptake-Birnin Kudu The reasons for default are shown in the figure below:
Figure25: Reasons for default-Birnin Kudu LGA The carers whose children had defaulted were interviewed. Most carers were not able to keep up with the regular follow-up at the CMAM site because the health facilities were too far. Cases of absenteeism were evident from the routine data. This may be associated with defaulting, that is high in Birnin Kudu.
.VII.3.3. Gwiwa LGA A hypothesis was developed to be tested and to confirm possible patch coverage in Gwiwa LGA.
Hypothesis: “Coverage is likely to be ≥ 50% in areas that are near the CMAM sites (within 5 km or 2 hours walking distance with accessible road to the CMAM site) while coverage is likely to be < 50% in areas that that are remote (more than 5km or 2 hours walking distance and with limited access roads to CMAM site27”.
The findings and results of the small area survey for Gwiwa are summarized in the tables 10 and 11 below.
Table10: Findings of the small area survey in Gwiwa LGA Guri villages SAM cases Reasons: not covered Areas remote/with poor Total SAM Found 15 road access and far from SAM Cases in the 4 the CMAM sites (more than Programme 5 Km or two hours away SAM cases not in the 6 Carer not aware the child is from CMAM sites: Programme (11) malnourished Dodal (9km), Guntai(3km- poor road access), 1 Carers prefers traditional Dan’abzin(7km), Burma healer/medicine Ruwa(1km-poor road 1 Far distance access), Jigawa Fulani(6km)
3 Carer rejected at CMAM site/Long waiting time at program site/Stock-out of RUTF Areas near the CMAM site Total SAM Found 18 (within 5 Km or less that 2 SAM Cases in the 15 hours walk from CMAM Programme site): SAM Cases not in the 1 Carer not aware the child is Kwalare Yamma (1km), Firji Programme (3) malnourished (0.5km), Korayal Geri 1 Far distance (0.5km), Gwiwa (0.5km). 1 Carer rejected at CMAM site.
The results of the small area survey are summarized below:
27 The time estimate is the average time it would take a carer to walk to the program site or to be able to afford equivalent of regular cost of motorbike or tuk tuk
Table11: Results of the small area survey in Gwiwa LGA Areas of small are survey Parameters Calculation of Conclusion. decision rule Areas remote/with poor Coverage standard 50% Number of cases road access and far from (p) covered (4) is less that the CMAM sites (more than Cases covered 4 decision rule (7).
5 Km or two hours away Decision Rule (d) [n x 50/100]= Therefore: 28 29 from CMAM sites: ( n = (4+11) ) =⌊ ⌋=7 Dodal (9km), Guntai (3km- Coverage is likely to be poor road access), <50% Dan’abzin(7km), Burma Ruwa(1km-poor road access), Jigawa Fulani(6km) d 7
Areas near the CMAM site Coverage standard 50% Number of cases (within 5 Km or less that 2 (p) covered (15) is more hours walk from CMAM Cases covered 15 than the decision rule site): Decision Rule (d) [n x 50/100]= (9). Therefore:
Kwalare Yamma (1km), Firji (n= (15+3) =9 (0.5km), Korayal Geri Coverage is likely to be (0.5km), Gwiwa (0.5km). >50% d 9
The findings confirm the hypothesis. The quantitative and qualitative findings as well as the small area survey leads to a conclusion that the coverage is heterogeneous in Gwiwa. The
28 Number of cases found in the small area investigations 29 Sum of SAM children in villages barricaded by swamps or prone to flooding
barriers to program access and uptake found in the small area survey are shown in the figure 26.
Figure26: Barriers to access and uptake of the program-small area survey in Gwiwa LGA
.VII.3.4. Prior setting for Guri, Birnin Kudu and Gwiwa LGAs The information that has been collected and analysed as barriers and boosters is used to set the prior mode of each of the investigations in Guri, Birnin Kudu and Gwiwa LGAs. The boosters and Barriers were arranged into 4 top boosters and 4 top barriers in Guri and Birnin Kudu respectively and 3 top boosters and barriers in Gwiwa. Thus to be able to calculate the prior mode the following steps were followed: 1. Each of the booster and barrier was ranked and given a value. 1 denoting the lowest possible score while 4 denoting highest possible score (see the rank scores in the tables showing barriers and boosters for respective LGAs above). These scores are deliberated by the SQUEAC core team and given dependent on the influence each barrier and booster has on the program coverage. This effect is illustrated in the figure 27 below. Weighing was then done on the boosters and barriers. 2. Weights were calculated. This was based on: 1) the best “informed guess” by the persons actively doing SQUEAC 2) assuming that all barriers and booster bears same weight, assigned them equal scores to calculate un-weighted barriers and boosters. Reference is made to SQUEAC technical manual on weighing barriers and boosters. 3. The minimum and maximum possible coverage were deliberated using a calibrated number line from 0(minimum) to 100 (maximum). 0 denotes the lowest possible coverage while 100 the highest possible coverage. This procedure produced the lowest possible coverage and the highest possible coverage in each LGA.
4. Prior was calculated based on the weights derived in barriers and boosters as well as the informed guess of the SQUEAC team in point 2 above. The parameters used in calculation of the mode prior are tabled and annexed in this report.
Figure 27: effect of booster and barriers on coverage
.VII.4. Stage 3: Wide area survey-overall estimate of program coverage
The wide area survey was conducted for each LGA of Guri, Birnin Kudu and Gwiwa.
The Bayesian statistics was employed to analyse the information collected in all the three stages of the SQUEAC investigation.
To be able to estimate the overall coverage of the program the following procedures were used: The quantitative and the qualitative information was analysed into barriers and boosters and used to develop prior( this has been described in the previous section) Information collected from the Likelihood survey was collected. Posterior determination was done. Conjugal analysis of the prior information and the likelihood survey results yielded the posterior.
The procedures are described below, with exception of determination of prior which has been described in previous section.
Likelihood survey sample size calculation A formula for calculating the sample size for wide area survey30 is in the table below. The likelihood survey sample size was calculated based on the parameters in the table in annex 2 in this report. The sample size was calculated for each of the LGA of Guri, Birnin Kudu and Gwiwa. Thus: Table12: summary of the sample size calculation for Guri, Birnin Kudu and Gwiwa LGAs
Main ⌈ ⌉ Sample size formula-n ( ) (likelihood) Guri ⌈ ⌉ 47.63=⌈ ⌉
Birnin Kudu ⌈ ⌉ 47.26=⌈ ⌉
Gwiwa ⌈ ⌉ 52.76=⌈ ⌉
The sample sizes for Guri, Birnin Kudu and Gwiwa LGAs are 48, 48 and 53 current SAM cases respectively. Spatial sampling technique A complete list of villages was prepared for the individual LGAs of Guri, Birnin Kudu and Gwiwa. Spatial systematic sampling (described below) identified sample of villages in which a census of almost all the current SAM children in the villages was done. The entire population was stratified into LGA, Ward and the individual villages as show the structure below:
Figure28: Stratified villages for each LGA-Guri, Birnin and Gwiwa
The sampling was done in two stages:
30Semi Quantitative Evaluation of Access and Coverage (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Technical reference. Mark Myatt et al. October 2012
Stage 1: Spatial sampling method; To be able to estimate the number of SAM children expected per village, the SAM estimates were used derived from the SMART nutrition survey results (2011 and 2012) of Jigawa state. The SAM by MUAC reported rate according to this report was 3.6% (2.3-5.7%)31. The SAM rates of 2.3%, 2.1% and 2.3% were adopted as conservative estimates to calculate the number of SAM cases per village in Guri, Birnin Kudu and Gwiwa respectively. It is important to note that the prevalence used here is the estimated prevalence of the program’s admitting case definition. This is not the weight for height estimate reported in SMART survey32. The number of villages (n) that would need to be visited to get the calculated sample size was estimated using the following formula and summarized in the table below:
31 The SAM rate was derived from MUAC severe acute malnutrition. 32 Semi Quantitative Evaluation of Access and Coverage (SQUEAC)/Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Technical reference. Mark Myatt et al. October 2012
Table 13: Calculation of number of village’s per LGA for the wide area survey.
LGA villages ( ) ⌈ ⌉
Guri =31.96
⌈ ⌉
Birnin =11.04
Kudu ⌈ ⌉
Gwiwa =17.65
⌈ ⌉
The Sampling Interval was calculated and a random number picked between 1 and the sampling interval to get a starting point to start systematic spatial sampling.
Sampling was then done to choose each of the LGA’s derived villages (Guri (40), Birnin Kudu (20) and Gwiwa (25)) from list of complete villages provided for each LGA33. This yielded reasonably even spatial sample from the entire program catchment area.
Stage 2: Within community sampling method Active and adaptive case finding method to find the SAM cases in the sampled village was used. The current SAM cases identified in the community were classified into: Current cases attending the program (covered) Current cases not attending the program (not covered) and Recovering cases in the program.
Presentation of the likelihood survey results The results of the coverage of the program before combing prior and the likelihood to yield posterior (overall program coverage) was calculated as illustrated below: Point coverage using current cases only. It provides snapshot of the program performance and places strong emphasis on the coverage and timeliness of case finding and recruitment. Period coverage that uses data for both current and recovering cases. Includes children that are still in the program because they have not yet met the discharge criteria.
33 List of villages obtained from Expanded Program on Immunization (EPI) in the State Ministry of Health (SMOH)-Jigawa State. December 2013
Parameters for the calculations of the coverage: Current SAM cases attending the program=x Current cases not attending the program (non-covered cases) =y Recovering cases in the program=t Total current cases (x+y)=n
Thus: Point coverage is given by:
Period coverage is given by:
The parameters that are used to set prior calculate sample size and make conjugate analysis for Guri and Gwiwa are summarized in the table in annex 1. The results of the wide area surveys are summarized below:
.VII.4.1. Guri LGA The parameters used in the calculations with the summarized results are presented in the table 14 below: Table14: parameters used in calculation of coverage-Guri LGA Coverage Formula Parameters Guri LGA Coverage estimator Values estimates (at 95%; CI (Credible Interval) Point Total current cases (n) 50 Point coverage Current cases attending 26 coverage=50.4% the program (x) (39.9-61.0%) Current cases not 24 attending the program (y) Conjugate analysis for posterior estimate and overall program coverage estimate for Guri LGA The prior information that was shaped by the parameters (in table 1 in the annexes section of this report) was used for the analysis Alpha prior and Beta prior was combined with the likelihood survey data using a conjugate analysis. The conjugate analysis of the alpha and beta prior shape parameters for mode prior and numerator and denominator of the likelihood estimator yielded posterior probability density. Conjugate analysis is illustrated in figure 29 and 30 below.
Figure29: Conjugate analysis-Guri LGA The prior, likelihood and posterior coverage estimates are illustrated in the BayesSQUEAC calculator in figure 30 below.
Figure30: Estimation of coverage using BayesSQUEAC Calculator34-Guri LGA
34 BayesSQUEAC Version 3.01 available at www.brixton.com
The figures above summarize the prior and the posterior distribution for Guri LGA wide area survey. Point coverage estimator (here referred as the posterior) is preferred in reporting wide area coverage for Guri LGA.
The program coverage Guri LGA is therefore:
Point coverage=50.4% (39.9-61.0%; CI35 95%)
Barriers to program access and uptake in wide are survey-Guri LGA
Figure31: Barrier to Program access and uptake-Guri LGA36
.VII.4.2. Birnin Kudu LGA Table15: Parameters used in calculation of coverage in Birnin Kudu Coverage Formula Parameters Birnin Kudu Likelihood estimator LGA Values results (at 95%; CI (Credible Interval) Point Total current cases (n) 86 Point coverage Current cases attending 12 coverage=14.0 % the program (x) (6.6%-20.6%) Current cases not 74 attending the program (y)
35 Credible interval 36 Others include: Insecurity on the road (1), Child relapsed after previous treatment(1), Carer not able to attend due to pregnancy(1)
Conjugate analysis for posterior estimate and overall program coverage estimate-Birnin Kudu LGA The conjugate analysis for the prior and the likelihood shows strong evidence of prior-likelihood conflict (p=0.0004)37. The prior was overestimated resulting to the true coverage (likelihood results) differing significantly. Therefore, the beta binomial conjugate analysis was rejected and the likelihood survey results were adopted.
Barriers to program access and uptake are shown in figure 32 below
The program coverage Guri LGA is therefore:
Point coverage (likelihood results) =14.0 (6.6-20.6%; CI 95%)
Barriers to program access and uptake in wide are survey-Birnin Kudu LGA
Figure32: Barrier to Program access and uptake-Birnin Kudu LGA Majority of the carers in Birnin Kudu who had non-covered SAM cases were not aware that their child was malnourished. They had no knowledge of program case definition of a child who will qualify for admission in a CMAM program.
.VII.4.3. Gwiwa LGA The results of the wide area survey in Gwiwa LGA are summarized below:
37 If p<0.025 then there is a moderate evidence of prior-likelihood conflict. If p<0.001 then there is very strong evidence of prior- likelihood conflict. When p<0.025 then the conjugate analysis is rejected. This is what has been applied in Birnin Kudu LGA wide area results.
Table16: Parameters used in calculation of coverage in Gwiwa LGA Coverage Formula Parameters Gwiwa LGA Coverage estimator Values estimates (at 95%; CI (Credible Interval) Point Total current cases (n) 56 Point coverage Current cases attending 28 coverage=48.1% the program (x) (38.0-58.7%) Current cases not 28 attending the program (y)
Conjugate analysis for posterior estimate and overall program coverage estimate-Gwiwa LGA
Figure33: Conjugate analysis-Gwiwa LGA BayesSQUEAC calculator presented the program coverage as shown in figure 34 below
Figure34: Estimation of Coverage using BayesSQUEAC calculator-Gwiwa LGA The program coverage in Gwiwa LGA is:
Point coverage=48.1% (38.0-58.7%; CI 95%)
Barriers to program access and uptake in wide are survey-Gwiwa LGA
Figure35: Barriers to program access and uptake of wide are survey in Gwiwa LGA.
.VIII. DISCUSSIONS
The LGAs of Guri, Birnin Kudu and Gwiwa have CMAM services that have unique barriers and boosters. The CMAM services in all the LGAs have the following common features: There is no program to treat moderate acute malnutrition. However the SAM cases are retained in the program for as long as the care is needed and until they have a MUAC of 125mm and above and released directly into the community. RUTF supply break has been noted as a barrier in Jigawa CMAM program. This has contributed to default of beneficiaries as there were over 3 months of supply break in 2013.
The individual coverage estimates and programs are described below.
.VIII.1.1. Guri LGA The CMAM program in Guri has good retention from admission to recovery. However the exits over time depict high defaulter rate. There is active case finding although there are significant SAM cases that are admitted into the program with relatively low MUAC (below 110mm). there is good opinion about the program by the community and when the CMAM services are integrated into the health facility, there is evidence of beneficiaries who are identified passively at the health facility when they came to be treated for other ailments especially childhood diseases related to malnutrition such as diarrhoea and respiratory tract infections as well as malaria.
One of the major factors hindering access is limited geographic accessibility especially for areas that are barricaded by floods or are susceptible to flooding.
Point coverage is the measure used for the program coverage due to the fact that: Recovering cases are more likely to be absentees who would likely remain in the program for longer period from admission to recovered or who would most likely become defaulters. Active case finding is done but not well established. As such, the knowledge that the community has about the program has contributed largely to referrals from the community such as mother to mother referral or word of mouth referrals. There is evidence of carers who brought suspected SAM cases into the program. However there is still large proportion of SAM cases who are not attending the program. This coverage aims to give a picture of the ability of the program to actively get SAM cases into the program.
.VIII.1.2. Birnin Kudu LGA Birnin Kudu CMAM program coverage has some important points. There is not much knowledge of the program among the Majalisa38, Traditional birth attendants and religious leaders. Majority of carers brought their children for CMAM services when they are referred from the health facilities where they sought medical services for other ailments other than malnutrition. Low knowledge on CMAM by health workers resulted in poor beneficiary records, discharge of children erroneously as discharged recovered. Nevertheless, there was a good working relationship between the CVs and the health workers.
RUTF and drug stock outs were a constraint in the program and contributed to increased default rate and long Length of Stay.
38 Majalisa refers to the local social gathering of men in the market areas or near shopping areas or in tea venders shops
Some villages do not have CVs and some are far away from CMAM sites. The number of children that are not in the program among the total current cases investigated in the survey form a large part.
Point coverage is ideally used to describe the program coverage.
.VIII.1.3. Gwiwa LGA
The Gwiwa CMAM program has relatively active case finding and recruitment. This is evident from the MUACs on admission plots (110/109mm). There are also outreach activities that target case finding, defaulter tracing and community mobilization. Peer referrals and also, self-referrals are evident. The patient cohort consisted mainly of the uncomplicated incident cases that can be cured quickly. The program has relatively good recovery rate and low number of defaulters. The median Length of Stay (LoS) is 7 weeks. It is ideal when the SAM cases stay from admission to discharge as recovered for 8 weeks (56 days).
In a nutshell the program can be described to have acceptable case finding that ensures majority of admissions are uncomplicated incident cases which leads to good outcomes (above 75%39 recovery rate and death rates of below 10%) and also good retention from admission to discharged as recovered ensuring there are minimum defaulting (below 15%).
Point coverage is still used to describe the program coverage. Proportion of children that were not attending the program were significant (about half) of all current SAM cases investigated in the survey.
.IX. RECOMMENDATIONS
It is recommended that another SQUEAC investigation be done in 8 months. The program recommendations are summarized as actions with deliverables. The recommendations below places special emphasis on individual LGAs the table 21 below.
39 SPHERE minimum standard indicators for a rural Therapeutic Feeding Program (TFP)
Table 17: Common recommendations to CMAM program in Guri, Birnin Kudu and Gwiwa Common recommendations for Guri, Birnin Kudu and Gwiwa Target area Process Verification Outcome Recruitment of Identify religious leaders and List of potential religious leaders with potential Increase in early referral of malnourished community case finders community leaders by catchment for indoctrination in the program cases41 population Schedule of meeting/ appointments with Decreased default rate and LoS. Identify community volunteers religious leaders Increased knowledge about the program in (CVs) for recruitment in the areas List of potential CVs. the community. far from CMAM sites and from Schedule of training for the CVs areas prone to flooding. List of community (Majalisa40) case finders to Identify Majalisa points where liaise closely with program staff. volunteer case finders may be MUAC tapes for wiling community case finders. recruited Referral slips for volunteer case finders
Identify carers who have attending At the health facilities, consult and create a list of Increased peer and self-referrals as cases as model early case finders mothers who would be willing to be model evidenced by the number of cloak tickets. mothers. Increased early detected cases admitted into Recruit model mothers counselled Train the model carers/mothers on use of MUAC the program 42 in IYCF as case finders. tapes and referral. Reduced defaulter rate Create cloak tickets that would be given to carers of malnourished children upon referral by model carers/mother. Community Map current CV in LGAs catchment List of CV per village/group of villages per CMAM Increased admissions43. sensitization and population for spatial catchment area Increased knowledge about malnutrition. mobilization representation. List of the mapped community groups according Early referral of malnourished children. Map the village heads, the health to LGAs Increased knowledge on modalities of facility committees, ward Training schedules to meet and sensitize the CMAM program development committees and safe community groups regularised with health
40 Refers to the social groupings where male members of the community meet to upraise the events that have happened in the community 41 Religious leaders will advise faithful of the benefits of staying into the program and adhering to regular attendance and proper use of RUTF 42 model carers prevail upon carers with malnourished children to stay in program until discharged as recovered by health facility personnel 43 Evidence on admission registers/cards.
motherhood committees. facility outreach visits Prepare key messages for training community on basic messages on malnutrition
Scaling-up of CMAM to Map CMAM sites in the LGA to Every month with a list of the new satellite sites Reduced distance to site other HFs/establish identify better/less covered areas which are planned in harmony with the current Decrease in Rate of defaulting. 44 CMAM satellite sites On the map, identify HFs with and static CMAM sites. Early case finding increases. without CMAM services Shorter length of stay. Develop a framework for start-up Schedule showing timeline for opening the of satellite sites in areas with less satellite sites and also roll-out of CMAM services HF coverage. in non CMAM sites.
Strengthen case Prepare Weekly/monthly schedule Referral chits/cloak tickets available for each CV Early case recruitment resulting to short LOS referral mechanism to CV activities and action points. with knowledge on their use. Reduced rejection at CMAM site and Equip and train health workers on Maintain file for filled up chits for follow-up of therefore good opinion about the program use of MUAC45 DNAs and defaulters and sustaining active case Number of non-respondents in the program Identify villages with high finding. reduce defaulters. Documentation of reasons for defaulting Improved program effectiveness due to Create a defaulters list. List if villages with defaulters. reduced defaulter and DNAs46. Schedule for defaulter tracing. Increased community referrals of SAM Supervision visit reports RUTF stock supply. Incorporate procurement of RUTF Availability of stocks throughout project cycle. Reduced number of stock outs in the CMAM buffer stock in CMAM Updated month/quarterly stock request sheets sites. programming shared Reduced LoS Work joint schedule for request Reduced defaults as a result of program and delivery of supplies with donor constraints agencies Steady case finding and admissions not affected by RUTF or drug stock outs
44 CMAM satellite offer CMAM services and are associated with a static site (Health facility where CMAM is running along other health services. 45 This prevents referral of cases who have MUAC of more than 115mm to CMAM site 46 Did Not Attend cases
Advocacy Make key messages for use to Tailored messages for advocacy Sustainable RUTF supply at health facility advocate for support of treatment Agreement or draft paper recognising Increased number of health workers trained of malnutrition by government47. partnership between ACF and government and on CMAM Incorporate into the SMoH Agenda the expected deliverables from them Increased geographic coverage of CMAM the burden of malnutrition and Nutrition issues feature as part of the agenda in programs. strategy to address it. SMoH planning/strategic forums. Broadcasting nutrition Identify the local station and Campaign strategy with clear nutrition messages Increased awareness on malnutrition. messages design a nutrition education Early referral of malnourished cases campaign Increased knowledge on modalities of CMAM program
Table 188: Disaggregated recommendations to CMAM program in Birnin Kudu and Gwiwa Birnin Kudu LGA Target area Process Verification Outcome
Strategy to Prepare key messages on Key messages for delivery to the Increased knowledge about program and communicate program program modalities for the community. modalities in the community modalities community Schedule of sessions in certain locations. Improved performance of the CMAM program Identify the key areas that List of health workers to be sensitized. Reduced referral of children with MUAC of above would need sensitization of Training materials as follow-up to CMAM 115mm health worker to adhere to training More accurate follow-up tools such as registers program modalities for instance and OTP patient cards the process of readmission of Decreased gap in SAM case definition between defaulters into CMAM program. program and community.
Training of community Prepare refresher trainings for Standard training manual prepared and Continuity in recruitment and training CV in volunteers current Community volunteers made available to program staff for use to event of turnover as program matures (number with clear training plan. train on key areas relevant to CV. of CV per area). Complete the training plan with
47 This could be inform of profiles that report the magnitude of burden of malnutrition to the economy of the State/country
a clear work plan system
Gwiwa LGA Target area Process Verification Outcome Capacity build the Prepare training resources for List of health workers to be trained Improved capacity of health worker to health workers on health workers. implement quality CMAM program CMAM Identify Health workers for Training materials as follow-up to CMAM Early recruitment of malnourished children due CMAM training. training to improved passive case finding. Prepare CMAM monitoring tool Decreased gap in SAM case definition between with key areas of CMAM program and community. knowledge for health workers
.X. ANNEXES
.X.1.1. Parameters used in the report Table 19: Parameters used in prior setting, sample size calculation and conjugate analyses Guri LGA Birnin Kudu Gwiwa LGA Prior mode by un-weighted barriers and boosters LGA Total barriers 16 15 17 Total Boosters 14 11 11 Average score given to a barrier and booster + or - 3 + or - 3 + or – 3 Total weight- barriers 48 45 51 Total weight -Boosters 42 33 33 Contribution of barriers to coverage 52% 55% 49% Contribution of boosters to coverage 42% 33% 33% Average coverage 47.0% 44% 41% Prior by weighted barriers and boosters48 Total weights to barriers 58% 53% 52% Total weight to boosters 53% 43% 40% Contribution of barriers to coverage 42% 47% 48% Contribution of boosters to coverage 53% 43% 41% Average coverage 47.5% 45% 44.5% Prior calculated from unweighted barriers and boosters and weighted barriers and boosters 47% 44.5% 42.75% Fixed quantity (uncertainty) 25.0% 25% 25 Minimum probable value 22.3% 19.5% 17.75% Maximum probable value 72.3% 69.5% 67.75% Determination of ranges of prior in belief histogram Data: Min –Prior 0.25 0.2 0.2 Max –Prior 0.75 0.7 0.75 Mode -Prior 0.4725 0.445 0.4275 Posterior estimate Precision 0.109 0.109 0.109
48 The system of weighing barriers and boosters involved using trained SQUEAC team who gave values to each barrier and booster based on the perceived impact of each on the program. +or -1&+or -4 were used as minimum and maximum for boosters and barriers respectively.
Median Village Population 360 1150 725 % of 6-59 Months 0.18 0.18 0.18 SAM Prevalence 0.023 0.021 0.023 Sample Size 48 48 53 Derivatives Not (δ) 0.083333333 0.083333 0.091667 Mu(μ) 0.481666667 0.446667 0.443333 Alpha prior (α) 16.83502067 15.45038 12.57733 Beta prior (β) 18.11657933 19.14002 15.79259 Likelihood Sample Size 47.63882084 47.26644 52.76547 17.65784 31.9637821(round 11.0421 (rounded to Number Of Villages to 40) (rounded to 20) 25)
.X.1.2. Locations visited in the SQUEAC survey in Jigawa State Table 20: Locations, Sources and methods in SQUEAC-Guri, Birnin Kudu and Gwiwa LGAs Date of visit Guri LGA-Locations Sources and Method used
25/11/2013 Adiyani CMAM, Health Facility Visit and observation Adiyani Tsangaya CMAM facilities: Data Extraction Tsakiyar, Adiyani Interviews: Health Worker Interview (Four Health Nasardi, Zorio workers -2 males and 2 females), CVs, Carers, in- community Interviews (Community leader, religious leader, a TBA, and a Majalisa gathering), traditional healers, provision seller, opinion leaders.
26/11/2013 Lafia CMAM, Adiyani Health Facility Visit and observation Tsangaya Tsakiyar, Arin CMAM facilities: Data Extraction Community, Interviews: Health Worker Interview (Four Health Madamuwa workers -2 males and 2 females), CVs, Carers, in- Community, Lafia community Interviews (Community leader, religious Community leader, a TBA, and a Majalisa gathering), traditional healers, provision seller, opinion leaders. 27/11/2013 Musari CMAM, Yelwa Health Facility Visit and Observation community, Musari CMAM sites: Data Extraction community, Kujurin Interviews for CV, Program staff, carers of defaulter, Gabbas, Zugobia carers of current SAM cases, Community Interviews community, Guri (a community leader, religious leader, Majalisa gatherings), TBAs. 30/11/2013 & Dribda, Takazza, Small Area Survey and Small Study 1/12/2013 Gaduwa, Kassaga, Tsabowa, Kassaga, Tsohowa, Karidu, & Ajibukaram 3/12/2013 Guri Case-study of Health Worker and organization of qualitative information gathered. 4th 5th & 6th Sampled villages49 Wide Area Survey December 2013 Date Birnin Kudu & Gwiwa Sources and Method used and LGA-Locations 10/12/2013 Firji CMAM and Health Facility Visit and observation communities Data Extraction Duwigi Community Interviews: Health Worker, CVs, careers for current cases attending the program, community interviews Kangire and Yalwan (Community leaders, a religious leader, tea shop CMAM sites seller, Majalisa gatherings, and opinion leaders), tea
49 Sampled villages are annexed to this reports
shop seller 11/12/2013 Korayal CMAM, Sada Health Facility Visit and Observation community, Jallawa CMAM sites: Data Extraction community, Korayal Interviews: CV interviews, carers of defaulter, Maikasua, Rorau community Interviews (a community leader, religious community leader, Majalisa gathering, and a patent-medicine- Yalwan Damai CMAM dealer. 12/13/2012 Dabbi CMAM, Karshi Health Facility Visit and Observation Community Interviews: Health Worker, CVs, carer of current cases attending the program. Bamaina CMAM Case study Communities: Kwalare Small Area Survey and Small Study 13/12/2013 Yamma, Dodal, Firji, Guntai, Dan’abzin, Burma Ruwa, Jigawa Fulani 15/12/2013 Communities: Firji & Case Studies: 2 Community Volunteers, community Shafe leader, Carers in-programme 17th 18th & Sample of villages Wide Area Survey 19th December 2013
.X.1.3. Active and adaptive case finding50 procedure
Visit the community gathering place first and seek permission to visit the village. Request the village leader to provide a key informant of choice. Ask the key informant the case finding question ‘can you show us child who is under-five years and is ‘Tamawa, khana, yabushe, kadawoo, dorti, shanyanyen, Duwawo’, was recently sick (with diarrhea or cough and fever) and is recovering, or is currently vulnerable to ‘Local term of malnutrition’ due to being orphaned, poverty” 50 Local terms of malnutrition usedGo are to fromthe first Guri house LGA in where Jigawa a State,potential Nigeria. case may be found
Check the child is aged between 6 and 59 months Explain the purpose of the survey to the parents and what you will do Measure the MUAC of the child Check for the Oedema sign
Does the child have bilateral Oedema or is the MUAC < 115mm?
Current SAM case Not a Current SAM case
Is the child in OTP? Is the child in OTP?
Ask to see sachet of RUTF and Ask to see sachet of RUTF and health card health card
Current SAM case not in the Recovering SAM case Current SAM case in Normal child, No program the program history of SAM 1. Fill out the tally
1. Fill out the tally sheet sheet 1. Fill out the tally 1. Not included in the 2. Apply questionnaire 2. Thank the caregiver sheet study.
3. Refer the child to 3. Ask case finding 2. Thank the caregiver 2. Thank the caregiver CMAM program site question 3. Ask case finding 3. Ask case finding
4. Thank the caregiver question question 5. Ask case finding
question
Use additional sources or other key informants to inform and improve the search Always ask parents of the SAM children identified whether they know of other cases Continue until no new cases are indicated by any source or all leads point to previously identified cases .X.1.4. Sources and Methods-Guri, Birnin Kudu and Gwiwa Table 21: Sources and methods-Guri, Birnin Kudu and Gwiwa LGAs, S. No Source Method 1 Observation (Health facility) Observation Check list
2 Health workers Simple Structured Interview 3 CVs Simple Structured Interview 4 Community leaders Simple Structured Interview 5 Religious leader (Imam) Simple Structured Interview 6 Traditional healers Simple Structured Interview 7 "Majalisa"-Community groupings in tea places In-depth group discussions 8 Provision store keeper Simple Structured Interview 9 Traditional birth attendant Simple Structured Interview 10 Data (Program data) Data Retrieval 11 Carer (current cases) Simple Structured Interview 12 Program Staff Simple Structured Interview 13 Chemists Simple Structured Interview 14 Carers (defaulters) In-depth group discussions 15 Observation (community) Observation Check list 16 Case studies In-depth group discussions
.X.1.5. Summary of qualitative findings Community perception/knowledge of the program and malnutrition
The community appreciated the program for treatment of malnutrition. Malnutrition in the local dialect is referred by the terms Dauda, Tamouwa, Manyan Kwaniya which variously translates to an exceptionally thin child or the one with an “extended stomach”. Other local terms used in this reference are Malalachia, Mayankwaniya, Tamouwa, Daoda, Tayara, Bayamma, Lallachewa, Yinwa.
Among the Kanuri community in Guri the local names for malnutrition are Chaned and wina (oedema). In Hausa language malnutrition is referred as Datti and Ba abinci.
Among 4 carers that were interviewed at Kangire health facility, one mentioned that she has knowledge of the child who is malnourished in her community whom carer came to the program but there was RUTF stock out. When she visited the facility again, it had closed early (1pm) and she could not access CMAM services.
Carers are generally enlightened and know about the services that treat malnutrition. They mentioned that most of the carers would take their children to health facilities as opposed to traditional healers.
RUTF is known to have instant impact on the children. CVs have continuously enlighten the community that RUTF is medicine for children, however, some community persons interviewed said that the “sweet food” is sometimes shared between the children and it can been enjoyed by adult as well.
The Majelisa have knowledge about the program that gives the “sweet food” to the “sick children”. They call it Tamauwa. Some interviews with Majelisa revealed that there are children who have malnutrition that are treated by traditional healers. They were aware and could mention names of some children treated at Tamauwa. They however mentioned that no one specifically talked to them about the program.
The traditional healers, Majelisa had seen the RUTF but had not seen MUAC tape.
Interviews with health workers Interviews with the health worker reveal that most of the facilities in Birnin kudu especially Kangire CMAM sites have challenges such as; stock out of routine drugs (Amoxicillin, ACT and Albendazole, stock out of RUTF and high defaulting cases attributed to the far distance carers come from. The worst case is when the carer come far distances to access care and they are not able to be given RUTF due to unavailability. Usually, CMAM sites become congested when the stocks arrive after a period without stocks.
Adiyani health workers said that the major challenges are: increase in number of defaulters and reduced number of admission during farming and rainy season due to labour demand and flooding. In addition said that harvest of Pepper makes carers to default or absent as they prefer to go to the farm.
More so, husbands' refusal to provide transportation fare to carers for those coming from far distances contribute significantly to high default rate. Sharing of RUTF by Carers among other children and adults and routine drug (amoxicillin) stock out are major challenges.
Health facilities had active screening for all the children referred to the CMAM site. The health facility staff in Yalwan Damai mentioned that his staff had limited knowledge on CMAM, but the heath facility in charge had received training from ACF but had not cascaded the training done to them. As such observations revealed that defaulters were discharged as recovered on the records.
In Adiyani, All the health workers interviewed reported that about 5-10 new SAM cases are admitted weekly in Adiyani CMAM OTP site, while average clients coming for follow-up is between 28-35. However, they confirmed that other clients accessing the health facility services other than CMAM average between 15 and 25 children on daily basis, including ANC services.
Also the health workers interviewed in Adiyani OTP confirmed that new beneficiaries are referred to the OTP site by CVs through active case finding. Some community leaders and TBAs refer SAM
cases to the OTP site. In addition, near Villages were identified as Tsagayar Tsakiya, Zorio, and Ajibukaran which ranges between 15 minutes to 1 hour to travel by foot while the far villages are Dagana and Arin were identified as far villages where beneficiaries come from to access the CMAM services which will take about 2 hours while travelling by foot. However, other far villages are located in Yobe State, these include Waccakal, Dabba which takes about two and half hours to travel on foot.
Regarding perception about the program the Adiyani health worker said that the communities are very appreciative of the CMAM programme and are always thanking the health workers for the work they are doing for their malnourished children.
To counter defaulting active CVs track and send the defaulters back to the program. They also inform the community leaders who assist in tracing the defaulters in the villages.
The major diseases reported in Adiyani are malaria, diarrhoea, measles, malnutrition, cold and catarrh
Observations Kangire: In Kangire health facility, data tools are not well organized and some OTP cards were missing. However, the benches where the carer sits are well organized. Children that were getting admitted at Kangire CMAM site had MUACs of 113/114mm which indicates early treatment for SAM. Adiyani: Had well organized, with benches and mats, and smooth flow of clients. Data tools were well arranged and the Health Workers are well motivated and exhibited friendly attitude towards the clients and carers.
However, it was observed that some children were admitted wrongly (with MUAC more than 115mm) also noticed during data extraction. Routine Drug stock-out was also noticed, especially, Amoxycilline which has been out-of-stock for since August months to our visit also confirmed from the Health workers.
Some beneficiaries are coming from Daba (Nguru LGA) of Yobe State, contributing to the defaulters.
Most of the defaulters were observed to come from neighboring State (Yobe) and some nearby communities under Adiyani OTP catchment that are separated by a River, which during the rainy season reduces access to CMAM site as water level rises. The communities separated by rivers include Kassaga Sabowa and Kassaga Tsohowa.
Referral mechanism and health seeking behaviour Interviewed carers attending in Kangire CMAM brought their children to the health facilities to be treated for other ailments and were effectively referred for CMAM services. 2 carers first visited
the traditional healer and chemists before they came to the CMAM services. There were 3 carers who visited the health services first when the child became sick. There were evidence of carers who had information about the program from an in-law, another carer or a neighbour. Some carers mentioned that the best person who are able to do referrals for SAM are traditional healers, Majalisa, TBAs and CVs. There are many referrals done by CVs especially in Yalwan Damai Health facility. However, it is difficult to trace beneficiaries who come from far especially outside the LGA. All the workers interviewed reported that they recieve referrals from other H/F that are not providing CMAM services.
Access to CMAM services There are some communities who live far from the CMAM services and some areas do not have community volunteers. The bulk of the CVs come from Kangire health facility51. Most of the cases that were screened in the community by CVs were admitted early into the program. Health worker mentioned that carers would walk to the facility for distances up to 40 minutes. Most would also use the motorized transport such as busses, tuk tuk and motorcycles. Health workers reported that some carers had difficulty in paying transport to come regularly at the program as needed
Case studies: Kangire CMAM site. Two CVs
There are 2 CVs who reside in Ajuwan Tudu. Their names are Auwalu Basiru and Sule Adamu. Both have worked as volunteers in the CMAM program for 18 months by the time SQUEAC investigation was done. Sule Adamu said that he moved from community to community to enlighten people on Tamawa (malnutrition) and describes to them how it is treated and recovered. He mentioned that received 3 training from ACF.
Auwalu Basiru said he has been trained 5 times by ACF, he knows how to access Severe acute malnutrition using the MUAC tape. Bothe CVs feel satisfied when they help their community get malnutrition treated. They mentioned this satisfaction as their motivation to work as Volunteers. Bamaina CMAM site
10 CVs supporting Bamaina CMAM site
They are Saadu Ali from Kadale village, Kabiru Luwanu from Gajara, Maikudi Abdullahi from Bamaina, Harira Yunusa from Bamaina, Yakubu Iliasu from Tsangaya Bamuwa, Salisu Adamu from Bamaina, Mohamed Salisu from Bamaina, Yau Abubakar from Bamaina, Muahmed yakubu from Jigawa, Kamis Liman from Bamaina.
51 Interview with Kangire CMAM site health worker.
Summary of the CVs life in the program
How long have you worked as a CV?
All respondents affirm they have been working as volunteers for over 1 year.
Brief histories of work as CV
Makudi abdullahi and Saadu Ali said they volunteered his service to help his people.
Kabiru Luwanu affirm that his work as a CV is so tasking but his joy remains that his service has brought joy to most served.
Harira is happy with the work confirming that she visit gatherings like naming ceremonies to assess malnourish children, she claim to have a cordial working relationship with the health workers.
Yakubu Ilyasu said he has had a close working relationship with health workers and he mobilizes people in his community and tells them of the program.
Mohammed Salisu has been a CV for almost 2 years stating that he has been actively doing the job even without getting stipends.
Kamis Liman has been trained 5 times by ACF, he routinely attends meetings organized by MoH under support of ACF.
Salisu Adamu said mobilization and active case findings are key to accessing service, he has thus been doing such since he took up the role of a CV.
Yau Abubakar offered himself as a CV to assist in tracking and helping the numerous malnourished children in and around his community.
Mohammed Yakubu said his role as a CV has extended to him being assigned as the guard for the store housing the RUTF. He has thus ensured that supplies are safe.
History of training (organised by MoH but supported by ACF)
The CVs said they have been trained between 3-5 times with 4 being trained 5 times while 3 had been trained 4 times.
.X.1.6. List of the participants-Jigawa SQUEAC
Table 22: List of participants in the Jigawa SQUEAC survey S/N Name of participants Trainers 1 Ifeanyi Imandusi 2 Dr. Bimba Enumerators 1 Aisha M. Umar 2 Munirat Ibrahim Umar 3 Muhsin Mansur 4 Adegbola Janet 5 Ali Baba Bimba 6 Hajia Alti Hamisu 7 Hussaina Muhammad 8 Nafinatu H. Abdullahi 9 Waziri Y. Waziri 10 Dorothy Pius 11 Farouk Abdulsalam 12 Babannan Mato
.X.1.7. Birnin Kudu concept map
Key: Green eclipses: positive factors promoting the program Red eclipses: negative factors affecting the program
.X.1.8. Guri LGA-Concept map
77
.X.1.9. Gwiwa LGA-Concept map
78