Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) Damaturu LGA CMAM Program Yobe State, Northern Nigeria May-June 2014
Action Against Hunger | ACF International
1
ACKNOWLEDGEMENTS
The SQUEAC1 assessment in Damaturu has been completed through the support from the Children Investment Fund Foundation (CIFF).
The Yobe State Primary Health Care Board (YSPHCB) gave authorization for conducting the survey in Damaturu. Mr. Usman Adams, the Assistant Director Primary Health Care (PHC) was very supportive throughout the exercise, acting as the link between YSPHCB and the CMAM Coverage assessment team. Ms. Aisha Abdu Salle, the Acting PHC Coordinator and Ms. Fatima Mustapha the Nutrition Focal Person (NFP) for Damaturu LGA are appreciated for providing the team with client records, anecdotal program information and security information during the stage 2 and 3 of the assessment.
The ACF Damaturu base team led by the Head of Base Ibrahim S. Adamu, is acknowledged for the information they provided and other support they gave to the SQUEAC team in the course of SQUEAC implementation. This include but not limited to the Community based Management of Acute Malnutrition (CMAM) program team led by Stella Esedume, and the Water Sanitation and Hygiene (WASH) team led by Bwakat Hollong.
Peter Magoh, the security manager of ACF; Morris Ramnaps, the Flying Logistic Officer; and Abubakar Kawu, the Program Support Officer; conducted security and logistics assessments prior to the SQUEAC assessment to ensure the team settled comfortably before beginning the work. The mentioned persons especially Abubakar, stayed with the team and gave support during the implementation period.
Joseph Njau (ACF Coverage Program Manager) provided technical support during the implementation of the assessment while 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 Abdulmalik, 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 despite the security challenges in Damaturu LGA cannot be overemphasized.
Finally, all the health workers, caregivers, traditional and religious leaders, traditional birth attendants and other interviewees who freely gave very useful information regarding the CMAM Program in Damaturu LGA are highly appreciated.
Joseph Njau & Ifeanyi Maduanusi
1 Semi Quantitative Evaluation of Access and Coverage
2
Table of contents 1. Executive summary ...... 6 2. Introduction ...... 6 3. Objectives ...... 7 4. Methodology ...... 7 5. Description of field activities ...... 9 6. Results and findings ...... 9 6.1. Stage 1: Routine monitoring data-identifying potential areas of low and high coverage...... 10 6.1.1. Routine monitoring of Beneficiary cards and performance data ...... 10 6.1.1.1. Program exits (discharge outcomes) ...... 10 6.1.1.2. Admission trends ...... 10 6.1.1.3. MUAC or oedema at admission ...... 11 6.1.1.4. Length of stay from admission to cure ...... 12 6.1.1.5. Number of visits before default ...... 13 6.1.1.6. Time to travel to CMAM HFs- plot of distance to treatment centre ...... 13 6.1.1.7. Distribution of villages across the HFs providing CMAM services ...... 14 6.1.2. Conclusion of the routine monitoring (beneficiary cards) analysis ...... 14 6.2. Stage 1: Qualitative data-Investigation of factors affecting program and coverage...... 15 6.2.1. Qualitative sampling Framework ...... 15 6.2.2. Qualitative information ...... 15 6.2.2.1. Health seeking behaviors in the communities ...... 15 6.2.2.2. Community Sensitization, mobilization and awareness of the program ...... 15 6.2.2.3. Community Volunteer (CV) activity and trainings ...... 16 6.2.2.4. Community perception about the program and RUTF ...... 16 6.2.2.5. Activity of Health workers ...... 16 6.2.2.6. Generalized stock-out of routine drugs...... 16 6.2.2.7. Stock-out of RUTF due to unsustainable supply chain management ...... 16 6.2.2.8. Insecurity in Damaturu LGA ...... 17 6.2.3. Data triangulation ...... 17 6.2.4. Concept map...... 19 6.3. Stage 2: Confirmation of areas of high and low coverage...... 19 6.3.1. Study Type ...... 19 6.3.2. Sampling Methodology ...... 19 6.3.3. Case Definition ...... 19 6.3.4. Result of Small Area Survey ...... 19 6.3.5. Conclusion of small area survey...... 21 6.4. Developing the prior...... 21 6.4.1. Histogram of Belief...... 21 6.4.2. Concept Map ...... 21 6.4.3. Un-weighted barriers and boosters ...... 22 6.4.4. Weighted barriers and boosters...... 22 6.4.5. Prior estimate from recent SLEAC Assessment ...... 24 6.4.6. Triangulation of Prior...... 24 6.4.7. Bayes Prior Plot and Shape Parameters ...... 24 6.5. Stage 3: Wide area survey ...... 25 6.5.1. Calculation of Sample Size and Number of Villages to be visited for likelihood survey ...... 25 6.5.2. Quantitative sampling framework ...... 25 6.5.3. Case Finding Method ...... 25 6.5.4. Qualitative data Framework ...... 25 6.5.5. Results of the wide are survey ...... 26 6.5.6. Posterior/Coverage Estimate ...... 27 7. Discussions ...... 28 8. Recommendations ...... 29 9. Annexure ...... 32
3
9.1. Annex1: Schedule of implemented activities in Damaturu SQUEAC...... 32 9.2. Annex2: Parameters used in prior building and sample size calculation...... 33 9.3. Annex3: Concept maps-Team A and B...... 34 9.4. Annex4: Active and adaptive case finding procedure...... 36 9.5. Annex5: List of participants in Damaturu SQUEAC assessment ...... 37 9.6. Annex6: Comparison of the May 2012 and May 2014 SQUEAC findings (Barriers) in Damaturu LGA ...... 38
List of figures
FIGURE 1: POLITICAL MAP OF NIGERIA (LEFT) SHOWING YOBE STATE AND YOBE STATE MAP SHOWING DAMATURU LGA (ALL INDICATED BY BLUE ARROWS). MAPS CAN BE DOWNLOADED AT WWW.MAPSOFTHEWORLD.COM ...... 6 FIGURE 2: THE 2 STANDARD 3 CLASS CLASSIFIER USED IN SLEAC AND ADAPTED FOR USE IN SQUEAC ...... 8 FIGURE 3: BETA BINOMIAL CONJUGATE ANALYSIS IN STAGE 3 OF SQUEAC ASSESSMENT ...... 9 FIGURE 4: EXIT TRENDS FOR DAMATURU LGA CMAM PROGRAM-JANUARY 2013 TO APRIL 2014...... 10 FIGURE 5: ADMISSION TRENDS AND THE SEASONAL CALENDAR OF EVENTS ...... 10 FIGURE 6: ADMISSION MUAC FOR DAMATURU LGA CMAM PROGRAM ...... 11 FIGURE 7: TRIANGULATION OF OEDEMA AND MUAC AT ADMISSION >=115MM ...... 11 FIGURE 8: LENGTH OF STAY IN PROGRAM BEFORE RECOVERY ...... 12 FIGURE 9: PROPORTION OF THE MUAC MEASUREMENT OF CHILDREN DISCHARGED AS RECOVERED...... 12 FIGURE 10: PLOT OF NUMBER OF VISITS BEFORE DEFAULT ...... 13 FIGURE 11: DEFAULTER’S MUAC ON EXIT ...... 13 FIGURE 12: TIME TO TRAVEL TO CMAM SITE FOR CURRENT CASES AND DEFAULTERS-ALL CMAM SITES ...... 13 FIGURE 13: TIME TO TRAVEL TO CMAM SITE-INDIVIDUAL PLOTS OF 10 CMAM SITES ASSESSED ...... 14 FIGURE 14: DISTRIBUTION OF VILLAGES SERVED PER HEALTH FACILITY IN DAMATURU-< 2HOURS AND MORE THAN 2 HOURS ...... 14 FIGURE 15: REASONS FOR NOT ATTENDING THE CMAM PROGRAM-SMALL AREA SURVEY ...... 20 FIGURE 16: ILLUSTRATION OF TRIANGULATION OF PRIOR ...... 24 FIGURE 17: BAYESSQUEAC BINOMIAL DISTRIBUTION PLOT FOR PRIOR SHOWING THE SHAPE PARAMETERS AND THE SUGGESTED SAMPLE SIZE...... 24 FIGURE 18: BARRIERS TO PROGRAM ACCESS AND UPTAKE-WIDE AREA SURVEY ...... 26 FIGURE 19: BAYES PLOT SHOWING PRIOR, LIKELIHOOD AND POSTERIOR ...... 28 FIGURE 22: CONCEPT MAP SHOWING THE RELATIONSHIP BETWEEN FACTORS AFFECTING CMAM PROGRAM AND COVERAGE-TEAM A ...... 34 FIGURE 23: CONCEPT MAP SHOWING THE RELATIONSHIP BETWEEN FACTORS AFFECTING CMAM PROGRAM AND COVERAGE-TEAM B ...... 34
List of tables
TABLE 1: PARAMETERS ANALYZING LIKELIHOOD SURVEY ...... 8 TABLE 2: THE QUANTITY AND PROPORTION OF RUTF GIVEN ON ADMISSION ...... 12 TABLE 3: SOURCES AND METHODS USED TO GET INFORMATION IN A BBQ TOOL...... 17 TABLE 4: BARRIERS, BOOSTERS & QUESTIONS FINDINGS AND SOURCES OF INFORMATION ...... 17 TABLE 5: SIMPLIFIED LOT QUALITY ASSURANCE CLASSIFICATION OF SMALL AREA SURVEY RESULTS ...... 20 TABLE 6: WEIGHTED BARRIERS AND BOOSTERS-DAMATURU SQUEAC ASSESSMENT ...... 22 TABLE 7: PARAMETERS FOR SAMPLE SIZE CALCULATION FOR LIKELIHOOD SURVEY ...... 25 TABLE 8: BARRIERS TO PROGRAM ACCESS AND UPTAKE ...... 26 TABLE 9: RESULTS OF THE LIKELIHOOD (WIDE AREA) SURVEY ...... 26 TABLE 10: DISAGGREGATED SAM CASES PER WARD FOUND IN THE WIDE AREA SURVEY ...... 27 TABLE 11: FRAMEWORK OF ACTION POINTS TO ADDRESS BARRIERS IF DAMATURU CMAM PROGRAM ...... 29 TABLE 12: BARRIERS IN 2012 AND 2014 SQUEAC ASSESSMENTS ...... 38
4
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 ECHO European Commission of Humanitarian Aid FMOH Federal Ministry of Health HF Health Facility IEC Information Education and Communication IYCF Infant and Young Child Feeding 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 YSPHCMB Yobe State Primary Health Care Management Board
5
1. Executive summary Action against Hunger | ACF International (ACF) is supporting the implementation of Community-based Management of Acute Malnutrition (CMAM) in Damaturu since June 2011. A SQUEAC assessment of CMAM program in Damaturu LGA2, had been implemented by ACF in May & June 2012 with a coverage estimate of 50.4% (41.1-59.9%; CI 95%). A more recent SLEAC assessment3 conducted by Valid International (VI) unveiled a moderate classification4 of coverage with 29 children out of total of 95 SAM cases found in Damaturu LGA being in the program. This giving a coarse estimate of 30.4% which helps to show a picture of program coverage. Damaturu LGA had been chosen for a SQUEAC assessment to identify positive and negative factors affecting CMAM program coverage, build capacity of SMOH and LGA staff, and proffer recommendations to improve the CMAM coverage. Program data and client records from January 2013 to April 2014 were extracted and analyzed. Eight out of eleven CMAM sites5 and sixteen villages in Damaturu LGA were visited to obtain additional qualitative information about the CMAM program from different sources6 and using different methods7. All the findings were triangulated by these various sources and methods and analyzed into negative factors (barriers) and positive factors (boosters) which affect program coverage. The key barriers to program access and coverage identified include; Shortage of trained health workers in CMAM sites, non- adherence to CMAM protocol in terms of admission and discharge criteria, High defaulter rate and lack of mechanism to trace defaulters by community volunteers (CVs), lack of CVs in some areas, Poor support from Local government council in transportation of RUTF from LGA store to health facilities (HFs) and in procurement of routine drugs, stock-out of RUTF and routine drugs, insecurity due to insurgency8. The key boosters include; good opinion of the program in most communities, peer-to-peer referrals by caregivers, good working relationship between Health workers (HWs) and CVs existed, good attitude of HWs towards caregivers motivated caregivers to attend, personal contribution of community leaders and HWs to cost of transporting RUTF from LGA store to HFs in some areas ensuring beneficiaries get RUTF regularly, regular training of CVs by ACF contributed to their long stay in supporting the program. Patchy coverage was confirmed in Damaturu LGA. Unavailability of CVs in some areas and insecurity were the confirmed factors that led to patchy coverage in Damaturu LGA. A point coverage estimate of 28.2% (20.3% - 38.1%. CI; 95%)9 (which is consistent with the SLEAC findings) was arrived at after conjugate analysis of the prior and likelihood survey. Recommendations: to improve the CMAM program in Damaturu were proffered. These include; increase in number of health workers in health facilities, motivate community volunteers (CVs) through a sponsored sustainable cooperative project, improve community mobilization and sensitization through community dialogues and meetings, radio jingles and drama, and dissemination of IEC materials about the CMAM program, strengthening of the supply chain of RUTF, provision of routine drugs, training and retraining of new and existing health workers, as well as CVs.
2. Introduction Yobe State is located in the Northeast of Nigeria. It is bordered by Jigawa and Bauchi States on the West, Gombe on the South, and Borno State on the East, and an international border on the North with Niger Republic.
Figure 1: political map of Nigeria (left) showing Yobe state and Yobe state map showing Damaturu LGA (all indicated by blue arrows). Maps can be downloaded at www.mapsoftheworld.com
2 The Damaturu SQUEAC assessment in 2012 recommended a repeat SQUEAC in May 2013. However, this SQUEAC came after the 2012 one and has been implemented in May- June 2014. 3 Chrissy B., Bina S., Safari B., Ernest G., Lio F. & Moussa S.; Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Survey of Community-based Management of Acute Malnutrition program; Northern States of Nigeria-(Sokoto, Kebbi, Zamfara, Kano, Katsina, Gombe, Jigawa, Bauchi, Adamawa, Yobe, Borno). Valid International. February 2014 4 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% 5 Three CMAM sites (Gabai, Sassawa and Gambir) could not be visited due to unpredictable security situation 6 Care-givers, Health Workers (HWs), Community Volunteers (CVs), community leaders, religious leaders, majalisa6, teachers, traditional healers, traditional birth-attendants (TBAs), and women group, program staff, etc. 7 Semi-structured interview, in-depth interview, observations and informal group discussions 8 Damaturu has been under a state of emergency since 31st December 2011 till date due to insurgency 9 Results are expressed with a credible interval of 95%.
6
Damaturu is a Local Government Area in Yobe State, Nigeria. Its headquarters are in the town of Damaturu, the State capital. 8. The LGA has had series of insurgency activities for a long time and has been under state of emergency since year 2011. Previous major attacks within the LGA include attack to a school by Boko Haram militants where people were killed in June 2013 and also a battle between security forces and the militants when the militants raided a hospital in Damaturu10. In May, a number of attacks took place in Yobe State in general. These include attacks in Buni Yadi town under Gujba LGA, an attempt to attack in Damaturu police barrack, attacks along Damaturu Maiduguri road, and lastly the abduction of the village head of Gumsa, in Gaidam LGA. Implementation of Community-based Management of Acute Malnutrition (CMAM) in Damaturu LGA commenced in June 2011 with establishment of seven CMAM sites. The program is supported by ACF International with funding from European Commission of Humanitarian Office (ECHO). In April 2012 four additional CMAM sites were established. ACF works in collaboration with YSPHCMB in providing CMAM services in Damaturu. The health workers (HWs; employees of the YSPHCMB) in the eleven health facilities (HFs) provide CMAM & IYCF services at the health facility level together with the Community Volunteers (CVs) , while ACF team provides technical support, capacity building, regular monitoring and supervision and on-the-job coaching to the service providers at LGAs and state levels. A previous SQUEAC investigation had been carried out in Damaturu by ACF (between 21st May and 1st June 2012) to evaluate the CMAM program coverage. The findings indicated a coverage estimate of 50.4%, some recommendations to strengthen the program were also proffered. A key contributing factor to adequate coverage11 in 2012 SQUEAC was the start-up of OTP in additional health facilities just two weeks prior to the investigation. More than one year has elapsed between the SQUEAC done in May 2012 to the current one (May 2014). Nevertheless, security situation around the communities attending HFs offering CMAM services in 2014 may be much higher than year 2012. Evidence in the 2014 SQUEAC assessment has shown that caregivers are not able to attend CMAM site as freely as would have been 2 year back12. A recent SLEAC study conducted late 2013 by Valid International showed a coarse estimate of 30.5% (in which 29 SAM cases were covered by CMAM program out of 95 SAM cases found in the assessment) in Damaturu LGA. Damaturu CMAM coverage was classified as moderate and was one of the LGAs identified for an in-depth investigation through a SQUEAC assessment in order to gather additional information on positive and negative factors affecting access to and coverage of CMAM program. SQUEAC assessment was therefore, undertaken in Damaturu LGA in the month of May 2014.
3. Objectives The SQUEAC investigation was guided by the following specific objectives. To; 1. Investigate the barriers and boosters to program and coverage. 2. Evaluate the spatial pattern of program coverage. 3. Estimate overall program coverage. 4. 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 in Damaturu LGA CMAM program. The assessment used a set of quantitative and qualitative sets of data to describe the factors that affect the CMAM program and its coverage. The methodology used are explained as follows; Stage 1 data
Quantitative data: Routine program data, and data from client information recorded in the beneficiary cards were extracted and analyzed into various plots; 1) time to travel to site; 2) length of stay from admission to cure; 3) admission trends; 4) program exits/discharge outcomes; 5) MUAC at admission; and 6) number of visits before default. 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 Barrier booster and questions: Qualitative data was analyzed into a barriers, boosters and question (BBQ) tool to capture the positive and negative factors that affect the program and its coverage. The information was collected from different sources and methods and triangulated to adduce evidence. Additional information was 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
10 http://en.wikipedia.org/wiki/damaturu 11 SPHERE standards recommend a minimum coverage of 50%, 70% and 90% for rural, urban and camp Therapeutic feeding programs respectively 12 See the Damaturu LGA SQUEAC report barriers for 2012 and 2014 in annex 9.6, table 12
7
The small area survey data were analyzed using simplified lot quality assurance technique. This was done by examining the number of Severe Acute Malnutrition (SAM) cases found (n) and the SAM cases covered 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 standard used as a measure of coverage13. In Damaturu SQUEAC assessment the standard (p) was adapted from the two standard three class classifier14 used in the previous SLEAC survey15. The previous SLEAC assessment coarse estimate (of 30.4%) indicated that the coverage in Damaturu LGA may be generally low, that is below 50% (as a minimum standard that measures coverage of a rural TFP programs). In this SQUEAC assessment it was scrupulous to adapt the SLEAC classifier to allow for use of a minimum threshold of 20% which would be plausibly sensitive to disparities in spatial coverage in Damaturu. The adapted 2 standard 3 class classifier is illustrated below:
Figure 2: The 2 standard 3 class classifier used in SLEAC and adapted for use in SQUEAC
The value of (p) used that was used was 20%. The formula for deriving (d) is shown below:
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 reasons for coverage failure obtained from the small area survey were plotted in figure 15. Stage 3 data
The prior: The tools that have been used revealed a rich set information about coverage and identified barriers to access and care and also spatial coverage of the program. Bayesian technique16 was helpful to provide information about overall coverage of the program. 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. In other words, the analysis of routine program data; the intelligent collection of qualitative data; and the findings of the small-area surveys in stage 1 and stage 2 provided huge amount of relevant information about program coverage that was entered into a process of creating the prior. 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.
Therefore the prior of the program was estimated though use of the following tools17:
• Belief histogram • Weighted barriers and boosters • Calculation of the total positive and total negative factors illustrated in the concept map. • The coarse estimates of the previous SLEAC survey in Damaturu LGA The prior was established in a beta prior distribution with prior shaping parameters and plotted on Bayes calculator. The beta prior distribution18 expresses the findings of stage 1 and 2 in similar ways to the likelihood survey as described below. The Bayes 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 tabulated in the table 1 below. The binomial distribution of the likelihood results are shown in figure 19 in results section of this report. Table 1: Parameters analyzing likelihood survey
Parameters Values Current cases in the program (x) Current SAM cases not in the program (y)
13 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. Previously conducted SLEACS or SQUEAC headline coverage results were considered to set the value of “p”. 14 The two class three standard classifier classifies coverage as follows: Low coverage-20% and below; Moderate coverage-greater than 20% up to 50%; high coverage-above 50% 15 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. 16 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 17 Each listed process is discussed in detail in the body of the report 18 As illustrated in the example in figure 19 in results section of this report.
8
Total current SAM cases (n) Point coverage19. CI 95%
The program coverage.
The process of combining the prior and the likelihood to arrive at the posterior (also referred as conjugate analysis20) 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) were, the likelihood, expressed as a probability density as described process of developing a prior above. The conjugate analysis combined the beta distributed prior with a binomial distributed likelihood to produce a beta distributed posterior in a process illustrated in the figure below:
Figure 3: Beta binomial conjugate analysis in stage 3 of SQUEAC assessment
Met need is calculated as:
5. Description of field activities
SQUEAC planning, training and implementation ACF coverage team arrived in Damaturu on 30th April 2014 to commence the SQUEAC investigation. A letter from Federal Ministry of Health (FMoH) requesting the team be allowed to carry-out the assessment was delivered to Yobe State Primary Health Care Management Board (YSPHCMB) and LGA authorities and the purpose of the SQUEAC assessment shared. The YSPHCMB allowed the team to carry out the assessment and also gave access to the beneficiary records. A total of 2,763 beneficiary cards with information of beneficiaries from 10 CMAM sites between Jan 2013 to May 2014 were extracted. However, it was observed that this was lower than the actual number of cases admitted within the reference period as quite a number of the beneficiary cards at the health facilities had been damaged by termites and as such the information could not be extracted. The beneficiary cards from Sassawa health facility could not be accessed21. Out of the total number of cards (2,763), 291 were currently enrolled in the CMAM program while the remaining had exited. Among the exits, the recovered were 1,148, while total of 920 defaulted, 18 died, 81 were transferred and 130 were non-recovered. Information from the cards were extracted into a computer database and analyzed. (See the annex 1 for chronology of events in SQUEAC assessment). The advertisement for enumerators were done, and recruitment accomplished before a training for successful enumerators for 2 days (that is 13th and 14th May). Qualitative information gathering commenced on 15th and ended on 21st May, where eight health facilities providing CMAM services and sixteen villages were visited and various respondents interviewed. The small area survey was conducted on 27th and 28th May, while the likelihood survey was conducted between 30th May and 2nd of June 2014. A meeting to share the findings of SQUEAC with the program staff, was held on 6th June. The meeting was also meant to get inputs of the program staff on the possible actions that would be recommended to improve the program. Dissemination of the SQUEAC findings with YSPHCMB, Damaturu LGA and traditional council was planned for 24th June 2014, much later after completion of the SQUEAC assessment. The delay in dissemination happened when state officers representing YSPHCMB had to attend to other competing priorities before an appropriate date could be set for the SQUEAC dissemination and ensure full participation of all stakeholders.
6. Results and findings
This section summarizes the results of stage 1, 2 and 3. The various data were organized as follows:
19 Point coverage gives overall accurate measure of this program 20 A conjugate analysis requires that the prior and the likelihood are expressed in similar ways. 21 The security situation in Sassawa would not allow the team to go and access the cards from the facility. In the previous month, the health in charge of the facility had passed away and the HF was not operational at the time of the assessment
9
6.1. Stage 1: Routine monitoring data-identifying potential areas of low and high coverage.
In stage one 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. This analysis formed the basis of mapping locations that would be suitable to collect the qualitative data that would provide more information regarding factors affecting the CMAM program. The findings of the quantitative and qualitative data analysis is described in the following sections
6.1.1. Routine monitoring of Beneficiary cards and performance data
6.1.1.1. Program exits (discharge outcomes)
The trend of exit was plotted and smoothened with spans of median and average of three months (M3A3). The plots are presented in figure 4 below.
Figure 4: Exit trends for Damaturu LGA CMAM program-January 2013 to April 2014
Observation of the figure above shows that the Damaturu CMAM program did not performing optimally in the period January 2013 to April 2014. The recovery rate was seen to be below SPHERE standard of 75% throughout the period under review. Similarly, defaulter rate was also very high, and above the recommended 15% standard throughout same period. Only the death rate was lower than the recommended standard.
6.1.1.2. Admission trends
The figure 5 below shows the admission trend of Damaturu LGA with seasonal calendar of events.
Figure 5: Admission trends and the seasonal calendar of events
There are low admissions in the months of January and February often due to female labour demand in processing of grains that were harvested the previous 2-3 months. Towards the end of February (when the female labour gets less), number of admissions
10
increase steadily from March through April and peaks in May. One of the contributing factors of the increased admission during this period is reduced food at the household level. This is also the period when there is a steady increase in diarrhea22 episodes due to commencement of rainy season contributing to the incident SAM cases. The slight decline in admissions from June to August may attribute to the combination of predominant wedding activities that contributes to absenteeism during this period; flooding that restrict access to the health services, and female labour demand (in form of weeding of planted farm lands) as most of the population in Damaturu LGA are farmers. This engages more of caregivers’ time so that they are not able to make follow-up visits to the CMAM program as regularly as required. The peak in September could be attributed to reduced female labor demand which has an effect of creating time for caregivers to attend the CMAM programs. There is also, continued unavailability of food in household coupled with increased food prices and may contribute to reduced food availability at Household level and increased incident cases of SAM. Factors that may have majorly contributed to decline in admission in October include the stock-out of RUTF and the rise of insecurity incidents during this period in most areas in Yobe State. Case in point is the Sassawa health facility that was closed for more than 7 months due to consistent insurgency activities. This contributed to the conspicuous drop in number of the program admissions.
6.1.1.3. MUAC or oedema at admission
Figure 6: Admission MUAC for Damaturu LGA CMAM program
The median MUAC on admission was found to be 110mm. In an ideal CMAM program, children should be identified early enough when they slip into SAM condition (that is below 115mm). The plot indicates that a relatively large group of SAM cases (below 110mm) have stayed a while in the community before being identified and referred accordingly. This is a pointer to low or inconsistent active case finding in the LGA. Erroneous MUAC measurements (as seen in digit preference and heaping at 110mm) by the MUAC measurers during admission is suspected. It was also noted that, some clients were admitted with MUAC above 115mm. During the analysis, such MUAC were filtered out before analyzing for the median MUAC on admission. Some of the children with MUAC >= 115 mm were cases who presented with oedema on admission. Therefore, the data was triangulated and analyzed further to include cases with oedema on admission, and the result is presented in figure 7 below.
Figure 7: Triangulation of Oedema and MUAC at admission >=115mm
A total of 145 children had MUAC >=115mm. Out of 90 oedema cases presented on admission, only 41 had MUAC >= 115, with most of oedema cases coming from Damakasu and Dikumari HFs. Suspected erroneous admissions were noticed in all the CMAM sites, but they were prominent in Nayinawa, Murfakalam and Gambir HFs. The largest proportion of the cases admitted with MUAC of >=115mm in Damakasu and had Oedema on admission. However, this is not so in the other HFs such as Gabai, Gambir, Gwange, Kukareta Nayinawa and Nutrition & Diet (General Hospital) where cases of children admitted into the program with MUAC of >=115mm and no oedema. Given that the major criteria for admission are: 1) presence of oedema; 2) MUAC of <115mm and 3) Weight for height of < -3 Z-scores (of which 1 and 2 are done in the program), then the other cases would be suspected cases of poor MUAC measurement at the point of referral in the community as well as those at the point of admission. Analysis of the RUTF given on admission revealed a large proportion of SAM cases that were admitted received erroneous quantities when compared to the national CMAM guideline. This is illustrated in the table 2 below:
22 Diarrhoea diseases contribute burden of disease and are associated with high malnutrition
11
Table 2: The quantity and proportion of RUTF given on admission23
Parameter Count Proportion Given Less RUTF on admission 669 24.2% Given more RUTF on admission 513 18.6% Total children analyzed 2762 100.0%
6.1.1.4. Length of stay from admission to cure
Figure 8: Length of Stay in program before recovery
There is evidence of less than satisfactory CMAM program performance due to low recovery rates and high defaults rates (below the recommended 75% and above 15% SPHERE standards respectively). The median length of stay (LoS) (as shown in Figure 7) is plotted from all the discharged recovered children in the program with MUAC at exit was 125mm and above. Earlier analysis of MUACs on exit indicated that a significant proportion of the children discharged as recovered had often not attained a MUAC of 125mm or more, which pointed to a likely poor monitoring of SAM cases under treatment in the program before discharge. These proportions are illustrated in the figure 9 below:
Figure 9: proportion of the MUAC measurement of children discharged as recovered.
The plot (in figure 8) shows that most beneficiary stayed in the program and exited as recovered at 7 weeks. This is within the recommended 8 weeks for a good program24. These results are, however, treated with caution. Analysis of the discharge outcomes MUAC at admission reflect a poorly performing program and late admissions into the program respectively. This means that children admitted late into the program are likely to stay longer before recovering from SAM. The quality of the monitoring data was suspected to have errors and therefore, the short length of stay which is desirable for a good program and which this analysis shows may not reflect a true picture of the Damaturu LGA CMAM program. This is not to mention the inadequate amounts of RUTF give to a significant proportion of SAM cases. This would ideally, have an effect of increasing the episode of treatment in the program.
23 The prescription of RUTF (weekly) recommended in CMAM national guideline by the Federal Ministry of Health in Nigeria: 92g packet of RUTF that provides an average of 500kcals per packet is given according to child’s weight at a dose of 200cal/kg/day. 24 SPHERE standards recommend that SAM case is treated in a TFP program for 56 days (approximately 8 weeks)
12
6.1.1.5. Number of visits before default
Figure 10: Plot of number of visits before default
Analyses of the number of visits by the beneficiaries before default showed that significant number of the defaults occur early in the program, the median number of visits before default was found to be 3 visits. This indicated that a large proportion of the defaulters in the community are more likely to be current SAM cases25. This is confirmed by findings of analysis of MUAC of defaulters in figure 11 below. This indicates that SAM cases who should be in the program getting treatment go back to the community and remain un- covered. This analysis confirms the high defaulter rate as shown by the discharge outcomes.
Figure 11: Defaulter’s MUAC on exit
6.1.1.6. Time to travel to CMAM HFs- plot of distance to treatment centre
The beneficiary information on village names and time to travel to the HFs were noted to be either missing or erroneous (when the information was triangulated with that obtained from interview with local residents to check its reliability). Additionally, out of all the 2,762 cards analyzed, approximately half (1,469) had information on time to travel to the HFs. The information in the beneficiary cards was later compared with what was provided by the local residents, and was reconciled. The available information was analyzed to come up with the plots of time it took the beneficiaries to travel to HFs that are illustrated below.
Figure 12: Time to travel to CMAM site for current cases and defaulters-All CMAM sites
The plot indicated that most beneficiaries come from the villages within walking distance of 2 hours. There is a number of beneficiaries that came from distant communities. These beneficiaries (usually coming from adjacent LGAs of Buni Yadi in Yobe State and Maiduguri in Borno State) would often use motorized transport to access CMAM services. Such plots for individual CMAM facilities indicated that clients come from distant communities to access treatment in Damakasu HF and General Hospital (Nut & Diet) as illustrated in the figure 13 below.
25 Defaulters that have had below 4 visits are likely to be current cases of SAM. Refer to SQUEAC/SLEAC technical manual.
13
Figure 13: Time to travel to CMAM site-Individual plots of 10 CMAM sites assessed
6.1.1.7. Distribution of villages across the HFs providing CMAM services
The proportion of villages from which the beneficiaries come (when they are identified by their LGAs of origin) is an indicator of beneficiaries attending HF from other neighboring LGAs. Normally the beneficiaries attend the HF close to them or that which they feel offers them better service26. The distribution of attendance in Damaturu is illustrated in the figure 14 below.
Figure 14: distribution of villages served per health facility in Damaturu-< 2hours and more than 2 hours 6.1.2. Conclusion of the routine monitoring (beneficiary cards) analysis
The information obtained from the beneficiary cards allowed to identify potential areas of low coverage as well as factors that affect program and would need to be investigated further. For example:
• Time the beneficiaries use to travel to the HFs providing CMAM services gave an indication of the distant as well as the close settlements.
26 There is evidence of some health facilities which are preferred by beneficiaries compared to others.
14
• Defaulter information indicated the likely areas of low coverage. • The LoS indicated how long the beneficiaries’ episodes of treatment could be. The following section involved in-depth investigation of the factors affecting program and coverage.
6.2. Stage 1: Qualitative data-Investigation of factors affecting program and coverage.
6.2.1. Qualitative sampling Framework
In order to augment the information from the quantitative data analyses of the extracted client records, qualitative information on the CMAM program were solicited and obtained from different sources using diverse methods and sources and then triangulated. Eight out of 11 HFs providing CMAM services which were accessible in Damaturu LGA were visited27. At least three caregivers (who were randomly chosen), the HWs, and CVs in each HFs were interviewed. The choice of the communities (that is affiliated to any of the CMAM sites) visited and respective persons were interviewed based on 1), distances from CMAM site, that is, distant community and a community near to the CMAM site; 2) communities where most defaulters seemed to be coming from and 3) communities that were closer to areas where insecurity was unpredictable. Therefore, a total of sixteen communities were visited. In each communities, 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 using different methods to obtain information about the programme. As information comes to fore, it was identified or analyzed depending on the effect it had on the program (that is as a booster or a barrier to the program). Each piece of information was considered and verified if needed, then a different source with/or different method was used to seek further evidence in a process of triangulating information by source and methods. The questions that arose called for more investigation so as to put more clarity on the piece of information before it was finalized as a barrier or booster. Different methods and sources were thorough to a point where no new information could be obtained28. 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. Health seeking behaviors in the communities
Evidence from the previous SQUEAC in Damaturu LGA reported poor health seeking behavior in communities. The current assessment indicated some changes29 (compared against findings in the previous SQUEAC assessment) in health seeking behavior of caregivers of malnourished children in various communities, however, it is still mixed30. As such the pathway to seek health care is not very clear across the communities; while some respondents reported visiting health facility for treatment when their child gets sick, others said they visited traditional healers and patent medicine vendors before accessing the HFs. This means that some clients were admitted late in the HFs after the child has deteriorated due to their preference of visiting traditional healers. This was also collaborated by some low MUAC on admissions31 despite the fact that the CMAM program in Damaturu LGA is not a new program which is expected to be admitting mostly prevalent cases with low MUACs
6.2.2.2. Community Sensitization, mobilization and awareness of the program
There was evidence of community sensitization and mobilization activities done for selected categories (for instance dwelling on traditional leaders and hairdressers) of people in Damaturu LGA32. This includes village heads and religious leaders who are then expected to sensitize their various communities and heads of settlements under their jurisdiction. The responses of some traditional leaders, TBAs, Majalisa and religious leaders in some near and distant villages among the eight CMAM sites visited, indicated that they were not sensitized on the CMAM program. This was also the case for some lower echelon of village heads33. However, they reported they learnt about the program from caregivers of malnourished children who had benefited from the program and from other members of the community. It was evident that the communities were well aware about the CMAM programs as the program has been ongoing for about 3 years. It was also evident that the good perception of CMAM program seen in communities, is mostly due to the former beneficiaries from various communities who received the treatment earlier.). Nevertheless, the team witnessed a community mobilization meeting holding in Nayinawa health facility during one of their visit. The community mobilization and sensitization are in place, however, larger bracket of community actors need to be brought into the bracket of those being mobilized and sensitized. It was also evident that many caregivers withdraw their children from the program before being declared recovered/cured by health workers. Couple of reasons that may be attributed to this include: caregivers/mothers view their children as improved and no longer attend HF, some caregivers attend other tasks (mostly farming activities) or travel to attend community events (such as weddings) and skip or abandon follow-up visits to the programs . These are also, the factors that may contribute to high defaulting.
27 Three CMAM sites (Gabai, Sassawa and Gambir) could not be visited due to unpredictable security situation 28 The process of collection of the information is iterative and as such pieces of information are investigated repeatedly until no new information is forthcoming. 29 Some caregivers and community members reported that presently, they take malnourished and sick children to health facilities which was not reported in the previous SQUEAC. 30 Respondents gave both CMAM sites/health facilities and traditional healers as places where malnourished children are taken for treatment. 31 See figure 6 illustrating distribution plot of MUAC at admission 32 The ACF Programme Staff in charge of community mobilization and sensitization in Damaturu LGA 33 There are echelons of community leaders from the Emir, District head, ward head and then village head.
15
6.2.2.3. Community Volunteer (CV) activity and trainings
Most of the CVs interviewed in Nayinawa, Dinkumari, Kalalawa, Damakasu, Murfakalam, and Gwange HFs reported that they conduct active case-finding through house to house screening for SAM cases. However, it was noted that none of the caregivers reported being referred to the program by a CV. The analysis of the routine program data and information extracted from the beneficiary cards showed that default rate was quite high. Interviews with the CVs indicates that they hardly undertake defaulter tracing because they cannot afford transport costs to access distant places and trace defaulters. The CVs also mentioned that they feel hindered to work effectively. ACF program Staff mentioned that it is difficult to identify CVs for the Nutrition and Diet HF as the facility is situated in the central urban area of Damaturu where a CV might expect monetary support to enable their work. It was observed and confirmed that some CVs in Dinkumari, Nayinawa, Damakasu, and Murfakalam HFs are used to run all services except medical consultation for clients. This has an advantage of ensuring continuity of CMAM services. However, there are errors in monitoring beneficiaries suspected such as wrong admissions, misuse of RUTF by giving rations to healthy children & adults and assigning of MUAC & weight measurements to clients without proper measurements. (See analysis of MUAC at admission in section 6.1.1.3). During the visits of the health facility, it was also observed that some CVs at Nayinawa and Dinkumari HFs had difficulties to take MUAC measurements correctly. All the CVs interviewed, reported that they received trainings from ACF more than three times since inception of the CMAM program in June 2011. It was also found that the last training they received was less than six months prior to the SQUEAC investigation.
6.2.2.4. Community perception about the program and RUTF
Evidence showed that there is good opinion and good awareness about CMAM program in communities. Majority of the caregivers in the program mentioned that they were referred to the program by their peers (fellow women) in their communities who had benefited from the program earlier. However, it was found in various communities that RUTF is shared among the siblings of malnourished child. Older people in some communities mentioned that RUTF should also be given to the adults. During the SQUEAC investigations healthy children gathered in some communities around the investigators asking for jedta34. A hospital messenger, among the majalisa interview in Gwange, showed the team seven packets of RUTF in his possession. This raised suspicion among the investigators as it was not in the right hands and as he did not have SAM children admitted in the hospital where he worked. However, he declined to give further information to the team as other members of the majalisa signaled him to stop. Some spouses were also reported to have refused caregivers to attend HF for CMAM services in Kukareta Area35. Thus spouses thought that the RUTF caused the SAM cases under treatment to vomit after consuming it. They also, feared that the sick child will contract measles at the HF when they visit. Tamowa was found to be the general Hausa word for malnutrition. Datti used to describe bad breast milk believed to be produced by a lactating mother after intercourse, rana wasting, skinny or drying-up, iska-possession by evil spirit, while tsunburarre was used to refer to ‘shrinking and or dryness’ and Ciwon yunwa used for hunger in communities. Bajul (Fulani word for oedema), and tuhundi were also given as Fulani words for different types of malnutrition in Fulani communities. Kadawu (Kanuri word for wasting), Kumberi (oedema), Kinna (hunger), were used to refer to malnutrition in Kanuri language.
6.2.2.5. Activity of Health workers
The HWs were observed to be very motivated in fulfilling their duty. It was also evident that there is a cordial relationship between HWs and CVs. Also, the HWs were seen to have good attitude towards caregivers. Referrals from non-CMAM HFs were also evident. However, lack of enough HWs was observed in all HFs as the number of HWs per HF is generally low. Some HF had only one HW, Thus CVs were heavily engaged to run CMAM services offered weekly. Sometimes the HF can be closed if the only HW traveled. All the HWs interviewed reported that they have gone through several trainings on CMAM by ACF international36 in the previous six month period before this SQUEAC investigations. However, the HWs had difficulties to fill the information in the beneficiary cards. There were also examples where the National CMAM guidelines were not strictly followed (see barriers in another section of this report). Information obtained from the beneficiary cards showed that children were discharged as recovered with a MUAC less than 125 mm (see section on admission MUAC). This raised suspicion on authenticity of the weekly program data generated at the HF.
6.2.2.6. Generalized stock-out of routine drugs
Stock out of routine drugs has existed in the HFs for the periods of February and May 2014 which spanned 4 months. This information was obtained from observations and interviews done with HWs, LGA’s NFP, acting PHC Coordinator and ACF program staff. The buffer stocks of the routine drugs that the ACF’s nutrition program had procured to bridge the shortage of the drugs and the HFs were delayed by the government authorities at the point of entry during the clearing process before being released into the country. As such the shortage of the routine drugs persisted. As such, HWs had been asking the caregivers to purchase prescribed routine drugs from patented chemists. Some responses from the caregivers indicated that some would occasionally afford while some caregivers would not and therefore, give RUTF to their sick children without routine drugs in this period.
6.2.2.7. Stock-out of RUTF due to unsustainable supply chain management
A nationwide stock-out of RUTF occurred between October and November 2013 which also affect Yobe State. From their buffer stock, ACF provided significant support to the state with the supply of RUTF without interruption of CMAM treatment.
34 Local term of RUTF 35 Source of information: CV interviewed at Kukerata health facility. 36 ACF is an international organization that supports CMAM as well as other services in Damaturu
16
Even when the RUTF supply is stable at State level, often the HFs faced challenges with the regular supply due to the fact that there is no proper mechanism for transporting RUTF from state to LGAs and further to the HFs37. Often HWs and the traditional leaders engaged personal resources to transport RUTF from the LGA to their various HFs. The contribution by HWs and some community members was on going at the time of the SQUEAC investigation. Although this arrangement is seen in a good sense, it is, however, not sustainable as this is one of the major responsibilities that state has to fulfill to ensure the optimum treatment and save the lives of the malnourished children.
6.2.2.8. Insecurity in Damaturu LGA
Since November 2011, Damaturu LGA has been witnessing insurgency attacks by a group referred as Boko Haram in security circles. This insurgency necessitated the Federal and the State Government to impose movement restrictions (curfew) over the last two years. Moreover, in May 2012, a State of Emergency was declared by the President in Yobe State. Such situation overall affect the majority of the population to access the health services in general. The insecurity also led to fear of attack by insurgents or arrest by security forces as the caregivers travel to the HFs. The level of insecurity resulted in closure of Sassawa HF, situated very close to the areas with intense insecurity, since October 2013. There was also restricted visits for ACF team to Gabai and Gambir HF, affecting close supervision by ACF. Insecurity is strongly attributed to low active case-finding by CVs in these areas as well as low attendance by caregivers in program.
6.2.3. Data triangulation
Information in the SQUEAC investigation was obtained when SQUEAC tools38were 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 data that is obtained as analyzed as barriers and boosters and the relationship between them drawn to give a clearer “picture of the program coverage”. (See the activities that ensured in the investigation in annex 1). 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 a BBQ tool.
S/N SOURCE METHOD CODES39 1. Health Worker Semi Structured Interview/In depth interview A,C 2. Health Facility Visit’s Observations Observation study B 3. Community Volunteer (CV) Semi Structured Interview /In-depth Interview A,C 4. ACF Program Staff In-depth Interview C 5. Caregivers Semi Structured Interview A 6. Religious Leaders Semi Structured Interview A 7. Traditional Leaders Semi Structured Interview A 8. Traditional Birth Attendant Semi Structured Interview A 9. Patent Medicine Vendor Semi Structured Interview A 10. Traditional Healer Semi Structured Interview A 11. Community Health Extension Workers Semi Structured Interview A 12. Teachers Semi Structured Interview A 13. Majalisa Informal Group Discussion D 14. Nutrition Focal Person Damaturu LGA In-depth Interview C 15. PHC Co-ordinator Damaturu LGA In-depth Interview C 16. Women Group Informal Group Discussion D 17. Health Facility Cook Semi Structured Interview A 18. Routine Data Extraction E 19. Small Area Survey Semi Structured Interview A
Table 4: barriers, boosters & questions findings and sources of information
BARRIERS SOURCE BOOSTERS SOURCES
Health workers have been trained on Shortage of trained health workers in HFs 1A, 2B,14C,1C 1 CMAM severally to improve knowledge 1A, 1C and skills
There were areas with active case finding Inadequate seats/mats for caregivers at 1A,2B,3A by some CVs, through house-to-house the HFs (Nayinawa), leading to poor 2 1A,3A,3C screening. Few motivated CVs follow-up client flow and control on defaulters (Nayinawa)
Generalized stock-out of routine 1A, 2B, 14C, 1C, 3 Peer-to-peer & self-referrals was evident 1A,5A drugs(amoxicillin) 3C
37 interviews done to the ACF’s programme staff in Damaturu, the NFP of Damaturu LGA, the HWs in all the CMAM sites visited and one traditional leader in Damakasu community 38 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. 39 The code resonates with the method used, thus: A=Semi structured interview; B=Observation; C=In-depth interview; D=Informal Group Discussion (IGD); E=Data extraction
17
1A, 3A, 5A, 6A, Sharing of RUTF among healthy siblings by 1A, 2B, There was a good opinion of the CMAM 7A, caregivers and consumption of RUTF by 8A,9A,13D,14C 4 programme in most communities 8A,9A,10A,11A, adults 12A,13D,1C,3C
RUTF is used by CVs & health workers 2B, 9A, 13D, 1C, Passive referrals from health workers in inappropriately. For instance, It would be 7A 5 other primary health care services was 1A, 5A given as appreciation when CVs work at evident CMAM site40.
Some CVs do not know how to take 2B, 4C Good working relationship between MUAC 41 reading correctly, including 6 1A, 2B, 3A Health workers and CVs existed. those who assist in operating CMAM sites 42
Poor health seeking behavior by some 3A, 11A,8A, Motivated CVs that are used to run the 7 2B, 1C, 3C communities 10A,5A, 9A, 6A, HFs ensure continuity of services. 13D,7A
Training and retraining of CVs by ACF High defaulter rate and lack of 3A, 3C 8 contributed to their long stay in 3A, 3C mechanism to trace defaulters by CVs43. supporting the program
3A, 5A, 7A, Stock-out of RUTF at HFs 44. 3A,4C,14C,15C1 Good health seeking behavior by some 9 13D, 6A, 9A, C members of communities 8A, 10A, 16D
Un-decentralized (same category of 8A,10,7A,9A,6A, Good attitude of health workers towards people) community sensitization and 12A,13D,11A, 4C 10 5A, 2B caregivers mobilization leading to low awareness about CMAM program in some areas
Non-adherence to CMAM protocols 2B, 18E, 19A
a. Non-compliance with discharge criteria (discharge with MUAC There is good awareness of programme in 8A,7A,11A,13A, 11 <12.5cm) the community 9A,6A,10A Arbitrary assigning of MUAC measurements, evident by erratic MUAC measurements.
Personal contribution of community Poor support from Local government 14C, 15C leaders and Heath Workers to cost of council in terms of transportation of RUTF 12 transporting RUTF from LGA store to HFs 14C, 1C, 7A from LGA store to HFs and in procuring in some areas ensured beneficiaries get routine drugs (Virtually no mechanism is RUTF regularly in place)
Health workers share RUTF 13D, 10A indiscriminately in the community as 13 favor to healthy adults, among colleagues and CVs at the close of OTP days45
Insecurity leading to closure of HF 1C,4C,14C, 18E (Sassawa) for the last 7 months; resulting 14 to inaccessibility to carry out supervision, community mobilization, and active case finding in some areas.
Some caregivers do not understand how 1C, 18E, the programme works:
a. Withdrawal of caregivers before being declared by the HW as 15 recovered/cured. b. Caregivers travel to other areas and do not attend programme, resulting to defaulting of beneficiaries
40 HFs : Nainawa, Kalalawa, Kukarita and Dikumari; Nutrition & Diet General hospital 41 Mid Upper Arm Circumference 42 This points to lost opportunities in active case finding. 43 Some CVs had intimated that they would need monthly stipends and transportation as a motivation 44 Unsustainable RUTF supply chain between LGA headquarters and CMAM site was evident. There was RUTF stock out in period October.-November 2013) 45 45 HFs : Nayinawa, Kalalawa, Kukareta and Dikumari; Nutrition & Diet General hospital
18
Husbands refusal of spouse to return to 3C, programme after child vomits;
16 some caregivers are not attending CMAM programme as they allege that their children will contract measles at the HF (Kukareta)
Lack of CVs to support referrals of cases 1C,17C 17 at HFs (Nut. & Diet unit- General Hospital)
Lack of transportation fare for caregivers 1C,3C,4C who preferred Nut. & Diet unit- General 18 Hospital compared to the HFs which are closer to their communities
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 the information obtained from the field. Epigram software-version 1.1046 was used to draw the concept maps presented in the figures 15 & 16 in annex 2.
6.3. Stage 2: Confirmation of areas of high and low coverage.
Based on the information that was collected during stage 1 the coverage team critically looked at the information gathered in order to identify the possible factors that are affecting high or low (patchy) coverage in Damaturu LGA. 1. CV activity and insecurity were the major factors identified. Based on these two factors, the team formulated two hypothesis as follows Hypothesis 1: Coverage is classified as equal to or below 20%47 in areas that do not have active case finding by CVs. Coverage is classified as greater than 20% in areas with evidence of active case finding by CVs, and in the areas that have active CVs will be higher than 20%. To test this hypothesis two communities of Fulatari and Hausari (which are located in Damaturu Central) without CVs were identified, while two communities of Bullama Kurma and Pawari (in Gwange and Nayinawa ward respectively) with CVs were selected. Hypothesis 2: The coverage of the wards considered not to have security challenges have coverage classification >20%, while coverage wards considered to have security challenges will have coverage classification <= 20%. Two communities (Fulatari Isari and Dumbari) were selected in Sassawa ward where there is insecurity, while two communities (Kuareta and Murfa) with less insecurity in Kukareta and Murfakallam wards were selected respectively.
6.3.1. Study Type
Small area survey was used in each of the selected communities in order to test the formulated hypothesis.
6.3.2. Sampling Methodology
The villages were selected purposively based on the characteristics used in setting the hypothesis. In addition door-to-door screening was done in communities that are urban while active and adaptive case-finding was conducted in rural communities during the small area survey.
6.3.3. Case Definition
Severe Acute Malnutrition (SAM) is 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 HF or in hospital (Stabilization Centre) The status was verified when beneficiary showed the RUTF packets and/or ration Card to the investigator. SAM case not covered: Refers to a SAM case who was not enrolled in a CMAM program or the hospital (SC) at the time of this assessment. The case was also confirmed as not in the program when the beneficiary unable to show the RUTF packets and/or ration Card. Recovering case: A child (6-59months) with MUAC above 115 and was enrolled in a CMAM program at the time of the investigation. This case was verified when beneficiary showed the RUTF packets and/or ration Card.
6.3.4. Result of Small Area Survey
The results of small area survey are presented in the table 5 below
46 Epigram software was developed by Mark Myatt and is available on www.brixtonhealth.com 47 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.
19
Table 5: Simplified Lot Quality Assurance classification of small area survey results
Characteris Wards Location Total SAM Decision rule Covered(C) Not Recovering Coarse estimate48 tics under ) covered case (RC) investigati (NC) on
1st Hypothesis No CVs Damaturu Fulatari 4 1 3 1 20% available Central Hausari 1 0 1 0
With CVs Gwange Bullama 1 0 1 1 0% available Kurma
Nayinawa Pawari 0 0 0 0
2nd Hypothesis With Sassawa Fulatari 15 0 15 0 0% security Isari 5 challenge Dumbari 13 0 13 0
Without Kukareta Kukareta 13 6 7 6 46% insecurity challenge Murfakallam Murfa 0 0 0 0
Figure 15: 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: Hypothesis one:
• Areas with no CVs (some communities in Damaturu Central)
The number of SAM cases found that were covered was 1. The Decision rule calculated above is =1 Since 1 is not greater than 1, the coverage in the area with no community volunteers is classified as being below 20%.
• Areas with CVs
The number of SAM cases found that were covered was 0. The decision rule calculated was 0.
Since 0 is not greater than 0, the coverage in the surveyed area, is however, classified as greater than 20% (high coverage) for the reasons that are explicitly described below49.
Hypothesis two:
• Communities with security challenges
The number of covered cases was 0, the decision rule was 5. Since 0 is not greater than 5, the coverage in the surveyed area is classified as being below 20%.
• Communities without security challenges
49 See the section 6.3.5 on conclusion of the small area survey
20
From the table the number of covered cases found was 6, the decision rule is 2. Since 6 is greater than 2, the coverage in the area without security challenges is classified as being above 20%.
Hypothesis 2 was confirmed and accepted. Communities in areas without security challenges have higher coverage than areas with security challenges.
6.3.5. Conclusion of small area survey
Hypothesis one:
The results of the test of hypothesis one, if looked at literally (on the basis of the derived classification) may make the program seem not to have good potential coverage in areas where CVs have not been recruited. However, available data indicated that there were virtually no SAM case found during the small area survey (with just one case found). The area under investigation had incident cases identified early and referred to the program, hence very few SAM cases were found at the time of the assessment. This can be explained as good coverage as it indicates that the SAM cases are identified and admitted early into the CMAM program before they deteriorate. There are also few complicated cases and the cure rate for such cases50 is close to 100%. The treatment episodes for such cases will also be shorter. In this case, if the hypothesis is rejected, the program will be penalized when we consider these factors in the areas where the CVs are present.
The areas where there are no CVs had significantly high proportion of SAM cases who were not in the program compared to those in the program. Simplified LQAS results yields classification that is below the threshold and therefore low coverage compared to the areas with the CVs.
Hypothesis two
The comparison of the LQAS classification of communities without security challenges and that of with insecurity indicates potential disparity in coverage. The hypothesis tested positive for patchy coverage in Damaturu on the basis of security situation prevailing in the areas under investigation. Therefore, the security situation can affect coverage in Damaturu LGA.
6.4. Developing the prior. 6.4.1. Histogram of Belief.
The belief of the coverage team on what the program coverage could be was obtained. A histogram of beliefs was constructed with teams discussing and reaching consensus on what could be the most likely coverage, identifying the minimum and maximum possible coverage. The central tendency of the histogram on the coverage of beliefs was set at 45% with a minimum 20 % and maximum 70%. Prior 1: Histogram of belief = 45%
6.4.2. Concept Map
The Coverage 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. Team A concept map has a total of 18 barriers, and 12 boosters while team B had 22 barriers and 11 boosters. Further description is given below: Prior calculated from concept map Team A:
Team ‘A’ prior estimation=35%
Prior calculated from concept map B:
= 44 + (100− 88)/2= 28 Team ‘B’ prior estimation=28% Prior 2: Average prior calculated from concept map of Team ‘A’ and Team ‘B’ = 35+ 28)/2= 31.5
50 These cases are referred as the uncomplicated incident cases. These cases can be cured quickly and cheaply (see the SQUEAC technical manual)
21
Prior of developed by use of concept map =31.5% 6.4.3. Un-weighted barriers and boosters
The barriers and boosters were consolidated and refined. The maximum score was scaled so that neither the sum of positive scores nor the sum of the negative scores can exceed 100%. Thus: The number of negative scores was 18. Therefore;
To calculate the contribution of barriers and boosters to coverage:
= 60 + (100− 90 2= 35
Prior 3: Prior estimated from un-weighted barriers and boosters = 35%
6.4.4. Weighted barriers and boosters.
Team A and B were asked to assign weights to the barriers and boosters independently. The highest possible weight was 5 and the lowest possible weight was 1. Team members discussed extensively how they perceive each of the barrier or booster listed affect the CMAM program in Damaturu. A score is finally agreed upon and is assigned to each barrier or booster after discussion by team members. The table below shows that the weight assigned by the teams and average/final score given to the barriers and boosters. Table 6: Weighted barriers and boosters-Damaturu SQUEAC assessment
BOOSTERS Tea Team Ave BARRIERS Tea Tea Ave m A B rage m A m B rage
Scor Scor e e
Health workers have been trained on CMAM 5 3 4 Shortage of trained health 4 5 4.5 1 severally to improve knowledge and skills workers in HFs
There were areas with active case finding by 3 4 3.5 Inadequate seats/mats for 1 1 1 some CVs, through house-to-house caregivers at the HFs (Nayinawa), 2 screening. Few motivated CVs follow-up on leading to poor client flow and defaulters (Nayinawa, Murfakalam) control
Peer-to-peer & self-referrals was evident 5 5 5 Generalized stock-out of routine 5 5 5 drugs(amoxicillin) 3
There was a good opinion of the CMAM 5 5 5 Sharing of RUTF among healthy 1 3 2 4 programme in most communities siblings by caregivers and consumption of RUTF by adults
Passive referrals from health workers in 1 3 2 RUTF is used by CVs & health 1 4 2.5 other primary health care services was workers inappropriately. For 5 evident instance, It would be given as appreciation when CVs work at HFs.
Good working relationship between Health 3 5 4 Some CVs do not know how to 1 1 1 51 6 workers and CVs existed. take MUAC reading correctly, including those who assist in operating the CMAM program52
Motivated CVs that are used to run the HFs 2 4 3 Poor health seeking behavior by 3 3 3 7 ensure continuity of services. some communities
Training and retraining of CVs by ACF 5 3 4 High defaulter rate and lack of 3 5 4 8 contributed to their long stay in supporting mechanism to trace defaulters by the program CVs53 .
51 Mid Upper Arm Circumference 52 This points to lost opportunities in active case finding. 53 Some CVs had intimated that they would need monthly stipends and transportation as a motivation
22
Good health seeking behavior by some 3 3 3 Stock-out of RUTF at HFs54. 1 3 2 9 members of communities
Good attitude of health workers towards 3 5 4 Un-decentralized (same category 5 3 4 caregivers of people are mobilized over and again) community sensitization 10 and mobilization leading to low awareness about CMAM program in some areas
There is good awareness of programme in 3 4 3.5 Non-adherence to CMAM 5 5 5 the community protocols b. Non-compliance with discharge criteria (discharge with MUAC <12.5cm) 11 c. Arbitrary assigning of MUAC measurements, evident by erratic MUAC measurement.
Personal contribution of community leaders 5 3 4 Poor support from Local 5 5 5 and Heath Workers on cost of transporting government council in 12 RUTF from LGA store to HFs in some areas transportation of RUTF from LGA ensured beneficiaries get RUTF regularly store to HFs and in procurement of routine drugs
Health workers share RUTF 2 1 1.5 indiscriminately in the community 13 as favor to healthy adults, among colleagues and CVs at the close of OTP days
Insecurity leading to closure of a 5 4 4.5 HF (Sassawa CMAM site) for the previous 7 months; resulting to 14 inaccessibility to carry out supervision, community mobilization, and active case finding in some areas.
Some caregivers do not 2 2 2 understand how the programme works: c. Withdrawal of caregivers before being declared by the HW as 15 recovered/cured. d. Caregivers travel to other areas and do not attend programme, resulting to defaulting of beneficiaries
Husbands refusal of spouse to 2 1 1.5 return to programme after child vomits; 16 some caregivers are not attending CMAM programme as they allege that their children will contract measles at the HFs (Kukareta)
Lack of CVs to support referrals of 5 4 4.5 17 cases at hospitals (Nut. & Diet unit- General Hospital)
Lack of transportation fare for 1 1 1 caregivers who preferred Nut. & 18 Diet unit- General Hospital other than HFs closer to their communities
Total 45 Total 52 56 54
Therefore:
Prior 4: Prior estimate from weighted barriers and boosters = 45.5%
54 Unsustainable RUTF supply chain between LGA headquarters and CMAM site was evident. There was RUTF stock out in period October.-November 2013)
23
6.4.5. Prior estimate from recent SLEAC Assessment
Estimated prior from SLEAC 2013 report calculated from Damaturu LGA coverage classification where 95 SAM cases were found and 29 were covered by the program to arrive at estimate of 30.5% Therefore; Coarse estimate
Prior 5: Coarse estimate of recent SLEAC for Damaturu LGA = 30.5% 6.4.6. Triangulation of Prior
Prior estimate55 was then calculated by triangulation of all the prior estimates obtained from various methods. It is illustrated by figure 16 below. = 45+ 31.5+ 35+ 45.5+ 30.5/5= 37.5 Prior mode = 37.5%.
Figure 16: Illustration of triangulation of prior
6.4.7. Bayes Prior Plot and Shape Parameters
The lowest and highest possible coverage56 for Damaturu CMAM program was estimated by the team. It was believed that coverage in Damaturu LGA could not be lower than 20% and not higher than 70%. Then alpha (13.4) and beta prior (20.1) shaping parameters were calculated using the Bayesian SQUEAC calculator. This also resulted into a binomial 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.
Figure 17: BayesSQUEAC binomial distribution plot for prior showing the shape parameters and the suggested sample size.
55 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. 56 These are also referred as minimum probable value and maximum probable value for coverage.
24
6.5. Stage 3: Wide area survey
Information collected in stage 1 and 2 was used to develop a prior mode and its distribution. In stage 3 of the SQUEAC investigations, the likelihood survey was built into the Damaturu SQUEAC investigation to add to the existing information (analyzed in stage 1 & 2) to provide a headline coverage of the program. This was accomplished through the process of: 1) A likelihood survey 2) Developing a Posterior mode57or the overall coverage
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 Bayes calculator to be 54 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 54 SAM children aged 6-59 months was calculated using the formula below:
Table 7: Parameters for sample size calculation for likelihood survey
Parameters Value 1 SAM cases 54 2 N(Median population size of all ages) 400 3 Percentage of under-fives in the 18% population58 4 SAM prevalence59 2.0 (1.1-3.4%.CI; 95%)
The median population was preferred to average population in calculating the number of villages to be visited. This is because median population excluded extreme population values (such as 15000 and 40) found in the complete list of communities/settlement in Damaturu LGA. 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:
38 villages had to be visited so as to get a minimum of 54 SAM cases.
6.5.2. Quantitative sampling framework
Spatial systematic sampling was done using sampling frame of complete list of communities/settlements stratified by political wards. This list was obtained from the Damaturu Local Government. The list was used because a scaled map of Damaturu LGA could not be obtained.
A sampling interval was obtained by dividing the total number of villages by the number of villages to be visited in Damaturu LGA. The first village was randomly selected using Microsoft Excel RAND function for random numbers ranging between 1 and the sampling interval. A total of 38 villages were selected for wide area survey by adding the sampling interval to each preceding selection.
6.5.3. Case Finding Method
Both door-to-door and active and adaptive case-finding methods were used during the wide area survey. Active and adaptive case- finding was used in rural communities/settlements where key-informants can help in identifying SAM cases. Door-to-door case finding method was employed in some wards (with many communities/settlements) that are urban, where the use of key- informants for active and adaptive case-finding was not feasible. The case definition was a child: • Aged 6-59 months • With a MUAC less than 11.5 cm, and or • With bilateral pitting oedema
6.5.4. Qualitative data Framework
Each SAM case identified in the likelihood/wide area survey, who was not in the CMAM program60 were regarded as non-covered case. In such cases a questionnaire was administered to the caregiver 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 18 and table 8 below
57 See the description of process of developing prior mode, likelihood and posterior in methodology section 58 Source: National Bureau of Statistics 59 Severe Acute Malnutrition results of Mid Upper Arm Circumference (MUAC) for the SMART Nutrition survey of September 2012 were used. 60 As verified by show of RUTF or ration card by beneficiary
25
Figure 18: barriers to program access and uptake-wide area survey
Table 8: barriers to program access and uptake
Reasons Number HFs (CMAM site) closed 18 Not aware child is malnourished 6 Does not know about CMAM program 6 Does not understand how the program works 3 Caregiver could not give reason for non-attendance*61 3 Others*62 3 Prefers traditional healer 2 Far distance 2 Belief-first son should not be cared for (Badawai tribe) 1 Didn't enroll because husband's friend gives them RUTF 1 Husband refused 1
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 below. The disaggregated results are shown in table 9 below.
Table 9: Results of the Likelihood (wide area) survey
Parameter Values 1 Total SAM cases 62 2 SAM cases in the program 14 3 SAM cases not in the program 48 4 Recovering cases in the program 15
The likelihood was calculated using the following standard formula for point coverage:
Therefore:
Likelihood = 22.6%
61 *The caregivers who could not give reason for non-attendance (3) come from Sassawa area where insecurity is high. The team who had been trained to do active and adaptive case finding in the area has some degree of acceptance within the community and were able to do undertake small area and wide area surveys in the areas respectively. However, it was not able to be established why 3 caregivers would not give reasons for non-attendance. 62 *Caregiver was sick (1), Caregiver said she did not want to access the program (1) and caregiver was ashamed to attend the CMAM services (afraid of ridicule)
26
Table 10: disaggregated SAM cases per Ward found in the wide area survey
S/ Ward Settlements Total SAM SAM not Recovering Coarse Numbe No. of Urban N SAM Covered covered Estimate r of HFs CMAM /Rural Sites 1 DAMAKASSU Aisori 2 2 0 1 75% 1 1 Rural 2 Dapsa 2 1 1 1 3 Kolori 0 0 0 2 4 NJIWAJI/GWA New 0 0 0 0 0% 1 1 Urban NGE Foundation 5 Old Gwange 0 0 0 0 6 Bulabulin 1 0 1 1 Central 7 Bulama Modu 0 0 0 0 8 DAMATURU Low cost 0 0 0 0 50% 9 1 Urban CENTRAL estate 9 3 Bedroom GB 0 0 0 0 area 10 Bulamari block 2 1 1 0 11 Buhari estate 1 0 0 0 0 Bedroom 12 Buhari estate 2 0 0 0 0 Bedroom 13 M Bulamari 0 0 0 0 14 GAMBIR/MO Kafitulowa 0 0 0 0 - 1 1 Rural DURI Fulatari 15 Ngurduri 0 0 0 0 16 KALALLAWA/ Hujjobe 0 0 0 0 0% 3 2 Rural 17 GABBAI Ngellemoye 1 0 1 0 18 KUKARETA/W Alhajiri 0 0 0 0 0% 1 1 Rural 19 ARSALA Guwori 4 0 4 0 20 Ngubtori 3 0 3 0 21 Kachallari 1 0 1 0 22 MAISANDARI/ W I. Extension 1 0 1 0 30% 4 0 Urban W I North 23 3 Bed Room 2 1 1 0 South 24 Red Brecks 3 2 1 0 North 25 Sumsumma 4 0 4 1 Lawanti South 26 MURFAKALA Tsangaya 0 0 0 3 75% 2 2 Rural 27 M Dikumari B 4 3 1 4 28 NAYINAWA Tukuban 2 1 1 0 60% 1 1 Urban Akawu 29 Abbari By Pass 0 0 0 0 30 Anguwan 3 2 1 2 Makarbata 31 Sani Dara West 0 0 0 0 32 BINDIGARI/PA Makera 1 1 0 0 100% 2 0 Urban 33 WARI Police Barrack 0 0 0 0 34 T Z L D 0 0 0 0 35 SASAWA/KAB Sainnari 5 0 5 0 0% Rural 36 ARU Fulatari Baffa 7 0 7 0 37 Goni bintumiri 6 0 6 0 38 Kajiro Mari 8 0 8 0 62 14 48 15 Total
6.5.6. Posterior/Coverage Estimate The Bayes coverage estimate (posterior) of 28.2% (20.3% - 38.1%) was arrived at after combining the Prior and Likelihood in a conjugate analysis using the SQUEAC Coverage Estimate Calculator version 3.01 (see figure 19 below). 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) and therefore there is no prior- likelihood conflict 2) The posterior being narrower than the prior indicated that the likelihood survey had reduced uncertainty.
27
Figure 19: Bayes plot showing prior, likelihood and posterior
The point coverage of the program is therefore: Point coverage=28.2% (20.3% - 38.1%. CI; 95%)63 Point coverage was used to indicate the headline coverage of the CMAM program. It would not be ideal to calculate the period coverage for this program due to the following reasons: • There is poor retention from admission to cure of SAM cases in the program, with high defaulter rates consistently observed between periods January 2013 to April 2014. Analysis of routine data indicates defaulting mostly occurring at less than 4 weeks in the program indicating that the defaulting cases are likely current cases of SAM. Period coverage places strong emphasis of good retention of SAM cases from admission to cure. • The recovery rate has been below the recommended standard throughout the review period. (See figure 3). This indicates that the proportion of the number who have been treated to recovery was consistently lower than 75%64 • Poor adherence to CMAM protocol due to suspected erroneous case monitoring, stock-out of routine drugs & RUTF, and sharing of RUTF within household and communities strongly indicates that the Damaturu CMAM program is not effective. Poor adherence makes a SAM child stay longer in the program and may contribute to high defaulting and a potential to degrade the opinion of the program. Therefore point coverage would give the evaluation of program coverage at the point in time when the SQUEAC investigation was implemented. 7. Discussions The coverage estimate result of 28.2% (20.3-38.1%.CI; 95%) for Damaturu LGA was lower than 50.4% reported by the SQUEAC conducted in 2012 by ACF International. Some barriers that were identified in the 2012 SQUEAC assessment were more of less similar to those in 2014 SQUEAC assessment65, thus special emphasis has been placed on all the barriers in making relevant recommendations to improve the program. It is slightly lower than the coarse estimate of 30.5% unveiled in the Damaturu LGA’s recent SLEAC study completed in the February 2014. However, it is worth to mention that coarse estimate of the 11 political wards of Damaturu LGA varied significantly intra-ward. SAM cases were found in 10 out of the 11 wards of the LGA. Out of these 10 wards, half (5) had a coarse estimate of 50% and above. At the time of the investigation, the CMAM program in Damaturu LGA is largely affected by unpredictable security situation. Due to insurgency attacks, Damaturu LGA is presently under emergency rule with armed soldiers, policemen, and other security agents manning security checkpoints along many roads in the LGA. This emergency rule has lasted for about 2 years. Three out of the 11 existing HFs providing CMAM services, namely Gabai, Gambir and Sassawa are not presently accessible to ACF team for supportive supervision and monitoring due to insecurity. Out of these three, Sassawa was closed down since October 2013, about 7 months prior to the SQUEAC investigation. According to ACF program team, plans to resuscitate Sassawa HF was subdued after the death of the Officer-in-charge of the health facility in January 2014. More so, health workers posted to that facility are reluctant to resume duties there. Most caregivers in this area could not access the HFs in other places as they were afraid of being arrested by security operatives. It is important to note that, all the SAM cases found in Sassawa ward in the wide area survey were not covered by any CMAM program. The met needs of the Damaturu program area can be calculated as follows: Met need: Met need = Coverage x Median recovery rate = 28.8 x 0.652 = 18.77%
63 Results are expressed with a credible interval of 95%. 64 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. 65 See table 12 in Annex 9.6 for barriers in 2012 and 2014 SQUEAC reports.
28
8. Recommendations
A participatory recommendation session was held with stakeholders at the State and LGA levels. The following action points were collaboratively recommended to be taken in order to improve the CMAM program in Damaturu. The following recommendations were agreed upon by the participants to address the identified barriers to CMAM program. Ø The Director General YSPHCMB, Damaturu LGA Chairman and Commissioner of Health have agreed to increase the number of HWs in each facility implementing CMAM program in Damaturu to at least 5 HWs. The HWs should be retained at least for one year so that the benefits of the training that has been offered by partner percolate to the community in quality health care service. Ø State Social Mobilization Committee Chairman, to liaise with the State Nutrition Officer, and ACF program team to roll-out social mobilization plan for Damaturu LGA and Yobe State. This include but not limited to IEC materials, sponsored radio jingles and television drama (in Hausa and Kanuri Languages), community dialogues, reorientation of HWs and CVs on community sensitization and mobilization strategies. The Emirate Council to support the mobilization activities within the Damaturu Emirate through the involvement of the District, Ward and Village Heads. Ø Adequate seats to be provided in Nayinawa CMAM site by PHC coordinator of Damaturu LGA. Ø Partner agency to support in producing transport and tracking (for RUTF and drugs) mechanism to identify the gaps in routine drug supply in Damaturu LGA in liaison with SNO, NFP and Damaturu LGA PHC coordinator which would be presented to the State PHC Director. The PHC Director to facilitate provision of the routine drugs already identified above, under the obligation of the Director General YPHCMB and Damaturu LGA Chairman. Ø Implementation of the tracking mechanism of RUTF consumption through monitoring of the services and the providers and close M&E. Ø State Nutrition Officer to facilitate the training and retraining of HWs and CVs on CMAM in Damaturu LGA with funding from UNICEF. Ø Work with clients who have successfully completed the program to track defaulters properly and there is need to pay/motivate those that trace/track defaulters inform of co-operative66 or loan to enable transport themselves and work effectively. Ø Damaturu PHC Coordinator in liaison with the Nutrition Focal Person to put in place a mechanism for supply chain of RUTF and drugs, and to facilitate availability of funds by the LGA Chairman to manage supply chain mechanism and commodities from LGA to HFs. Ø The State Nutrition Officer to identify the best form of cooperative project that is preferred by the CVs which would be sponsored by State and used as a sustainable way motivate the CVs so that they can start generating funds within themselves instead of payment of stipends and incentives.
This recommendations were developed further into an action Framework in the table 11 below. Table 11: framework of action points to address barriers if Damaturu CMAM program
Main area of activity Processes Responsible Verification Outcomes/expectations Community mobilization and sensitization Community Mobilization Identify wards and Community Mobilization Number of communities and Increased programme and sensitization communities for town Officers and Senior wards to conduct town hall awareness in communities hall meetings District Heads meetings done
Town Hall meeting in State Social Mobilization Number of town hall meetings each ward with village Committee Chairman, held heads (Hakimi, Opinion Coordinator PHC,DPHC, leaders, religious leaders, and District Heads with and women leaders) Support from State, LGA and ACF(community Conduct mobilization officers) training/sensitization of Number of sensitization meetings religious leaders (Imams) Coordinator PHC,DPHC, held for sensitization of and District Heads with faithful during Jumaa Support from State, LGA Number of Religious leaders prayers and ACF(community trained/sensitized. mobilization officers) Develop Strategy to Radio jingles and dramas Social Mobilization Number of radio jingles and radio Increased understanding of communicate programme on community radio Committee Chairman, drama developed on CMAM how the programme works modalities stations about the CMAM SNO/State Health programme Educator/LNO Number times per month the Number of community leaders, jingle/drama is aired on Radio religious leaders, caregivers, who have knowledge about the program Dissemination of IEC SNO/State Health materials in Hausa, fulani Educator/LNO Number of people given IEC Increased community and Kanuri Languages materials in Hausa, Fulani and understanding of the Kanuri Languages programme
66 Such a proposal will originate from the state. The persons identified would be identified in relation to the work they have done in CMAM
29
Establish strategy to Identify individual State Social Mobilization Community mobilization and Improved opinion about the communicate knowledge community Committee, partner, LGA sensitization plan for LGA CVs programme. on malnutrition. definition/understanding PHC Coordinators and of malnutrition and NFP Improved opinion about the opinion of the program programme Recruitment of case finders and community persons to be volunteers-to begin with (NUT/DIET) to strengthening of active case-finding in Damaturu LGA/motivation and training of existing CVs Community/religious Identify religious leaders, District Heads and ACF List of potential religious leaders, Increase in SAM early leaders, TBAs and community leaders, TBAs, Community Mobilization community leaders, TBAs, enrollment. traditional healers. Traditional Healers, and Officers Traditional Healers, and Majalisa Majalisa mapped by with potential for indoctrination Increased knowledge about catchment population in the program. CMAM program in the who can volunteers on community. specific roles within the . Schedule of meeting/ Community. appointments with religious leaders
Key messages to be used on faithful at mosque Existing CVs Train existing community Coordinator PHC, DPHC, Number of new and existing CVs Referrals done by CVs tracked volunteers on MUAC NFP with support from trained thorough CV activity sheets measurements, LGA, State and partners.. community sensitization techniques etc.
Identify and map existing NFP, partner agency, Number of caregivers trained Number of referrals done by mother to mother Unicef sensitized on CMAM, who caregivers of MtMSG support group understands malnutrition and can identify and refer SAM cases Mother to mother support 67 Make/share existing key groups (MtMSG ) messages on community sensitization on malnutrition, CMAM program, identification and referral of SAM cases Carry out a pilot is Proposal made at CMAM List of mothers willing and Admissions from peer and self- recruitment and use of coordination forum at trained as model mothers. referrals increase. “Role model caregivers” “model mothers” as early federal level case finders and as sensitization avenue. Partners agency, NFP Indoctrinate into the List of private doctors willing to Increased recruitment of SAM program willing patent- directly cooperate with the cases Patent medicine vendors medicine dealers for program in referral. direct referral from their Short LOS clinics. Indoctrinate willing List of Traditional Healers willing Increased recruitment of SAM Traditional healers Traditional Healers for to directly cooperate with the cases direct referral from their programme in SAM referral clinics Identify a cooperative SNO/NFP and CVs List of cooperative community Increased motivation of CVs project that can be used projects identified for possible to motivate CVs funding Motivate CVs Sponsor the cooperative DG YSPHCB, LGA identified above for Chairman and partners. community volunteers to Number of cooperative generate funds community projects sponsored for CVs Service delivery Improve staff strength and Advocacy to relevant Director General YSPHCB, Number of new HWs recruitment Increased knowledge of CMAM quality of service delivery authorities for Commissioner of Health guidelines by HWs in health facilities recruitment of HWs to Yobe State. strengthen the staff Increased adherence to CMAM capacity protocol
Identify 55 HWs (5per HFs SNO/NFP and PHC Schedule training/refresher Increase in number of trained and/or catchment area) Coordinator training of HWs on CMAM HWs on CMAM for training/refresher training on CMAM.
SNO supported by List of HWs trained or that had Improved effectiveness of UNICEF refresher training service delivery
Monitoring and evaluation Joint Supportive Develop supportive SNO, partner , and NFP Supportive supervision work plan Improved programme serviced supervision supervisory work plan developed by State, ACF and delivery quality and tools for State, LGA Damaturu LGA and ACF
Share and harmonize
67 Mother to mother support groups are formed to bring information on infant and young child feeding closer to the caregivers. The groups are formed with objective to offer training, counselling, practice and follow-up on the appropriate IYCF practices. The groups are ideal as vehicles for sensitizing communities where they exist on CMAM program, malnutrition and referral of SAM cases.
30
supervisory work plan
Use of SQUEAC tools Produce SQUEAC Partner agency, NFP and Existing mapping of where Addressed barriers and analytical tools of: plots SNO beneficiary come from boosters to travel to site, admission MUAC, CV Mapping of CV activity and Improved program coverage activity reports/mapping corresponding outputs etc. RUTF and routine drugs stocks pipeline Strengthen Supply Chain of Provision of funds for PHC Coordinator/NFP Amount dedicated to supply of RUTF stock within HF RUTF and drugs transportation to RUTF monthly by the LGA strengthen supply chain of RUTF by LGA Chairman Director General/Director PHC YSPHCB and LGA Establish mechanism for Supply of routine drugs to stock delivery, tracking Chairman through the CMAM sites. and reporting LGA Coordinator PHC and Stocks records showing routine NFP drugs within state’s and LGA’s Number of stock outs per store quarter
31
9. Annexure
9.1. Annex1: Schedule of implemented activities in Damaturu SQUEAC. Dates Villages visited HF Sources of information 13th-14th May GAAT Hotel Training of Training of Enumerators, SMOH/LGA staff Enumerators, SMOH/LGA staff 15/5/ 2014 Nayinawa HF, Nayinawa HF, 2 religious leaders (one in each village) near 1(Fawari) <1km to HF 2 community leaders far 1(Nayinawa tsaleke) 1 Majalisa (40 to 60 years) >=2km to HF; 4care-giver 1 teacher 2 patent medicine vendors 2 traditional birth attendants 1 community health worker 1 traditional healer 16/5/2014 Kalalawa HF Kalalawa HF 2 religious leaders near 1(Kallalawa) <1km to 2 community leaders HF, majalisa (40 to 60 years) far 1(dapchi) 75km to HF; 2 patent medicine vendors 3 care -givers 2 traditional birth attendants 2 community health workers 2 traditional healers 19/5/2014 Gwange HF and Dimkumari Gwange HF 2 religious leaders HF Dimkumari HF 2 community leaders Gwange HF majalisa (38 to 50 years) near 1(Masalachin kuruna) 5 care-givers <1km to HF, 2 patent medicine vendors far 1(Tsumtsumma) >=10km 2 traditional birth attendants to HF 2 community health workers Dimkumari HF 2 traditional healers near 1(Angwan Amina) <1km to HF, far 1(Angwan Kuka) >=30km to HF 20/5/2014 Nutrition & Diet unit Nutrition & Diet 2 religious leaders (General hospital Damaturu) unit- General 2 community leaders near 1(Ganye uku) <4km to Hospital Damaturu 4 Majalisa group (40 to 60 years) HF, 3 carers far 1(Kartako) >20km to HF 1 traditional healers 2 patent medicine vendors 2 traditional birth attendants 1 community health workers 1 traditional healer 21/5/2014 Damakasu dispensary Damakasu 2 religious leaders near 1(Dapsa) <2km to HF, dispensary 2 community leaders far 1(Aisori) >15km to HF 4 Majalisa group (40 to 60 years) 3 carers Murfakalam HF 1 traditional healers near 1(Murfa) <2km to HF, 2 patent medicine vendors far 1(Dungurum) >8km to HF Murfakalam HF 2 traditional birth attendants Kukerata HF 1 community health worker near 1(Kimeri) <0.5km to HF, 1 traditional healer far 1(Warsala) >3km to HF 1 provision shop seller Kukerata HF 26/5/2014 Damaturu Central Gwange HF Small area survey Hausari Fulatari 26/5/2014 Gwange Gwange HF Small area survey Bulama kuruna Nayinawa Nayinawa HF Fawari 27/5/2014 Murfakalam Murfakalam HF Small area survey murfa Kukareta HF Kukareta Kukareta 30/5/2014 Sassawa Sassawa HF Small area survey Fulatari isari & Duablin
Damaturu Central Low cost estate, M. Bulameri, Malari B, GRA 3 bed roam, Bulamari, Buhari Estate 1 & 2 bed room Wide area survey
Njiwaji/Gwange Gwange HF New foundation, old Gwange, Nayinawa HF Bulabulin, central, Bulama Nut & Diet unit modu,
Nayinawa Tukuban Akawu, Motter cat, Daki tara, Abbari bye-pass, Angwa makar buta
32
Pindigari/Pawari Sani dara west, Makera, Police barrack, T22LD 31/5/2014 Damakasu/Moduri Damakasu HF Wide area survey Modu kemeri, Amatari, Anguwar, Kassai, Bulama, Zarama Murfakalam HF
Murfakalam Tsangaya, Dikumari, Kerel, Kukareta HF
Kukareta Alhajiri, Dabugu, Kachallari 02/6/2014 Kallalawa Kallalawa HF Wide area survey Sindir Ngelluwa
9.2. Annex2: Parameters used in prior building and sample size calculation.
Parameters Values Parameters continued Values
Total barriers 18 Prior calculated from belief histogram, un-weighted barriers and 37.5% boosters and weighted barriers and boosters and SLEAC results etc. Total Boosters 12 Maximum probable value 70% Belief histogram 45% Minimum probable value 20% Concept map 31.5% Min –Prior(proportion) 0.2 Coarse estimate (SLEAC) 30.5% Mode –Prior (proportion) 0.375 Un-weighted barriers and boosters 35.0% Posterior estimate Precision 0.1 Prior by weighted barriers and boosters68 45.5% Median Village Population 400 Not (δ) 0.0833 % of 6-59 Months 18% Mu(μ) 0.4 SAM Prevalence 2.0% Alpha prior (α) 13.424 Sample Size 54 Beta prior (β) 20.136
Number Of Villages 38
68 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.
33
9.3. Annex3: Concept maps-Team A and B. Figure 20: Concept map showing the relationship between factors affecting CMAM program and coverage-Team A
Shortage of H/ W trained on CMAM
contributes to lead to leads to
Insecurity
lead to Closure of Sasawa OTP Health workers share RUTF indiscriminately.
increases Lack of transportation
lead to Encourages Good working relationship b/w Poor tracing of defaulters Community Volunteer and Health Workers reduces gives room to
Lack of CV (nut and diet)
leads to Husband refusal promote
leads to Consumption of RUTF by adults contributes to Distance encourages
HW trained on CMAM severally. CVs clamouring for Incentives.
Stock out of routine drugs leads to reduces increases
leads inhibites reduces
poor support of LGA increases Misuse of RUTF leads to Active Case finding
reduces promotes leads to leads leads COVERAGE
affects carers dont understand how the hinders Low community sensitization programme works leads to encourages
promotes
affects increases affects leads leads to promotes
Lenght of Stay
Poor health seeking behaviour prolongs DEFAULTERS CVs trained and retrained on CMAM
promotes Motivated CVs used to run CMAM reduces site promotes
leads to prolongs
Early admission promotes Good health seeking behaviour
reduces / inhibits
Low awarenes of CMAM programme
promotes promotes promotes Community leaders and H/ Ws uses their Reduces money to convey RUTF to CMAM sir=tes.
encourages
Good opinion of the programme. promotes makes Good awareness of programme.
reduces
wrong admission encouraged lack of supply chain for supply of RUTF to 0TP sites
leads to leads to hinders promotes promotes
Arbitrary assigning of MUAC leads to
leads to leads to
Peer to peer refferral
encourages wrong discharge Good attitude of H/ W toward carriers Non adherence to CMAM protocols enhances
Figure 21: Concept map showing the relationship between factors affecting CMAM program and coverage-Team B
34
35
9.4. Annex4: Active and adaptive case finding procedure69.
Visit the community gathering place first and seek permission to visit the village.
Request the village leader to provide a key informant of choice.
Ask the key informant the case finding question ‘can you show us child who is under-five years and is ‘Tamowa, Datti, rana (wasting, skiny or drying-up), iska (possession by evil spirit), and tsunburarre (Hausa)-‘shrinking and or dryness’ & Ciwon yunwa (Hausa word for hunger) Bajul (Fulani word for oedema), and tuhundi- Fulani words for different types of
Check the child is aged between 6 and 59 months
Explain the purpose of the survey to the parents and what you will do Measure the MUAC of the child
Does the child have bilateral Oedema or is the MUAC < 115mm?
Current SAM case Not a Current SAM case
Is the child in HF? Is the child in HF?
Ask to see sachet of RUTF and Ask to see sachet of RUTF and health card health card
Current SAM case not in the Recovering SAM case Current SAM case in Normal child, No history program the program of SAM 1. Fill out the tally
1. Fill out the tally sheet sheet 1. Fill out the tally 1. Not included in the 2. Apply questionnaire 2. Thank the caregiver sheet study.
3. Refer the child to 3. Ask case finding 2. Thank the caregiver 2. Thank the caregiver CMAM program site question 3. Ask case finding 3. Ask case finding
4. Thank the caregiver question question 5. Ask case finding
question
Use additional sources or other key informants to inform and improve the search Always ask parents of the SAM children identified whether they know of other cases
69 Local terms of malnutrition used are from Damaturu LGA in Yobe state, Northern Nigeria.
36
9.5. Annex5: List of participants in Damaturu SQUEAC assessment S/N NAME OF PARTICIPANTS ORGANIZATION DESIGNATION 1 JOSEPH NGANGA ACF PROGRAM MANAGER 2 IFEANYI MADUANUSI ACF DEPUTY PROGRAM MANAGER 3 ABUBAKAR CHIROMA KAWU ACF PROGRAM SUPPORT ASSSISTANT 4 ZULAI ABDULMALIK ACF COVERAGE OFFICER 5 ADEGBOLA JANET ADEOYE ACF COVERAGE OFFICER 6 CHIKA OBINWA ACF COVERAGE OFFICER 7 FRANCIS OGUM ACF COVERAGE OFFICER 8 IDRIS ADAMU ACF DRIVER 9 YUSUF MUSTAPHA ACF DRIVER 10 FATI MUSTAPHA DAMATURU LGA NUTRITIONAL FOCAL PERSON 11 YABAWA ABBA YBSMOH DEPUTY STATE NUTRITION OFFICER 12 USMAN ADAMS YBSMOH ASSISTANT DIRECTOR PHC (NUTRITION) 13 AISHA ABDU SALLE YBSMOH ACTING PHC COORDINATOR 14 FANNA MUSA ACF ENUMERATOR 15 ALHAJI YARO MUSA ACF ENUMERATOR 16 AUWALU GARBA ACF ENUMERATOR 17 AISHA SULAIMAN ACF ENUMERATOR 18 MARYAM ZANNA ACF ENUMERATOR 19 LABARA MUSTAPHA ACF ENUMERATOR 20 JUMMAI BARDE ACF ENUMERATOR 21 HAMMAN USMAN ACF ENUMERATOR 22 BABA ALI MUSTAPHA ACF ENUMERATOR (SASAWA) 23 MUHAMMAD AHMAD ACF ENUMERATOR (SASAWA) 24 FATIMA MUSTAPHA ACF ENUMERATOR (SASAWA) 25 YAGANA NGAMA ACF ENUMERATOR (SASAWA)
37
9.6. Annex6: Comparison of the May 2012 and May 2014 SQUEAC findings (Barriers) in Damaturu LGA Table 12: Barriers in 2012 and 2014 SQUEAC assessments 2012 SQUEAC 2014 SQUEAC Barriers Barriers 1 Defaulting high High defaulter rate and lack of mechanism to trace defaulters by CVs70 . 2 Insecurity Insecurity leading to closure of a HF (Sassawa CMAM site) for the previous 7 months; resulting to inaccessibility to carry out supervision, community mobilization, and active case finding in some areas. 3 Lack of communication on how CMAM works Some caregivers do not understand how the programme works: a. Withdrawal of caregivers before being declared by the HW as recovered/cured. b. Caregivers travel to other areas and do not attend programme, resulting to defaulting of beneficiaries 4 Stock outs Generalized stock-out of routine drugs(amoxicillin) Stock-out of RUTF at HFs71. 5 Limited geographic distribution of treatment Results of the small area survey depicts heterogeneity of coverage. 6 CVs expectation for payment RUTF is used by CVs & health workers inappropriately. For instance, It would be given as appreciation when CVs work at HFs. 7 CVs lack of precision with MUAC (leading to Some CVs do not know how to take MUAC72 reading correctly, including rejection) those who assist in operating the CMAM program73 8 Lack of CVs in Nut and Dietetic area Lack of CVs to support referrals of cases at hospitals (Nut. & Diet unit- General Hospital) 9 Long distance/transport cost Lack of transportation fare for caregivers who preferred Nut. & Diet unit- General Hospital other than HFs closer to their communities 10 Understanding the importance/use of RUTF Sharing of RUTF among healthy siblings by caregivers and consumption of RUTF by adults RUTF is used by CVs & health workers inappropriately. For instance, It would be given as appreciation when CVs work at HFs. Health workers share RUTF indiscriminately in the community as favor to healthy adults, among colleagues and CVs at the close of OTP days 11 Father not allowing child to attend treatment Husbands refusal of spouse to return to programme after child vomits; some caregivers are not attending CMAM programme as they allege that their children will contract measles at the HFs (Kukareta) 12 Inadequate awareness about malnutrition Poor health seeking behavior by some communities 13 Lack of sensitization of all key stakeholders Un-decentralized (same category of people are mobilized over and again) community sensitization and mobilization leading to low awareness about CMAM program in some areas
70 Some CVs had intimated that they would need monthly stipends and transportation as a motivation 71 Unsustainable RUTF supply chain between LGA headquarters and CMAM site was evident. There was RUTF stock out in period October.-November 2013) 72 Mid Upper Arm Circumference 73 This points to lost opportunities in active case finding.
38