Anthropometric and Retrospective Mortality Survey in the County of Aweil East, Northern Bhar El Gazal State South Sudan

June 2009 Aweil East South Sudan

Anthropometric and Retrospective Mortality Survey In the County of Aweil East, Northern Bhar El Gazal State South Sudan

Funded by

ACKNOWLEDGMENTS

Action Against Hunger would like to express its deep gratitude for the support given during the Anthropometric and retrospective Mortality Survey 2009 in Aweil East County.

We would like to thank ACF-USA staff, particularly the support team without which the survey wouldn’t have been possible.

Furthermore, we would like to thank the survey teams, for their endurance, dedication and team spirit which enabled to obtain good quality data. Thanks also to all drivers who ensured timely and safe movement of the survey teams.

A special thanks to the SSRRC of Aweil East County for providing vital information on the geographical area and for participating in the survey and to the UN World Food Program for providing assistance in transport.

We finally like to say many thanks to the individual families who pleasantly allowed the survey teams measure their children and provided the survey team with the information required to make it a success.

For the funding of Anthropometric and Retrospective Mortality Survey Aweil East County, ACF-USA thanks the European Commission Humanitarian Office (ECHO)

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

TABLE OF CONTENTS

.I. EXECUTIVE SUMMARY ...... 6 .II. INTRODUCTION ...... 11 .III. OBJECTIVES ...... 14 .IV. METHODOLOGY ...... 14 .IV.1. Type of Survey ...... 14 .IV.2. Sampling Methodology ...... 14 .IV.3. Data Collection and Field Work ...... 16 .IV.4. Guidelines and Formulas Used ...... 19 .IV.5. Survey Constraints ...... 21 .V. RESULTS OF THE ANTHROPOMETRIC AND MORTALITY SURVEY ...... 22 .V.1. .Child Nutrition and Health ...... 22 V.1.1 Distribution by Age and Sex ...... 22 .V.2. Anthropometric Analysis ...... 23 .V.3. Vaccination Coverage ...... 25 .V.4. Children’s Morbidity ...... 26 .V.5. Composition of the Households ...... 26 .VI. RESULTS OF THE QUALITATIVE QUESTIONNAIRE ...... 27 .VI.1. Socio- demographic characteristics of the respondents ...... 27 .VI.2. Food Security and Livelihoods ...... 27 .VI.3. Health ...... 31 .VI.4. Water and Sanitation ...... 33 .VI.5. Maternal and Child Care Practices ...... 35 .VII. DISCUSSION ...... 36 .VIII. RECOMMENDATIONS ...... 38 .IX. APPENDICES ...... 40 .IX.1. Sample Size and Cluster Determination ...... 40 .IX.2. Anthropometric Survey Questionnaire ...... 49 .IX.3. Household enumeration data collection form for a death rate calculation survey (one sheet/household) ...... 50 .IX.4. Enumeration data collection form for a death rate calculation survey (one sheet/cluster) ...... 51 .IX.5. Calendar of Events ...... 52 .IX.6. Qualitative Questionnaire ...... 54

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

INDEX OF TABLES Table 1: Results Summary ...... 8 Table 2: UN Agencies, International NGOs and Ministries in Aweil East County ...... 13 Table 3: Population Figure, Prevalence, Precision, and Sample Sizes, Aweil East ...... 15 Table 4: Distribution of Age and Sex of Sample ...... 22 Table 5: Prevalence of Acute Malnutrition (based on weight-for height z-scores and/or oedema) ...... 23 Table 6: Distribution of Acute Malnutrition and Oedema (based on weight-for-height z-scores) ...... 23 Table 7: Global and Severe Acute Malnutrition in z-scores ...... 24 Table 8: Prevalance of Malnutrition by Age (based on weight -for- height percentage of the median and oedema) ...... 25 Table 9: Measles vaccination coverage ...... 25 Table 10: Mortality Rate ...... 25 Table 11: Prevalence of Reported Illness in Children Two Weeks Prior to Inverview )n=835) ...... 26 Table 12: Illness Breakdown in Children Two Weeks Prior to Inverview (n=835) ...... 26 Table 13: Household Composition ...... 26 Table 14: Main Livelihood Activities (n=682) ...... 28 Table 15: How Households are Obtaining Main Food Sources (n=682) ...... 29 Table 16: Top Coping Strategies When Food Stocks Decline (n=670) ...... 29 Table 17: Most Commonly Consumed Foods ...... 30 Table 18: Current Water Sources (n=673) ...... 33 Table 19: Time to and from the Water Source ...... 33 Table 20: Location of Defecation if there is no Toilet (n=654) ...... 34 Table 21: Household Use for Soap (n=265)...... 34

INDEX OF FIGURES Figure 1: Distribution of sex by age group ...... 22 Figure 2: Weight for Height in z-scores compared to WHO population ...... 24 Figure 3: Number of Feddans Planted or to be Planted in 2009 ...... 28 Figure 4: First Place of Treatment When Sick ...... 31 Figure 5: Distance to the Nearest Health Facility ...... 32 Figure 6: Number of Times per day that Children are Fed ...... 36

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

LIST OF ABBREVIATIONS

ACF-USA Action Contre la Faim- USA, Action Against Hunger-USA AMURT Ananda Marga Universal Relief Team ENA Emergency Nutrition Assessment FAO Food and Agricultural Organization GAM Global Acute Malnutrition IOM International Organization for Migration IDP Internally Displaced People INGO International Non Governmental Organization IRC International Rescue Committee MUAC Mid Upper Arm Circumference MSF-F Médecins Sans Frontières-France NBeG Northern Bahr el Ghazal NCHS National Center for Health Statistic OTP Outpatient Therapeutic Program PHCU Primary Health Care Unit SAM Severe Acute Malnutrition SFP/C Supplementary Feeding Program/Center SMART Standardized Monitoring and Assessment of Relief and Transitions SPHERE Humanitarian Charter and Minimum Standards in Disaster Response SPLM/A Sudan People Liberation Movement/Army SPSS Statistical Package for the Social Sciences SSRRC Southern Sudan Relief and Rehabilitation Commission TFP/C Therapeutic Feeding Program/Center UN United Nations UNICEF United Nations Children’s Fund VSF Veterinaires Sans Frontieres WFH Weight for Height WFP World Food Program WHO World Health Organization WVI World Vision International

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.I. EXECUTIVE SUMMARY

Introduction

Aweil East County is one of the five counties that make up the state of Northern Bahr el Ghazal in Southern Sudan. The county consists of the seven administrative payams of Malualbai, Baac, Madhol, Mangartong, Mangok, Yargot and Wunlang running from north to south. Population figures for the total population of the county, according to the World Health Organization and confirmed by the state level Ministry of Health, is estimated at 616,105.

Aweil East is traditionally an agro-pastoralist region. However, a majority of the population does not own livestock and hence they are reliant on a more diversified livelihoods base. This livelihoods base includes: grass and firewood collection; charcoal burning; casual labor; fishing; petty trade; and wild food collection. These households tend to be heavily reliant on cash income, wild food collection and daily purchasing to meet their needs as they lack the asset base to make longer term investments.

The community is currently facing a very serious hunger gap due to the fact that last year’s harvest was poor yielding as a result of concurrent drought, floods and crop pest infestations. This early onset of the hunger season and an increased reliance on market purchases adds particular pressures onto the most vulnerable households who lack livestock and other assets. The delayed rains this year are also hampering planting which will result in a shorter growing season in which to plant or re-plant seeds in hopes of obtaining good yields.

Aweil East County can be considered a location in which there is a chronic level of acute malnutrition above the emergency threshold. The combination of food insecurity, lack of access to clean water and sanitation facilities, disease outbreaks, and poor child care practices have a negative impact on the nutrition status of children under five years of age.

Nutritional and retrospective mortality surveys are undertaken annually in Aweil East County by ACF in order to estimate the malnutrition and the mortality rates in this county. This survey took place from June 10-23, 2009.

Survey Methodology

The Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology was utilized in the implementation of the Anthropometric and Mortality survey. Children aged 6-59 months formed the target group. Complimenting these two survey tools, a qualitative questionnaire was also administered to households. The objectives of the surveys included:

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

. To assess the prevalence of acute malnutrition in children aged 6-59 months . To estimate the crude and under five mortality rate . To estimate the coverage of measles among targeted children . To identify the underlying causes and factors of malnutrition

The SMART survey methodology was used in the planning, training, field data collection and analysis of the anthropometric and mortality surveys. Since village population was not available to help with cluster selection, the total population of the county was used as a starting point and then accessible villages were entered according to proportion-to-size information. Based on this information, the targeted sample sizes were 841 children for Anthropometric survey and 4344 for the Mortality survey. In order to reach these targets, the survey needed to be administered in 42 randomly selected clusters.

A qualitative questionnaire was also administered to 683 households to collect information on food security and livelihoods, water and sanitation, child care practices, and health. The households that were chosen for participation were primarily those that participated in the Mortality survey.

At the conclusion of the surveys, the Anthropometric survey included 8441 children, the Mortality survey included 689 households encompassing 4431 residents, and the qualitative questionnaire included 683 households. Sample size target numbers were exceeded for both the Anthropometric and Mortality surveys. The sample size target of 714 for the household questionnaire was only slightly missed.

Summary of Findings

Key nutrition and mortality findings:

The June 2009 ACF nutritional anthropometric survey in Aweil East County revealed GAM rates of 29.8% (25- 35.2%) and SAM rates of 7.8% (5.5-11.1%) (WHO 2005 standards). Both these rates exceed the WHO emergency threshold for GAM and SAM, and are also a significant increase over June 2008 survey results of GAM rates of 19.9% (16.1-23.8) and SAM rates of 3.8% (1.9-5.6) (WHO 2005 standards). Immunization rates are low with 76.4% of children between 9-59 months being vaccinated for measles; a proxy indicator for overall immunization and health. Additionally, mortality rates were 0.2 (0.1-0.4%) for total crude mortality rates and 0.34 (0.1-1.0) for under five mortality rates. Both of these rates are below the alert and emergency levels for total crude mortality and under five mortality rates. The Results Summary Table can be found on the next page.

1 While the survey incorporated 844 children, the final analysis incorporated 805 children after exclusion of 39 children due to incoherency.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Table 1: Results Summary

INDEX INDICATOR RESULTS Global Acute Malnutrition 29.8% Z-scores W/H< -2 z and/or oedema [25.0% - 35.2%] WHO (2005) N=805 Severe Acute Malnutrition 7.8% W/H < -3 z and/or oedema [5.5% - 11.1%] Global Acute Malnutrition 29.1% Z- scores W/H< -2 z and/or oedema [24.5% -34.1%] Severe Acute Malnutrition 3.1% NCHS (1977) W/H < -3 z and/or oedema [1.9% - 5.1%] N=805 Global Acute Malnutrition 18.0 % % Median W/H < 80% and/or oedema [14.0% - 22.8%] Severe Acute Malnutrition 0.7 % W/H < 70% and/or oedema [0.3 – 1.9%] 10.9% Global Acute Malnutrition (<120mm) Height >=65 cm [8.8% - 13.1%] MUAC N=805 2.9% Severe Acute Malnutrition (<110mm) [1.7% - 4.0%] Total crude retrospective mortality (last 3 months) /10,000/day 0.2 [0.10-0.40] Under five crude retrospective mortality /10,000/day 0.34 [0.12-1.0] By card 12.8% Measles immunization coverage According to caretaker2 10.7% (N= 806 children ≥ 9months old) Not immunized 76.4%

Key food security and livelihoods findings:

• The surveyed population dependents on livelihood activities such as crop farming (59.2%), petty trade (16%), and employment (9.2%) primarily. • The main sources of income reported were petty trade (55.6%), sale of livestock and livestock products (10.9%), sale of assets (7.6%), casual labor (9.2%), and permanent job salary (7.8%). • The majority of the population has planted or will plant less than one feddan (35.1%) or between one and two feddans (47.7%) this growing season. • 95.9% of households do not have sufficient food sources and 63.4% of them have already depleted their food stocks. Therefore, most households’ main food sources were purchase (75.4%), cultivation (24.7%), and wild food collection (35.2%). • As food stocks are declining, households are primarily resorting to wild food collection for consumption or sale (51.2%), borrowing money (13.6%), selling personal assets (12.6%), and selling livestock assets (10.8%).

2 When no EPI card was available for the child at the household, measles vaccination information was collected according to the caretaker/ mother of child

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

• The food consumption patterns showed that the most commonly consumed foods are cereals (84.2%), sugar and honey (40.7%), meat (33.9%), oils and fats (23.7%), fish (19.9%) and pulses (18.7%).

Key water and sanitation findings:

• The main water source(s) cited for household use were boreholes (58.9%) and unprotected wells (28.7%). • 14.6% of households remarked that they boil, filter/sieve or chemically treat their water prior to consumption. • 52.3% of households commented that it takes less than 30 minutes to walk to and from the water source; the rest stated a longer time. • Most households collect between two (39.5%) and three (39.7%) times per day. • Based on an average Sudanese household size of 6.6 members, this amount of water collected per day equates to 6-9 liters/capita/day which is far below the SPHERE emergency standard of 15 liters/capita/day. • Only 7% of the households surveyed said they had access to a latrine. For those that did not, a majority of households used the bush (85.8%) or open field (12.5%). • 35.4% of households remarked that they had soap in the home at the time of the survey. Of this 35.4% that had soap, only 15.5% washed before eating, 2.3% washed before feeding their children, and 1.5% washed after defecation.

Recommendations

Health and Nutrition

• To scale up emergency therapeutic feeding programs (expansion of OTP sites and expansion of intake capacity at TFC) • Partners to scale up/continue with General Food Distribution for households facing food shortage • Continued surveillance of the food security and nutrition situation in Aweil East • Education and provision of bed nets to reduce the incidence of malaria; especially for children under the age of five years

Food Security and Livelihoods

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

• A contingency is as well needed to enable appropriate responses to continuous food insecurity and malnutrition in 2010 due to a prospected weak harvest in 2009. • A safety net approach should be considered to respond to recurring annual food insecurity and malnutrition, which could be facilitated through a regular cash transfer to the most vulnerable households. • Provision of seeds and tools, including agriculture training on improved techniques and plant protection to all households in need for improving the agricultural production at household level • Support increased household food production through diversification by introducing early maturing crop varieties, vegetable gardens, and small animal livestock keeping • Support the diversification of income sources through income generating activities • The local riverside communities should be empowered on appropriate fishing techniques, processing and preservation techniques

Water and Sanitation

• Prevention and mitigation measures for communities affected by recurring cholera/AWD • Training in latrine construction at the community and household levels and provision of digging kits • Increase the number of adequate and safe water schemes through construction, rehabilitation and training on operation and maintenance

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.II. INTRODUCTION

Aweil East County is one of the five counties that make up the state of Northern Bahr el Ghazal in Southern Sudan. Its borders include Gogrial West County to the east, Southern Kordofan to the north-east, Southern Darfur to the north, and Aweil South County to the south. Aweil East lies in the western flood plain livelihood zone which is prone to seasonal flooding; especially in August and September. The flat terrain and sandy and clay soils contribute to this flooding pattern. The county consists of the seven administrative payams of Malualbai, Baac, Madhol, Mangartong, Mangok, Yargot and Wunlang running from north to south. Population figures for the total population of the county, according to the World Health Organization and confirmed by the State level Ministry of Health, is estimated at 616,105.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Northern Bahr el Ghazal is traditionally an agro-pastoralist region, and cattle ownership remains the primary determinant of wealth and status. Livestock are sold for cash, traded for other products, form marriage dowries and act as a source of milk and meat.

However, given that up to seventy percent of the population does not own any cattle, and over forty percent do 3 not own any livestock at all , the majority of the population is reliant on a more diversified livelihoods base. Agriculture is commonly undertaken during the annual cropping season, although generally at a subsistence level that covers barely 3-6 months worth of the household’s staple food requirements. Very poor and poor households (those with no or few livestock) undertake a range of seasonal and year-round livelihoods activities including: grass and firewood collection; charcoal burning; casual labor; fishing; petty trade; and wild food collection. These households tend to be heavily reliant on cash income, wild food collection and daily purchasing to meet their needs as they lack the asset base to make longer term investments.

The population of Aweil East County has seen a high influx of returnees in the previous year, with an estimated 4 24% of all returnees in Northern Bahr El Ghazal State having settled in Aweil East. From March to May 2009 5 Northern Bahr el Ghazal State recorded 18,335 spontaneous returnees into the state. Additionally, an estimated 56% of Northern Bahr El Ghazal’s Internally Displaced People (IDP) population is currently settled in Aweil East.

According to the 2008/2009 Annual Needs and Livelihood Assessment, only three counties in Southern Sudan were expected to be severely food insecure in 2009. One of these counties was Aweil East County. A below average crop harvest in 2008 as a result of wide spread flooding in the region, translated into an early onset of the hunger gap by about a month and a half. In addition, regular market assessments by ACF have shown that currently food prices are rising for staple food items and the cost of livestock is decreasing.

A rapid water and sanitation assessment in Aweil East County conducted in March 2009 by ACF showed that only 24% of the population has access to clean water. It also demonstrated that while some households show interest in having latrines, the sanitation coverage rate in Aweil East is almost negligible.

Aweil East County can be considered a location in which there is a chronic emergency state of acute malnutrition, whereby the Global Acute Malnutrition rate is always above the WHO emergency level of 15%. A nutrition survey conducted by ACF in June 2008 revealed a Global Acute Malnutrition (GAM) rate of 19.9% (16.1%-

3 Food Security Assessment and Cross Sectional Survey on Nutrition, Measles and Vaccination Coverage and Retrospective Mortality, Northern Bahr el Ghazal – Medecins Sans Frontieres (December 2008) 4 IOM – South Sudan North Bahr El Ghazal Sub-Office 5 Humanitarian Action in Southern Sudan Report Week 25, 15-21 June 2009

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

6 23.8%) and a Severe Acute Malnutrition (SAM) rate of 3.8% (1.9%-5.6%) . In May 2008, ACF therapeutic feeding programs admitted 148 severely malnourished children for nutritional treatment, while in May 2009 a total of 285 children were admitted for treatment of severe acute malnutrition. Interviews with key community informants during the survey indicated that communities believe that malnutrition is a cause for mortality in the population, specifically for children.

There are several UN agencies, international NGOs (INGO), and Ministries operating in Aweil East to help with basic infrastructural needs such as health clinics and schools, and to assist the general population with livelihoods and food security, water and sanitation, and nutrition. Table 2 below shows some of the key partners working in Aweil East.

Table 2: UN Agencies, International NGOs and Ministries in Aweil East County IOM Returnee monitoring, food security, WASH WFP General Food Distribution, Blanket SFP, Food For Training UNICEF Nutrition, education, child survival, WASH FAO Food Security and surveillance World Vision International Nutrition, food aid, emergency response Save the Children UK Education, protection, WASH, food security Save the Children Sweden Education, child protection IRC Medical treatment and child survival programs AMURT Food security, education Mercy Corps Food security, economic recovery and development, cash for work VSF Livestock surveillance, vaccination, outbreak reporting, and restocking Ministry of Health Medical treatment, immunizations Ministry of Agriculture Food Security Ministry of Infrastructure and WASH Rural Water Resources ACF Nutrition, surveillance, food security, WASH Tearfund Health Christian Solidarity Health International

6 WHO 2005, Z-score

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.III. OBJECTIVES

The objectives for the Anthropometric and Retrospective Mortality Survey were as following:

.IV. METHODOLOGY

.IV.1. Type of Survey

The Anthropometric and Retrospective Mortality survey implemented in Aweil East County used the Standardized Monitoring and Assessment of Relief and Transitions (SMART) methodology. During the survey, anthropometric and mortality data were simultaneously collected. Qualitative data, in the forms of a qualitative questionnaire, key informant interviews, focus group discussions, and general observation, were also collected in order to complement the anthropometric survey findings.

.IV.2. Sampling Methodology

A two-stage cluster sampling method was used to collect data for both the Anthropometric and Retrospective Mortality Surveys:

At the first stage, the sample size was determined by entering necessary information into the ENA for SMART software for both Anthropometric and Retrospective Mortality surveys. The information included the estimated population sizes, estimated prevalence rates of mortality and malnutrition and the desired precision and design effect. As population figures for villages were not known, the total population figure of 616,105 was assumed and then only the accessible villages were entered with populations proportion-to-size. This information was confirmed by SSRRC, State level MoH and ACF staff. Table 3 shows how the sample size numbers were calculated.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Table 3: Population Figure, Prevalence, Precision, and Sample Sizes, Aweil East Anthropometric survey Retrospective Mortality survey Population 123.221 616.105 Estimated prevalence 20 1 ± desired precision % 4 0.4 Design effects 2 1.5 Sample sizes 765 3.949

Anthropometric survey: Using a malnutrition prevalence of 20% based on previous surveys with a precision of 4% and a design effect of 1.5; a sample size of 765 was obtained. A buffer of 10% was included in the total sample in order to compensate for missing data, thus, resulting in a sample size of 841 children from 6 months to 59 months.

Given the operational circumstances and the fact that one cluster needed to be finished in one working day, 20 children aged 6-59 months were estimated to be measured in one cluster which yielded a total of 42 clusters.

Mortality survey: Using an estimated mortality prevalence of 1.0%, a desired precision of 0.4, a design effect of 1.5 and a recall period of 90 days; a sample size of 3949 was obtained. A buffer of 10% was included in the total sample in order to compensate for any missing data, thus, resulting in a sample size of 4344 persons. For the mortality survey, 104 people present at the time of the survey were included for each cluster (4344/42 clusters)

At the second stage, selection of households to be visited in each cluster was done using the EPI method. In each selected cluster, the survey teams were led to the center of the village. The pen was spun in order to ascertain which direction to walk. At the edge of the village, the pen was spun again, and the team then walked along this second line counting and marking the households within a few arm’s length. The first house to be visited was selected at random. The second house and each following were chosen by proximity, always choosing the houses on the right hand when standing with the back to the main door of the sleeping quarters of the mother. In areas where the houses were very sparsely located and where there was no house to the right, the nearest house was selected to participate in the survey.

In every selected household, all children aged 6-59 months were included in the Anthropometric survey. If there was more than one wife/ caretaker in the household7, each wife was considered separately. If there were no children in the household, the house remained a part of the sample that contributed zero children to the Anthropometric survey. The survey team continued until a target of 20 was obtained. If the last household had

7 A household refers to a mother and her own or adopted children

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

more children than needed to obtain the target of 20, then each child within the household was still measured and the target number exceeded for that cluster. Households that were absent or where a parent was not present were recorded as such and revisited at the end of the day; time and weather allowing. The age of the children were recorded based on a combination of the parents’ knowledge of the birth date, the use of the ACF calendar of events, birth cards, and child height.

The Mortality survey was administered to all households that had an adult member present.

Qualitative data was primarily collected through administration of a qualitative survey. This survey was administered to 683 households total with a target of 17 households per team per day. These households constituted primarily those that participated in the Mortality survey. However, if the number of household members ‘present now’ for the Mortality survey was met before the quota for the household survey, then the same methodology applied as before and houses to the right of the mother’s sleeping quarters were chosen to complete the household questionnaire. Additionally, focus group discussions with both men and women from Aweil East, and key informant interviews with community members, SSRRC, other NGOs, and Ministries were conducted.

Anthropometric and Mortality Surveys, and qualitative questionnaires were administered in each household until the daily quota for each one had been successfully obtained. The quotas were 20 children for anthropometric, 104 household members “present now” for mortality, and 17 households for qualitative. In total, the minimum sample size for both the Anthropometric and Mortality surveys were met. The Anthropometric survey included 844 children, and the Mortality survey included 689 households encompassing 4431 residents. The sample size target for the household questionnaire was 714 households with an actual 683 households that completed the questionnaire in its entirety. Anthropometric, mortality and qualitative information were asked to the mother in the household; in her absence the father of the household was asked.

.IV.3. Data Collection and Field Work The survey teams, each consisting of 1 supervisor, 1 enumerator and 2 measurers were subjected to a standardization test to ascertain their capability in taking accurate and precise measurements, so as to minimize errors during data collection. The surveys were completed in 12 days.

.IV.3.1 Anthropometric Survey For each eligible child aged 6-59 months, information was collected during the anthropometric survey using an anthropometric questionnaire (Appendix .IX.2). The information included:

. Age: Recorded with the help of a local calendar of events (Appendix .X.5)

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

. Gender: Male or female.

. Weight: Targeted children were weighed without clothes using a SALTER balance of 25kg (precision of 100g).

. Height: Children were measured on a measuring board (precision of 0.1cm). Children less than 85cm were measured lying down, while those greater than or equal to 85cm were measured standing up.

. Mid-Upper Arm Circumference: MUAC was measured at the mid-point of the left upper arm for measured children (precision of 0.1cm). ACFIN MUAC tapes were used.

. Bilateral oedema: Assessed by the application of normal thumb pressure for at least 3 seconds to both feet.

. Measles vaccination: Assessed by checking for measles vaccination on EPI cards and probing caretakers.

. Household status: Information was sought on the duration of stay in the area. This was used to determine whether households were residents, displaced, returnees or temporarily in the area. 6 months stay and reason for movement were used as criteria.

. BCG scar: Assessed by checking the arm for the scar indicating that the child had received this series of vaccinations

. Vaccination card: Assessed whether the child had received vaccines in the past

. Illness in the last 2 weeks: Assessed to see whether children were suffering from common childhood diseases. This was used to see if they had had malaria, diarrhea, skin infections, worms or respiratory infections.

All children who were found to be severely malnourished were referred to either the ACF-USA Outpatient Therapeutic feeding Program or taken directly to the Therapeutic Feeding Centre; depending on presence of medical complications and level of oedema. A total of 28 children were referred during the survey.

.IV.3.2 Mortality Survey Each family selected at random (even if there was no child 6-59 months), was asked to state all family members and indicate their age and sex. The family was then asked to indicate which of the listed family members were present now and at the beginning of the recall period, which member joined or left during the recall period, and which members were born or died during the recall period. (Appendix .IX.3).

.IV.3.3 Food Security and Livelihoods, and Water and Sanitation Food security and livelihoods, and water and sanitation data were collected from the same households the mortality data was collected in order to collect information to complement the Global Acute Malnutrition rates.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Information was collected during the anthropometric survey using a qualitative questionnaire (Appendix .IX.6). Food security and livelihoods information included:

. Main livelihood: Information was sought on what they consider to be their main livelihood(s)

. Main source of income: Information was sought on what they consider to be their main source(s) of income

. Current month’s food source: Information was sought on what is the household’s main food source(s) for the current month

. Sufficiency of main food sources: Information was sought to determine if household thought their main sources of food were sufficient

. Coping strategies: Information was sought on what the household does when it’s food stocks decline in order to determine coping mechanisms

. Current food stock: Information was sought on how long their current food stock would last

. Ensuring enough food: Information was sought open-endedly on what the household thought could be done to ensure enough food for household consumption

. Crops cultivated: Information was sought on what crops were cultivated or will be cultivated this planting season

. Feddans cultivated: Information was sought on how many feddans of land the household cultivated or planned to cultivate this planting season

. Livestock owned: Information was sought on which type of livestock the household owned

. Fishing: Information was sought on whether the household was engaged in fishing, and why not if they said no

Water and sanitation information included:

. Current water source: Information was sought on what is the household’s current water source(s)

. Time to water source: Information was sought on how many minutes it took to go to the water source and back. This was to determine the time it took to obtain water.

. Times per day collecting water: Information was sought on how many times a day water was collected

. Water collection container: Information was sought on what type of container was used to actually collect the water in.

. Water storage container: Information was sought n what type of container was used to store the water once it arrived at the household.

.IV.3.4 Data Quality Control Assurance

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

The use of an anthropometric standardization and cluster control sheet, thorough training of the 4 teams (each consisting of 1 supervisor, 1 enumerator and 2 measurers), and close supervision by the Surveillance Program Manager ensured that data collected was of good quality.

.IV.3.5 Field Exercise The training was followed by a field exercise in a village not selected for the surveys. The methodology was tested; the precision and the accuracy of the data collection were assessed, and the measurement techniques were assessed. Additionally all the data collection forms were tested during the exercise. The exercise was successful and the actual survey started the next day.

.IV.3.6 Data Entry and Analysis Anthropometric and mortality data processing and analysis was conducted using SMART/ENA software. Extreme value flags and WHO verification guidelines were used to identify Z-score values where there was a strong likelihood that some of the data items were incorrect; these data were not used in the analysis. The food security, water and sanitation data entry was done in SPSS version 12. The data analysis was performed with SPSS software version 12.

.IV.4. Guidelines and Formulas Used

.IV.4.1.1. Acute Malnutrition

 Weight for Height Index

Low weight-for-height identifies wasted children. It is normally very useful when exact ages of children are difficult to determine. This index is appropriate when examining short-term effects such as seasonal changes in food supply or short-term nutritional stress brought about by illness.

Acute malnutrition rates are estimated from the weight for height (WFH) index values as well as presence of bilateral oedema. The WFH indices are expressed in both Z-scores (standard deviation score) and percentage of the median, according to both NCHS and WHO references.

Other than having a true statistical meaning; expression in z- score conveys malnutrition rates more precisely and allows for inter-study comparison. The percentage of the median on the other hand, estimates weight deficits more accurately and is commonly used in determining eligible children for targeted feeding programs.

The following guidelines were thus used in expression of results in Z-score and percentage of the median.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Guidelines for results expressed in Z-score:

. Severe malnutrition is defined by WFH < -3 SD and/or existing bilateral oedema on the child’s lower limbs. . Moderate malnutrition is defined by WFH < -2 SD and ≥ -3 SD and no oedema. . Global acute malnutrition is defined by < -2 SD and/or existing bilateral oedema.

Guidelines for results expressed in percentage of median:

. Severe malnutrition is defined by WFH < 70 % and/or existing bilateral oedema on the child’s lower limbs. . Moderate malnutrition is defined by WFH < 80 % and ≥ 70 % and no oedema. . Global acute malnutrition is defined by WFH <80% and/or existing bilateral oedema

 Mid-Upper Arm Circumference (MUAC)

The weight for height index is the most appropriate index to quantify wasting in a population. However, MUAC is a useful tool for rapid screening at a higher risk of mortality. MUAC measurements are used in children with a height of 65 cm and above. The guidelines are as follows:

. MUAC < 110 m and/or oedema Severe malnutrition and high risk of mortality . MUAC ≥ 110 mm and <120 mm Moderate malnutrition and risk of mortality . MUAC ≥ 120 mm and <125 mm High risk of malnutrition . MUAC ≥ 125 mm and <135 mm Moderate risk of malnutrition . MUAC ≥ 135 Adequate nutritional status

.IV.4.1.2. Mortality SMART methodology was utilized in mortality data collection over a 90 day recall period. The data gathered was then used to calculate the Crude mortality rate (Appendices .X.3 and .X.4). It is calculated using the following formula. The result is expressed per 10,000 people/day. Crude Mortality Rate (CMR) = 10,000/a*f/ (b+f/2-e/2+d/2-c/2), where: a = Number of recall days (90) b = Number of current household residents c = Number of people who joined household d = Number of people who left household e = Number of births during recall f = Number of deaths during recall period

Thresholds are defined as follows8:

Crude Mortality Rate (CMR):

8 Health and nutrition information systems among refugees and displaced persons, Workshop report on refugee’s nutrition, ACC / SCN, Nov 95.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Alert level: 1/10,000 persons/day Emergency level: 2/10,000 persons/day

Under five Mortality Rate (U5MR): Alert level: 2/10,000 persons/day Emergency level: 4/10,000 persons/day

.IV.5. Survey Constraints

Access: Some villages were not included in the cluster selection as they were considered inaccessible either due to physical access during the rainy season or for security reasons. This may have resulted in some villages from not having a chance to be surveyed.

Recall bias: There was a 90 day recall for the mortality questionnaire that did not have a significant memorable event attached to it and therefore recall bias could have been introduced into the data collection process.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.V. RESULTS OF THE ANTHROPOMETRIC AND MORTALITY SURVEY

.V.1. .Child Nutrition and Health

V.1.1 Distribution by Age and Sex

The distribution of the sex and age group shows that the total boy/ girl ratio is within the acceptable range of 0.8-1.2. Similarly, the sex ration within the age groups indicates a normal distribution.

Table 4: Distribution of Age and Sex of Sample Age groups in Boys Girls Total Ratio months N % N % N % Boy:girl 6-17 89 51.1 85 48.9 174 21.6 1.0 18-29 96 53.0 85 47.0 181 22.5 1.1 30-41 106 52.0 98 48.0 204 25.3 1.1 42-53 90 50.0 90 50.0 180 22.4 1.0 54-59 35 53.0 31 47.0 66 8.2 1.1 Total 416 51.7 389 48.3 805 100.0 1.1

Figure 1: Distribution of sex by age group

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.V.2. Anthropometric Analysis

.V.2.1. Distribution of Acute Malnutrition in Z-scores In the complete sample, the prevalence of Global Acute Malnutrition was found to be 29.8% (25.0% - 35.2%). The prevalence of Severe Acute Malnutrition was 7.8% (5.5%-11.1%). In Table 5 the weight for height distribution by age groups is demonstrated. There are no significant statistical differences of malnutrition levels between the age groups.

Table 5: Prevalence of Acute Malnutrition (based on weight-for height z-scores and/or oedema)

Moderate wasting Age Severe wasting Normal (>= -3 and <-2 z- Oedema groups Total (> = -2 z score) in N (<-3 z-score) score ) months N % N % N % N % 6-17 174 18 10.3 29 16.7 127 73.0 0 0.0 18-29 181 22 12.2 38 21.0 121 66.9 0 0.0 30-41 204 5 2.5 44 21.6 154 75.5 1 0.5 42-53 180 11 6.1 48 26.7 121 67.2 0 0.0 54-59 66 6 9.1 18 27.3 42 63.6 0 0.0 Total 805 62 7.7 177 22.0 565 70.2 1 0.1

Out of the total of 805 children measured one child was found to have kwashiorkor (bilateral oedema), 62 children were found marasmic (severe wasting), and no children presented with marasmic- kwashiorkor. The child with kwashiorkor was referred to the Therapeutic Feeding Centre in Malualkon for nutritional treatment. (See Table 6)

Table 6: Distribution of Acute Malnutrition and Oedema (based on weight-for-height z-scores) <-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor N= 0 N= 1 (0.0 %) (0.1 %) Oedema absent Marasmic Normal N= 62 N=742 (7.7 %) (92.2 %)

The figure below shows the weight for height distribution curves of the Anthropometric survey samples in Z- scores for comparison with WHO populations. The curve is shifted to the left, with a mean Z- score of -1.43 and a standard deviation of 1.04, which indicates that the population surveyed exhibits a poor nutrition status compared with the WHO reference population.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Figure 2: Weight for Height in z-scores compared to WHO population

Table 7: Global and Severe Acute Malnutrition in z-scores NCHS reference WHO reference Global Acute Malnutrition 29.1 (24.5%-34.1%) 29.8% (25%-35.2%) Severe Acute Malnutrition 3.1% (1.9%-5.1%) 7.8% (5.5%-11.1%)

According to both NCHS and WHO reference z-scores, Table 7 above shows that both the GAM and SAM rates exceed the WHO emergency thresholds of 15% and 4% respectively. When comparing the severe acute malnutrition rates between the WHO and NCHS reference populations, the severe acute malnutrition rate according to the WHO reference population is significantly higher than the severe acute malnutrition rate according to the NCHS reference population.

.V.2.2. Distribution of Acute Malnutrition in percentage of the median In the overall sample the prevalence of global acute malnutrition was found to be 10.7% (6.9%-14.5%) and the prevalence of severe acute malnutrition was found to be 0.1 % (-0.2%-0.5%). In Table 8 the prevalence of acute malnutrition is presented in weight for height percentage of the median and oedema, and it can be seen that no children were found to be suffering from severe wasting (N=0).

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Table 8: Prevalance of Malnutrition by Age (based on weight -for- height percentage of the median and oedema) Age Moderate wasting Severe wasting Normal groups Total (>=70% and <80% Oedema (<70% median) (> =80% median) in N median) months N % N % N % N % 6-17 174 0 0.0 21 12.1 153 87.9 0 0.0 18-29 181 0 0.0 26 14.4 155 85.6 0 0.0 30-41 204 0 0.0 11 5.4 192 94.1 1 0.5 42-53 180 0 0.0 19 10.6 161 89.4 0 0.0 54-59 66 0 0.0 8 12.1 58 87.9 0 0.0 Total 805 0 0.0 85 10.6 719 89.3 1 0.1

.V.3. Vaccination Coverage Table 9 shows the vaccination coverage among the surveyed population. The source of information used during the data collection was as following: Child health card or recall by the mother.

Table 9: Measles vaccination coverage Population >= 9 months N= 804 12.8% Immunized by card (10.4% -15.1%) 10.7% Immunized by recall (8.5%-12.8%) 76.4% Not immunized (73.4%-79.3%)

.V.4. Mortality The retrospective death rate was calculated based on the data collected with a 90 days recall period. Data was collected from families with or without children under 5 years. The results are summarized in the Table 10 below.

Table 10: Mortality Rate

Demographic data N Current residents HH 4431

Current residents < 5 years in HH 1013

People who joined HH 132 < 5 years who joined HH 26 People who left HH 200

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

<5 years who left HH 22 Birth 74 Death 8 Death < 5 years old 3 Recall period (days) 90

CMR (death/10.000 people/ day 0.20 (0.10-0.40)

U5MR (deaths in children < 5 years/ 10.000/day 0.34 (0.12-1.00)

.V.4. Children’s Morbidity

Table 11: Prevalence of Reported Illness in Children Two Weeks Prior to Interview (n=835)

6-59 months Prevalence of Reported 56.3% Illness

Table 12: Illness Breakdown in Children Two Weeks Prior to Interview (n=835)

Illness 6-59 months Malaria 19.1% Diarrhoea 12.4% Acute Respiratory Infection 6.3% Skin Infection 4% Worms 2.3% Other 5.2%

As Tables 11 and 12 above depict, in the two weeks prior to the survey, over half the children surveyed had a reported illness (56.3%). The top two illnesses included malaria (19.1%) and diarrhoea (12.4%). Both of these preventable illnesses increase the child’s susceptibility to malnutrition as food quantity consumed and nutrient absorption are decreased when the child is sick.

.V.5. Composition of the Households

Mortality survey was administered in 689 households during the survey. As seen in Table 13 below, the findings revealed an under- five year average of 1.5 children per household and an overall average of 6.6 persons per household.

Table 13: Household Composition Average per Age group N % household

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Under 5 years 1013 22.9 1.5 Adults 3418 77.1 5.1 Total 4431 100 6.6

.VI. RESULTS OF THE QUALITATIVE QUESTIONNAIRE

A qualitative questionnaire was administered to 683 households to collect information on food security and livelihoods, water and sanitation, child care practices, and health9. Information collected was supplemented by information collected by key informant interviews, focus group discussions, and observation.

.VI.1. Socio- demographic characteristics of the respondents

This survey revealed that households are primarily residents (94.3%) but the population also consists of returnees (3.2%), temporary residents (1.6%), and IDPs (0.7%).The status of each household was characterized by the household themselves. Therefore, it is possible that they may consider themselves as residents rather than returnees, temporary residents or IDPs. This could account for any discrepancies found in returnee percentages found in other reports.

Survey respondents were 95.5% female; male respondents were surveyed if the female adult household member was not present or unable to respond.

.VI.2. Food Security and Livelihoods

The main habitants of the Aweil County are Dinka who are predominantly agro-pastoralists. While cattle ownership remains the primary determinant of wealth and status, this survey shows that a majority of the population does not own cattle (79.2%). As Table 14 shows, they are dependent on other livelihood activities such as crop farming (59.2%), petty trade (16%), and employment (9.2%). The main sources of income reported were petty trade (55.6%), sale of livestock and livestock products (10.9%), sale of assets (7.6%), casual labor (9.2%), and permanent employment salary (7.8%). It is important to note that 21.1% of households reported that they had no source of income.

9 Many of the questions asked could have multiple responses thereby leading to percentages for individual questions not equaling 100%.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Table 14: Main Livelihood Activities (n=682) Livelihood Activity Percentage

Crop Farming 59.2%

Agro-pastoralism 24.5%

Petty Trade 16%

Employment 9.2%

The population in Aweil East practices small scale farming mainly of cereals and pulses such as sorghum, millet, maize, cowpeas, groundnut, and simsim. Sometimes, vegetables such as okra, pumpkin, tomatoes and cabbages are planted. Qualitative survey results reflect this pattern of cropping with 93.6% of households saying they have or will plant sorghum, 8.1% maize, 14.8% simsim, and 13.9% groundnuts. Only 4.2% of respondents said they will plant vegetables but those mentioned were primarily okra and pumpkin. The majority of the population has planted or will plant less than one feddan (35.1%) or between one and two feddans (47.7%) this growing season; see Figure 3. When asked during assessments by ACF and other humanitarian organizations why they are not cultivating more land especially given that it is plentiful, the majority of people cited reasons including insufficient seed supply, drought and too much flooding.

Figure 3: Number of Feddans Planted or to be Planted in 2009

30 20

Percen 10 0 .00 less than 1 1-2 2-3 3-4 more than 4 Number of feddans

This ACF June 2009 survey shows that 95.9% of households do not have sufficient food sources and 63.4% of them have already depleted their food stocks. Most households’ main food source(s) for July 2009 were purchase (75.4%), cultivation (24.7%), and wild food collection (35.2%) (See Table 15).The ability to purchase food is a

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

source of distress as the main livelihoods reported are crop farming (59.2%) and agro-pastoralism (24.5%) which are severely affected by the delayed rains and hence cultivation activities do not provide a positive outlook.

Table 15: How Households are Obtaining Main Food Sources (n=682) Activity Percentage Purchasing 75.4% Cultivation 24.7%

Wild Food Collection 35.2%

Livestock 5%

Kinship 2.3%

Fishing 1.2%

Food Aid 0.6%

Additionally, Table 16 shows that as food stocks are declining, households are primarily resorting to wild food collection for consumption or sale (51.2%), borrowing money (13.6%), selling personal assets (12.6%), and selling livestock assets (10.8%). These types of coping strategies are to be expected but also have a negative impact on household assets and hence their ability to recover following this food insecurity period.

Table 16: Top Coping Strategies When Food Stocks Decline (n=670) Strategy Percentage

Wild Food Collection 51.2%

Borrow Money 13.6%

Sell Personal Assets 12.6%

Sell Livestock Assets 10.8%

Ask for Food Gifts 9.1%

Decrease Number of 7.8% Meals

During this survey, households were asked what could be done to ensure that they had enough food. The majority of respondents said cultivation (56.1%), procurement of seeds and tools (20.9%), and food aid (14.9%).

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

As this was an open ended question, participants commented on their own without influence. It is possible that those that mentioned cultivation could have been referring to the beginning of rains so that they could plant or to provision of seeds and tools for planting.

Only half of the households surveyed owned livestock (53.3%). Of those that did, the major type owned was goat (31.2%), chickens (23.6%) and cows (20.8%).

Fishing activities in Aweil East typically take place in March and April and then again from October to December of every year; though some people are able to carry on with fishing throughout the rest of the months of the year. The tools used for fishing include modern net, hooks, or locally made fishing equipment like Thoi and Roke. There are not many household in Aweil East that fish and therefore fishing contributes minimally to livelihoods in this county. Survey results show that only 8.3% participate in fishing activities with the main constraints being no access to fishing points (45.7%), lack of labor to fish (19%), or lack of fishing equipment (16.1%).

Households were asked what types of food they consumed within the previous 7 days before the survey. The food consumption patterns showed that the most commonly consumed foods were cereals (84.2%), sugar and honey (40.7%), meat (33.9%), oils and fats (23.7%), fish (19.9%) and pulses (18.7%)10. The absence of vegetables and fruits in the diet highlights the risk of deficiencies in micronutrients, which is a major cause leading to malnutrition.

Table 17: Most Commonly Consumed Foods Food Percentage Cereals 84.2% Sugar and Honey 40.7% Meat 33.9% Oils and Fats 23.7%

Fish 19.9%

Pulses (Beans and Lentils) 18.7%

10 Of the households that ate meat, 75.% of them only ate it once or twice per week.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.VI.3. Health

Health services in Aweil East are supported by MoH and NGOs. The 33 health facilities in Aweil East consist of one Hospital in Akuem, six PHCCs and 26 PHCUs. Two PHCCs (Malualkon and Malualbaai) and six PHCUs (Warawar, Bakou, Amethgolduang, Mangartong, Nyanlath and Adoor) are jointly run by IRC and government. Tear Fund is running two PHCUs in Omdurman and Waragany, Diocese of Rumbek and the government one PHCC in Gordhim, and Christian Solidarity International one PHCC in Mabel. The government is running one PHCC in Wanyjok, and 15 other PHCUs within the county.

According to qualitative research, the majority of the population seeks medical treatment when ill and only goes to see traditional healers when the problem persists. There are some people, including those cut off by long distances, who use the traditional/witchdoctors as they are the only people available in their vicinity. These statements can be complimented by this survey’s results (see Figure 4) showing that when household members are first sick they go to PHCC/Us (62.1%), the hospital (17.2%), or the pharmacy (9.3%) primarily; only 4.6% said they go to traditional healers first. There are people however who do refuse to go to health centers even if they are nearby. Key informants say that often pregnant women are the first to refuse health care in health centers and only like to do so if they are either seriously sick or when they hear that others had successful prenatal care services and treatment.

Figure 4: First Place of Treatment When Sick

60 50 40 30

Percent 20 10 0 Traditional healer PHCC/U Relatives/friend No assistance No response CHW Hospital Pharmacy Other Treatment Provider

Most health units are located in the mid-land with no real facility in the far-low and high-lands. Therefore, key informants say that it is on average a 6-15 kilometer walk to reach a health centers. This is supported by Figure 5 below that 49.7% say it takes more than 2 hours to reach a health facility, 18.2% say 1-2 hours; only 14.8% say they are less than a 30 minute walk.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Figure 5: Distance to the Nearest Health Facility

Nearest Health Facility less than 30 minutes 30 minutes to 1 hour 1-2 hours 2 hours or more

With 33 existing health units, there are several operational problems that hinder the functioning of the units and the ability to treat patients. As reported by the County Health Preventive and Curative Officers, the health centers lack proper drugs, staff motivational strategies, and interventions/supports. Drugs were previously supplied directly by NGOs however the government took over drugs procurement responsibility, and while supplies are supposed to reach health units every 3 months, there has been no shipment received since February 2009.

This survey revealed 0.2 [0.05 – 0.35] /10,000/day crude mortality rates and 0.35 [-0.07 – 0.75] /10,000/day under-five mortality rates. Both findings are below the respective 1% alert and 2% emergency levels. However, mortality data have to be considered with great caution as people are reluctant to talk about death and the reported rates could be highly underestimated.

Immunization rates continue to be low in Aweil East County. Survey results show that only 21.4% of children have received the BCG vaccination and 23.5% received the measles vaccine. Extended Programs on Immunization (EPI) have frequently been arranged and implemented by WHO but by March 2009 children have not been immunized against the six killer diseases. Reasons cited include WHO not receiving the vaccines from government/MoH.

In the two weeks prior to the survey, over half the children surveyed had a reported illness (56.3%). The top two illnesses included malaria (19.1%) and diarrhoea (12.4%). Recurrent acute watery diarrhoea (AWD)/cholera outbreaks have plagued Aweil East County since October 2008, with the most recent cases being reported during the last week of June 2009. Outbreaks were registered in many payams/ bomas with most hard hit areas being Makuac, Kiir, Peth, Ameth and Akuem.

The June 2009 ACF nutritional anthropometric survey in Aweil East County revealed GAM rates of 29.8% (25- 35.2%) and SAM rates of 7.8% (5.5-11.1%) (WHO 2005 standards). Both rates exceed the SPHERE standards for

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

GAM (>15%) and SAM (>4%), and are also a significant increase over June 2008 survey results which showed GAM rates of 19.9% (16.1-23.8) and SAM rates of 3.8% (1.9-5.6) (WHO 2005 standards).

.VI.4. Water and Sanitation

Qualitative survey results show that households in Aweil East County are using a variety and combination of water sources to meet their daily needs. The main water source(s) cited were boreholes (58.9%) and unprotected wells (28.7%) with rivers, ponds, seasonal springs, and surface run-off also as small contributors (see Table 18). The source(s) of water used is dependant on many factors including seasonal availability, borehole breakdowns, lack of alternative water sources, and distance to the source. Borehole water is considered the only safe water supply and while 58.9% of the population is accessing this at some point, a large portion of the population is using unsafe water supplies. Also, the survey results show that only 14.6% of households remarked that they boil, filter/sieve or chemically treat their water prior to consumption.

Table 18: Current Water Sources (n=673) Source Percentage Borehole 58.9%

Unprotected Well 28.7% River 5.6% Ponds/Apiir 4.8%

Seasonal Spring 1.9%

Surface Runoff 1.9%

The time it takes for households to walk to and from the water source varies; half the households (52.3%) commented that it takes less than 30 minutes, the other half stating longer time. (See Table 19).

Table 19: Time to and from the Water Source Time N=671 Less Than 30 Minutes 52.3% 30-60 Minutes 28.8% More Than 60 Minutes 17.1%

The number of times/day that water is collected varies upon the distance to the source, household duties, and number of household members able to collect and carry the water. Most households collect between two (39.5%)

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

and three (39.7%) times per day. Water is usually collected in 20 liter jerry cans (77.6%) with storage mainly being in locally crafted pots (65%).

Based on an average household size of 6.6 members, this amount of water equates to 6-9 liters/capita/day which is far below the SPHERE emergency standard of 15 liters/capita/day. There are several reasons that could contribute to this low daily per capita consumption rate including distance to the water source, number of households sharing the daily water source, water scheme breakdowns, unreliable water supply, long queue time, and lack of containers for carrying and storing water.

Sanitation needs for Aweil East County are also not adequate. Only 7% of the households surveyed said they had access to a latrine. For those that did not, a majority of households used the bush (85.8%) or open field (12.5%). A small percentage (1.4%) did remark that they go to the river which could be an important factor for the spread of water-borne illnesses. (See Table 20). Stools of children under the age of 3 were primarily thrown outside the house compound (88%) with only 2.5% being buried or thrown in a latrine.

Table 20: Location of Defecation if there is no Toilet (n=654) Location Percentage Bush 85.8% Open Field 12.5% River 1.4% Other 0.2%

Health and hygiene practices for treating water prior to consumption, and proper handwashing practices also were not observed or reported. Only 35.4% of households remarked that they had soap in the home at the time of the survey. As depicted in Table 21, of this 35.4% that had soap, only 15.5% washed before eating, 2.3% washed before feeding their children, and 1.5% washed after defecation. The majority of households that had soap used it to wash clothes (93.6%), wash utensils (29.4%) or bathe (23.8%). A small minority of households that did not have soap did tell us that they used alternates; 3.5% used tree bark, 2.2% used ash, and 0.9% used thou kou.

Table 21: Household Use for Soap (n=265) Use Percentage Wash Clothes 93.6% Wash Utensils 29.4% Bathe 23.8%

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Wash Before Eating 15.5% Wash Before Feeding 2.3% Children Wash After Defecation 1.5%

Lack of adequate quality and quantity of water, of proper sanitation, and of good hygiene practices lead to the spread of diarrhea and other illnesses; and could be a factor in the reoccurring acute watery diarrhea (AWD)/cholera outbreaks which have plagued Aweil East County since October 2008.

.VI.5. Maternal and Child Care Practices

Adequate child care practices are acknowledged as a significant determinant of good wellbeing and nutrition among children. The UNICEF causal framework for malnutrition places care practices as one of the top three underlying causes of malnutrition; the other two being food security and health. Breastfeeding and complementary feeding practices are two major components of proper care practices.

This survey revealed that 83.6% of women initiated breastfeeding within one hour after delivery, with 75.5% of women breastfeeding the child on demand (i.e. when the child cried, reached for the breast, etc). Of the 504 women that had children under the age of 5 years old, 70.4% were still breastfeeding. Of those that had ceased breastfeeding, this primarily occurred when the child was over 24 months (59.1%) or between the ages of 18-24 months (22.8%). While it is not known if Sudanese women in Aweil East County are exclusively breastfeeding their children for the first 4-6 months11, it is being shown that the majority are breastfeeding through at least 18 months of age. The length of breastfeeding is also promoted traditionally in the community in Aweil East County, where it is accepted that a child be allowed to breastfeed for two to three years. However, there is a portion of the community that is more affluent and ceasing to breastfeed at 12 months of age, but the older generation is against this. Survey results also show that for children that were no longer breastfeeding, the main reasons were because the child was too old or the mother was pregnant. While the cultural implications of breastfeeding while pregnant were not explored in this survey, it is important to sensitize mothers to the fact that breastfeeding while pregnant should continue and will not harm the unborn child.

Until the child reaches the age of 6 months, all nutrients are provided through breast milk and other food sources are not needed or advisable. After this age, it is necessary to introduce complimentary foods to the child so that they can receive all the essential nutrients needed to maintain a good health and nutritional status. Survey results showed that only 15.4% of children between the ages of 4-6 months received complimentary foods at this essential growth stage. The majority of children received complimentary foods from the age of 6-10

11 Exclusive breastfeeding was not researched in this survey

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

months (33.7%) or after the age of 10 months (38.3%). There is a need to sensitize mothers on timely complimentary feeding practices.

In addition to the proper timing for food introduction, the types of food and numbers of meals provided to the children each day is very significant to meeting their overall nutritional needs. Respondent’s said that the foods they feed to their 6-59 month old children include porridge (65.4%), breast milk (27.5%), other foods such as sorghum and normal ‘adult’ foods in combination with wild foods (23.7%), and cow/goat milk (22.2%). Figure 6 below shows that children were fed between 0 and more than 3 times per day with the highest percentage receiving 2 meals a day.

Figure 6: Number of Times per day that Children are Fed

30 20

Percen 10 0 nil once twice thrice > 3 times Number of Times Per Day

.VII. DISCUSSION

The June 2009 ACF Anthropometric survey in Aweil East County revealed GAM rates of 29.8% (25-35.2%) and SAM rates of 7.8% (5.5-11.1%) (according to WHO 2005 standards). Both these rates exceed the WHO emergency threshold for GAM and SAM, and are also a significant increase over June 2008 survey results of GAM rates of 19.9% (16.1-23.8) and SAM rates of 3.8% (1.9-5.6) (WHO 2005 standards). Key indicators are pointing to the development of an acute nutritional emergency that is considerably higher than the normal chronic emergency levels. The results could be linked to the following:

Food intake and food insecurity

The community is facing a very serious hunger gap due to the fact that last year’s harvest was poor yielding as a result of concurrent drought, floods and crop pest infestations. Based on an ACF post harvest crop assessment in October 2008, some households only harvested enough food to last until December 2008; thereby instigating an earlier hunger gap than in a normal year. Discussions with key informants have also concluded that the 2008 harvest was far below than that of 2007. Additionally, rains are late this year and planting is being delayed. This

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

is initiating fear that not only will yields be reduced for the October harvest but also that the annual floods could come on time in August/September, and hence further reducing crop yields.

Increases in staple food prices, and decreased selling prices (and terms of trade) for cattle, are also affecting the food security situation and the ability of households to obtain sufficient food quantity. This is especially problematic given that during the survey, 63.4% of households reported that their food stocks were already depleted; with an additional 28.6% stating that all stocks will be used by mid-July. The combination of these factors not only contributes to the current food insecurity and malnutrition situation, but could also have negative push-on effects for households’ livelihoods and next year’s food security and nutrition situation.

Disease prevalence:

Over half the children surveyed had a reported illness (56.3%) within 14 days prior to the survey. The top two illnesses included malaria (19.1%) and diarrhoea (12.4%). Survey results show that a small percentage of children have been vaccinated in this county; 21.4% had BCG vaccination, 23.5% had measles vaccine. Extended Programs on Immunization (EPI) had not occurred by March 2009 to immunize children against the six killer diseases. Additionally, acute watery diarrhea (AWD)/cholera outbreaks have been reoccurring since October 2008, with the most recent cases being reported during the last week of June 2009. Preventing disease and immunizing children are important to keep a healthy population that is less susceptible to high malnutrition rates.

Inadequate water and sanitation situation:

In Aweil East County, only 58.9% of the population is accessing clean water through boreholes as a main water source, and 7% have access to proper latrines and sanitation. Handwashing procedures are also low with only 15.5% washing hands before eating, 2.3% washing before feeding their children, and 1.5% washing after defecation. These low percentages can be contributed to distance and sources of water, functionality of water schemes and pumps, ability and knowledge to build latrines, and knowledge and implementation of proper hygienic practices.

Lack of adequate quality and quantity of water, proper sanitation, and good hygiene practices leads to the spread of diarrhea and other illnesses; and is a key instigating factor in the reoccurring acute watery diarrhea (AWD)/cholera outbreaks which have plagued Aweil East County since October 2008, with the most recent cases being reported during the last week of June 2009. Diarrhea and other illnesses lead to reduced food quantity intake and decreased nutrient absorption thereby contributing to a decline in nutritional status.

Inappropriate maternal and child care practices:

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Poor child care practices in Aweil East County may be a contributing factor to high malnutrition rates. While there is good breastfeeding initiation after delivery, and prolonged breastfeeding duration primarily through the age of 18-24 months, complimentary feeding practices need to be improved. This survey reveals that only 15.4% of children are receiving foods at the proper age. This has a serious affect on the health and nutritional status of a child if he/she is not receiving the proper quantity and quality of foods at vital growth stages.

.VIII. RECOMMENDATIONS

Health and Nutrition

Food Security and Livelihoods

• Contingency and preparedness measures need to be established to be able to respond to a deteriorating food security and livelihoods situation due to the current dry spell and for the flood onset in August and September 2009, preventing the nutrition situation from further deterioration • A contingency is as well needed to enable appropriate responses to continuous food insecurity and malnutrition in 2010 due to a prospected weak harvest in 2009. • A safety net approach should be considered to respond to recurring annual food insecurity and malnutrition, which could be facilitated through a regular cash transfer to the most vulnerable households. • Provision of seeds and tools, including agriculture training on improved techniques and plant protection to all households in need for improving the agricultural production at household level • Support increased household food production through diversification by introducing early maturing crop varieties, vegetable gardens, and small animal livestock keeping • Support the diversification of income sources through income generating activities • The local riverside communities should be empowered on appropriate fishing techniques, processing and preservation techniques

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

Water and Sanitation

• Contingency planning for prevention and mitigation measures in communities affected by recurring cholera/AWD • Training in latrine construction at the community and household levels and provision of digging kits • Increase the number of adequate and safe water schemes through construction, rehabilitation and training on operation and maintenance • Hygiene promotion to encourage good hygienic practice with a specific focus on hand washing

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX. APPENDICES

.IX.1. Sample Size and Cluster Determination

Payam Boma Geographical unit Population size Assigned cluster BAAC ANGOOT MACHAR CHUEL 1039 BAAC ANGOOT RUMAKOC 1039 BAAC ANGOOT RUM GIER 1039 BAAC ANGOOT RIANG AWAI 1039 BAAC ANGOOT ANGOT 1039 1 BAAC MALOU MAYOM ROK 1039 BAAC MALOU RUMDIER 1039 BAAC MALOU MABOK 1039 BAAC MALOU JOOC 1039 BAAC MALOU WUN TIT 1039 BAAC MALOU RUMDHAAL 1039 BAAC MALOU LUETH MALUAL 1039 BAAC MALOU HONG AKOL 1039 BAAC MALUALKON MATHIANG 1039 BAAC MALUALKON RIANG AGUER MABIOR 1039 BAAC MALUALKON WARATANY 1039 BAAC MALUALKON MAJAK PAGONG 1039 BAAC MALUALKON MALUALKON 6962 2 BAAC MALUALKON AUCHUEI 1039 BAAC MALUALKON KON CI BEK 1039 BAAC MALUALKON WUN TIT 1039 BAAC MALUALKON HONG KOU 1039 BAAC MALUALKON RIANG LOCH 1039 BAAC MANYIEL CIYOK 1039 BAAC MANYIEL BAAC 1039 BAAC MANYIEL WAR UGAP 1039 BAAC MANYIEL MAYEN BAAC 1039 3 BAAC MANYIEL ANGOT NHOM 1039 BAAC MANYIEL AUCHER 1039 BAAC MANYIEL ANUEI 1039 BAAC MANYIEL LOL MADIING 1039 BAAC MANYIEL CHUOM 1039 BAAC MANYIEL MARIAL PIOL 1039 BAAC MANYIEL AKUOL LUAL 1039 BAAC MANYIEL MABIOR 1039 BAAC MANYIEL WUN TIT 1039 BAAC PARIAK RUM AGUER ABIEM 1039 BAAC PARIAK LUETH LUAL 1039 BAAC PARIAK PARIAK/PARAK LANG 6754 4

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

BAAC PARIAK PAN ABYEI 1039 BAAC PARIAK MAJOK 1039 BAAC PARIAK NYUM DIT 1039 BAAC PARIAK NYUM THII 1039 MALUAL BAAI ADOOR ATUET 1039 BAAC RULNYIN MALEK 1039 BAAC RULNYIN AJOK ARIATH 1039 BAAC RULNYIN HONG ANGUI 1039 BAAC RULNYIN OMDURUMAN/MADHURMAN 7793 5 BAAC RULNYIN RUOL NYIN 1039 BAAC RULNYIN RUMYUOL 1039 BAAC RULNYIN WARLANG 1039 BAAC WARAWAR ALUETH 1039 BAAC WARAWAR WAR REL 1039 BAAC WARAWAR YAR ACHOT 1039 BAAC WARAWAR AUCHIR 1039 BAAC WARAWAR MATHIANG DIT 6754 6 BAAC WARAWAR WARAWARTHII 1039 BAAC WARAWAR AKUAC 1039 BAAC WARAWAR MAROL 1039 BAAC WARAWAR WAR CHUM 1039 BAAC WARAWAR YITH AKEN 1039 BAAC WARAWAR WARAWAR 8312 7 BAAC WARAWAR KUORUEI 1039 BAAC WARAWAR HONGAU 1039 BAAC WARAWAR KUETH DIT 1039 BAAC WARAWAR MABIL CHAN 1039 BAAC WARAWAR ADUT ADHOT 5715 8 BAAC WARAGANY WARGUET 1039 BAAC WARAGANY AGOK 1039 BAAC WARAGANY WARLIET 1039 BAAC WARAGANY RIANG AWAI 1039 BAAC WARAGANY BACHMANGAN 1039 BAAC WARAGANY LUETH NYANG 1039 BAAC WARAGANY MAJOK AKEN 1039 BAAC WARAGANY MAJAK 1039 BAAC WARAGANY WARPIEN 1039 BAAC WARAGANY BERIC 1039 BAAC WARAGANY BACH ANEI 1039 BAAC WARAGANY MATHIANG 1039 BAAC WARAGANY MARENG TENG 1039 BAAC WARAGANY RUM RAAN DIT 1039 9 MADHOL AJIEP LONGJAL 1039 MADHOL AJIEP AJIEP 1039 MADHOL AJIEP AKOL DIT 1039 MADHOL AJIEP MANGAR AKOL 1039 MADHOL AJIEP RUM DENG AYUP 1039 MADHOL AJIEP MAKER AJIETH 1039 MADHOL AMAR JAL MATHIANG 1039 MADHOL AMAR JAL MACHER ABYEI 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

MADHOL AMAR JAL AMER JAL 1039 MADHOL AMAR JAL WAR AJAP 1039 MADHOL DOK KUL WARTHOU 1039 MADHOL DOK KUL ANGUOTH 1039 MADHOL DOK KUL MAN AWAN 1039 MADHOL DOK KUL WUNKUEL 1039 10 MADHOL DOK KUL RUMBUOL 1039 MADHOL DOK KUL DOKUL 6754 MADHOL DOK KUL PANHIAL 1039 MADHOL MABOK TONG MADHOL 5715 11 MADHOL MABOK TONG RUM MALONG 1039 MADHOL MABOK TONG MANYIEL 5715 MADHOL MABOK TONG PANMUOI 1039 MADHOL MABOK TONG PAN RIANG 1039 MADHOL MABOK TONG MABOK TONG 6234 12 MADHOL MABOK TONG ATUET 8001 MADHOL MAJOK DUT NYETHKOU 1039 MADHOL MAJOK DUT AKUAC DIT 1039 MADHOL MAJOK DUT WARLANG 1039 MADHOL MAJOK YITHIOU MAJOK YINYHIOU 1039 MADHOL MAJOK YITHIOU TOM KIEU 1039 13 MADHOL MAJOK YITHIOU WARAYEN 1039 MADHOL MAJOK YITHIOU MAROL 1039 MADHOL MAJOK YITHIOU MADHIAU AGUR DINY 1039 MADHOL MAJOK YITHIOU KAARIC 1039 MADHOL MAJOK YITHIOU WARNYIEL 1039 MADHOL MAJOK YITHIOU WARCHUM 1039 MADHOL MAJOK YITHIOU TUUL 1039 MADHOL MAJOK YITHIOU WURAWAR 1039 MADHOL MALUALDIT MALUALDIT 1039 MADHOL MAROL AKOT MATHIANG 1039 MADHOL MAROL AKOT RUMNYAL 1039 MADHOL RUM ROL MAJAK DHIEU 1039 MADHOL MAROL AKOT MAKUEI DENG 1039 MADHOL MAROL AKOT MAROL AKOT 1039 14 MADHOL MAROL AKOT GUAR DHUEC 1039 MADHOL PAGAI MABIOR DENG DIT 1039 MADHOL PAGAI ADIMMALEK 1039 MADHOL PAGAI PAGAI 6858 MADHOL PAGAI ADUANY 1039 MADHOL PAGAI MABIL THONY 1039 MADHOL PAGAI MABIL LANG 1039 MADHOL PAGAI PANTHONY 1039 15 MADHOL RUM ROL MAYOM GAI 1039 MADHOL RUM ROL WARAWAR BAI 1039 MADHOL RUM ROL ATUONG RIAL 1039 MADHOL RUM ROL WARAWAR 1039 MADHOL RUM ROL TIT ADUONG 1039 MADHOL RUM ROL MANYIEL 1039 MADHOL RUM ROL RUM THOI 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

MADHOL RUM ROL RUM ROL 1039 MADHOL RUM ROL AMIN 1039 MADHOL RUM ROL RUMJOCH 1039 MADHOL RUM ROL KUEL 1039 MADHOL WAR BAAI MALITH ALEK YAI 1039 MADHOL WAR BAAI RUTH THIEP 1039 MADHOL WAR BAAI RUMALUIL 1039 16 MADHOL WAR BAAI MACHAR DUT WOL 1039 MADHOL WAR BAAI KONGDAI 1039 MADHOL WAR BAAI RUMAPUOTH 1039 MADHOL WAR BAAI MADHOL KON 1039 MADHOL WAR BAAI WARBAI 1039 MADHOL WAR BAAI KUANYTHUOR 1039 MALUAL BAAI ADOOR ATUET 1039 MALUAL BAAI ADOOR WARCUEI 1039 MALUAL BAAI ADOOR THOI 1039 MALUAL BAAI ADOOR TOYOR 1039 MALUAL BAAI AJIERIAK ATHIANG 1039 MALUAL BAAI AJIERIAK MANGAR TONG ADHEN 1039 MALUAL BAAI AJIERIAK RUMDENG ANGOL 1039 MALUAL BAAI AJIERIAK MAKUEI DONG 1039 17 MALUAL BAAI AJIERIAK MADHOL 1039 MALUAL BAAI AJIERIAK HECHEC 1039 MALUAL BAAI AJIERIAK AWANG THOU 1039 MALUAL BAAI AJIERIAK MATHIANG DUT AKOT 1039 MALUAL BAAI AJIERIAK RUM GENG ATEM 1039 MALUAL BAAI AJIERIAK LUETH DENG 6234 MALUAL BAAI AJIERIAK TIT 1039 MALUAL BAAI AJIERIAK AJIERIAK 1039 MALUAL BAAI AJIERIAK PAN GUK 1039 18 MALUAL BAAI AJIERIAK MADING AMEL 1039 MALUAL BAAI AJIERIAK MAYEN 1039 MALUAL BAAI LIETH MAJAK ARIEU 1039 MALUAL BAAI LIETH ATADOU 1039 MALUAL BAAI LIETH MULO 1039 MALUAL BAAI LIETH WATHJONG 1039 MALUAL BAAI LIETH LOL KOU 1039 MALUAL BAAI LIETH ARIEU 6754 19 MALUAL BAAI LIETH GUATBOT 1039 MALUAL BAAI LIETH MABOK 1039 MALUAL BAAI LIETH MABIOR 1039 MALUAL BAAI LIETH MATHIANG WETTHOU 1039 MALUAL BAAI LIETH MAKUEI ARIANG 1039 MALUAL BAAI LIETH RUMGOT 1039 MALUAL BAAI LIETH MARIIK 1039 MALUAL BAAI LIETH WARLANG 1039 MALUAL BAAI LIETH LIETH 7273 20 MALUAL BAAI LIETH RUMMANYIEL 1039 MALUAL BAAI LIETH WARRUAL 1039 MALUAL BAAI LIETH MAYEN 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

MALUAL BAAI MARIALNGAP WUNANGOR 1039 MALUAL BAAI MARIALNGAP AKUAACDIT 1039 MALUAL BAAI MARIALNGAP AKONG 5195 MALUAL BAAI MARIALNGAP JERAKOL 1039 MALUAL BAAI MARIALNGAP WUNMUONYDIT 1039 MALUAL BAAI MARIALNGAP AMETHWEDUANG 1039 21 MALUAL BAAI MARIALNGAP TITUBIT 1039 MALUAL BAAI MARIALNGAP MAJAK PALUIL 1039 MALUAL BAAI MARIALNGAP MAROL AKEN 1039 MALUAL BAAI MARIALNGAP RUMWEL 1039 MALUAL BAAI MARIALNGAP ARENG 1039 MALUAL BAAI MARIALNGAP MAYOMDOHOK 1039 MALUAL BAAI MARIALNGAP DIERADHEL 1039 MALUAL BAAI MARIALNGAP MARIALNGAP 1039 MALUAL BAAI MARIALNGAP KUELABUOK 1039 MALUAL BAAI MARIALNGAP AGUATH 1039 MALUAL BAAI MAROL LACH MINIK 1039 MALUAL BAAI MAROL LACH MALUAL BAI 5715 22 MALUAL BAAI MAROL LACH AWALA 1039 MALUAL BAAI MAROL LACH MAKUAC ALAJAM 1039 MALUAL BAAI MAROL LACH DENYIC 1039 MALUAL BAAI MAROL LACH ROLCOL 1039 MALUAL BAAI MAROL LACH MANYIEL 1039 MALUAL BAAI MAROL LACH PANDHAK 1039 MALUAL BAAI MAROL LACH PANAPUOTH 1039 MALUAL BAAI MAROL LACH ALOL 1039 MALUAL BAAI MAROL LACH AMETH/MAJAK/MALEK DENG 6234 23 AGUER MALUAL BAAI PETH PANTER 1039 MALUAL BAAI MAROL LACH MATHIANG ADIM 5923 MALUAL BAAI PETH WARCUEI/TOCH ADHOT MAJAK 5195 24 MALUAL BAAI PETH PETH CENTER 5195 MALUAL BAAI WUNK KUEL WARLANG 1039 MALUAL BAAI WUNK KUEL ARAMKUETH 1039 MALUAL BAAI WUNK KUEL WUNKUETH 1039 MALUAL BAAI WUNK KUEL MADING 1039 MALUAL BAAI WUNK KUEL CHUOM 1039 MALUAL BAAI WUNK KUEL MAJOK 1039 MALUAL BAAI WUNK KUEL RIANG ANGUI 1039 MALUAL BAAI WUNK KUEL MAROL DUER 1039 25 MANGARTONG KANAJAK MAJAK AJUONG 1039 MANGARTONG KANAJAK WANYJOK 7273 MANGARTONG KANAJAK PAROT 1039 MANGARTONG KANAJAK WARAPATH 1039 MANGARTONG KANAJAK KANAJAK 1039 MANGARTONG KANAJAK PANAPUOTH 1039 MANGARTONG KANAJAK PANLIET 1039 MANGARTONG MABIL MABIL 6234 26 MANGARTONG MANGARTONG MACHIER 1039 MANGARTONG MANGARTONG PAN TIT 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

MANGARTONG MANGARTONG MATHIANG 1039 MANGARTONG MANGARTONG RUMAGOK 1039 MANGARTONG MANGARTONG AMATNHIEN 1039 MANGARTONG MANGARTONG NYIANG MIR 1039 MANGARTONG MANGARTONG WUT RIONG 1039 MANGARTONG MANGARTONG ROL NGOR 1039 MANGARTONG MANGARTONG BUR AKOL 1039 27 MANGARTONG MANGARTONG RUPTHIOU 1039 MANGARTONG MANGARTONG MANYIER 1039 MANGARTONG MANGARTONG WAK KON 1039 MANGARTONG MANGARTONG RUP AGIEU 1039 MANGARTONG MANGARTONG ABYEI 1039 MANGARTONG MANGARTONG MANGOK LUAL 1039 MANGARTONG MAROL AJUONG MABOK NGOR 1039 MANGARTONG MAROL AJUONG CIBILIK 1039 MANGARTONG MAROL AJUONG THANY THANY 1039 MANGARTONG MAROL AJUONG MAKER MAWEIN 1039 MANGARTONG MAROL AJUONG RUP MALEK 1039 MANGARTONG MAROL AJUONG WARANYAK 1039 MANGARTONG RIALDIT MABOK APUOK 1039 MANGARTONG RIALDIT RUP ROL 1039 28 MANGARTONG RIALDIT RIAL DIT 1039 MANGARTONG RIALDIT RIAL DIT BAK 1039 MANGARTONG RIALDIT MANGOK 1039 MANGARTONG RIALDIT GUM ROU 1039 MANGARTONG RIALDIT THOC AJUONG 1039 MANGARTONG RIALDIT MAPER PING DONG 1039 MANGARTONG RIALDIT RUP WET AROU 1039 MANGARTONG RIALDIT MALOU WET WOL 1039 MANGARTONG RIALDIT WAR KECH 1039 MANGARTONG RIALDIT MARIAL ADAL 1039 MANGARTONG RIALDIT CHITING 1039 MANGARTONG RIALDIT RUM WET KOR 1039 MANGARTONG RIALDIT RUM ANGOR 1039 MANGARTONG RIALDIT MAJAK DHIAMA 1039 29 MANGARTONG RIALDIT WAR AKECH 1039 MANGARTONG RIALDIT WAR PACH 1039 MANGOK MABIOR WUNDING MAKOL 1039 MANGOK MABIOR WUNDING MABOK AGANY 1039 MANGOK MABIOR WUNDING MANYANG 1039 MANGOK MABIOR WUNDING MAKUEIKOU 1039 MANGOK MABIOR WUNDING AKEKROF 1039 MANGOK MABIOR WUNDING WUTLOL 1039 MANGOK MABIOR WUNDING MANGAR DHEL 1039 MANGOK MAKUACH MALEK PALUAL 1039 MANGOK MAKUACH MAJAK AKUENY 1039 MANGOK MAKUACH RUMWEL 1039 MANGOK MAKUACH AJIEP 1039 MANGOK MAKUACH AGOR 1039 30 MANGOK MAKUACH NGAMHAR 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

MANGOK MAKUACH MAKUACH 1039 MANGOK MAKUACH MARIAL NGOP 1039 MANGOK MAKUACH MAYEN GOP 1039 MANGOK MAKUACH MAWUT MANGA 1039 MANGOK MAKUACH NYAANG DIT 1039 MANGOK MAMEER DEEK WOLAPUK 1039 MANGOK MAMEER GUK AMOL 1039 MANGOK MAMEER MAYOM AKOT 1039 MANGOK MANGOK RIANG AKOT 1039 MANGOK MANGOK MALITHBUOL 1039 MANGOK MANGOK MALOU 1039 MANGOK MANGOK MANGOK 1039 MANGOK MANGOK AYIIDHIOP 1039 31 MANGOK MANGOK PANGI 1039 MANGOK MANGOK MAJOK MOU AKUENY 1039 MANGOK MANGOK MABOK 1039 MANGOK MANGOK MARIAL ADOT 1039 MANGOK MANGOK AKONDOK 1039 MANGOK MANGOK WUNRAK 1039 MANGOK MANGOK AGOL 1039 MANGOK MANGOK WARCHUEI 1039 WUNLANG GAL WAKMACHAR 1039 WUNLANG GAL GAL 1039 WUNLANG GAL WARNYIEL 1039 WUNLANG GAL MATHIANG 1039 WUNLANG GAL ABYEI/THURLANG 1039 WUNLANG MAJAK GIER HONGAGOK/MAJAKGIER 1039 32 WUNLANG MAKUEI AGEP RIANGMEI 1039 WUNLANG MAKUEI AGEP LUETHWEK 1039 WUNLANG MAKUEI AGEP PANTHOU 1039 WUNLANG MAKUEI AGEP NGAWEL 1039 WUNLANG MAKUEI AGEP WUT-RUAL 1039 WUNLANG MAKUEI AGEP MAKUEI AGEP 1039 WUNLANG MAKUEI AGEP AJOK LUAL 1039 WUNLANG MANYIEL MALOU 1039 WUNLANG MANYIEL MAYOM 1039 WUNLANG MANYIEL WUNHONG 1039 WUNLANG MANYIEL DOOR ABUUN 1039 WUNLANG MANYIEL MANYIEL 1039 WUNLANG MANYIEL WUN YIK 1039 33 WUNLANG MANYIEL MAYOM 1039 WUNLANG MANYIEL MARIAL KUEL 1039 WUNLANG RUM MANYIEL LUONY LUAL 1039 WUNLANG RUM MANYIEL RUMMAJOK 1039 WUNLANG RUM MANYIEL MAYOM WOL 1039 WUNLANG RUM MANYIEL MAYEN NUOK 1039 WUNLANG RUM MANYIEL RUMLOC 1039 WUNLANG RUM MANYIEL MABOK 1039 WUNLANG RUM MANYIEL MALEK BOL AKON 1039 WUNLANG RUM MANYIEL RUMMANYIEL 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

WUNLANG RUM MANYIEL LULIC 1039 WUNLANG RUM MANYIEL RUMMAPER 1039 WUNLANG RUM MANYIEL WARPAC 1039 WUNLANG RUM MANYIEL PAOLUIL 1039 34 WUNLANG RUM MANYIEL MALOU 1039 WUNLANG RUM MANYIEL MANY-KOOR 1039 WUNLANG RUMATHOI AKUAC 1039 WUNLANG RUMATHOI RIANGBAR 1039 WUNLANG RUMATHOI MANYIEL/ABYEI 1039 WUNLANG RUMATHOI MACHAR 1039 WUNLANG RUMATHOI RUMATHOI 1039 WUNLANG RUMATHOI MANGAR 1039 WUNLANG TONG-GOI JOKTHOK 1039 WUNLANG TONG-GOI ALAKOU 1039 WUNLANG TONG-GOI KOUIC 1039 WUNLANG TONG-GOI LOL NHOM 1039 WUNLANG TONG-GOI TONG-GOI 1039 WUNLANG TONG-GOI ARENGPINY 1039 35 WUNLANG TONG-GOI KAR ARIATH 1039 WUNLANG WAR-DONG YOMDIT 1039 WUNLANG WAR-DONG MACHAR TIT 1039 WUNLANG WAR-DONG AJUAJA 1039 WUNLANG WAR-DONG WAR KUEL THII 1039 WUNLANG WAR-DONG LONGANGUEK 1039 WUNLANG WAR-DONG WAR LANG 1039 WUNLANG WAR-DONG WARDONG 1039 WUNLANG WAR-DONG WAR KUELDIT 1039 WUNLANG WAR-DONG AJIEP 1039 WUNLANG WAR-DONG RIALDIT 1039 YARGOT HALBUL KUNYUK 1039 YARGOT HALBUL ANGUEK 1039 YARGOT HALBUL HALBUL 5715 36 YARGOT HALBUL GEER 1039 YARGOT HALBUL MATIAT 1039 YARGOT MAJOK BUOL RUM DHUK AYUOPACHEL WEI 1039 YARGOT MAJOK BUOL MALUALDIT 1039 YARGOT MAJOK BUOL MAKUAL MEL 1039 YARGOT MAJOK BUOL RUM ACHOL 1039 YARGOT MAJOK BUOL LOLKOU 1039 YARGOT MAJOK BUOL MALEK LOL 1039 YARGOT MAJOK BUOL AMIIR DENG ABUK 1039 37 YARGOT MAJOK BUOL MAJOK BUOL 1039 YARGOT MAJOK BUOL MAJOK AKECH 1039 YARGOT MAJOK BUOL GEEU 1039 YARGOT MAJOK BUOL KARICH 1039 YARGOT MAKUAC AKUEL TITCHUOR 1039 YARGOT MAKUAC AKUEL RUM ALEU 1039 YARGOT MAKUAC AKUEL DAI ABAL 1039 YARGOT MAKUAC AKUEL HONG THOK 1039 YARGOT MAKUAC AKUEL MAGAK 1039

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

YARGOT MAKUAC AKUEL ACHOK KUEI 1039 YARGOT MAKUAC AKUEL PANYIKUEL 1039 YARGOT MAKUAC AKUEL ATUEK CHOK 1039 YARGOT MAKUAC AKUEL RUMBUOL 6754 38 YARGOT MAKUAC AKUEL LANGIC 1039 YARGOT MAKUAC AKUEL GOKTHOK 1039 YARGOT MAKUAC AKUEL AKUEMKOU 7273 YARGOT MAKUAC AKUEL MACHAR TUOP 1039 39 YARGOT MAKUEI WET TONG MAKOL PANYIER 1039 YARGOT MAKUEI WET TONG MAKOL PANLUIL 1039 YARGOT MAKUEI WET TONG DHAL AKOT 1039 YARGOT MAKUEI WET TONG WUNYIIK/WAR AYAK 7793 YARGOT MAKUEI WET TONG MAKUEI WET TONG 1039 YARGOT MAKUEI WET TONG HONG AKONG 1039 YARGOT MAKUEI WET TONG MARIAL AKECJOK 1039 40 YARGOT MAKUEI WET TONG PANKOU 1039 YARGOT MAKUEI WET TONG YARGOT 6234 YARGOT MAKUEI WET TONG LUALABAK 1039 YARGOT MAKUEI WET TONG UDOM 1039 YARGOT MAKUEI WET TONG MALEK ANGUEI 1039 YARGOT NYAKRAL GUENGKOU 7273 41 YARGOT NYAKRAL CHUEI MALWAL/ AGUOK MADING 1039 YARGOT NYAKRAL KARMAKUOCH 1039 YARGOT NYAKRAL ANGOT 1039 YARGOT NYAKRAL NYAKRAL 1039 YARGOT NYAKRAL WAKABIL 1039 YARGOT YARGOT WUNTHOU 5715 42 YARGOT YARGOT MAROL NGOR 1039 YARGOT YARGOT RUMKOU 1039 YARGOT YARGOT MATTIIANG 1039 MANGOK MAKUACH MANGARANGUEI 6754

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX.2. Anthropometric Survey Questionnaire

In the BCG Vaccination last HH. HH Age Sex Weight Height Malnutrition Oedema MUAC Measles Scar card illness, if No. No. Status mth (F/M) Kg Cm Status Y/N Cm C/M/N Present Present? any did Y/N Y/N the child have? 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 (1) Status: 1= Resident, 2= Displaced (Because of fighting, flood, etc length < 6months), 3= Family temporary resident in the village (Cattle camp, water point, visiting family…..), 4= Returnees (2) Measles: C= according to EPI card, M=according to mother, N=not immunized against measles (3) Illness in the last two weeks: 1=No illness, 2=Malaria, 3=Diarrhoea, 4=Respiratory infections, 5=Measles, 6=Skin Infection, 7=Worm infestation, 8=Others (Specify)

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX.3. Household enumeration data collection form for a death rate calculation survey (one sheet/household)

Survey Payam: Village: Cluster number:

HH number: Date: Team number:

1 2 3 4 5 6 7 Present at beginning of recall Date of Born Died (include those not present now HH Present birth/or during during the ID and indicate which members were Sex member now age in recall recall not present at the start of the years period? period recall period ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Tally (these data are entered into Nutrisurvey for each household):

Current HH members – total Current HH members - < 5 Current HH members who arrived during recall (exclude births) Current HH members who arrived during recall - <5 Past HH members who left during recall (exclude deaths) Past HH members who left during recall - < 5 Births during recall Total deaths

Deaths < 5

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX.4. Enumeration data collection form for a death rate calculation survey (one sheet/cluster)

Survey Payam: Village: Cluster number:

HH number: Date: Team number:

Current HH Past HH members Current HH members who who left during Births Deaths during recall N member arrived during recall recall during (exclude births) (exclude deaths) recall Total < 5 Total <5 Total < 5 Total < 5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX.5. Calendar of Events

MONTH Seasons 2004 2005 2006 2007 2008 2009 JANUARY 53 41 29 17 5 (NYIETH) Signing of CPA in Naivasha War btn Dinka Transplantation of tobacco malual/Misiriya. CPA Well digging celebratio Cattle taken to Toch(lowland) n in Malakal FEBRUARY 52 40 28 16 4 (KOL) War btn Dinka Malakal malual/Misiriya. fighting Cutting grass for roofing the houses. Threshing of Dura btn SAF Wild food collection and SPLA. MARCH 51 39 27 15 3 Clearing of field in preparation for planting. Roofing of the Mengitis outbreak. War btn Dinka Issuance houses. malual/Misiriya.Malong Paul of arrest

Cutting tree for fencing fields appointed as governor of warrant. Harvesting of tobacco NBGS. APRIL 50 38 26 14 2 Pope John Paul’s Census done in all of South Fishing. Bush Burning for cultivation/hunting . Rain begins. Creation of death. Guinea worm filters Sudan. Maloda making(blacksmithing) bomas/payams(SPLM) distributed. Peace conference btn Garden fencing councils Dinka malual and Misiriya. MAY 49 37 25 13 1 SPLA 16 anniversary. Tradition 20 govt officials died in al chiefs’ plane crash in Rumbek. conferen SAF and SPLA fought in ce in Abyei Bentiu. Integration of katiba Evangelis Planting Sim sim, groundnuts, sorghum, millet, Maize. Cattle Ajer led by t Mark brought to midland.Fishing.fencing continues Abdalbagi Ayii with Dan’s SPLA crusade in Wanyjok/ Aweil town JUNE 48 36 24 12 Measles ACF –French nutrition planting of maize groundnut begins. Weeding of first plants. immunization survey. Cattle in mid/high land campaign. Malaria Nhomlaau radio opened in

campaign. malualkon JULY 59 47 35 23 11 Death of Dr. Garang North-south road Flooding. Hunger begins. Collection of wild fruits. Weeding completed. in a plane crash. construction begun. Chasing birds from crop fields. Planting groundnut/maize. Local chiefs Cattle in concentration camps in midland. received sorghum to sell at cheap prices. AUGUST 58 46 34 22 10 Salva Kiir swon in as president in Harvest of sorghum weeding of gnuts. Cattle in highland. Khartoum. Kuol

Flooding Manyang appointed as caretaker for NBGS. SEPTEMBER 57 45 33 21 9 Appointment of

Harvesting. Initiation ceremonies. wedding ceremonies Mareng as first

governor. OCTOBER 56 44 32 20 8 Governor visited all Frequent traditional dancing. A lot of liquor brewing. fruit - the counties in the trees producing/ripening state. NOVEMBER 55 43 31 19 7 Malakal conflict btn Dinka malual/misiriya war Harvesting of gnuts. . Rain stops. Circumcision. Marriage SAF and SPLA. ceremonies. Mudding of houses. Flood water receding. Madut Biar Cattle in midland. Cold dry wind appointed as NBGS governor. DECEMBER 54 42 30 18 6 (KONPIU) Christmas celebration CANS started Traditional dancing by women. Holiday for school children. Fishing. Cattle to toch. Marriages ceremonies. Male circumcision

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

.IX.6. Qualitative Questionnaire

No Questions HH1 HH2 HH3 HH4 HH5

1 Status of the Household 1=Resident 2=IDP 3= Temporarily resident 4=Returnee 2 Sex of respondent 1=Male 2=Female 3 What is your main livelihood activity(s)? A= Pastoralism B= Fishing C= Crop farming D= Employment E= Agro-pastoralist F= Petty trade G= Other (specify) 4 What is/are your main source(s) of income? A=No income B= Sale of livestock C= Sale of livestock products D= Sale of crops E= Petty trading e.g. sale of firewood F=Casual labour G=Permanent job H= Sale of personal assets I= Remittance J=Other(Specify) 5 What is/are the household’s main food source(s) in the current month? A=Cultivation B= Livestock C= River(fishing) D=Buying E= Food Aid F= Wild food collection G=Kinship H= Other (specify)

6 Are your main sources of food sufficient for your household? 1=Yes (skip to qn 9) 2= No

7 If your answer is NO in question 6 above give reasons for

insufficient food sources? (Note the answers in the 54 field and code later for analysis- refer to the attached). ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

No Questions HH1 HH2 HH3 HH4 HH5

8 What do you do when your household food stock declines? A=Borrow money B=Receive money from relatives C=Ask for food from relatives, friend, neighbours (no repayment) D=Rely on food distribution (WFP, NGOs) E=Sell livestock assets F=Sell personal assets (other than livestock) G=Send children away (to relatives, friends e.t.c) H=Wild food collection I=Eat immature crops J=Reduce number of meals K=Other (specify) 9 How long will your current food stock last you? l= Less than a month 2= 1-3 months 3= 4-6 months 4= More than 6 months 5= Already completed. 10 What do you think could be done to ensure enough food?

11 Which crops did you cultivate/are you cultivating this year? 1=Sorghum 2= Maize 3=Cowpeas 4=Groundnuts 5=Simsim 6=Other 12 How many feddans of land did you cultivate/are you cultivating this year? 1=Less than 1 2= One to two 3= Two to three 4=Three to four 5= More than four 13 Which livestock do you own? 1=None 2=Cows 3=Goats 4=Chicken

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

5=Donkey 6=Sheep 7=Others

14 Does this household do fishing? 1=Yes (skip to qn 16) 2=No

No Questions HH1 HH2 HH3 HH4 HH5

15 If no, why? A= Lack of fishing equipments B= Lack of enough fish in the fishing points C= Lack of labour D= No access to fishing point E=Other (Specify) 16 What is the frequency of the following foods the HH A= B= A= B= A= B= A= B= A= B= consumed in the last 7 days? (no. days/wk) C= D= C= D= C= D= C= D= C= D= A=Meat and offal B=Cereals (sorghum, maize etc) E= F= E= F= E= F= E= F= E= F= C= Milk and milk products D= Beans, lentils and nuts G= H= G= H= G= H= G= H= G= H= E= Vegetables F= Fruits G= Fish and seafood H= Eggs I= J= I= J= I= J= I= J= I= J= I=Spices, condiments and beverages J= Sugar/honey K= L= K= L= K= L= K= L= K= L= K= Roots and tubers L= Oil/fats 17 Do you have children under 5 years old? 1 =Yes 2=No (skip to qn 25) 18 When did you start breastfeeding your youngest child after birth? (for those who do not answer immediate, probe why) 1=Immediate (within 1 hr) 2= More than 1 hr 3= 1 day 4=More than 1 day 5=Others (specify) 19 How do you breastfeed your youngest child (less than or equal to 24 months)? 1= On demand 2= Own choice 3= Others (specify) 20 How old was your youngest child when he/she stopped breastfeeding?

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

1= Child still breastfeeding (skip to qn 22 ) 2= Less than 3 months 3= 4-6 months 4= 6-12 months 5= 12-18 months 6= 18-24 months 7= Older than 24 months 21 Why did the child stop breastfeeding at this age? 1= Mother was pregnant 2= Mother couldn’t produce enough milk 3= Mother died 4= Child refused the breast 5=Heath worker/traditional healer said to stop No Questions6= Child too old to be breastfeeding 7= Other (specify) HH1 HH2 HH3 HH4 HH5

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

22 At what age did your youngest child (less than or equal to 24 months) start to receive food other than breast milk, vitamins, minerals and medicines? 1= Less than 4 months 2= More than or equal to 4- 6 months 3= More than or equal to 6-10months 4=More than 10months 23 Which foods are fed to your children; 6-59 months? A=Breast milk B= Cow/goat milk C= Porridge (specify) D= Vegetables E= Other (specify) 24 How many times did you feed your younger children (6- 59 months) in a day in the last 7 days? 1= None/nil 2= Once 3=Twice 4= 3 times 5= More than 3 times 25 What is/ are your main current water source(s) for household consumption/use? A= River B= Lake C= Borehole D= Unprotected well E= Surface run-off F= Rain water G=Swamp water H= Seasonal spring I=Laga J= Other (specify) 26 How long does it take to go to the main source of water and back? 1= Less than 30 minutes 2= 30 minutes to 1 hour 3=More than 1 hour 27 How many times a day do you collect/fetch water? 1= 1time 2= 2 times 3=3 times 4=More than 3 times

28 What type of container do you use to collect water? 1= Jerry can (10 liter) 2= Jerry can (20 liter) 3=Pot 4= Bladder 5= Other (specify)

29 What type of container do you use to store water? 1= Jerry can (10 liter) 2= Jerry can (20 liter) 3=Pot 4= Bladder 5=Other (specify)

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

No Questions HH1 HH2 HH3 HH4 HH5

30 What do you do to water before drinking? A= Nothing B= Boiling C= Use of traditional methods D= Use chemicals E= Filter/sieves F=Decant 31 Does your household have access to a toilet facility? 1=Yes 2= No (skip to qn 33) 32 If yes, who owns the toilet? 1= Own toilet 2= Neighbours 3=Community 4=Others (specify)

33 If no, where do you go/use (probe)? A= Bush B=Open field C=Near the river D= Behind the house E= Other(specify) 34 What happens with the stools of young children (0-36 months) when they do not use the latrine or toilet facility? A=Children always use toilet or latrine B=Thrown into toilet or latrine C=Thrown outside the yard D=Buried in the yard E=Not disposed of or left on the ground F= Wash in river or water point G=No young children in the household H=Other(specify)

35 Do you have soap in the household? 1=Yes (go to qn 36) 2=No (probe if other alternatives to soap are used and specify which ones and skip to qn 37)

36 If yes what do you use it for? A= Wash hands before eating (self) B=Before feeding children C= After defecation/soiling D=Wash clothes E=Other (specify)’

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

No Questions HH1 HH2 HH3 HH4 HH5 37 When a member of your household is sick where does he/she first seek treatment? 1=Traditional healer 2=Community health worker 3=PHCC/U 4=Hospital 5=Relative/friend 6= Pharmacy 7= No assistance 8= Other(specify) 38 How long does it take to walk to the nearest health facility? 1= Less than 30 minutes 2= More or equal to 30 minutes and less than 1hr 3= More or equal to 1 hr and less than 2 hrs 4= More or equal to 2 hrs 1. Exclusive breastfeeding: 4-6 months 2. Timely complimentary feeding: ≥6 and ≤10 months

Question 7; if your answer is NO in question 6 give reasons for insufficient food sources?

A=Not enough rain (drought) B=Too much rain (flood) C=Diseases killed livestock/destroyed crops D=Pests killed livestock/destroyed crops E=Insecurity F=Livestock/crops stolen G=Not enough livestock H=Not enough land I=Not enough fish in the rivers J=Not enough people in household for production K=Not enough materials (seeds, tools, equipment) for production L=Do not have enough skill/training/education to increase production M=Sickness (illness) or handicapped N=Too young or too old

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009

O=Do not have enough money to buy food P=Prices of food are too high Q=Lack of/inadequate income R=Other (specify)

Additional information to be gathered through key informant interviews and observation

WFP food aid (FFR, FFW, FFE etc) - obtain information from WFP and SSRRC.

Markets: Include how many markets are in the Payam; whether they sell food stuffs; food price changes

Livestock: Where does the community buy or sell livestock; whether livestock are at home or in the cattle camps; milk availability

Fishing: Probe whether there are any fishing grounds in the surveyed location and which months of the year the community does fishing.

Water: Consider availability of portable water such as seeking to know how many boreholes/taps are in the area, how many are functional e.t.c

Health care: Find out how many health facilities (PHCCs and PHCUs) are available in the surveyed area as well as additional information on health care seeking patterns.

ACF-USA nutrition surveys, Aweil East County, South Sudan, 2009