Semi Quantitative Evaluation of Access and Coverage ......Abdul Rasheed Muhammad (Local Technical...

Semi Quantitative Evaluation of Access and Coverage (SQUEAC) of Outpatient Therapeutic program

Bungudu, Bakura & Shinkafi LGAs, Zamfara State, Nigeria

25th January – 12th March 2014

ACF international & Save the Children International

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TABLE OF CONTENTS .I. Acknowledgements ...................................................................................................................... 5 .II. Acronyms ..................................................................................................................................... 6 .III. EXECUTIVE SUMMARY ................................................................................................................ 7 .IV. INTRODUCTION ........................................................................................................................ 10 .V. METHODOLOGY ......................................................................................................................... 12

.V.1. SQUEAC approach and screening model ............................................................................ 12

.V.2. SQUEAC investigation in Zamfara State LGAs. ................................................................... 14 .VI. RESULTS .................................................................................................................................... 16

.VI.1. Stage 1 investigation-Routine program data ....................................................................... 16 .VI.1.1. Plot of admission over time - Bungudu, Bakura & Shinkafi LGAs ...................... 17 .VI.1.2. Bugundu LGA –Analysis of routine data ........................................................... 19 .VI.1.3. Bakura LGA-Analysis of routine data ................................................................ 24 .VI.1.4. Shinkafi LGA-Analysis of routine data .............................................................. 28 .VI.1.5. Summary of quantitative data analysis ............................................................ 32

.VI.2. Stage 1: investigation-qualitative data analysis ................................................................... 32 .VI.2.1. Bungudu LGA ................................................................................................... 33 .VI.2.2. Bakura LGA ...................................................................................................... 35 .VI.2.3. Shinkafi LGA .................................................................................................... 37

.VI.3. Stage 2: Confirmation of high and low coverage areas using small area survey and small studies......................................................................................................................... 41

.VI.3.1. Bungudu LGA ................................................................................................... 42

.VI.3.2. Bakura LGA ...................................................................................................... 45

.VI.3.3. Shinkafi LGA .................................................................................................... 47 .VI.4. Forming the prior ................................................................................................................ 50 .VI.5. Stage 3: Wide area survey - overall estimate of program coverage ..................................... 52

.VI.5.1. Bungudu LGA ................................................................................................... 55

.VI.5.2. Bakura LGA ...................................................................................................... 57

.VI.5.3. Shinkafi LGA .................................................................................................... 59 .VII. DISCUSSIONS ........................................................................................................................... 61

.VII.1.1. Bungudu LGA .................................................................................................. 61

.VII.1.2. Bakura LGA ..................................................................................................... 62

.VII.1.3. Shinkafi LGA.................................................................................................... 63 .VIII. RECOMMENDATIONS ............................................................................................................. 64 .IX. ANNEXES................................................................................................................................... 68

.IX.1.1. Parameters used in the report ......................................................................... 68

.IX.1.2. Survey Questionnaire for caretakers with cases NOT in the programme ................................................................................................................. 69 .IX.1.3. Case study with CV from Fulani community ..................................................... 70 .IX.1.4. Active and adaptive case finding procedure ..................................................... 72 .IX.1.5. Mind map-Bungudu LGA .................................................................................. 73 .IX.1.6. Mind map-Bakura LGA ..................................................................................... 73 .IX.1.7. Mind map-Shinkafi LGA ................................................................................... 73 .IX.1.8. List of the participants-Zamfara SQUEAC ......................................................... 74

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.IX.1.9. Weighted and un-weighted barriers and boosters-Bungudu LGA ................. - 75 -

.IX.1.10. Weighted and un-weighted barriers and boosters-Bakura LGA .................. - 77 -

.IX.1.11. Weighted and un-weighted barriers and boosters-Shinkafi LGA................. - 80 -

.IX.1.12. Concept map-Bungudu LGA ....................................................................... - 83 -

.IX.1.13. Concept map-Bakura LGA .......................................................................... - 84 -

.IX.1.14. Concept map-Shinkafi LGA ......................................................................... - 85 - LIST OF TABLES TABLE 1: SUMMARY OF BARRIERS AND BOOSTERS - ZAMFARA STATE LGAS ......................................................... 7 TABLE 2: THE WIDE AREA SURVEY COVERAGE ESTIMATES PRESENTED FOR ZAMFARA STATE AND ITS 3 LGAS ......... 8 TABLE 3: STABILIZATION CENTRE AND CMAM SITES PER LGA ................................................................................ 11 TABLE 4: THE LIMITATIONS IN THE SQUEAC ASSESSMENT AND POSSIBLE MITIGATION MEASURES ........................ 16 TABLE5: BARRIERS AND BOOSTERS-BUNGUDU LGA .............................................................................................. 33 TABLE6: BARRIERS AND BOOSTER-BAKURA LGA .................................................................................................... 35 TABLE7: BARRIERS AND BOOSTERS-SHINKAFI LGA ................................................................................................ 37 TABLE 8: SOURCES AND METHODS FOR BARRIERS AND BOOSTERS ....................................................................... 39 TABLE9: FINDINGS OF THE SMALL AREA SURVEY IN BUNGUDU LGA ...................................................................... 43 TABLE10: RESULTS OF THE SMALL ARE SURVEY ANALYSIS-BAKURA LGA ................................................................ 46 TABLE11: FINDINGS OF THE SMALL AREA SURVEY IN SHINKAFI LGA ...................................................................... 48 TABLE 12: MODE PRIORS ...................................................................................................................................... 51 TABLE13: SUMMARY OF THE SAMPLE SIZES FOR EACH LGA .................................................................................. 53 TABLE14: PARAMETERS USED IN CALCULATION OF COVERAGE-BUNGUDU LGA .................................................... 55 TABLE15: PARAMETERS USED IN CALCULATION OF COVERAGE IN BAKURA LGA .................................................... 57 TABLE16: PARAMETERS USED IN CALCULATION OF COVERAGE IN SHINKAFI LGA .................................................. 59 TABLE 17: COMMON RECOMMENDATIONS FOR THE CMAM PROGRAM IN BUNGUDU, BAKURA AND SHINKAFI LGAS

................................................................................................................................................................... 65 TABLE18: PARAMETERS USED IN PRIOR SETTING, SAMPLE SIZE CALCULATION AND CONJUGATE ANALYSES .......... 68 TABLE 19: LIST OF PARTICIPANTS IN THE ZAMFARA SQUEAC STUDY ...................................................................... 74 TABLE20: BARRIERS AND BOOSTERS-BUNGUDU LGA ........................................................................................ - 75 - TABLE21: BARRIERS AND BOOSTER-BAKURA LGA .............................................................................................. - 77 - TABLE22: BARRIERS AND BOOSTERS-SHINKAFI LGA........................................................................................... - 80 - List of figures FIGURE 1: ZAMFARA STATE MAP SHOWING COLOURED LGAS WHERE SQUEAC WAS CONDUCTED ........................ 10 FIGURE2: PLOT OF ADMISSIONS OVER TIME-BUNGUDU, BAKURA & SHINKAFI LGA ............................................... 17 FIGURE3: EXITS OVER TIME-BUNGUDU CMAM PROGRAM .................................................................................... 19 FIGURE 4: PLOT OF TIME TO TRAVEL TO SITE BY CAREGIVERS-BUNGUDU LGA ....................................................... 20 FIGURE5: PLOT OF TIME TO TRAVEL TO SITE BY CAREGIVERS-3 CMAM SITES IN BUNGUDU LGA ............................ 20 FIGURE6: LENGTH OF STAY BUNGUDU LGA ........................................................................................................... 21 FIGURE7: ADMISSION MUACS BUNGUDU LGA ...................................................................................................... 22 FIGURE8: WEEKS IN PROGRAM BEFORE DEFAULT-BUNGUDU LGA ........................................................................ 23 FIGURE9: EXITS OVER TIME BAKURA LGA .............................................................................................................. 24 FIGURE10: PLOT OF TIME TO TRAVEL TO SITE-BAKURA LGA .................................................................................. 25 FIGURE11: PLOT OF TIME TO TRAVEL TO SITE-4 CMAM SITES IN BAKURA LGA ....................................................... 26 FIGURE12: LENGTH OF STAY-BAKURA LGA ............................................................................................................ 26 FIGURE13: MUAC ON ADMISSION TALLY-BAKURA LGA .......................................................................................... 27 FIGURE14: WEEK IN PROGRAM BEFORE DEFAULT-BAKURA LGA ............................................................................ 27

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FIGURE15: EXITS OVER TIME SHINKAFI LGA .......................................................................................................... 28 FIGURE16: TIME TO TRAVEL TO SITE PLOT-SHINKAFI LGA ...................................................................................... 29 FIGURE17: TIME TO TRAVEL TO SITE PLOT-3 CMAM SITE IN SHINKAFI LGA ............................................................ 30 FIGURE18: TALLY OF LENGTH OF STAY FROM ADMISSION TO CURE-SHINKAFI LGA ................................................ 30 FIGURE19: TALLY OF ADMISSION MUACS-SHINKAFI LGA ....................................................................................... 31 FIGURE20: WEEK IN PROGRAM BEFORE DEFAULT-SHINKAFI LGA .......................................................................... 31 FIGURE21: BARRIERS TO PROGRAM ACCESS AND UPTAKE- SMALL AREA SURVEY IN BUNGUDU LGA ...................... 44 FIGURE22: REASONS OF DEFAULT BY PROGRAM BENEFICIARIES IN SMALL AREA SURVEY IN BUNGUDU LGA ......... 45 FIGURE23: BARRIERS TO PROGRAM ACCESS AND UPTAKE-BAKURA LGA ............................................................... 47 FIGURE24: BARRIERS TO ACCESS AND UPTAKE OF THE PROGRAM-SMALL AREA SURVEY IN SHINKAFI LGA ............. 49 FIGURE 25: EFFECT OF BOOSTER AND BARRIERS ON COVERAGE .......................................................................... 51 FIGURE26: PRIOR MODE-BUNGUDU, BAKURA AND SHINKAFI LGAS ....................................................................... 52 FIGURE27: STRATIFIED VILLAGES FOR EACH LGA-BUNGUDU, BAKURA AND SHINKAFI ............................................ 54 FIGURE28: ESTIMATION OF COVERAGE USING BAYESSQUEAC CALCULATOR-BUNGUDU LGA ................................. 56 FIGURE29: BARRIERS TO PROGRAM ACCESS AND UPTAKE-BUNGUDU LGA ............................................................ 57 FIGURE30: ESTIMATION OF COVERAGE USING BAYESSQUEAC CALCULATOR-BAKURA LGA .................................... 58 FIGURE31: BARRIER TO PROGRAM ACCESS AND UPTAKE-BAKURA LGA ................................................................. 59 FIGURE32: ESTIMATION OF COVERAGE USING BAYESSQUEAC CALCULATOR-SHINKAFI LGA ................................... 60 FIGURE33: BARRIERS TO PROGRAM ACCESS AND UPTAKE OF WIDE ARE SURVEY IN SHINKAFI LGA. ....................... 60 FIGURE 34: ILLUSTRATION OF THE RECOMMENDED SQUEAC AUDIT CYCLE FOR ZAMFARA STATE .......................... 64

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.I. Acknowledgements

The SQUEAC1 study in Zamfara has been completed with generous support from the UKaid through the department for international development (DFID) project that is funding a project titled ‘Working to improve Nutrition in Northern Nigeria’ (WINNN). ACF international and Save the children International (SC) worked in a coordinated effort in the entire process of planning, coordination and supervision of the SQUEAC study in the three LGAs of Bungudu, Shinkafi and Bakura. In particular Tamanna Ferdous (ACF’s Nutrition Coordinator) and Karina Lopez (SC’s Senior Nutrition Advisor) pre-planned the study and gave a general orientation in regard to the timing and implementation. Oramalu Adaeze (SC’s Nutrition Advisor) was pivotal in organising and coordinating the SC field teams to participate in the SQUEAC study. Joseph Njau (ACF’s CMAM Program Coverage Manager) remotely supervised, gave direction in SQUEAC implementation process and offered technical support in the execution of the SQUEAC study. Ifeanyi Maduanusi (ACF CMAM Program Coverage Deputy Manager) cascaded the SQUEAC knowledge in a training to the SC, SMoH & enumerators and also supervised all the stages of the SQUEAC study in Zamfara. Taiye Babarinsa (SC’s field manager in Zamfara) was valuable in organising all the field activities while Lawani Babatunde (State Technical Advisor-Zamfara) facilitated various sessions in training of the SQUEAC tools. Abdul Rasheed Muhammad (Local Technical Advisor-Shinkafi) and Ayowumi Ogunjobi (M&E Advisor-Zamfara) gave valuable support in supervising field activities. The State Ministry of Health (SMoH) who granted permission for the SQUEAC team to gain access to the Local Government Areas (LGA) and implement the SQUEAC assessment are commended. Last but not least, the caregivers of the CMAM beneficiaries who allowed the SQUEAC teams to interview them and freely shared needed information are, also, acknowledged. Authors: Joseph Njau & Ifeanyi Maduanusi ACF International

1 Semi Quantitative Evaluation of Access and Coverage

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.II. Acronyms

ACF Action Contre la Faim|Action Against Hunger|ACF International CV Community Volunteer CI Confidence Interval CBO Community Based Organization 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 HW Health Worker IYCF Infant and Young Child Feeding LoS Length of Stay LGA Local Government Authority MUAC Middle Upper Arm Circumference INGO International Non-Governmental Organization OTP Outpatient Therapeutic Programme SAM Severe Acute Malnutrition SC Stabilization Centre SCI Save the Children International 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

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.III. EXECUTIVE SUMMARY

Save the Children International (SCI) is supporting State Ministry of Health (SMoH) to implement CMAM program in the Local Government Areas (LGAs) Bungudu, Shinkafi and Bakura in Zamfara State. A Semi Quantitative Evaluation of Access and Coverage assessment (SQUEAC) was implemented in each of these LGAs individually, taking cognisance of the specific characteristics of each program including diversity in the demographic and socio economic makeup of these areas. Each independent SQUEAC investigation covered stages 1, 2 and 3 of the methodology. The barriers and boosters2 were therefore, unique to each LGA under investigation. Three principal barriers and boosters for each LGA have been tabulated below. Table 1: Summary of barriers and boosters - Zamfara State LGAs LGAs Barriers Boosters Bungudu Non-adherence to CMAM protocols3. Communities have good opinion of the

programme Poorly motivated health workers (HWs)

resulting in low levels of commitment. The HWs see CMAM as additional burden.

Good linkage between CMAM services and the community4.

Lack of Community Volunteers (CVs) in villages far from CMAM sites contributing to low level of active case finding

Good awareness of the CMAM programme in communities

Bakura Insecurity in some political wards (within

many communities/settlement5) Good opinion of the programme by care givers and the community members.

CVs are not evenly distributed; they are concentrated in villages close to the CMAM sites6

Strong community ownership and participation7.

2 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 3 This includes but not limited to: incomplete filling of client cards (sensitive client information not captured; walking distance, weight, date of admission/follow-up visits, no outcome indicated); many cases admitted on MUAC ≥ 115; non-confirmation of MUAC by health workers at OTP sites; total mix-up of OTP cards making it hard to track SAM cases once admitted in the program, some cards are not demarcated by outcome. 4 Evidenced by self-referral of caregivers, mother-mother referral, spouse referral, referrals by TBA. Also referrals by community leaders, traditional healer and patent-medicine dealer were evident. 5 This affects access to CMAM Services in Damri and Dambo 6 Places/communities far from OTP Sites do not have CVs for case finding 7 Evidence is seen in provision of shades, toilets and water by CVs in some CMAM sites (OLPC and Danmanau); provision of lunch by community leaders to CVs, health workers, and some time carers (Danmanau, Dambo & OLPC) and also digging of water well in Rini CMAM site by ward development committee.

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There is low awareness of the program in settlements far from the CMAM site8

Mother to mother referrals. Also referrals are done by community leaders and patent medicine vendor9.

Shinkafi CVs absent in communities far from CMAM

sites but concentrated in areas close to them (CMAM sites).

Strong community ownership10

CMAM sites are cut-off or rendered inaccessible by flood during the rainy season11

Good awareness of the programme in communities

RUTF12 given to Severe Acutely Malnourished (SAM) cases at CMAM site is shared among adults at home.

CMAM services hosted at the health facilities.

Assessing percentage coverage in each LGA, the parameters are as follows. The results are in table 2 below:

• x=current SAM cases attending the program • y=current SAM cases not attending the program • n=total current SAM cases

Table 2: The wide area survey coverage estimates presented for Zamfara State and its 3 LGAs

LGAs Current cases in the

program (x)

Current SAM cases not in the

program (y)

Total current SAM cases (n)

Point coverage13.

CI 95%

Bungudu 22 46 68 35.0%(26.3-44.7)14 Bakura 38 40 78 47.4%(38.4-56.5)15 Shinkafi 22 25 47 47.0%(36.3-58.0)16 Total cases (3 LGAs)

82 111 193

The coverage estimates for the LGAs in Zamfara State are all below the recommended minimum SPHERE standard (50%)17.

8 This is due to low sensitization and also low active case finding in places far from CMAM sites. 9 This term refers to the sellers who have government permission to sell patent medicine that treats human ailments/diseases. Patent medicine refers to proprietary drugs that are considered safe to sell to the general public in prepackaged form, and include common drugs for instance: pain-relieving tablets and cough syrups 10 Community has built shades, provided benches, water and food at Fakai, Galadi, Shanawa CMAM sites 11 The CMAM sites made inaccessible are Fakai and Galadi. 12 Ready to Use Therapeutic Food 13 Point coverage gives overall accurate measure of this program because generally: there was evidence high default, erroneous discharge of SAM cases as cured. Most carers were sent home without RUTF. 14 Z=0.8; p=0.4228 15 Z=0.44; P=0.6606 16 Z=0.04; p=0.9686 17 SPHERE: Program implemented in rural areas

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The identified barriers in this SQUEAC assessment should be addressed to improve the program coverage. The following areas of improvement should be considered18:

• Improved Community Mobilization and sensitization in all wards of the Local Government Area LGA

• Increase awareness on program modalities • Recruitment of additional case finders besides the Community Volunteers (CVs) • Consider innovative ways to motivate of CVs. • Establish mechanism for consistent RUTF supply. • Consider health workers for training to improve SAM case monitoring in the CMAM

program. • Consider health workers and community health workers for training on CMAM • Scale-up of CMAM services to include HF not implementing CMAM • A repeat SQUEAC assessment where feasible in each of the LGAs in 6 to 12 months.

18 Refer to the comprehensive processes, outputs and expectations for improvement of the program in table 17 which contains recommendations for improving the program

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.IV. INTRODUCTION

Zamfara state has 14 local governments19. It covers a land area of 38,414 square kilometres. It has Sokoto state to the North, Kebbi and Niger states to the west, Katsina to the east and Kaduna to the south.

Figure 1: Zamfara State map showing coloured LGAs where SQUEAC was conducted

Save the Children is implementing the CMAM program under the WINNN project in Zamfara State in Partnership with the State Ministry of Health20. Start-up of the CMAM program began with 15 CMAM sites (5 in each LGAs of Bungudu, Bakura and Shinkafi) in September 2012, with 1 Stabilization Center in each of the LGAs. Initially, the emphasis was on advocacy and training in close collaboration with the SMoH prior to implementation of actual CMAM services (September 2012). The program scaled up by adding 5 additional CMAM sites in March 2013 (2 sites each in Bungudu and Bakura LGAs, and 1 site in Shinkafi LGA). Another stabilization centre

19 Anka, Bakura, Birnin Magaji/Kiyawa, Bukkuyum, Bungudu, Chafe, Gummi, Gusau, Kaura, Namoda, Maradun, Maru, Shinkafi, Talata Mafara & Zurmi 20 Prior to implementation of the project a Memorandum of Understanding (MoU) was signed between Save the Children and Zamfara State

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was opened in July the same year in Bungudu. The number of CMAM OTP sites in each of the LGAs is illustrated in table below:

Table 3: Stabilization centre and CMAM sites per LGA

S/N LGA Number of CMAM OTP sites (September 2012)

Number of CMAM OTP sites (March 2013)

Number of Stabilization Centers

1 Bungudu 5 7 2 2 Bakura 5 7 1 3 Shinkafi 5 6 1 The CMAM program is managed in a structure which has three program staff (1 Local Technical Advisors – LTA, 2 Local Government Community Engagement Consultants) in each LGA who support implementation of the CMAM programme. At the State level, the State Technical Advisor –STA, Infant and Young Child Feeding (IYCF) Advisor, and Monitoring and Evaluation Coordinator provides technical support and coordinates the CMAM activities, with a Field Manager as the administrative head. Additionally, Nutrition Advisors and M&E Advisor at the Federal Capital are in charge of the technical aspects of the program while the Chief of Party is the overall Program Manager. The CMAM services (which are delivered once a week in all the three LGAs) are implemented through the existing government structures (primary health care and the general hospital21) in close collaboration with the government health workers and community volunteers. The capacity of the HWs working in these facilities is built through training and on-the-job coaching to provide quality services. A SQUEAC assessment was therefore planned for the three LGAs to evaluate the coverage of the CMAM program. The SQUEAC investigation was undertaken between 27th January and 12th March 2014. It 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 of the CMAM program.

21 Bungudu: Fantaru MDG, Yarkatsina, Tofa PHC, WCWC Bungudu, Nahuche PHC, Samawa PHC & Kurar Mota PHC; Bakura: Danmanau PHC, Dambo PHC, Rini PHC, OLPC, Yarkofiji PHC, Damri PHC & Dankaiwa; Shinkafi: Kware PHC, kurya PHC, Fakai PHC, Katuru PHC, Galadi PHC & Shanawa PHC

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5. To build the capacity of the staff of the State Ministry of Health (SMoH)22in conducting a coverage assessment.

.V. METHODOLOGY

.V.1. SQUEAC approach and screening model

The SQUEAC approach consists of a set of tools designed to identify and investigate CMAM program coverage and factors influencing it. SQUEAC is semi-quantitative, which means that it blends quantitative and qualitative data. The process of SQUEAC has been described as investigative, iterative, innovative, interactive, intelligent, informal and a community process23. Information is analysed and re-analysed in an on-going process which aims to triangulate data from various sources and through various methods to the point of saturation (i.e. there is no more new information)24.

The process is as follows:

• Routine performance monitoring data on admissions and exits is analysed to highlight positive and negative trends at facility level. Data that is normally included on beneficiary record cards such as MUAC at admission and home villages of the program beneficiaries (including distance from the CMAM sites) is also analysed in order to get a sense of if and how geography plays a role in access and coverage and whether there is a trend of late admissions.

• A seasonal calendar is created during informal group discussions with a variety of informants in order to understand how admission and exit trends may be affected by seasonal fluctuations including labour demands, climatic conditions, disease burden and availability and access to food.

• Qualitative data is collected via case studies, simple structured interviews, informal group discussions and in-depth interviews with a broad section of the community (including health care workers, caregivers, community elders, religious leaders, Traditional Birth Attendants (TBAs), Community Volunteers (CVs), Community Health Extension Workers (CHEWs) and during community gatherings25). The aim of this step is

22 The SQUEAC survey incorporated the State 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 government owned health facilities using government health officers. 23 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 24 The term is used to refer to the process where information is sought repetitively and exhaustively using variety of methods from different sources till it yields consistent information. 25 Here the community gatherings refer to social groupings of people in market place, near tea shops, social events or other discussions.

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to identify boosters and barriers to access in a manner which provides an in-depth understanding of the factors identified.

• All of the quantitative and qualitative information thus far collected is triangulated according to data collection method and source. It is then organized using a mind mapping tool (x-mind) and concept maps are developed in order to understand how the factors interlink.

• Based on the above analysis a hypothesis is formulated which aims to identify areas with high and low coverage according to certain key boosters and barriers.

• Small studies and small area surveys are conducted to confirm or deny this hypothesis and the results are fed into the ongoing analysis.

• Boosters and barriers are weighted according to the number of times they appear in the data set, their reliability (assessed via triangulation) and their perceived level of impact on access and coverage.

• The weighted barriers and boosters are then used to calculate the prior mode of the program. Prior mode estimates the coverage of the program using the existing data and is understood to be an informed guess of coverage.

• Data from the recent anthropometric surveys26 is used to estimate the number of SAM cases per village and a sample size for the wide area survey is generated using a Bayesian calculator developed by Brixton Health27.

• A wide area survey is conducted, using active case finding methodology to identify all cases of SAM in the sampled villages and establishing whether or not these children are currently enrolled in the CMAM program.

• Bayesian techniques are employed to estimate overall program coverage. • An action plan is developed aimed at diminishing barriers and strengthening existing

boosters (and extending them where feasible to other areas). This action plan should include means, methods and targets to monitor progress.

Design of the SQUEAC assessment

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 the program staff engaged by Save the Children International program staff28, caregivers, CVs and community members29. The sources and methods used in the SQUEAC assessment are annexed in this report.

26 In this case from 2011 and 2012 27 http://www.brixtonhealth.com 28 Save the children International staff who give technical support to the MoH staff at LGA and State level 29 Community members are people who live in the community and have a lot of information on its social systems and events. Some of them are community elders, local farmers, men, other caregivers, shopkeepers, teachers and opinion leaders

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In this stage the information obtained was treated as existing data and was analysed into barriers and boosters. Questions that arose in the process of analysing the negative and the positive factors affecting the program in the course of the investigation were recorded. These questions were used when undertaking further investigations and made use of new sources and methods until search for relevant collaborative information was exhaustive. This is the principle of sampling to redundancy that was employed in the investigative process. When this was done 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 area survey. Analysis of spatial pattern of coverage was done and in this stage the barriers and boosters were revised basing on the current new evidence.

Forming the prior

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 was done and further developed into an “informed guess” or a “belief” of what the program coverage was likely to be30.

Stage 3:

Bayesian techniques were used to estimate overall program coverage with a wide area survey using spatial sample survey31. Statistical analysis was done using the Bayes SQUEAC software. Likelihood survey was implemented in the wide area survey where a census of all or nearly all 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.1.4 of this report

.V.2. SQUEAC investigation in Zamfara State LGAs.

The LGAs of Bungudu, Bakura and Shinkafi were considered to have unique characteristics despite the fact that they belong to the same State. The diverse features marking out individual LGA can be described in terms of:

• Diverse population settlement patterns • Security situations affecting different sections of the LGA • Geographical features unique to each LGA.

30 Reference is made to SQUEAC technical manual in developing prior mode. 31 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

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• 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.

Planning of the SQUEAC assessment

The Zamfara SQUEAC assessment was planned and carried out from 25th February – 13th March 2014. A planning meeting with the SCI Zamfara WINN Team, Field Manager and ACF’s CMAM Coverage DPM was held on 26th March. Between 27th and 31st January 2014, a security meeting and a briefing by the WINN and LGA Local Nutrition Officers were held to identify wards and communities that were faced with security challenges and could not be accessed during the SQUEAC investigation32. Additionally, requests for LGA maps and list of communities and population was also sent to the Director National Population Commission, Gusau, and followed up. Recruitment and training of the enumerators Enumerators were sourced from the locality (Bungudu, Bakura and Shinkafi) as much as possible. Where this was infeasible; enumerators from Gusau (the State Capital) were used to complete the coverage team. Sixteen enumerators were shortlisted who had previous experience in qualitative research at the community level for a four-day training on SQUEAC and CMAM (4th – 7th February). Twelve enumerators (four males and eight females) were finally selected based on their level of participation and team skills shown during the training. Among these were three health workers, 2 teachers, an LGA secretarial staff, local NGO workers, and school leavers who had previous experience in supervising immunization campaigns at community level. In addition, two SCI staff (LTA Shinkafi and the State M&E Coordinator) participated as team supervisors, with ACF’s CMAM Coverage DPM as the lead in the field. Brief description of the field activities implementation. Bungudu was the first LGA selected for the SQUEAC assessment (on 10th – 23rd February). It was conducted with all the team in order to give them hands-on experience and develop their skills. The SQUEAC team was split into two (that is team A and B, each with six enumerators and a supervisor). Quality checks of the process of data collection were ensured by regular phone contact between the lead and the supervisors who visited various field locations. The coverage teams collected the OTP cards and extraction was initiated during the SQUEAC training, however, cards that were not done during the training period were done on the site. At the end of stage 2 in Bungudu ( on 17th February); Team A conducted the wide area survey (stage 3) of 32 In Bungudu Kura Morta ward was not accessed due to security challenges; Damri and Dakko wards were not accessed in Bakura LGA; however all the wards in Shinkafi LGA were accessed. It is likely that the coverage estimate in Bungudu and Bakura LGAs could be lower as there was low program activity and performance in the wards mentioned due to insecurity.

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Bungudu, while Team B was dispatched to commence the SQUEAC assessment of Shinkafi LGA along with a different team leader. Shinkafi LGA SQUEAC activities commenced on 18th February. The activities of the two LGAs ended on 3rd March while that of Bakura LGA ended on 10th March 2014 with all the enumerators participating in the wide area survey. The team headed back to Gusau on 12th March. Debriefing of the SCI, SMOH and LGA Staff, and participatory recommendations of action plans for program improvement was done on 13th of March, after which the Team technical lead left for Abuja on the 14th of March 2014.

The limitations and possible mitigation measures are tabled below:

Table 4: The limitations in the SQUEAC assessment and possible mitigation measures

Limitation Mitigation 1 The OTP cards had some data that were not

recorded well, had an error or were missing in a large proportion of the cards. The incorrectly written or missing information include: 1) Distance to travel to site by the beneficiary 2) dates of admission 3) suspect MUAC measurements

The SQUEAC analysis dwelt on the cards with full information. Complete list of the villages from which beneficiaries come from was done and other persons knowledgeable of the geographic locations of the villages engaged to estimate distances in kilometres and walking time.

2 Insecurity in parts of Bungudu and Bakura LGAs Wards and communities with serious security threats were not accessed during the SQUEAC study (see details in below)

3 Accommodation challenges for enumerators in Shinkafi and Bakura LGAs33

LTAs of Shinkafi and Bakura assisted in providing 2 separate accommodations for male and female enumerators with support of the Traditional Leaders

.VI. RESULTS

.VI.1. Stage 1 investigation-Routine program data

The routine data that was analysed was obtained from the selected CMAM sites in Bungudu, Bakura and Shinkafi LGAs. The routine program information was analysed into the following plots:

33 The males and females needed to be accommodated in different compounds despite low availability place of stay

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• Plot of program admissions over time (Program inception, September 2012 till February 2014)

• Exits over time-Standard indicator graph showing cure, defaulter, defaulter and death trend.

• Time to travel to CMAM site by beneficiaries34 • Length of stay from admission to cure • MUAC at Admission for exits in last 6 months and current cases in the program. • Weeks SAM cases stayed in program before defaulting.

The investigations on routine data are discussed under each LGA as follows:

.VI.1.1. Plot of admission over time - Bungudu, Bakura & Shinkafi LGAs

Figure2: Plot of admissions over time-Bungudu, Bakura & Shinkafi LGA

The trend in admission was compared to the seasons within the year to establish the ability of the program to meet the needs of the community. It is worth to note that the trend of 34 In an IGD involving 20 caregivers in Fakai CMAM site. The caregivers indicated the perception of distances as: Very near<2km; Near 2-3km; Not far 4-5km; Not near 6-7km; Far 8-10km; Very far is >10km

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admissions for each of the LGAs for the period were remarkably similar, therefore, the admission data was combined and the trend in admission explained using figure 2 above.

• The CMAM services began in September 2012 with high community mobilization activities, active case finding by volunteers in the communities, and presence of large number of prevalent cases resulting in large number of clients. As the programme progressed from September through December 2012, a steady decline in admission was seen due to many reasons. Firstly, the harvest period was beginning and CVs were hence unable to engage in program activities. As such active case finding was affected. Equally, caregivers went to farms to participate in harvesting and crop processing. This was also a period of marked increase in per capita food availability in the household (HH). Food availability at the HH level has a rapid effect on the increase or decrease in severe acute malnutrition in children. Despite prevalence of malnutrition due to other cases such as disease, it may be that there was a general increase of food at the HH level resulting in a marked reduction of both prevalent and incident35 of SAM cases in communities. In Shinkafi, the decline in admissions from September 2012 to March 2013, could be attributed to migration of nomadic Fulani to Mashekarin36 areas for pasture as they move away with their entire households.

• The slight increase in admissions from December to February 2013 may be attributed to increased diarrheal diseases and respiratory tract infections as the hot season peaked, and as the harvest of farm produce got depleted.

• In March 2013, five additional CMAM sites were established (2 each in Bungudu and Bakura, and 1 in Shinkafi LGA) which resulted in community mobilization and active case-finding in new areas in the 3 LGAs, leading to the steady rise in admissions. In Shinkafi, the Fulani people migrate and return to their original habitation, thus some of the admissions could be attributed to SAM cases from the community.

• Similarly, an increase in, diarrheal diseases and malaria37, in the month of April/May 2013 as well as reduction in household food reserves saw the gradual increase of the admissions during this period.

• The decrease in admissions June through August 2013 coincides with the period of the rainy season and flooding which rendered many CMAM sites inaccessible which may have resulted in reduced active case finding and decreased attendance of SAM cases to the CMAM sites. As the flooding subsided in September, admissions began to increase.

35 Prevalent SAM cases have been existing at the time of the program implementation while incident SAM cases develop when normally nourished children slip to moderate acute malnutrition and eventually to SAM due to various reasons (as defined in UNICEF conceptual framework on cases of malnutrition) in children < 5years. 36 Mashekarin is the word for grazing reserve 37 Information provided by the health workers

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Admissions were seen to decrease steadily again from November 2013 through February 2014. This could be attributed to increased female labour demand in harvesting and processing thus making caregivers not be able to attend HF due to changes in their prorities. This is coupled by a considerable decrease in hunger within the HH due to food availability at HH level.

Generally, it appears that seasonal fluctuations may account for much of the increase and decrease in admissions throughout the year in Zamfara. At the very least, there are no unusual peaks or troughs that defy a logical explanation.

.VI.1.2. Bugundu LGA –Analysis of routine data Exits over time (discharges)

Figure3: Exits over time-Bungudu CMAM program

The CMAM program in Bungudu LGA has the following characteristics: • Between September 2012 and February 2013 the recovery rate improved significantly

and remained at above the minimum SPHERE standards of 75%. • In a similar manner the defaulter rate decreased and from the beginning of February

2013, generally remained below the maximum defaulter rate recommended by SPHERE standards, which is 15%.

• The non-recovered rate has been generally low (below 5%). This is generally expected of a good CMAM program.

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• The death rate is remarkably low (below the maximum rate recommended by SPHERE standards, which is 10%).

The programme can be described to have be effective in performance. Plot of time to travel to CMAM site

Figure 4: Plot of time to travel to site by caregivers-Bungudu LGA

The plot shows that most of the beneficiaries that attended live close to the CMAM site. This trend is generally observed among all the beneficiaries attending the CMAM sites in Bungudu. The distance to the CMAM site affects the spatial coverage of the CMAM program in Bungudu LGA. Significant number of clients were admitted from villages/communities close to the CMAM site. However, large number of cases were observed to access CMAM sites from communities that are over 4 hours. These are mostly from outside the catchment areas of the CMAM OTP sites and Bungudu LGA. Most of these clients use motor vehicles as medium of transport. Absence of community volunteers in these areas and cost of transportation could be the major reason for sizeable default for these group of clients.

Figure5: Plot of time to travel to site by caregivers-3 CMAM sites in Bungudu LGA

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Length of Stay from admission to cure

Figure6: Length of Stay Bungudu LGA

The median length of stay from admission to cure, 6 weeks, was satisfactory for this program. The cases seen to have 1 or 2 weeks LoS were largely those admitted with MUACs of >115mm. They had been admitted into the program to avoid negative perception of the program due to rejection of caregivers. These cases were particularly within the beginning periods of the program. Cases that stayed for a long time in the program (>8 weeks) may also be the SAM cases which had very low MUACs on admission (see plot of admission below). They were cases that were late admission and may have taken longer to resolve before being officially discharged as recovered cases.

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MUAC on Admission for the current cases in program

Figure7: Admission MUACs Bungudu LGA

The median MUAC at admission falls between 107-108 mm. Cases admitted with MUACs that tend towards 100mm and below are considered late admissions. It may have been that these cases are from the areas which are far from the CMAM site that were identified and admitted as the radius within which active case finding was done increased.

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Weeks stayed in the program before default

Figure8: weeks in program before default-Bungudu LGA

There were many cases which defaulted upon their first or second visit to the CMAM site. It may be that they came large distances especially from outside the LGA and were not able to sustain regular follow-up by attending the CMAM site regularly.

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.VI.1.3. Bakura LGA-Analysis of routine data Exits over time

Figure9: Exits over time Bakura LGA

In the period between September 2012 and August 2013 the recovery rate remained below the recommended rate by SPHERE (75%) while the defaulter rate was above 15% (as recommended by SPHERE standards) for most of the months. The SAM children classed as non-recovered reached above 5% between January and March 2013. Program effectiveness can therefore be described as low. A very high defaulter rate was witnessed in the first six months of the program, September 2012 – February 2013. This may largely be a result of caregivers withdrawing their children from the CMAM site immediately after their condition improved without the children being declared recovered by health workers. Additionally, the period of October through January coincides with high female labour demand (harvesting and processing). It is believed that continuous community sensitization, and nutrition counselling over time contributed in bringing down defaulting as the programme implementation progressed. The slight increase in the defaulter rate in July and August 2013 was due to flooding as some carers could not access the CMAM sites during this period.

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Plot of time to travel to CMAM site

Figure10: Plot of time to travel to site-Bakura LGA

Generally as the distance to and from the CMAM site increases the number of beneficiaries accessing the site decreases. Thus distance to the CMAM site appears to be a factor that affects the coverage of the program. This is illustrated in figures 10 and 11. Most clients coming from places with walking time above 90 minutes usually access the CMAM sites with motor vehicles; those coming from areas with walking time above 240 minutes (4 hours) were mostly from neighbouring LGAs and Sokoto State. OLPC CMAM site being situated in Bakura town with access roads and other amenities usually plays host to such clients, and the sizeable number of defaulters witnessed among such clients was due to the absence of community volunteers to trace and follow-up on absentees from such areas. Nevertheless, uneven distribution of community volunteers within the LGA makes tracing and follow-up ineffective within the LGA.

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Figure11: Plot of time to travel to site-4 CMAM sites in Bakura LGA

Length of stay

Figure12: Length of Stay-Bakura LGA

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The median length from admission to cure, 6 weeks, was satisfactory for this program. MUAC on Admission for the current cases in program

Figure13: MUAC on admission tally-Bakura LGA

The median MUAC at admission may be considered low at 109--110 mm. Week in program before default

Figure14: week in program before default-Bakura LGA

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Many SAM cases defaulted early when attending the program with median number of weeks before default found to be 3. In Dambo site, most of the defaults occurred during rainy season and overflow of the River banks making the site inaccessible. In Danmanau most of the early defaulters were found to be coming from distant communities and neighbouring LGA, which is also the case for OLPC CMAM site where most of the defaulters are from neighbouring LGAs and Sokoto State where there are no community volunteers to do follow-up of absentees.

.VI.1.4. Shinkafi LGA-Analysis of routine data

Exits over time-Shinkafi LGA

Figure15: Exits over time Shinkafi LGA

The very high defaulter rate witnessed at the early stage of the programme implementation (September 2012 to January 2014) was due to a combination of reasons. As the programme was brand new, caregivers may not have understood fully the programme modalities, therefore, most carers withdraw their children once they noticed that the condition has improved while the child is yet to reach the discharge criteria, Many of the clients coming from distant communities in Shinkafi LGA, neighbouring LGAs and States also ended up as defaulters. Moreover, a very large turnout of clients during this period resulted in long waiting time, and as female labour demand (harvesting and processing) during October through January is very high, many carers tend to default as a result of conflicting priorities. However, the performance indicators improved significantly after March 2013 as the programme was targeted for improvement by the programme staff of the implementing partner (SC); an additional CMAM site was established in Shinkafi LGA, which came with increased community mobilization and

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sensitization activity in all the CMAM sites, improved accessibility in terms of proximity, and reduced waiting as the new CMAM site pulled down the crowds in existing sites. Plot of time to travel to CMAM site

Figure16: Time to travel to site Plot-Shinkafi LGA

The number of those admitted and those who defaulted compared to time taken by beneficiaries to travel to CMAM site did not show significant differences except for those who took less than 30 minutes to travel to the CMAM site and those who took more than 3 hours (240 minutes) to arrive at the site. This is more so in Fakai CMAM site (see below). Generally, it can be said that caregiver were willing to access the programme despite the distance. There are areas that were mainly located outside the LGA and were served by the CMAM sites38

38 Fakai CMAM: S/Kamawawa; Shanawa CMAM: Bafarawa, Arume, Kwangomi, Tsullawa, Suruddubu, Moriki, Dumfawa, Dan Zanke, Yan Biki, Kaura and Alawa; Galadi CMAM: Ruddunu, Dole Moriki, Zazzaka, Jidibali, M/Rini, Shinkafi, Bula, Arume, Turba, Tungar Kahau, Gamoji, Tangyalla, Satiru, T/Bore, Sabon Gida.

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Figure17: Time to travel to site Plot-3 CMAM site in Shinkafi LGA

The individual facility plots generally indicates that time to travel to site may affect coverage of the program with exception of Fakai CMAM site which may have relatively homogenous distribution of beneficiaries attending the CMAM site. It is observed that the areas that are outside the intended catchment area (for instance outside the LGA where the CMAM site is located) have high attendance. These are beneficiaries coming from adjoining LGAs which are far from the CMAM site in question. Length of stay (LOS)

Figure18: Tally of Length of stay from admission to cure-Shinkafi LGA

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The LoS can be described as satisfactory. The Sphere standard for length of state in an OTP is 8 weeks. MUAC on Admission for the current cases in program

Figure19: Tally of admission MUACs-Shinkafi LGA

There is a significant number of children that were admitted relatively late. This is expected of the program at inception as most of the cases that present late for treatment of SAM have been prevalent SAM cases. The dataset analysed showed that there are significant number of cases that were admitted relatively late in CMAM sites as evidenced in Figure 19 above. Week in program before default

Figure20: Week in program before default-Shinkafi LGA

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.VI.1.5. Summary of quantitative data analysis

The data points to two main aspects affecting program coverage: effectiveness and timely admission of cases.

The relatively low MUAC on admission shows that cases which should have been in the program stayed for a long time in the community before coming for treatment. Regarding the Length of Stay a substantial number of cases stayed in the program beyond 9 weeks. The median length of stay of 9 weeks before default by clients in Shinkafi may be due to two reasons: 1) caregivers withdraw their children as they “noticed” improvement in their recovering child though HW was yet to declare the clients recovered. Thus caregivers would not come to the program anymore after having regular contact with the CMAM site for long time; 2) possible sharing of RUTF ration for SAM cases within the households among siblings and adults.

The analysis shown by the routine data suggests heterogeneity in program coverage. For this reason, there was a need to carry out further investigations to complement the evidence obtained from routine data analysis.

.VI.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 persons39, observations, referral mechanism and health seeking behaviour & access to CMAM services. A cases study has also been annexed. The information was continuously stored into the mind mapping tool (the x-mind40 software). The information is also presented as follows:

• Barriers and boosters as presented in the tables below. • Concept maps as annexed to this report

The analysis and presentation is summarized for each LGA’s investigation.

39 Community members are people who live in the community and have a lot of information on its social systems and events. Some of them are community elders, local farmers, men, other caregivers, shopkeepers, teachers and opinion leaders 40 The trial version of the mind-mapping tool (x-mind) is free and available at http://www.xmind.net/download/

.VI.2.1. Bungudu LGA

The barriers and boosters for Bungudu LGA are analysed and presented in the table below. A table listing the sources from which information were obtained and codes for the methods used is shown in table 6 below.

Table5: Barriers and Boosters-Bungudu LGA

S/n

Boosters (Bungudu LGA) Sources S/n

Barriers (Bungudu LGA) Sources

1 Communities have a good opinion of the programme

3C,4C,5C,6C,7C,8C,9C,10C,11D,12C

1 Non-adherence to CMAM protocols41. 1A,2B,3C,6C,12C

2 Good linkage between CMAM services and the community42.

3C,4C,7C,10C,13C

2 Poorly motivated health workers in their work resulting in low commitment. The HW see CMAM as an additional burden.

2B,3C,6C,12C

3 Good awareness of the CMAM programme in communities

5C,7C,8C,9C,10C,11D,12C

3 Lack of CVs in villages far from CMAM sites contributing to low active case finding

1A,3C,6C,12C,14C

4 Good working relationship between CVs and HWs

2B,3C,5C 4 Stock out of data tools, RUTF and routine drugs

3C,6C,7C,12C

5 Support of CMAM program by CBO43 with routine drugs, and recent support by State and LGAs

2B,6C,12C 5 Insecurity in many parts of the LGA affecting attendance of CMAM services negatively.

2B,3C,6C,7C,11D,12C

41 This includes but not limited to: incomplete filling of client cards (sensitive client information not captured; walking distance, weight, date of admission/follow-up visits, no outcome indicated); many cases admitted on MUAC ≥ 115; non-confirmation of MUAC by health workers at OTP sites; total mix-up of OTP cards making it hard to track SAM cases once admitted in the program. Some cards are demarcated by outcome. 42 Evidenced by self-referral of caregivers, mother-mother referral, spouse referral, referrals by TBA. Also referrals by community leaders, traditional healers and patent-medicine dealer were evident 43 Community Based Organizations: Zanna Organization and Yerima like-mind Foundation

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6 Integration of CMAM with other child health services MNCH (Maternal New Born and Child Health) week

2B,3C,6C,12C

6 Shortage of health workers trained on CMAM generally. Interns run the CMAM site in Nahuche CMAM site.

2B,3C,4C,6C,12C,

7 Ownership of the program demonstrated by some community members who provide food and stipends for CVs and HWs for each OTP day, and contribution to OTP daily expenditure (in HF-Tofa)

2B,3C,6C,12C

7 Poor referral of SAM cases by existing CVs; CVs not motivated, soliciting for motivation in terms of stipends/allowances

2B,3C,5C,6C,12C,14C

8 Good interface between OTP and SC 1A,3C,6C 8 Stigmatization of malnutrition in some communities

3C,5C,4C,12C

9 Poor understanding of CMAM program modalities by caregivers leading to frequent misunderstanding with health workers

2B, 3C,8C,6C

10 Poor organization at the CMAM site44. 1A,2B

11 Confusion of CMAM with polio immunization limits access by clients45.

1A,2B

12 Far distance limiting accessibility of services in many communities

3C,4C,5C,6C,7C,12C

13 Refusal/unfriendly attitude to clients by health workers

2B,4C,6C,12C

14 Early closure of OTP sites makes it inaccessible to the SAM cases who are brought from far

2B,3C,4C,6C,12C,

44 Poor seating arrangement; lack of shades for carers; lack of adequate number of benches/seats making caregiver fatigued. Caregivers also stand queuing for long periods waiting for their children to be attended to. 45 Community do not have trust on Polio immunization. When the CMAM service is associated with polio, attendance to the OTP is negatively affected.

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.VI.2.2. Bakura LGA Barriers and Boosters-Bakura LGA

Table6: Barriers and booster-Bakura LGA

S/n

Boosters (Bakura LGA) Sources. S/n Barriers (Bakura LGA) Sources

1 Good opinion of the programme by care givers and the community members.

2B,3C,4C,5C,6C,7C,8C,9D,10C,11D.

1 Insecurity in some political wards (with many communities/settlement46

11D,3C,15F

2 Strong community ownership and participation47. 2B,3C,4C,11D,17G

2 CVs are not evenly distributed; they are concentrated in villages close to the OTP sites48

14C,13B&C,11D,3C,9D,6C,7C,8C.

3 Mother to mother referrals. Also referrals are done by community leaders and patent medicine vendors.

3C,4C,5C,6C,10C,11D

3 There is low awareness of the program in settlements far from the CMAM site49

6C,7C,8C,9D,11D

4 Cooperation between adjacent CMAM sites in sharing responsibilities to attend to CMAM clients.50

3C,11D 4 Preference of the traditional treatment for SAM cases.51

16C,6C,8C,12A&3C

46 This affects access to CMAM Services in Damri and Dambo) 47 Evidence is seen in provision of shades, toilets and water by CVs in some CMAM sites (OLPC and Danmanau); provision of lunch by community leaders to CVs, health workers, and some time carers (Danmanau, Dambo & OLPC) and also digging of water well in Rini CMAM site by ward development committee. 48 Places/communities far from OTP Sites do not have CVs for case finding 49 This is due to low sensitization and also low active case finding in places far from CMAM sites. 50 Training of select health workers from neighboring facilities to the Bakura CMAM site to assist during OTP days 51 Poor health seeking behavior because some communities engage/prefer traditional healers to treat SAM.

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5 Health workers are motivated and enthusiastic in rendering services

2B, 3C, 11D.

5 Not all health workers who have had training on CMAM devote themselves fully to attending to SAM cases. This results in long waiting times as clients wait for the services.

3C,11D

6 CVs are trained and used as support staff for health workers in those facilities with shortage of staff for anthropometric measurements/rescreening of SAM cases and nutrition/health education.

2B, 3C, 4C, 11D.

6 Poor defaulter and DNA52 tracing. There are lost opportunities to get some cases back into to the program53

11D,3C,4C

7 Integration of CMAM with other child health services (MNCH week activities)

3C,11D, 7 There is a general shortage of CMAM trained health workers in most CMAM sites54

2B, 11D.

8 Integration of the CMAM sites within the HF has ensured good turnout of clients55

2B, 3C, 11D.

8 Poor arrangement of data and monitoring tools making it difficult to track SAM cases in the process of treatment or to identify defaulters for follow-up.

1A,2B,11D

9 CMAM site are well organized/arranged. 2B, 3C, 11D.

9 Non adherence to protocols evident on admission of children with MUAC>115mm.

1A,

10

Good working relationship between health workers and CVs leading to good quality service to clients.

2B,3C,4C,5C,11D

10 Previous stock out of RUTF (September /October) contributed significantly to rise in default rates. Clients were sent home without RUTF in this period.

1A,12B,3C,11D

11

Active case finding and referrals by some CVs: and passive referrals by some health workers

3C,4C,5C,11D

11 Sharing of RUTF among entire households (children and adults) in communities

3C,11D,19E,20G

52 Did not attend 53 CVs concentrated to communities close to the CMAM sites do not do defaulter tracing in close communities. There is no follow-up of SAM cases in relatively far communities. 54 Damri, Dankaiwa, Rini, Danmanau. OLPC, Dambo. 55 This has been observed in most sites visited (Danmanau, OLPC, Yarkofoji) underscoring accessibility of CMAM services by clients.

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12

Availability of Basket Fund with LGA contributions for provision of routine drugs.

3C,11D,18A

12 Flooding due to overflow of river bank limits access to CMAM site56

2B,5C,14C

13 There was persistent stock-out of essential routine drugs, especially amoxicillin and ACT-Malaria drugs (September/October 2013).

1A,3C,11D,12B

14 Unevenly distributed CMAM sites57. 2B,11D.

15 Poor integration of CMAM services with polio immunization service58

11D,3C

.VI.2.3. Shinkafi LGA Table7: Barriers and boosters-Shinkafi LGA

S/n Boosters Sources S/n Barriers Sources

1 Strong community ownership59 1B,3C,4C,11G,13C

1 CVs absent in communities far from CMAM sites but concentrated in areas close to them (CMAM sites).

3C,4C,11G,12E,13C,15A

56 This happens in Dambo during rainy season significantly contributing to defaults and reduced admissions. Incident cases are not captured early resulting in late admissions to the program. 57 Dambo and OLPC CMAM sites are close to each other (6KM apart). This has meant that the HFs are under-utilized while some other communities in more remote areas do not have access to CMAM services) 58 In most health facilities health workers are taken away for the polio campaign thereby disrupting services to CMAM clients (Danmanau, Dambo & Rini 59 Community has built shades, provided benches, water and food at Fakai, Galadi, Shanawa CMAM sites

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2 Good awareness of the programme in communities

1B,3C,4C,5C,6C,7C,8D,9C,10C,11G

2


4C,5C,6C,11G,12E,13C

3 CMAM services hosted at the health facilities. 3C,4C,6C,8D,9C,10C,11G,12E,13C,15A

3

RUTF given to SAM cases at CMAM site is shared among adults at home.

6C,7C,8D,9C,10C

4 Willingness of care givers to travel far distances to access CMAM services

1B,2A,3C,4C,5C,11G,7C,8D,13C

4 Previous stock out of routine drugs, especially, amoxicillin drugs (September and October, 2013)

3C,11G,13C

5 Self-referral, peer-to-peer referral, husband, grand-mother, and religious leader referrals evident

3C,5C,11G,12E,13C

5 Dilapidated Health Facility with leaking roofs, making it difficult to host clients, keep RUTF and drugs well and store data tools safely (Fakai CMAM site)

2A,3C,6C,7C,11G,12E

6 Integration of CMAM services with immunization and MNCH activities enhances passive case finding to identify SAM

3C,6C,13C 6 Preference for traditional healers and medicine vendors over health facility. This is in treatment of SAM and diseases associated with SAM (RTI and Malaria)

4C,5C,9C,10C

7 Good opinion of the programme in communities 2A,3C,6C,12E

7 Poorly motivated CVs.61 2A,3C,5C,6C,12E, 14C

8 Good community linkage evidenced by use of traditional leaders as CVs

1B,3C,5C,6C,8D

8 Long waiting times at the HF as caregivers wait for their children to be attended to. There are often no seating arrangement for caregivers who stand as they wait.

1B,3C,4C,5C,13C


1B,3C,4C,11G,13C

9 Non adherence to protocols by health workers is evident (admission MUAC >115)

3C,8D,6C

60 The CMAM sites made inaccessible are Fakai, Galadi. 61 This affects the active case finding negatively

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10 Provision of ACT drugs by Malaria Action Programme for State (MAPS)in two CMAM sites (Galadi and Kware)

3C,11G,13C

10 Shortage of HWs trained on CMAM to render services to clients (Fakai)

1A,3C,13C

11 Data tools well-arranged and easy for tracking patient's records

1B,3C,13C 11 Masking of defaulters in HF records make it hard to track proper treatment episodes for SAM cases. Galadi CMAM site has long LOS that is associated with this.

1A,2A

12 Continuous training of HWs in facilities not implementing CMAM on CMAM protocols. This enhances referrals from facilities not implementing CMAM to facilities implementing CMAM services

3C,11G,13C

12 Far distance limits access to CMAM services to caregivers living far from the site.

2A,5C,14C

13 Poor tracking of the migrating Fulani community by program such that SAM cases would default when the community migrates.

2A,3C,6C,8D,11G,12E,13C

Table 8: sources and methods for barriers and boosters

S. No Source Method S/n Method Code 1 Client Cards A 1 Extraction A 2 Health Facility visit B 2 Observation B 3 Health Worker Interview C 3 Simple Structured Interview C 4 Carer Interview C 4 Informal Group Discussion D 5 Community Volunteer

Interview C 5 Case Study E

6 Program Staff C 6 Briefing F 7 Community leaders C 7 In-depth Interview G 8 Religious leader (Imam) C

40

9 TBAs C 10 Traditional Healer C 11 Majalisa62 D 12 Program Staff C 13 Chemists/patent medicine

dealers C

14 Small Area Survey C

62 Refers to the Community group gathering of males in social places particularly market areas in tea shops and shades

.VI.3. Stage 2: Confirmation of high and low coverage areas using small area survey and small studies

In stage one of the SQUEAC investigation evidence was gathered to give a picture of the program coverage, with qualitative data complementing routine data analysis. Both types of data suggested that coverage may not be uniform across each LGA. Stage two of the SQUEAC investigation therefore focused on:

1. Investigating heterogeneity of coverage of the program. This was important to establish before moving on to stage three of SQUEAC investigation which gives a headline estimate of coverage.

2. Collecting additional information on factors affecting program coverage. Design of the small area survey Prior to the assessment area characteristics were drawn up based on the information obtained in stage one. These were: 1) areas that are far from the CMAM site based on the perception of walking distance by the beneficiaries; 2) areas with more dense program activities compared to those seen to have less dense activities (particularly CV activity). These characteristics were used to draw the hypothesis that would be tested for areas identified for each specific LGA. Collection of data from the community used a technique of active and adaptive case finding63. This involved identifying SAM children (6 and 59 months) in the community and getting information of those covered and those not covered by the CMAM (OTP) program. Semi structured interviews (SSI) were also carried out with caregivers of non-covered cases to establish the reasons why their child was not enrolled in the programme or had defaulted. Analysis of the 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 was 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. In this case standard (p) for rural settlements is 50% coverage64 and it was used to gauge coverage for all the three LGAs65 in Zamfara 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. 63 See the process of active and adaptive case finding annexed to this report. 64 SPHERE standards recommends a threshold of 50%, 70% and 90% coverage for TFP program run in the contexts of rural, urban and camp areas respectively. 65 Bugundu, Bakura and Shinkafi LGAs

42

The findings, results and conclusions of the small area surveys, barriers to access and coverage and reasons for default are presented per LGA in the tables and figures below.


The quantitative information and qualitative information indicated patchy coverage in Bungudu LGA. As such a small area survey was conducted to determine spatial pattern of coverage. Hypothesis:

‘The catchment areas that are located far from the CMAM site (within 5km radius) are likely to have coverage of > than 50%. The areas located outside the radius are likely to have coverage of <50%’.

The summary of the findings in Bungudu LGA are summarized in the table 9 below. The analysis of barriers to access and uptake is shown in figure 21 below.

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Table9: Findings of the Small area survey in Bungudu LGA

Villages (Bungudu LGA)

SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are far and have least contact with CVs: Gamraki (17km), Tungan Mai Kawo (19km) and Rawaya (31km), Karakai (30km)

Total SAM Found 23 Coverage standard (p)

50% Number of cases covered (6) is less than decision rule (11). Therefore: Coverage is likely to be <50%

SAM Cases in the Programme

6 Cases covered 6

SAM cases not in the Programme (17)

6

Caregiver not aware that child is malnourished

Decision Rule (d) n66= (6+17)

[n x 50/100]=

= =11

2 Caregiver rejected or did not come because she knows other care-givers who have been rejected

1 Caregiver overwhelmed by other chores

2 Caregiver sick or pregnant and would not come

4 CMAM site is far 1 Caregiver thought program

ended

1 Caregiver thought she will not get RUTF when she attends

Villages (Bungudu LGA)

SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are close to CMAM site and


50% Number of cases covered (1) is less

66 Number of cases found in the small area investigations in communities that are far and have least contact with CVs

44

have contact with CVs Jandutsi (2km), Awala (3km), Yarkaita (<half km) Kukoki (0.5km)


1 Cases covered 1 than the decision rule (2). Therefore: Coverage is likely to be <50%

SAM Cases not in the Programme (4)

3 Care-giver not aware that child is malnourished


[n x 50/100]=

= =2 1 Care-giver overwhelmed by

other chores

Barriers to program access and uptake.

Figure21: barriers to program access and uptake- small area survey in Bungudu LGA

67 Number of cases found in the small area investigations in Communities that are close to CMAM site and have contact with CVs

45

Reasons for default by program beneficiaries

Figure22: Reasons of default by program beneficiaries in small area survey in Bungudu LGA

.VI.3.2. Bakura LGA

To be able to confirm heterogeneity or homogeneity of program coverage in Bakura LGA a small are survey was undertaken to test the hypothesis below: Hypothesis:

The program coverage in areas that are far (more than 3 hours walk) from the CMAM sites and have less contact with CVs is less than 50% while the that of areas which are near the CMAM site (less than 3 hours) and have presence of CVs is more than 50%.

The summary of the findings in Bakura LGA are summarized below:

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Table10: Results of the small are survey analysis-Bakura LGA

Villages (Bakura LGA) SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are far and have least contact with CVs: Rakukuma (25km), Masgo (22km) and Tungar Rihenu (20km)




0 Cases covered 0


5 Care-giver not aware of CMAM program


[n x 50/100]=

= =4 3 Spouse refused caregiver to

attend due to far distance from CMAM site

Villages (Bakura LGA) SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are close to CMAM site and have contact with CVs Balgare (2km), Rogoji (1.5km), Danmanau Gabbas (0.5km)


50% Number of cases covered (4) is more than the decision rule (3). Therefore: Coverage is likely to be >50%


4 Cases covered 4

SAM Cases not in the Programme (2)

1 Care-giver not aware child is malnourished


[n x 50/100]=

= =3 1 Caregiver sick and could not

take child to HF

68 Number of cases found in the small area investigations in Communities that are far and have least contact with CVs 69 Number of cases found in the small area investigations in Communities that are close to CMAM site and have contact with CVs

47

Barriers to program access and uptake-Bakura LGA.

Figure23: Barriers to program access and uptake-Bakura LGA

.VI.3.3. Shinkafi LGA

The 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 site70”. The findings and results of the small area survey are summarized in the table 12 below: 70 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 “keke”

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Table11: Findings of the small area survey in Shinkafi LGA

Villages (Shinkafi LGA) SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are far from CMAM site: Sabeji (13km), Tungar Bole (15km), Tungar Kayeye (12km), Tafkin Dawaki (19km), Badarawa (6km)




3 Cases covered 3


1 Care giver not aware of the programme


[n x 50/100]=

= =5 1 Care giver says she cannot

afford transport to CMAM site

2 Far distance to the CMAM site

1 Care giver not aware child is malnourished

1 SAM child < 6 months rejected at the CMAM site.

1 Care giver was rejected at the CMAM site

Villages (Shinkafi LGA) SAM cases Reasons: not covered Parameters Calculation-Decision rule

Conclusion

Communities that are close to CMAM site. Kafin Mazuga (4km), Kagara (3km), Katsele


50% Number of cases covered (0) is less than the decision rule (1). Therefore:


0 Cases covered 0

71 Number of cases found in the small area investigations in Communities that are far and have least contact with CVs

49

(4 to 5 km) SAM Cases not in the Programme

2 Care giver not aware of the programme

Decision Rule (d) n72= (2)

[n x 50/100]=

= =1

Coverage is likely to be <50%

1 Husband refusal

The barriers to program access and uptake found in the small area survey are shown in the figure 24 below.

Figure24: Barriers to access and uptake of the program-small area survey in Shinkafi LGA

72 Number of cases found in the small area investigations in Communities that are close to CMAM site and have contact with CVs

50

Conclusion of the small area survey in Bungudu, Bakura and Shinkafi LGAs As indicated in each hypothesis, patchy coverage was envisaged in all the three LGAs due to substantial differences in geographical access to CMAM services. However, only in Bakura LGA did this prove to be the case. In Bungudu and Shinkafi, on the contrary, the studies indicated that coverage was generally low. Despite the fact that most non-covered cases were found in distant communities, the hypothesis in Bungudu and Shinkafi were not confirmed. Thus, coverage was confirmed to be heterogeneous in Bakura alone, but can be said to be homogeneous in Bungudu and Shinkafi. Normally it is recommended to undertake a wide area survey in situations where coverage is homogenous so as to come up with a headline measure of coverage that would reflect a true picture of most areas of the LGAs where SQUEAC was done. Nevertheless, it was necessary to undertake wide area surveys in all LGAs in Zamfara regardless of homogeneity/heterogeneity in order to contribute to the solid learning process for the SQUEAC teams which included Ministry of Health staff. It is however important to note that in the Zamfara SQUEAC as much as the wide area results were expected to give a headline coverage, the heterogeneity of geographical coverage would be noted so that interpretation of the results to show true context of the surveyed areas.

.VI.4. Forming the prior

In light of the small area survey findings the boosters and barriers were confirmed by the SQUEAC teams and arranged into the 4 top boosters and 4 top barriers in all the LGAs. Next, in order to calculate the prior mode the following steps were followed:

1. Each of the boosters and barriers were ranked and given a value. A score 1 of denoted the lowest possible score while 5 denoted 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 teams and given scores dependent on the influence each barrier and booster has on the program coverage. This effect is illustrated in the figure 25 below. The process of weighing of the boosters and barriers was done. (see weighted and un-weighted boosters and barriers in annex IX.1.11)

2. Weights of individual barriers and boosters were calculated 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 for each of the prior building in each LGA.

51

4. Prior was calculated based on the weights derived in barriers and boosters as well as the informed guess of the SQUEAC team in Stage 2 above. The parameters used in calculation of the mode prior are tabled and annexed in this report

Figure 25: effect of booster and barriers on coverage The prior building resulted in the following mode priors for each of the LGA. Thus:

Table 12: Mode priors

LGA Prior mode Minimum & maximum possible coverage73

Shaping parameters

Min Max α β Bungudu 41% 16 66 13.9 19.9 Bakura 44.25% 19 69 15.3 19.1 Shinkafi 49.25% 24 74 16.5 18.3 The prior plots for the three LGAs are illustrated on the figure 26 below:

73 The minimum and maximum possible coverage was set at +or-25%. Also see parameters used in prior determination in the Annex

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Figure26: Prior mode-Bungudu, Bakura and Shinkafi LGAs74

.VI.5. Stage 3: Wide area survey - overall estimate of program coverage

The wide area survey was conducted for each LGA of Bungudu, Bakura and Shinkafi. Bayesian tools were used to analyze 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 the prior( this has been described in the previous section)

• The likelihood survey was implemented. • Posterior was determined

The overall program coverage was established through a process of binomial conjugate analysis of the prior information and the likelihood survey results to yield the posterior.

The procedures leading to the above are described below, with exception of determination of prior which has been described in previous section. Likelihood survey sample size calculation

74 Alpha and Beta prior distribution shaping parameters in table 13

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A formula for calculating the sample size for wide area survey75 is illustrated below.

The likelihood survey sample size was calculated based on the parameters in the table in annex IX.1.1 in this report. The sample size was calculated for each of the LGA of Bungudu, Bakura and Shinkafi. Thus:

Table13: summary of the sample sizes for each LGA

LGA Sample size Bungudu 45 Bakura 63 Shinkafi 46 Calculation of the number of villages to be visited To be able to estimate the number of SAM children expected per village, SAM estimates derived from the 2013 SMART76 nutrition survey for Zamfara state were used. The SAM rate of 1.5% (MUAC estimate) was adopted as conservative estimate to calculate the number of SAM cases per village in Bungudu, Bakura, and Shinkafi LGAs. 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 survey77. The number of villages (n) that would need to be visited to get the calculated sample size was estimated using the following formula below:

N (villages)

The parameters are defined as: N (villages) =sample of villages n=sample size of SAM cases desired for the likelihood survey. The number of villages to be visited for Bungudu, Bakura and Shinkafi was 40, 62 and 20 villages respectively (see annex IX.1.1) Spatial sampling technique

75Semi 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 76 Standardized Monitoring and Assessment of Relief and Transitions 77 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

54

A complete list of villages was prepared for the individual LGAs of Bungudu, Bakura and Shinkafi. Spatial systematic sampling (described below) identified the 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 below:

Figure27: Stratified villages for each LGA-Bungudu, Bakura and Shinkafi

The sampling was done in two stages: Stage 1: Spatial sampling method; When the list of the villages had been obtained, a 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 villages that will be visited from list of complete villages provided for each LGA78. This yielded a reasonably even spatial sample from the entire program catchment area that would be visited to get the minimum number of SAM children to fulfil the sample. Stage 2: Within-community sampling method Active and adaptive case finding method to find the SAM cases in the sampled village was used. The found current SAM cases 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.

78 List of villages obtained from Expanded Program on Immunization (EPI) in the State Ministry of Health (SMOH)-Zamfara. January 2014

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Presentation of the likelihood survey results The results of the coverage of the program (that is before combining prior and the likelihood to yield the posterior, i.e. overall program coverage) was calculated using the formula for point coverage as opposed to period coverage.Point coverage uses current cases only and provides a snapshot of the program performance, placing strong emphasis on the coverage and timeliness of case finding and recruitment. Parameters for the calculations of the coverage:

• Current SAM cases attending the program=x • Current cases not attending the program (non-covered cases) =y • Total current cases (x+y)=n

Thus: Point coverage is given by:

The parameters that were used to set the prior, calculate sample size and make conjugate analysis for Bungudu, Bakura and Shinkafi are summarized in annex IX.1.1 The results of the wide area surveys are summarized below:


The parameters used in the calculations with the summarized results are presented in the table 15 below:

Table14: parameters used in calculation of coverage-Bungudu LGA

Coverage estimator

Formula Parameters Bungudu LGA Values

Likelihood results

Point coverage

Total current cases (n) 68 32.3% Current cases attending the program (x)

22

Current cases not attending the program (y)

46

Conjugate analysis for posterior estimate and overall program coverage estimate The mode prior and the likelihood survey results were combined using the Bayesian tool in a beta binomial 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. The posterior is also the final coverage of the program.

56

The prior, likelihood and posterior coverage estimates are illustrated in the Bayes SQUEAC calculator in figure 28 below.

Figure28: Estimation of coverage using BayesSQUEAC Calculator79-Bungudu LGA

The figures above summarize the prior and the posterior distribution for Bungudu wide area survey. Point coverage estimator (here referred as the posterior) is preferred in reporting wide area coverage for Bungudu LGA. The program coverage in Bungudu LGA is therefore:

Point coverage=35.0% (26.3-44.7%; CI80 95%)

79 BayesSQUEAC Version 3.01 available at www.brixton.com 80 Credible interval

57

Barriers to program access and uptake in wide are survey-Bungudu LGA81

Figure29: Barriers to Program access and uptake-Bungudu LGA

.VI.5.2. Bakura LGA

Table15: Parameters used in calculation of coverage in Bakura LGA

Coverage estimator

Formula Parameters Bakura LGA Values

Likelihood results

Point coverage


38


40

Conjugate analysis for posterior estimate and overall program coverage estimate The conjugate analysis is illustrated below:

81 1=Caregiver “tired of giving too many” RUTF; 2=Caregiver says child vomits after taking RUTF; 3=Caregiver says child is “teething” and would not take RUTF

58

Figure30: Estimation of coverage using BayesSQUEAC Calculator-Bakura LGA

The program coverage Bakura LGA is therefore:

Point coverage =47.4% (38.4-56.5%; CI 95%)

59

Barriers to program access and uptake in wide are survey-Bakura LGA

Figure31: Barrier to Program access and uptake-Bakura LGA

.VI.5.3. Shinkafi LGA

Table16: Parameters used in calculation of coverage in Shinkafi LGA

Coverage estimator

Formula Parameters Shinkafi LGA Values

Likelihood results

Point coverage


22


25

Conjugate analysis for posterior estimate and overall program coverage estimate The Bayes SQUEAC calculator presented the program coverage as shown in figure 32 below

60

Figure32: Estimation of Coverage using BayesSQUEAC calculator-Shinkafi LGA

The program coverage in Shinkafi LGA is:

Point coverage=47.0% (36.5-57.4%; CI 95%)

Barriers to program access and uptake in wide are survey-Shinkafi LGA82

Figure33: Barriers to program access and uptake of wide are survey in Shinkafi LGA.

82 In the community, “teething” refers to the stage where the child is developing teeth.

61

Description of the Bayesian curves for Bugundu, Bakura and Shinkafi The prior drawn for each of the LGAs shows moderately strong prior distribution. The prior curves are relatively uncertain compared to the likelihood’s (illustrated by wider credible intervals for the prior curves). In all cases the likelihood curves are relatively strong and overlap well with Prior curves. As there is no conflict between prior and likelihood curves it can be said that the conjugate analysis given by the posterior is reliable.

.VII. DISCUSSIONS

.VII.1.1. Bungudu LGA

The Bungudu CMAM program is well linked to the community (as shown by evidence by self-referral of caregivers, mother-mother referral, spouse referral, referrals by TBA. Also referrals by community leaders, traditional healer and patent-medicine dealer). Generally, there is good awareness about the program in the community and the community’s opinion about the program is positive. The CMAM sites are integrated within the normal health services making it possible to utilise opportunities whereby the child is attending the facility for other reasons to screen for acute malnutrition. The shortcomings that greatly affected coverage of the program include non-adherence to CMAM protocols by CMAM staff which includes but is not limited to: incomplete filling of client cards with some information missing such as walking times by caregivers to the CMAM site, weight, date of admission/follow-up visits and some cards have no outcome indicated. Also, many cases have been admitted on MUAC ≥ 115 which has in some occasions resulted to some referred caregivers being sent away from CMAM site. The general organization of cards was poor making it difficult to do follow-up. There is lack of CVs in some villages making active case finding and follow-up in those communities non-existent. Stock out of data tools, RUTF and routine drugs was also a barrier that has contributed to low coverage. The coverage estimate used for Bungudu LGA was point coverage after consideration of various factors.

• Late admission of SAM cases for treatment indicated that children who were malnourished were likely to have a longer length of stay than when they have been admitted early as would be the case in an ideal situation. This is evidenced by the analysis of the routine data for Bungudu.

• Though the program performance can be described as effective, the defaulter rate, especially early into attendance indicate that significant proportion of SAM cases default when they are still likely to be SAM cases. As such, this contributes significantly to reducing the ability of the program to meet needs.

62

The point coverage of the program at 35.0% (26.3-44.7%; CI83 95%) is considerably lower than the minimum recommended for a CMAM program implemented in a rural context (50%). Met need is calculated as:

Given that median recovery rate for Bungudu LGA is 85%84 and the program coverage is 35.0%, then:

This indicates that 30% of the total SAM cases in the community have been treated successfully and discharged as recovered. The Bungudu LGA CMAM is described as a low coverage program.

.VII.1.2. Bakura LGA

The CMAM program is characterised by strong ownership and participation by the community. There was evident in the provision of shades, constructed toilets and water stations in CMAM sites (OLPC and Danmanau); sometimes the community leaders would provide lunches to CVs, health workers, and sometimes carers (Danmanau, Dambo & OLPC); there was a well in Rini CMAM site due to the efforts of the ward development committee. This has a direct impact on increasing coverage of the program in terms of motivating health workers and CVs and provision of required amenities for use by caregivers and SAM cases for a good CMAM site. Generally the community has a good opinion about the program and there are regular referrals done by community leaders as well as mother to mother referrals. The insecurity close to some sites have directly affected attendance and ultimately coverage. This was observed in Damri and Dakko areas. Also, flooding due to over flow of river bank limits access to the CMAM site85. The CVs are not well distributed (especially to areas far from the CMAM site) with a good number concentrated close to the CMAM site. This contributes to low awareness of the program and non-existent active case finding in settlements that are far from the CMAM site. Often children with MUAC of > than 115mm have been admitted into the program mainly because the CVs and some health workers were not taking MUAC measurements appropriately before referring and admitting cases respectively. More so, health workers sometimes admit referrals not fulfilling the admission criteria for fear of alienating caregivers, thereby damaging the reputation of the programme.

83 Credible interval 84 Program performance data for Bungudu LGA; SC’s database. 85 This happens in Dambo during rainy season significantly contributing to defaults and reduced admissions. Incident cases are not captured early resulting in late admissions into the program.

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The point coverage estimate was preferred for Bakura for similar reasons to those of Bungudu. General late admission and high defaulter rates will need to be addressed to improve the coverage of the program. The point coverage of 47.4% (38.4-56.5%; CI 95%) is slightly below the minimum recommended for a CMAM program implemented in a rural context (50%). Met need is calculated as:

Given that the median recovery rate for Bakura LGA is 75%86 and the program coverage is 47.4%, then:

This indicates that 36% of the total SAM cases in the community in Bakura LGA have been treated successfully and discharged as recovered. The Bakura LGA CMAM is described as a low coverage program

.VII.1.3. Shinkafi LGA

The CMAM program is valued by the community in Shinkafi. The community demonstrated ownership of the program by building shades, providing benches, water and food at Fakai, Galadi, Shanawa CMAM sites. Shinkafi can be described as the CMAM program where significant number of caregivers walk long distances to attend CMAM program. The program is hosted within the health facilities utilizing opportunities for treating SAM from caregivers who have brought their children for treatment of other ailments; just like the other programs in Bungudu and Bakura. CVs are not well distributed in the catchment area while some CMAM sites get cut off and are rendered inaccessible by floods during the rainy season87. Sharing of RUTF in the households was evident and preference for using traditional healers and medicine vendors over the health facility is common. This is the case for SAM and diseases associated with SAM such as RTI and Malaria.

Point coverage is still used to describe the program coverage. Similarly, the point coverage of 47.0% (36.5-57.4%; CI 95%), just like that of Bakura LGA is slightly below the minimum recommended for a CMAM program implemented in a rural context (50%). 86 Program performance data for Bakura LGA; SC’s database. 87 The CMAM sites made inaccessible are Fakai, Galadi.

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Met need is calculated as:

Given that median recovery rate for Shinkafi LGA is 77%88 and the program coverage is 47.0%, then:

This indicates that 36% of the total SAM cases in the community in Shinkafi LGA have been treated successfully and discharged as recovered. The Shinkafi LGA CMAM is described as a low coverage program

.VIII. RECOMMENDATIONS

A repeat SQUEAC investigation should be done in 6-12 months as guided by the audit cycle (figure 34 below). The program recommendations are summarized as actions with deliverables. These recommendations were proffered in a consultative process involving ACF International, the SCI program staff and the state ministry of health who are implementers of the CMAM program

Figure 34: illustration of the recommended SQUEAC audit cycle for Zamfara state

88 Program performance data for Shinkafi LGA; SC’s database.

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Table 17: Common recommendations for the CMAM program in Bungudu, Bakura and Shinkafi LGAs Common recommendations for the 3 LGAs

Main area of activity Processes Verification Expectations Community Mobilization and sensitization in all wards of the LGA

Identify/list wards and communities for town hall meetings in places far from CMAM sites

Number of communities and wards to conduct town hall meetings done

Increased programme awareness in communities.

Increased number of caregivers who have awareness about program and malnutrition

Identify/list nomadic Fulani communities for sensitization e.g. (Fulani Ardos)

Prepare community sensitization tools for use by CMAM program staff/translate to other languages.

Number of Fulani Ardos sensitized

Checklist of tools and translated messages for sensitization

Increased admissions of SAM cases from Fulani Ardos. Number of mother to mother referral from Ardo community

Town Hall meeting in each ward with village heads (Hakimi89, opinion leaders, religious leaders, and women leaders).

Number of town hall meetings held. Number of SAM cases referred by community/religious leaders

Conduct training/sensitization of religious leaders (Imams) for sensitization of faithful’s during Jumaa Prayers

Checklist of key messages for reference by religious leaders.

Community mobilization and sensitization plan for LGA CVs

Number of sensitization meetings held

Number of religious leaders trained/sensitized and stratified by wards/villages

Reduced stigmatization of malnutrition in some communities Number of SAM cases referred from communities sensitized Proportion of caregivers who have no awareness about malnutrition or knowledge about the program.

Programme modalities Radio jingles and dramas on community radio stations about the CMAM programme

Number of radio jingles and radio drama developed on CMAM

Number times per month the jingle/drama is

Increased number of caregivers who have knowledge on program modalities. Proportion of HH where RUTF is shared with other siblings/adults

89 Opinion/social leaders in the community

66

aired on Radio Dissemination of IEC materials in Hausa and Fulani Languages

Number of settlements/communities given IEC materials in Hausa and Ajami Languages

Identify individual community definition/understanding of malnutrition and reconcile with program definition. Identify/list points of contact with target health care workers, caregivers and community leaders

List of health care workers who follow-up on this task. List of communities to be visited and sensitized

Improved knowledge on program case definition in the community.

Case finders List of villages stratified by wards that will need active case finding. Identify & list potential caregivers who can be case finders within their communities.

Identify religious leaders, community leaders, TBAs, Traditional Healers, and majalis90 mapped by catchment population who can work as case finders

Prepare a plan/schedule to train/educate and engage identified groups/ individuals who have shown positive response to be engaged a case finders

A list of potential, caregivers, religious leaders, community leaders, TBAs, Traditional Healers, and majalis with potential for indoctrination in the program.

Schedule of meeting/ appointments with religious leaders, community leaders, TBAs, Majalis, and Traditional leaders that are to be engaged as Case finders

Training plan/schedule for the identified persons

List of active case finders per CMAM site

Community mobilization work-plan developed by CVs per CMAM site

Increased peer and self-referrals.

Increased MUAC at admission indicating early identification of SAM cases.

Reduced defaulter rate.

Number of villages visited for case finding and case follow-up per week/ month.

Improved recovery rate especially for Shinkafi LGA

Improved treatment coverage of the CMAM program

CV motivation Continue the incentives given by SCI to community volunteers during monthly meetings

Integrate fully the motivation of community

Number of community volunteers that received incentives from SCI monthly meetings

Number of community volunteers that

Monthly budget line allocation and activity plan for CVs. Number of SAM referral cases from the CVs.

90 Social groups of people usually male who hold small social gatherings neat the market or shopping centre.

67

volunteers into the funds provided by the Basket Funds from the LGA.

received incentives from the Basket Fund provided by LGAs

Number of volunteers supporting CMAM activities per CMAM site/catchment area.

RUTF supply Develop monthly distribution plan of RUTF from State to LGA and to OTP sites.

Transport and supply chain of RUTF from State Capital to LGA and down to OTP sites

Distribution plan developed by State

Distribution plan developed by LGA

Number of health facilities with stock-outs monitored bi-weekly

CMAM training Identify HWs and CHWs91 (3 per OTP) and 1 per HF not implementing CMAM for training/refresher training.

Schedule refresher training of HWs on CMAM

List of Health Workers that had training/refresher training

Number of OTP cards adequately completed without missing information. Existence of tracking records on stocks, absences and defaulters of beneficiaries

CMAM program monitoring Form LGA supervisory team

Build capacity of LGA team on supportive supervision of CMAM programme

Develop supportive supervisory work plan

Conduct continuous supportive supervision

LGA supportive supervision work-plan developed

Schedule of the number of sites to be visited for supportive supervision per month by each LGA team

Number of sites to be visited by the State Nutrition team per quarter.

Monthly report on CMAM performance indicators from HF

Number of times each HWs came into contact with supervisor team per quarter

Scale-up of CMAM Establish two more CMAM sites in Bungudu LGA

Establish one more OTP Site in Bakura LGA

Establish one more OTP site in Shinkafi LGA

Number of new CMAM sites established per LGA

Increased accessibility of CMAM sites by carers and coverage Low defaulter rate and high recovery rate. Increased new admissions

SQUEAC assessment Plan for another assessment in 6-8 months Full-scale SQUEAC assessment implemented SQUEAC report/Action points

91 Community Health workers

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.IX. ANNEXES

.IX.1.1. Parameters used in the report

Table18: Parameters used in prior setting, sample size calculation and conjugate analyses

Prior mode by un-weighted barriers and boosters Bungudu LGA Bakura Kudu

LGA Shinkafi LGA

Total barriers 14 15 13 Total Boosters 8 12 12 Average score given to a barrier and booster (Unweighted) + or – 3 (%age) + or – 3 + or – 3 Total weight- barriers 42 45 39 Total weight –Boosters 24 36 36 Contribution of barriers to coverage 58 55 61 Contribution of boosters to coverage 24 36 36 Average coverage 41 45.5 48.5 Prior by weighted barriers and boosters92 Total weights to barriers 49 45 37 Total weight to boosters 31 31 37 Contribution of barriers to coverage 51 55 63 Contribution of boosters to coverage 31 31 37 Average coverage 41 43.5 50 Prior calculated from un-weighted barriers and boosters and weighted barriers and boosters 41 44.25 49.25 Fixed quantity (uncertainty) + or – 25% + or – 25% + or – 25% Minimum probable value 16 19.25 24.25 Maximum probable value 66 69.25 74.25 Proportions used in determination of ranges of prior in belief histogram

Min –Prior 0.16 0.1925 0.2425 Max –Prior 0.66 0.6925 0.7425 Mode -Prior 0.41 0.4425 0.4925 Posterior estimate Precision 0.1 0.1 0.109 Median Village Population 410 376 853 % of 6-59 Months 0.18 0.18 0.18 SAM Prevalence 0.015 0.015 0.015

92 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.

69

Sample Size (SAM cases) 45 63 46 Derivatives Not (δ) 0.83 0.08 0.83 Mu(μ) 0.41 0.44 0.475 Alpha prior (α) 13.87 15.38 16.58 Beta prior (β) 19.96 19.18 18.32 Likelihood Sample Size 45 63 46 Number Of Villages 40 62 19

.IX.1.2. Survey Questionnaire for caretakers with cases NOT in the programme

State: ________________ LGA: ______________ WARD: ______________

Village: _____________ Team No: ____________

Child Name: __________________________________

1a. DO YOU THINK YOUR CHILD IS SICK? IF YES, WHAT IS HE/SHE SUFFERING FROM? ___________

__________________________________________________________________________________

1. DO YOU THINK YOUR CHILD IS MALNOURISHED? YES NO

2. DO YOU KNOW IF THERE IS A TREATMENT FOR MALNOURISHED CHILDREN AT THE HEALTH CENTRE? YES NO (stop)

3. WHY DID YOU NOT TAKE YOUR CHILD TO THE HEALTH CENTRE? Too far (How long to walk? ……..hours) No time / too busy Specify the activity that makes them busy this season __________________________ The mother is sick The mother cannot carry more than one child The mother feels ashamed or shy about coming No other person who can take care of the other siblings Service delivery issues (specify ………………………………………………….) The amount of food was too little to justify coming The child has been rejected. When? (This week, last month etc)________________ The children of the others have been rejected My husband refused The mother thought it was necessary to be enrolled at the hospital first The mother does not think the programme can help her child (prefers traditional healer, etc.) Other reasons: ___________________________________________________

4. WAS YOUR CHILD PREVIOUSLY TREATED FOR MALNUTRITION AT THE HC (OTP/SC)? YES NO (=> stop!)

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If yes, why is he/she not treated now? Defaulted, When?.................Why?.................. Discharged cured (when? ............) Discharged non-cured (when? .............) Other:___________________________________________ (Thank the mother/carer)

.IX.1.3. Case study with CV from Fulani community

Name of CV: Madawaki Wakili

Community/location: Katuru community of Katuru CMAM site.

Madawaki said he got to know about the programme when he saw a community gathering in the facility about 16 months ago, and sought more clarification from the community leader who told him that it is a programme meant for children and is free of charge. The community leader also told him that volunteers are needed for awareness creation within their individual community. He was also told that the programme is conducted every Tuesday. He indicated his willingness to volunteer for the programme to the Community leader who afterwards sent him to the Health Facility In-Charge named Mallam Bala.

Mallam Bala explained to him was told that there would not be any payment made to volunteers in the course of the programme and he (Madawaki) agreed to these terms. Madawaki was trained in August 2012 on how to identify SAM children and was given a MUAC tape which he readily showed to the investigators.

Madawaki is the only normadic Fulani CV in Katuru facility. He said he has referred many SAM cases from various Fulani communities among which is a special case of a four year old girl that was severely malnourished, and was sick for three years, unable to walk. However, within three weeks of treatment at Katuru OTP site, she made great progress and as a result many carers bring their children to his house every time or send for him whenever they think their child is sick to assess them for malnutrition. He usually refers children who fall below MUAC cut-off of 115mm (red) to the OTP site where their MUAC is confirmed before they are enrolled into the programme. He reported that he has continually discouraged carers whose children are enrolled in the CMAM programme from migrating with other family members, as he encouraged such carers to stay in a nearby settlement until their children are discharged from the OTP site before rejoining their family wherever they have moved to.

71

However, Madawaki said the biggest challenge to this programme is migration of nomadic Fulanis to other places in search of pastures and water for their livestock during dry season. For example he cited that people from Kafin Mazuga (in Shinkafi LGA) migrate to Arume in Isa LGA of Sokoto State, Fulanis from Geza Katuru migrate to Tungar Kayaye in Shinkafi LGA, while some move from Satiru to Katsalle in Isa LGA of Sokoto State. According to Madawaki he is constrained from following up clients that refuse to stay behind to get their children discharge/cured before migrating due to the long distance to such places and inability of such carers to settle in one place. Moreover, Madawaki said the migrating Fulanis always return after six months (November to May).

Another important challenge is that while every other tribe will permit anybody to touch their child/measure their MUAC, the nomadic Fulani would never allow any person other than someone of Fulani origin to touch/measure their child. He therefore suggested that in those communities where the nomadic Fulanis have migrated the assistance of Ardo (Fulani Leaders) should be solicited for them to allow their children to be screened and referred to OTP sites. Madawaki Mukatari cited a striking example of a Fulani husband refusing permission for his wife to attend OTP follow-up because he was not given any prior notification by Madawaki (as the carer came by herself to access the CMAM services). As a result she was absent until he was able to trace her home and resolve the issue with her husband.

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.IX.1.4. Active and adaptive case finding93 procedure

93 Local terms of malnutrition used are from Guri LGA in Zamfara State, Northern Nigeria.

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

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” Go to the first house where a potential case may be found

Does the child have bilateral Oedema or is the MUAC < 115mm?

Not a Current SAM case

Is the child in OTP?

Ask to see sachet of RUTF and health card

Current SAM case

Is the child in OTP?

Ask to see sachet of RUTF and health card

Current SAM case not in the program

1. Fill out the tally sheet 2. Apply questionnaire 3. Refer the child to

CMAM program site 4. Thank the caregiver 5. Ask case finding

question

Current SAM case in the program

1. Fill out the tally sheet

2. Thank the caregiver 3. Ask case finding

question

Recovering SAM case

1. Fill out the tally sheet


question

Normal child, No history of SAM

1. Not included in the study.


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

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.IX.1.5. Mind map-Bungudu LGA

.IX.1.6. Mind map-Bakura LGA

.IX.1.7. Mind map-Shinkafi LGA

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.IX.1.8. List of the participants-Zamfara SQUEAC

Table 19: List of participants in the Zamfara SQUEAC study

S/N Name of participants Trainers 1. Ifeanyi Maduanusi 2. Babatunde Lawani Enumerators 1 Hassan Hudu 2 Janet Adeoye 3 Funmilayo Ajayi 4 Rukaiya Bello 5 Saratu Abdullahi 6 Kareema Idris 7 Asmau 8 Santos Esievo 9 Nura Aminu 10 Hafsat Haliru Anka 11 Sakina Adamu 12 Lawali Yahaya

.IX.1.9. Weighted and un-weighted barriers and boosters-Bungudu LGA

Table20: Barriers and Boosters-Bungudu LGA

S/n

Boosters (Bungudu LGA) Unweighted

weighted

Sources. S/n Barriers (Bungudu LGA) Unweighted

weighted

Sources

1 Communities have a good opinion of the programme

3 5 3C,4C,5C,6C,7C,8C,9C,10C,11D,12C

1 Non-adherence to CMAM protocols94.

-3 -5 1A,2B,3C,6C,12C

2 Good linkage between CMAM services and the community95.

3 5 3C,4C,7C,10C,13C

2 Poorly motivated health workers in their work resulting in low commitment. The HW see CMAM as an additional burden.

-3 -5 2B,3C,6C,12C

3 Good awareness of the CMAM programme in communities

3 5 5C,7C,8C,9C,10C,11D,12C

3 Lack of CVs in villages far from CMAM sites contributing to low active case finding

-3 -5 1A,3C,6C,12C


3 4 2B,3C,5C 4 Stock out of data tools, RUTF and routine drugs

-3 -3 3C,6C,7C,12C

5 Support of CMAM program by CBO96 with routine drugs, and recent support by State and LGAs

3 4 2B,6C,12C 5 Insecurity in many parts of the LGA affecting attendance of CMAM services negatively.

-3 -4 2B,3C,6C,7C,11D,12C

6 Integration of CMAM with other child health services MNCH (Maternal New Born and Child Health) week

3 3 2B,3C,6C,12C

6 Shortage of health workers trained on CMAM generally. Interns run the CMAM site in Nahuche CMAM site.

-3 -3 2B,3C,4C,6C,12C,

94 This includes but not limited to: incomplete filling of client cards (sensitive client information not captured; walking distance, weight, date of admission/follow-up visits, no outcome indicated); many cases admitted on MUAC ≥ 115; non-confirmation of MUAC by health workers at OTP sites; total mix-up of OTP cards making it hard to track SAM cases once admitted in the program. Some cards are demarcated by outcome. 95 Evidenced by self-referral of caregivers, mother-mother referral, spouse referral, referrals by TBA. Also referrals by community leaders, traditional healers and patent-medicine dealer were evident 96 Community Based Organizations: Zanna Organization and Yerima like-mind Foundation

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7 Ownership of the program demonstrated by some community members who provide food and stipends for CVs and HWs for each OTP day, and contribution to OTP daily expenditure (in HF-Tofa)

3 3 2B,3C,6C,12C

7 Poor referral of SAM cases by existing CVs; CVs not motivated, soliciting for motivation in terms of stipends/allowances

-3 -2 2B,3C,5C,6C,12C

8 Good interface between OTP and SC

3 2 1A,3C,6C 8 Stigmatization of malnutrition in some communities

-3 -2 3C,5C,4C,12C

9 Poor understanding of CMAM program modalities by caregivers leading to frequent misunderstanding with health workers

-3 -3 2B, 3C,8C,6C

10 Poor organization at the CMAM site97.

-3 -4 1A,2B

11 Confusion of CMAM with polio immunization limits access by clients98.

-3 -2 1A,2B

12 Far distance limiting accessibility of services in many communities

-3 -4 3C,4C,5C,6C,7C,12C

13 Refusal/unfriendly attitude to clients by health workers

-3 -3 2B,4C,6C,12C

97 Poor seating arrangement; lack of shades for carers; lack of adequate number of benches/seats making caregiver fatigued. Caregivers also stand queuing for long periods waiting for their children to be attended to. 98 Community do not have trust on Polio immunization. When the CMAM service is associated with polio, attendance to the OTP is negatively affected.

77

14 Early closure of OTP sites makes it inaccessible to the SAM cases who are brought from far

-3 -4 2B,3C,4C,6C,12C,

Sum +24% +31 Sum -42 -49 Lowest value anchor 0% 0% Upper value anchor 100% 100% Totals 24% 31% Total 58% 51% .IX.1.10. Weighted and un-weighted barriers and boosters-Bakura LGA

Table21: Barriers and booster-Bakura LGA

S/n

Boosters (Bakura LGA) unweighted

weighted

Sources. S/n Barriers (Bakura LGA) unweighted

weighted

Sources

1 Good opinion of the programme by care givers and the community members.

3 5 2B,3C,4C,5C,6C,7C,8C,9D,10C,11D.

1 Insecurity in some political wards (with many communities/settlement99

-3 -5 11D,3C,15F

2 Strong community ownership and participation100.

3 5 2B,3C,4C,11D,17G

2 CVs are not evenly distributed; they are concentrated in villages close to the OTP sites101

-3 -5 14C,13B&C,11D,3C,9D,6C,7C,8C.

3 Mother to mother referrals. Also referrals are done by community leaders and patent medicine vendors.

3 5 3C,4C,5C,6C,10C,11D

3 There is low awareness of the program in settlements far from the CMAM site102

-3 -5 6C,7C,8C,9D,11D

99 This affects access to CMAM Services in Damri and Dambo) 100 Evidence is seen in provision of shades, toilets and water by CVs in some CMAM sites (OLPC and Danmanau); provision of lunch by community leaders to CVs, health workers, and some time carers (Danmanau, Dambo & OLPC) and also digging of water well in Rini CMAM site by ward development committee. 101 Places/communities far from OTP Sites do not have CVs for case finding

78

4 Cooperation between adjacent CMAM sites in sharing responsibilities to attend to CMAM clients.103

3 1 3C,11D 4 Preference of the traditional treatment for SAM cases.104

-3 -2 16C,6C,8C,12A&3C

5 Health workers are motivated and enthusiastic in rendering services

3 2 2B, 3C, 11D.

5 Not all health workers who have had training on CMAM devote themselves fully to attending to SAM cases. This results in long waiting times as clients wait for the services.

-3 -2 3C,11D

6 CVs are trained and used as support staff for health workers in those facilities with shortage of staff for anthropometric measurements/rescreening of SAM cases and nutrition/health education.

3 3 2B, 3C, 4C, 11D.

6 Poor defaulter and DNA105 tracing. There are lost opportunities to get some cases back into to the program106

-3 -4 11D,3C,4C

7 Integration of CMAM with other child health services (MNCH week activities)

3 2 3C,11D, 7 There is a general shortage of CMAM trained health workers in most CMAM sites107

-3 -2 2B, 11D.

8 Integration of the CMAM sites within the HF has ensured good turnout of clients108

3 2 2B, 3C, 11D.

8 Poor arrangement of data and monitoring tools making it difficult to track SAM cases in the process of treatment or to identify defaulters

-3 -3 1A,2B,11D

102 This is due to low sensitization and also low active case finding in places far from CMAM sites. 103 Training of select health workers from neighboring facilities to the Bakura CMAM site to assist during OTP days 104 Poor health seeking behavior because some communities engage/prefer traditional healers to treat SAM. 105 Did not attend 106 CVs concentrated to communities close to the CMAM sites do not do defaulter tracing in close communities. There is no follow-up of SAM cases in relatively far communities. 107 Damri, Dankaiwa, Rini, Danmanau, OLPC, Dambo. 108 This has been observed in most sites visited (Danmanau, OLPC, Yarkofoji) underscoring accessibility of CMAM services by clients.

79

for follow-up.

9 CMAM site are well organized/arranged.

3 1 2B, 3C, 11D.

9 Non adherence to protocols evident on admission of children with MUAC>115mm.

-3 -1 1A,

10 Good working relationship between health workers and CVs leading to good quality service to clients.

3 2 2B,3C,4C,5C,11D

10 Previous stock out of RUTF (September /October) contributed significantly to rise in default rates. Clients were sent home without RUTF in this period.

-3 -3 1A,12B,3C,11D

11 Active case finding and referrals by some CVs: and passive referrals by some health workers

3 2 3C,4C,5C,11D

11 Sharing of RUTF among entire households (children and adults) in communities

-3 -3 3C,11D,19E,20G

12 Availability of Basket Fund with LGA contributions for provision of routine drugs.

3 1 3C,11D,18A

12 Flooding due to overflow of river bank limits access to CMAM site109

-3 -3 2B,5C,14C

13 There was persistent stock-out of essential routine drugs, especially amoxicillin and ACT-Malaria drugs (September/October 2013).

-3 -3 1A,3C,11D,12B

14 Unevenly distributed CMAM sites110.

-3 -3 2B,11D.

109 This happens in Dambo during rainy season significantly contributing to defaults and reduced admissions. Incident cases are not captured early resulting in late admissions to the program. 110 Dambo and OLPC CMAM sites are close to each other (6KM apart). This has meant that the HFs are under-utilized while some other communities in more remote areas do not have access to CMAM services)

80

15 Poor integration of CMAM services with polio immunization service111

-3 -1 11D,3C

Sum +36% +31 Sum -45 -45 Lowest value anchor 0% 0% Upper value anchor 100% 100

%

Totals 36% 31% Total 55% 55% .IX.1.11. Weighted and un-weighted barriers and boosters-Shinkafi LGA

Table22: Barriers and boosters-Shinkafi LGA

S/n Boosters (Shinkafi) Unweighted

weighted

Sources S/n Barriers Unweighted

weighted

Sources

1 Strong community ownership112 3 5 1B,3C,4C,11G,13C

1 CVs absent in communities far from CMAM sites but concentrated in areas close to them (CMAM sites).

-3 -5 3C,4C,11G,12E,13C,15A

2 Good awareness of the programme in communities

3 5 1B,3C,4C,5C,6C,7C,8D,9C,10C,11G

2


-3 -5 4C,5C,6C,11G,12E,13C

3 CMAM services hosted at the health facilities.

3 5 3C,4C,6C,8D,9C,10C,11G,12E,13C,15A

3

RUTF given to SAM cases at CMAM site is shared among adults at home.

-3 -5 6C,7C,8D,9C,10C

111 In most health facilities health workers are taken away for the polio campaign thereby disrupting services to CMAM clients (Danmanau, Dambo & Rini 112 Community has built shades, provided benches, water and food at Fakai, Galadi, Shanawa CMAM sites 113 The CMAM sites made inaccessible are Fakai, Galadi.

81

4 Willingness of care givers to travel far distances to access CMAM services

3 4 1B,2A,3C,4C,5C,11G,7C,8D,13C

4 Previous stock out of routine drugs, especially, amoxicillin drugs (September and October, 2013)

-3 -3 3C,11G,13C

5 Self-referral, peer-to-peer referral, husband, grand-mother, and religious leader referrals evident

3 4 3C,5C,11G,12E,13C

5 Dilapidated Health Facility with leaking roofs, making it difficult to host clients, keep RUTF and drugs well and store data tools safely (Fakai CMAM site)

-3 -2 2A,3C,6C,7C,11G,12E

6 Integration of CMAM services with immunization and MNCH activities enhances passive case finding to identify SAM

3 2 3C,6C,13C 6 Preference for traditional healers and medicine vendors over health facility. This is in treatment of SAM and diseases associated with SAM (RTI and Malaria)

-3 -3 4C,5C,9C,10C

7 Good opinion of the programme in communities

3 4 2A,3C,6C,12E

7 Poorly motivated CVs.114 -3 -3 2A,3C,5C,6C,12E

8 Good community linkage evidenced by use of traditional leaders as CVs

3 2 1B,3C,5C,6C,8D

8 Long waiting times at the HF as caregivers wait for their children to be attended to. There are often no seating arrangement for caregivers who stand as they wait.

-3 -2 1B,3C,4C,5C,13C


3 2 1B,3C,4C,11G,13C

9 Non adherence to protocols by health workers is evident (admission MUAC >115)

-3 -1 3C,8D,6C

114 This affects the active case finding negatively

82

10 Provision of ACT drugs by Malaria Action Programme for State (MAPS)in two CMAM sites (Galadi and Kware)

3 1 3C,11G,13C

10 Shortage of HWs trained on CMAM to render services to clients (Fakai)

-3 -2 1A,3C,13C

11 Data tools well-arranged and easy for tracking patient's records

3 1 1B,3C,13C 11 Masking of defaulters in HF records make it hard to track proper treatment episodes for SAM cases. Galadi CMAM site has long LOS that is associated with this.

-3 -2 1A,2A

12 Continuous training of HWs in facilities not implementing CMAM on CMAM protocols. This enhances referrals from facilities not implementing CMAM to facilities implementing CMAM services

3 2 3C,11G,13C

12 Far distance limits access to CMAM services to caregivers living far from the site.

-3 -1 2A,5C,14C

13 Poor tracking of the migrating Fulani community by program such that SAM cases would default when the community migrates.

-3 -3 2A,3C,6C,8D,11G,12E,13C

Sum +36% +32 Sum -39 -37 Lowest value anchor 0% 0% Upper value anchor 100% 100% Totals 36% 32% Total 61% 63%

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.IX.1.12. Concept map-Bungudu LGA

84

.IX.1.13. Concept map-Bakura LGA

85

.IX.1.14. Concept map-Shinkafi LGA

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