NUTRITION AND MORTALITY SMART SURVEY

FINAL REPORT

FORMER IKWOTO COUNTY, FORMER EASTERN EQUATORIA STATE,

SOUTH SUDAN

Funded by:

June 2019

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Acknowledgements This nutrition SMART survey would not have been possible without the financial support from the Italian Agency for Development Cooperation.

Appreciation goes to the County Health Department of greater Ikwoto and the Relief and Rehabilitation Commission of South Sudan for their generous support during the implementation of the survey. Further, the consultant is grateful to the local administration and community leaders for their assistance during data collection.

Sincere gratitude goes to AVSI staff in charge of survey implementation particularly Luca Scarpa (Country Representative), Bruno Nazim (Head of Monitoring and Quality Assurance), Mariam Joshi (M&E Coordinator), Patrick Okot (Area Team Leader, Torit), and Abdu James (Logistics Manager, Torit) for supporting survey planning, training and logistics.

A profound thank you goes to the field supervisors, Joel Komakech and Papa John. Special thanks are expressed to the survey teams for their dedication and hard work.

Gratitude is extended to mothers and caregivers for providing information during the interviews and allowing their children to be measured. The technical support of Kiross Tefera and Ismail Kassim of UNICEF and the entire South Sudan Nutrition Information Working Group is highly acknowledged.

Consultant Morris Chui Public Health Nutrition Consultant Email: chui.morris@gmail.com Mobile Tel: + 254 721 882 662 Skype: morris.chui

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Table of Contents Acknowledgments ................................................................................................................................... ii

List of Tables ........................................................................................................................................... v

Acronyms .............................................................................................................................................. vii

Executive summary ...............................................................................................................................viii

Specific recommendations are outlined below; .......................................................................... xi

1 Introduction ................................................................................................................................ 1

1.1 Background ................................................................................................................................. 1

1.2 Humanitarian assistance ............................................................................................................. 3

1.3 Survey Justification ..................................................................................................................... 3

1.4 Main objective of the survey ....................................................................................................... 3

1.4.1 Specific Objectives ........................................................................................................ 3

1.5 Timing of the survey ................................................................................................................... 4

2 Methodology .............................................................................................................................. 5

2.1 Study design ............................................................................................................................... 5

2.2 Study population ........................................................................................................................ 5

2.3 Sample size determination .......................................................................................................... 5

2.4 Sampling methodology ............................................................................................................... 7

2.4.1 First stage sampling ............................................................................................................ 7

2.4.2 Second stage sampling ....................................................................................................... 7

2.5 Survey implementation ............................................................................................................... 8

2.5.1 Survey management and coordination ......................................................................... 8

2.5.2 Survey teams training, supervision and data quality...................................................... 8

2.5.3 Data collection tools ..................................................................................................... 9

2.6 Data collected ............................................................................................................................. 9

2.6.1 Mortality ...................................................................................................................... 9

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2.6.2 Individual information per survey child - Anthropometry ............................................ 10

2.7 Data entry, analysis, and report writing........................................................................................ 10

2.8 Classifying malnutrition............................................................................................................. 11

2.8.1 Weight for height (WFH) and MUAC – Wasting among children .................................. 11

2.8.2 Weight for age (WFA) – Underweight ......................................................................... 11

2.8.3 Height for age (HFA) – Stunting ................................................................................... 11

2.9 Results dissemination ............................................................................................................... 11

2.10 Possible bias and assumptions ............................................................................................... 11

2.11 Ethical considerations ............................................................................................................ 12

3 Survey results ........................................................................................................................... 13

3.1 Age and sex pyramid ................................................................................................................. 13

3.2 Anthropometric results (based on WHO standards 2006) .......................................................... 13

3.2.1 Distribution by age and sex ......................................................................................... 13

3.2.2 Prevalence of acute malnutrition based on Weight for Height and by sex ................... 14

3.2.3 Prevalence of acute malnutrition by age based on Weight for Height ......................... 16

3.2.4 Distribution of acute malnutrition and oedema based on WFH Z-scores ..................... 16

3.2.5 Prevalence of acute malnutrition based on MUAC cut off’s and/or oedema by sex ..... 17

3.2.6 Prevalence of acute malnutrition based on MUAC cut off’s and/or oedema by age ..... 17

3.2.7 Prevalence of underweight based on weight for age Z scores by sex ........................... 18

3.2.8 Prevalence of underweight based on weight for age Z scores by age .......................... 18

3.2.9 Prevalence of stunting based on height for age Z scores ............................................. 19

3.2.10 Mean Z-scores, design effects and excluded subjects .................................................. 19

3.3 Mortality results (retrospective over 98 days prior to interview) ............................................... 20

4 Other results ............................................................................................................................. 22

4.1 Food Security and Livelihoods ................................................................................................... 22

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4.1.1 Income sources for households in the past 30 days..................................................... 22

4.1.2 Household hunger scale .............................................................................................. 22

4.1.3 Household food consumption score ............................................................................ 23

4.1.4 Household dietary diversity ........................................................................................ 24

4.1.5 Reduced coping strategy index ................................................................................... 25

4.1.6 Livelihoods based coping strategies ............................................................................ 25

5 Discussion ................................................................................................................................. 27

5.1 Nutrition status ......................................................................................................................... 27

5.1.1 Acute malnutrition ..................................................................................................... 27

5.1.2 Underweight............................................................................................................... 27

5.1.3 Chronic malnutrition ................................................................................................... 28

5.2 Mortality................................................................................................................................... 28

5.3 Food Security and Livelihoods ................................................................................................... 28

6 Conclusion ................................................................................................................................ 30

7 Recommendations .................................................................................................................... 31

8 Appendices ............................................................................................................................... 32

List of Tables

Table 1: Summary of survey results ........................................................................................................ ix

Table 2: Other agencies present in Ikwoto County ................................................................................... 3

Table 3: Survey sample size determination .............................................................................................. 5

Table 4: Percent of households and children 6-59 months included in the survey .................................... 7

Table 5: Distribution of age and sex sample ........................................................................................... 14

Table 6: Prevalence of acute malnutrition based on weight-for-height Z-scores and/or oedema by sex . 15

Table 7: Prevalence of acute malnutrition by age based on weight-for-height Z-scores and/or oedema. 16

Table 8: Distribution of acute malnutrition and oedema based on WFH Z-scores ................................... 17

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Table 9: Prevalence of acute malnutrition based on MUAC cut off's and/or oedema by sex ................... 17

Table 10: Prevalence of acute malnutrition based on MUAC cut off's and/or oedema by age ................ 18

Table 11: Prevalence of underweight based on weight-for-age Z-scores by sex ...................................... 18

Table 12: Prevalence of underweight based on weight-for-age Z-scores by age ..................................... 19

Table 13: Mean Z-scores, Design Effects and excluded subjects ............................................................. 19

Table 14: Demographic profile of the respondents ................................................................................ 20

Table 15: Household hunger scale ......................................................................................................... 23

Table 16: Household food consumption score ....................................................................................... 23

Table 17: Household dietary diversity score ........................................................................................... 24

Table 18: Reduced coping strategy index ............................................................................................... 25

Table 19: Livelihoods based coping strategies used by households ........................................................ 25

List of Figures

Figure 1: IPC Phase Classification and location of the former County of Ikwoto. ....................................... 1

Figure 2: South Sudan Seasonal Calendar ................................................................................................ 4

Figure 3: Population Pyramid of the former County of Ikwoto ............................................................... 13

Figure 4: Frequency Distribution of WFH Z-scores for children 6-59 months .......................................... 16

Figure 5: Main sources of income in Ikwoto ........................................................................................... 22

Figure 6: Households consuming each food group ................................................................................. 24

Figure 7: Trend in the prevalence of GAM and SAM in children 6-59 months ......................................... 27

Figure 8: Trend in mortality rates in Ikwoto ........................................................................................... 28

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Acronyms

AVSI Association of Volunteers for International Service

CHD County Health Department

CMAM Community Management of Acute Malnutrition

CMR Crude Mortality Rate

ENA Emergency Nutrition Assessments

FAO Food and Agriculture Organization of the United Nations

FCS Food Consumption Score

GAM Global Acute Malnutrition

HAZ Height for Age Z scores

HDDS Household Dietary Diversity Score

HLSS Health Link South Sudan

HH Household

HHS Household Hunger Scale

IPC Integrated Food Security Phase Classification

MAM Moderate Acute Malnutrition

MUAC Mid Upper Arm Circumference

NIWG Nutrition Information Working Group

OTP Out-Patient Therapeutic Programme

PLW Pregnant and Lactating Women

PPS Probability Proportional to Size

RRC Relief and Rehabilitation Commission of South Sudan

SAM Severe Acute Malnutrition

SD Standard Deviation (measure of spread around the mean)

SMART Standardized Monitoring and Assessment for Relief and Transitions

TSFP Targeted Supplementary Feeding Program

U5MR Under 5 Mortality Rate

UNICEF United Nation Children’s Fund

WAZ Weight for Age Z scores

WFP World Food Program

WHO World Health Organisation

WHZ Weight for Height Z scores

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Executive summary

The AVSI Foundation with support from the Italian Agency for Development Cooperation conducted a SMART nutrition and mortality survey covering all the eight Payams of the former County of Ikwoto in the former Eastern Equatoria State. The main objective of the survey was to assess: current prevalence of acute malnutrition, crude and under-five retrospective death rates, and to analyze possible factors contributing to malnutrition.

The Standardized Monitoring and Assessment for Relief and Transitions (SMART) which applies a two-stage cluster sampling was used. A total of 613 children aged 6-59 months from 570 households in 39 clusters were sampled for anthropometric measurements. The mortality assessment was conducted concurrently in all 570 households.

Additional information on the food security and livelihoods situation of Ikwoto was collected in the 570 households visited, in order to provide more insight into possible risk factors associated with acute malnutrition.

The prevalence of global acute malnutrition (GAM) defined as Weight-for-Height <-2 Z scores and/or oedema for the County was 9.2% (6.6 - 12.8 95% C.I.) and the severe acute malnutrition (SAM) prevalence defined as Weight-for-Height <-3 Z scores and/or oedema was 2.0% (1.0 -4.0 95% C.I.). The GAM rate indicated a poor nutrition situation while SAM prevalence was classified as critical according to the WHO classification. There were no oedema cases identified during the assessment.

The crude mortality rate (CMR) and the under-five mortality rate (U5MR) were 0.42 (0.22-0.79) and 0.46 (0.15-1.44) respectively. Both the CMR and the U5MR were classified as normal as per the WHO emergency thresholds of 1/10,000/day and 2/10,000/day respectively. Most deaths were due to illness (81.8%).

Food security indicators had varied responses. Overall, a significant number of households were found to be at risk of food insecurity at the time of the survey. Only 38.6% of the surveyed households had acceptable food consumption. Most of the households (66.3%) said they were experiencing little to no hunger. Additionally, 61.1% of sampled households had low diet diversity. The mean HDD score for all households in the survey was 4.1 food groups which show that on average, households consumed 4 or fewer food groups the previous day of the interview. The vast majority of households (95.8%) had a low reduced coping strategy index. However, a substantial number of households (47.7%) reported using one or more livelihood based coping strategies in the month prior to the survey.

The results of key indicators are summarized in table 1 below.

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Table 1: Summary of survey results

Anthropometry - Children 6-59 months based on WHO 2006 standards

Index Indicator N n Percent (95% C.I)

WHZ- scores

Prevalence of global acute malnutrition (<-2 z-score and/or oedema)

595 55

9.2% (6.6 - 12.8)

Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema)

595

43

7.2% (5.1 – 10.1)

Prevalence of severe malnutrition (<-3 z-score and/or oedema)

595

12

2.0% (1.0 - 4.0)

WAZ- scores

Prevalence of underweight (<-2 z-score)

606

163

26.9% (22.1 - 32.3)

Prevalence of moderate underweight (<-2 z-score and >=-3 z-score)

606 137 22.6% (18.7 - 27.1)

Prevalence of severe underweight (<-3 z-score)

606

26

4.3% (2.7 - 6.7)

HAZ- scores Prevalence of stunting (<-2 z-score) calculated with a SD of 1

31.9%

MUAC

Prevalence of global acute malnutrition (< 125 mm and/or oedema)

613 21 3.4 % (2.3 - 5.1)

Prevalence of moderate acute malnutrition (< 125 mm and >= 110 mm, no oedema)

613 17 2.8 % (1.8 - 4.3)

Prevalence of severe acute malnutrition (< 115 mm and/or oedema)

613 4 0.7 % (0.2 - 1.7)

Retrospective Mortality - 98 days recall period

Mortality

CMR Deaths/10,000 people/day

2671.5 11 0.42 (0.22 - 0.79)

U5 MR Deaths/10,000 children U5/day

666.5 3 0.46 (0.15 - 1.44)

Food Security and Livelihoods

Household hunger scale

Little to no hunger: 0 to 1 Moderate hunger: 2 to 3 Severe hunger: 4 to 6

570 378 192 0

66.3 (62.5-69.8) 33.7 (29.8-37.5) 0

Food consumption score

Poor food consumption Borderline food consumption Acceptable food consumption

570 100 250 220

17.5 (14.6-20.9) 43.9 (39.8-47.7) 38.6 (34.6-42.5)

Household dietary diversity

Low: 0 to 4 Medium: 4 to 5 High >6

570 348 150 72

61.1 (56.8-64.7) 26.3 (22.8-30.0) 12.6 (9.8-15.6)

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Reduced coping strategy index

Low: 0 to 15 Medium: 16 to 40 High>40

570 546 24 0

95.8 (94.2-97.4) 4.2 (2.6-6.0) 0

Households not coping

570

298 52.3 (48.2-56.5)

Stress coping strategies

Sent household members to eat elsewhere

17 3.0 (1.8-4.6)

Spent savings/sold more animals 67 11.8 (9.3-14.2)

Sold household assets e.g. furniture, utensils

12 2.1 (1.1-3.3)

Borrow money/ purchase food on credit 92 16.1 (13.2-19.1)

Crisis coping strategies

Withdrew children from school 13 2.3 (1.2-3.5)

Sold productive assets or means of transport

5 0.9 (0.2-1.8)

Reduced essential non-food expenses such as health, education, etc.

27 4.7 (3.2-6.5)

Emergency coping strategies

Entire household migrated 19 3.3 (1.9-4.7)

Engaged in degrading jobs or risky income generating activities

6 1.1 (0.4-1.9)

Begged 8 1.4 (0.5-2.5)

Sold last female animal 5 0.9 (0.2-1.8)

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Specific recommendations are outlined below:

1. Continue implementing the ongoing CMAM program in order to treat and prevent acute malnutrition among vulnerable children. Taking into account the high rate of SAM cases noted by the survey, AVSI should consider expanding or creating more health facility-based or outreach sites to help address the high caseload of SAM children.

2. Considering that a number of malnourished children were referred to health facilities and nutrition centers for treatment, there is need to scale up community level screening in order to detect all malnourished cases that need to be enrolled in treatment programs. This can be achieved through regular training and providing incentives to community nutrition volunteers.

3. According to survey findings, boys were more affected by both acute and chronic malnutrition compared to girls. There is a need to conduct a Nutrition Causal Analysis (NCA) study to understand why boys tend to be more malnourished in the County and also identify the predominant causes of malnutrition to be prioritized for intervention.

4. Survey results have shown that chronic malnutrition is a burden for the County. Therefore, more efforts are needed for stunting prevention through various multi-sectoral interventions. For a start, AVSI should consider intensifying health and nutrition education activities on ways of diversifying the household diet, good IYCF practices, improving household hygiene and sanitation practices, improving health-seeking behaviors as well as sensitizing the community on the domestic treatment of drinking water.

5. A significant number of households were found to be at risk of food insecurity as measured by their food consumption. The current FAO food security and livelihoods program targeting local communities in Ikwoto should be expanded to include other simple and cost-effective livelihoods interventions meant to improve food security such as: supporting farmers to produce fast-growing edible crops, promoting utilization of indigenous food, training farmers on food processing and preservation among other interventions.

6. The survey noted that the majority of the households had poor dietary diversity and mostly consumed food of low nutritional value (cereals and tubers, vegetables and oil/fat). Community members should be targeted with continuous health education on strategies for dietary diversification such as: establishing kitchen gardens at the household level, keeping small animals, and demonstrating to mothers or caregivers how to prepare quality diets.

1 Introduction

1.1 Background

The former County of Ikwoto is one of the eight counties that made up the former Eastern Equatoria state in the Republic of South Sudan. Ikwoto County borders Budi County to the East, Torit County to the North, Magwi County to the Southwest, and Uganda to the South.

According to the South Sudan National Bureau of Statistics, the greater Ikwoto County has a projected population of 129,620 and an area of 3,512 km². The County is mainly inhabited by the Lango ethnic group who depend on rain-fed agriculture supported by the rearing of livestock and exploitation of natural products from the forest.

This former County is made up of eight Payams that include: Ikwoto, Hatire, Imotong, Losite, Isohe, Chahari, Chorokol and Bira.1

Figure 1: IPC Phase Classification and location of the former County of Ikwoto.

Ikwoto falls under the high land forest and sorghum livelihood zone, which is characterized by highlands and foothills along border areas with Uganda where the altitude ranges from 1200 – 2000 m above sea level, while the valley and floodplains in the northeast and northern parts range 600 – 900 m above sea level.2 Soils vary in type and include lateritic soil in the mountainous areas, sandy soils in the foothills, and clays and loam soils in the valley plains. Soils are relatively fertile and suitable for crop production. The zone has a unimodal rainfall pattern with an average annual precipitation of 900 – 1000 mm. There are two distinct seasons: a rainy season from April to November and a short dry season from December to March. There is one major growing season from April to July although localized areas in the highlands have a second growing season for groundnuts and sesame from September to December.

Following the outbreak of violence in Juba in July 2016, the conflict expanded to previously peaceful areas, including the Eastern Equatoria region. While security deteriorated in some areas of Eastern Equatoria, Ikwoto was largely unaffected by direct fighting through most of 2017. Due to relative safety in the County, in the first quarter of 2018, some of those displaced by conflict started returning from their displacement areas outside and within South Sudan, mostly to settle in population centres perceived to be secure and to look for income-generating activities.3 Between July and September 2018, humanitarian sources continued to report returns of South Sudanese refugees who had been staying in Uganda to the counties of Eastern Equatoria that border Ugandan refugee camps, including Torit, Magwi and Ikotos Counties.4

1 AVSI Nutrition and Mortality SMART Survey Ikwoto County, former Eatern Equatoria State, October 2017. 2FEWS NET. 2018. Livelihoods Zone Map and Descriptions for the Republic of South Sudan. Washington, DC: FEWS NET.

3 REACH. Situation Overview: Greater Equatoria, South Sudan. January - March 2018. 4 REACH. Situation Overview: Central and Eastern Equatoria, South Sudan. July - September 2018.

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Findings from of a multi-sector survey conducted by AVSI Foundation in May 2018, indicated that the major concerns in the former Ikwoto County are food insecurity, human sickness, and delay of rains/lack of water infrastructure. Other basic services are also missing in some areas of Ikwoto county, especially health services, schools, and markets.5

According to the IPC South Sudan, Food Security Outlook update in January 2019, Ikwoto County was classified at IPC phase 3 (crisis level). Further, IPC classification projections for the period between May and July 2019 indicated that the County would remain in IPC phase 3 or likely to drop at least one phase down in the absence of humanitarian intervention or assistance.

Irregular rains in 2018 led to a below average harvest – although better than those registered in the previous years, when insecurity, pests, and a severe drought disrupted the livelihood activities of a large section of the population. Besides the negative climatic events, food insecurity in Ikwoto is determined by limited access to nutrient-rich food based on FSNMS round 23 preliminary data released on November 2018. The unbalanced diet is one of the major causes of stunting, the most visible manifestation of chronic malnutrition, which in Eastern Equatoria affects almost 30% of children, the highest level registered in South Sudan as per the latest IPC analysis.6

The AVSI Foundation has been implementing the community management of acute malnutrition programme (CMAM) covering the entire Ikwoto County. Through the programme, AVSI provides nutrition care and treatment services for malnourished children, pregnant and lactating women. Nutrition services are provided through the health facility-based Outpatient Therapeutic Programme (OTP), Targeted Supplementary Feeding Programme (TSFP) and community outreach.

Other complementary nutrition services provided include the promotion of appropriate IYCF practices through mother to mother support groups, supporting nutrition capacity building of clinical staff and community nutrition volunteers and provision of technical equipment (weighing scales, pads, height boards, reporting materials) to the local health facilities.

Results from the last SMART survey conducted in Ikwoto by Health Link South Sudan in November 2018 showed high malnutrition rates, with a GAM rate of 13.5% (10.6-17.2 95% C.I) and SAM prevalence at 2.6% (1.5-4.3 95% C.I). The GAM prevalence indicated a serious nutrition situation while SAM was classified as critical as per the WHO classification.

To determine the trend of malnutrition prevalence in the former County of Ikwoto, AVSI proposed to conduct a pre-harvest SMART survey to assess the nutritional status of the vulnerable groups in the month of June 2019.

5 https://www.humanitarianresponse.info/en/operations/south-sudan/assessment/2018-greater-ikwoto-multi-sector-survey 6 https://fscluster.org/south-sudan-rep/document/food-security-and-nutrition-monitoring-1

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1.2 Humanitarian assistance

There are a number of agencies currently working in the former County of Ikwoto including WFP, UNICEF, Red Cross, CARE International, CARITAS, and Health Link South Sudan. These organizations support and implement various programs as outlined in table 2 below.

Table 2: Other agencies present in Ikwoto County

Organization Sector

WFP TSFP (RUSF distribution)

UNICEF OTP (RUTF distribution)

Red Cross Disaster and emergency response, health, WASH, and education programs

CARE International Programs in food security and livelihoods, protection (gender-based violence)

CARITAS Programs in food security and livelihoods, WASH, protection and health services support at Isohe Mission Hospital

Diocese of Torit Programs to support the provision of health and nutrition services

Health Link South Sudan Programs in health, nutrition, and WASH

1.3 Survey Justification

The survey was necessary to determine the level of acute malnutrition among vulnerable groups in the former Ikwoto County. Results from a SMART survey conducted in Ikwoto by Health Link South Sudan in November 2018 showed a serious nutrition situation, with a GAM rate of 13.5% (10.6-17.2 95% C.I.) and SAM rate of 2.6% (1.5-4.3 95% C.I.). This assessment was therefore conducted as part of surveillance and monitoring of the nutrition situation in the County during the pre-harvest period.

Results of this survey will provide up to date information on nutrition, mortality rates, and the food security situation, to be used to promote evidence-based decision-making process. Further, survey findings will provide recommendations on appropriate nutrition and food security interventions to be implemented in order to improve the existing situation.

1.4 Main objective of the survey

The main objective of the survey was to assess the nutrition status among children aged 6-59 months, to estimate the crude and under 5 retrospective death rates, and to define the current state of food security.

1.4.1 Specific Objectives

1. To estimate the prevalence of acute malnutrition (GAM and SAM) among children aged 6-59 months.

2. To estimate the retrospective crude and under-five mortality rates.

3. To assess the current household food security situation in the County.

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4. To recommend appropriate nutrition and food security interventions based on survey findings.

1.5 Timing of the survey

The Nutrition SMART Survey was conducted from 10th to 24th June 2019, during the lean season as shown in the seasonal calendar below.

Figure 2: South Sudan Seasonal Calendar

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2 Methodology

2.1 Study design

The nutrition survey adopted a cross-sectional household survey design using the two-stage cluster sampling based on the SMART methodology. Clusters were selected using probability proportional to population size (PPS). Stage one sampling involved the sampling of clusters to be included in the survey while the second stage sampling involved the selection of households from the sampled clusters.

2.2 Study population

The target population for the anthropometric survey was children aged 6-59 months while all households were targeted for retrospective mortality assessment. Mothers or caregivers were interviewed to obtain information on food security and livelihoods.

2.3 Sample size determination

The sample size for anthropometry and retrospective mortality was determined using the Emergency Nutrition Assessment (ENA) for SMART software July 9th, 2015 version.

For anthropometry, a total of 518 children in 437 households was calculated and for mortality, a total of 3215 persons in 574 households was calculated using the parameters summarized in the table below.

Table 3: Survey sample size determination

Parameters/values Anthropometric

survey

Mortality survey

Rationale/ Source

Estimated prevalence (%)

13.5 0.88 Based on the November 2018 survey by Health Link South Sudan. The County remained relatively stable and although the survey was to be conducted during the lean season, the nutrition situation was considered not to have changed significantly since the last survey, hence point prevalence was used for anthropometry.

In November 2018, CMR was 0.88 (0.56-1.39 95% C.I.) with a DEFF of 1.07 which showed the occurrence of death was homogeneous. The security situation was calm with no major disruptions affecting the population since the last survey;

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During planning, sample size calculation for anthropometry and mortality yielded two different household samples and therefore the survey used the larger of the two samples (574 households) for both mortality and anthropometric surveys as recommended by the SMART survey guidelines7. However, after identifying the recall event, the recall period in days increased from the initial 93 days used for planning to 98 days. This resulted in a decrease in the mortality sample size from 574 HHs to 544 HHs.

7 SMART Methodology. Updated SMART Manual Version 2.0 published in 2017.

therefore, the point prevalence was used for mortality.

Desired precision 3.5 0.4 According to SMART Survey guidelines based on estimated prevalence

Design effect 1.3 1.3 DEFF for anthropometry and CMR was based on the November 2018 survey by HLSS. Both GAM and CMR were homogenous, and therefore a more conservative design effect was considered.

Average household size 5.9 5.9 Based on the November 2018 survey by HLSS.

Percent of U5 children 23.5 N/A Based on the November 2018 survey by HLSS.

Percent of none responsive households

5 5 According to SMART Survey guidelines (usually a range of 3-5%).

Recall period in days N/A 98 days From 14th March 2019 marked by the visit of the first Vice President of South Sudan Mr. Wani Igah to Ikotos County, to the survey data collection mid-point on 19th June 2019.

Children to be included 475 N/A

Households to be included

390 374

Population to be included

N/A 2671

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The survey used the highest number of households (574) as the final sample size in order to ensure the minimum number of children 6-59 months (518) would be achieved.

All randomly selected households were included in the survey. The total number of households included was 570 out of the 574 initially planned which shows 99.3% achievement of the sample size. The total number of children 6-59 months included in the survey was 613 which was higher than the minimum sample size required (518). This indicates 118.3% achievement.

Table 4: Percent of households and children 6-59 months included in the survey

2.4 Sampling methodology

The survey applied a two-stage cluster sampling based on the SMART methodology with clusters being selected using probability proportional to population size (PPS).

2.4.1 First stage sampling

The first stage involved the assignment of the smallest sampling unit called clusters which in this case were villages. A sampling frame for clusters (villages) with their respective population sizes and the indication of their accessibility was prepared with assistance from the local Payam administrators. All accessible villages along with their respective populations were entered into the ENA for SMART software.

Random selection of clusters from the total number of villages in the County was done using the planning screen of ENA for SMART software based on probability proportional to population size (PPS). Clusters were defined as villages within Bomas in all the Payams of the County.

A total of 39 clusters were selected from the sampling frame generated from the list of all villages in the former County of Ikwoto.

2.4.2 Second stage sampling

Simple random sampling was used to select 15 households per cluster to be surveyed. The household heads in the villages were listed with the support of the village chiefs. Once a complete list of household heads was obtained, enumerators used ENA generated random numbers tables to select the households from the sampling frame (household list).

2.4.3 Number of households per cluster

The number of households to be completed per day was determined according to the time the team could spend in the field excluding transportation, other procedures and break times. The details below are taken into consideration when performing this calculation based on the given context:

Number of HHs

planned

Number of HHs

surveyed

% surveyed /planned

Number of children 6-59

months planned

Number of children 6-59 months

surveyed

% surveyed /planned

574 570 99.3% 518 613 118.3%

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1. Departure from office at 8:00 am and back at 6:00 pm. 2. Average travel time (to and from the field): 3 hrs. 3. Duration for initial introduction and selection of households: 1 hour. 4. Break: one lunch break of 1 hour.

The above gave an average of 5 hours of working time in each cluster. It was determined that on average teams could spend 15 minutes in each HH and 5 minutes moving from one HH to another and therefore, each team could comfortably reach 15 HHs per day. The total number of households in the sample was then divided by the number of households to be completed in one day so as to determine the number of clusters to be included in the survey. Based on this calculation 39 clusters (574 HH/15 HH per day = 38.3 rounded up to 39) were selected to be included in the survey.

Out of 294 villages, 39 villages, corresponding to 39 clusters along with 4 reserved clusters were included in the survey. 4 clusters were inaccessible due to their remote location at the mountain top with difficult terrain that would require teams to walk for hours amid heavy rains experienced at the time of data collection.

Reserve Clusters (RCs) were used in accordance with the requirement that they are only surveyed if 10% or more clusters are impossible to reach during the field data collection. To allow easier identification, and entry of data into ENA software, replacement clusters were assigned different cluster numbers as follows: RC1 (Cluster 40), RC2 (Cluster 41), RC3 (Cluster 42) and RC4 (Cluster 43).

2.5 Survey implementation

2.5.1 Survey management and coordination

The survey protocol was developed and presented to the Nutrition Information Working Group for validation. Relevant information on security and access was also obtained before the survey from County authorities. Meetings were held with local authorities on arrival in order to brief them on the survey objectives, methodology and procedures including requesting from them updated information on security, access, and village level population.

2.5.2 Survey teams training, supervision and data quality

The survey was conducted by four teams. Each team consisted of three members (1 team leader and 2 enumerators). Survey teams were trained on the SMART methodology for four days from 10th to 13th June 2019.

The training focused on survey objectives, sampling methodology, anthropometric measurements, field procedures, administration of the survey tools and interviewing techniques.

On the third day of training, the standardization test was conducted to evaluate the accuracy and precision of the survey enumerator’s measurements. Each enumerator took measurements of 10 children (aged 6-59 months) twice.

Data collection tools were pre-tested as part of the training of enumerators in a village close to the training venue in Torit County.

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Data collection was undertaken for ten days starting on 14th to 24th June 2019. A rest day was provided for the teams in order to avoid fatigue due to the prolonged data collection period.

All 39 clusters were surveyed; they included 35 of the originally sampled clusters and 4 replacement clusters. One cluster (cluster 33) was repeated after it was discovered that the team had visited a different village that had a similar name as the one selected.

At the field level, close supportive supervision by the consultant and two dedicated AVSI staff ensured the quality of data collected from the field was good. Any mistakes that were noted during data collection were corrected.

Every other day after data collection, all the teams were able to submit the data electronically. A central data manager was on standby to receive and upload data to a configured server where it could be retrieved and analyzed.

Anthropometric data were analyzed using ENA for SMART software July 9th, 2015 version. Based on the results, the survey manager gave feedback to the survey teams. For errors and flagged data observed after daily entry and analysis, the feedback was given to the teams to ensure continuous improvement as data collection progressed.

2.5.3 Data collection tools

Structured questionnaires were used to collect quantitative data. Data was collected using the KoBo Toolbox digital platform. Data collection tools included: Mortality questionnaire (targeting all households), anthropometric questionnaire (targeting children 6-59 months) and a Food Security and Livelihoods questionnaire (targeting mothers/caregivers).

All questionnaires used were the standard SMART survey data collection tools recommended by the Nutrition Information Working Group (Appendix 4).

2.6 Data collected

In all selected households, all children 6-59 months were included in the anthropometric survey. The age of the children was determined using birth certificates where available and a local calendar of events for children without birth certificates. If there were no children 6-59 months in the household, the household was still interviewed for mortality. All survey data were collected by recall.

The following case definitions were used in the assessment:

2.6.1 Mortality

Retrospective mortality data were collected in all the visited households, including those with no children aged 6-59 months. A recall period of 98 days was used.

Individual mortality questionnaire was used to collect the following data:

• Total number of people in the household

• Number of children under five years

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• Number of people who left the household within the recall period (total and children under five years)

• Number of people who joined the household within the recall period (total and children under five years)

• Number of births in the household within the recall period

• Number of deaths in the household within the recall period (total and children under five years)

• Cause of deaths

2.6.2 Individual information per survey child - Anthropometry

Age: The primary source for this information was the child’s immunization card, birth certificate or birth notification. In the absence of these documents, a local calendar of events was used to estimate the age (Appendix 5).

Gender/Sex: This was recorded as either ‘f’ for female or ‘m’ for male.

Weight: A digital weighing scale (SECA) was used to measure children’s weight. Children were weighed with minimal or no clothing and weight recorded to the nearest 0.1kg.

The teams on daily basis calibrated the electronic scales using a standard weight to ensure accuracy.

Height/Length: This was measured using a standard UNICEF height/length board – taking a standing height for children 24-59 months (or >87 cm) and recumbent length for children 6-23 months (or <87 cm). Both height and length were measured to the nearest 0.1 cm. Measurement was done by a measurer and recorded with assistance from the child’s mother/caretaker.

MUAC: Mid Upper Arm Circumference was measured on the left arm at the middle point between the tip of the elbow and the tip of the shoulder bone while the arm was at right-angle. MUAC was measured to the nearest mm. In the event of a disability on the left arm or a left-handed child, the right arm was used.

Bilateral Oedema: This was assessed by the application of moderate thumb pressure for at least 3 seconds on both feet. If a depression formed upon pressure application, the presence of bilateral oedema was confirmed.

2.7 Data entry, analysis, and report writing

After every other day of data collection, data was downloaded from the KoBo Toolbox application and entered into the ENA software for analysis.

Data analysis for anthropometry and mortality was done using ENA for SMART software, July 9th, 2015 version. Anthropometric indices were computed in ENA and results reported with the exclusion of SMART flags.

Data on food security and livelihoods were analyzed using Excel and SPSS.

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2.8 Classifying malnutrition

2.8.1 Weight for height (WFH) and MUAC – Wasting among children

The prevalence of wasting is presented as global acute malnutrition (GAM) and severe acute malnutrition (SAM) using weight-for-height (WFH) Z- scores and MUAC indices described below:

• Children whose WFH Z-scores fell below -2 standard deviations from the median of the WHO growth standards or had bilateral oedema were classified as wasted (to reflect GAM).

• Children whose WFH Z-scores fell below -3 standard deviations from the median of the WHO growth standards or had bilateral oedema were classified as severely wasted (to reflect SAM).

• A cut-off point of <125mm MUAC was used to denote GAM among the under-fives.

2.8.2 Weight for age (WFA) – Underweight

The prevalence of underweight is presented as weight for age (WFA) Z scores defined below:

• Children whose WFA Z-scores fell below -2 standard deviations from the median of the WHO growth standards were classified as underweight.

• Children whose WFA Z-scores fell below -3 standard deviations from the median of the WHO growth standards were classified as severely underweight.

2.8.3 Height for age (HFA) – Stunting

The prevalence of stunting is presented as height-for-age (HFA) Z scores defined below:

• Children whose HFA Z-scores fell below -2 standard deviations from the median of the WHO growth standards were classified as stunted.

• Children whose HFA Z-scores fell below -3 standard deviations from the median of the WHO growth standards were classified as severely stunted.

2.9 Results dissemination

Preliminary results of the survey were reported using the recommended MS Word template. Results in PowerPoint slides were presented to AVSI and the NIWG for validation.

The consultant later produced a final draft report in MS Word accompanied by ENA and Excel data sets. Final survey documents and reports were submitted to AVSI after incorporating feedback given and for circulation.

2.10 Possible bias and assumptions

The survey excluded some villages which were considered insecure and others inaccessible. This meant that the coverage area for this survey had to be restricted to areas that were considered safe and accessible for effective data collection. It is worth noting that the excluded villages could be areas presenting poorer health and nutrition situation since they are more difficult to access

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and often miss out on essential services provided by humanitarian agencies. Although some villages were excluded, the survey still covered the entire County.

Recall bias, as well as the intentional exclusion of some children, could have occurred. 69% of the children did not have records indicating birth dates.

Anthropometric measurements are prone to measurement errors. To minimize errors, survey teams were adequately trained on measurement techniques and standardization test carried out in order to improve the accuracy and precision of measurements.

It is important to acknowledge the limitations of comparing survey results between November 2018 and June 2019. First, this is a significant time gap between two surveys which would likely have an impact on the current malnutrition prevalence. Secondly, the two surveys were conducted in different seasons; since the level of malnutrition may change depending on seasonality this limits meaningful comparability of the two survey results.

Causes of death should also be interpreted with caution because they were reported by family members based on observation, with no verification of the clinical cause of death. To further mitigate against potential biases, questions were translated into local language and tools were refined through pretesting.

2.11 Ethical considerations

Participation in the survey was voluntary. Verbal consent was sought from all respondents (mothers and caregivers of children) before starting the interview.

Children were weighed naked if the mother or caregiver gave the authorization if not weighing was done with the minimum possible clothing.

Mothers and/or caregivers of moderately and severely acutely malnourished children (assessed with Weight-for-Height or with MUAC) were informed of their condition and a referral slip to the nearest nutrition site was provided.

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3 Survey results

3.1 Age and sex pyramid

Overall, male to female sex ratio was 0.82. There are differences between age groups with male to female sex ratio found to be low in the 18-49 (0.67) age group. Individuals between the ages of 18-49 years made up more than two-fifths (42.6%) of the surveyed population. Notable were the indents in the population pyramid among males in the age group (20-24). One probable contributing factor could be the higher rate of out-migration noted in the survey. A higher proportion of the population is female than male. A higher percentage of children under five was observed (24.9% of those surveyed).

Figure 3: Population Pyramid of the former County of Ikwoto

3.2 Anthropometric results (based on WHO standards 2006)

3.2.1 Distribution by age and sex

Anthropometric measurements were taken on a total of 613 children (315 boys and 298 girls) aged 6-59 months to assess acute malnutrition. The distribution of the assessed children by age and sex shows that boys and girls were equally represented with a ratio of 1.1.

The age ratio of 6-29 months to 30-59 months was 1.39 (The value should be around 0.85). This was found to be statistically significant (p-value = 0.000). The representation of the older age category (54-59 months) was lower compared to younger children. The under-representation of children in the 54-59 months age group was mainly due to the absence of these children as many were reported: i) attending school; ii) participating in family farming activities; iii) sent to the refugee camps in Uganda. As explained in the latter part of the preceding statement, the under-representation of older children is consistent with the gender bias observed, especially considering that farming is conducted mainly by females and that they also receive more

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attention in terms of child care practices (as proved by the significant gap in nutrition results by gender – see table 6). This occurrence is similar to the survey result observed by AVSI in its previous SMART survey, where the total number of children between 54 and 59 months of age was found to be 4%.

Distribution of age and sex is shown in Table 5 below.

Table 5: Distribution of age and sex sample

Boys Girls Total Ratio

AGE (mo) no. % no. % no. % Boy:girl

6-17 92 48.4 98 51.6 190 31.0 0.9

18-29 87 52.1 80 47.9 167 27.2 1.1

30-41 62 46.6 71 53.4 133 21.7 0.9

42-53 55 57.9 40 42.1 95 15.5 1.4

54-59 19 67.9 9 32.1 28 4.6 2.1

Total 315 51.4 298 48.6 613 100.0 1.1

3.2.2 Prevalence of acute malnutrition based on Weight for Height and by sex

The prevalence of Global Acute Malnutrition (GAM) rate for the former County of Ikwoto was 9.2% (6.6 – 12.8 95% C.I.), and the severe acute malnutrition (SAM) rate was 2.0% (1.0 – 4.0 95% C.I.). GAM indicates a poor nutrition situation while SAM is classified as critical according to WHO classification.

Results also showed a significant difference between boys and girls in the prevalence of acute malnutrition estimated by Weight for Height Z-score (p-value = 0.0291). Boys were more malnourished compared to girls with GAM rate at 12.5% for boys and 5.9% for girls. This suggests a higher vulnerability to acute malnutrition by boys compared to girls.

Acute malnutrition levels seemed to have declined from November 2018 to June 2019 (GAM: 13.5% to 9.2%, SAM: 2.6% to 2.0% although such a comparison is not valid since the two surveys were done in different seasons. The apparent improvement in GAM prevalence should be considered cautiously since GAM by WHZ was found to be much higher among boys (12.5%) than girls (5.9%).

The reduction in acute malnutrition may be attributed to the robust multi-sector and inter-agency interventions currently being implemented in Ikwoto County. These include but not limited to the UNICEF funded nutrition program, FAO’s food security and livelihoods program, HPF health program among others implemented by AVSI and other collaborating partners. The situation was further boosted by a better performance of the rain season in the last quarter of 2018 which contributed to improved harvest and availability of food and pasture for animals thereby impacting positively on the food security situation at the household level. Additionally,

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a low level of conflict supported greater household movement, improved trade flows, and the resumption – to some degree - of typical livelihood activities.8

Lastly, it should not be underestimated that the movement of people from Ikwoto to Ugandan refugee camps has a great impact in that, although not any more motivated by insecurity, it continues to be significant during the lean season, and tends to slow down during the harvest season, when people from the camps return to Ikwoto. In this context, the departure of the most food-insecure households may have contributed to keeping malnutrition levels low during the lean season.

Table 6: Prevalence of acute malnutrition based on weight-for-height Z-scores and/or oedema by sex

All n = 595

Boys n = 305

Girls n = 290

Prevalence of global malnutrition (<-2 z-score and/or oedema)

(55) 9.2 % (6.6 - 12.8 95% C.I.)

(38) 12.5 % (8.4 - 18.1 95% C.I.)

(17) 5.9 % (3.7 - 9.3 95%

C.I.)

Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema)

(43) 7.2 % (5.1 - 10.1 95% C.I.)

(32) 10.5 % (7.1 - 15.3 95% C.I.)

(11) 3.8 % (2.1 - 6.6 95%

C.I.)

Prevalence of severe malnutrition (<-3 z-score and/or oedema)

(12) 2.0 % (1.0 - 4.0 95%

C.I.)

(6) 2.0 % (0.8 - 4.9 95%

C.I.)

(6) 2.1 % (1.0 - 4.4 95%

C.I.)

No case of oedema was observed during the assessment.

Graphical presentation of the above results shows that the curve had deviated to the left, with a mean of -0.74 and SD of ±0.98. The standard deviation of ±0.98 is within the acceptable range of 0.8 to 1.2. The design effect (DEFF) determined was 1.55 which shows the existence of some inter-cluster differences. The cases of malnutrition appeared to be aggregated into certain clusters as indicated by the index of dispersion (ID) values for WHZ <-2: ID=1.44 (P=0.037).

8 FEWS NET South Sudan Food Security Outlook. June 2019 – January 2020.

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Figure 4: Frequency Distribution of WFH Z-scores for children 6-59 months

3.2.3 Prevalence of acute malnutrition by age based on Weight for Height

The prevalence of acute malnutrition (WHZ<-2 and/or oedema) by age shows that children aged (6-17 months) were more malnourished. This may be attributed to sub-optimal feeding practices among these children. Contrastingly, the prevalence of severe wasting (WHZ<-3 and/or oedema) affected children in all age groups except those aged 54-59 months.

Table 7: Prevalence of acute malnutrition by age based on weight-for-height Z-scores and/or oedema

Severe wasting (<-3 z-score)

Moderate wasting

(>= -3 and <-2 z-score )

Normal (> = -2 z score)

Oedema

Age (mo) Total no.

No. % No. % No. % No. %

6-17 182 4 2.2 23 12.6 155 85.2 0 0.0

18-29 164 3 1.8 9 5.5 152 92.7 0 0.0

30-41 129 3 2.3 6 4.7 120 93.0 0 0.0

42-53 93 2 2.2 3 3.2 88 94.6 0 0.0

54-59 27 0 0.0 2 7.4 25 92.6 0 0.0

Total 595 12 2.0 43 7.2 540 90.8 0 0.0

3.2.4 Distribution of acute malnutrition and oedema based on WFH Z-scores

There were no oedema cases identified during the survey, however, 23 marasmic cases were identified as shown in Table 8 below.

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Table 8: Distribution of acute malnutrition and oedema based on WFH Z-scores

<-3 z-score >=-3 z-score

Oedema present Marasmic kwashiorkor No. 0

(0.0 %)

Kwashiorkor No. 0

(0.0 %)

Oedema absent Marasmic No. 23 (3.8 %)

Not severely malnourished No. 590 (96.2 %)

3.2.5 Prevalence of acute malnutrition based on MUAC cut off’s and/or oedema by sex

Table 9 below shows the prevalence of global acute malnutrition based on MUAC (<125 mm) and/or oedema which was 3.4 % (2.3 – 5.1 95% C.I.) and of severe acute malnutrition (MUAC<115 mm and/or oedema) was 0.7 % (0.2 – 1.7 95% C.I.).

The prevalence of acute malnutrition using MUAC is significantly lower compared to using Weight for Height Z-scores. It is worth noting that mass screening for malnutrition is done via MUAC in this target population, and the majority of cases admitted to OTP/TSFP are done through MUAC. It is, therefore, possible that malnutrition, as identified by MUAC, is identified and treated more quickly than those defined through WHZ, resulting in lower malnutrition rates when defined by MUAC.

Table 9: Prevalence of acute malnutrition based on MUAC cut off's and/or oedema by sex

All n = 613

Boys n = 315

Girls n = 298

Prevalence of global malnutrition (< 125 mm and/or oedema)

(21) 3.4 % (2.3 - 5.1 95%

C.I.)

(10) 3.2 % (1.8 - 5.6 95%

C.I.)

(11) 3.7 % (2.1 - 6.5 95%

C.I.)

Prevalence of moderate malnutrition (< 125 mm and >= 115 mm, no oedema)

(17) 2.8 % (1.8 - 4.3 95%

C.I.)

(6) 1.9 % (0.9 - 4.1 95%

C.I.)

(11) 3.7 % (2.1 - 6.5 95%

C.I.)

Prevalence of severe malnutrition (< 115 mm and/or oedema)

(4) 0.7 % (0.2 - 1.7 95%

C.I.)

(4) 1.3 % (0.5 - 3.4 95%

C.I.)

(0) 0.0 % (0.0 - 0.0 95%

C.I.)

3.2.6 Prevalence of acute malnutrition based on MUAC cut off’s and/or oedema by age

Generally, younger children aged 6-17 months were more affected by both moderate (MUAC cut off >115 mm and < 125 mm) and severe acute malnutrition (MUAC cut off <115 mm).

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Table 10: Prevalence of acute malnutrition based on MUAC cut off's and/or oedema by age

Severe wasting (< 115 mm)

Moderate wasting

(>= 115 mm and < 125 mm)

Normal (> = 125 mm)

Oedema

Age (mo) Total no.

No. % No. % No. % No. %

6-17 190 2 1.1 13 6.8 175 92.1 0 0.0

18-29 167 1 0.6 1 0.6 165 98.8 0 0.0

30-41 133 0 0.0 0 0.0 133 100.0 0 0.0

42-53 95 1 1.1 0 0.0 94 98.9 0 0.0

54-59 28 0 0.0 0 0.0 28 100.0 0 0.0

Total 613 4 0.7 14 2.3 595 97.1 0 0.0

3.2.7 Prevalence of underweight based on weight for age Z scores by sex

The prevalence of underweight among children was 26.9% (22.1 – 32.3 95% C.I.) with 4.3 % (2.7 – 6.7 95% C.I.) being severely underweight.

More boys were underweight (34.5%) than girls (18.9%), with the difference in the prevalence of underweight between boys and girls being statistically significant (p-value = 0.0021).

Table 11: Prevalence of underweight based on weight-for-age Z-scores by sex

All n = 606

Boys n = 310

Girls n = 296

Prevalence of underweight (<-2 z-score)

(163) 26.9 % (22.1 - 32.3

95% C.I.)

(107) 34.5 % (27.4 - 42.4

95% C.I.)

(56) 18.9 % (14.5 - 24.3

95% C.I.)

Prevalence of moderate underweight (<-2 z-score and >=-3 z-score)

(137) 22.6 % (18.7 - 27.1

95% C.I.)

(90) 29.0 % (23.0 - 35.9

95% C.I.)

(47) 15.9 % (11.9 - 20.8

95% C.I.)

Prevalence of severe underweight (<-3 z-score)

(26) 4.3 % (2.7 - 6.7 95%

C.I.)

(17) 5.5 % (3.2 - 9.2 95%

C.I.)

(9) 3.0 % (1.5 - 5.9 95%

C.I.)

3.2.8 Prevalence of underweight based on weight for age Z scores by age

Prevalence of underweight by age shows children in the age group 18-29 months were most affected by moderate underweight. On the other hand, younger children aged 6-17 months were more likely to be affected by severe underweight.

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Table 12: Prevalence of underweight based on weight-for-age Z-scores by age

Severe underweight (<-3 z-score)

Moderate underweight

(>= -3 and <-2 z-score )

Normal (> = -2 z score)

Oedema

Age (mo) Total no.

No. % No. % No. % No. %

6-17 186 13 7.0 35 18.8 138 74.2 0 0.0

18-29 165 5 3.0 46 27.9 114 69.1 0 0.0

30-41 133 5 3.8 34 25.6 94 70.7 0 0.0

42-53 94 3 3.2 16 17.0 75 79.8 0 0.0

54-59 28 0 0.0 6 21.4 22 78.6 0 0.0

Total 606 26 4.3 137 22.6 443 73.1 0 0.0

3.2.9 Prevalence of stunting based on height for age Z scores

The overall prevalence of stunting (HAZ <-2) was 31.9% calculated with a SD of 1.9 This result is classified as serious according to WHO classification of chronic malnutrition.

3.2.10 Mean Z-scores, design effects and excluded subjects

The mean Z scores for wasting (WHZ), underweight (WAZ) and stunting (HAZ) were; -0.74±0.98, -1.35±0.99 and -1.53±1.21 respectively, all indicating a poorer nutrition situation compared to WHO reference population. The standard deviations for WHZ and WAZ were within the acceptable range of 0.8-1.2 but higher for HAZ (1.21) indicating a possible overestimation of stunting rates. The sample design effect values of 1.55 (WHZ) and 1.72 (WAZ) showed moderate inter-cluster variability, but for the 2.29 for (HAZ) indicated higher inter-cluster variability.

Table 13: Mean Z-scores, Design Effects and excluded subjects

Indicator n Mean z-scores ± SD

Design Effect (z-score < -2)

z-scores not available*

z-scores out of range

Weight-for-Height 595 -0.74±0.98 1.55 0 18

Weight-for-Age 606 -1.35±0.99 1.72 0 7

Height-for-Age 600 -1.53±1.21 2.29 0 13

* contains for WHZ and WAZ the children with oedema.

9 If the SD of a given anthropometric index is greater than 1.2, it is recommended that the calculated prevalence (with a SD of one) is reported in the final survey report. The SMART Plausibility Check for Anthropometry 2015

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3.3 Mortality results (retrospective over 98 days prior to interview)

Mortality data was collected using the mortality individual questionnaire. The survey revealed a death rate of 0.42 (0.22-0.79) per day among the adult population, while the under-five mortality rate was 0.46 (0.15-1.44) as reported in the previous 98 days. Both the CMR and the U5MR were below the WHO emergency thresholds of 1/10,000/day and 2/10,000/day respectively.

Most deaths were as a result of illness (81.8%) while injuries contributed 18.2% of deaths. 45.5% of deaths occurred in the current location, 18.2% occurred during migration and another 36.4% of deaths occurred in the place of the last residence.

Mortality results are presented in table 14 below.

Table 14: Demographic profile of the respondents

Parameters for Mortality Results (C.I 95%)

CMR (deaths per 10 000/day 0.42 (0.22-0.79 95% CI)

U5MR (deaths in children <5/10 000/day 0.46 (0.15-1.44 95%)

Persons recorded within the recall period 2671.5

Current residents <5 years old 666.5

Total deaths during the recall period 11

Total deaths during the recall period <5 years old 3

Recall Period (days) 98

Population to be included 3215

Average HH Size 4.7

Households to be included 574

Birth rate 1.18

In-migration rate (Joined) 9.21

Out-migration rate (Left) 14.97

Cause of death % Location of death %

1. Illness 81.8 1] In current location 45.5

2. Injury/traumatic 18.2 2] During migration 18.2

3. Unknown 0 3] In the place where person migrated

36.4

4] Other 0

The survey covered 570 households that were randomly selected in the survey area. The total household members studied were 2671.5 with children aged below 5 years totaling to 666.5 (male-340, female-326.5). The average family size was approximately 5 (4.7) which was derived from the household members. The limited number of average household members is related to the movement of people from Ikwoto to the refugee camps in Uganda which peaks during the lean season. Ample anecdotal evidence shows that refugee camps attract especially young children reaching school age (from 4 to 10), who face different barriers in accessing education in South Sudan, forcing families to send them to refugee camps to enable them access food and

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education. It is therefore unsurprising that the level out-migration of people (15%) is much higher compared to the in-migration rate at 9%. These figures are also consistent with data gathered by AVSI during its previous SMART survey (November 2017), with regard to both the significant net outflow of people and the limited numerosity of households found previously to be 4.4.

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4 Other results

4.1 Food Security and Livelihoods

Household-level interviews included questions to derive a number of international standard composite food security indicators to evaluate the food security status of households. These included: The Household Hunger Scale (HHS), Food Consumption Score (FCS), Household Dietary Diversity Score (HDDS), Reduced Coping Strategy Index (rCSI) and the Livelihoods Based Coping Strategies.

4.1.1 Income sources for households in the past 30 days

Overall, all the surveyed households reported at least one source of income and mostly relied on the sale of crops (51.2%). Another 18.9% of households indicated they depend on the sale of natural resources (firewood; charcoal; grass etc.) to earn income. Although many households reported having a source of income, the types of income sources described were low-skill, temporary or offered only limited earning potential. Sources of income are shown in figure 6.

Figure 5: Main sources of income in Ikwoto

4.1.2 Household hunger scale

The Household Hunger Scale (HHS) is a measure of household food access that captures insufficient food quantity based on the physical consequences of hunger experienced in a household over the past 30 days. This index measures whether households fall into severe and moderate categories of hunger or whether they experienced little to no hunger. Using this composite indicator, a respondent can score between zero and six depending on their answers. Individuals scoring from zero to one experience the least hunger and respondents scoring six experience the most hunger. Survey results showed that 66.3% of respondents experienced little to no hunger while 33.7% indicated they experienced moderate hunger as shown in table 19 below.

0.2

51.2

7.5

2.3

8.4

0.4

18.9

0.2

2.3

0.4

2.8

1.8

3.3

0.4

No income

Sale of Crops

Sale of Livestock

Sale of Livestock products

Brewing

Sale of fish

Sale of Natural Resources

Sale of Food Aid

Casual labour

Skilled labour

Salaried Work

Small business/petty trading

Family support

Other

HH main income sources

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Table 15: Household hunger scale

Household Hunger Scale Frequency Proportion (95% C.I)

Little to no hunger: 0 to 1 378 66.3 (62.5-69.8)

Moderate hunger: 2 to 3 192 33.7 (29.8-37.5)

Severe hunger: 4 to 6 0 0

Total 570 100

4.1.3 Household food consumption score

The household food consumption score (FCS) uses the information provided about weekly food consumption by type of food and frequency of consumption (how many days per week) and weights it against the nutritional value to provide an indication of a household’s consumption patterns.

Only 38.6% of the sampled households had acceptable food consumption, 43.9% of households had borderline food consumption with 17.5% of surveyed households reporting poor food consumption. The results are as shown in the table below.

Table 16: Household food consumption score

Food consumption score Frequency Proportion (95% C.I)

Poor food consumption: 0 to 21 100 17.5 (14.6-20.9)

Borderline food consumption: 21.5 to 35

250 43.9 (39.8-47.7)

Acceptable food consumption: >35 220 38.6 (34.6-42.5)

Total 570

As presented in the chart below, nearly all sampled households (98.4%) reported consuming cereals, tubers and roots in the week prior to the household interview. Similarly, the vast majority of households (97.9%) reported consuming vitamin A rich vegetables and leaves. In addition, most households (76.3%) indicated that they consumed oil or fats.

Plant-based protein-rich foods (pulses, legumes, and nuts) were consumed by only half of the sampled households (51.6%).

Consumption rates of nutrient-rich foods including meat, fish, and eggs were average at 52.8%. Similarly, milk and milk products were consumed by 54.7% of households.

Household food consumption patterns show that many households are prioritizing intake of high-energy foods (cereals, roots, and tubers) and vitamin A rich vegetables and leaves including oil or fats. Nutrient dense foods including meat, eggs, fish, milk, and dairy products were consumed by about half of the sampled households, and this may explain why fewer households had acceptable food consumption.

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Figure 6: Households consuming each food group

4.1.4 Household dietary diversity

Dietary diversity is a qualitative measure of food consumption that reflects household access to a variety of foods and is also a proxy for nutrient adequacy of the diet of an individual.

The survey questionnaire comprised the twelve major food groups including: cereals, roots and tubers, legumes, vegetables (the vegetable food group is a combination of vitamin A rich vegetables and tubers, dark green leafy vegetables and other vegetables including wild vegetables and leaves), fruits (the fruit group is a combination of vitamin A rich fruits and other fruits including wild fruits), meat and poultry, eggs, fish and seafood, milk and milk products, sugar and sweets, fats and oils, and condiments (spices, sweets, unsweetened beverages).

Only 12.6% of households consumed six or more food groups in the previous twenty-four hours and can be said to have acceptable dietary diversity. Another 26.3% of households were found to have medium dietary diversity with most households (61.1%) having low dietary diversity.

The mean HDD score for all households in the survey was 4.1 food groups which show that on average, households consumed 4 or fewer food groups the previous day of the interview.

Table 17: Household dietary diversity score

Household dietary diversity Frequency Proportion (95% C.I)

Low diversity (0-4 food groups) 348 61.1 (56.8-64.7)

Medium diversity (5 -6 food groups) 150 26.3 (22.8-30.0)

High diversity (>6 food groups) 72 12.6 (9.8-15.6)

570

98.4

51.6

52.8

97.9

11.8

54.7

24.6

76.3

Cereals, tubers & roots

Pulses, legumes & nuts

Meat, eggs & fish

Vit A rich vegetables & leaves

Vit A rich fruits & wild fruits

Milk and dairy products

Sugar and honey

Oil/fats

% of HHs consuming each food group

Page 25

4.1.5 Reduced coping strategy index

The reduced coping strategy index assesses how households cope when faced by food shortages or declining access to food. Households were assessed based on five strategies which were then weighted based on their severity. The higher the score, the more frequent and severe these strategies are, and this indicates high vulnerability to food insecurity by a household.

Overall, 95.8% of households had a low reduced copying strategy index and no household had a high rCSI.

Table 18: Reduced coping strategy index

Reduced coping strategy index Frequency Proportion (95% C.I)

Low rCSI (0-15) 546 95.8 (94.2-97.4)

Moderate rCSI (16-40) 24 4.2 (2.6-6.0)

High rCSI >40 0 0

570 100

4.1.6 Livelihoods based coping strategies

Livelihood based coping strategies were collected using a 30-day recall period and were used to measure households’ use of negative coping strategies relating to increased livelihood stress and asset depletion. Respondents were asked if they used any of the listed strategies over the 30 days before the interview and, if not if the reason was that they had exhausted that possibility or because they did not need to. The strategies were then classified into three categories: stress strategies, crises strategies, and emergency strategies.

Overall, 47.7% of households used coping strategies to meet their basic food needs. Varied responses were given by the assessed households. The main reported coping strategies were stress coping strategies of borrowing money or purchasing food on credit (16.1%), and spending savings or selling more animals (11.8%). Only a few households reported using crisis and emergency coping strategies, and this indicates that households are using less severe measures. Nonetheless, the use of stress coping strategies shows that some of the households are at risk of food insecurity.

Table 19: Livelihoods based coping strategies used by households

Rating Coping strategy N n Proportion (95% C.I.)

Households not coping

570

298 52.3 (48.2-56.5)

Stress coping strategies

Sent household members to eat elsewhere

17 3.0 (1.8-4.6)

Spent savings/sold more animals 67 11.8 (9.3-14.2)

Sold household assets e.g. furniture, utensils

12 2.1 (1.1-3.3)

Borrow money/ purchase food on credit 92 16.1 (13.2-19.1)

Page 26

Crisis coping strategies

Withdrew children from school 13 2.3 (1.2-3.5)

Sold productive assets or means of transport

5 0.9 (0.2-1.8)

Reduced essential non-food expenses such as health, education, etc.

27 4.7 (3.2-6.5)

Emergency coping strategies

Entire household migrated 19 3.3 (1.9-4.7)

Engaged in degrading jobs or risky income generating activities

6 1.1 (0.4-1.9)

Begged 8 1.4 (0.5-2.5)

Sold last female animal 5 0.9 (0.2-1.8)

Page 27

5 Discussion

5.1 Nutrition status

5.1.1 Acute malnutrition

According to the survey findings, the global acute malnutrition (GAM) rate of 9.2% (6.6 – 12.8 95% C.I.) indicates a poor nutrition situation as per the WHO malnutrition thresholds. Contrastingly, the severe acute malnutrition (SAM) rate of 2.0% (1.0 - 4.0 95% C.I.) is classified as critical.

Acute malnutrition levels declined from November 2018 to June 2019 (GAM: 13.5% to 9.2%, SAM: 2.6% to 2.0%). The apparent improvement in GAM prevalence should be considered cautiously since GAM by WHZ was found to be much higher among boys (12.5%) than girls (5.9%). This suggests a higher vulnerability to acute malnutrition by boys compared to girls. This phenomenon needs to be further investigated.

The general trend in malnutrition prevalence indicates that both GAM and SAM have remained stable and consistently below the WHO emergency threshold of ≥15%.

Figure 7: Trend in the prevalence of GAM and SAM in children 6-59 months

5.1.2 Underweight

The prevalence of underweight among the children was 26.9% (22.4-31.9 95% C.I.) while 4.3% (2.7-6.6 95% C.I.) were severely underweight. This is classified as “serious’’ according to WHO classification. Underweight was found significantly higher in boys (34.5%) than in girls (18.9%). Severe underweight level was found to be 4.3%.

Survey results seem consistent with previous SMART survey findings where the overall prevalence of underweight was reported at 22.5% in November 2018 and 24.9% in November 2017. Therefore, underweight is still a burden to the County.

1.62.6 2

10.3

13.5

9.2

November 2017 November 2018 June 2019

Pre

vale

nce

Trend in prevalence of GAM & SAM in children 6-59 months

SAM GAM

Page 28

5.1.3 Chronic malnutrition

The rate of chronic malnutrition in Ikwoto is worrying. The results show a level of stunting considered "serious" at 31.9% calculated with a SD of one. Similar to underweight rate, the overall prevalence of stunting has not changed much since November 2017 when the stunting rate was at 31% and at 28.4% in November 2018. The results indicate that children are suffering from long term nutritional deprivation. This, therefore, highlights the need to prioritize stunting prevention interventions in Ikwoto.

5.2 Mortality

The crude mortality rate (CMR) was 0.42 (0.22 - 0.79 95% C.I.) and the under-five mortality rate was 0.46 (0.15-1.44). Both the CMR and the U5MR were below the WHO emergency thresholds of 1/10,000/day and 2/10,000/day respectively.

A decreasing trend in mortality is apparent since November 2017 when CMR stood at 1.79%; 0.88% in 2018; and 0.42% currently. Between November 2017 and November 2018, the U5MR showed little change, however, the mortality rate reported in this survey shows a substantial decline.

Figure 8: Trend in mortality rates in Ikwoto

5.3 Food Security and Livelihoods

Findings indicated variations in food security indicators among the assessed population. The main source of income was from the sale of crops notwithstanding the fact that the population was assessed during the lean season.

Most of the households (66.3%) said they were experiencing little to no hunger but only 38.6% of the surveyed households had acceptable food consumption. A close examination of consumption of specific food groups showed that the majority of households were prioritizing

1.24 1.44

0.46

1.790.88

0.42

0

0.5

1

1.5

2

2.5

3

3.5

November 2017 November 2018 June 2019

Pre

vale

nce

Trend in mortality rates

U5MR CMR

Page 29

intake of high-energy foods (cereals, roots, and tubers) and vitamin A rich vegetables and leaves including fats or oil. Nutrient dense foods including meat, eggs, fish, milk, and dairy products were consumed by about half of the sampled households (52.8%), and this may explain why fewer households had acceptable food consumption.

Results also showed that most of the households (61.1%) had low diet diversity. The mean HDD score for all households in the survey was 4.1 food groups which indicated that on average, households consumed 4 or fewer food groups the previous day of the interview.

The vast majority of households (95.8%) had a low reduced coping strategy index. However, a substantial number of households (47.7%) reported using one or more livelihood based coping strategies in the month prior to the survey.

These results show that a considerable number of households are food insecure or at risk of food insecurity.

Page 30

6 Conclusion Based on the WHO classification, the prevalence of global acute malnutrition in the former County of Ikwoto is considered “poor’’ and calls for continuation and expansion of programmes meant to treat and prevent acute malnutrition.

Survey findings also indicated that underweight and chronic malnutrition is a major concern. An estimated 26.9% of children aged 6-59 months were found to be underweight while 31.9% suffered from stunting. These results are classified as serious according to WHO malnutrition thresholds.

Various indices of food security indicated that a significant number of households were at risk of food insecurity as measured by their food consumption; diversity of foods consumed; as well as the use of coping mechanisms.

The interventions in place currently should be maintained in order to prevent deterioration of acute malnutrition among children below five years of age. Activities should also increasingly focus on the high levels of both underweight and chronic malnutrition.

Integrated multisectoral approaches should be continually implemented to reduce risk factors associated with both acute and chronic malnutrition particularly: morbidity, food insecurity, poor child caring practices, lack of safe drinking water and limited sanitation and hygiene facilities. In addition to these interventions, there is a need to develop longer-term strategies to enhance the provision of basic services, livelihood support, and social protection mechanisms.

Page 31

7 Recommendations 1. Continue implementing the ongoing CMAM program in order to treat and prevent acute

malnutrition among vulnerable children. Taking into account the high rate of SAM cases noted by the survey, AVSI should consider expanding or creating more health facility-based or outreach sites to help address the high caseload of SAM children.

2. Considering that a number of malnourished children were referred to health facilities and nutrition centers for treatment, there is need to scale up community level screening in order to detect all malnourished cases that need to be enrolled in treatment programs. This can be achieved through regular training and providing incentives to community nutrition volunteers.

3. According to survey findings, boys were more affected by both acute and chronic malnutrition compared to girls. There is a need to conduct a Nutrition Causal Analysis (NCA) study to understand why boys tend to be more malnourished in the County and also identify the predominant causes of malnutrition to be prioritized for intervention.

4. Survey results have shown that chronic malnutrition is a burden for the County. Therefore, more efforts are needed for stunting prevention through various multi-sectoral interventions. For a start, AVSI should consider intensifying health and nutrition education activities on ways of diversifying the household diet, good IYCF practices, improving household hygiene and sanitation practices, improving health-seeking behaviors as well as sensitizing the community on the domestic treatment of drinking water.

5. A significant number of households were found to be at risk of food insecurity as measured by their food consumption. The current FAO food security and livelihoods program targeting local communities in Ikwoto should be expanded to include other simple and cost-effective livelihoods interventions meant to improve food security such as: supporting farmers to produce fast-growing edible crops, promoting utilization of indigenous food, training farmers on food processing and preservation among other interventions.

6. The survey noted that the majority of the households had poor dietary diversity and mostly consumed food of low nutritional value (cereals and tubers, vegetables and oil/fat). Community members should be targeted with continuous health education on strategies for dietary diversification such as: establishing kitchen gardens at the household level, keeping small animals, and demonstrating to mothers or caregivers how to prepare quality diets.

Page 32

8 Appendices

Appendix 1: Plausibility report

Overall data quality Criteria Flags* Unit Excel. Good Accept Problematic Score

Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-7.5 >7.5

(% of out of range subjects) 0 5 10 20 5 (2.9 %)

Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001

(Significant chi square) 0 2 4 10 0 (p=0.492)

Age ratio(6-29 vs 30-59) Incl p >0.1 >0.05 >0.001 <=0.001

(Significant chi square) 0 2 4 10 10 (p=0.000)

Dig pref score - weight Incl # 0-7 8-12 13-20 > 20

0 2 4 10 0 (3)

Dig pref score - height Incl # 0-7 8-12 13-20 > 20

0 2 4 10 0 (7)

Dig pref score - MUAC Incl # 0-7 8-12 13-20 > 20

0 2 4 10 0 (4)

Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >=1.20

. and and and or

. Excl SD >0.9 >0.85 >0.80 <=0.80

0 5 10 20 0 (0.98)

Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6

0 1 3 5 1 (-0.21)

Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6

0 1 3 5 1 (0.24)

Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001

0 1 3 5 1 (p=0.037)

OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 18 %

The overall score of this survey is 18 %, this is acceptable.

Page 33

Appendix 2: List of selected clusters

Geographical unit Population size Cluster

Logele 672 1

Lohutulo 1668 2

Hiba 1668 3

Alaongorohou 828 7

Lagire kaka 4188 8

Leny-leny 1140 RC

Tarafafa 1536 9

Lomogoro 3468 10

Tolok 1500 11

Logire Hutukhinyany 4272 12

Sarianga 1656 RC

Lomwo 4500 13

Gum 912 14

Lorum 1344 15

Kator 2 216 16

Drivers Quators 120 17

Imee 204 18

Kibirit 1428 19

Ifune Centre 1620 20

Lolongi 2334 22

Lokudaru 3120 23

Lalanga 2826 24

Iteuso 3066 25

Ncoutube 1770 26

Lofayo 1218 RC

Iyak 612 27

NangoleJore 1044 RC

Werewere 294 28

Turkana 1584 29

Obuto 282 30

Napwajire 990 31

Lodurumo 180 32

Nahichoel 342 33

Ingone 192 34

Lodik 900 35

Loluo 1320 36

Nahicod 750 37

Page 34

Tanglobilong 1500 38

Sadit 468 39

Appendix 3: Standardization test results

Standardisation test results

Precision Accuracy OUTCOME

Weight

subjects mean SD max

Technical error

TEM/mean

Coef of reliability

Bias from superv

Bias from median result

# kg kg kg TEM (kg) TEM (%) R (%) Bias (kg) Bias (kg)

Supervisor 10 11 2.5 0.1 0.04 0.4 100 - 0.37 TEM good R value good

Enumerator 1 10 10.9

2.6 0.1 0.04 0.4 100 -0.02 0.35 TEM good

R value good

Bias good

Enumerator 2 10 10.9

2.6 0.2 0.07 0.6 99.9 -0.05 0.31

TEM acceptable

R value good

Bias good

Enumerator 3 10 11

2.6 0.3 0.08 0.7 99.9 -0.01 0.35

TEM acceptable

R value good

Bias good

Enumerator 4 10 10.9

2.5 0.7 0.17 1.5 99.6 -0.05 0.31 TEM poor

R value good

Bias good

Enumerator 5 10 10.9

2.6 0.1 0.05 0.5 100 -0.03 0.33

TEM acceptable

R value good

Bias good

Enumerator 6 10 10.9

2.6 0.3 0.09 0.8 99.9 -0.04 0.32

TEM acceptable

R value good

Bias good

Enumerator 7 10 11

2.6 0.2 0.06 0.5 99.9 0 0.36

TEM acceptable

R value good

Bias good

enum inter 1st 7x10 10.9

2.5 - 0.09 0.8 99.9 - - TEM good R value good

enum inter 2nd 7x10 10.9

2.5 - 0.07 0.6 99.9 - - TEM good R value good

inter enum + sup 8x10 10.9

2.5 - 0.08 0.7 99.9 - - TEM good R value good

Page 36

TOTAL intra+inter 7x10 - - - 0.12 1.1 99.8 -0.03 0.34

TEM acceptable

R value good

Bias good

TOTAL+ sup 8x10 - - - 0.11 1 99.8 - -

TEM acceptable R value good

Height

subjects mean SD max

Technical error

TEM/mean

Coef of reliability

Bias from superv

Bias from median result

# cm cm cm TEM (cm)

TEM (%) R (%) Bias (cm) Bias (cm)

Supervisor 10 84.4 9.8 1.1 0.31 0.4 99.9 - 0.33 TEM good R value good

Enumerator 1 10 84

9.6 0.8 0.28 0.3 99.9 -0.39 -0.07 TEM good

R value good

Bias good

Enumerator 2 10 83.9

9.6 2.1 0.57 0.7 99.6 -0.53 -0.2

TEM acceptable

R value good

Bias good

Enumerator 3 10 83.9

9.6 1.2 0.37 0.4 99.9 -0.52 -0.19 TEM good

R value good

Bias good

Enumerator 4 10 84.4

9.6 1.4 0.47 0.6 99.8 0.02 0.34

TEM acceptable

R value good

Bias good

Enumerator 5 10 84.4

9.5 1.4 0.52 0.6 99.7 -0.02 0.31

TEM acceptable

R value good

Bias good

Enumerator 6 10 84.2

9.4 1.7 0.6 0.7 99.6 -0.26 0.07

TEM acceptable

R value good

Bias good

Page 37

Enumerator 7 10 84.1

9.4 1.8 0.61 0.7 99.6 -0.28 0.05 TEM poor

R value good

Bias good

enum inter 1st 7x10 84.2

9.4 - 0.39 0.5 99.8 - - TEM good R value good

enum inter 2nd 7x10 84.1

9.3 - 0.55 0.7 99.6 - -

TEM acceptable R value good

inter enum + sup 8x10 84.2

9.3 - 0.51 0.6 99.7 - -

TEM acceptable R value good

TOTAL intra+inter 7x10 - - - 0.69 0.8 99.4 -0.28 0.08

TEM acceptable

R value good

Bias good

TOTAL+ sup 8x10 - - - 0.71 0.8 99.4 - -

TEM acceptable R value good

MUAC

subjects mean SD max

Technical error

TEM/mean

Coef of reliability

Bias from superv

Bias from median result

# mm mm mm

TEM (mm)

TEM (%) R (%) Bias (mm) Bias (mm)

Supervisor 10 14.5 1.3 0.5 0.16 1.1 98.5 - -0.4 TEM good

R value acceptable

Bias good

Enumerator 1 10 14.5

1.4 0.5 0.18 1.2 98.3 -0.03 -0.43 TEM good

R value acceptable

Bias good

Enumerator 2 10 14.5

1.3 0.6 0.15 1.1 98.7 0 -0.39 TEM good

R value acceptable

Bias good

Enumerator 3 10 14.9

1.3 0.4 0.13 0.8 99 0.4 0 TEM good

R value good

Bias good

Page 38

Enumerator 4 10 14.7

1.2 0.7 0.27 1.8 95.4 0.16 -0.23 TEM good

R value acceptable

Bias good

Enumerator 5 10 14.6

1.2 0.7 0.25 1.7 95.9 0.12 -0.27 TEM good

R value acceptable

Bias good

Enumerator 6 10 13.9

2.6 10.1 2.27 16.3 25.3 -0.57 -0.97

TEM acceptable

R value reject

Bias good

Enumerator 7 10 14.9

1.3 0.5 0.16 1.1 98.5 0.37 -0.02 TEM good

R value acceptable

Bias good

enum inter 1st 7x10 14.5

1.8 - 1.28 8.8 49.8 - - TEM good R value reject

enum inter 2nd 7x10 14.6

1.3 - 0.25 1.7 96.1 - - TEM good R value acceptable

inter enum + sup 8x10 14.6

1.5 - 0.72 4.9 76.2 - - TEM good R value reject

TOTAL intra+inter 7x10 - - - 1.27 8.7 33 0.07 -0.34 TEM good

R value reject

Bias good

TOTAL+ sup 8x10 - - - 1.19 8.2 38.5 - - TEM good R value reject

Suggested cut-off points for acceptability of measurements

Parameter MUAC mm

Weight Kg Height cm

individual good <2.0 <0.04 <0.4

TEM acceptable <2.7 <0.10 <0.6

(intra) poor <3.3 <0.21 <1.0

Page 39

reject >3.3 >0.21 >1.0

Team TEM good <2.0 <0.10 <0.5

(intra+inter) acceptable <2.7 <0.21 <1.0

and Total poor <3.3 <0.24 <1.5

reject >3.3 >0.24 >1.5

R value good >99 >99 >99

acceptable >95 >95 >95

poor >90 >90 >90

reject <90 <90 <90

Bias good <1 <0.04 <0.4

From sup if good acceptable <2 <0.10

<0.6

outcome, otherwise poor <3 <0.21

<1.4

from median reject >3 >0.21 >1.4

Appendix 4: Data collection tools

DEMOGRAPHY & MORTALITY QUESTIONNAIRE

DATE OF INTERVIEW: [ D ][ D ]/[ M ][ M ]/ [ Y ][ Y ]

Was anyone in the household pregnant at the start of the recall period? No [ ] Yes [ ] If yes, how many? _______

10 HH definition: Group of people living under same roof & sharing food from the same pot for a period of at least 6 months. In home with multiple wives, those living and eating in

different houses are considered as separate HHs. Wives living in different houses and eating from same pot are considered as one HH.

COUNTY: PAYAM: NAME OF INTERVIEWER:

BOMA: VILLAGE:

CLUSTER NO. [ ][ ] TEAM NO. [ ][ ] HOUSEHOLD10 NO. [ ][ ]

01 02 03 04 05 06 07 08 09 10

No.

Name

Sex

(M/F)

Age

(years)

Joined on or after:

Left on or after:

Born on or after:

Died on or after:

Cause of death

1= illness 2=injury

66=unknown

Location of death

1=current location 2=during

migration 3=in place of last residence

4=other

__________________________ (Start date of the recall period - ex. Jan. 1, 1900)

WRITE ‘Y’ for YES. Leave BLANK if NO.

a) List all the people that slept in this household last night.

1 2

3 4 5

6 7

8 9

10

11 12

13 14 15

16 17

18 b) List all the people that slept in this household on the first night of the recall period (FILL IN DATE/EVENT) but did NOT sleep in the household last night.

1 Y

2 Y 3 Y 4 Y

5 Y 6 Y

7 Y c) List all the people that slept in this household on the first night of the recall period but have since died

1 Y

2 Y 3 Y

4 Y 5 Y

ANTHROPOMETRIC QUESTIONNAIRE (to be conducted in every SELECTED HH with children 6-59 months)

Date (DD/MM/YY): …./……/….... Cluster No:……… Team No…..… State:……………….. County……..…….. Payam:………………. Boma………..…….. Village:………....……

1 2 3 4 5 6 7 8 9 10

Child No.

HH NO

Child Name Sex

m = Male f = Female

Date of Birth

(DD/MMYY)

Age in months Weght in Kg (eg 12.4) Height in cm (eg 78.1) Oedema

n = No y =Yes

MUAC in

cm (eg 11.3)

1

2

3

4

5

6

7

8

9

10

11

Page 42

12

13

14

15

16

17

18

19

20

21

Page 43

FOOD SECURITY AND LIVELIHOOD QUESTIONNAIRE

(conduct the interview in all selected HHs whether there are children or not)

Date (D/M/Y): ……./….…/…..... Cluster No:……… Team No…..… State:……………….. County……..…….. Payam:………………. Boma………..…….. Village:………....……

3.10 3.11 3.12 3.13 3.14 3.14a 3.15 3.15a 3.16 3.16a

HH NO*

What is the residency status of HH? 1 = Resident 2 = Returnee (in the last 1yr) 3 = IDP 4 = Refugees 66=Other specify

Is the HH head male or female? 1 = Male 2 = Female

What was your HHs main source of income in the last 30 days? (choose 1 option) 0= No income 1= Sale of crops 2= Sale of livestock 3= Sale of livestock products 4= Brewing 5= Sale of fish 6= Sale of natural resources(firewood; charcoal; grass) 7= Sale of food aid 8= Casual labor 9= Skilled labor 10= Salaried work 11= Small business/other petty trading 12= Family support 66= Other-Specify

In the past (4 weeks/30 days), was there ever no food to eat of any kind in your house because of lack of resources to get food? 0= No (if No, Skip to Q3.15) 1= Yes

How often did this happen in the past (4 weeks/30 days)? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times)

In the past (4 weeks/30 days), did you or any household member go to sleep at night hungry because there was not enough food? 0= No (If No, Skip to Q 3.16) 1= Yes

How often did this happen in the past [4 weeks/30 days]? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times)

In the past [4 weeks/30 days], did you or any household member go a whole day and night without eating anything at all because there was not enough food? 0= No (If No, move to next section Q4.0) 1= Yes

How often did this happen in the past [4 weeks/30 days]? 1 = Rarely (1–2 times) 2 = Sometimes (3–10 times) 3 = Often (more than 10 times)

Page 44

Page 45

4.0 Household Dietary Diversity

Did any member of your household consume food from any these food groups in the last 24 hours (from this time yesterday to now)? Include any snacks consumed (0=No 1=Yes)

HH

NO

Cer

eals

and

cer

eal p

rod

uct

s

Wh

ite

Ro

ots

and

Tub

ers

Legu

mes

, nu

ts a

nd s

eed

s

Dar

k gr

een

leaf

y ve

geta

ble

s

Yello

w o

r o

ran

ge f

lesh

ed

tub

ers

an

d

vege

tab

les/

Vit

amin

A-r

ich

Oth

er v

eget

able

s

Vit

amin

A r

ich

Fru

its

Oth

er F

ruit

s

Mea

t &

Off

al

Eggs

Fish

an

d s

ea f

oo

ds

Milk

an

d m

ilk p

rodu

cts

Suga

r an

d h

on

ey

Oils

/ fa

ts

Mis

cella

neo

us

(e.g. maize, spaghetti, rice, caanjera, bread, biscuits)?

(e.g. white potatoes, yams, cassava and their products)?

(e.g. beans, lentils, green grams, cowpeas; peanut)?

(including Vitamin A-rich sweet pepper, local and wild leafy vegetables)?

(e.g. squash/juices pumpkins, carrots, sweet potatoes that are orange or yellow inside)?

(other local and wild vegetables that are not dark green or leafy e.g cabbage, lettuce, greeen pepper)

(e.g. ripe mangoes, pawpaw, etc which are yellow or orange fleshed)?

(other local and wild fruits that are not yellow or orange fleshed e.g bananas)?

(e.g. beef, sheep/goat/camel or game meat, poultry& products)?

(Spices and Caffeine, tea or coffee)?

Page 46

Page 47

5.0 Food Consumption Score 6.0 Reduced Coping Strategy Index How many days over the last 7 days, did members of your household eat the following food items, prepared and/or consumed at home, and what was their source?

During the last 7 days, were there days when your household had to employ one of the following strategies (to cope with a lack of food or money to buy it)? if so, how many how many days did you use any of the following strategies?

HHNO

Cer

eals

& t

ub

ers

(m

aize

, ric

e, s

org

hu

m,

cass

ava,

po

tato

es, e

tc)?

Pu

lses

(b

eans

, pea

s, g

rou

nd

nu

ts, g

ree

n gr

ams,

etc

)?

Veg

etab

les?

Fru

its?

An

imal

so

urc

e fo

od

s (m

eat,

fis

h, e

ggs)

?

Milk

& d

airy

pro

du

cts?

Suga

r an

d h

on

ey (

e.g

. sw

eete

ned

fo

ods,

dri

nks

, cho

cola

tes,

sw

eets

, ca

ndie

s, e

tc)?

Oils

/fat

s (e

.g. f

at

or

oil,

butt

er, g

hee

, m

arg

ari

ne

add

ed t

o fo

od o

r us

ed f

or

coo

king

)?

a. R

ely

on le

ss p

refe

rred

an

d le

ss

exp

ensi

ve f

oo

ds?

b. B

orr

ow

fo

od,

or

rely

on

hel

p f

rom

a

frie

nd

or

rela

tive

?

c. L

imit

po

rtio

n si

zes

at m

eal t

imes

?

d. R

estr

ict

cons

um

ptio

n b

y ad

ult

s so

th

at s

mal

l ch

ildre

n c

an e

at?

e. R

edu

ce t

he

nu

mb

er o

f m

eals

eat

en

in a

day

?

Page 48

7.0 Livelihoods based coping strategies: 7.1 During the past 30 days, did anyone in your household have to engage in any of the following activities because there was not enough food or money to buy food?

7.2 Withdrew child(ren) from school? 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

7.3 Sent household members to eat elsewhere 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4= Not applicable

7.4 Spent savings or sold more animals than usual 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already sold those assets in the last 12 months and cannot continue doing it 4 = Not applicable

7.5 Sold household assets e.g. furniture, utensils 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already sold those assets in the last 12 months and cannot continue doing it 4 = Not applicable

7.6 Borrow money/ purchase food on credit 1 = Yes 2 = No because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

7.7 Sold productive assets or means of transport e.g. sewing machines, wheel barrows, etc.) 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

7.8 Reduced essential non-food expenses such as health, education, etc. 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

7.9 Entire household migrated 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

8 Engaged in degrading jobs or risky income generating activities 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

8.1 Begged 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable

8.2 Sold last female animal 1 = Yes 2 = No, because I did not face a shortage of food 3 = No, because I have already engaged in this activity in the last 12 months and cannot continue doing it 4 = Not applicable 5 = No, because I diid not want to sell any more livestock

Page 49

Page 50

Appendix 5: Local calendar of events Month Season 2014 2015 2016 2017 2018 2019

January Taking of cattle to cattle camps/ Dry period

CPA signing day New year celebrations

CPA signing day New year celebrations 53

CPA signing day New year celebrations

41

CPA signing day New year celebrations 29

CPA signing day New year celebrations 17

CPA signing day New year celebrations 5

February Dry period Lam festival

Lam festival

52

Lam festival

40

Lam festival Appointment of Governor Tobiolo Oromo of Imotong State 28

Lam festival 16

Lam festival 4

March Clearing bushes

Fighting between the youth and SPLA in Ikotos centre 51

39

27

15

Vice president Wani Igah visited Ikotos County 3

April Clearing bushes

Death of Paramount chief Okulang of Ikotos

50

38

Cattle raiding in Isohe village by youth from Ofi 26

Death of Alfred Kayumba the first Director General of Finance of the former EES 14

EPI immunization campaign in Ikotos

2

May Start of rains/Animals returning from cattle camps

SPLA DAY May 16th

SPLA DAY May 16th

49

SPLA DAY May 16th

37

SPLA DAY May 16th

Appointment of new County commissioners in greater Ikwoto 25

SPLA DAY May 16th

13

Mass vaccination of cattle against East Coast fever in greater Ikwoto

1

June Planting of crops; maize, sorghum

48

Inauguration of new counties in the greater Ikwoto

36

Food distribution by Act for Humanity in greater Ikwoto 24

12

0

July Weeding of farms Independence Day /martyr day Cholera outbreak in greater Ikwoto

59

Independence Day celebration /martyr day

47

Independence Day celebration /martyr day Fighting in Juba between Government & rebels

35

Independence Day celebration /martyr day 23

Independence Day/martyr day Outbreak of East Coast fever in Kekerek 11

August Weeding of farms 58

President Salva Kiir signs peace agreement 46

President Salva Kiir signs peace agreement

34

Mass movement from greater Ikwoto to Palabek

22

10

September Maize harvesting 57

45

33

21

9

Page 51

October Sorghum harvesting

Annual Comboni day celebrations St Teresah day celebration

56

Annual Comboni day celebrations St Teresah day celebration 44

Annual Comboni day celebrations St Teresah day celebration

32

Annual Comboni day celebrations St Teresah day celebration

20

Annual Comboni day celebrations. Cattle raiding in Chorokol by the Didinga youth

8

November Sorghum harvesting

55

43

31

19

Peace agreement btwn Logir & Buya communities

7

December Christmas Day celebrations

54

Division of the country into 28 States Appointment of Governor Nartisho of Imotong 42

30

18

6