Health and Nutrition Report

7/27/2019 Health and Nutrition Report

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Health and Nutrition Situation

A Rapid Assessment in 5 States in

Southern Sudan

Consultants

Faith M. Thuita

Stephen Macdowell

August 2009

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TABLE OF CONTENTS

1. INTRODUCTION

1.1 EXECUTIVE SUMMARY ……………………………………………………………… 4

1.2 BACKGROUND TO ASSESSMENT…………………………………………………………… 8

2. METHODOLOGY

2.1 STUDY METHODS …………………………………………………………………….. 10

2.2 NUTRITION STATUS INDICES …………………………………………………………… 11

3. FINDINGS

3.1 DESCRIPTION OF THE SURVEY SAMPLE………………………………………………….. 13

3.2 ANTHROPOMETRIC RESULTS: CHILDREN (BASED ON WHO STANDARDS 2006)...... 13

3.3 MORTALITY RESULTS (Retrospective over 91 days prior to interview)………………………. 19

3.4 CHILDREN’S MORBIDITY…………………………………………………………………….. 20

3.5 VACCINATION RESULTS…………………………………………………………………….. 21

3.6 VITAMIN A SUPPLEMENTATION/SLEEPING UNDER LLITN…………………………… 21

3.7 PROGRAMME COVERAGE…………………………………………………………………… 22

3.8 SUPPLEMENTARY RATION ADEQUACY…………………………………………………… 23

3.9 INFANT FEEDING PRACTICES……………………………………………………………….. 23

3.10 SECONDARY DATA REVIEW…………………………………………………………………. 25

STATE REVIEW…………………………………………………………………………………. 26

4. DISCUSSION……………………………………….…………………………… 31

5. CONCLUSIONS…………………………………………………………..…………… 32

6. RECOMMENDATIONS AND PRIORITIES………………………………………………. 32

APPENDICES………………………………………………………………………………………… 35

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Abbreviations

ACF-USA - Action Contraire Faim-USA

ANOVA - Analysis of Variance

ARTI - Acute Respiratory Tract Infection

ASAL - Arid and Semi-Arid Lands

CCC - Comprehensive Care Center

CDR - Crude Death Rate

CHS - Community Health StrategyCI - Confidence Interval

CBR - Crude Birth Rate

CMR - Crude Mortality Rate

CSB - Corn Soya Blend

EMOP - Emergency Operations Programs

ENA - Emergency Nutrition Assessment

EPI - Extended Programme of Immunization

FGDs - Focus Group Discussions

GAM - Global Acute Malnutrition

GFD - General Food Distribution

GOK - Government of Kenya

HAZ - Height-for-Age Z-score

IEC - Information, Education and Communication

IDP - Internally Displaced Persons

IMR - Infant Mortality Rate

ITN - Insecticide Treated Mosquito Nets

L/HAZ - Length/ Height for Age –Z-score

MOH - Ministry of Health

MSF-B - Medecins Sans Frontieres-Belgium

MUAC - Mid-Upper Arm Circumference

NCHS - National Centre for Health Statistics

OTP - Out-patient Therapeutic Program

PHC - Primary Health Centres

PLM - Pregnant and Lactating Mothers

SAM - Severe Acute Malnutrition

SC - Stabilization Centre

SD - Standard Deviation

SFP - Supplementary Feeding Programme

SMART - Standardized Monitoring and Assessment of Relief and Transitions

SMOH - Sudan Ministry of Health

TFC - Therapeutic Feeding Centre

U5MR - Under Five-Mortality Rate

UNICEF - United Nations Children’s Fund

URTI - Upper Respiratory Tract Infection

WHM - Weight for Height Median

WAZ - Weight-for-Age Z-score

WHZ - Weight-for-Height/length Z-scores

WFP - World Food ProgrammeWHO - World Health Organization

GoSS - Government of Southern Sudan

ACSI - Accelerated Child Survival Initiative

PCA - Partners Cooperative Agreement

SSRRC - Southern Sudan Relief and Rehabilitation Commission

ANLA - Annual Needs and Livelihoods and Anthropometric Assessment

OCHA - Office of Coordination of Humanitarian Affairs

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EXECUTIVE SUMARY

Since the Annual Needs and Livelihoods and Anthropometric Assessment (ANLA) was conducted inOctober 2008, the food security and nutrition situation in Southern Sudan has continued todeteriorate because of heightened cross boarder and internal conflicts, populationdisplacements, delayed rains, deterioration in road networks/market access, and rise in foodprices. This has given rise to concerns that crisis conditions may be emerging. In view of this,the Southern Sudan Relief and Rehabilitation Commission (SSRRC) and the Office of Coordination of Humanitarian Affairs (OCHA) recommended a rapid assessment to determine

the extent of deterioration in five states: Jonglei, Upper Nile, Eastern Equatoria, Warap,Northern Bahr el Ghazal. The assessment involved a rapid nutritional assessment in the fivestates as well as a review of secondary data which sought to distinguish seasonal deviationsfrom chronic issues to identify potential crises.

The nutritional assessment coupled with a review of current available secondary health datasuggests seasonally stable health and nutrition conditions. The data also indicates threepopulations with acute health needs and an elevated risk of morality which are of immediateconcern - the Murle households affected by Lou Nuer massacres who remain in Lekongolearea or were displaced into Pibor town; Lou Nuer households from Nyandit, displaced by Murlemassacres into Akobo town and displaced populations from Abyei in Aweil East. Within thesegroups, children under 5 are a particular concern. Each of the five states examined also has

populations of concern which should be monitored in the coming months. Areas within eachstate that should be monitored in the coming months were identified by comparing livelihoodssystems against potential impacts of rain or crop failure.

Key Findings

• The prevalence of Global Acute Malnutrition (GAM) among all children surveyed was 15.8% and is indicative of critical nutritional situation based on the WHO standards. Theprevalence of Severe Acute Malnutrition (SAM) of 3.6 % is also suggestive of widespreadmalnutrition among children. However, these rates are seasonally common.

• High morbidity rates were reported in the five states two weeks prior to the study. Theselevels were however consistent with seasonal morbidity patterns recorded from healthfacilities in these states. Both crude and under five mortality rates are at emergency levelsfor emergency situations.

• Overall, data from both the nutritional survey and review of secondary data showsseasonally stable health & nutrition conditions in the 5 states, but with pockets of worsening nutritional situation among populations with acute health & nutrition needs in 3states in Jonglei (Pibor and Akobo); NBEG (Aweil East County) and Upper Nile (Longichukand Sobat area - Maiwut, Nasser & Uranga).

• Other populations groups in the 5 states that require close monitoring in the comingmonths were identified. Eastern interior areas of Upper Nile, Lou Nuer communities incentral Jonglei, and communities which may be impacted by insecurity or which mayreceive IDPs should there be conflict in Abyei are areas which should be prepared forpotential increase in medical and nutritional needs and should exercise increasedsurveillance. Surveillance should also be increased for areas of the west of EasternEquatoria.

• The key underlying causes of the high rates of chronic malnutrition include high morbidity,inadequate coverage of selective feeding programmes, poor IYCF and care practices and

household food insecurity.

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• Amend existing PCA with ACF – USA to accommodate increased case load of severely

malnourished children

• Support on-going nutritional surveillance

NBEG - Awiel East county

• Advocacy with WFP for scale up of SFP in Akobo and Pibor counties

• Amend existing PCA with ACF – USA to accommodate increased case load of severely malnourished children

In addition,

• Scale up of targeted SFP in the 5 states, coupled with active case finding in order toidentify moderately malnourished children for admission into SFP is critical. Nutritionalstatus is highly sensitive to changes in the risk factors and therefore a child’s nutritionalstatus is likely to fluctuate considerably with seasonal changes. In addition, pregnantwomen and lactating mothers with infants less than 6 months require supplementaryfeeding during this period of high food stress..

• Measles vaccination coverage is inadequate in the five states. Given the high prevalenceof disease and severe acute malnutrition, it is necessary to implement a vaccinationcampaign. Specific attention should be made to ensuring that areas that are difficult toaccess are adequately covered, providing and reinforcing the importance of vaccinationcards.

• Rehabilitation of acutely malnourished children through the existing selective feeding andoutreach programs coupled with active case finding until household food security isrestored is critical. Public health issues of concern identified and detailed in the secondarydata should be are addressed.

Medium - Long-Term Interventions

• Intensify health and nutrition education activities at the household level to address childcare, targeting caregivers. The main areas of focus should include promoting exclusivebreastfeeding, appropriate young child feeding, diet diversification and improvement inhousehold hygiene including health care practices. This should also include developmentof local IEC nutritional education materials for community level health promoters.

• Establish a regular nutrition surveillance system.

• On-going capacity building of SMOH staff and the community to manage severemalnutrition

• Assess and strengthen programmes and strategies currently addressing IYCF with a viewto improving the protection, promotion, and support of optimal infant and young childfeeding.

• Work to improve coverage of life saving interventions such as Vitamin A supplementation,immunization and use of LLITNs by children and pregnant mothers.

• Strengthening of hygiene practices to reduce the incidence of diarrhoeal disease includinghealth education to educate the community on treatment of drinking water.

• Strengthening mobile clinic initiatives to cover the rural populations and support outreachservices and community strategy to encourage caregivers to seek health services

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Background to Assessment

An Annual Needs and Livelihoods and Anthropometric Assessment (ANLA) was conducted in

Southern Sudan in October 2008 to provide a benchmark for assessing the scope of food

security and nutrition needs in Southern Sudan for 2009, and also to guide food security and

nutrition programming. This was a joint effort between SSRRC, FSTS/SSCCSE, WFP, UNICEF,

MOA, FAO and MOH. Since the ANLA was conducted in October 2008, the food security and

nutrition situation has continued to deteriorate because of heightened cross boarder andinternal conflicts, population displacements, delayed rains, market fluctuations, deterioration

in road networks/market access, and rise in food prices. This has given rise to concerns that

crisis conditions may be emerging. In view of this, the Southern Sudan Relief and

Rehabilitation Commission (SSRRC) and the Office of Coordination of Humanitarian Affairs

(OCHA) recommended a rapid assessment to determine the extent of deterioration in five

states: Jonglei, Upper Nile, Eastern Equatoria, Warap, Northern Bahr el Ghazal. These states

were chosen based on pre-existing levels of food security and 2009 events.

The assessment involved a rapid emergency nutritional assessment in the five states as wellas a review of secondary data. Information for the secondary data was derived from MOH

Integrated disease surveillance reports, consultations with health facilities and with localNGOs as well as a review of admission rates in selective feeding Programs. The health andnutrition assessment was designed to identify deviations from seasonal norms which mayindicate deterioration which may lead to crisis conditions. This report presents findings of boththe rapid nutritional assessment and secondary data review for Health and Nutritionconditions.

MethodologyStandard anthropometric procedures were used in assessment of nutritional status.Measurements of weight, height and mid upper arm circumference were taken for children in

households with a child under five years. Age of children was entered in months, usingdocumentation when available, else recorded as given by the mother to the nearest month.Weight was taken using Salter scales calibrated to 25 kg while length boards were used forlength and height measurements of children. A precision of 0.1cm was used for recordingheight measurements and 0.1kg for weight measurements. The Mid-Upper Arm circumference(MUAC) was measured at the mid-point of the left upper arm (precision of 0.1 cm), usingstandard MUAC tapes.The presence of bilateral pitting oedema was assessed for all children,by applying pressure on the dorsal side of both feet for at least 3 seconds. If the pit remainedafter removing the pressure, the outcome was considered to be positive. Persons trained forthe ANLA survey were used for data collection

Retrospective morbidity: Caretakers were asked for episodes of illness in the last 2 weeks (14

days) prior to the survey. The cause of illness and whether treatment was sought was alsorecorded. The following case definitions were used:

Fever with chills (Malaria) Diarrhea (watery stool >3/24H); Bloody (watery stool with blood >3/24H) Respiratory infection (fever with difficulty breathing or cough); Measles (fever with red rash)

Coverage of life saving interventions: Measles vaccination and Supplementation with VitaminA: Assessed by checking for measles vaccination and vitamin supplementation on EPI cardsand/or verbal confirmation from the caretakers. Caregivers asked about utilization of treatednets.

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Mortality data: Retrospective mortality data was collected using the current household censusmethod in all the households visited, including those with no children aged less than fiveyears old. The recall period was 94 days. Information was collected on the number of deathsover the recall period. The presumed causes of death were recorded based on the followingcase definitions:

Secondary data

Information for the secondary data was derived from MOH Integrated disease surveillancereports, consultations with health facilities, local NGOs as well as a review of admission ratesin selective feeding Programs for OTP and SFP. Information on current health conditionssought to utilise existing health sector information systems. Health facility monthly morbidityreports were provided by supporting agencies. Agency and key stakeholder interviews helpedinform on communities of special concern. To identify deteriorating health or nutritionconditions required distinguishing between chronically poor nutritional status indicators, theirseasonal fluctuations and to identify deviation from the seasonal norm. The South SudanLivelihood Profiles report was used to interpret the impact of current security and climaticconditions on livelihoods and their potential impacts on health conditions Nutrition andmortality baseline levels were estimated using historic survey data (1998 to 2009) and

compared against 2008 ANLA levels. Morbidity baselines and health facility consultationsused the Picture of Health Project findings and extrapolated those findings for the formerUpper Nile Region to include Bahr el Ghazal and eastern Equatoria.

Procedure for data AnalysisData processing and analysis for the anthropometric data was carried out using ENA(SMART), ENA and SPSS 15.0 software. The calculation and analysis of anthropometricdata and mortality was done using ENA, with the WHO 20061 and NCHS 19772 sexaveraged reference standards selected for the calculation of nutritional indices and cut-off points for malnutrition. The software flagged off any extremes, potentially incorrect or out of range values. The following lower and upper bounds are fixed to identify these extremes or

potentially incorrect z-score values. All flagged off z-scores were excluded from the analysis.

SMART flag limits:

Indicator

Lowerbound

Upperbound

WHZ -3.0 +3.0

HAZ -3 +3WAZ -3 +3

To determine the nutritional status the following variables were considered for analysis: sex,

age, weight, height or length and oedema. The cluster number was also included forsegregation purposes and to allow for smooth merging up of data with the other householdvariables in the SPSS software. During the z-score calculations the following facts were takeninto consideration.

1. If Sex is missing the observation is excluded from analysis.

2. If Weight is missing, no WHZ and WAZ are calculated, and the programme derives onlyHAZ.3. If Height is missing, no WHZ and HAZ are calculated, and the programme derives onlyWAZ.

5. For any child records with missing age (age in months) only WHZ was calculated.

1

WHO Child Growth Standards- WHO Multicentre Growth Refrence Study2 National Centre for Health Statistics (1977) NCHS growth curves for children, birth – 18years. United States Vital Health

Statistics. 165, 11-74.

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6. If a child has oedema only his/her HAZ is calculated.

Additional analyses for frequencies, descriptive, correlations and cross – tabulations wereconducted using SPSS 15.0 for Windows ENA Epi Info and Excel.Immunization/Supplementation, Morbidity and household questionnaire data was alsoanalyzed using SPSS.

Nutritional Status Indices

Acute Malnutrition Indices

Weight-for-height (WFH) index

Acute malnutrition rates are estimated from the weight for height (WFH) index values andoedema. The WFH indices obtained were derived from comparison of children in the survey tothe WHO 20063 references and are reflective of current nutritional conditions. WFH indiceswere expressed both in Z-scores Indicators of acute malnutrition using WFH z-scores andPercentage of the Median of the reference population:

Table 1: Acute Malnutrition Indicators

Weight for Height z-score

Weight for Height %of the median

Global AcuteMalnutrition

<-2 SD and/or oedema < 80% and/ or oedema

Moderate AcuteMalnutrition

<-2 SD and ≥ -3 SD < 80% and ≥ 70%

Severe AcuteMalnutrition

<-3 SD and/or oedema < 70% and/or oedema

Global acute malnutrition (GAM) is therefore defined as ‘the proportion of children presenting with a weight for height index less than -2 Z scores or less than 80%

percent of the median and/or oedema’

Mid-Upper Arm Circumference (MUAC)

MUAC, like weight for height, is used to quantify wasting in a population. MUAC is easier tomeasure than Weight for Height and has recently been shown to be a better indicator of acutemalnutrition than W/H4, using WHO standards for MUAC-for-age5. MUAC is used as a tool forrapid screening at community level and is a good predictor of the risk of mortality. Theguidelines are as follows:

MUAC < 11.5 cm Severe malnutrition and high risk of mortalityMUAC ≥ 11.5 cm and <12.5 cm Moderate malnutrition

MUAC ≥ 12.5 and < 13.5 cm At risk of malnutritionMUAC ≥ 13.5 cm Satisfactory nutritional status

Chronic Malnutrition IndexHeight-for-Age (HFA) - Stunting

Chronic malnutrition rates are estimated from the height-for-age (HFA) index values. The HFAindices were compared with WHO standards and are reflective of long-term malnutrition. HFAindices are expressed in Z-scores and HFA % of median.

3 WHO Child Growth Standards.

4 Myatt M, Duffield A(2007). Weight for Height and MUAC for estimating the prevalence of acute malnutrition? A review of survey data collected between September 1992 and October 2006. IASC Global Nutrition Cluster 5 Current cut-off for severe acute malnutrition revised at MUAC < 115

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Table 2: Height for Age indices

Height for Age z-score

Height for Age % of the median

Global ChronicMalnutrition

<-2 SD < 90%

Moderate ChronicMalnutrition

<-2 SD and ≥ -3 SD < 90% and ≥ 80%

Severe ChronicMalnutrition

<-3 SD < 80%

• Global chronic malnutrition is therefore defined as ‘the proportion of children presenting with a weight for age index less than -2 Z scores or less than 90% percent of the median and/or oedema’ Weight-for-Age (WFA)-Underweight

Weight-for-Age (WFA)-Underweight

WFA is a composite index that reflects both wasting and stunting. It is not a good indicator of current nutritional stress but can be used to follow individual children longitudinally in thecommunity. The HFA indices were compared with WHO standards. WFA indices are expressed

in Z-scores and WFA % of median.Table 3: Weight for Age indices

Weight for Age z-score

Weight for Age % of the median

Global Underweight <-2 SD and/or oedema < 90% and/ or oedema

Moderate Underweight <-2 SD and ≥ -3 SD < 90% and ≥ 80%

Severe Underweight <-3 SD and/or oedema < 80% and/or oedema

Mortality Indices

The crude mortality rate (CMR) is determined for the entire population surveyed for a given

period. The CMR using the current census method is calculated as follows6:CMR = 10,000 people x number of deaths during recall =Deaths/10,000/day

Number of recall days number of current residents

+1/2 (No. of deaths during recall) -1/2 (No. of births during recall)

The proportion of deaths among children under-five years of age (U5MR) is also calculated thesame way using the under five population data. The thresholds are defined as follows7:

Table 4: Mortality Thresholds

Total populationCMR

Under-five populationU5MR

Alert level: 1/10,000 people/day 1/10,000 children/day

Emergencylevel:

2/10,000 people/day 2.3/10,000 children/day

6

Save the Children (November 2004) “Emergency Nutrition Assessment Guidelines for field workers”7 The Sphere Standards, 2006

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3. FINDINGS

3.1 DESCRIPTION OF THE SURVEY SAMPLE

Table 8: Sample characteristics: Health and nutrition Survey in 5 states ( July 2009)

Number of children 0-59 months surveyed 731

Number of children 6-59 months analysed 671Number of anthropometry data excluded (SMARTFlags)

31

Household Census:

Number of total population surveyed for mortality 5424

Number of children under five surveyed for mortality 1393

Number of HH covered in the mortality survey 739

Number and % of children referred to SFP(WHZ <-2SD)

15.8% 101

3.2 Anthropometric Results: Children (based on WHO Standards 2006)

3.1.2 Distribution by age and sex

Table 9: Distribution of age and sex of sample

Boys Girls Total Ratio

no. % no. % no. % Boy:girl

6-17

months

91 49.5 93 50.5 184 27.0 1.0

18-29months

106 52.7 95 47.3 201 29.5 1.1

30-41months

108 59.7 73 40.3 181 26.5 1.5

42-53months

51 51.0 49 49.0 100 14.7 1.0

54-59months

8 50.0 8 50.0 16 2.3 1.0

Total 364 53.4 318 46.6 682 100.0 1.1

Of the children measured, 53.4% were boys and 46.6% were girls (Table 9). Despite the fact

that there were more boys than girls in the sample, the overall sex ratio (calculated bydividing the total number of boys with the total number of girls) was 1.1 which is within therecommended range of 0.8 – 1.28 demonstrating an unbiased sample. The age and sexdistribution of the study group is graphically shown in Figure 6.

8 Assessment and Treatment of Malnutrition in Emergency Situations, Claudine Prudhon, Action Contre la Faim (Action Against

Hunger), 2002.

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Figure 6: Distribution by Age and Sex

3.1.3 Prevalence of acute malnutrition based on weight-for-height z-scores (and/oroedema) and by sex (Based on WHO and NCHS References)

Table 10: Prevalence of acute malnutrition based on weight-for-height z-scores(and/or oedema) and by sex

WHOReference

NCHSReference

WHOReference

NCHSReference

WHOReference

NCHSReference

Alln = 640

Alln = 651

Boysn = 340

Boysn = 345

Girlsn = 300

Girlsn = 306

Prevalence of globalmalnutrition(<-2 z-scoreand/or oedema)

(101)15.8 %(10.7 -

22.7 95%C.I.)

(110) 16.9%

(11.3 -24.5 95%

C.I.)

(57)16.8 %(9.2 -28.695%C.I.)

(64) 18.6%

(11.6 –28.2 95%C.I.)

(44) 14.7%

(8.9 - 23.195% C.I.)

(46) 15.0 %(9.5 - 22.995% C.I.)

Prevalence of moderatemalnutrition(<-2 z-score and>=-3 z-score, nooedema)

(78) 12.2%

(8.0 –18.2 95%

C.I.)

(93) 14.3%

(9.2 - 21.495% C.I.)

(48)14.1 %(7.4 –25.295%C.I.)

(60) 17.4%

(10.2 –28.1 95%

C.I.)

(30) 10.0%

(6.6 –15.0 95%

C.I.)

(33) 10.8 %(7.0 - 16.395% C.I.)

Prevalence of severemalnutrition(<-3 z-scoreand/or oedema)

(23) 3.6%(1.3 - 9.495% C.I.)

(17) 2.6 %(0.8 – 8.095% C.I.)

(9) 2.6%(0.9 -

7.6 95%C.I.)

(4) 1.2 %(0.3 - 4.795% C.I.)

(14) 4.7 %(1.5 - 13.595% C.I.)

(13) 4.2 %(1.3 – 12.995% C.I.)

The prevalence of oedema is 1.4 %

Table 10 shows that the prevalence of Global Acute Malnutrition (GAM) among all childrenwas 15.8 % (10.7 - 22.7 95% C.I.) indicative of a critical nutritional situation based on theWHO standards9. The prevalence of Severe Acute Malnutrition (SAM) of 3.6 %( 1.3 - 9.4 95%C.I.) is suggestive of widespread malnutrition among children. Analysis of the data by sexshows that a higher proportion of boys 16.8 % (9.2 - 28.6 95% C.I.)is malnourished than girls

9 WHO cut off points for wasting using Z scores (<-2 Z scores in populations: <5% acceptable; 5-9% poor; 10-14% serious; >15%critical).

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14.7 %(8.9 - 23.1 95% C.I.). However the overlapping confidence limits indicated that thedifference in malnutrition between the boys and girls was NOT statistically significant - bothgenders are at equal risk of malnutrition.

Figure 7:Comparative graph of GAM and WHO 2006 Reference population

Prevalence of acute malnutrition by age based on weight-for-height z-scores and/oroedema

Table 11: Prevalence of acute malnutrition by age based on weight-for-height z-scores and/or oedema (WHO 2006)

Severe wasting(<-3 z-score)

Moderatewasting(>= -3 and <-2

z-score )

Normal (> = -2 z score)

Oedema

Age(mths

)

Total no.

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

6-17 173 4 2.3 19 11.0 145 83.8 5 2.9

18-29 192 4 2.1 23 12.0 162 84.4 3 1.6

30-41 169 5 3.0 18 10.7 145 85.8 1 0.6

42-53 95 1 1.1 15 15.8 79 83.2 0 0.0

54-59 11 0 0.0 3 27.3 8 72.7 0 0.0

Total 640 14 2.2 78 12.2 539 84.2 9 1.4

The above table categorizes the distribution of the acute malnutrition rate by age groupbased on weight and height in z-scores and or oedema. No consistent trend is evident foreither severe or moderate wasting but analysis of variance (ANOVA) revealed that there wasa highly significant differences (p=0.015) in group means. To verify this, the age groups arere-categorized in Table 13 below, for sub-analysis.

Table 12: Distribution of acute malnutrition and oedema based on weight-for-height z-scores

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

Oedema present Marasmic kwashiorkorNo. 2

(0.3 %)

KwashiorkorNo. 7

(1.0 %)

Oedema absent MarasmicNo. 27(4.0 %)

Not severelymalnourished

No. 635(94.6 %)

This table shows that there were 9 cases of nutritional oedema observed in the analysedsample- 2 with marasmic kwashiorkor and 7 with frank kwashiorkor. There are 27 children(4.0%) who are severely wasted (marasmus). These point to a critical emergency situation.

Prevalence of acute malnutrition (weight-for-height z-scores) by comparison of agegroups

Table 13: Prevalence of acute malnutrition based on weight-for-height z-scores and by age group

To analyze the effect of age on nutritional status, the whole sample was compared to a sub-sample of children (6-29months). This sub-sample would coincide approximately with theweaning period in a child’s life cycle and thus highlight the effect of infant-feeding practices.

The GAM of the younger children was slightly higher than the overall GAM and the burden of severe wasting (SAM) also higher. There is no statistically significant difference betweenacute malnutrition rates observed among children aged 6-29 months and the whole sample

(p<0.05). This in this sample, age is not a risk factor for malnutrition.

Prevalence of acute malnutrition based on the percentage of the median and/oroedema

Table 14: Prevalence of acute malnutrition based on percentage of the medianand/or oedema

NCHS Referencen = 673

Prevalence of global acute malnutrition(<80% and/or oedema)

(74) 11.0 %(7.6 - 16.8 95% C.I.)

Prevalence of moderate acutemalnutrition(<80% and >= 70%, no oedema)

(55) 8.2 %(6.1 - 11.6 95% C.I.)

Prevalence of severe acute malnutrition(<70% and/or oedema)

(19) 2.8 %(0.9 - 8.8 95% C.I.)

Table 15: Prevalence of malnutrition by age, based on weight-for-height percentage of the median and oedema (NCHS 1977)

Severewasting

(<70% median)

Moderatewasting

(>=70% and <80% median)

Normal

(> =80%

median)

Oedema

14

6-59monthsn = 640

6-29 monthsn = 387

Z -

Scores

Global Acute Malnutrition:

W/H < -2 Z-score and/or oedema

(101) 15.8 %

(10.7 - 22.7 95% C.I.)

(58) 15.9% (12.1-

20.6 C.I.)

Severe Acute Malnutrition:W/H < -3 Z-score and/or

oedema

(23) 3.6 %(1.3 - 9.4 95% C.I.)

(16) 4.4% (1.3-13.3C.I.)



Age(mths

)

Total no.

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

6-17 173 2 1.2 15 8.7 160 92.5 5 2.9

18-29 195 2 1.0 20 10.3 174 89.2 3 1.5

30-41 174 5 2.9 11 6.3 162 93.1 1 0.6

42-53 96 1 1.0 8 8.3 89 92.7 0 0.0

54-59 13 0 0.0 1 7.7 14 107.7 0 0.0

Total 651 10 1.5 55 8.4 599 92.0 9 1.4

Once again, ANOVA revealed a significant difference in nutrition status between groups andthis was explored in Table 16 below:

Table 16: Prevalence of acute malnutrition based on percentage of the median and by age group

6-59monthsn=673 6-29monthsn = 381

% Median Global AcuteMalnutrition:W/H < 80% and /or oedema

(74) 11.0 %(7.6 - 16.8 95% C.I.)

(47) 12.3 %(8.3 - 19.2 95% C.I.)

Severe AcuteMalnutrition:W/H < 70% and /or oedema

(19) 2.8 %(0.9 - 8.8 95% C.I.)

(12) 3.1 %(0.9 - 11.5 95% C.I.)

Based on percentage of the median, the younger age group (6-29months) has a higher rate of

GAM and SAM. However, a chi-square analysis revealed no statistically significant differencebetween malnutrition rates observed among children aged 6-29 months and the whole group(p<0.05).

Risk of Mortality: Children’s MUAC (WHO Standards for MUAC-for-Age)

Table 17: Distribution of MUAC by Nutritional StatusNutritional Status MUAC Criteria Number Percentage

n %

Severe malnutrition <11.5cm 17 2.5

Moderate malnutrition >=11.5 and <12.5cm 52 7.6

At risk of malnutrition >=12.5 and <13.5cm 154 22.6

Satisfactory nutritionalstatus

>=13.5cm 457 67.2

TOTAL 680 100

From Figure 8, it is evident that the younger children have a higher rate of GAM than those30-59 months. According to the MUAC index, prevalence of malnutrition i.e. GAM(MUAC<12.5cm) in 680 children was 10.1% and severe malnutrition (SAM) at 2.5% (Table 17).

Figure 8: Acute Malnutrition expressed in MUAC in Age Groups

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Generally, the younger age groups seem much more affected than the older age groups. Two-way ANOVA and chi-square analysis indicates that there is indeed a highly significantdifference (p=0.00) between the younger (6-29 m) and whole (6-59) sample.

Table 18: Prevalence of Acute Malnutrition by State

States GAM MAM SAM ODEMA

EEQ 7.8 5.5 2.3 1.6

Warrap 19.4 15.7 3.7 0

U.Nile 24.4 17.7 7.3 5.7

NBEG 15.2 13.1 2 0

Jonglei 18.6 17.1 1.4 0

All States 16.6 14.2 2.7 1.4

Prevalence of Chronic Malnutrition (stunting) expressed in Z scores

Table 18: Chronic Malnutrition expressed by Z scores

Level of malnutrition

Alln = 553

Boysn = 288

Girlsn = 265

Global CM(<2SD and/oroedema)95% CI

(116) 21.0 %

(12.3 - 33.5 95% C.I.)

(64) 22.2 %

(13.4 - 34.5 95% C.I.)

(52) 19.6 %

(10.6 - 33.4 95% C.I.)

Moderate CM(<-2- ≥ -3 SD)95% CI

(92) 16.6 %

(10.0 - 26.4 95% C.I.)

(54) 18.8 %

(11.5 - 29.1 95% C.I.)

(38) 14.3 %

(8.0 - 24.3 95% C.I.)

Severe CM

(<-3SDand/oroedema)

(24) 4.3 %

(1.9 - 9.7 95% C.I.)

(10) 3.5 %

(1.2 - 9.7 95% C.I.)

(14) 5.3 %

(2.4 - 11.4 95% C.I.)

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95% CI

Height for age (stunting) is an indicator of long term malnutrition arising from deprivationrelated to persistent poor food security situation, micronutrient deficiencies and recurrentillnesses and other factors which interrupt normal growth. A high rate of stunting, 21.0 %(12.3 - 33.5 95% C.I.) was reported. There was no difference in the level of stunting (Table 18)between the boys and girls, as judged by the overlap in the Confidence Intervals.

Figure 9: Height-for-Age z-scores

3.2 MORTALITY RESULTS (retrospective over 3 months prior to interview)

Mortality Indices The crude mortality rate (CMR) is determined for the entire population surveyed for a givenperiod. The CMR using the current census method is calculated as follows10:CMR = 10,000 people x number of deaths during recall =Deaths/10,000/day

Number of recall days number of current residents

+1/2 (No. of deaths during recall) -1/2 (No. of births during recall)

The proportion of deaths among children under-five years of age (U5MR) is also calculated thesame way using the under five population data. The thresholds are defined as follows11:

Total population Under-five population

10 Save the Children (November 2004) “Emergency Nutrition Assessment Guidelines for field workers”11 The Sphere Standards, 2004

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CMR U5MR

Alert level: 1/10,000 people/day 1/10,000 children/day

Emergencylevel:

2/10,000 people/day 2.3/10,000 children/day

Mortality was assessed using the current household census method. There were 5424individuals alive at the time of the survey, 1393 of who were children under-five years old.

For all the deaths recorded, 41(35.3%) occurred in children less than 5 years and 75(64.7%)occurred in persons greater than 5 years old.

Table 19: Mortality rates

CMR (total deaths/10,000 people / day): 2.35 (1.07-5.10) (95% CI)

U5MR (deaths in children under five/10,000 children under five / day): 3.22 (1.21-8.34) (95% CI)Out of 739 households sampled for mortality data, a total of 41 children under five years and75 over-5s were reported to have died 3 months before the survey, thus yielding a specificunder five mortality rate of 3.22/10,000/day and crude mortality rate of 2.35/10,000/dayrespectively. Both the mortality rates are at emergency levels, by Sphere Standards.

Table 20: Causes of Death

Cause of Death <5 (n=25) >5 (n=47)

1 Fever/Malaria 14 26

2 Diarrhoea 10 0

3 Cough 0 0

4 Unknown 1 5

5 Others 0 16

The main presumed causes of death among children under-five years were ‘Fever/malaria’

and ‘diarrhoea’. 1 unknown death occurred. For deaths over 5 years, 26 deaths were causedby ‘Fever/Malaria’, 5 by unknown causes and 16 by other specified illnesses. The informationon number of deaths should be viewed with caution due to the inherent survey limitation of difficulties in measuring mortality. Possible reasons for this include manipulation of information and poor recall of date of death by the survey population, both of which that maylead to an overestimation of incidence of death12.

3.3 CHILDREN’S MORBIDITY

Table 21: Prevalence of reported illness in children in the two weeks prior tointerview (n= 682)

Symptoms of reported episodes of illness

N of responses

%

Watery Diarrhoea 275 42.4Bloody Diarrhoea 51 7.9

Cough with breathing difficulty 296 45.4

Fever 398 61.2Some children had more than one reported disease during the recall period of 2 weeks priorto the survey, and thus the cumulative percentage of reported illness exceeds 100%. Thus,there were 1020 cases of disease reported in 682 children.

Figure 10: Prevalence of Reported Illness

12 Save the Children (November 2004) “Emergency Nutrition Assessment Guidelines for field workers”- Difficulties withmeasuring mortality

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The prevalence of reported illness was determined based on a two-week recall period(inclusive of the day of the survey). Figure 10 illustrates that most of the children sufferedfrom cough/acute respiratory infections (ARIs), watery dirrhoea and fever. The three mainconditions mentioned above, account for >95% of all the cases of disease reported.

Figure 11: Caretakers health Seeking behaviour

Health care seeking behaviour of caretakers determines the preference and quality of healthcare services obtained whenever a child falls ill. Of the 545 caretakers interviewed, More thanhalf (67.7%) of caretakers sought assistance for illness of the child outside home. Quality of health care services and duration taken before a sick child receives medical attentioncontributes to the severity of the illness. However, only 49 (7.2%) of sick children receivedmedical assistance from the hospital. This is indicative of poor health-seeking practices.

3.4 VACCINATION

Table 23: Vaccination coverage: Measles for 6-59 months

Measles (with card)6-59 monthsN: 644

Measles (with card orconfirmation)6-59monthsN: 644

Yes

n = 22434.8%(31.3-38.4 95%C.I)

n = 31348.6%(42.8-55.0 95%C.I)

When estimating measles coverage, only children 6 months of age or older were taken into

consideration as they are the ones who were eligible for routine vaccination. The vaccination coverage was calculated as the proportion of children immunized based on

19



records and recall. Using both card and confirmation, there seems to be a low coverage rate(48.6%) for measles vaccinations- way below the recommended SPHERE coverage cut-off point of 90%13. Approximately 28% of the coverage reported here was based on recall and notevidenced by a health card.

3.5 VITAMIN A SUPPLEMENTATION/SLEEPING UNDER LLITN

Table 24: Vitamin A supplementation coverage

Vitamin A supplement (6-

59months)N:648

LLITN Utilization (6-

59months)N:648

n = 24446.8 % (43.1-50.6 95%C.I)

n = 24437.7% (34.2-41.4 95%C.I)

Vitamin A supplementation was assessed over a 6-month recall period during which thechildren should have received at least 1 dose. The coverage of Vitamin A supplementation isat 46.8%, below the optimum cut-off of ≥90%. The MoH reported a slightly rate of coveragefor W. South district for the period Jan-June2009, averaging at 44.5%. This is a worrying trendas adequate micronutrient supplementation is crucial in the reduction of chronic malnutrition.

This survey showed that only a very small proportion (37.7%) of the children was sleepingunder LLITNs. Malaria remains a leading cause of morbidity and mortality for children andpregnant mothers in Southern Sudan. Pregnant women and young children are at particularrisk of malaria infection. Health records for the month of January to February 2008 from thedistrict hospital showed that malaria ranked two and one out of ten most common diseasesaffecting children under five years old and adults respectively.

3.6 PROGRAMME COVERAGE

Table 25: Selective Feeding Programmes Coverage

Programme type

Supplementary feedingprogramme coverage

16.7 %

Therapeutic feeding programmecoverage

95.7 %

The currently accepted methods14 of estimating the coverage of selective feedingprogrammes uses the two-stage cluster- sampling survey methodology to estimate theprevalence of acute malnutrition in the programme area. Coverage is estimated eitherdirectly or indirectly using different formulas.

The direct method was used in estimating coverage in this survey. It is the most commonlyused method to assess coverage and it involves adding a question to the anthropometricquestionnaire to record whether or not a child is currently registered in the feedingprogramme.

Cumulative number of children registered in SFP: 13Cumulative number of children registered in OTP/TFP: 22Cumulative number of children referred for SFP/(Children with WHM<80% and >= 70% that are not enrolled): 65

13 For the population to be protected against epidemics.14 Save the Children (November 2004) “Emergency Nutrition Assessment Guidelines for Field Workers”

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Cumulative number of children referred for OTP/TFP(Children with WHM<70% that are not enrolled): 1

SFP coverage = Children registered in SFP in last 3 months x100

Children registered in SFP+ children with WHM<80% and >= 70% that are notenrolled

= 13/13+65 x 100 = 16.7%OTP coverage = Children registered in OTP /TFP in last 3 months x100

Children registered in OTP + children with WHM<70% that are notenrolled

= 22/22+1 x 100 = 95.7%

This formula estimats RECENT coverage in a given period (PERIOD PREVELENCE).

This limitation has been minimised in this survey by inclusion of children recently dischargedfrom the SFP/OTP i.e. those discharged in the past 3 months before survey date. This gives amore accurate picture of the programme impact in the survey area without logistical bias(caused by discharge before the survey period).

The SFP coverage rate (16.7%) calculated using the formula above is below the acceptedlevels of ≥50.0% in rural areas according to the SPHERE Standards (2004). This may bepartially attributed to the fact that children discharged before May 2009 (and still in therecovery phase) were not captured in this formula. The OTP coverage, on the other hand,was very good at 95.7%, within acceptable standards. It is important to note that the reportedcoverage rates at best, estimate the true picture of what is on the ground because of thelimitations inherent in the survey design and formula.

3.7 SUPPLEMENTARY RATION ADEQUACY

For the supplementary ration to be effective in improving the nutrition status of the childenrolled in the SFP programme, it has to be adequate in terms of quantity and availability. Tomeasure this, the duration which the ration lasts in the household was asked. In the 86 HHeligible, the average duration of ration utilization was 0.6weeks (SD 1.1) i.e. less than a weekas shown below:

This implies that the child did not have SFP ration for the duration of time recommended untilthe next distribution.

To measure dilution of the ration by HH member usage of the ration, it was acknowledgedthat 5 (1.3%) of 396 HH share the SFP ration. These two findings are contradictory as the

low SFP ration duration in the HH seems to suggest that its utilization was not sorely forthe child. It may be beneficial to look for other underlying reasons for this situation. Thismay include reselling in the market, bartering or saving for seed.

3.8 INFANT FEEDING PRACTICES

Information on infant and young child feeding practices was obtained based on a 24-hourrecall, in line with the WHO guidelines to minimize recall bias and thus obtain more validinformation. The indicators used for infant feeding practices are based on Knowledge, Practiceand Coverage (KPC) 2000+15 founded on WHO guidelines. These are also the key indicators

15 Arimond M & Ruel T. M “Generating Indicators of Appropriate Feeding of children 6 through 23 months from KPC 2000+”November 2003. Food & Nutrition Technical Assistance Project (FANTA).

21



for the Global Strategy for Infant and Young Child feeding. The information on breastfeedingpractices was obtained for children aged 0-24 months.

Table 26: Infant Feeding Practices

N = 491 %

Ever breastfed

Yes 441 89.8

No 50 10.2

Child Currently breastfeeding

Yes 320 65.3

No 170 34.7

Initiation of breastfeeding

0 – 1 hour 349 73.9

> 1 hour 100 21.3

Don’t Know 23 4.8

Frequency of Feeding

None 81 18.4

Once 121 27.4

2 - 3 three times 216 49.0

4 – 5 times 23 5.2

Age at Introduction of Complementary feeds

Average 6.2 mons (Sd2.4)

Average duration of breastfeeding 16.5 mons (Sd 8.1)

Initiation of breastfeeding was nearly universal with 95.2% of the mothers having breastfedtheir children. Over half (65.3%) of the children 0-24 months were still being breastfed at thetime of the survey. Approximately three quarters of the children (73.9%) had been to put tothe breast within one hour after birth as recommended by WHO16. The other percentage of caretakers started breastfeeding later or did not know the BF initiation. It will be important toestablish the underlying causes of the prevalent IYCF practices to facilitate design of feasibleinterventions to improve IYCF practices which are a key underlying cause of malnutritionobserved.

16 WHO (1989): Protecting, promoting and supporting breastfeeding: special role of maternity services: a joint WHO/UNICEFStatement. Geneva, WHO.

22



The survey questionnaire asked the caretakers when (in months) they had introduced foodother than breast milk (if the child was still not being exclusively breastfed). The introductionto complementary foods should happen at 6months (24wks) of age. This is because breastmilk alone is not sufficient to provide all the required nutrients for the child’s optimal growthfrom this age onwards. From the table above, the average age of introduction tocomplementary foods is 6.2 months (mode=6.00) which implies that exclusive breastfeedingis practiced for up to 6 months by the majority of caretakers. This is a good practice thatshould continue to be encouraged.

The average duration of BF is 16.5 months (SD 8.1) which implies that the majority of caretakers are not breastfeeding as recommended for at least 2 years. Beliefs and practiceson IYCF in communities should be assessed and documented as a basis for programmeplanning.

Complementary feeding practices

Complementary feeding rateFor the average healthy breastfed infant, meals of complementary foods should be 2-3 timesper day at 6–8 months of age and 3–4 times per day at 9–24 months of age, with additional

nutritional snacks offered 1–2 times per day as desired (FANTA, 2003, WHO, 2003). As awhole, the 341 children assessed (6-24 months of age) who received food/drinks (excludingbreast milk and water) ate 1.8(0.39SD)) times. Analysis by age groups indicated that children6-8 months of age ate 2.55 times (0.8 SD) and those 9-24 months ate 2.4 times (0.8 SD). Thefindings indicate that both the younger and older age group of children received inadequatenumber of meals within the recommended range of an average feeding frequency of 4-5times per day.

3.9 Secondary dataSecondary data reviewA review of the secondary health and nutrition data from MoH IDSR, health facilityconsultations and nutrition feeding programmes admission rates indicates seasonally normalconditions. The potential failure of rains or crops this year will likely have differing levels of

health impact across the five states assessed. It is worth noting that at this time of the year,malnutrition rates can be almost twice WHO emergency thresholds and are some of thehighest in the world. The data indicates the following three populations with acute healthneeds and an elevated risk of morality. The three populations of immediate concern are:

• The Murle households affected by Lou Nuer massacres who remain in Lekongole areaor were displaced into Pibor town

• Lou Nuer households from Nyandit, displaced by Murle massacres into Akobo town

• Displaced populations from Abyei in Aweil East. Within these groups, children under 5are a particular concern.

Each of the five states examined also has populations of concern which should be monitoredin the coming months. These areas were identified by comparing livelihoods systems againstpotential impacts of rain or crop failure. There is currently no reported deterioration in theseareas, but there is adequate reason to justify active surveillance.

Based on recent history, current stable conditions could be expected to continue even withdecreased crop yields in some areas. Rainfall fluctuations 2002 and 2005 resulted indramatic grain reductions for some communities. Mortality and nutrition surveys done at thattime were at seasonal expected levels. It would suggest that coping options utilised byaffected communities are adequate to negotiate the loss of food or income source withminimal effect on morbidity. Notwithstanding unforeseen shocks, it would be unexpected to

have a markedly different health or nutrition outcome than in 2002 or 2007.

The assessment also examined other data sources to help explain the health and nutrition23



stability during times of stress. In times of crisis in Sudan, children under-five can account for50% of excess mortality17. The stress to households due to rainfall variation of the magnitudeseen this year or 2002 does not appear likely to significantly disrupt child care to triggerdeterioration into illness or malnutrition. Scale up of support to existing health services andnutrition programmes is however important because many health and nutrition indicators,even in the absence of an emergency are chronically above emergency thresholds.Continuing efforts to provide for the basic health needs for mothers and the elderly are alsocrucial in the coming months. Maternal mortality remains one of the highest in the world.

Lastly, while stable conditions may be expected, additional unanticipated shocks in an alreadyvulnerable community can lead to a spike in mortality and malnutrition18. A high level of vigilance and preparedness is important during these periods.

3.10State review

The following are highlights for each of the states from a review of secondary date on thenutrition situation, morbidity, mortality

Jonglei

The primary concern for Jonglei state are the displaced and victims of violence fromLekongole (Murle) or Nyandit (Lou Nuer). Dry conditions also increase the importance to thehighly pastoral communities (particularly Lou Nuer) to move to the dry season grazing areas.If insecurity should interrupt that seasonal movement, mortality and malnutrition couldincrease as it has in the past. Beyond these areas of concern, indications are of otherwisestable conditions.

• Malnutrition and child morbidity rates recorded in the ANLA wereANLA were amongst the

highest in the five states, but not exceptional for Jonglei at that time of year. While someareas of the state have relatively good health facility coverage and quality of services,others have no access to services.

• Outside the communities which experienced insecurity mortality may be stable .stable. Although there were no mortality surveys conducted this year in Jonglei, there were noreports of excess mortality from health facilities in the state. Mortality reported in the2008 ANLA was comparable to mortality historic rates.

• Morbidity may be seasonally stable, outside of Pibor and Akobo. Morbidity reports fromWuror, an area experiencing very poor rainfall19, indicated seasonal numbers and types of consultations. A seasonal decrease in ARI/pneumonia and diarrhoeas was observedthrough the dry season. The coming of the rains saw an increase in malaria/feverconsultations, which is expected to continue and rise. The exceptions to the stableconditions were reported in Pibor and Akobo where comparatively higher U5 diarrhoeaswere reported.

• Admission to OTP programmes in Wuror wereAdmissions to OTP programmes in Wurorwere reported to be comparable to the previous year. Supplementary feeding

17 De waal, 1989.18 Such a spike was noted in 2002, in Lou Nuer areas. Lou communities in the Waat/Lankien area were engaged in a tribal fight

which prevented their seasonal migration to grazing areas. The year was also a very dry one. They were forced to remain in wetseason homes where there was little water and food. MSF Belgium responded to and documented those conditions.19 Fewsnet, 2009

24



programmes programmes had slightly more admissions but were declining from theirseasonal high point in May.

• Given the reports of diarrhoea in both Pibor and Akobo, sanitation may be a concern forthe displaced in Akobo where queues at water points were observed. There are alsoindications of acute health needs in Pibor/Lekongole and Akobo, particularly for children,especially among the displaced populations. General conditions in Akobo are also poor asthe area has been cut off from Malakal.

• In a worst case scenario, and outside of Pibor and Akobo, concern would be primarily forthe Lou Nuer communities. Generally, for the state, if the crops were to fail or be reducedmortality and morbidity rates may experience little fluctuation. Mortality rates andmalnutrition rates in previous dry years did not vary significantly from levels reportedbefore and after those events.

• The greatest concern will be for the Lou Nuer of Wuror, Dirror, Nyrol. The areas from Pibornorth-west through Wuror to Lankien are experiencing the most dramatic fluctuations inrainfall. Those counties are marginal rain-fed agricultural areas to which is accommodatedin the Lou Nuers predominatly pastoral lifestyle. They are much more pastoral than manyof their Gawar or Jikan neighbours. Disruption of their seasonal movement to the dry

season grazing areas, particularly in drier years, has had significant impacts on mortalityand malnutrition.

• Communities along the Nile, Zeraf and Pibor will may not be as as adversely affected bycurrent rainfall fluctuations as they have more coping options. Annuak and Kachipocommunities on the Ethiopian border, while predominantly agricultural benefit from robustagricultural conditions in those highland areas. They have not recorded crisis in previousdry years. The withdrawal of a long-standing health service provider from Pochalla leavesan important gap in service provision. Environmental health conditions and healthbehaviours are not expected to change.

Upper Nile

• The primary concerns in Upper Nile State are the potential effects of delayedof delayed rainfall and insecurity related to a Lou Nuer attack on a village outside of Nasir.Depending on the final performance of the rains and harvest, there may be concerns forincreased morbidity in rural areas of Latjor.

• Upper Nile State generally performed very well in the ANLA exercise. Child morbidity,immunization, vitamin and latrine coverage ranked as one of the highest. Mosquito netutilisation was good. Malnutrition rates were amongst the lowest. In contrast, access toimproved water sources was one of the poorest and they also reported some of the

highest levels of chronic malnutrition.

• It is important to bear in mind the extremes that exist in the state between the relativelydeveloped conditions and services available in Malakal or Nasir versus the regions to theeast and north that continue to exist without development or services.

• There have been no reports of excess mortality in the state, beyond the 30 to 40 deaths inthe village outside of Nasir. The ANLA CMR and U5CMR rates were consistent with ratesdocumented in mortality surveys.

• Health facilities have reported seasonally normal levels and types of consultations

(Fashoda, Panyikang). Fevers/malaria are increasing and are expected to continue toincrease through the rains. The IDSR reported diarrhoeas in Nasir/Luakpiny and

25



fluctuating levels of malaria. Health facility coverage and quality varies with more andbetter services available along the Nile and Sobat.

• Malnutrition is likely to be seasonally normal. Malnutrition prevalence reported in theANLA 2008 is similar to levels recorded in surveys previously during that time of year. OTPprogrammes are not reporting exceptional trends in admissions.

• In a worst case scenario, health issues may emerge in the drier areas in the interior if the

rains fail. If seasonal movement is impeded (for example through insecurity) or if there isimpeded access to grain, it could place additional stress on communities possibly leadingto increased morbidity (ARI and diarrhoeas during the dry season).

• Alternate means of survei llance will be important as PHC coverage is weaker in theinterior areas. Communities along the Nile and Sobat have many more coping and marketoptions, reducing the impacts of a poor harvest year.

• Mortality surveys conducted during dry years report mortality levels similar to those innon-dry years. Malnutrition surveys conducted in dry years do not report significantdeviation of either rates or their seasonal trends. Environmental health is unlikely tofluctuate considerably in rural locations, as are the behavioural health factors

Eastern Equitorial

• The State ranges from very dynamic economic areas in the west, along the Ugandanborder to some of the most traditional pastoralist communities in the entire Sudan in theeast of the country. This may explain the mixed performance of the state in the ANLA.EEQ reported the lowest malnutrition levels but highest chronic malnutrition. It had thelowest prevalence of child morbidity and poorest immunization coverage.

• The primary concern in Eastern Equatoria is the impact of the rainfall on the secondseason crops of the agricultural communities in the west of the state. The more pastoralcommunities are unlikely to be as affected by current rainfall conditions.

• Mortality may be stable. There have been no reports of fluctuations in mortality thoughfew surveys have been conducted in this state. ANLA crude mortality rate was higher thanrates reported in mortality surveys while the U5MR was lower than rates reported in aprevious mortality survey (Kapoeta).

• Health facility coverage is fairly extensive in the east of the state, supported by agencieswith a long-standing presence in those communities. They also have regularcommunication with Juba. The IDSR reported diarrhoeas, and fluctuating levels of malaria.

• Nutrition is likely to be at previously experienced levels. ANLA nutrition results werecomparable to rates which had been recorded in the State. The only nutrition programmewhich was run in the region, operated for displaced Dinka communities, was closed inapproximately 2004. A June 2005 nutrition survey in neighbouring Kajo Keji, reported GAMof 8.6, one of the lowest recorded in South Sudan.

• No new OTP admissions were recorded in Magwi in June. Increases in OTP admissions forApril to June at SMoH were similar to those recorded in SMoH in CEQ and Jonglei states.

There is no data on changes to baseline environmental health or social health factors.

• In a worst case scenario, the rains will result in a poor first and second harvest for theagricultural communities in the west of the state. The more pastoral communities in theWest may be less affected as long as there are no impediments to their seasonal

movement. Access to markets, and alternate coping strategies will assist households tocope, but it will place additional stress.

26





• The main concern for Northern Bahr el Ghazal is the delayed rainfall. The state is alsorecovering from a cholera outbreak this year. Some communities in Aweil East receivedpersons displaced from Abyei 2008, and there concerns over the likelihood and potentialrepercussions of fighting in Abyei.

• Displaced households from Abyei continue to report serious health conditions and furtherconflict and fighting would likely exacerbate those conditions. In the absence of thatconflict, conditions may be expected to remain stable across the state, including for

returnee populations. Higher levels of AWD reported for <5’s raises the need forsurveillance of small children in particular as they may be more at risk of fevers in thispeak season for fevers.

• Apart from malnutrition, the ANLA presented a generally poor picture of conditions in theState with child morbidity being one of the highest in the states surveyed. Immunizationand vitamin A coverage rates were also some of the lowest. Access to improved watersources and sanitation are also some of the poorest.

• Acute malnutrition rate were however the third lowest and chronic malnutrition wasranked in the middle. Mosquito net coverage also ranked in the middle. The coverageand support to health facilities is comparable to other states.

• Mortality appears to be stable. Rates reported by ANLA in October were comparable toprevious surveys. ACF USA conducted a mortality survey in Aweil East in June 2009 whichreturned some of the lowest CMR and U5MR rates recorded for the County. A choleraoutbreak occurred throughout NBEG earlier this year. It was identified and managed andcase mortality was similar to previous cholera outbreaks in South Sudan.

• Morbidity incidence is reported to be seasonally stable in the state, with ongoing concernfor cholera/watery diarrhoea and possibly higher than normal malaria in Aweil East.Consultations in Aweil South are reported to be at levels and types comparable to lastyear. Anecdotal reports for other locations mirrored these observations. The IDSR still

reports cases of cholera, almost entirely from Aweil Centre. Reported cases were for >5’s.Watery diarrhoea is reported in all counties, but at higher rates in Aweil Centre. Waterdiarrhoea reports are mostly for <5’s. Malaria is also being reported, particularly in AweilEast.

• Despite a report of a high rate of GAM in Aweil East, malnutrition prevalence may be atseasonally typical levels. The acute malnutrition for the state reported in the ANLA waslow, even for that time of year. Historic rates will regularly double from the annual post-rains low points.

• Reported rates of malnutrition are almost uniformly high across the state. There were twonutrition surveys conducted in the state in 2009. A VSFG survey in February in Aweil Westreported rates GAM rates which were seasonally low and SAM rates which were commonlyreported. The ACF USA survey in June in Aweil East, reported high GAM and SAM rates butat levels comparable to ones recorded in the past.

• OTP programmes are reporting admissions in June which are at or aboveadmissions for May. Admissions seasonally decrease in June or July. Levels of admissions are comparable to last year. ACF USA is reporting a spike inadmissions continuing through June, when admissions normally decrease. Theyhave attributed the increase to a combination of both the displaced from Abyei andthe effects of the cholera/AWD outbreak.

28



• In previous years of stress, like 2009, there have been recorded an extension of the seasonal peak in admissions through June and sometimes into July. SFPadmissions are also reported to be increasing through June. Given the lowcoverage of both OTP and SFP programmes, a shift in incidence may reflectmothers’ decisions to enter their children in the programme rather than anincrease in prevalence. There are also many difficulties with children enteringmultiple programmes in Aweil South. Both of these trends suggest increased

energies expended to access particularly supplementary foods, not necessarily dueto a change in the acute malnutrition prevalence.

• Concerns in a worst case scenario would focus on the displaced from Abyei.Otherwise, the state while under stress, may not show deterioration in mortality ormorbidity. Poor rainfall and crop harvest appear to be most pronounced in the eastof the state, and its impacts would be localised to certain communities. LikeWarap, concern for a population level health crisis as a result of returnees, may beoverstated. The vulnerable within that population is likely to be less than itsestimated 0.8% of the total population.

• Morbidity and mortality may be comparable to 2005/6. After a very poor harvest in2005 (75% staple crop production), nutrition and mortality surveys reported resultscomparable to previous and subsequent years. While crisis conditions would not beexpected, as it is a year of greater stress for many households across the state,increased surveillance will be important. Surveillance should particularly consider<5’s in areas which reported higher levels of AWD.

4. DISCUSSION

4.1 Nutritional Status The prevalence of severe malnutrition is indicative of critical levels of acutemalnutrition since they are above the WHO critical levels set at 15%20. These rates arehowever seasonally expected though the results must be interpreted with cautionsince the sample used is not statistically representative of the states covered. Thelevels of underweight (WFA) 18.7% and stunting (HFA) at 20.7% are also high butwithin range for sub Saharan Africa. The reported high rate of stunting is reflective of the cumulative effects of chronic food insecurity and recurrent illnesses. Lookingbeyond the seasonal shifts, it is important to address the causes of chronicmalnutrition. Adequate food alone does not result to improved nutritional status if care practices and other underlying factors are not addressed.

Morbidity and Mortality The mortality under five rates reported in this survey are unacceptably high and couldpredict future mortality especially if prevailing conditions of insecurity and internaldisplacements worsen. The main causes of U5 mortality in the area include malaria,ARTIs and diarrhoea.

High morbidity rates were reported in the five states two weeks prior to the study. These levels were consistent with seasonal morbidity patterns recorded from healthfacilities in these states. Analysis showed that there was a strong significantassociation (p=0.01) between the two. As expected, morbidity has direct relationshipwith malnutrition where illness leads to increased nutritional demands to repair worn

20Global Acute Malnutrition (GAM): prevalence of GAM <5% termed as acceptable, 5-9% poor, 10-14% serious and >15%

critical.

29



out tissues and at the same time interfering with the intake, digestion, absorption andutilization of the nutrients in the body. Health seeking behaviour for ill children waspoor with 93% of caretakers not seeking health care.

Health and Nutrition Programme Coverage

Poor coverage for health and nutrition programmes are important risk factors to poor nutritionsituation. There was a very poor coverage rate for measles vaccinations (48.6%), using the

SPHERE cut-off point of 90%. This situation is precarious and the U5 population is vulnerableto an outbreak due to inadequate cover for an epidemic.

Also of concern was the Vitamin A supplementation much lower than cut-off at 46.8%. TheLLITN coverage for malaria during survey was low at 37.7%. This predisposes the vulnerablepopulation (U5s and pregnant mothers) to a high risk of malaria. It is no wonder that thehighest cause of morbidity and mortality in both U5 and over 5s is ‘fever/malaria’.The SFPcoverage is poor while the OTP/TFC coverage is adequate. In general, the health and nutritionindices are poor and there is urgent need to address the immediate and underlying causes of malnutrition before the situation deteriorates further.

Infant and Young Child Feeding and Care practicesPoor child care and feeding practices persist and have been associated with high levels of malnutrition. Knowledge Attitudes and Practices on breastfeeding are mainly controlled byculture, customs and the social environment, such as through maternal grandmothers andother elderly women in the community. Though breastfeeding is acceptable to all caretakers,initiation of breastfeeding within the first hour of birth is not practiced universally (65%).Exclusive breastfeeding for the first six months of life is practiced by the majority of caretakers, as evidenced by the introduction of complementary foods at about 6.2 months onaverage. The average duration of breastfeeding is about 16 months. Lack of knowledge,inappropriate beliefs and very close birth spacing are major obstacles to successfulbreastfeeding for up to two years. It is also noteworthy that only 5.2% of the children aged 6‐

24 months were fed for the recommended 4-5 times per day. Poor breastfeeding andcomplementary feeding habits expose children to morbidity, malnutrition and death.

5. CONCLUSION

The prevalence of Global Acute Malnutrition (GAM) among all children was 15.8 % and isindicative of critical nutritional situation based on the WHO standards. The prevalence of Severe Acute Malnutrition (SAM) of 3.6 % is also suggestive of widespread malnutritionamong children. However, these rates are seasonally common. High morbidity rates werereported in the five states two weeks prior to the study. These levels were however consistentwith seasonal morbidity patterns recorded from health facilities in these states. Both crudeand under five mortality rates are at emergency levels for emergency situations.

Overall, data from both the nutritional survey and review of secondary data shows seasonallystable health & nutrition conditions in the 5 states, but with pockets of worsening nutritionalsituation among populations with acute health & nutrition needs in 3 states in Jonglei (Piborand Akobo); NBEG (Aweil East County) and Upper Nile (Longichuk and Sobat area - Maiwut,Nasser & Uranga). Other populations groups in the 5 states that require close monitoring inthe coming months were identified. Eastern interior areas of Upper Nile, Lou Nuercommunities in central Jonglei, and communities which may be impacted by insecurity orwhich may receive IDPs should there be conflict in Abyei are areas which should be preparedfor potential increase in medical and nutritional needs and should exercise increasedsurveillance. Surveillance should also be increased for areas of the west of Eastern Equatoria.

• The key underlying factors are high morbidity, inadequate coverage of selective feeding

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programmes, poor IYCF and care practices and household food insecurity.

• There is weak capacity for prevention and management of acute malnutrition in the fivestates. This is attributed to:

o Limited partners supporting management of malnutrition including withdrawal insome areas (Jonglei – World Relief; Upper Nile – ACF)

o Limited funding for nutrition response: GoSS, UN, NGOs (*human resources,capacity building & surveillance needs)

o Low technical capacity of SMOH in management of severe malnutritiono Weak PHC infrastructure for integrating management of acute malnutrition to

increase coverage especially at community level.o Coordination challenges at central and state level

6. RECOMMENDATIONS

Intervention efforts that address both immediate needs for the acute malnutrition cases andchronic malnutrition in the vulnerable population should be mobilized. An integrated approachshould be adopted in addressing these underlying causes at community level. Measures toincrease access to health facilities and improved coverage of selective feeding programmes

would play a critical role in both preventing and treating morbidity and malnutrition.

An effective nutrition surveillance system needs to be established including an appropriatecontext specific tool that facilitates assessment of issues that are contributing to acutemalnutrition. This will allow a clearer understanding of the underlying causes and thereforeimprove the specificity and design of future interventions. In addition, developing longer termstrategies to enhance the provision of basic services, sustainable strategies for livelihoodsupport and social protection mechanisms are recommended. Specific recommendationsinclude:

Immediate Interventions

In line with the findings of this assessment, immediate action to avert worsening health and

nutrition conditions among populations in the three states with acute health and nutrition

needs is recommended as follows:

Upper Nile (Longichuk county)

• Conduct a SMART coverage survey

• Strengthen appropriate clinical and therapeutic management of severe cases of malnutrition through existing therapeutic feeding programmes.

• Strengthen capacity of community based organisations and mother support groups

that were previously trained to undertake active case finding and referral. Thesegroups should concurrently promote appropriate infant feeding practices and

hygiene promotion.

Immediate implementation of ACSI in upper Nile state beginning with counties inthe Sobat area e.g Longichuk.

Jonglei (Akobo, Pibor and Wuror

• Pibor: Monitor on-going therapeutic response as per amended PCA with MSF –

Belgium for Pibor county

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• Akobo West: Monitor on-going therapeutic response as per amended PCA with

Save the Children’s Fund – UK.

• Akobo East: Develop programme corporation agreement with IMC for facility

and community based management of severe acute malnutrition.

• Advocacy with WFP for scale up of the supplementary feeding programme in

Akobo and Pibor counties

•Amend existing PCA with ACF – USA to accommodate increased case load of severely malnourished children

• Support on-going nutritional surveillance

NBEG - Awiel East county

• Advocacy with WFP for scale up of SFP in Akobo and Pibor counties

• Amend existing PCA with ACF – USA to accommodate increased case load of

severely malnourished children

In addition,

• Scale up of targeted SFP in the 5 states, coupled with active case finding in order toidentify moderately malnourished children for admission into SFP is critical. Nutritionalstatus is highly sensitive to changes in the risk factors and therefore a child’s nutritionalstatus is likely to fluctuate considerably with seasonal changes. In addition, pregnantwomen and lactating mothers with infants less than 6 months require supplementaryfeeding during this period of high food stress..

• Measles vaccination coverage is inadequate in the five states. Given the high prevalenceof disease and severe acute malnutrition, it is necessary to implement a vaccinationcampaign. Specific attention should be made to ensuring that areas that are difficult toaccess are adequately covered, providing and reinforcing the importance of vaccinationcards.

• Rehabilitation of acutely malnourished children through the existing selective feeding andoutreach programs coupled with active case finding until household food security isrestored is critical. Public health issues of concern identified and detailed in the secondarydata should be are addressed.

Medium - Long-Term Interventions

• Intensify health and nutrition education activities at the household level to addresschild care, targeting caregivers. The main areas of focus should include promotingexclusive breastfeeding, appropriate young child feeding, diet diversification andimprovement in household hygiene including health care practices. This shouldalso include development of local IEC nutritional education materials forcommunity level health promoters.

• Establish a regular nutrition surveillance system.

• On-going capacity building of SMOH staff and the community to manage severemalnutrition

• Assess and strengthen programmes and strategies currently addressing IYCF with a viewto improving the protection, promotion, and support of optimal infant and young child

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SOUTH SUDAN - RAPID NUTRITION ASSESSMENT TOOL

NOTE TO ENUMERATORS: ONE form should be filled out for EVERY child in the

household less than 59 months old.

SECTION 11 – CHILD NUTRITION

Questions for adult caretaker of child

11.1 Relationship of respondent to child 1 Mother

2 Father

3 Other

11.2 Sex of child 1 Male 2 Female

11.3a Age

|__|__| months

If Child is greater than 24months SKIP to 11.10

Questions 11.4 to 11.9 only for children 0- 24months

11.4 Has this child ever been breastfed? 1 Yes

2 No

3 Don’t Know

11.5 Is this child still breastfeeding now? 1 Yes

2 No

3 Don’t Know

11.6 If not breastfeeding now, how many months did you breastfed this child? |____|____| months

11.7 How long after birth did you start breastfeeding? 1 0-1hour

2 More than 1hour

3 Don’t know

11.8 At what age did you begin to feed this child daily with any food or fluids

other than breastmilk?|______|_____| months

|__| Still exclusively

breastfeeding

11.9 Since this time yesterday, how many times was this child given porridge

(madida) or mashed food or solids?

1 None

2 Once

3 Two to three

4 Four to five

Questions 11.10 to 11.23 are for children 6-59 months old

11.10 Has this child received a Vitamin A capsule in the last 6 months?

Show capsules for different doses:

1 Yes

2 No

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Blue for children 6-11 months old

Red for children 12-59 months old

3 Don’t Know

11.11 Since 2 weeks ago has this child had watery diarrhoea?

(Diarrhoea is three or more loose or watery stools per day.)

1 Yes

2 No

3 Don’t Know

11.12 Since 2 weeks ago has this child had bloody diarrhoea?

(Bloody diarrhoea is three or more loose or watery stools with blood in

them per day)

1 Yes

2 No

3 Don’t Know

11.13 Since two weeks ago has this child had a cough during which he/she had

difficulty breathing?

1 Yes

2 No

3 Don’t Know

11.14 Since two weeks ago has this child had a fever? 1 Yes

2 No

3 Don’t Know

If respondent answered yes to 11.11 or 11.12 or 11.13 or 11.14 ask question 11.15. If they answered no, proceed to question 11.16

11.15 Did you seek advice or treatment for the il lness outside of the home? 1 Yes

2 No

3 Don’t Know

11.15a From where did you seek care?

Circle all mentioned- but do not prompt respondent

1 Hospital

2 Government clinic (PHCC/PHCU)

3 NGO clinic (PHCC/PHCU)

4 Mobile/outreach clinic

5 Village health care worker

6 Private physician

7 Relative or friend

8 Shop

9 Traditional practitioner

10 Pharmacy

11 Other- specify

11.16 Has this child received a measles vaccination? 1 Yes, by card

2 Yes, by maternal recall/verbal history

3 No

4 Unknown

11.17 Did this child sleep under a mosquito net (LLITN) last night? 1 Yes

2 No

3 Don’t Know

11.18a 1 Yes

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Health and Nutrition Report

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