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Semi-Quantitative Evaluation of Access & Coverage
(SQUEAC)
Twic County
Warrap State
Republic of South Sudan
November 2012
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ACRONYMS
ACF ---------- Action Against Hunger
CMAM ------ Community Management of Acute Malnutrition
CSAS --------- Centric Systematic Area Sampling
CSB-----------Corn Soy Blend
CNV ---------- Community Nutrition Volunteer
CNW --------- Community Nutrition Worker
EPI----------- Extended Programme for Immunization
GMP--------- Growth Monitoring Programme
IYCF ---------- Infant and Young Child Feeding
MAM-------- Moderate Acute Malnutrition
MUAC ------- Mid Upper Arm Circumference
OTP ---------- Outpatient Therapeutic Programme
PHCC -------- Primary Health Care Centre
SAM --------- Severe Acute Malnutrition
SC ------------ Stabilisation Centre
SMART ------ Standardized Monitoring and Assessment of Relief and Transition
SQUEAC ---- Semi QUantitative Evaluation of Access and Coverage
SD ------------ Standard Deviation
SSRRC ------ South Sudan Relief Rehabilitation Commission
RTI-----------Respiratory Tract Infection
RUTF -------- Ready to use Therapeutic Food
TFP ----------- Therapeutic Feeding Programme
U5 -----------Under Five
WHZ-------- Weight for Height Z-score
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ACKNOWLEDGEMENTS
Action Against Hunger (ACF) South Sudan mission would like to express its deep gratitude to the Common
Humanitarian Fund (CHF) for funding the 2012 nutrition coverage investigation conducted in Twic County in
November 2012.
A special thanks to the SSRRC and County Health Manager of Twic County for providing vital information on
the demographic and geographical information, participation and support during the investigations.
The team would like to thank the ACF nutrition programme in Wunrok for active participation and provision of
community nutrition workers in the building of the Prior and particularly the ACF support team for the
logistical facilitation.
Finally, the team would also wish to thank the investigation enumerators, community leaders and individual
families who pleasantly allowed the investigation team members to assess their children and provided the
investigation team with the information required that made this exercise a success.
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EXECUTIVE SUMMARY
The Twic County ACF nutrition programme coverage investigation using Semi QUantitave Evaluation of Access
and Coverage (SQUEAC) methodology was conducted from Nov 9th – Nov 20th 2012. The objectives of the
investigation were:
1. To estimate the overall coverage of ACF Therapeutic Feeding Programme (TFP) in Twic 2. To identify boosters and barriers to access and uptake of the Community Management Acute
Malnutrition (CMAM) services provided by the ACF nutrition treatment programme in Twic 3. To provide key recommendations for strengthening ACF nutrition treatment services in improving
quality and coverage of the CMAM programme
The evaluation used a simplified version of the standard, 3-stage; Bayesian beta-to-binomial conjugate
analysis. CMAM coverage in Twic County was estimated to be:
Point Coverage1: 36.7% (27.5% - 47.0%)
Period Coverage2: 59.7% (51.6% - 67.3%).
In December 2011 ACF implemented a SQUEAC investigation in Twic. Following the recommendations, ACF
increased screening for children under-5 using the village mapping. Also additional Community Nutrition
Workers (CNW) were recruited to strengthen the Outpatient Therapeutic Programme (OTP) implementation.
Finally weekly community mobilization was implemented by the Community Nutrition Volunteers (CNV),
Community Mobilizers and Community Nutrition Workers
The point coverage found during the November 2012 SQUEAC investigation was slightly higher than the point
coverage (27.3% (19.7% - 36.6%)) found during the SQUEAC investigation in December 2011. Barriers to higher
coverage will be discussed at length in the report.
Point coverage still was found to be relatively low (< 50%). The
SQUEAC investigation confirmed that areas within 2 hours walk from
TFP services show a high coverage (> 50%) and areas further than 2
hours walk show a low coverage. This implies that coverage across the
programme area is patchy and NOT homogenous, and that far away
areas are likely to have low coverage. Main barriers found included:
poor awareness on malnutrition and treatment, supply shortages,
Outpatient Therapeutic Programme (OTP) service provision (including
rejection of children as they don’t meet the admission criteria),
distance and physical barriers to the OTP, sub-optimal community
mobilization in far away villages, and lastly stigma.
The SQUEAC investigation recommends the following: increase
awareness on CMAM services provided, continue screening and case
finding, increase efforts on defaulter tracing and mapping of defaulters, increase on-the-job training for
1 Point coverage: this estimator uses data for current cases only. It provides a snapshot of service performance and places a strong emphasis on the timeliness of case finding and recruitment. 2 Period Coverage: this estimator uses data for both current and recovering cases. Recovering cases are children that should be receiving treatment because they have not yet met discharge criteria.
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Community Nutrition Workers (CNW), develop uniform nutrition education messages, continue health &
nutrition education at the community level, involve traditional health practitioners and community leaders in
the sensitization on malnutrition and CMAM services, continue additional mobile OTPs in areas with high
prevalence of malnutrition, integrate CMAM services in the health facilities and improve supply chain
management in collaboration with UNICEF.
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CONTENTS
1. BACKGROUND ................................................................................................................................... 7
2. STAGE ONE...................................................................................................................................... 10
2.1 Admission by service delivery unit (OTP) ................................................................................... 11
2.2 Admissions vs. Needs ................................................................................................................. 12
2.3 Spatial coverage of Admissions .................................................................................................. 13
2.4 MUAC at admission .................................................................................................................... 13
2.5 Qualitative Data ......................................................................................................................... 19
2.5.1 Synthesis of Quantitative and Qualitative Data ................................................................... 19
2.5.2 Understanding of Malnutrition ............................................................................................ 19
2.5.3 Health Seeking Behavior ...................................................................................................... 20
2.5.4 Barriers to Access ................................................................................................................. 20
2.6. Areas of High and Low Coverage ......................................................................................... 22
3. STAGE TWO ..................................................................................................................................... 22
3.1 Active and adaptive case finding ................................................................................................ 22
3.2 The definition of a case .............................................................................................................. 22
3.2.1 High coverage areas ............................................................................................................. 23
3.2.2 Low coverage areas .............................................................................................................. 23
4. STAGE THREE .................................................................................................................................. 24
4.1 Developing a Prior ...................................................................................................................... 24
4.2 Sampling Methodology .............................................................................................................. 25
4.2.1 Minimum Sample Size .......................................................................................................... 25
4.2.2 Minimum number of villages ............................................................................................... 26
4.2.3 Spatial Representation ......................................................................................................... 26
4.3 Wide Area Survey Results .......................................................................................................... 27
4.4 Overall Coverage Estimation ...................................................................................................... 27
5. CONCLUSIONS & RECOMMENDATIONS ......................................................................................... 28
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1. BACKGROUND
Twic County is one (1) of the counties in Warrap State in Southern Sudan. The county consists of six (6)
administrative payams; Wunrok, Mayen Abun, Turalei, Aweng, Akochthon and Ajak Kuac (Figure 1). Twic
County borders Gogrial West, Unity State and Abyei region. Twic county has two (2) main livelihood zones
namely agro-pastoral and mixed farming.
During the fifth population census in 2008, the population of the county was estimated at 204,905.
Considering an annual population growth of 2.05% in 2011 and additional returnees of 13,592 persons brings
the total population in 2012 to 235,299 persons. The multi-indicator survey conducted by GOAL South Sudan in
Twic county in April 2012 showed that the Global Acute Malnutrition (GAM) and Severe Acute Malnutrition
(SAM) rates were 32.0 %( 26.8-37.6) and 7.5 %( 4.9-11.2).
ACF has been working in Warrap state since 2007. ACF is implementing integrated nutrition, food security &
livelihoods and WASH activities in Twic County to address the chronic emergency situation. The activities aim
to save the lives of children, men and women threatened by hunger and disease in Warrap State. The
Therapeutic Feeding Programme admits and treats severely malnourished children under-5 years in 6
Outpatient Therapeutic Programme (OTP) sites and those with medical complications and/or no appetite at
Stabilisation Centre (SC) level.
Figure 1: Map of ACF operational sites in Twic County
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ACF conducted a coverage investigation in December 2011 and estimated the point coverage to be 27.3%
(19.7% - 36.6%) and recommended actions to be implemented. The table below shows the recommendations
made in the 2011 coverage investigation and the activities put in place as a response.
Table 1: Recommendations and implementation of activities following the SQUEAC of December 2011
Recommendations December 2011 SQUEAC investigation Activities implemented based on the recommendations
Increase screening activities and rapid assessments in the area to monitor the nutrition situation of the County
As recommended, the actual screened number of children increased. This happened as the result of implementing screenings using the village mapping, more involvement of CNVs and increased number of CNWs working in comparatively high case load areas.
Put in place contingency measures such as creating mobile OTPs during the high influx periods
During the coverage survey, it was anticipated that there would be a need to respond with more OTPs, as there were predictions of more returnees to the country. However, in reality the border was blocked and few returnees arrived in Twic. Monitoring of the situation using admission data in OTPs also indicated that the situation was not as alarming as predicted. Hence, there was no need to respond with additional mobile OTPs. On the contrary, one mobile OTP (Anyiel Kuac) was closed due to significant reductions in admissions.
Prepositioning of supplies and increased remote management especially in times of increased insecurity for some identified OTPs
In 2012, the programme included a plan for prepositioning of supplies. Accordingly, ACF managed to procure 1000 cartons of Ready to Use Therapeutic Food (RUTF) as reserve stock (on top of the pipeline supplies from UNICEF). Wunrok received 300 cartons. However, the supplies arrived during the peak admission season and it did not last for more than one month. Due to the constant shortages of RUTF, the reserve could not be replenished.
Need for synergy between community mobilization officer, CNWs and CNVs; this link needs strengthening and will be able to improve awareness.
In 2012, weekly community mobilization was done to strengthen the link between CNWs, CNVs and community mobilizers. The community link was strengthened by introducing CNV incentives, more frequent refresher trainings, and requiring that the CNVs come to the respective OTPs during the screening day.
Mapping of all villages per OTP catchment area and plan for community mobilization, follow up and spot check to be conducted by the community mobilizers in areas that have been mobilized, and identify villages that have not yet been visited.
Mapping of all villages in a 10km radius was completed (although needs further refinement). The mapping was used by the community mobilizers to plan and execute mobilization. It is also a tool that describes which villages were reached and which ones remain.
Volunteers need to be spread across the catchment area and thus selection of volunteers from villages near the OTP should be reviewed.
There are 15 active CNVs in each OTP catchment area. They are linked with the villages. The locations of some CNVs were very close to one another while others were far apart. The programmes do have frequent review of CNVs.
Standardizing and sustaining the motivation strategy for volunteers through:
- CNVs motivation and incentives standardized across all CNVs and OTPs
- Refresher trainings - Provision of incentives e.g. t-shirts, caps,
raincoats etc. - Transportation during mobilization days
The CNVs ration from the World Food Programme (WFP) that was planned to motivate CNVs failed. WFP did not provide the CNV rations. ACF used in-kind support for motivation (gumboots, t-shirts and a small transportation reimbursement). Provision of refresher trainings were also used as incentives for the CNVs
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Need to ensure constant supply of the commodity ACF tried to do everything possible to ensure a constant flow of supplies through early requisition, providing reminders and even paying the cost of transportation of RUTF from the central UNICEF store to the ACF base. In some serious cases, discussions were held with UNICEF and WFP to improve supply delivery.
Uniform nutrition education messages for improving knowledge of community members on malnutrition
In 2012 the nutrition education messages were developed to be uniform by making the UNICEF counselling cards available in all OTPs and SCs. In addition, trainings were also provided. Recently, we have also ordered different sizes of the messages (laminated) to be used by ACF staff and even smaller sizes for the CNVs.
Increased on-job training for CNWs by the OTP supervisor, increased contact with the programme officer and manager.
The OTP supervisor is required to spend more time in the OTPs to provide on-the-job training to the CNWs, while the Targeted Supplementary Feeding Programme (TSFP) and community mobilization teams are functioning in other sites (i.e. they are using a single vehicle to return to the base)
In 2012, ACF planned a second SQUEAC investigation to follow up from last year’s investigation. The Twic
coverage investigation using the SQUEAC methodology was conducted from Nov 9th – Nov 20th 2012. The
objectives of the investigation were:
1. To estimate the overall coverage of ACF Therapeutic Feeding Programme (TFP) in Twic 2. To identify boosters and barriers to access and uptake of the Community Management Acute
Malnutrition (CMAM) services provided by the ACF nutrition treatment programme in Twic 3. To provide key recommendations for strengthening ACF nutrition treatment services in improving
quality and coverage of the CMAM programme
The SQUEAC3 tool was developed to provide an efficient and accurate method for identifying existing barriers
to service access and evaluating coverage in a non-emergency context.
This investigation was based on the principle of triangulation of data. This means that data was collected and
validated by different sources and different methods. The exercise ended when there was redundancy; i.e. no
new information was being gained from further investigation using different sources or methods.
The investigation achieved its efficiency by using a three stage approach: the development of the Prior, the
development of the Likelihood and the generation of the Posterior. The first two stages aimed at identifying
potential barriers and providing two individual estimations of coverage. During the Prior building process,
existing routine data which had previously been collected and compiled was combined with qualitative data to
produce a coverage “picture”; the “picture” was drawn by using the xMind software. Building the Prior
provided a projection of coverage levels for both the entire target area and also on specific areas suspected of
relatively high or low coverage within the programme’s target zone.
The Likelihood was built with data collected during a wide area field survey in randomly selected villages. The
Active and Adaptive Case Finding (AACF) method was used to identify severely malnourished children as well
3 Mark Myatt, Daniel Jones, Ephrem Emru, Saul Guerrero, Lionella Fieschi. SQUEAC & SLEAC: Low resource methods for evaluating
access and coverage in selective feeding programs. http://www.brixtonhealth.com/SQUEAC.Article.pdf
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as children enrolled in the programme who were still malnourished or recovering. During the wide area
survey, additional qualitative data was collected in order to explain why some severely malnourished children
were not enrolled in the OTP.
The last stage, the generation of the Posterior, combined the two initial stages and provided the overall
coverage estimation, including Credibility Intervals4 (C.I), by taking into account the “strength” of each
component of the equation. The Posterior was calculated using the Bayesian calculator.
2. STAGE ONE
The first stage of SQUEAC investigations began with an analysis of routine programme monitoring data which
included admissions, exits and data that is already collected on beneficiary record cards such as admission by
Mid Upper Arm Circumference (MUAC) and beneficiary address (home villages) etc.
The objective of Stage One was to identify areas of low and high coverage and the reasons for coverage failure
using routine programme data or easy-to-collect quantitative and qualitative data.
2.1 Programme Admissions
Among the ACF nutrition programme operational areas, Twic county admits a high number of severely
malnourished cases. For purposes of this investigation data analyzed covers a period that extends from Nov
2011-Oct 2012. During this period, the programme admitted 4,716 5 children to its five (5) operational OTPs.
The evolution of overall admissions is illustrated in Figure 2. The admission trend reveals a steady increase
from December 2011 up until June 2012 and a decrease thereafter with a slight increase again in September.
Admissions in January 2012 were relatively high and can be explained by the lack of availability of food at the
household level and also improved security that made the population movements more stable.
The pronounced peak in admissions in June 2012 was explained as the period when the reserve food stocks for
the household were depleted and there was migration to the big centers which increases the population in
places like Turalei and Wunrok.
In addition, during the period analysed there was an influx of a high number of IDPs from Abyei in Mayen
Abun. The July 2012 drop was partly attributed to the reduced availability of food, and further explained by
movements back to the cultivation areas as well as floods which hindered beneficiary movements. Also in July
2012 the mobile OTP was stopped at Anyiel Kuac as the number of admissions had decreased, but this may not
have affected the net admissions as it could have resulted in an increase in admissions in the nearest static
OTP.
4 The Bayesian approach is about beliefs and updating beliefs with data, the estimation interval is called the credible interval.
5 The admission numbers exclude mobile OTPs which was not part of the survey
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Graph 1: OTP Admissions over time, Nov 2011-Oct 2012, Twic County
2.2 Admission by service delivery unit (OTP) Overall admissions to the programme were further analyzed by service delivery unit in order to identify
potential disparities in admissions across the different facilities.
Graph 2: Admissions by OTP, Nov 2011-Oct 2012, Twic County
Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Oct
Total Admissions 269 177 250 273 340 587 551 761 523 392 427 305
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Total Admissions over Time
Wunrok TuraleiMayenAbun
Aweng Molbang
Total Admissions 1468 1153 738 818 641
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The analysis identified significant differences between the OTP sites. The investigation concluded this can be
attributed to the location of the OTPs. OTPs located near the primary road (Wunrok, Turalei and Aweng)
connect the different centres and these locations are generally larger. These locations experience in-migration
during the hunger gap, and in these locations primary health care is available, all contributing to more
admissions. Additionally, Molbang has shared beneficiaries coming from Biemnhom County of Unity State due
to proximity. The admission trends over time per health facility are illustrated in graph 3 below.
Graph 3: Admissions over time by health facility, Nov 2011-Oct 2012, Twic County
Wunrok OTP had the highest admissions during the peak season followed by Turalei OTP. The OTP admissions
trends by site overtime were different in most of the months. For most OTPs, admissions increased starting
from December. Two OTPs (Mayen Abun and Aweng) reached their peak in April while others extend until June
and July (Wunrok and Turalei). The changes in admissions are related to the location of the county as it hosts
IDPs (mainly Wunrok and Turalei) from internal conflicts with unpredictable movements. In several locations,
admissions dropped for one only month. Such cases are related to supply shortages for that specific month, in
the respective location.
2.3 Admissions vs. Needs Seasonal peaks are based on the local seasonal calendar and critical events, which highlight the highest food
insecurity (i.e. pre-harvest) and in-migration to population centres during the peak periods of March- May. In
linking admission trends and the seasonal calendar one can see the trends correspond to needs as in Annex 1,
though this is largely based on assumptions as the time frame is not long enough (over years) to illustrate this
adequately.
The decrease in admissions was also related to an increase in the agricultural activities of the communities,
mainly in the month of July. More involvement of the caregivers in cultivation or migration to earn wages
during the peak cultivation period seems to affect the attendance of malnourished children at the OTPs.
During this period the caregivers place more priority on cultivation.
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Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Oct
# o
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Molbang
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2.4 Spatial coverage of Admissions A geographical mapping exercise of admissions, defaulters and CNV’s was used to assess the pattern of spatial
coverage across the real catchment area of the programme. However, due to limitations on availability of good
quality maps6 , the spatial coverage assessment was modified. From the database, villages with corresponding
admissions, defaulters and CNV’s were generated.
The overall admission trend analysis was done to understand the specific locations of beneficiaries admitted to
the programme. The analysis showed that admissions were higher in communities along the network of roads
within the county and admissions were also higher in densely populated areas. It is also clear that coverage is
high with populations within a short radius from the OTP (5 km radius); this was corroborated by the fact that
most villages far from the OTP had low admissions purely because of the distance (between 5km to 10km).
This was a key exercise towards guiding the investigation in the formulation of two different hypotheses on
coverage for testing as part of Stage Two.
2.5 Mid Upper Arm Circumference (MUAC) at admission The measurement of the MUAC at admission was also part of the data available on the individual admission
card and as well captured by the database. The compilation of data collected from each OTP site makes it
possible to investigate the timeliness of treatment seeking behaviors.
In order to further understand whether the programme is reaching SAM children early, the MUAC at admission
was plotted for all recorded admissions from November 2011-October 2012. The results are as shown in graph
4 below. The median MUAC at admission was nearly 115mm. The slight discrepancy with some MUAC
measurements being greater than 115 mm is influenced by the fact that the programme admission criteria
uses weight for height z-score (WHZ) of <3 in addition to MUAC <115mm. This captures children earlier before
they reach a MUAC of <115mm. However, despite the use of WHZ, about 30% of children are malnourished by
MUAC definition7.
6 The mapping exercise proved difficult because the available maps did not have villages or settlements on it. It was also difficult for the team to locate most of the villages on the map from the list provided by the program. 7 One point to note is that the people of this area are tall and may easily be considered to be having low WHZ in reference to the standard height.
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Graph 4: Admission MUAC Nov 2011-Oct 2012, Twic County
Overall, children appear to be arriving in a relatively timely manner for treatment, as the median MUAC on
admission can be used as an indicator of beneficiaries' treatment-seeking behavior. More specifically, it
reflects how early or late they seek care. A late presentation may also affect the length of stay in the
programme. When the median MUAC on admission is higher than the cut-off point (115 mm), this indicates
better health seeking behaviour of the beneficiaries. On the other hand, a median MUAC on admission of <
115 mm usually indicates late treatment-seeking behaviour. For the above analysis in the Twic county CMAM
programme, the admission median MUAC was 115 mm indicating the programme is able to capture SAM
children early.
This early arrival seems to be positively influenced by the use of WHZ as one of the admission criteria. The
investigation revealed that slightly above two thirds of admissions are by WHZ. This implies that the children
are admitted before they reach the <115 mm (the MUAC admission criteria).
2.6 Programme Exits Programme exits met the SPHERE standards8, with the proportion of discharges from therapeutic care who
died at <10%, recovered >75% and defaulted <15%. In February and June, the proportion of deaths was above
10% due to an increase in disease and hunger. There was evidence of high defaulting in May and June, which
can be explained by land preparation in the lowland areas (opportunity cost) by caregivers of beneficiaries.
8 SPHERE Standards Handbook 2011
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.2 74
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Frequency
Median frequency
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Graph 5: Programme performance over time, Nov 2011-Oct 2012, Twic County
2.7 Review of Defaulter Records
To better understand the factors affecting defaulting and potentially affecting coverage, various stages of
analysis were conducted specifically to analyse defaulting. This was a key exercise towards guiding the
investigation in the formulation of two different hypotheses on coverage for testing as part of Stage Two.
Defaulters were treated as uncovered cases; the number of defaulters was examined for signs of worryingly
high trends over time.
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Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
cured
Default
Death
Non-respondent
16
2.7.1 Defaulting versus admissions over time In bringing together admissions and defaulters per health facility it was possible to identify a few trends in the
graph below.
Graph 6: Defaulters versus admissions per OTP, Nov 2011-2012, Twic County
In almost all nutrition facilities (except Aweng), the proportion of defaulters was low. This is attributed to an
increase in community mobilization activities and slightly improved availability of RUTF as well as supplies for
the treatment of Moderate Acute Malnutrition (MAM). Supplies are distributed based on the number of
beneficiaries at each OTP and what is predicted for the remaining period. The actual number of beneficiaries
and the predicted number vary. Hence, some OTPs will have more stock than others. The rate of defaulters in
Aweng reached around 30% of the total admissions. The potential correlation between defaulting and
admissions over time in different nutrition facilities is described in graph 7 below.
Wunrok TuraleiMayenAbun
Aweng Molbang
Total Admissions 1468 1153 738 716 641
Defaulting 72 44 67 218 46
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Graph 7: Defaulting over time, Nov 2011-Oct 2012, Twic County
Admission records were also sorted and analyzed according to time of defaulting. Many factors contributed to
defaulting, including distance and the movement of soldiers9. The Aweng OTP experienced a particularly high
number of defaulters. In the months of February and June the OTPs experienced more defaulters due to the
diversion of caretaker attention to land clearance and cultivation. In February defaulter follow-up was poor as
CNVs were limited both in number and availability. In the first 2 weeks of February, there were RUTF shortages
in most of the OTPs which affected admission rates and also overall programme performance in March (as
shown in figure 3). Additionally in February GOAL staff (who also implement nutrition treatment programmes
in Twic) went on strike requesting salary increases; during this strike the community thought ACF had also
stopped its activities. ACF subsequently communicated to leaders and caregivers that ACF activities were on-
going. During this period, community mobilization in Aweng was less effective, contributing to the rise in
defaulters.
2.7.2 Early versus Late Defaulting The investigation also analysed the timing of defaulting, in an effort to determine possible reasons behind it10.
Discharged defaulter cards were gathered and separated into categories according to number of visits
recorded (Graph 8). Most children defaulted in the 8th week. This is the point when children have improved
and appear cured. Other children do not come back again after admission because caretakers believe the food
ration is too little to justify traveling long distances to the health facilities. Due to a shortage of supplies,
beneficiaries tend to stay in the programme for a longer period and caregivers may lose confidence in the
programme, further contributing to defaulting linked to the time of stock outs.
9 When soldiers move, the families of the soldiers also move including severely malnourished cases admitted in the TFP
10 Early defaulters generally suggests; 1) that the child did not recover on its own after (i.e. is affecting coverage) and; 2) it happened
most likely for significant reasons. If it happened late it generally means that; 1) the child probably recovered on its own and; 2) it probably happened precisely because “sufficient” progress had been made and cost-benefit balance shifted
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Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sept Oct
Defaulting Over Time
Wunrok
Turalei
Mayen Abun
Aweng
Molbang
18
Graph 8: Defaulting by week on admission, Nov 2011-Oct 2012, Twic County
2.7.3 Defaulters by Home Location The investigation also considered the need for locating where the programme has high defaulting by mapping
the villages from which defaulters come from.
Table 2: Villages with higher number of defaulters (>5)
Village Payam Number of defaulters
Pandit Turalei 6
Mayen Abun Wunrok 6
Madiar Aweng 6
Agok Aweng 6
Malou hol Wunrok 7
Marial maper Aweng 7
Anyangout Aweng 8
Wunrok Wunrok 10
Marial Ajakkuac 10
Ajakkuac Ajakkuac 10
Majok Noon Aweng 26
Maper Aweng 36
The highest number of defaulters was in Maper and Majok Noon (62 cases), located within Aweng payam,
where there were reported security risks. Marial and Ajakkuac are villages within the Ajakkuac payam where
military barracks are located. These threats to security affect the movement of beneficiaries. Most of the other
villages with the number of defaulters in the 6-8 range were also from Aweng payam. The total number of
defaulters in Aweng (218) accounted for 27% of the total admissions (818). Defaulting in Wunrok and Turalei is
associated with the months when there were supply shortages.
0
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30
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60
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22
Defaulting by week of admission
# of defaulters
19
2.8 Qualitative Data
2.8.1 Synthesis of Quantitative and Qualitative Data In this investigation, a mind mapping exercise was used to synthesize all quantitative data analyzed and
qualitative information gathered. The exercise allowed the investigation team to collate all the data in such a
way as to facilitate discussion and interpretation. The exercise was done by putting together pieces of flip
chart paper to create a “wall” to write on. The theme or topic being investigated, which in this case was
coverage, was put at the centre of the wall and then sub-topics based on the various sources of quantitative
data and qualitative information were written down branching out from the central theme. Corresponding
data and information were then written down per sub-theme or topic and the process continued until all
points of investigation were exhausted. The mind mapping exercise can also be facilitated and captured using
XMind11, a mind mapping software that allows for the recording of a mind map electronically.
2.8.2 Understanding of Malnutrition Malnutrition is partially recognized in the communities of Twic County which to some extent can affect early
detection and treatment.
The caregivers do not initially think of malnutrition when a child is unwell. In listing the common health
problems experienced in their communities, the Focus Group Discussion (FGD) respondents (caregivers) were
aware of malaria, diarrhea, pneumonia, eye infections, cough, fever, vomiting, measles, skin rash, and malaria,
and Respiratory Tract Infections (RTI). Malnutrition was also mentioned as a disease by respondents. Diseases
mentioned and ranked according to occurence were: Diarrhea (yach), malaria and vomiting. Diarrhea was said
to occur frequently during the rainy season (March to April) and malaria from May to September. The
respondents further mentioned that malnutrition usually occurs between the month of April and August.
The community described symptoms of malnutrition as loss of weight, oedema, high fever, and loss of
appetite, diarrhea, thinness and skin rashes. The terms used to refer to malnutrition are: Adoor which means
thin; aluot which means oedema, and ‘Majak ador’, ‘meth ci pial’ Thiang which refers to a child who is thin,
vomiting and has diarrhoea. The respondents also understood that among the U5 children, malnutrition
affects mainly the youngest children between the ages of 1-2 years. Rapid growth in this age group was
mentioned as a reason why they are more easily affected. Other confounding factors mentioned included
poor hygiene, insecurity, poverty, diseases, inadequate food intake, drought and hunger.
Caregivers listed the following sources of information as to how they learned of ACF’s CMAM services: CNVs,
Community Nutrition Workers (CNW’s), Extended Programme for Immunization (EPI) vaccinators, Growth
Monitoring Programme (GMP), other beneficiaries, and the staff of GOAL.
The communities also mentioned that in order to find malnourished children in their areas, the best question
to ask is: Meth Ce dor’’, or meth Ce nyuin?, or meth Ce paal Wei? ’’ meaning ‘”is there any thin child?” The
respondents mentioned that in order to identify malnourished children in their communities, the best people
to ask are the community leaders, elders and the caregivers of beneficiaries.
11
XMind can be downloaded free at www.xmind.com as supporting tool but not part of SQUEAC investigation
20
2.8.3 Health Seeking Behaviour For the purpose of assessing the health seeking behavior, the community members and the caregivers were
asked what they do when their children get sick. The informal discussions revealed a strong traditional health
seeking behaviour across the programme areas.
Information collected across the catchment areas suggests that children are first taken to spear master
(witchdoctor). Some started by giving herbs and others remove the cloth of the child and then tear it, burn it
into ashes, dissolve it into water and then give it to the child for the treatment of malnutrition. The use of
roots and thorn trees (Lang, gumel, Akiar, anyan thiith) were believed to have a therapeutic effect.
The timing of the health seeking behaviour is very important, not only because early presentation ensures the
best health outcomes, but because it also suggests that the programme is known, trusted and perhaps most
importantly, accessible to the population.
When the child’s health status becomes very serious the traditional healer may then advise the caregivers to
take the child to the Primary Health Care Centre (PHCC), but to continue to come back to him to check on the
progress of the child. The child will be given advice and medication from the PHCC. Due to lengthy stays under
the traditional healer’s care, some of the cases arrive only after they are seriously ill. The factors considered
before seeking treatment at the PHCC’s include: distance to the PHCC, cost of treatment, accessibility of the
health centers due to flooding and impassable roads and the element of common practice. When they were
asked where such conditions are treated ; they mentioned in local terminology the ‘ panakim’’ or “pan miith
ador’’ meaning feeding centre. At the centre, respondents indicate that RUTF, Corn Soy Blend (CSB) and drugs
for treatment of malnourished children are provided. Walking distance to the OTPs was described as a
determining factor in seeking nutrition treatment, as it becomes unmanageable when it is more than 2 hours
one way.
2.8.4 Barriers to Access For gathering relevant information on health seeking behaviours in Twic, a series of cultural and programmatic
factors linked to malnutrition were reviewed. The table below summarizes key findings on possible reasons for
defaulting and non-attendance.
Table 3: Reasons for Defaulting & Non-Attendance
Awareness on
malnutrition &
treatment
available
Many of the cases admitted into programme have not come on their own. Instead, they were referred by CNVs. Awareness on malnutrition and utilization of the nutrition facilities is regularly done by the community worker as well as the CNVs in outreach activities. Malnutrition is recognized as a distinct disease by the majority of the communites. Thus, when a child presents with signs and symptoms of malnutrition, caregivers seek ‘first line’ treatment from the traditional healer (witch doctor); herbs and roots (lang, gumel, Akiar, anyan thiith) that are believed to have medicinal benefits and rituals removing the cloth of the malnourished child which is torn and burnt into ashes before being dissolved in water and then given to the child. The programme is known and accepted by villages in closer proximity to the OTP. Those with more than 10 km distance from the OTP still have the challenge of accessing treatment, and mobilization activities are less likely to have reached them.
21
Supply shortages
Most OTPs reported shortages of supplies, sometimes caused by a communication
breakdown between the base and the OTPs. However, the team also experienced
shortages due to a lack of RUTF supply from UNICEF. Consequently when caregivers travel
long distances and are not able to get the supplies they will go back and discourage other
caregivers from coming for the next visit, resulting in increased defaulting.
OTP Service
Provision
The OTP services are well accepted and caregivers are satisfied about it. The caregivers highlighted the following positive points: continous weekly follow-up, provision of RUTF/medication and a fair waiting time. However a common complaint was that children are turned away from the OTP when they do not meet the admission criteria. This may in part be caused by a lack of awareness of the caregivers on admission criteria. There is a need to educate the caregivers both at the community and facility level on admission criteria, the services provided and what it means to be rejected. The programme as well needs to be very careful referring children from the community level or else it may exacerbate the sense of rejection.
Distance
The catchment of villages around each OTP is a 10Km radius. Distance is a concern voiced by the respondents. The distance traveled varies with most people traveling up to 2 hours while some were travelling 3 to 6 hours. This long distance to travel has especially impacted defaulting. Caregivers bring their children to the programme and then fail to return due to the distance. When caregivers travel long distances and then there are supply ruptures at the OTP, or their children are not admitted, they return home feeling frustrated and discouraged, leading to defaulting.
Physical Barrier
Most lowland areas become inaccessible during the rainy season thus limiting access to the OTP’s. In some areas the road becomes damaged and impassable while in other areas flooding prevents travel. It was also noted by some caretakers that they may meet drunken people while traveling, thus feeling insecure.
Community
Mobilization
Some of the villages visited that were furthest from nutrition facilities (around 10 km) reported having not seen ACF nutrition outreach workers providing education nor conducting screening in their villages. The CNV’s complained of the difficulty accessing these villages during mobilization days. Lack of transport has hampered community mobilization in these areas.
Rejection
Some of the rejected cases were due to incorrect referrals by the CNVs. The caregivers
may not then return for screening even if their children later become malnourished. They
feel cheated and frustrated by the staff when their children are not admitted in the
programme.
RUTF
Perceptions /
Acceptance
In the community, RUTF is recognized as a medicine and its local term “Atom” (meaning
food for malnourished child) is becoming more popular. When the beneficiaries were
asked on how they normally give RUTF to the malnourished children, they reported that
they feed 3 to 4 times a day without sharing with other children.
Stigma
Shame or social stigma about malnutrition was reported amongst the younger caregivers.
It is believed to be linked to parents who had sex at times when the child was still
breastfeeding. Some community members also perceive that OTPs are for poor and
irresponsible families.
22
2.9 Areas of High and Low Coverage Based on the information collected and analyzed in Stage One, there were observations of high and low
coverage as seen in the admissions per home location. The investigation concluded that coverage is likely to be
determined by three factors: distance to the OTP, awareness about the programme and community
mobilization.
The hypothesis was therefore that:
Coverage is high in areas within a 2 hour walk to the OTP.
Coverage is low in areas beyond a 2 hour walk to the OTP.
To test this hypothesis, eight villages were selected, based on the investigation, as the most representative of
the hypothesis. The second stage is then undertaken to confirm the hypothesis.
3. STAGE TWO
The objective of Stage Two was to confirm the locations of areas of high and low coverage as well as the
reasons for coverage failure identified in Stage One (above) using small area surveys.
Eight villages were sampled. It was expected that in four (4) of the eight (8) villages the coverage would be high
as they are located within a 2 hour walk while in the other four (4) villages the coverage would be low due to
longer distance (more than 2 hours walk). Four (4) teams were formed for the small area survey which was
conducted in two days.
3.1 Active and Adaptive Case Finding
Active = Target SAM cases instead of doing house to house screening
Adaptive = Use key informants to help find SAM cases
= Key informants:
a. Village headman/elder b. Traditional healer c. Senior women and elders d. Religious leaders e. Beneficiaries
3.2 The definition of a Case MUAC less than 115 mm Bilateral oedema Aged 6-59 months <-3 Z-score; in this investigation, the criterion of Z-score was not considered to identify cases since it is
a criterion generally used at the health facility level only and this measurement is not part of the community based approach12.
12
Duffield A, & Myatt M, 2007, Weight-for-height and MUAC for estimating the prevalence of acute under-nutrition: a review of survey
data.
23
Based on the information collected, coverage was classified against a threshold of 50%13. A decision rule (d)
was calculated using the following formula:
d= n x p
100
n = total number of cases found
p = coverage standard set for the area
3.3 Stage Two – Small Area Survey (Findings)
In the test of hypothesis exercise for high coverage areas, the following results were found and calculated in
order to classify coverage are as follows.
3.3.1 High coverage areas Total SAM found = 8; IN programme = 6; NOT in programme = 2; D = 50/100*8=4
Since 6 > 4, then coverage is above 50% and the hypothesis was accepted.
3.3.2 Low coverage areas Total SAM = 16; IN programme =3; NOT in programme = 13; D = 50/100*16=8
Since 3 < 8, then the coverage is below 50% and the hypothesis was accepted.
The actual numbers for each village are represented in the table below.
Table 4: Results of the small area survey
High Coverage villages
Villages SAM cases
Cases Not In
Programme
Cases in Programme
Recovering
Majak Aher 6 5 1 0
Agok 0 0 0 0
Bulyom 2 1 1 1
Marial Guot 0 0 0 3
Low Coverage villages
Aweng Thou 4 4 0 0
Apapiny 0 0 0 1
Kor Liet Molbang 4 2 2 0
Ayien Amuol 8 7 1 0
Total 24 19 5 5
The hypothesis that coverage is high in areas around the programme up to a distance of a 2 hour walk was
statistically proven, showing that the actual coverage of the programme is limited to areas within a 2 hour
walk around programme sites, this implies that coverage across the programme area is patchy and NOT
homogenously distributed, and that far away areas are likely to have low coverage.
13
Threshold was set at 50% based on the SPHERE minimum for rural areas.
24
4. STAGE THREE
The objective of Stage 3 was to provide an estimate of overall programme coverage using Bayesian techniques.
To do this, the evaluation relied on the standards Bayesian beta to binomial conjugate analysis.
4.1 Developing a Prior All the positive and negative factors identified affecting the coverage were listed, ranked and weighted
according to their relative contribution to the overall coverage. Positive and negative factors ranked highest
were automatically given a ±5% weight while lowest ranked factors were weighted ±1%. Factors ranked in
between were given weights of ±3% according to their perceived positive or negative contribution to the
coverage. The positive and negative weights for the factors were then added up.
All positive factors were added to the minimum possible coverage (0%) while all the negative factors were
subtracted from the highest possible coverage (100%).
Table 5: Measuring Contributing Factors (Prior)
Positive Factors Value Negative Factors
Admission Vs Time 3 3 Health seeking behavior
Performance rate 5 4 Defaulting
Understanding of malnutrition 3 5 Distance
Awareness of CMAM 2 5 Rejection
Perception of CMAN 3 3 RUTF Stock outs
Communication 1 5 Opportunity cost
Follow up 1 4 Insecurity
Outreach/community mobilization 1 5 Flooding
Transport 1 5 Work material
4 Motivation of CNVs
5 Movement of population
5 Limited number of staff
Added to Minimum Coverage (0%) 20 53
Subtracted from Maximum Coverage (100%)
Median 33.5
α value 10.9 20.4 β value
The distribution of prior coverage estimate was determined through a beta distribution of the belief of
perceived coverage estimates. This was done by using the Bayes SQUEAC calculator14 to plot the mode and all
the perceived other possible coverage proportions. An average was calculated and used as the median for a
trial distribution curve (Prior) plotted using the Bayes SQUEAC Calculator. The final curve that was generated is
as shown in graph 9.
14
Software specifically designed and developed for SQUEAC investigations and can be downloaded free at www.brixtonhealth.org
25
Graph Prior of Programme Coverage
4.2 Sampling Methodology
4.2.1 Minimum Sample Size To estimate the minimum number of cases (children) needed in the small area survey (n), the following
formula is used:
n= mode x (1-mode) - (α + β – 2)
(precision / 1.96)2
Using α (10.9) and β (20.4) values and a mode of 33.5% (see section 4.1), the following minimum sample is as
follows:
n= [ (0.335 x (1-0.335) / (0.11515/1.96)2 – (10.9+ 20.4– 2)]
n= (0.247975/0.003443) – 29.3
n= 72.03182 – 29.3
n= 43
n=43
In order to achieve a confidence of +/- of 10%, a minimum of 43 cases needs to be identified.
15
The precision was put at 10% + 1.5% for non respondent
26
4.2.2 Minimum number of villages The minimum number of villages to be sampled was then calculated with the use of the following values.
Target Sample Size: 43
Average village population: 400
Prevalence of SAM: 2.6%
% Children aged 6-59 months: 20%
Values were used in the following formula:
n villages = 43/ (400 x 0.2 x 0.026)
n villages = 43/2.08
n villages = 20.76308
n villages = 21
As a result, a minimum of 21 villages had to be sampled in order to reach the minimum sample size of children.
4.2.3 Spatial Representation In order to achieve spatial representation, the Stage Three investigation involved a two-stage sampling:
1) Village selection: First, a list of all the villages in the catchment areas was generated. The following
procedure was then followed in selecting villages to be surveyed:
a) List of villages was generated from the database b) Sort them according to distance to the respective OTP c) Assigned numbers 1- 205 d) The required number of villages is 21, the sampling interval was 9. e) Randomly selected the beginning number, which was 4 f) Added 9 to the starting number 4 and continued adding 9 till 184 g) From those numbers, the villages were identified.
2) Within-community sampling method: a combined active & adaptive case-finding & mass screening approach
was used in Stage Three to ensure selected communities were sampled exhaustively. The wide area survey
was carried out over four (4) days (Nov 16th – 20th) by four (4) teams of four (4) people each; each team
composed of three (3) enumerators and was overseen by the surveillance and nutrition programme officers
and the surveillance deputy programme manager. The case definition used in Stage Two was reviewed with
field teams and replicated in this stage of the process.
n Villages = n
average village population all ages x percent of population6-59 months x prevalence
100 100
27
4.3 Wide Area Survey Results The main results for the wide area survey are summarized in Table 6.
Table 6: Stage Three (wide area survey) Main Findings
Types of Cases Number of cases
Number of current (SAM) cases 64
Number of current (SAM) cases attending the programme 24
Number of current (SAM) cases not attending the programme 40
Number of recovering cases attending the programme 14
The main reasons for not attending CMAM services are summarized.
Graph 10: Main reasons for Non-Attendance
The main reason for not-attendance in the nutrition programme by the caregivers of malnourished children
was found to be “not admitted to the programme”. The majority of these caregivers might not have a good
awareness of the programme and it may also be the result of a mistake in referral for which the caregiver
attends the health facility but is not admitted.
4.4 Overall Coverage Estimation Point coverage is presented as it provides a more accurate picture of the actual coverage of SAM cases at the
time the investigation was conducted. Period coverage16 is as shown in the foot note 12.
Point Coverage
Point coverage provides a snapshot of programme performance and places strong emphasis on the coverage
and timeliness of case-finding and recruitment. To calculate point coverage, the numerator and the
denominator were selected from the results for the wide area survey using the formula
16
Period Coverage = 59.7% (51.6% - 67.3%).
0 10 20 30 40 50 60
Not aware child is Malnourished
Not aware of the Program
Too far
No time/Too busy
No one else to take care of other…
Mother is sick
Mother feels ashame/shy
Child previously Rejected
Necessary to be enrolled in hosp firts
Other ( previsiously discharged)
Not admitted to the program
28
Selected data was used as a denominator (64) and numerator (24) when inputted into the Bayes SQUEAC
Calculator.
Graph 11: Programme coverage showing prior, likelihood and posterior curves
Based on the existing prior and wide area survey (likelihood) point coverage was estimated to be 36.7%
(27.5% - 47.0%)17, while the period coverage was estimated to be 59.7% (51.6% - 67.3%). The point coverage
found during this SQUEAC investigation was slightly higher than the point coverage (27.3% (19.7% - 36.6%))
found during the SQUEAC investigation in December 2011.
5. CONCLUSIONS
The point coverage in Twic County in 2012 showed an improvement when compared to that of last year.
Changes that contributed to this increase in point coverage include: increased community based screening,
screenings using the village mapping, more involvement of CNV’s and an increased number of CNW’s working
in comparatively high case load areas. In 2012, weekly community mobilization meetings were also held to link
the CNW’s, CNV’s and community mobilizers. A mapping of all villages in a 10km radius was used by the
nutrition outreach team to plan and execute mobilization activities.
17
All values were calculated using the Bayes SQUEAC calculator.
29
Point coverage may have been increased further if not for the following barriers: poor awareness on malnutrition and treatment, supply shortages, rejection of children when they don’t fit the admission criteria, distance and physical barriers to the OTP, sub-optimal community mobilization in far away villages, and lastly stigma.
6. RECOMMENDATIONS
For further improvement of the programme coverage, the SQUEAC investigation team recommends:
Continued active case finding and defaulter tracing at the community levels and mapping for defaulter follow up.
Increased on-the-job training for CNWs by the OTP Officer, and increased contact with the Programme Managers. This has the impact of improving the staffing of the OTPs, and improving referrals and monthly reporting.
Community members first consult traditional health practitioners who in most cases delay them before they can seek appropriate health care in the PHCC and OTP. There is the need for continued provision of health education to the community and to work with traditional health practitioners to reduce the delay in seeking appropriate health care. Hence, targeted and appropriate health and nutrition education messages for improving the knowledge of community members on malnutrition should be prioritized. Additionally, cases are not easily recognized by the care-takers as needing to be taken to an OTP.
There is a need for more active screening to help in detecting cases earlier to avoid late admissions.
The lack of supplies causes a high number of malnourished cases to default, in all of the nutrition facilities. Hence, it is highly recommended to keep a reserve of supplies, particularly RUTF.
Continued operation of mobile OTP’s to reduce the distance travelled by caretakers who travel up to 3-5 hours to reach treatment.
ACF has admitted a high number of cases into the programme, however there is a need to scale up if optimal coverage is to be reached.
Monitoring and supervision of OTPs should be strengthened.
More effort is needed to integrate the CMAM program into the existing health services, especially in remote areas.
30
7. ANNEXES
Annex 1: Admission versus Seasonal calendar of Events, Twic County
Jan Feb March April May June July August Sept Oct Nov Dec
RTI
Seasonal Illness
Malaria
Diarrhea
ARI
Food security
Hunger Season
Cultivation
Weeding
Enough food
Harvest
Rains
Burning of Bushes
Fishing
Dry season
Other Important seasonal
events
Flooding
Migration
Annex 2: List of villages Sampled for Wide Area survey
Village Sampled Number Team Number
Abyei dau 4 1
Achuanic 13 2
Adol 22 3
Ajong 31 4
Ametdai 40 1
Anyoun 49 2
Awilgiik 58 3
Biemnhom 67 4
Daunyep 76 1
Gool 85 2
Kurum Hol 94 3
Machar 103 4
Malek Adiany 112 1
Makol 121 2
Malualtor 130 3
Manyiel 139 4
Matiir 148 1
Muon 157 2
Pagorum 166 3
Panachuei 175 4
31
Panor 184 1
Pawil Anyoun 193 2
Rumger 202 3
Annex 3: Defaulters by home Location
Village # Village # Village # Village # Village #
Thak yen 1 Achuan 1 Ajakkuac 10 Akoot 1 Anyien Amau 1
Abaping 2 Achuet 1 ajiel 1 Akoot 1 Anyiet 1
Abbping noon 1 Achulop 2 Ajing 2 Akoutouk Ayang 1 Anyuon 4
Abianatue 1 Agar 2 Ajok kuac 1 Ameth 4 apabing noon 1
Abii 2 Agok 6 ajong leit 1 Ametweer 1 Apiir 1
Abing 1 Agok Nyang 1 Ajoung 1 Angot 1 Apioth 1
abyei kom 1 Agook 1 Akew 1 anyangout 8 Aroum 1
Achan 1 Agor 3 Akoctok 1 Anyangut 1 Atong 1
Achel 1 agor wiir rock 1 Akol Adiang 1 Anyiel 3 Ator 1
Achmca 1 Aguc 1 akol thuc 1 anyiel abiel 1 Awal 3
Achol 1 Ajak 2 Akonak 1 Anyiel kuac 1 Awany 1
Biemnhom 1 Bok Anyara 1 Gor Ayak 3 Luit Aweng 1 Majong noon 1
Bolkuaclom 1 Bokanyara 2 Guk 2 luitic 1 Maker 4
Bulyom 2 bol 1 Jok nyang 1 maayen nyuon 1 makol 4
Burwol 1 Dhuec 2 Joknyang 1 Mabek 1 Makuac 1
Buyuom 1 Dokul 1 Kabara 1 Mabil 1 Makuac Alal 1
Crol 1 dong piny 1 kenkou 2 Mabior 1 makual 1
Cuimet 1 Dongpiny 1 kodogo 1 Machar 4 Malek 1
Dhuec 2 Doum 1 Kol Lual 1 Maching Ageng 1 malek adiang 1
Dokul 1 Duanyep 1 kuac gok 1 Madiar 6 Malek Adiany 2
Dong piny 1 Duoyep 1 Kuediec 1 Mading 4 malekadiang 1
Dongpiny 1 Gok 1 Kueimat 1 Mading Nyang 2 Malou Awet 4
Doum 1 Gol 1 Kuel 1 Majak 1 malou hol 7
Duanyep 1 goom lual 2 Kuethdhiec 3 Majak Ahor 2 Malual 1
Duoyep 1 gor ajak 1 Lan Akon 1 majak ayii 1 Malual Diit 1
Maper 36 Nyagor 1 Lengic 1 majak nimou 1 Malual Tor 4
Marial 10 Nyanyuon 1 liet aweng 1 Majok 1 malual tou 1
Marial maper 7 Nyindeng Ayuel 1 Lietik 1 Majok noon 26 Mandior 1
Mariat 1 Nyinthok 1 Lolkou 1 Majoknoon 3 mankuac 1
Marol 1 Nyithok 1 Miakol nyok 1 Pandit 6 Pagumoum 1
Matek 1 pagak 3 Molbang 2 Pangap 1 Paleng 4
Mathawan 1 Pagap 2 Mulbang 1 Pangok 1 Pamuk 2
Mathiang 1 Pagumoum 1 Nyagor 1 Pangup 1 Panabiei 2
Mayen 2 Paleng 4 Nyanyuon 1 Pan-leng 1 Panagok 5
Mayen Abun 6 Pamuk 2 Nyindeng Ayuel 1 Panliet 1 Panameth 3
32
Mayom 4 Panabiei 2 Nyinthok 1 Panmeth 1 Panbaai 1
Mayom anyuen 1 Panagok 5 Nyithok 1 Panrual 1 Pandit 6
Mayom Anyuon 4 Panameth 3 pagak 3 pantet 1 Pangap 1
Methawan 1 Panbaai 1 Pagap 2 Panther 2 Pangok 1
Panyok 1 Tuele 1 pantet 1 Telei 1 Pangup 1
Patai 3 Tunyawer 1 Panther 2 Teny 1 Pan-leng 1
Pawil 1 Turalei 1 Panuk 1 thauic 1 Panliet 1
Payuai 1 Twele 1 Panyai 1 Thuranyier 1 Panmeth 1
Pongap 1 Wunbut Awan 1 panyang 1 Tongliet 1 Panrual 1
Rai 1 Wundeeng 1 rumcour 1 Wurieng 3
Renkachouk 1 Wundeng 1 Rumguer 1 Wurok 1
Riak 3 Wunkueth 3 Telei 1 wutcuei 1
Rum Ariik 1 Wunrok 10 Teny 1 Yok cien 1
Rum arit 1 Wunthou 3 thauic 1 Youm 1
