Semi-Quantitative Evaluation of Access and Coverage ... · Usman Tahir (Director General GHSB), and Kabir Ibrahim (Director ... Sanitation and Hygiene (WASH) Program and Suleiman

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Semi-Quantitative Evaluation of Access and Coverage (SQUEAC)

Kiyawa LGA CMAM Program

Jigawa State, Northern Nigeria

June-July 2014

Joseph Njau, Ifeanyi Maduanusi, Chika Obinwa, Francis Ogum, Zulai Abdulmalik, and Janet

Adeoye

ACF International

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ACKNOWLEDGEMENTS

Kiyawa LGA SQUEAC1 has been implemented through generous support of the Children Investment

Fund Foundation (CIFF). The ACF Nigeria Coverage Assessment Team are grateful to the following for

their valuable contributions towards the successful completion of the Kiyawa Local Government Area

(LGA) SQUEAC assessment.

Firstly, Gunduma Health System Board (GHSB) Jigawa State authorized the implementation of the

SQUEAC assessment in Kiyawa LGA. Usman Tahir (Director General GHSB), and Kabir Ibrahim (Director

Primary Health Care GHSB) were very supportive throughout the exercise, acting as the link between

the State and the CMAM2 Coverage assessment team. Aisha Aminu Zango, the State Nutrition Officer

(SNO), Husaini Ado of National Population Commission (NPopC), Ibrahim Haruna Head of Department

(HoD) Kiyawa LGA Water, Sanitation and Hygiene (WASH) Program and Suleiman Muhammad the

Nutrition Focal Person (NFP) of Kiyawa LGA are acknowledged for providing the coverage assessment

team with client/beneficiary records, anecdotal program information and the LGA maps.

Gloria Njoku, the ACF’s Head of Base, at Dutse office is appreciated for introducing the team to the

various stakeholders and giving logisitical support to the SQUEAC investigation team. Peter Magoh the

ACF security manager, and Abubakar Kawu (program support officer) conducted a very helpful security

and logistics assessment prior to the implementation of the SQUEAC assessment. Abubakar played a

key role in logistics and administrative support during the SQUEAC implementation period.

Joseph Njau (ACF CMAM Coverage Program Manager) provided technical support in the

implementation of the assessment while Sophie Woodhead (of Coverage Monitoring Network team -

CMN) gave useful insights in compilation and validation of this SQUEAC report. Ifeanyi Maduanusi

(ACF Coverage Deputy Program Manager) led the coverage assessment team in training enumerators,

supervision of the assessment and writing of the SQUEAC report. The ACF CMAM Program Coverage

Officers (Chika Obinwa, Zulai Abdul Malik, Janet Adeoye and Francis Ogum) led the enumerator teams

and were instrumental to ensure the quality of daily SQUEAC study activities. The effort of the

enumerators -‘the foot soldiers’ in collecting information during the study is acknowledged.

Different stakeholders including health workers, caregivers, traditional and religious leaders,

traditional birth attendants and other interviewees who, despite their busy schedule gave very useful

information regarding the CMAM Program in Kiyawa LGA are highly appreciated.

Coverage Assessment Team

ACF International

1 Semi Quantitative Evaluation of Access and Coverage 2 Community-based Management of Acute Malnutrition

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Table of contents

1.1.1.1. EXECUTIVE SUMMARY ............................................................................................................................. 7

2. INTRODUCTION ....................................................................................................................................... 8

3. OBJECTIVES ........................................................................................................................................... 10

4. METHODOLOGY .................................................................................................................................... 10

5. DESCRIPTION OF FIELD ACTIVITIES ........................................................................................................ 14

6. RESULTS AND FINDINGS ........................................................................................................................ 14

6.1. STAGE 1: ROUTINE MONITORING AND PERFORMANCE DATA-IDENTIFYING POTENTIAL AREAS OF LOW AND HIGH

COVERAGE. .......................................................................................................................................................... 14

6.1.1. ROUTINE MONITORING (OTP CARDS) AND PERFORMANCE DATA ................................................................. 15

6.1.1.1.6.1.1.1.6.1.1.1.6.1.1.1. PROGRAM EXITS (DISCHARGE OUTCOMES) ............................................................................................... 15

6.1.1.2.6.1.1.2.6.1.1.2.6.1.1.2. ADMISSION TRENDS ............................................................................................................................. 16

6.1.1.3.6.1.1.3.6.1.1.3.6.1.1.3. MUAC AT ADMISSION ......................................................................................................................... 18

6.1.1.4.6.1.1.4.6.1.1.4.6.1.1.4. LENGTH OF STAY (LOS) FROM ADMISSION TO RECOVERY ............................................................................ 19

6.1.1.5.6.1.1.5.6.1.1.5.6.1.1.5. NUMBER OF VISITS BEFORE DEFAULT ....................................................................................................... 20

6.1.1.6.6.1.1.6.6.1.1.6.6.1.1.6. DEFAULTERS AND ALL EXITS BY LOCATION ................................................................................................ 21

6.1.1.7. TIME TO TRAVEL TO CMAM (OTP) HF PLOT/DISTANCE FROM TREATMENT CENTRE ....................................... 22

6.1.2.6.1.2.6.1.2.6.1.2. CONCLUSION OF THE ROUTINE MONITORING (OTP CARDS) ANALYSIS ........................................................... 24

6.2. STAGE 1: QUALITATIVE DATA-INVESTIGATION OF FACTORS AFFECTING PROGRAM AND COVERAGE. ........................ 24

6.2.1. QUALITATIVE SAMPLING FRAMEWORK .................................................................................................... 24

6.2.2. QUALITATIVE INFORMATION ................................................................................................................. 25

6.2.2.1. AWARENESS AND PERCEPTION OF THE PROGRAM, COMMUNITY MOBILIZATION, AND PEER-TO-PEER REFERRALS IN

COMMUNITIES ..................................................................................................................................................... 25

6.2.2.2.6.2.2.2.6.2.2.2.6.2.2.2. COMMUNITY VOLUNTEER (CV) ACTIVITY AND HIGH DEFAULT RATE ............................................................ 25

6.2.2.3.6.2.2.3.6.2.2.3.6.2.2.3. HEALTH SEEKING BEHAVIOUR IN COMMUNITIES ....................................................................................... 26

6.2.2.4.6.2.2.4.6.2.2.4.6.2.2.4. STOCK-OUT OF DATA TOOLS AND ROUTINE DRUGS LEADING TO CHARGES FOR OTP CARDS AND ROUTINE DRUGS26

6.2.2.5. STOCK-OUT AND MISUSE OF RUTF, AND OCCASIONAL CLOSURE OF OTP SITES .............................................. 27

6.2.2.6. LARGE TURN-OUT OF BENEFICIARIES, LONG WAITING TIME, AND UNAVAILABILITY OF SHADES, MATS AND BENCHES

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6.2.2.7. HEALTH WORKERS ACTIVITIES, NON-ADHERENCE TO CMAM GUIDELINES, AND TRAINING .............................. 28

6.2.3. DATA TRIANGULATION ......................................................................................................................... 29

6.2.4. CONCEPT MAP .................................................................................................................................... 31

6.3. STAGE 2: SMALL AREA SURVEY AND SMALL STUDY. ...................................................................................... 31

6.3.1 SMALL AREA SURVEY ............................................................................................................................... 32

6.3.1.2 CASE DEFINITION ................................................................................................................................ 32

6.3.1.3 RESULT OF SMALL AREA SURVEY ............................................................................................................ 33

6.3.2 SMALL STUDIES ....................................................................................................................................... 34

6.3.2.1 SAMPLING METHODOLOGY ................................................................................................................... 34

6.3.2.2 CASE DEFINITION ................................................................................................................................ 34

6.3.2.3 RESULT OF QUANTITATIVE SMALL STUDY ................................................................................................ 34

6.3.3 SMALL STUDY ON DEFAULTERS .................................................................................................................. 35

6.3.3.1 RESULTS OF DESCRIPTIVE SMALL STUDY .................................................................................................. 36

6.3.4 CONCLUSION OF SMALL AREA SURVEY AND SMALL STUDY .............................................................................. 37

6.4 DEVELOPING THE PRIOR. ........................................................................................................................... 38

6.4.1 HISTOGRAM OF BELIEF. ............................................................................................................................ 38

6.4.2 CONCEPT MAP ........................................................................................................................................ 39

6.4.3 UN-WEIGHTED BARRIERS AND BOOSTERS ..................................................................................................... 39

6.4.4 WEIGHTED BARRIERS AND BOOSTERS. ......................................................................................................... 40

6.4.5 TRIANGULATION OF PRIOR ........................................................................................................................ 42

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6.4.6 BAYES PRIOR PLOT AND SHAPE PARAMETERS ............................................................................................... 42

6.5 STAGE 3: WIDE AREA (LIKELIHOOD) SURVEY ................................................................................................. 43

6.5.1 CALCULATION OF SAMPLE SIZE AND NUMBER OF VILLAGES TO BE VISITED FOR LIKELIHOOD SURVEY ....................... 43

6.5.2 QUANTITATIVE SAMPLING FRAMEWORK ...................................................................................................... 44

6.5.3 CASE FINDING METHOD AND CASE DEFINITION ............................................................................................ 45

6.5.4 QUALITATIVE DATA FRAMEWORK ............................................................................................................... 45

6.5.5 RESULTS OF THE WIDE ARE SURVEY ............................................................................................................. 47

6.5.6 POSTERIOR/COVERAGE ESTIMATE .............................................................................................................. 49

7 DISCUSSIONS ......................................................................................................................................... 51

8 RECOMMENDATIONS ............................................................................................................................ 51

9 ANNEXURE ............................................................................................................................................ 56

ANNEX1: SCHEDULE (DETAILED) OF IMPLEMENTED ACTIVITIES IN KIYAWA SQUEAC. ....................................................... 56

9.4 ANNEX2: PARAMETERS USED IN PRIOR BUILDING AND SAMPLE SIZE CALCULATION. ............................................. 61

9.5 ANNEX3: CONCEPT MAPS-TEAM A AND B. .................................................................................................. 62

9.6 ANNEX 3: ACTIVE AND ADAPTIVE CASE FINDING PROCEDURE ........................................................................... 64

9.7 ANNEX6: SUMMARY OF THE SMALL STUDY FINDINGS-KIYAWA LGA ................................................................. 65

List of figures

FIGURE 1: MAP OF NIGERIA (TOP RIGHT) SHOWING JIGAWA STATE AND JIGAWA STATE MAP SHOWING KIYAWA LGA (INDICATED BY

ARROW). MAPS CAN BE DOWNLOADED AT HTTP://LOGBABY.COM/ENCYCLOPEDIA/HISTORY-OF-JIGAWA-

STATE_10024.HTML#.U_CQ7SIG-P8................................................................................................................... 9 FIGURE 2: MAP OF KIYAWA LGA SHOWING THE LOCATION OF THE 5 CMAM HFS (HFS) ......................................................... 9 FIGURE 3: EXIT TRENDS FOR KIYAWA LGA CMAM PROGRAM-JANUARY 2013 TO APRIL 2014 ................................................ 16 FIGURE 4: ADMISSION TREND OF KIYAWA LGA CMAM PROGRAM AND THE SEASONAL CALENDAR OF EVENTS ............................ 17 FIGURE 5: ADMISSION MUAC FOR KIYAWA LGA CMAM PROGRAM .................................................................................. 18 FIGURE 6: LENGTH OF STAY (LOS) FROM ADMISSION TO RECOVERY ..................................................................................... 19 FIGURE 7: PROPORTION OF EXIT MUACS -RECOVERED ..................................................................................................... 20 FIGURE 8: PLOT OF NUMBER OF VISITS BEFORE DEFAULT.................................................................................................... 20 FIGURE 9: DEFAULTERS’ MUAC ON EXIT ....................................................................................................................... 21 FIGURE 10: PROPORTION OF EXIT MUACS AT POINT OF DEFAULT ....................................................................................... 21 FIGURE 11: DEFAULTERS VS EXITS-KIYAWA AND OTHER LGAS ............................................................................................ 22 FIGURE 12: TIME TO TRAVEL TO CMAM SITES -CASES IN PROGRAM AND DEFAULTERS ............................................................ 23 FIGURE 13: TIME TO TRAVEL TO CMAM SITES FOR EACH OF THE 5 CMAM HEALTH FACILITIES ................................................ 23 FIGURE 14: REASONS FOR NOT ATTENDING THE CMAM PROGRAM-SMALL AREA SURVEY ........................................................ 33 FIGURE 15: ANALYSIS OF REASONS FOR DEFAULT AND CONDITION OF CASES FOUND IN THE STUDY IN STAGE 2 ............................. 37 FIGURE 16: ILLUSTRATION OF TRIANGULATION OF PRIOR ................................................................................................... 42 FIGURE 17: BAYESSQUEAC BETA-PRIOR DISTRIBUTION PLOT SHOWING THE SHAPE PARAMETERS AND THE SUGGESTED SAMPLE SIZE 43 FIGURE 18: KIYAWA MAP DIVIDED INTO QUADRANTS FOR SPATIAL SAMPLING OF VILLAGES ....................................................... 45 FIGURE 19: BARRIERS TO PROGRAM ACCESS AND UPTAKE-WIDE AREA SURVEY (WAS) ............................................................ 46 FIGURE 20: DISTRIBUTION OF QUADRANTS ACCORDING TO COARSE COVERAGE ESTIMATES ....................................................... 49 FIGURE 21: BAYES PLOT SHOWING PRIOR, LIKELIHOOD AND POSTERIOR (CONJUGATE ANALYSIS) ................................................ 50

List of tables

TABLE 1: PARAMETERS USED IN ANALYSES OF LIKELIHOOD SURVEY ....................................................................................... 13 TABLE 2: SUMMARY OF OUTCOME DATA EXTRACTED FROM BENEFICIARY/OTP CARDS AT A GLANCE. .......................................... 15 TABLE 3: SOURCES AND METHODS USED TO GET INFORMATION IN THE BBQ TOOL FOR KIYAWA LGA. ........................................ 29 TABLE 4: BARRIERS, BOOSTERS & QUESTIONS FINDINGS AND SOURCES OF INFORMATION.......................................................... 30 TABLE 5: SIMPLIFIED LOT QUALITY ASSURANCE CLASSIFICATION OF SMALL AREA SURVEY RESULTS .............................................. 33 TABLE 6: SIMPLIFIED LOT QUALITY ASSURANCE CLASSIFICATION OF SMALL STUDY RESULTS ...................................................... 35 TABLE 7: CMAM HFS AND VILLAGES SELECTED FOR DEFAULTER STUDY ................................................................................ 36

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TABLE 8: WEIGHTED BARRIERS AND BOOSTERS OF KIYAWA LGA CMAM PROGRAM .............................................................. 40 TABLE 9: PARAMETERS FOR SAMPLE SIZE CALCULATION FOR LIKELIHOOD SURVEY .................................................................... 44 TABLE 10: BARRIERS TO PROGRAM ACCESS AND UPTAKE-WAS .......................................................................................... 46 TABLE 11: RESULTS OF THE LIKELIHOOD (WIDE AREA) SURVEY ............................................................................................ 47 TABLE 12: DISAGGREGATED SAM CASES PER QUADRANT AND COARSE ESTIMATE DURING THE WIDE AREA SURVEY ....................... 47 TABLE 13: FRAMEWORK OF ACTION POINTS TO ADDRESS BARRIERS OF KIYAWA CMAM PROGRAM ............................................ 52

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ABBREVIATIONS

ACF Action Contre La Faim | Action Against Hunger | ACF International

CIFF Children Investment Fund Foundation

CMAM Community-based Management of Acute Malnutrition

CV Community Volunteer

FMOH Federal Ministry of Health

GHSB Gunduma Health System Board

IEC Information Education and Communication

LGA Local Government Area

NFP Nutrition Focal Person

INGO International Non-governmental Organization

OTP Outpatient Therapeutic Program

PHC Primary Health Care

RUTF Ready to Use Therapeutic Food

SAM Severe Acute Malnutrition

SLEAC Simplified Lot quality assurance sampling Evaluation of Access and Coverage

SNO State Nutrition Officer

SMART Standardized Monitoring Assessment of Relief and Transitions

SMOH State Ministry of Health

SQUEAC Semi Quantitative Evaluation of Access and Coverage

VI Valid International

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1.1.1.1. Executive summary The Community-based Management of Acute Malnutrition (CMAM) in Kiyawa LGA of Jigawa

State commenced in October 2011. CMAM is implemented in the five CMAM sites3 by the

Jigawa State’s Gunduma Health System Board (GHSB), Kiyawa Local Government Area (LGA)

and primarily supported by UNICEF ‘D’ Field Office, Bauchi. Since inception of the CMAM

program a SQUEAC4 assessment had not been done in Kiyawa LGA. However, a recent SLEAC

assessment56 by Valid International (VI) unveiled a low classification7 of coverage with 7

children out of total of 47 SAM cases found in Kiyawa LGA being in the program. This gave a

coarse estimate of 14.8% which give a picture of program coverage. Kiyawa LGA was chosen

for a SQUEAC assessment to identify positive and negative factors affecting CMAM program

coverage, build capacity of SMoH and LGA staff, and to proffer recommendations to improve

the CMAM coverage.

Routine program data and client/beneficiary/Out Patient Therapeutic Program (OTP) records

from May 2013 to May 2014 were extracted and analyzed. Five health Facilities (HFs) which

offer the CMAM services8 and 10 villages9 which form part of the catchment population of

these HFs in Kiyawa LGA were visited to obtain additional qualitative information about the

CMAM program using different sources10 and using different methods11. All the information

was triangulated by the various sources and methods until no new information was

forthcoming and then analyzed into negative factors (barriers) and positive factors (boosters)

which affect program coverage.

The key barriers to program access and coverage identified included; high number of

defaulters, lack of motivation of community volunteers to conduct case-finding and defaulter

tracing, non-adherence to CMAM national guideline/protocol, generalized stock-out routine

drugs, stock-out of data tools for recording beneficiary information, consumption of RUTF by

adults, and siblings of SAM children in communities, weak mechanism for delivery of RUTF to

HFs. The key boosters include; large turnout of beneficiaries12, good awareness of the

program in communities, good opinion of the program in communities, willingness of

beneficiaries to stay in the program despite challenges13, peer-to-peer referrals by caregivers,

good working relationship between Health workers (HWs) and CVs.

3 All the five CMAM sites Katuka, Garko, Maje, Kwanda and Katanga 4 Semi Quantitative Evaluation of Access and Coverage 5 D’ Field Office is located in Bauchi, Bauchi State. 6 Chrissy B., Bina S., Safari B., Ernest G., Lio F. & Moussa S.; Simplified Lot Quality Assurance Sampling Evaluation of Access

and Coverage (SLEAC) Survey of Community-based Management of Acute Malnutrition program; Northern States of

Nigeria-(Sokoto, Kebbi, Zamfara, Kano, Katsina, Gombe, Jigawa, Bauchi, Adamawa, Yobe, Borno). Valid International.

February 2014 7 The SLEAC used a three-class classifier with 20% and 50% as the thresholds; <= 20% is low; >20% to <=50% is moderate

coverage while >50% 8 All the five CMAM sites Katuka, Garko, Maje, Kwanda and Katanga were visited. 9 Villages visited include; Nafara, Dangoli, Tsirma, Jama ‘a Dawa, Kakarawa, Gorumo, Yelwa, and Danfasa. 10 Care-givers, Health Workers (HWs), Community Volunteers (CVs), community leaders, religious leaders, majalisa,

teachers, traditional healers, traditional birth-attendants (TBAs), and women group, etc. 11 Semi-structured interview, in-depth interview, observations and informal group discussions 12 This is a potential for enhancing peer to peer referral and less dependence on CVs 13 For instance there are caregivers who sleep-over at CMAM facilities till they are attended.

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Homogenous coverage across the wards in Kiyawa LGA was confirmed. Program failures

caused by placing some costs (hidden charges) on OTP cards and routine drugs to be borne

by beneficiaries was also confirmed; defaulters were studied and reasons for defaulting were

also analyzed.

After conjugate analysis of the prior and likelihood survey, a point coverage estimate of 48.5%

(41.3% - 55.8%. CI; 95%)14 was reported. Thus the coverage was significantly higher than that

unveiled in the recent SLEAC results for Kiyawa LGA due to reasons explained in detail in the

report.

Recommendations: key recommendations include provision of data tools for CMAM sites,

provision of routine drugs, removal of the hidden charges on beneficiaries to ensure free

CMAM services, strengthening of the delivery mechanism of RUTF to HFs, supportive

supervision of HWs at CMAM HFs to enhance adherence to CMAM protocol and

establishment of innovative ways to improve motivation of CVs.

2. Introduction Jigawa State is located in the Northwest of Nigeria. It is bordered by States of Kano and Katsina

in the West, Bauchi in the South, Yobe in the East and shares nn international border with

Niger Republic in the North.. The State has 27 Local Government Areas (LGAs)15. According to

the 2006 census, the State has a total population of 4,348,649 million inhabitants16. The

population growth of the state is estimated at 3.5 % with about 48 % of the population falling

under the age of fifteen. Out of the total population about 2.9 million are considered to be

productive adults. Eighty per cent (80%) of the population is found in the rural areas and is

made up of mostly Hausa, Fulani and Manga (a Kanuri dialect). The pattern of human

settlement is nucleated, with defined population centres17.

Kiyawa LGA is about 30 km away from Dutse (Jigawa State Capital). It is bordered by Dutse,

Birnin Kudu, Buji, Kafin Hausa, and Jahun LGAs, as well as Bauchi State. The people of Kiyawa

are mostly Hausa and Fulani. The major occupation is farming; while the religion is

predominantly Islam. Kiyawa LGA is under the traditional leadership of the Emir of Dutse, with

two district heads in Shuwarin and Kiyawa districts in charge of the traditional affairs of the

LGA.

At the commencement of implementation of Community-based Management of Acute

Malnutrition (CMAM) in Kiyawa LGA in October 2011, 5 CMAM sites were established18. The

program is supported by UNICEF ‘D’ Field Office, which provides technical support. The health

workers19 run the services at the CMAM Health Facilities (HFs) on a weekly basis. A Nutrition

Focal Person (NFP) for the LGA supervises the five CMAM sites and reports to the State

14 Results are expressed with a credible interval of 95%. 15 Auyo, Babura, Biriniwa, Birnin Kudu, Buji, Dutse, Gagarawa, Garki , Gumel, Guri, Gwaram, Gwiwa, Hadejia, Jahun, Kafin

Hausa, Kaugama, Kazaure, Kiri Kasama, Kiyawa, Maigatari, Malam Madori, Miga, Ringim, and Roni 16 National Population Commission 17 http://logbaby.com/encyclopedia/history-of-jigawa-state_10024.html#.U_Cq7sIg-P8 18 UNICEF Nigeria uses a model of 5 CMAM sites in each LGA for treatment of malnourished children. This is presently,

being reviewed for possible scale-up. 19 HWs are employees of the Gunduma Health System Board -GHSB)

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Nutrition Officer (SNO) who coordinates all nutrition activities in the State. The SNO who

reports to the State Director of Primary Health Care, also serves as the linkage to the UNICEF

Nutrition Specialists in UNICEF regional Office.

Figure 1: Map of Nigeria (top right) showing Jigawa state and Jigawa state map showing

Kiyawa LGA (indicated by arrow). Maps can be downloaded at

http://logbaby.com/encyclopedia/history-of-jigawa-state_10024.html#.U_Cq7sIg-P8

Figure 2: Map of Kiyawa LGA showing the Location of the 5 CMAM HFs (HFs)

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SQUEAC has not been carried out since the commencement of CMAM program in Kiyawa to

determine access and coverage of the program. However, a recent SLEAC study conducted

late 2013 by Valid International reported a coarse estimate of 14.89% (where 7 SAM cases

were covered by the CMAM program out of 47 SAM cases found in the LGA during the

assessment). Therefore, Kiyawa LGA CMAM coverage was classified as low and was one of

the LGAs (Kiyawa) identified for an in-depth investigation through a SQUEAC assessment. The

purpose was to gather additional information on positive and negative factors affecting

access and coverage of CMAM program. The SQUEAC assessment was undertaken in Kiyawa

LGA in the month of July 2014.

3. Objectives The SQUEAC investigation was guided by the following specific objectives;

1. To investigate the barriers and boosters to program and coverage in Kiyawa LGA.

2. To evaluate the spatial pattern of program coverage in Kiyawa LGA.

3. To estimate overall program coverage in Kiyawa LGA.

4. To make relevant recommendations in order to reform and to improve the CMAM

program,

5. Build the capacity of SMoH and LGA staff to conduct a SQUEAC assessment.

4. Methodology The SQUEAC methodology was adapted to suit the SQUEAC investigation of Kiyawa LGA

CMAM program.

The methodology used are explained in detail as follows;

Stage 1

Quantitative data:

Routine program data, and data from client information recorded in the OTP cards were

extracted and analyzed into various plots; admission trends; exit trends; time to travel to site;

Length of stay from admission to cure; MUAC at admission; number of visits before default;

MUAC at default; defaulters by location.

The derived plots indicated some factors that affect the program such as existence of active

case finding, met need, far distance and defaulting tendencies.

Qualitative data

Barriers, boosters and questions: Qualitative information was collected from different

sources, using different methods. The information was analyzed into a barriers, boosters and

questions (BBQ) to capture the positive and negative factors that affect the program and its

coverage. The data was triangulated to adduce evidence. Additional information was

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collected to confirm the evidence gathered before, until no further information was

forthcoming around a certain theme/topic in a process referred as sampling to redundancy.

This information was further analyzed into weighted and un-weighted barriers and boosters

which were scored according to the perceived weight each barrier or booster had on the

program coverage (either negatively or positively). The process of weighing barriers and

boosters is discussed in detail in separate section (making the prior section).

Concept map: the factors that affect the program were analyzed into a concept maps that

showed the relationship between them. The concept maps are annexed in section 9.3 annex

3.

Stage 2 data

Based on the information and evidence gathered in stage 1, a small area survey and two small

studies were conducted to investigate the spatial pattern of coverage and factors that may

be affecting coverage.

Small Area Survey

The small area survey data was analyzed using simplified Lot Quality Assurance Sampling

(LQAS) technique to test a hypothesis. This was done by examining the number of Severe

Acute Malnutrition (SAM) cases found (n) and the SAM cases covered (c) in the program. The

threshold value (d) was used to determine if the coverage was classified as satisfactory or not.

Value (p) was used to denote a minimum standard used as a measure of high, moderate or

low coverage20. The recent SLEAC assessment coverage classification (with a coarse estimate

of 14.98%) indicated that the coverage in Kiyawa LGA was low, that is below 20%21. Given

this coverage classification in Kiyawa LGA, consideration could have been given to use the 2

standard 3 class classifier to set the values of p1 and p2 as the lower and higher limit of

classification of coverage to test spatial coverage hypothesis in the current SQUEAC

assessment. However, in Kiyawa SQUEAC assessment the standard (p) was adapted as 50%22

for analyses of small area survey data using simplified LQAS. This was because it was highly

likely that the coverage could have considerably improved at the time of this SQUEAC

investigation compared to the period when SLEAC assessment was implemented. The low

coverage unveiled by SLEAC assessment could be attributed to long duration of stock-out of

RUTF at the time23. In the current SQUEAC investigation, the information gathered in stage 1

gave a picture of a program that was likely to have a high coverage. Therefore, using the

20 SPHERE standards has recommended minimum coverage for Therapeutic programs in rural, urban, and camp

settlements. These thresholds are 50%, 70% and 90% coverage for TFP program run in the contexts of rural, urban and

camp areas respectively. 21 The two standard three class classifier classifies coverage as follows: Low coverage-20% and below; Moderate coverage-

greater than 20% up to 50%; high coverage-above 50% 22 Previously conducted SLEAC coarse estimate was not considered to set the value of “p” in Kiyawa LGA. Therefore, the

sphere standard of coverage in a rural setting <= 50% for low coverage; and >50% for high coverage was used. 23 The Wide Area Survey in SLEAC assessment considers the children who are in the program (covered) against the total

number of SAM cases found during the survey. Large number of Current (SAM) cases were not covered during SLEAC due

to lack of RUTF stocks in HFs at the time; which may have made many beneficiaries stay home till RUTF was available at the

HFs

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SPHERE standard of coverage mentioned above is sufficiently sensitive to show disparities in

spatial coverage in Kiyawa as the context has changed. Therefore:

The value of (p) used that was used was 50%. The formula for deriving (d) is shown below:

� = �� × �� = �� × 50100� =�2

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.

The results of the small area survey were shown in table 5 and reasons for coverage failure

obtained from the small area survey were plotted in figure 6 in results section of this report.

Small Study

Two small studies were conducted; that is, a quantitative and a descriptive small study

respectively. The small study investigated the number of beneficiaries that were charged for

CMAM routine drugs and OTP registration cards and therefore, establish whether this was

major cause of coverage failure. The data obtained was analyzed quantitatively using

simplified LQAS and utilized the threshold (p) of 50% (as use in small area survey). This way

the classification thereof, established whether charging beneficiaries for the mentioned

service contributed to coverage failure. In this study, the (n) parameter was number of

caregivers with SAM cases that were interviewed. A descriptive small study of defaulters was

done to understand the reasons for defaulting and results presented on table 6 and illustrated

in Figure 14.The results of the small study are explained in the results section of this report.

Stage 3 data

The prior: The tools that have been used revealed a rich set information about coverage. It

also identified barriers to access and care, as well as spatial coverage of the program.

Therefore the prior of the program was estimated through use of the following tools24:

• Belief histogram

• Weighted barriers and boosters

• Un-weighted barriers and boosters

• Calculation of the total positive and total negative factors illustrated in the concept

map.

The prior was established in a beta prior distribution with prior shaping parameters and

plotted on BayesSQUEAC calculator25. The beta prior distribution26 expresses the findings of

24 Each listed process is discussed in detail in the body of the report. The recent SLEAC coarse estimate was not used

incorporated while calculating the prior. This was because it was observed that it could possibly bias the result since CMAM

sites were closed down at the time coinciding with the SLEAC survey in Kiyawa LGA in 2013. 25 Downloaded free from www.brixtonhealth.com 26 As illustrated in the example in figure 21 in results section of this report.

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stage 1 and 2 in similar ways to the likelihood survey’s (binomial distribution) as described in

a sections below. The BayesSQUEAC calculator also suggested a sample size at 10% precision.

The likelihood survey yielded data that was analyzed to give program coverage. The data

was organized into the parameters shown in the table 1 below. The binomial distribution of

the likelihood results are shown in figure 21 in results section in this report.

Table 1: Parameters used in analyses of likelihood survey

Parameters Values

Current cases in the program (x)

Current SAM cases not in the program (y)

Total current SAM cases (x+y=n)

Point coverage27. CI 95% �� = (�)(� + �)�100

The program coverage (posterior).

The process of combining the prior and the likelihood to arrive at the posterior (also

referred as conjugate analysis28) was used in arriving at the program coverage in this

SQUEAC investigation. This meant that the prior information about coverage (i.e. the findings from the analysis of

routine programs data; the intelligent collection of qualitative data; and the findings of small-

area surveys, and small studies) collected using the bayesian technique29 was helpful to

provide information about overall coverage of the program (expressed in a beta prior

distribution). As such, all the relevant information that was collected in stage 1 and 2 were

used together with the survey data that were collected in stage 3 of the SQUEAC investigation

to give an overall picture about the program and to unveil the headline coverage.

A conjugate analysis which is used to provide the program coverage (as described below)

requires that the prior and the likelihood are expressed in similar ways.

The conjugate analysis combined the beta distributed prior with a binomial distributed

likelihood to produce a beta distributed posterior (see figure XX).

Met need is calculated as:

!"�!!� = #$%!&'(!(!))!*"+%!�!,,) × !�+'�&!*$%!&-&'"!

27 Point coverage gives overall accurate measure of this program 28 A conjugate analysis requires that the prior and the likelihood are expressed in similar ways. 29 Bayesian methods allowed findings from work done prior to a survey to be combined with data from the survey. In this

case survey data are treated as just another source of information and are used to update the prior information The main

advantage in using the Bayesian approach in this as well as in all SQUEAC investigation are:1) Smaller survey sample sizes

are required compared to larger population based dummy surveys2) It provides a framework for thinking about SQUEAC

data that has been collated and analyzed in stage 1 and stage 2

14

5. Description of field activities

SQUEAC planning, training and implementation

A letter from the Federal Ministry of Health (FMOH) notifying the State Ministry of Health

(SMOH) of the intended SQUEAC assessment of Kiyawa CMAM program was delivered

through the SNO. The ACF Coverage Assessment Team arrived in Dutse30 on 14th May 2014 to

commence the SQUEAC investigation. A letter requesting approval to conduct the assessment

was sent to Jigawa State Health Research Ethics Committee through Gunduma Health System

Board (GHSB) before the daily SQUEAC activities could begin. The Coverage Assessment Team

was granted access to visit the CMAM HFs and to extract data from beneficiary/client records

as at 24th June 2014.

Advertisement for enumerators and recruitment was completed, with most of the

enumerators being indigenous members of the Kiyawa LGA. A total of 6,231 Outpatient

Therapeutic Program (OTP) card information of beneficiaries from 5 CMAM sites who have

exited the program from May 2013 to May 2014 were extracted into a computer database.

Two days theoretical training for successful enumerators, SMOH, LGA and National

Population Commission (NPopC) staff was done in 2 days (30th June and 1st July 2014).

Qualitative information was gathered by visiting all the five CMAM sites and 10 villages (2

villages per OTP site). Due to the fasting period (Ramadan), intermittent breaks were given to

the enumerators during the SQUEAC assessment so that they could rest. The small area

survey and two small studies were conducted on 12th and 13th July while the likelihood survey

was conducted in the period 15th and 18th July 2014.

Dissemination workshop of the SQUEAC findings was done at the State level and involved

GHSB, Kiyawa LGA, as well as the traditional ruling council of Kiyawa (conducted on 4th of

August 2014). A joint session of all the stakeholders helped make recommendations to

improve the Kiyawa CMAM program. A detailed list of daily activities during the Kiyawa

SQUEAC assessment is contained in annex 1 of this report.

6. Results and findings

This section summarizes the results of stage 1, 2 and 3. The various data were organized as

follows:

6.1. Stage 1: Routine monitoring and performance data-identifying

potential areas of low and high coverage.

In stage 1 the routine data, anecdotal program information and performance data were

analyzed to study the effectiveness of the CMAM program, the outreach activities and

potential areas of low and high coverage, time taken to travel to site among others. This

analysis formed the basis of identifying locations that would be suitable to collect the

30 Jigawa State capital where the coverage team resided during Kiyawa SQUEAC. Dutse is about 30 kilometers from Kiyawa.

15

qualitative data that would provide more information regarding factors affecting the CMAM

program. The data extracted from OTP records of the five HFs in Kiyawa LGA are summarized

in Table 2 below.

Table 2: Summary of outcome data extracted from beneficiary/OTP cards at a glance.

CMAM HF Exit

records

Dead Defaulter Non-

recovered

Recovered Transferred

to SC

Missing

data

Garko 484 0 3 1 480 0 0

Katanga 2866 1 1672 147 850 0 196

Katuka 1676 0 875 1 774 8 18

Kwanda 887 0 383 1 365 0 138

Maje 318 1 91 1 213 0 12

Total 6231 2 3024 151 2682 8 364

Proportion 100% 0.03% 48.53% 2.42% 43.04% 0.13% 5.84%

The findings of the quantitative and qualitative data analysis is described in the following

sections

6.1.1. Routine monitoring (OTP cards) and performance data

6.1.1.1.6.1.1.1.6.1.1.1.6.1.1.1. Program exits (discharge outcomes)

The exit trend of the performance data was plotted and smoothened in spans of 3 months

median and average (M3A3) respectively. The plots are presented in figure 3 below.

16

Figure 3: Exit trends for Kiyawa LGA CMAM program-January 2013 to April 2014

From the exit trends above, the Kiyawa CMAM program is observed not to be effective. The

recovery rate is below SPHERE standard31 of 75% throughout the period under review.

November and December 2013 had zero recovery rate with no client discharged as recovered.

During this time the CMAM sites were not administering services due to stock-out of RUTF.

On the other hand, defaulter rate was observed to be high, above the recommended 15%

standard throughout same period. The peak of defaulting was in the month of November

2013, when all the children in the program had become defaulters as the CMAM services

were not operational. The summary of extracted routine data tabulated in Table 1 above

showed that about 48% of all exits during the review period were defaulters. Compared to

the SPHERE standards only the death rate was low.

6.1.1.2.6.1.1.2.6.1.1.2.6.1.1.2. Admission trends

The figure 4 below shows the admission trend of Kiyawa LGA CMAM program in relation to

the seasonal calendar of events.

31 For a rural TFP program SPHERE standards and in line with Nigerian CMAM guidelines the recommended performance

rates are as follows: recovery rate >=75%; defaulter rate of <15%; death rate of <10%. A good program should have non

recovery rates of below 5%.

17

Figure 4: Admission trend of Kiyawa LGA CMAM program and the seasonal calendar of

events

The peak in admission in the month of May 2013 may have been as a result of high measles

incidence in this period32 (no evidence could be obtained from the LGA health records33) and

commencement of rainy season with increased diarrhea34 episodes contributing to incident

SAM cases presented for admission into the CMAM program. The decline in admissions from

May to June 2013 coincided with commencement of weeding of farmlands which is mainly

done by women. The slight increase in admissions from June to July 2013 may be attributed

to peak of hunger and food prices as the household food reserve had depleted through both

consumption and use of seeds for planting. As the rains and flooding increases the admissions

reduce steadily from July to September; this discourages some caregivers from attending the

CMAM site. At this period, the female labor demand (weeding35) increased; at the same time

32 Measles is associated with malnutrition. 33 The seasonal calendar was developed with key information of the HOD Health and the Agriculture Department of Kiyawa

LGA, however, this was based no verifiable record could be produced. 34 Diarrhea diseases contribute burden of disease and are associated with high malnutrition 35 Women are highly engaged in weeding of farm lands, as a result, they are likely not able to attend the CMAM services

due to the conflicting priority of weeding farms lands.

18

the rainy season and flooding subsided. The admission was seen to have increased

significantly in the month of October.

The increase in admission could not be sustained due to stock-out of RUTF in November and

December 2013. During the period of RUTF stock out, the five CMAM sites were closed, and

zero admission was recorded for the two months. As the CMAM sites reopened in January

2014, admissions were witnessed as defaulters return to seek treatment. The month of

January also coincides with the female labour demand in processing of harvested crops which

usually occurs from December to January, and as such some women are occupied and could

not access CMAM services. More so, availability of foods in households in January 2014 may

have contributed to reduced incident SAM cases. At the end of processing activities (which

engages women labour), caregivers had time to attend the program, thus, admissions may

have rose steadily from February to May 2014. Besides this, other reasons that can be

attributed to this increase in admissions include gradual depletion of household food

reserves, commencement of hot season which is associated with increased cases of measles,

and diarrhea which are major factors contributing to increase in incidence of malnutrition.

6.1.1.3.6.1.1.3.6.1.1.3.6.1.1.3. MUAC at admission

Figure 5: Admission MUAC for Kiyawa LGA CMAM program

The analyses of the extracted data showed a median MUAC on admission to be 105mm. There

were very small number of children admitted with MUAC above the admission criteria. The

plot also indicated that a relatively large group of SAM cases (below 110mm) were not

identified early and referred accordingly. This is a pointer to a probable weak active case

finding activity by CVs in the LGA. Digit preference and heaping at digits ending with ‘9’ and

‘4’ was noticeable, as such, erroneous MUAC measurement or lack of verification of MUAC

during admission was suspected.

19

6.1.1.4.6.1.1.4.6.1.1.4.6.1.1.4. Length of stay (LoS) from admission to recovery

Figure 6: Length of Stay (LoS) from admission to recovery

The median length of stay (LoS) of 6 weeks shown above was plotted from all the discharged

recovered children in the program during the period of May 2013 to May 2014 with exit

MUAC of 125mm and above. The satisfactory LoS result was treated with caution based on

two major reasons.

Firstly, the CMAM program performance indicators were observed to be less than satisfactory

due to low recovery rates and high defaulter rates did not meet the recommended SPHERE

standards of >=75% and less than 15%, respectively. (See section 6.1.1.1.). Similarly, table 1

above showed that about 48% of all exits during the review period were defaulters. A program

that has low performance is likely to have Long LoS and high defaulter rate. Secondly, further

analysis of MUACs on exit of those reported as recovered showed that a significant proportion

of the children discharged as recovered did not attain the exit MUAC of 125mm or more

(illustrated in figure 7 below). 30% of discharged recovered children are suspected to have

been discharged erroneously with MUAC of less than 125mm, with significant number being

current cases of SAM.

Therefore, the quality of the monitoring data and service delivery was suspected to have

errors and therefore, the short length of stay of 6 weeks showed above may not likely reflect

the true picture of the Kiyawa LGA CMAM program36.

36 A good CMAM program has a short length of stay (LoS). It could be that SAM cases were detected early and admitted

into the program. The treatment episode is shorter for SAM cases who are detected early and have no medical

complications. This also, keeps the cost of SAM treatment low.

20

Figure 7: Proportion of exit MUACs -recovered

6.1.1.5.6.1.1.5.6.1.1.5.6.1.1.5. Number of visits before default

Figure 8: Plot of number of visits before default

The median number of weeks SAM children stay in the program before default was found to

be 4 weeks. Significant number of beneficiaries were noticed to withdraw early in the

program. There is a likelihood that a large proportion of the defaulters in the community are

likely to be current SAM cases37. This is illustrated in further analysis of MUAC at default for

all the defaulters. The results are shown in figure 9 and 10 below.

37 Defaulters that have had below 4 visits are likely to be current cases of SAM. See SQUEAC/SLEAC technical manual on

interpretation of defaulters at < 4 weeks and those at >= 4 weeks.

21

Figure 9: Defaulters’ MUAC on exit

Figure 10: Proportion of exit MUACs at point of default

6.1.1.6.6.1.1.6.6.1.1.6.6.1.1.6. Defaulters and all exits by Location

The analysis of extracted client information in the period May 2013 to May 2014 showed that

about 85% of defaulters (2,459 out of 3,024 recorded) were mainly visiting Katanga and

Katuka health facilities38 in Kiyawa LGA. (See Table 1 above). The defaulters from other LGAs

near Kiyawa are also illustrated in figure 11 below.

38 Katanga and Katuka health facilities had the highest number of admissions from the client information and routine data

22

Figure 11: Defaulters vs exits-Kiyawa and other LGAs

The data was analyzed further for defaulters in Katanga and Katuka who exited in the last

quarter (March to May 2014) of the review period. The analyses showed that more than half

of the defaulters were from outside Kiyawa LGA (the catchment area) where there are no

CVs. However, significant number of defaulters were also coming from Kiyawa LGA despite

the presence of community volunteers. Therefore, it was suspected that that community

volunteers are not following-up with home visits for absentees. The LGAs where defaulters

were coming from were shown in Figure 11 above.

6.1.1.7. Time to travel to CMAM (OTP) HF plot/distance from treatment centre

Time to travel to site by caregivers were plotted as illustrated below. However, it was noted

that most beneficiary/OTP cards had missing or erroneous information on time to travel to

CMAM HF. Other sources and methods were used to get additional information on time to

travel to CMAM HF. Key informants in each of the health facility were used to give additional

information and also to verify the existing information on time to travel from different villages

to the CMAM HFs. The reconciled data was analyzed and plots of time it took the beneficiaries

to travel to each of the CMAM HF was presented below.

23

Figure 12: Time to travel to CMAM sites -cases in program and defaulters

Figure 13: Time to travel to CMAM Sites for each of the 5 CMAM health facilities

Median time to travel for all the CMAM health facilities by clients was found to be 1 hour 30

minutes. Significant number of the beneficiaries were observed to be travelling from villages

24

outside Kiyawa LGA, so also, are defaulters (see section 6.1.1.6). Most of the villages outside

Kiyawa LGA where beneficiaries were coming are mainly distant communities as recorded on

the beneficiary records. It was also observed that some beneficiaries use motor bikes

(Achaba) as the medium of transport, while significant number travel to the HF while walking.

Plots for individual CMAM facilities indicated similarities in time to travel to site of clients

come as shown in figure 13 above.

6.1.2.6.1.2.6.1.2.6.1.2. Conclusion of the routine monitoring (OTP cards) analysis

The analyses of information obtained from beneficiary/OTP cards gave indications of the

potential factors that were possibly affecting coverage in Kiyawa LGA. However, the

information above needed to be investigated and understood further using the qualitative

methodology. Major points noted for further investigation include;

1. Adherence to national CMAM guideline during admission and discharge

2. Charges incurred by caregivers while accessing CMAM services

3. High default rate

4. Distance as a factor affecting access

6.2. Stage 1: Qualitative data-Investigation of factors affecting

program and coverage.

6.2.1. Qualitative sampling Framework

Qualitative information about the CMAM program in Kiyawa LGA was sought and obtained

from different stakeholders (sources) so as to compliment the information obtained from the

quantitative data analyses of the extracted beneficiary/client records. Different methods

were used while collecting such information from stakeholders. The information collected

were analyzed, while sources and methods used were triangulated as evidence arose. All the

5 CMAM HFs were visited. In each of the HFs, at least four caregivers (who were randomly

chosen), HWs, and CVs were interviewed. Motorcyclists were also asked questions, as well as

husbands of caregivers of beneficiaries’ in-program. Ten communities were visited to collect

additional information about the program from community members. These communities

that were visited were purposively selected based on the following key reasons; 1) distances

from CMAM site, 2) pattern of admission and defaulting.

At least, a community leader, religious leader, teacher, provision shop seller, patent medicine

vendor, majalisa (community age-group social gathering in tea places and shades), and

women group were interviewed in each of the communities visited, using different methods

to obtain information about the CMAM programme in Kiyawa LGA. Information obtained

were identified and analyzed in respect of the effect it had on the program (that is as a booster

or a barrier to the program). Each piece of evidence was considered and if it needed further

verification, then a different source with/or different method was used to seek further

information which was compared with previously gathered information. The questions arose,

aided in collecting more information from the same or different sources using appropriate

methods to obtain more clarity on the piece of information before it was finalized as a barrier

25

or booster. In this way, using the different methods and sources repeatedly reached a point

where no new information could be obtained39. This is how sampling to redundancy was

achieved.

6.2.2. Qualitative information

The qualitative information obtained from the field are summarized and explained under

thematic headings as follows.

6.2.2.1. Awareness and Perception of the Program, Community Mobilization, and

Peer-to-peer Referrals in Communities

All respondents interviewed in different communities reported that they were aware of the

CMAM program in Kiyawa LGA. Nevertheless, there was evidence of poor community

mobilization in all the communities visited. Large number of respondents in the communities

reported that nobody had sensitized or told them about the CMAM program. However, it was

established that community mobilization was done when the Kiyawa LGA CMAM program

commenced in October 2011. The present number of beneficiaries accessing CMAM services

were mainly referred by their peers (peer-to-peer referral by caregivers) in communities. This

was noticed to thrive mostly on the good opinion of the Kiyawa CMAM program. Community

members said that they appreciated the CMAM program in the Kiyawa LGA. Notably, none of

the respondents reported negative opinion of CMAM program. The respondents interviewed

in both HFs and communities were able to relate the program with good outcome in terms of

recovery of many malnourished children that had accessed the CMAM program. Likewise,

communities are largely aware of the existence of the program which has largely been as a

result of the good opinion about the program that is shared within the community. In the

period the CMAM program has been running in Kiyawa LGA (for about three years),

information about what the program does in treatment of malnutrition had diffused to

communities in the LGA, with caregivers contributing immensely as ‘information or news

carriers’. The good opinion and awareness which the Kiyawa CMAM program enjoy in

communities within Kiyawa LGA was so strong that health workers in CMAM HFs are used to

resolve non-compliant40 cases during immunization campaigns. Non-compliant community

members on recognizing the health workers from CMAM health facilities allow their children

to receive immunization vaccines.

6.2.2.2.6.2.2.2.6.2.2.2.6.2.2.2. Community Volunteer (CV) Activity and High Default Rate

Each of the five CMAM HFs had 25 CVs assigned to them and documented. However, only a

handful of the CVs were reported to be working. The referral activity of CVs could not be

tracked because referrals slips were not being used. However, as the SQUEAC investigation

39 The process of collection of the information is iterative and as such pieces of information are investigated repeatedly

until no new information is forthcoming. 40Caregivers who hitherto would not allow their children to receive polio immunization were reported to have

accepted their children to be immunized once they see a health worker from a CMAM HF due to the good

opinion they have about the CMAM program.

26

was going on, referral slips were being distributed to the CMAM HFs for the first time by the

Nutrition Focal Person (NFP). Therefore, the SQUEAC investigation team could not verify

referral by CVs from the records. Though the CVs interviewed in all the CMAM sites

maintained that they refer SAM cases to the CMAM HFs, only two out of all caregivers41

interviewed in all the five CMAM sites reported that they were referred by a CV. On each OTP

day, the CVs come to the CMAM HFs to help the health workers in taking anthropometric

measurements and some other sundry activities. Some of the measurements taken by CVs

were noticed to be incorrect, and are not usually validated by health workers. Most CVs

reported that they are not able to carry out tracing of absentees and defaulter nor do

screening for malnutrition within communities properly due to lack of means of transport to

travel to distant areas. Therefore, the very high number of defaulters observed in quantitative

analyses of data could be linked to poor CV activities in terms of follow-up and tracing of

absentees/defaulters. Another factor reported by health workers and program staff is that

most beneficiaries come from neighboring LGAs42and States43 (see the section on defaulter

vs. exits by location). The CVs had been trained only once since the inception of the program

in Kiyawa LGA.

6.2.2.3.6.2.2.3.6.2.2.3.6.2.2.3. Health Seeking Behaviour in Communities

Responses on the health seeking behavior in communities on treatment of malnutrition

varied. Some respondents interviewed in different communities and HFs reported that

malnourished children are first taken to traditional healers and patent-medicine vendors

before coming to the CMAM HFs for treatment44. A local herb (Tsimi) was reported as being

used in communities for treating malnutrition. On the other hand, many respondents

reported that SAM children are taken to the health facility for treatment45. Therefore, the

health seeking behavior is presently mixed, and many caregivers, sometimes, visit elsewhere

first before going to the CMAM HFs for treatment. This could be the possible explanation for

the low median admission MUAC of 105mm which indicates that most admissions were

admitted late into the CMAM program.

6.2.2.4.6.2.2.4.6.2.2.4.6.2.2.4. Stock-out of Data Tools and Routine Drugs leading to charges for OTP

cards and Routine Drugs

During the extraction of client information from the OTP cards, it was observed that pieces of

papers were used as registration cards. These pieces were of varying sizes and colours, and

are written upon in different formats, with most of the sensitive beneficiary/client

information missing. Further inquiries revealed that there has been stock-out of data tools

41 A caregiver each in Maje and Katuka reported being referred by a CV. Others reported peer-to-peer referrals, and

passive referrals by health workers from non-CMAM HFs. 42 Dutse, Jahun, Birnin Kudu, and Miga LGAs 43 Bauchi and Kano 44 Two caregivers in Katuka CMAM HF, two CVs in Katuka HF, a provision shop seller in Nafara (3km from CMAM HF), a

religious leader from Dangoli (about 15km from CMAM HF); a caregiver from Garko CMAM HF, Traditional healer of Tsirma

(3km from Garko CMAM HF), a TBA, religious leader, majalisa and provision shop seller in Jama’ar Dawa reported that

traditional herbs are used to treat malnutrition because they are easily accessible in their communities. A Community

volunteer from Maje CMAM HF reported that traditional medicine is preferred for treating malnutrition in communities.

Traditional healer in Yelwa community (about 6 km) from Kwanda also reported that herbs are preferred for treating

malnutrition in communities. 45 This was reported by the rest of the interviewees in the various communities and HFs.

27

leading to HWs resorting to using different types of papers to record client information. Some

HWs reported that they used to contribute money among themselves to photocopy

beneficiary/OTP cards for clients46. Stock-out of drugs and data tools was reported by HWs in

all the CMAM HFs. However, the HWs did not have a clear reason for charging beneficiaries

varying charges for the OTP cards and routine drugs (especially, Amoxicillin and ACT). Some

caregivers-in-program47 reported that they pay for OTP registration cards, and are also

charged if their ration card got torn or missing.48 This information was also, collaborated by

that given by provision shop sellers, Achaba49 riders, husbands of care-givers in-program, and

four HWs50. A CV in Katanga intimated that sometimes they lend money to care-givers to buy

drugs so that the care-givers would not be sent home without RUTF. This is because RUTF is

never given to OTP beneficiaries who are unable to buy the routine drug (especially,

amoxicillin) on admission, which is a directive given to health workers51.

6.2.2.5. Stock-out and Misuse of RUTF, and occasional closure of OTP sites

Stock-out of RUTF was reported for the month of October to December 2013 (see section

6.1.1.2 Admission Trend). During the period mentioned, all the CMAM services were stopped

as there was no RUTF available to be given to the beneficiaries. The period of cessation of

CMAM services coincided with the time when Valid International conducted SLEAC survey in

Kiyawa. The result of the SLEAC survey reported that Kiyawa LGA had only 7 SAM cases

covered by the CMAM program out of 47 SAM cases found52. At the time of the SQUEAC

investigation, CMAM health facilities were reported to be closed. This was at the time when

the HWs were having a refresher training on CMAM at the LGA secretariat. On the other hand,

there were discrepancies in supply chain management of RUTF as was also observed in the

field. In Katuka CMAM HF, some caregivers were observed going home without weekly RUTF

ration. HWs on ground explained that the supply they received had finished in e process of

giving few RUTF to a large number of beneficiaries.

However, it was noted that RUTF was being misused by HWs and CVs. Two CVs in Katanga

CMAM HF reported that they are given 3 satchets of RUTF weekly to motivate them so that

they can lend a hand at the HF. They were quoted reporting ‘The RUTFs given to us were

meant as an incentive to motivate us’. A community leader from Jama’ar Dawa community

under Garko CMAM HF had reported that a HW from Katuka CMAM HF usually bring RUTF to

share to them in their community. However, the HW did not agree that this ever happened.

Consumption of RUTF by adults and siblings of SAM children in-program were also reported

46 The health worker in-charge of Katanga, Katuka and Garko CMAM HFs. 47 All 8 caregivers interviewed in Katuka CMAM HF, and the one interviewed in Maje CMAM HF. 48 The amount reported varied from NGN 200 to NGN 50 for new cards and for replacing thorn or missing cards. 49 Achaba refers to motor cycle as a medium of transport. The Achaba riders usually carry caregivers from their

communities to the HFs in OTP days and wait for them as their children get treatment before ferrying them back to the

community. 50 Two health workers each in Maje and Katanga CMAM HF said that sometimes care-givers are asked to pay N100 for

drugs. 51 This was also collaborated by the response of the SNO. 52 Chrissy B., Bina S., Safari B., Ernest G., Lio F. & Moussa S.; Simplified Lot Quality Assurance Sampling Evaluation of Access

and Coverage (SLEAC) Survey of Community-based Management of Acute Malnutrition program; Northern States of

Nigeria-(Sokoto, Kebbi, Zamfara, Kano, Katsina, Gombe, Jigawa, Bauchi, Adamawa, Yobe, Borno). Valid International.

February 2014

28

and observed53, respectively. Many children and adults in communities reported that they

have tasted the RUTF. Further inquiries showed that those children were never admitted SAM

cases in CMAM HFs.

6.2.2.6. Large Turn-out of Beneficiaries, Long waiting Time, and Unavailability of

Shades, Mats and Benches

During the visits to the CMAM HFs, very large number of OTP beneficiaries was observed in

Katuka and Katanga HFs. Garko and Kwanda HFs were also observed to have significant

number of OTP beneficiaries, however, Maje CMAM HF had significantly few number of OTP

beneficiaries. The large turnout of clients in CMAM HFs indicated accessibility, and willingness

of caregivers to utilize the CMAM services. The lack of capacity to control the crowd at the HF

resulted in long queues and waiting time for OTP beneficiaries. Additionally, lack of shades

for the beneficiaries in all the CMAM HFs was observed. Beneficiaries gladly make use of Dogo

Yaro tree as shelters, and sometimes have to queue under the sun, as observed by the

SQUEAC investigators. There is also, lack of mats and benches for caregivers at the CMAM

HFs. Most caregivers were also, seen sitting on the ground as they waited for their turn to be

attended to by the HW. In Katuka and Garko HFs, it was observed that some local merchants

display portable mats for caregivers to buy and use at the CMAM HF. Importantly, it was also,

reported that some caregivers sleep-over at HFs of Katuka, Katanga and Maje HFs prior to the

OTP day so that they would gain first entry and be attended to on time.

6.2.2.7. Health Workers Activities, Non-adherence to CMAM guidelines, and

Training

Health workers reported that sometimes they are under pressure by caregivers who insist

that their children should be admitted into the CMAM program. A CV serving Katuka HF,

reported that sometimes HWs may bow to the pressure so as to get the goodwill of the

caregivers. Further enquiries to explain the erroneous discharge of clients by HWs as observed

on the routine data revealed that HWs priority was to discharge clients as soon as the client

has been in the program for up to 8 weeks without necessarily placing emphasis on

improvement (in terms of weight or MUAC), or whether the beneficiary had recovered or

not54. This was possibly the reason for the erroneous discharge of clients with MUAC less than

125mm (as identified in the quantitative analyses of data). Furthermore, this situation may

have largely contributed to the short length of stay from admission to cure (see the section

on length of stay). Health workers were noticed not to be validating anthropometric

measurements taken by CVs at the CMAM HF in Katuka. A health worker in Kwanda CMAM

HF was observed assigning MUAC and weight arbitrarily without taking measurements. This

confirms suspicions raised in section 6.1.1.3.

53 A caregiver was observed giving RUTF to a healthy sibling that accompanied her to Katuka CMAM HF. 54The health workers were directed to follow this line of action since there are very large number of clients; those who

were not recovered after eight weeks were to be discharged and referred to inpatient care. Nevertheless, this was

observed to conflict with the guideline which directed that such action can be taken after a SAM child had stayed 12 weeks

in the program (13 visits) and is yet to be recovered.

29

The large turnout of beneficiaries may have been felt as a burden by the health workers. Some

of the health workers complained that they are not being paid any additional money for the

additional job they are doing compared to their counterparts in non-CMAM HFs. Health

workers in CMAM HFs clamor for incentives so as “to boost their morale”. This was also

pointed out by the NFP, who reported that he has received such complaints often from health

workers in all the CMAM HFs. On the other hand, some caregivers reported that some health

workers dispose bad attitude towards them, specifically, in Katuka and Katanga. Thus, terms

such as ‘friends of health workers’ and ‘the rich’ were used by the respondents to describe

those given preferential treatment, specifically, in Katuka CMAM HF. Some caregivers in

Katanga and Katuka reported that “threats of discharge” from the program was been used to

keep them complying with the health workers. However, some caregivers reported that they

are treated well by the health workers.

Passive referrals by health workers from non-CMAM HFs in Kiyawa LGA was reported in all

the CMAM sites by some of the caregivers interviewed during the health facility visits. Some

caregivers interviewed in Katuka, and Kwanda said they were referred by health workers in

and neighboring State (Bauchi) to the CMAM HFs in Kiyawa LGA. This was also confirmed by

the health facility in-charge of Kwanda CMAM HF.

6.2.3. Data triangulation

Information in the SQUEAC investigation was obtained when SQUEAC tools55 were used on

diverse sources using diverse methods. The triangulation process was done to ensure

conformity of the evidence accumulated before adoption as a negative or positive factor that

affects the program coverage. The information that was obtained was analyzed into barriers

and boosters and the relationship between them drawn to give a clearer “picture of the

program coverage” in concept maps. (See the activities that ensured in the investigation in

annex 1 and concept map in annex 3). The barriers and boosters identified, and the sources

and methods used to obtain such information during the investigation are tabulated in in the

tables 3 and 4 below.

Table 3: Sources and methods used to get information in the BBQ tool for Kiyawa LGA.

Codes Source Method Codes

1 Client record Extraction A

2 Carers Semi Structured Interview B

3 Health facility Observation D

4 Majalisa Informal Group Discussion E

5 SNO In-depth interview C

6 Health worker Semi Structured Interview /In-depth

Interview

B,C

7 NFP In-depth interview C

8 CVs Semi Structured Interview /In-depth

Interview

B,C

9 Religious leader Semi Structured Interview B

55 Tools include the simple structure, semi structured questionnaires; observation checklists; illustrations in form of

pictures, words or phrases; various forms to fill out extracted data from routine data etc.

30

10 Community leader Semi Structured Interview B

11 Provision shop Semi Structured Interview B

12 Patent medicine dealer Semi Structured Interview B

13 TBA Semi Structured Interview B

14 Traditional healer Semi Structured Interview B

15 Teacher Semi Structured Interview B

Table 4: barriers, boosters & questions findings and sources of information

BOOSTERS SOURCES BARRIERS SOURCE

1 Peer-to-peer referral 2B, 6B, 6C Poor Health seeking behavior 2B, 8B, 9B, 11B,

12B, 14B, 4E

2

Passive referrals by HWs in non-

CMAM health facilities; Health

workers from non-CMAM

facilities support in weekly

services to beneficiaries in

CMAM sites

2B, 6B, 6C Stock-out of Data tools (admission

and ration cards) resulting in use of

piece of paper as cards;

Caregivers are charged money for

OTP cards and for replacement of

lost/thorn OTP cards in Katuka,

Katanga, Maje, sites

1A, 2B, 3D, 6B,

8B, 6C

3

Good health seeking behavior 2B, 4E, 6B,

6C, 8B, 9B,

10B, 11B,

12B, 13B,

14B, 15B

Generalized stock-out of routine

drugs(amoxicillin) resulting in

caregivers paying for Amoxicillin and

ACT

2B, 6B, 8B, 11B,

6C, 8C

4 Large turnout of beneficiaries

accessing CMAM services

3D, 6B, 6C Poor attitude of health workers;

preferential treatment given to the

rich/friends of health workers

2B, 3D, 5C

5

Willingness of caregivers to sleep

over at OTP sites in order to

access CMAM services

3D, 6C, 8C,

10B

Over-burden of health workers due

to very large number of beneficiaries

accessing the OTP services; long

waiting time at CMAM sites

3D, 6B

6

Good opinion about the CMAM

program in communities

2B, 4E, 6B,

6C, 8B, 8C,

9B, 10B,

11B, 12B,

13B, 14B,

5B

Lack of shades for beneficiaries in all

the OTP sites; no mats and seats for

OTP beneficiaries

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

8C,

7

Health workers are trained thrice

on CMAM since inception (once

yearly)

6B, 6C, 7C Wrong measurement of weight and

MUAC by CV who are used for taking

anthropometric measurement.

HW assign MUAC arbitrarily.

3D, 8C

31

8

Good collaboration of health

workers and CVs and good

attitude of some health workers

towards caregivers

6B, 8B Sharing and consumption of RUTF

among healthy siblings and children

beyond the age of five years;

consumption of RUTF by adults;

Caregivers does not understand how

the program works

3D, 4E, 9B, 11B,

13B, 14B, 15B,

9

Referrals by some community

CVs

2B, 6B, 8B Community volunteers are not

motivated; conduct poor community

mobilization and sensitization, very

poor active case finding and defaulter

tracing.

Community volunteers clamor for

incentives

6B, 8B, 6C, 7C,

8C

10

Good awareness of the program

in communities

4E, 9B, 10B,

11B,

13B,14B,

15B

Faulty supply chain management

from LGA to CMAM sites leading to

stock-out on OTP days

2B, 3D, 6B, 7C

11

Selection of CMAM site for

intervention by SURE-P of

Federal Government

3D Non-adherence to CMAM protocols

a. Non-compliance with

discharge criteria (discharge

with MUAC <12.5cm

b. Arbitrary assigning of MUAC

measurements, evident by

erratic MUAC movements

on client cards

1A, 6B, 8B

12 7C, 8C High number of defaulters 1A, 6B, 6C, 5C,

7C

6.2.4. Concept map

The coverage team was split into two Teams that is, A and B. Each team drew a concept map

illustrating the positive and negative relationships existing between positive and negative

factors unveiled from the from the field visits interview and observations. Epigram software-

version 1.1056 was used to draw the concept maps presented in the in annex 3.

6.3. Stage 2: Small Area Survey and Small Study.

At the completion of stage 1, the major factors that may be affecting coverage in Kiyawa LGA

based on the results of analysis of information gathered were identified. Four factors

identified include;

1. Location and accessibility of CMAM Sites,

56 Epigram software was developed by Mark Myatt and is available on www.brixtonhealth.com

32

2. Accessibility of services in terms of fees paid for OTP cards and routine drugs by

beneficiaries,

3. Ability of the beneficiaries to stay in the program to recovery and factors affecting it

(in terms of LoS and Defaulting)

4. Quality of the CMAM services offered in terms of adherence to the CMAM protocol

by HWs.

To investigate the heterogeneity of the program probable coverage and factors that lead to

program failure, hypothesis were formulated around two of the above factors (that is Point 1

and 2). Two small studies were conducted to study each of point 3 and 4 above. Each of the

factors were studied in detail and reported below.

6.3.1 Small Area Survey

Hypothesis 1: Coverage is homogeneous in all areas, and is classified as above 50%57 in wards

where CMAM sites are located, as well as in wards where CMAM sites are not

located.

To test this hypothesis a total of four communities which are all distant from existing CMAM

HFs, and are located in Wards without CMAM HFs were selected. On the other hand, four

communities which are not distant from CMAM HFs and are in Wards where CMAM HFs are

located were selected.

Small area survey was used in each of the selected communities in order to test the

formulated hypothesis.

6.3.1.1 Sampling Methodology

The villages were selected purposively based on the characteristics used in setting the

hypothesis. Active and adaptive case-finding58 was conducted in the communities during the

small area survey.

6.3.1.2 Case Definition

Severe Acute Malnutrition (SAM case) was defined as Children (6-59) months, with MUAC

<115mm and or bilateral pitting oedema.

SAM case covered: Refers to a SAM case identified as defined above and is currently

enrolled in a CMAM site or Stabilization Centre (SC). The status is verified when beneficiary

shows the investigator the RUTF packets and/or ration Card.

SAM case not covered: Refers to a SAM case who is not currently enrolled in a CMAM

program or the SC. The case is also confirmed as not in the program when the beneficiary


57 This is based on the 2 standard 3 classification of coverage as less than 20% as low, 20% - 50% as moderate, and greater

than 50% as high for rural areas. 58 See the procedure of the active and adaptive case finding annexed to this report

33

Recovering case: A child (6-59) months old, with MUAC above 115mm and is enrolled in a

CMAM program at the time of the investigation. This case is verified when beneficiary


6.3.1.3 Result of Small Area Survey

The results of small area survey are presented in the table 5 below

Table 5: Simplified Lot Quality Assurance classification of small area survey results

Coverage Wards Location Total

SAM

(�)

Decision rule

. = � × /0100

Covered(C) Not

covered

(NC)

Recovering

case (RC)

Hypothesis basis

Wards

without

CMAM

sites

Balago Kalkutan 5 � = �10� = 10 3 2 4

Malkiba 9 5 4 4

Shuwarin Gurduba 5 3 2 6

Fake Chuwina 1 0 1 3

Wards

with

CMAM

sites

Katanga Katanga 10 � = �11.5� = 11 6 4 10

Garko Ali Sabon Gari 0 0 0 1

Maje Tesher

Gamji

2 0 2 0

Katuka Chikin Gari 11 9 2 9

Figure 14: Reasons for not attending the CMAM program-Small area survey

Interpretation of the results

LQAS classification technique was applied and the results are as follows:

• Wards without CMAM HFs

34

The number of SAM cases found that were covered was 11. The Decision rule

calculated above = 10

Since 11 is greater than 10, the coverage in the Wards without community volunteers was

classified as above 50%.

• Wards where CMAM HFs are located

The number of SAM cases found that were covered was 15. The decision rule calculated was

11.

Since 15 is greater than 11, the coverage in Wards where CMAM HFs are located, is

therefore classified as high (above 50%).

6.3.2 Small Studies

Two small studies were conducted. A quantitative small study was done to verify hypothesis 2,

based on accessibility of CMAM services in terms of hidden fees paid by OTP beneficiaries. While a

descriptive small study was done to shed light on defaulting in Kiyawa LGA CMAM program.

Hypothesis 2: CMAM services in Kiyawa LGA has hidden charges incurred by more than 50%

of the beneficiaries.

6.3.2.1 Sampling Methodology

Two CMAM HFs (Katuka and Katanga) where more than half of all the program beneficiaries

in Kiyawa LGA are accessing CMAM services were purposively chosen. On an OTP day, twenty

caregivers in-program in each of these CMAM HFs were selected randomly by balloting. The

selected caregivers were asked questions bordering on the fees they pay for beneficiary/OTP

registration cards and routine drugs.

6.3.2.2 Case Definition

Care-giver with a child (6-59) months currently enrolled in the CMAM program in Kiyawa

LGA.

Incurred hidden charges: Refers to care-giver as defined above who paid hidden fees for

OTP registration cards and drugs

Did not incur hidden charges: Refers to care-giver as defined above who did not pay hidden

fees for OTP registration cards and drugs.

6.3.2.3 Result of Quantitative Small Study

The results of the small study are presented in the table 6 below:

35

Table 6: Simplified Lot Quality Assurance Classification of small study results

CMAM HFs Care-givers in-

program

Interviewed (�)

. = � × /0100 Experienced hidden

charges

Did not experience

hidden charges

Katuka 20 10 18 2

Katanga 20 10 16 4

Interpretation of the results

LQAS classification technique was applied and the results are as follows:

Hypothesis Two:

• Katuka CMAM HF

The number of caregivers out of the total sampled who paid hidden charges for OTP card

and drugs were 18. The Decision rule calculated above was 10

Since 18 is greater than 10, the number of beneficiaries who paid hidden charges to access

CMAM services was classified to be more than 50%.

• Katanga CMAM HF

The number of caregivers who paid hidden charges for OTP card and drugs were 16. The

Decision rule calculated = 10

Since 16 is greater than 10, the number of beneficiaries who paid hidden charges to access

CMAM services are classified as greater than 50%.

6.3.3 Small Study on Defaulters A descriptive small study was conducted to understand the major reasons for defaulting in

Kiyawa LGA CMAM program.

From the client records extracted (see table 1 and session 6.1.1.1), it was observed that about

half of the exits of all the clients were defaulters. About two-third of the defaulters were from

Katanga and Katuka CMAM HFs. Furthermore, about half of these defaulters from Katanga

and Katuka CMAM HFs are from Kiyawa LGA, while the rest are from neighboring LGAs.

Therefore, Katanga and Katuka HFs were purposively selected to study defaulting. Two

villages under Katuka CMAM HFs and a village under Katanga HF where majority of the

defaulters were observed to be coming from were selected.

36

Table 7: CMAM HFs and villages selected for defaulter study

CMAM HFs Villages Number of Defaulters in

the Database

Number traced

Katuka Nafara 9 2

Katuka 10 0

Katanga Kawari 22 21

6.3.3.1 Results of Descriptive Small Study

Nafara community had nine defaulters on the database of client records. However, during the

defaulter tracing, only two out of the nine defaulters were traceable. The community leader

of Nafara explained that the remaining names (both that of the caregivers and children, as

well as their family name) are not from their community. The community leader pointed

further that those beneficiaries from Nafara Kudu community in Birnin kudu LGA, usually

report that they come from Nafara of kiyawa LGA while accessing the CMAM services.

The second community, Katuka very close to the CMAM site was also visited to trace 10

defaulters obtained from the client records. A CV that works in the Katuka CMAM health

facility was chosen as the village Guide. When the names of caregivers and children were

mentioned, the Guide confirmed that none of the names were from Katuka. Another CV from

Katuka community was recruited as a Guide for the defaulter tracing, however, she also said

they have no such names in their community. Finally, a third CV was chosen as a the village

guide to trace the defaulters, and he confirmed that beneficiaries from other communities

mostly outside Kiyawa LGA uses Katuka as their address on their OTP cards to avoid being

rejected since they are not under Kiyawa LGA.

However, in Kawari village under Katanga CMAM HF, twenty one defaulters were traced and

interviewed out of all the twenty-two defaulters obtained from the client database. The

responses of all the defaulters traced and interviewed during the defaulter study are

summarized in annexed 6 of this report. The analysis of the reasons of default are analyzed

in figure 15 below.

37

Figure 15: Analysis of reasons for default and condition of cases found in the study in stage 2

6.3.4 Conclusion of Small Area Survey and Small Study

Hypothesis 1:

The result of the LQAS comparison of the Wards with CMAM HF and the Wards without

CMAM HF pointed out possible homogeneity of coverage across Kiyawa LGA. The hypothesis

tested positive for probable homogenous coverage in Kiyawa LGA across political wards.

Therefore, the hypothesis that coverage is probably homogenous in all Wards despite location

of CMAM site was accepted.

Hypothesis two

Forty59 (40) caregivers were randomly sampled from two CMAM HFs and privately asked if

they incurred hidden charges. Initially, caregivers were reluctant to respond because they

were threatened by the health workers that they would be expelled from the program.

Therefore no reliable information was obtained from these caregivers.

In the face of this development the SQUEAC investigators adopted another method of enquiry

and interviewed caregivers who had received the RUTF ration from the CMAM HFs, and were

on their way back home. Thirty-four caregivers confirmed individually that they all paid

NGN200 (about USD1.25) for beneficiary/OTP cards on their first visit. Additionally, NGN 100

59 20 Caregivers in each of the facilities of Katanga and Katuka.

38

was also paid by each of the caregivers to buy a bottle of Amoxicillin syrup (routine drugs).

The caregivers reported that health workers in the CMAM HFs usually send any caregiver that

is not ready to pay any of these charges home. The team also observed a caregivers paying

money to a health worker for Amoxicillin syrup at Katuka CMAM HF.

Additional information from two Achaba riders, and three commercial motorists who were

waiting to convey caregivers back home, a gateman, and a husband of caregiver-in-program

in Katuka and the village head of Chikin Gari in Katuka confirmed that the OTP cards are sold

for (200) and torn/misplaced ones replaced with (100). More so, the in-charge of Katuka

CMAM HF finally confirmed that caregivers buy routine drugs as a measure so that the HF can

be able to purchase the same for them. The in charge noted that the SAM children would

deteriorate often because of lack of routine drugs at the HF. This meant that the routine drugs

were arranged to be delivered by the NFP and then given to the caregivers at a cost on OTP

days. The In-charge also confirmed that there was stock-out of beneficiary/OTP cards for

many months leading the health workers to make photocopies with their money but could

not confirm that they collect money from the caregivers as payment for the cards.

While the small study was going on, it was observed that at a particular point, the CMAM

services stopped, and caregivers sent home without RUTF ration for the week. Investigation

by the SQUEAC team revealed that the RUTF supply for the CMAM HF was exhausted.

Therefore, caregivers numbering more than two hundred (including those referred by the

SQUEAC investigation Team during the Small Area Survey active and adaptive case finding)

went home without RUTF. Caregivers and health workers alike confirmed that it was not the

first time RUTF supply was exhausted and caregivers sent home without RUTF ration.

Therefore, the small study was concluded by accepting the hypothesis that more than half of

the CMAM beneficiaries incur hidden charges while accessing the CMAM program in Kiyawa

LGA.

On defaulting, it could be seen that many of the defaulters who claim to be from Kiyawa LGA

give wrong address. Therefore, most of the defaulters in the Kiyawa LGA CMAM program can

be said to be from outside Kiyawa LGA (the catchment area).

6.4 Developing the prior.

6.4.1 Histogram of Belief.

A histogram of beliefs was constructed with teams discussing and reaching consensus on what

could be the most likely coverage in Kiyawa LGA CMAM program. The belief of the coverage

team on what the program coverage could be was obtained, and minimum and maximum

coverage was also identified by individuals within the team.

Individual team members were told to write on a paper what they believed should be the

coverage in Kiyawa LGA. This was used to construct a histogram of belief of the coverage team.

The central tendency of the histogram on the coverage of beliefs was set at 50% with a

minimum 30 % and maximum 70%.

Prior 1: Histogram of belief = 50%

39

6.4.2 Concept Map

The SQUEAC investigation team was split into Team ‘A’ and ‘B’, and each team drew a

concept map based on the barriers and boosters obtained in the field. A minimum of ‘1’

score was given to a factor that bore minimum impact of coverage while a maximum score

of ‘4’ was given to a facto that had a maximum impact on coverage.

Team ‘A’ concept map has a total of 20 barriers, and 12 boosters while team ‘B’ had 19

barriers and 12 boosters. To ascertain the contribution of positive and negative factors to

the program coverage the following calculations were done:

Prior calculated from concept map Team A:

*$�"&+34"+$�$)3'&&+!&, = 20 × 4 = 80

*$�"&+34"+$�$)3$$,"!&, = 12 × 4 =48

�&+$&!,"+7'"+$� = {(0 + 48) + (100 − 80)}2 = 34

Team ‘A’ prior estimation=34%

Prior calculated from concept map B:

*$�"&+34"+$�$)3'&&+!&, = 19 × 4 = 76

*$�"&+34"+$�$)3$$,"!&, = 12 × 4 = 48

�&+$&!,"+7'"+$� = {(0 + 48) + (100 − 76)}2 = 36

Team ‘B’ prior estimation = 36%

Average prior calculated from concept map of Team ‘A’ and Team ‘B’

�&+$&=(34+36)/2=35

Prior 2: Prior developed by use of concept map =35%

6.4.3 Un-weighted barriers and boosters

The barriers and boosters were consolidated and refined, and reduced to 12 barriers and 11

boosters in number. Using the largest number60, the maximum score was scaled so that

neither the sum of positive scores nor the sum of the negative scores can exceed 100%.

Thus:

Therefore;

60 The largest between the list of barriers and boosters is the barriers (12 in number). This is the number used to estimate

the value that can be assigned to the score indicating maximum impact. Here this value was assigned to each of the

barriers and boosters assuming that each has an equal impact on program coverage.

40

'@+747,*$&! = 10012 = �8.33� = 8To calculate the contribution of barriers and boosters to coverage:

*$�"&+34"+$�$)3'&&+!&, = 12 × 8 = 96

*$�"&+34"+$�$)3$$,"!&, = 11 × 8 = 88

�&+$&!,"+7'"+$�={(0+88)+(100−96)}/2=46

Prior 3: Prior estimated from un-weighted barriers and boosters = 46%

6.4.4 Weighted barriers and boosters.

The coverage team discussed and weighted each of the barriers and boosters with regards to

their perception on the contribution of each barrier or booster to the coverage of the CMAM

program in Kiyawa LGA. To reach on a score, team members discussed extensively before

finally agreeing upon a score. The highest possible score assigned to a barrier or booster was

10 while 1 was the lowest score.

The table below shows that the weight assigned by the teams and average/final score given

to the barriers and boosters.

Table 8: Weighted barriers and boosters of Kiyawa LGA CMAM Program

No BOOSTERS Average

Score

BARRIERS Average

Score

1 Peer-to-peer referral 10 Poor Health seeking behavior 2

2

Passive referrals by HWs in non-

CMAM health facilities; Health

workers from non-CMAM

facilities support in weekly

services to beneficiaries in

CMAM sites

6 Stock-out of Data tools (admission and

ration cards) resulting in use of piece

of paper as cards;

Caregivers are charged money for OTP

cards and for replacement of

lost/thorn OTP cards in Katuka,

Katanga, Maje, sites

8

3

Good health seeking behavior 6 Generalized stock-out of routine

drugs(amoxicillin) resulting in

caregivers paying for Amoxicillin and

ACT

6

4

Large turnout of beneficiaries

accessing CMAM services

10 Poor attitude of health workers;

preferential treatment given to the

rich/friends of health workers

3

41

5

Willingness of caregivers to

sleep over at OTP sites in order

to access CMAM services

5 Over-burden of health workers due to

very large number of beneficiaries

accessing the OTP services; long

waiting time at CMAM sites

3

6

Good opinion about the CMAM

program in communities

10 Lack of shades for beneficiaries in all

the OTP sites; no mats and seats for

OTP beneficiaries

3

7

Health workers are trained

thrice on CMAM since inception

(once yearly)

3 Wrong measurement of weight and

MUAC by CV who are used for taking

anthropometric measurement.

HW assign MUAC arbitrarily.

1

8

Good collaboration of health

workers and CVs and good

attitude of some health

workers towards caregivers

6 Sharing and consumption of RUTF

among healthy siblings and children

beyond the age of five years;

consumption of RUTF by adults;

Caregivers does not understand how

the program works

3

9

Referrals by some community

CVs

3 Community volunteers are not

motivated; conduct poor community

mobilization and sensitization, very

poor active case finding and defaulter

tracing.

Community volunteers clamor for

incentives

7

10


in communities

10 Faulty supply chain management from

LGA to CMAM sites leading to stock-

out on OTP days

3

11

Selection of CMAM site for

intervention by SURE-P of

Federal Government

4 Non-adherence to CMAM protocols

c. Non-compliance with discharge

criteria (discharge with MUAC

<12.5cm

d. Arbitrary assigning of MUAC

measurements, evident by erratic

MUAC movements on client cards

7

12 High number of defaulters 7

Total 73 Total 53

Therefore:

�&+$&!,"+7'"+$� = (73 + (100 − 53)2 = 60

42

Prior 4: Prior estimate from weighted barriers and boosters = 60%

6.4.5 Triangulation of Prior

Prior estimate61 was then calculated by triangulation of all the prior estimates obtained

from various methods. It is illustrated by figure 16 below.

�&+$& 7$�!= (46+50+35+60)/4=47.75

Prior mode = 47.75%

Figure 16: Illustration of triangulation of prior

6.4.6 Bayes Prior Plot and Shape Parameters

The teams were able to estimate (or give an informed guess of) the lowest and highest

possible coverage62 for Kiyawa LGA CMAM program. The SQUEAC investigation team believed

that coverage of CMAM in Kiyawa LGA could not be lower than 30%, and not higher than 70%.

Then alpha (26.8) and beta prior (28.2) shaping parameters were calculated using the

BayesSQUEAC calculator63. This also resulted into a Beta-prior distribution plot using the

SQUEAC calculator software, version 3.01 as shown in the figure 17 below. The Bayes plot of

the prior also suggested a sample size that was adopted in the likelihood survey described

below.

61 The average of the “coverages” is a credible value of the mode of the prior. It is also referred as the mode of the

probability density of the coverage. 62 These are also referred as minimum probable value and maximum probable value for coverage. 63 The BayesSQUEAC calculator can be downloaded free from www.brixtonhealth.com

43

Figure 17: BayesSQUEAC beta-prior distribution plot showing the shape parameters and the

suggested sample size

6.5 Stage 3: Wide area (likelihood) survey

After developing the prior mode using information collected in stage 1 and 2, the likelihood

survey was built into the Kiyawa SQUEAC investigation to add to the existing information

(analyzed in stage 1 & 2). This was done so as to provide a headline coverage of the program.

The procedures of implementing the wide area survey are described below.

6.5.1 Calculation of Sample Size and Number of Villages to be visited

for likelihood survey

The number of representative sample of SAM cases was calculated using the BayesSQUEAC

calculator to be 53 SAM cases at 10% precision (results are expressed at CI; 95%).

The number of villages that was needed to be visited to obtain a minimum of 53 SAM children

aged 6-59 months was calculated using the formula below:

Nvillages =(NSAMcases)

(N(medianpopulationsizeallages)xpercentageofunder − fivesinthepopulationx prevalence100 )

44

Table 9: Parameters for sample size calculation for likelihood survey

Parameters Value

1 SAM cases 53

2 N(Median population size of all ages) 400

3 Percentage of under-fives in the

population64

18%

4 SAM prevalence65 1.3%

The median population was preferred to average population in calculating the number of

villages to be visited. The median population size for all the communities/settlements was

calculated to be 400 and was used to calculate the number of villages to be visited in the

likelihood survey.

Therefore the sample calculation is given as:

Nvillages = 53(400 × 0.18x0.013) = Y56.6Z = 57

57 villages had to be visited so as to get a minimum of 53 SAM cases.

6.5.2 Quantitative sampling framework

Spatial sampling of villages was done by dividing the Kiyawa LGA maps into quadrants. The

quadrants were numbered accordingly. Only quadrants having up to half or more of its area

covering Kiyawa LGA map was selected. Furthermore, diagonals of the quadrants were drawn

so as to identify the center of each quadrants with a red dot. A total of 19 quadrants were

selected as shown in the figure 18 below.

Then the number of villages (n) to be visited in each quadrant was calculated as follows;

�[\]]^_`atobevisitedineachquadrant = defg = Y3Z = 3

The distribution of the quadrant on the Kiyawa map are illustrated in the figure below:

64 Source: National Bureau of Statistics 65 Severe Acute Malnutrition results of Mid Upper Arm Circumference (MUAC) for the National Nutrition and Health

Survey. May 2014.

45

Figure 18: Kiyawa map divided into quadrants for spatial sampling of villages

6.5.3 Case Finding Method and Case Definition

Active and adaptive case-finding method was used during the wide area survey.

The case definition was a child:

• Aged (6-59) months

• With a MUAC of less than 11.5 cm, and or

• With bilateral pitting oedema

6.5.4 Qualitative data Framework

During the likelihood/wide area survey, each SAM case that was identified and was not in the

CMAM program66 was regarded as non-covered case. Therefore, a questionnaire was

administered to the non-covered caregiver so as to collect information on possible reasons

for the SAM child not being in the program. The analysis of these reasons or barriers to access

and uptake is illustrated in figure 19 and table 9 below

66 As verified by show of RUTF or ration card by beneficiary

46

Figure 19: Barriers to program access and uptake-wide area survey (WAS)

Table 10: Barriers to program access and uptake-WAS

Reasons for non-attendance (barriers) Frequency

Lack of knowledge or have wrong information 21

Caregiver does not know child is malnourished 14

The carer didn’t know that her child can be readmitted 3

No knowledge of the program 1

Co wives convinced her that her child is healthy 1

A CV had told caregiver that her child was not eligible. 1

Caregiver thought it was necessary to be enrolled at the hospital first 1

Limited access to services 7

No transport fare 3

Far distance 3

Caregiver can’t afford money paid for card and drugs 1

Rejection 21

The child has been rejected before as not eligible 5

Caregiver said child has been discharged CMAM HF 12

Caregiver thought her child would be rejected 2

The caregiver said the child was rejected after defaulting 1

Rejected by health worker after child relapsed 1

Caregiver priorities prevents attendance 6

Caregiver said she was too busy to attend 3

The mother is sick 3

Others

Husband refused caregiver to attend 3

Child vomits or stools after eating RUTF 3

Caregiver prefers patent medicine dealer 1

Caregiver was not given RUTF when she attended the CMAM HF 1

47

6.5.5 Results of the wide are survey

The quantitative result of the case finding of the wide are survey (stage 3) is shown in the

table 11 below. The disaggregated results are shown in table 12 below. The distribution of

the quadrants using the coarse estimate is illustrated in figure 20 below

Table 11: Results of the Likelihood (wide area) survey

Parameter Value

1 Total SAM cases 122

2 SAM cases in the program 59

3 SAM cases not in the program 63

4 Recovering cases in the program 76

The likelihood was calculated using the following standard formula for point coverage:

h$+�"*$%!&'(! = ij *',!,+�"ℎ!�&$(&'7l$"'mij *',!,

Therefore:

n+o!m+ℎ$$� = 59 × 100122 = 48.36%

Likelihood = 48.36%

Table 12: Disaggregated SAM cases per quadrant and coarse estimate during the wide area

survey

Village Quadrant

Total

SAM Covered Not Covered Recovering

Coarse

Estimate (%)

Bugau

1

0 0 0 1

50

Agiyan Barnawa 2 1 1 4

Jaura Matta 0 0 0 1

Dugun Bakole

2

1 1 0 0

100

Gwayo 1 1 0 3

Kwanda 0 0 0 8

Kalagari Yamma

3

2 1 1 1

50

Lunkude 0 0 0 0

Kalagari Gabbas 0 0 0 2

Kadirawa

4

2 0 2 0

25

Katsinawa 2 1 1 0

Kwara 0 0 0 0

Katuka

5

12 11 1 4

87

Gidan Malu 1 1 0 1

Gidan Dachi 1 1 0 0

48

Karangiya

6

1 1 0 2

25

Tsallakawa Yamma 2 0 2 0

Malamawa 1 0 1 0

Tunanan C/Gari

7

9 2 7 2

42

Kwarin Gwaraji 0 0 0 0

Nassarawa 3 3 0 3

Garwa

8

1 1 0 0

50

Mai Kaji 2 1 1 0

Alalar Kimbagabag 1 0 1 1

Karabe

9

1 0 1 0

44

Galadimawa 5 1 4 2

Kakizali 4 3 1 2

Tsirma

10

4 1 3 2

29

Alalar Nagado 6 2 4 2

Maiywan Tudu 4 1 3 2

Dangolawa

11

1 0 1 0

75

Kalali 1 1 0 0

Gidan Taura/Harba 3 2 1 1

Debi

12

2 0 2 1

17

Andaza 2 0 2 1

Baure 2 1 1 0

Kawari Gabas

13

6 1 5 7

55

Kawari Yamma 5 5 0 4

Shadaka 0 0 0 1

Katanga

14

10 6 4 10

64

Dutse Shabe 0 0 0 1

Walawa 1 1 0 1

Yoland

15

0 0 0 0

50

Duhuwa 6 3 3 5

Kafin Baka 0 0 0 0

Fatara A

16

4 2 2 0

50

Raju 0 0 0 0

Gidan Gari 0 0 0 0

Zakwara Gabbas

17

1 0 1 1

0

Zakwara Yamma 0 0 0 0

Anifa Junaidu 1 0 1 0

Gidan Geji

18

0 0 0 0

75

Jigawa Bajida 3 2 1 0

Gidan Kwari 1 1 0 0

Gidan Dukawa

19

1 0 1 0

0

Kadirawa 3 0 3 0

Balkan 1 0 1 0

Total 122 59 63 76

49

Figure 20: Distribution of quadrants according to coarse coverage estimates

Out of the total quadrants visited more than half of the total quadrants visited had course

estimates of above 50%.

6.5.6 Posterior/Coverage Estimate

The Bayes coverage estimate (posterior) of 48.5% (41.3% - 55.8%) was arrived at after

combining the Prior and Likelihood in a conjugate analysis using the SQUEAC Coverage

Estimate Calculator version 3.01. The results of the conjugate analysis are credible and useful

in this study because:

1) The prior & likelihood are coherent, as the curves showed considerable overlap

(p>0.05)67 and therefore there is no prior-likelihood conflict

2) The posterior being narrower than the prior indicated that the likelihood survey had

reduced uncertainty (see figure 21 below).

67 p value=0.9691; z value=0.04

50

Figure 21: Bayes plot showing prior, likelihood and posterior (conjugate analysis)

The point coverage of the program is therefore:

Point coverage=48.5% (41.3% - 55.8%. CI; 95%)68

The rational for using point coverage to indicate the headline coverage of Kiyawa CMAM

program was informed by the following reasons:

� Significant number (almost half of all the exits) were defaulters, showing that there is

poor retention from admission to cure of SAM cases in the program (see table 1). It

was also evident from the analysis of routine data that most of the defaulters are likely

to be current SAM cases (see figure 8 and 9).

� The recovery rate during the period under review was below the recommended

standard (see figure 4), indicating that the proportion of the number who have been

treated to recovery was consistently lower than 75%69

� Poor adherence to CMAM protocol evidenced by wrongful discharge with significant

number of children classified as recovered not meeting the discharge criteria (see

figure 7).

68 Results are expressed with a credible interval of 95%. 69 SPHERE standards minimum is 75% of all the exits being discharged as cured/recovered. A program that attains this or

above is regarded as an effective program.

51

7 Discussions

The overall program coverage is 48.5% (41.3% - 55.8%. CI; 95%)70. Despite the good

awareness about the program treating malnutrition in the community, high proportion of

caregivers with SAM child not covered reported that they did not know their child is

malnourished. Therefore, the good awareness of the program in the community does not

translate to good knowledge about malnutrition.

Non-adherence to the national CMAM guideline/protocol was significantly affecting the

Kiyawa CMAM program. Significant number of SAM children found during the wide area

survey were discharged wrongly and told not to come back, while others were rejected at the

facility by the health workers. Generally, many of the SAM children not covered by the

program found during the likelihood survey were discovered to have been in the program

previously, but were discharged wrongly, or stopped attending after their ration card got

missing or torn.

Though the Kiyawa LGA CMAM program coverage is slightly lower than the minimum

recommended coverage according to SPHERE standard, most quadrants were having good

coverage as can be seen from the coarse estimates in Table 10 and figure 12 above. Out of a

total of 19 quadrants, 11 had a coarse estimate of above 50%. Two of the quadrants (that is

17 and 19) had coarse estimate of 0%. These were seen to be around the Maje CMAM HF

which was discovered to have no health worker residing in the community. The In-charge was

said to be residing in Kiyawa town from which he goes to work to Maje HF. Despite that health

workers from other HFs were sent to Maje CMAM HF on each of the CMAM OTP day (Fridays)

to compliment the efforts of the In-charge, unavailability of health worker residing in the

community was seen to be contributing to poor coverage around Maje CMAM HF. The lowest

number of admissions in the Kiyawa LGA CMAM program was also witnessed in Maje CMAM

HF as pointed out in Table 1.

The met needs of the Kiyawa CMAM program area can be calculated as follows:

Met need = Coverage x Median recovery rate

= 48.5 x 0.62 = 30.2%

8 Recommendations

In order to improve the Kiyawa LGA CMAM program, a debriefing and participatory

recommendation session was held with stakeholders from the LGA and State. On the

foregoing the following recommendations were proffered by the stakeholders.

70 Results are expressed with a credible interval of 95%.

52

Table 13: framework of action points to address barriers of Kiyawa CMAM program

Main area of activity Processes Responsible party Verification Expectations

Develop Strategy to

communicate about

malnutrition and

programme

modalities in

communities

Radio jingles and dramas on

community radio stations

about the CMAM programme

Dissemination of IEC materials

SNO/State Health

Educator/LNO

SNO/State Health

Educator/NFP

Number of radio jingles

and radio drama developed

on CMAM

Number times per month

the jingle/drama is aired on

Radio

Number of people given

IEC materials in Hausa

Languages

Increased understanding of

how the programme works

Supply of data tools

for recording

beneficiary

information

Budget for printing of data

tools for CMAM HFs in Kiyawa

LGA

Print data tools for CMAM HFs

Maintain constant distribution

of adequate quantity of data

tools to Kiyawa CMAM HFs

Director PHC, SNO and NFP

Quantity of data tools

printed for CMAM HFs in

Kiyawa LGA

Number of weeks without

stock-out of data tools in

Kiyawa CMAM HFs

Discontinuance of fees

charged on caregivers for

OTP registration cards

Improved quality of service

delivery at CMAM HFs

Improved opinion about

the Kiyawa CMAM

program

53

Motivate community

volunteers

Training and retraining of

community volunteers

Payment of stipends to

community volunteers

Kiyawa LGA, in close

collaboration with

Gunduma Health System

Board, and UNICEF

List of community

volunteers trained

Number of community

volunteers that receive

monthly stipend from

Kiyawa LGA

Increase in SAM early

recruitments

Increased community

volunteer activity.

Conduct Refresher

Training for Health

Workers

Identify 25 HWs (5 per OTP)

for refresher training

SNO and NFP with support

from LGA Chairman and

UNICEF

Schedule refresher training

of HWs on CMAM

List of Health Workers that

had refresher training

Increased knowledge of

CMAM guidelines by HWs

Increased adherence to

CMAM protocol

Increase in number of

trained HWs on CMAM

Improved quality of service

delivery at CMAM HFs in

Kiyawa LGA

Discontinuance of charges

on caregivers for CMAM

services

54

Strengthen Supply

Chain of RUTF and

routine drugs

Create a supply chain plan of

RUTF from State Capital to

LGA

Transport of RUTF from

Kiyawa LGA store to CMAM

HFs

Budget for the routine drug

needs for CMAM HFs

Create a distribution plan of

routine drugs

Integrate the supply chain of

RUTF and routine drugs

LGA to take care of

transportation funding

from LGA Chairman

Director Dutse Gunduma

Council and NFP to

maintain constant supply

of RUTF from the LGA store

to the CMAM HFs

NFP, HOD, SNO, and DPHC

NFP and SNO

SNO, NFP and CCO


stock out of RUTF at the

LGA Store


stock-out of RUTF at

CMAM HFs

Quantity of routine drugs

bought and supplied.


stock-out of routine drugs.

Improved availability of

RUTF

Improved availability of

routine drugs

Discontinuance of fees

charged on routine drugs

55

Strengthening of Joint

Supportive

supervision

Build capacity of LGA team on

supportive supervision of

CMAM programme

Develop supportive

supervisory work plan for

State and LGA

Integrate the State and LGA

supportive supervision work

plan

Conduct continuous

supportive supervision

NFP, LGA HOD WASH, , and

HE with support from SNO

NFP, SNO with support

from DPHC

LGA supportive supervision

work plan developed

Number of sites visited for

supportive supervision per

month by LGA and State

Team

Improved programme

serviced delivery quality

Increased capacity building

of HWs

56

9 Annexure

Annex1: Schedule (detailed) of implemented activities in Kiyawa SQUEAC. Date Activity/Villages visited HF Sources of

information

Information collected

14/6/2014 SQUEAC team travel from Damaturu

to Jigawa State

16/6/2014 SQUEAC team reported at ACF Dutse

base Introduction/meeting with staff

17/6/2014 Preliminary meeting with Director

Primary Health Care

18/6/2014 Preliminary meeting with State

Nutrition Officer

19/6/2014 Meeting with Director General

Gunduma health system board

20/6/2014 Meeting with Kiyawa Local

Government Area chairman

Collection of OTP cards from the

Central Store

21/6/2014 Travel to CMAM sites to retrieve OTP

cards (Kwanda, Katanga and Katuka

All OTP cards from May

2013-May 2014 were

sorted and collated

22/6/2014-

29/6/2014

Data extraction and entry Kwanda HF,

Maje HF,

Katanga HF,

Katuka HF,

Garko HF

OTP cards Name, age,

address/village, admission

weight, admission MUAC,

LOS, exit MAUC, RUTF at

admission, defaulter,

Transfer, discharge cured,

Died, discharge non cured

30/6/2014

&

01/7/2014

SQUEAC training

Kiyawa council

hall

List of

Participants

Enumerators

Nwaigwe,

blessing, Panyi

D. Annah,

Salamatu

Ibrahim, Jibrin

G. Ejura, Amina

Awaisu,

Maduka C.

Loveth,

Shamsiyya

Salisu, Hassana

G. Garba,

Hauwa S.

Suleiman,

At the end of day1:

participants were able to

explain CMAM

programme, identify

barriers & boosters and

role play on qualitative

information gathering

At the end of day2:

participants were able to

role play on how to gather

qualitative information,

identified SAM covered &

SAM not covered,

calculate point coverage

57

Amina M.

Abubakar,

Emmanuel W.

Meshelia,

Salisu Danladi,

Waziri Yerima,

Nasiru Yusuf,

Rugayyatu

Ismail.

State,LGA,

partners

Aisha…….SNOs,

Suleiman

Mohammed-

NFP, HOD

health,

Hussaini

NPopC

2/7/ 2014 Katuka HF,

Near community (Nafara) <3km to

OTP,

Far community (Dangoli) <3km to

OTP

Katuka HF 2 religious

leader

2 community

leader

3 Majalisa (35

to 60 years)

4care-giver

2 patent

medicine

dealer

2 traditional

birth

attendance

1 community

health worker

1 traditional

healer

Local terms

Tamowa, Dauda, maykwaniya

lisuwa bayamma Ayama

Yunwa Tundi Rama Kumburi

were local terms given for

malnutrition.

Description/perception

1. Maywaniya is a condition

of malnutrition resulting

when a breast feeding

mother have sexual

intercourse with any man be

it her husband or not.

2. Dauda: malnutrition

believed to arise when a

lactating mother has

intercourse with the husband

while still breast feeding,

3. Tamowa; wasting

4. Lisuwa; Hausa word for

wasting,paleness protrated

abdomen.

5. Bayamma: Hausa word for

oedema.

6. Yunwa: Hausa word for

hunger.

7. Ayama: Hausa

contaminated breast milk

from a mother taken by a

child.

58

8. Tundi: Fulani word for

malnutrition.

9. Rama: Hausa word for

emaciation.

10. Kumburi: Hausa word for

oedema

3/7/2014

Garko HF

Near community (Tsirma) <3km to

OTP

Far community (Jama’a dawa) <3km

to OTP

Garko HF

2 religious

leader

2 community

leader

3 Majalisa (35

to 60 years)

4care-giver

2 patent

medicine

dealer

2 traditional

birth

attendance

1 community

health worker

1 traditional

healer

Local terms

Dauda, yunwa, tamowa,

lisuwa


Dauda: malnutrition tends to

arise as a result of a lactating

mother having sexual

intercourse with husband

when still breastfeeding.

2,Yunwa:Hausa word for

Hunger

3, Tamowa: Malnutrition.

4, Lisuwa: wasting, paleness,

protruded abdomen.

4/7/ 2014 Maje HF

Near community (kakarawa) <3km to

OTP

Far community (Gorumo) <3km to

OTP

Maje HF

1 religious

leader

2 community

leader

3 Majalisa (35

to 60 years)

4care-giver

1 traditional

birth

attendance

2 health

worker

1 traditional

healer

Local terms

Dauda, yunwa, tamowa,

lisuwa, tundi, dauda


1, Yunwa: hausa word for

hunger.

2, Tamowa: Malnutrition.

3, Lisuwa: wasting, paleness,

protruded abdomen.

4. Tundi;Fulani word for

malnutrition.

5. Dauda: malnutrition tends

to arise as a result of a

lactating mother having

sexual intercourse with

husband when still

breastfeeding.

59

5/7/ 2014 –

6/7/2014

Data extraction and entry OTP cards

from May

2013-May

2014

Name, age,

address/village, admission

weight, admission MUAC,

LOS, exit MAUC, RUTF at

admission, defaulter,

Transfer, discharge cured,

Died, discharge non cured

7/7/ 2014 Kwanda HF

Near community (Yelwa pie) <3km to

OTP

Far community (Danfasa) <3km to

OTP

Kwanda HF

1 religious

leader

2 community

leader

2 Majalisa (35

to 60 years)

4 care-giver

2 traditional

birth

attendance

2 health

worker

1 traditional

healer

1 teacher

Local terms

Dauda, tindimiri tamow

maykwaniya


1, Tamowa: hausa word

meaning Malnutrition.

2. Tindimiri;Fulani word for

malnutrition.

3. Dauda: malnutrition tends

to arise as a result of a



husband when still

breastfeeding.

4. maykwaniya: malnutrition

tends to arise as a result of a



husband when still

breastfeeding.

8/7/ 2014 -

12/7/2014

Team 1( Janet, Hauwa, Ejura, HOD,)-

HF observation

Team 2(Francis, Amina, Loveth,

Meshelia,)-interview health workers

Team 3(Zulai, Salisu, Shamsiya,

Hussaini)-interview CVs

Team 1( Chika, Salamatu, Suleiman,

Aisha,)- interview care-givers

Kwanda HF,

Maje HF,

Katanga HF,

Katuka HF,

Garko HF

Survey

questionnaire

Update BBQ

Update mind map

14/7/2014-

17/7/2014

Communities visited: kalkuta,

chiwina, jigawa kiyawa, barka,

markibar, gudurbar, babari,

pilpia, tesher ganji, sabon gari,

katanga, cikin gari

Catchment area

to the following

CMAM sites:

Kwanda HF,

Maje HF,

Katanga HF,

Small area

survey

House-to-house active

case findings

60

Katuka HF,

Garko HF

Communities visited: Dungun bakole,

Gwawyo, kwanda, bugua, agiyan

barnawa, jauro matta, jigawa bajida,

fatara, dutse shabe, watawa,

kawariyamman, Katanga, shadaka,

kawari gabbas, tunancikin gari,

nasarawa, kwarin gwaraji, tsallakawa

yamman, debi, yoland, kwara, kafin

baka, duhuwa, baure, gidan gedi,

gidan kwari, anguwar madaki, gidan

malu, gidan dachi, garwa, mai kaji,

ala’ar kimba gabbas,kwara, kadirawa,

katsinawa, lukude, kalagari

gabbas,kalagari yamman, tsirma,

alalar nag ado, miyawa tudu, karabe,

galadimawa, kazizali, zakwaro,

gabbas, zakwaro yanma, jauda, kalali,

dagolawa, gidan jaura/harba, balkeri,

gidandukawa, kadirawa, karangiya,

and malamawa.

Catchment area

to the following

CMAM sites:

Kwanda HF,

Maje HF,

Katanga HF,

Katuka HF,

Garko HF

Wide area

survey

House-to-house active

case findings

22/7/2014- Continue updating mind map, BBQ,

and report writing

61

9.4 Annex2: Parameters used in prior building and sample size calculation.

Parameters Values Parameters continued Values

Total barriers 12 Prior mode calculated from belief

histogram, un-weighted barriers and

boosters and weighted barriers and

boosters

47.75%

Total Boosters 11 Maximum probable value 70%

Belief histogram 50% Minimum probable value 30%

Concept map 35% Min –Prior(proportion) 0.2

Un-weighted barriers and

boosters

46% Mode –Prior (proportion) 0.375

Prior by weighted barriers

and boosters71

60% Posterior estimate Precision 0.1

Not (δ) 0.6667 Median Village Population 400

Mu(μ) 0.485 % of 6-59 Months 18%

Alpha prior (α) 26.77 SAM Prevalence 1.30%

Beta prior (β) 28.42 Sample Size 53

Number Of Villages 57

71 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 -5 were used as minimum and maximum for

boosters and barriers respectively.

62

9.5 Annex3: Concept maps-Team A and B.

COVERAGE

GOOD OPINION

EARLY TREATMENT

LOSStockout of RUTFConsumption of RUTF by adults

Client incured hidden charges

Closure of CMAM site for 3weeks

Poor attitude of HWs towards carers

DEFAULTER

Referral by CV

Large turnout of client

No compliance to CMAM protocols

CVs not motivated

Peer-peer referral

Random transfer of trained HWs on CMAM

Poor community mobilization and sensitization

Poor healh seeking behaviour

CV clamouring for incentives

Good collaraboration of HWs and CVs


Passive referralStockout of routine drugs

Good attitude health workers towards carers

Health workers trained atleast once a year on CMAM

Health workers from other health facility visit during OTP days to

support

Over burden of HW due to large number of client

Poor active case finding

No, shade, mats & seats for carers at OTP site

Long waiting time

Good health seeking behaviour

Stockout of data tools

Williness of carer to sleep overat CMAM site to access CMAM

services

leads to

leads to

leads to

promotes

leads to

results to

promotes promotes

increases

increases

encourages

leads to

results to

leads to

results to

leads to

results to

results to

results to

results to

results to

leads to

resullts to increases

increases

encourages

results to

reduces

reduces

reduces

results to

63

64

9.6 Annex 3: Active and adaptive case finding procedure72727272

72 Local terms of malnutrition used are from Kiyawa LGA in Jigawa, Northern Nigeria.

65

9.7 Annex6: Summary of the small study findings-Kiyawa LGA

S/N

MUAC

at

Default

(mm)

Hidden Cost for

services

Reason for

Default

Present

MUAC Status

Action

Taken

Condition

of child

1 102 - Child died - Dead

2 123

Card N100 and

routine Drugs N100

each syrup

Ration card got

missing and

caregiver had no

money to pay for

replacement

108 SAM SAM

3 85 - Child died - - - Dead

4 92 - Child Died - - - Dead

5 120

Card N100 and

routine Drugs N100

each syrup

Caregiver

misplaced OTP

card and had no

money to replace

card.

122 Recovered recovered

6 115

Card N100 and

routine Drugs N100

each syrup

OTP card got torn

and she was not

ready to pay for

replacement of

torn OTP card.

112 SAM

Referred

to OTP

site

SAM

7 95

Card N100 and

routine Drugs N100

each syrup

Child was given

two sachets of

RUTF and she

stopped attending

96 SAM

Referred

to OTP

site.

SAM

8 92

Card N100 and

routine Drugs N100

each syrup

Caregiver said

child was

discharged by

health workers

98 SAM

Referred

to OTP

site

SAM

9 90

Card N100 and

routine Drugs N100

each syrup

Caregiver said

child was

discharged by

health workers

100 SAM

Referred

to OTP

site

SAM

10 130

Card N100 and

routine Drugs N100

each syrup

Caregiver (mother

)had surgery and

could not attend

OTP for two

months

107 SAM

Referred

to OTP

site

SAM

11 110

Card N100 and

routine Drugs N100

each syrup

She felt her child

was recovered 107 SAM

Referred

to OTP

site

SAM

12 100

Card N100 and

routine Drugs N100

each syrup

Lost OTP card and

was not ready to

pay 100 for

replacement.

112 SAM

Referred

to OTP

site

SAM

66

13 110

Card N100 and

routine Drugs N100

each syrup

Carer traveled to

Kano state with

the child

- - - absent

14 94

Card N100 and

routine Drugs N100

each syrup

Carer said she got

tired of waiting

hours at the OTP

before getting

RUTF.

130 Recovered - recovered

15 111

Card N100 and

routine Drugs N100

each syrup

She felt her child

has recovered 132 Recovered - recovered

16 100

Card N100 and

routine Drugs N100

each syrup

Mother said she

was fasting 108 SAM

Referred

to OTP

site

SAM

17 110

Card N100 and

routine Drugs N100

each syrup

Caregiver felt her

child has

recovered


18 110

Card N100 and

routine Drugs N100

each syrup

Caregiver was

pregnant and got

discouraged with

long waiting time

at OTP site.

113 SAM

Referred

to OTP

site

SAM

19 98

Card N100 and

routine Drugs N100

each syrup

Health worker

refused giving

them RUTF

because they felt

she was eating or

sharing the it

112 SAM

Referred

to OTP

site.

SAM

20 110

Card N100 and

routine Drugs N100

each syrup

Lost her OTP card

and health

workers refused

to attend to her

so she stopped

attending


21 108

Card N100 and

routine Drugs N100

each syrup

Caregiver said she

was busy 112 SAM

Referred

to OTP

site.

SAM

22 100

Card N100 and

routine Drugs N100

each syrup

Caregiver stopped

attending because

her child was not

improving after

taking the RUTF

112 SAM

Referred

to OTP

site.

SAM

23 105

Card N100 and

routine Drugs N100

each syrup

Caregiver felt child

has recovered 111 SAM

Referred

to OTP

site.

SAM

Documents

Semi-Quantitative Evaluation of Access and Coverage ... · Usman Tahir (Director General GHSB), and Kabir Ibrahim (Director ... Sanitation and Hygiene (WASH) Program and Suleiman