Integrated Nutrition and Mortality Smart Survey

INTEGRATED NUTRITION AND MORTALITY SMART SURVEY

REPORT

ELBARDE DISTRICT, BAKOOL REGION,

SOMALIA

NOVEMBER 2020

ACKNOWLEDGEMENTS

Action against Hunger (ACF), would like to acknowledge all the support provided during the preparation, training and field activities of the survey, which includes but not limited to; ➢ Technical and logistical support provided by Elbarde Municipality and the Ministry of Health in South West state of Somalia, facilitation during the training and field work. ➢ We would like to acknowledge the roles of the assessment teams including the team leaders, enumerators and community field guides and all the parents/caregivers who provided valuable information to the survey team, and participated the assessment. ➢ Assessment Information Management Working Group (AIMWG) members for the technical inputs and validations. ➢ Appreciation also goes to SIDA, for the generous financial supports to conduct this nutrition and mortality survey.

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Table of contents ACKNOWLEDGEMENTS ...... II

Table of contents ...... III

List of tables ...... IV

List of Figures ...... VI

1. INTRODUCTION ...... 1

1.1. Background ...... 1

1.2. Justifications ...... 1

1.3 Overall objective: ...... 2

1.3.1. Specific objectives ...... 2 2.0. METHODOLOGY ...... 3

2.1. Survey Design ...... 3

2.2. Study Population ...... 3

2.3. Geographic target area and population group ...... 3

2.4. Sample size calculation ...... 3

2.5. Cluster Sampling Strategy...... 5

The survey employed a two stage cluster sampling technique where ...... 5

2.6. Variables collected ...... 6

2.7 Case Definitions and inclusion Criteria ...... 6

2.7.1 Anthropometry ...... 6 2.7.2. Mortality ...... 8 2.7.3. Health ...... 8 2.7.4 Morbidity ...... 8 2.7.5. Water Sanitation and Hygiene (WASH) ...... 8 2.8. Data Management and Survey Teams...... 9

2.8.1. Survey Teams ...... 9 2.8.2 Quality Assurance ...... 9 2.8.3 Data Analysis and Reporting ...... 9 2.9. COVID-19 Preventive Measures ...... 10

3. RESULT ...... 11

3.1 Demographic ...... 11

3.2 Education Level ...... 11 III

3.3 Anthropometric results (based on WHO standards 2006): ...... 12 3.3.1 Acute malnutrition (Weight-for-height Z score (WHZ) ...... 12 3.3.2. Acute Malnutrition based on MUAC ...... 14 3.3.3. Prevalence of Underweight based on weight for Age Z - scores ...... 15 3.3.4. Prevalence of Stunting based on Height for Age Z - scores ...... 16 3.4 Mortality results (retrospective over x months/days prior to interview)...... 17

3.5. Children’s morbidity ...... 18

3.6. Health Seeking Practices ...... 19

3.7 Vaccination Results ...... 19

3.8 Programme coverage ...... 20

3.9 Vitamin A Supplementations and Deworming...... 20

3.10. Maternal Malnutrition ...... 20

3.10.1. Physical Status for Mothers ...... 20 3.10.2. Maternal nutrition and Multiple Micronutrient supplementations ...... 21 3.11. Water Sanitation and Hygiene (WASH) ...... 21

3.12. Food Security and Livelihood ...... 21

3.12.1. Dietary diversity ...... 21 3.12.2. Reduced Coping Strategies Index (rCSI) and Household Hunger Scores (HHS) ...... 22

4.0. DISCUSION ...... 23

5. CONCLUSIONS ...... 23

6. RECOMMENDATIONS AND PRIORITIES...... 24

7. REFERENCES ...... 1

8. APPENDICES ...... 2

Appendix 8.1: Plausibility Report for: Elbarde Deyr' 2020.as ...... 2

Appendix 8.2. Assignment of Clusters ...... 3

Appendix 8.3. Evaluation of Enumerators ...... 4

List of tables Table 1: Elbarde SMART Survey summary findings ...... IX

Table 2: Summary Recommendation ...... XI

Table 3: Anthropometric and Mortality Sample Size Calculation ...... 4

Table 4 : Culculating Number of Households per team per day ...... 5 IV

Table 5: Weight-for-height (WHZ), children 6-59 months ...... 7

Table 6: Cut offs points of the Height for Age index (HAZ) and Weight for Age (WAZ) ...... 7

Table 7: MUAC Cut offs, children 6-59 months ...... 7

Table 8: level of Education for Caregivers ...... 11

Table 9: Distribution of age and sex of sample ...... 12

Table 10: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex ...... 12

Table 11: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema ...... 13

Table 12: Distribution of acute malnutrition and oedema based on weight-for-height z-scores ...... 13

Table 13: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex ...... 14

Table 14: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema ...... 14

Table 15: Prevalence of combined GAM and SAM based on WHZ and MUAC cut off's (and/or oedema) and by sex* ...... 15

Table 16:Detailed numbers for combined GAM and SAM ...... 15

Table 17: Prevalence of underweight based on weight-for-age z-scores by sex ...... 15

Table 18: Prevalence of underweight by age, based on weight-for-age z-scores ...... 16

Table 19: Prevalence of stunting based on height-for-age z-scores and by sex ...... 16

Table 20: Prevalence of stunting by age based on height-for-age z-scores ...... 17

Table 22: Mean z-scores, Design Effects and excluded subjects ...... 17

Table 23: Mortality Rates ...... 18

Table 24: Prevalence of reported illness in children in the two weeks prior to interview (n= 127) ...... 18

Table 25: Symptom breakdown in the children in the two weeks prior to interview (n= 127) ...... 18

Table 26: Vaccination coverage: BCG for 6-59 months and measles for 9-59 months ...... 19

Table 27: Programme coverage ...... 20

Table 28: Vitamin A Supplementations and Deworming ...... 20

Table 29: Maternal nutrition and Multiple Micronutrient supplementations ...... 21

Table 30: WASH indicators ...... 21

Table 31: Reduced Coping Strategies and Household Hunger Scores ...... 22

Table 23: Recommendations ...... 24

List of Figures Figure 1: Livelihood map of southern inland pastoral (Elbarde-In red) ...... 1

Figure 2: Weight for Height Z Scores ...... 13

Figure 3: Prevalence of malnutrition by age based on MAUC ...... 14

Figure 4: Reported symptoms break down ...... 18

Figure 5: Health seeking practices ...... 19

Figure 6: Dietary Diversities ...... 21

ACRONYMS AND ABBREVIATIONS

ACF Action Against Hunger AIMWG Assessment and Information Management Working Group ARI Acute Respiratory Infections AWD Acute Watery Diarrhoea BCG Bacillus Calmette-Goerrin CDR Crude Death Rate DEFF Design Effect ENA Emergency Nutrition Assessment EPI Expended Program on Immunization FSNAU Food Security and Nutrition Analysis Unit GAM Global Acute Malnutrition HAZ Height for Age Z – Scores HDDS Household Dietary Diversity Scores HH Household IDP Internally Displaced Persons IPC Integrated Phase Classifications IYCF Infant and Young Child Feeding MM Millimetre MOH Ministry of Health MUAC Mid Upper Arm Circumference ODK Open Data Kit OTP Outpatient Therapeutic Program PPS Probability Proportionate to Size rCSI Reduced Coping Strategies Index SAM Severe Acute Malnutrition SIDA Swedish International Development Agency SC Stabilization Centre SD Standard deviation SMART Standardized Methods of Assessment in Relief and Transitions SRCS Somali Red Crescent Society TSFP Target Supplementary Feeding Program U5DR Under Five Death Rates UNICEF United Nations Children’s Fund WASH Water Sanitation and Hygiene WAZ Weight for Age Z –Scores WFP World Food Program WHO World Health Organization WHZ Weight for Height Z- Scores

VII

EXECUTIVE SUMMARY

Action Against Hunger (ACF) has been implementing key humanitarian interventions in Somalia during the last three decades, with programing in; integrated health and nutrition services through health centres, mobile teams and stabilization centres, Water sanitation and Hygiene (WASH), food security and livelihood (FSL) in Bakool Region. Action Against Hunger conducted integrated nutrition SMART survey in November 2020 in Elbarde District to assess the nutrition status among children 6 – 59 months, and the women of child bearing age (15 – 49) years among other indicators. A two - stage cluster sampling methodology was employed, where in the first stage a total of 33 clusters were sampled, using ENA for SMART software 20th January 2020 version, with probability proportionate to size (PPS). The second stage involved random selection of 468 households (36 clusters*13 households per cluster) using simple random sampling method. Five hundred and eight children were included in the sample and anthropometric indicators (Weight, Height and MUAC) taken. The findings indicated a Global Acute Malnutrition (GAM) by Weight for Height of 20.9% (16.7 – 25.8 95% CI) and Severe Acute Malnutrition (SAM) of 4.1% (2.5 – 6.7 95% CI). The combined GAM weight for height and MUAC indicated 24.6 % (20.2 - 29.5 95% C.I.). The findings indicate critical and persisted high acute malnutrition, since November 2019, where GAM of 20.4% (16.1 – 25.5 95% CI) was reported. The mortality findings from a 96-day recall period indicated low level with crude death rate CDR of 0.04 (0.01–0.34)/10,000/day with no households reported under five deaths during the recall period and those are below the WHO thresholds 1 person/10,000/days and 2 person/10,000/day for CDR and UDMR respectively. The survey findings further indicated 24.7% of children reported illness two weeks prior the assessment, mostly Fever (4.8%), Acute Watery Diarrhoea (AWD 37.9%) and Cough/ARI (25.2%). The coverages of measles and BCG immunizations were low (49.5%) and (26.3%) respectively. comparing to November 2019 BCG coverage is much lower in 2020 (71.9%), while measles has shown a slight increase than last year (32.6%) results but far below the recommended Sphere coverage (>95%) and WHO (>80%). Very low vitamin A supplementation (40.9%) and deworming (46.0%) was also recorded, showing very low coverages but better than 2019 coverage with vitamin A (13.5%) and deworming (12.0%). In conclusion, the nutrition status in Elbarde District remains critical with no improvement compared with 2019. The contributing factors to high malnutrition include high morbidity and chronic food insecurity. A summary of key survey findings key indicators and recommendations are shown in table 1 and 2.

VIII

Elbarde district SMART Survey - Preliminary results

Total children 508 Boys = 258, Girls = 250

Table 1: Elbarde SMART Survey summary findings # Indicators Number % (95% CI) Plausibility Scores 7% 1 Prevalence of Global Acute Malnutrition (All) 106 20.9% (16.7 – 25.8) Boys 49 19.0 (14.0 – 25.3) Girls 57 22.8% (17.1 – 29.7) 2 Prevalence of Severe Acute Malnutrition (All) 21 4.1% (2.5 – 6.7) Boys 11 4.3% (2.1 – 8.0) Girls 10 4.0% (2.2 – 7.2) 3 Oedema 0 0% 4 Design Effect 1.55 5 Prevalence of GAM MUAC (All) 57 11.1% (8.2 – 14.8) Boys 25 9.6% (6.4 – 14.1) Girls 32 12.7% (8.4 – 18.7) 6 Prevalence of SAM MUAC (All) 11 2.1% (1.1 – 4.0) Boys 3 1.1% (0.4 -3.6) Girls 8 3.2% (1.4 – 7.1) 7 Prevalence of Stunting (All) 34 6.7% (4.4 – 10.2) Boys 24 9.3% (5.6 – 15.1) Girls 10 4.0% (2.2 – 7.2) 8 Severe Stunting (All) 8 1.6% (0.7 – 3.8) Boys 7 2.7% (1.2 – 6.0) Girls 1 0.4% (0.1 – 3.0) 9 Prevalence of Underweight (All) 82 16.1% (12.4 – 20.7) Boys 47 18.3% (13.2 – 24.8) Girls 35 13.9% (9.7 – 19.4) 10 Severe Underweight (All) 11 2.2% (1.1 – 4.1) Boys 5 1.9% (0.8 – 4.5 Girls 6 3.2% (1.4 – 7.1) 11 Death Rates 12 Crude Death Rate/10,000/day 1 0.04 (0.01 – 0.34) 13 Under five Death Rates/10,000/day 3 0.0(0.0 – 0.0 ) 14 Design Effect 1.0 15 Morbidities 16 Child Illness prior two weeks (All) 127 24.7% (18.6 – 30.8)

17 Immunization Coverages IX

18 BCG vaccination (with scars) 135 26.3% (14.8 – 37.7) Measles Vaccination 254 49.5% (36.2 – 62.7)

Table 2: Summary Recommendation

No. Key Findings Recommendations By WHO. 1. Very high prevalence of acute Malnutrition Continuation of the Ongoing management of Severe and moderate Ministry of Health and at 20.9% (16.7 – 25.8 95% CI)) acute malnutrition (OTP, SC and TSFPs) as well as treatment Action Against Hunger categorized as critical and medium /management of communicable diseases (especially AWD, ARI and and other partners prevalence of Underweight at 16.1% (12.4 – Fever) and expending these services to any areas with possible gaps 20.7) 2 Low immunization and supplementation Demand generation activities to increase utilization of Ministry of Health and coverage. health/immunization services to improve immunization, Deworming Action Against Hunger Immunization coverages are far below the and vitamin supplementation coverages. and other partners international standards (sphere >95%), Such activities include: measles (49.5%) BCG (26.3%); Mass campaign, scaling up using CHWs, Very low vitamin A supplementations Improve immunization accesses using different strategies (fixed, (40.9%) outreach and mobile) Deworming: (46.0%) 4 Limited access to safe drinking water and Improve the access to safe drinking water through construction of Ministry of Health and the need for improved WASH practices protected water sources. Action Against Hunger Access to safe drinking water:19.0% (7.8 – and other partners 30.2) Construct new household latrines and promote positive hygiene Household Access to latrines: 42.9 %( 30.3 practices including hand-washing practices. – 55.5%) 5 High proportion of the population are still Address short-term food insecurity issue with programs like cash Ministries of planning and practicing high-reduced coping strategies. voucher or cash for work, as well as a long-term resilient plan on chronic agriculture, Action Against Households consuming from low dietary food insecurity. Hunger and other diversity 29.7% while 48.7% of households partners employing high coping strategies.

1. INTRODUCTION

1.1. Background

Action Against Hunger has been a key humanitarian actor in Somalia since May 1992 providing an array of services in Nutrition, Health, Food Security and Livelihood, Water Sanitation and Hygiene across 3 Regions; Bakool, Nugal and Banaadir . Elbarde is one of the districts in Bakool region in Southern Somalia and is located in the northern side of the region, where majority of its population are pastoralists (figure 1). ACF runs key live saving health and nutrition services in Elbarde, including the treatment of communicable diseases, OTP, TSFP and SC programs for the town and the 29 villages, Figure 1: Livelihood map of southern inland pastoral (Elbarde-In red) through Elbarde health centre and mobile teams. ACF also has food security activities in Elbarde district, including conditional and unconditional cash and WASH interventions like construction and rehabilitation of barkeds, rehabilitation of shallow wells, water treatment and construction of latrines.

1.2. Justification

The recurrent droughts of last decade has resulted to loss productive assets and increasing household food insecurity throughout Somalia. Bakool region is among the most affected areas, where beside the climatic shocks, conflict has worsened the situation due to limitation of road accessibilities to main markets, thus affecting the food prices and the overall livelihood status. In addition, 2019 SMART survey results indicated critical nutrition situation. The November 2020 SMART survey was planned to assess the current nutrition status in comparison to the surveys conducted surveys and further understand the challenges and the intervention gaps that need further improvement and provide the relevant recommendations. The study area in this survey was Elbarde district, which is mainly pastoral population. 1

1.3 Overall objective: To assess acute malnutrition rates amongst children 6-59 months and retrospective mortality rates amongst the population.

1.3.1. Specific objectives a. To estimate the prevalence of acute malnutrition amongst children aged 6-59 months. b. To assess the retrospective crude and under five mortality rates in the selected locations. c. To assess the coverage of vitamin A supplementation amongst children aged 6-59 months. d. To assess measles immunization coverage amongst children aged 9-59 months. e. To assess the coverage of immunization of polio and BCG amongst children aged 6-59 months f. To estimate the prevalence of common child illnesses in the target locations amongst children under 5 years two weeks prior to the survey. g. To assess water, sanitation and hygiene practices at the household level. h. To estimate levels of key food security and livelihood indicators of HDDS1, HHS and rCSI2.

1HDDS: Household Dietary Diversity Score 2 CSI: Coping Strategy Index 2

2.0. METHODOLOGY

2.1. Survey Design The survey applied a two-stage cluster sampling method with the clusters selected using the probability proportionate to population size (PPS). The sampling frame constituted sections of the town and list of the villages.

2.2. Study Population The target population for the survey were children aged 6 – 59 months for the anthropometric component, and women of reproductive age between 15 – 49 years for the maternal health indicators.

2.3. Geographic target area and population group The geographical coverage of the survey was limited only to Elbarde district populations, where the primary respondents for the survey was mothers and/or care takers of children for both household and child questionnaire.

2.4. Sample size calculation The survey sample size was calculated based on key parameters for the primary outcomes (mortality and GAM) in line with current SMART guidance. The table below represent the sample size calculations for both the anthropometric and mortality components. Using the Emergency Nutrition Assessment (ENA) software; January 20th, 2020 version.

Table 3: Anthropometric and Mortality Sample Size Calculation Total number of people accessible Approximately 48,1923 in Elbarde Type of population Rural, Resident/newly settled Parameters for Anthropometry Value Assumptions based on context Estimated Prevalence of GAM (%) 20.4 Point estimate of SMART Survey of November 2019 used. Secondary data review indicates no major changes in context. ± Desired Precision 5 Less precision required for high GAM >= 20%; precision determined by objectives of this assessment attributed to multi-indicators Design Effect (if applicable) 1.65 SMART survey November 2019 design effects. Children to be included 448 As calculated from ENA Average HH Size 5.5 SMART Survey, November 2019 % Children under- 5 20 Based on WHO polio population estimates ACF SMART survey in 2019. % Non-response Households 2 Experience during the November 2019 SMART Survey/to cater for unforeseen circumstances Households to be included 462 As calculated from ENA Parameters for Mortality Value Assumptions based on context Estimated Death Rate/ 10,000/day 0.15 November SMART Survey results ± Desired Precision /10,000/day 0.2 SMART guidelines with death rate < 1 0; Design Effect (if applicable) 1.2 November 2019 SMART survey Recall Period in Days 97 30th July 2020 (Eid Al-ad-ha Population to be included 1940 As calculated from ENA Average HH size 5.5 SMART Survey November 2019 % Non-response Households 2 Experience during the November 2019 SMART Survey/to cater for unforeseen circumstances Households to be included 360 As calculated from ENA **Higher household numbers from Anthropometric component of 462 to be used in cluster assignment.

3 WHO Polio Population Estimates 4

2.5. Cluster Sampling Strategy. The survey employed a two stage cluster sampling technique where; Stage one: cluster selection using updated sampling frame (list of villages in the study location and their population), which was entered into ENA for SMART software 20th January 2020. It is important to note that only secure and accessible areas were eventually included in the sampling frame, few villages near the border of Hudur District were excluded from the sampling due to security. The number of clusters was assigned based on the number of households a team can visit in a day as per calculations in the table 4. Table 4 : Culculating Number of Households per team per day Departure from Office 7:00 AM a. Daily morning Briefings 30 minutes b. Travel to clusters 45 minutes c. Introduction and HH list development 45 minutes d. Lunch/prayer break 30 minutes e. Travel back to base 45 minutes Total time taken 195 minutes Arrival back to Base 5:00 PM Total Available time in a day 10 hrs (600 minutes) Available time for work 600 - 195 minutes= 405 minutes Time taken to complete one questionnaire + 31 minutes moving one household to another

Stage two entailed random sampling of 13 households per cluster from an updated list of households. These were the households visited by the survey team. A household was defined as number of people living together and sharing a common cooking pot. Polygamous families were defined based on the same criteria, if each wife has her own pot, even if living in the same compound, this will be treated as different households. The village guide and the elders supported the teams in updating the list of households. Household selection techniques: For clusters with more than 250HHs, segmentation was used to select one portion of the cluster that will represent the cluster. This was done using probability proportionate to size (PPS). In all selected households, children under 5 years (based on immunization card or calendar of events) were included in the anthropometric survey. In households without children 6-59 months, other variables (mortality, Food Security, WASH) were collected. All children in the age range of 6-59 months were included in the anthropometric, immunization and health components. However, only children 9-59 months were assessed for measles immunization coverage as per WHO guidelines and immunization schedule.

2.6. Variables collected Age: Age of the child was recorded based on a combination of child health cards and mothers’/caretakers’ recall of the child’s birth date, using calendar of events for Elbarde town that was developed in collaboration with the survey teams. Sex: The gender of the child whether a male or female was recorded Bilateral Edema: normal thumb pressure was applied on the top part of both feet for 3 seconds Weight: Children were weighed when wearing minimal or light clothing. Weight was taken using Electronic scales, reading the nearest 0.1 Length/Height: children were measured bareheaded and barefooted using wooden UNICEF height boards with a precision of 0.1cm. Children under the age of two years were measured while lying down and those over two years while standing upright. Mid Upper Arm Circumference (MUAC): MUAC of children taken at the midpoint of the upper left arm using a MUAC tape and the nearest 0.1cm was recorded. Retrospective Morbidity of Children: Conducted, based on mother’s recall for all children (6-59 months) to assess the prevalence of common diseases, 2 weeks recall period was taken for Diarrhea, fever and pneumonia, while the recall period for the measles was based on last 30 days. Vaccination Status and Coverage: For all children 6-59 months, information on Oral polio Vaccine and measles vaccination was collected using the recall from caregivers. Vitamin A supplementation status: For all children aged 6-59 months, information on Vitamin A supplementation was collected based on mother/caregiver’s recall again. Information on whether the child had received supplementation in the last 6 months has been collected. Vitamin A capsule samples was also shown to mothers to support in recall. Household water source: This indicator is aimed at understanding the main water source of the households and whether they get from protected or unprotected source. Sanitation: Information on household accessibility to a toilet/latrine and the type of the latrine they use.

2.7 Case Definitions and inclusion Criteria

2.7.1 Anthropometry Acute malnutrition (Weight-for-height Z score (WHZ) Acute malnutrition in children 6-59 months can be expressed by using two indicators: Weight-for Height (WHZ) or Mid-Upper Arm Circumference (MUAC) as described below. A child’s nutritional status is estimated by comparing it to the WHZ curves of a reference population. These curves have a normal shape and are characterized by the median weight (value separating the population into two groups of the same size) and its Z scores

Table 5: Weight-for-height (WHZ), children 6-59 months Weight-for-height index (W/H) Nutritional status WFH  -2.0 Z scores None/Mild Children 6-59 WFH  - 3.0 and <-2.0 Z scores ,No Moderate acute malnutrition months oedema WFH <-3.0 Z scores and/or oedema Severe acute malnutrition

Chronic malnutrition (Height-for-age Z score (HAZ) The HAZ measure indicates if a child of a given age is chronically malnourished (stunted). This index reflects the nutritional history of a child rather than his/her current nutritional status. The height-for-age index of a child from the studied population expressed in Z-score (HAZ). The HAZ cut-off points in table 6.

Underweight (weight-for-age Z score (WAZ)) Underweight indicates the weight of the child compared to his age as expressed by the Weight- for-Age index and in Z-scores. Table 6 show underweight classes with their cut-off points.

Table 6: Cut offs points of the Height for Age index (HAZ) and Weight for Age (WAZ) Stunting Underweight Indicator (Height for Age -HAZ) (Weight for Age-WAZ) Global HFA < -2 S Z scores WFA< -2 Z scores Moderate HFA < -2 and ≥ -3 Z scores WFA < -2 and ≥ -3 Z scores Severe HFA < -3 Z scores WFA < -3 Z scores

Mid-Upper Arm Circumference (MUAC) The mid-upper arm circumference is a reliable indicator of the muscular status of the child and mainly used to identify children with a risk of mortality. In settings where height and weight measurement is not feasible, MUAC can be used as a screening tool for identifying severely malnourished children for management4 In the field , the criterion below was used to determine the status of children and appropriate referrals done based on the respective cut-offs (table7). Table 7: MUAC Cut offs, children 6-59 months Target group WHO thresholds Nutritional status 125≤135mm None /mild Children 6-59 months 115≤125mm Moderate acute malnutrition <115mm Severe acute malnutrition

4 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983511/

Nutritional bilateral pitting oedema Nutritional bilateral pitting oedema is one of the most severe clinical forms of severe acute malnutrition. In the field children with bilateral oedema was automatically categorized as being severely malnourished, regardless of their WHZ.

2.7.2. Mortality The mortality indicators included all households, regardless of the presence of children. All members of the household were listed using the household definition. Crude death rate (CDR) Number of persons in the total population that dies over a defined period.

Under-5 death rate (U5DR) The probability for those children aged 0-5 years to die during a specific time interval. Calculated as:

2.7.3. Health Immunization status (BCG and Measles) and vitamin A supplementation Mothers/caregivers of all children 6-59 were asked if children received all the necessary vaccinations. The vaccinations asked were; Bacillus Calmette–Guerin (BCG) and Measles for children 9-59 months while Vitamin A supplementation in the last six months was asked for children aged 6-59 months. If the vaccination card was not available to confirm, recall of the caregiver was considered. BCG vaccination was confirmed by checking the scar on the child upper arm (left/right while Vitamin A samples were shown to caregivers.

2.7.4 Morbidity Mothers/caregivers of children (0-59) months were asked if children had experienced any illness in the 14 days prior to the day of the survey. Acute respiratory infection (ARI), fever, malaria, measles and diarrhoea was recorded when symptoms matched the case definition described by the caregiver.

2.7.5. Water Sanitation and Hygiene (WASH) Household respondents were asked about the source of drinking water. The caregivers were also asked on what occasions they wash their hands and what they use to wash their hands to determine the hand washing practices and observe the availability and types of toilet facilities used.

2.8. Data Management and Survey Teams.

2.8.1. Survey Teams Number of Teams: Action Against Hunger as the lead in the survey oversaw the actual field level implementation of the survey from final review of protocol, enumerator selection, training, data collection, supervision, analysis and reporting. The survey encompassed 6 teams comprising of one team leader, two measurers and a village guide. Details on Field Supervision: Action against Hunger and Ministry of Health jointly conducted regular supervision with the teams to ensure that the data collection proceeds as expected. Data entry procedures: Data was collected on mobile platform (Open Data Kit) to allow direct entry and submission to central repository online. The survey used ODK (Android application) for data collection.

2.8.2 Quality Assurance The following measures were put in place by Action Against Hunger to ensure good quality work. a) Engaged qualified personnel while ensuring an enabling environment for operation. b) Ensured adherence to protocol and standards of quality survey implementation to include: • That the training of survey teams across all study locations were done using standardised material. • Undertook standardisation test as part of the training; taking appropriate steps thereafter based on performance of the survey teams. • Appropriate calibration of survey equipment. • Daily debriefing with the teams giving feedback geared at improving the quality of the measurements and congratulate the teams for job well done • Plausibility checks undertaken on daily basis, as data streamed in. c) Anthropometry data was auto analysed using ENA software anthropometry section. The same software used to analyse the mortality data. An updated calendar of events was used in estimating the ages of children; when documented proof of age is lacking.

2.8.3 Data Analysis and Reporting The survey data was analysed using ENA for SMART software July 2015 version, especially for both the anthropometric and mortality components. Additional data captured during the survey, such as food security, immunization, morbidity, and micronutrient variables were analysed using EPI Info. Statistical methods consisting of point prevalence calculations for each of the variables disaggregated by sex.

2.9. COVID-19 Preventive Measures

To prevent and reduce the likelihood of COVID-19 spreads, different mitigating activities were practiced during the training and field data collections, which includes:

1. All the team members used face masks during the training and data collection 2. Assessment team used sanitizers during the training and fieldwork as well. 3. Anthropometric equipment were cleaned with alcohol after each measurement. 4. Survey supervisors/coordinators followed and ensured that these preventive activities are practiced during fieldwork. 5. Social distancing also be practiced during field work and training.

3. RESULT

3.1 Demographic

Total of 468 households were assesed, where 352 out of these had children under five years, with average househod size of 4.9% and mid interval poplation of 2296. The population pyramid below is showing very close proportions as 49% of male and 51% of female were reported. (see figure XX)

3.2 Education Level The survey findings indicated a very low literacy level among care-givers with 70.7% not going to school, while 19.0% went to Koranic school, 5.3% at primary school and only 1.4% to secondary school as shown in table 8 below.

Table 8: level of Education for Caregivers

SN Education Level Number Proportion (95% CI) 1 Didn't go to school 133 70.7% (60.8 – 80.5)

2 Religious & Koranic school 89 19.0% (12.3 – 25.7) 3 Primary school 25 5.3% (2.7 – 7.9) 4 Secondary school 7 1.4% (0.0 – 3.1) 5 Technical School 11 2.3% (0.1 – 4.6) 11

6 Higher Education 1 0.2% (0.0 – 0.6) 7 Don’t Know 4 0.8% (0.0 – 1.8)

3.3 Anthropometric results (based on WHO standards 2006):

3.3.1 Acute malnutrition (Weight-for-height Z score (WHZ) Acute malnutrition in children 6-59 months can be expressed by using two indicators: Weight-for Height (WHZ) or Mid-Upper Arm Circumference (MUAC) as described below. A child’s nutritional status is estimated by comparing it to the WHZ curves of a reference population. These curves have a normal shape and are characterized by the median weight (value separating the population into two groups of the same size) and its Z scores

A total of 508 children 6-59 from 352 households were taken anthropometric measurement with distribution of boys (50.9%) and girls (49.1%) indicating boys girls ration of 1.0. Age distributions are shown in table 9.

Table 9: Distribution of age and sex of sample

Boys Girls Total Ratio AGE (mo) no. % no. % no. % Boy:girl 6-17 62 50.8 60 49.2 122 23.8 1.0 18-29 70 49.3 72 50.7 142 27.7 1.0 30-41 58 57.4 43 42.6 101 19.7 1.3 42-53 43 48.3 46 51.7 89 17.3 0.9 54-59 28 47.5 31 52.5 59 11.5 0.9 Total 261 50.9 252 49.1 513 100.0 1.0

The survey results indicates a prevalence of Global Acute Malnutrition (GAM-WHZ) of 20.9 % (16.7 - 25.8 95% C.I), with Severe Acute malnutrition (SAM-WHZ) at 4.1 % (2.5 - 6.7 95% C.I) indicating sustained critical acute malnutrition compared to November 2019 SMART results, where a critical GAM of 20.4% and SAM of 4.1% were reported. The prevalence of combined GAM (WHZ and MUAC) is 24.6 % (20.2 - 29.5) and this result further indicated that boys were more malnourished than girls though not statistically significant. Table below is showing the prevalence of acute malnutrition based on weight for height z scores/oedema.

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

All Boys Girls n = 508 n = 258 n = 250 Prevalence of global malnutrition (106) 20.9 % (49) 19.0 % (57) 22.8 % (<-2 z-score and/or oedema) (16.7 - 25.8 (14.0 - 25.3 (17.1 - 29.7 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate malnutrition (85) 16.7 % (38) 14.7 % (47) 18.8 % (<-2 z-score and >=-3 z-score, no (13.5 - 20.5 (10.2 - 20.9 (14.3 - 24.3 oedema) 95% C.I.) 95% C.I.) 95% C.I.)

Prevalence of severe malnutrition (21) 4.1 % (11) 4.3 % (10) 4.0 % (<-3 z-score and/or oedema) (2.5 - 6.7 95% (2.1 - 8.6 95% (2.2 - 7.2 95% C.I.) C.I.) C.I.) The prevalence of oedema is 0.0 %

Figure 2: Weight for Height Z Scores

Figure 2 is showing the normal distribution curve of weight for height z –scores, with mean±SD of 1.06±1.07, and design effect of 1.55. it skewed negetively and that is because of the high malnutrition seen Elberde (20.9%).

Analysis of acute malnutrtion by age group, indicate age group of 30 – 41 and 54 – 59 months have the highest SAM cases and have shown at 5% and 5.3% respectively, while higher level of moderate malnutrition were observed among the age groups of 54 – 59 (21.1%) and 6 -17 (20.%) months. Table 11 is showing the prevalence of acute malnutrition by age, based on weight for height z scores and/or oedema.

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

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 121 5 4.1 25 20.7 91 75.2 0 0.0 18-29 141 6 4.3 14 9.9 121 85.8 0 0.0 30-41 100 5 5.0 19 19.0 76 76.0 0 0.0 42-53 89 2 2.2 15 16.9 72 80.9 0 0.0 54-59 57 3 5.3 12 21.1 42 73.7 0 0.0 Total 508 21 4.1 85 16.7 402 79.1 0 0.0

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

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor. 0 Kwashiorkor. 0 (0.0 %) (0.0 %) Oedema absent Marasmic No. 25 (4.9 %) Not severely malnourished. 488 (95.1 %)

3.3.2. Acute Malnutrition based on MUAC A further analysis of malnutrition by Mid Upper arm Circumference (MUAC) <125 mm and /or oedema indicated a GAM of 11.1% (8.2 – 14.8), with Severe MUAC <115 mm and/or oedema 2.1% (1.1 – 4.095% C.I.), which is close to November 2019 SMART survey results of GAM by MUAC (12.6%) and SAM MUAC cases (2.2%). Girls have slightly higher malnutrition (12.7%) by MUAC than boys (9.6%). Table 13 is showing the prevalence of malnutrition based on MAUC

Table 13: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 513 n = 261 n = 252 Prevalence of global malnutrition (57) 11.1 % (25) 9.6 % (32) 12.7 % (< 125 mm and/or oedema) (8.2 - 14.8 95% (6.4 - 14.1 (8.4 - 18.7 95% C.I.) 95% C.I.) C.I.) Prevalence of moderate malnutrition (46) 9.0 % (22) 8.4 % (24) 9.5 % (< 125 mm and >= 115 mm, no oedema) (6.6 - 12.1 95% (5.5 - 12.7 (6.2 - 14.3 95% C.I.) 95% C.I.) C.I.) Prevalence of severe malnutrition (11) 2.1 % (3) 1.1 % (8) 3.2 % (< 115 mm and/or oedema) (1.1 - 4.0 95% (0.4 - 3.6 (1.4 - 7.1 95% C.I.) C.I.) 95% C.I.)

Figure 3: Prevalence of malnutrition by age based on MAUC

Younger age group of 6 – 17 months have higher severe (7.4%) and moderate (19.7%) acute malnutrition, based on MUAC <115mm and <125mm respectively, followed by the age groups of 18 – 29 and 30 – 41 months. Other older age groups of 54 – 59 months have the lowest prevalence of malnutrition based on MUAC. Both Figure 3 and table 14 are showing the prevalence of acute malnutrition by age, based on MUAC cut offs.

Table 14: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm)

Age Total No. % No. % No. % No. % (mo) no. 6-17 122 9 7.4 24 19.7 89 73.0 0 0.0 18-29 142 1 0.7 12 8.5 129 90.8 0 0.0 30-41 101 1 1.0 7 6.9 93 92.1 0 0.0 42-53 89 0 0.0 2 2.2 87 97.8 0 0.0 54-59 59 0 0.0 1 1.7 58 98.3 0 0.0 Total 513 11 2.1 46 9.0 456 88.9 0 0.0

Table 15: Prevalence of combined GAM and SAM based on WHZ and MUAC cut off's (and/or oedema) and by sex*

All Boys Girls n = 513 n = 261 n = 252 Prevalence of combined GAM (126) 24.6 % (55) 21.1 % (71) 28.2 % (WHZ <-2 and/or MUAC < 125 mm (20.2 - 29.5 (16.0 - 27.3 (21.9 - 35.4 and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of combined SAM (28) 5.5 % (13) 5.0 % (15) 6.0 % (WHZ < -3 and/or MUAC < 115 mm (3.5 - 8.3 95% (2.6 - 9.5 95% (3.3 - 10.5 and/or oedema C.I.) C.I.) 95% C.I.) *With SMART or WHO flags a missing MUAC/WHZ or not plausible WHZ value is considered as normal when the other value is available

Table 16:Detailed numbers for combined GAM and SAM

GAM SAM no. % no. % MUAC 20 3.9 7 1.4 WHZ 69 13.5 17 3.3 Both 37 7.2 4 0.8 Edema 0 0.0 0 0.0 Total 126 24.6 28 5.5 Total population: 513

3.3.3. Prevalence of Underweight based on weight for Age Z - scores Underweight is a composite indicator, affected by both wasting and stunting. The result of this assessment is showing medium prevalence of underweight 16.1 %( 12.4 – 20.7 95% C.I.) With severe cases of 2.2 % (1. 4.1 95% C.I.). The result further shows that boys were more underweight (18.3%) than girls (13.9%)as shown in table 17.

Table 17: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 509 n = 257 n = 252

Prevalence of underweight (82) 16.1 % (47) 18.3 % (35) 13.9 % (<-2 z-score) (12.4 - 20.7 (13.2 - 24.8 (9.7 - 19.4 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate underweight (71) 13.9 % (42) 16.3 % (29) 11.5 % (<-2 z-score and >=-3 z-score) (10.9 - 17.7 (11.6 - 22.5 (7.9 - 16.4 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe underweight (11) 2.2 % (5) 1.9 % (6) 2.4 % (<-3 z-score) (1.1 - 4.1 95% (0.8 - 4.5 95% (1.1 - 5.0 95% C.I.) C.I.) C.I.)

Analysis of underweight by age group indicates that children aged 6 to 29 months have higher underweight scores than other groups, while the children 30 to 41 Months have the highest proportion of severe cases (5.9%). Table 18 is showing the prevalence of underweight by age

Table 18: Prevalence of underweight by age, based on weight-for-age z-scores

Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % No. % (mo) no. 6-17 120 1 0.8 26 21.7 93 77.5 0 0.0 18-29 141 3 2.1 21 14.9 117 83.0 0 0.0 30-41 101 6 5.9 12 11.9 83 82.2 0 0.0 42-53 89 1 1.1 7 7.9 81 91.0 0 0.0 54-59 58 0 0.0 5 8.6 53 91.4 0 0.0 Total 509 11 2.2 71 13.9 427 83.9 0 0.0

3.3.4. Prevalence of Stunting based on Height for Age Z - scores Stunting is measured by a height-for-age z-score of more than 2 standard deviations below the World Health Organization (WHO) Child Growth Standards median 5 . Based on WHO classifications, the current stunting is showing low prevalence level (6.7%), with severe stunting cases of 1.6%. This is categorized under medium stunting6. Table 19 is showing the prevalence of stunting based on height for age z- scores by sex.

Table 19: Prevalence of stunting based on height-for-age z-scores and by sex

5 World Health Organization 2018, reducing stunting in children 6 https://www.who.int/nutrition/team/prevalence-thresholds-wasting-overweight-stunting-children- paper.pdf 16

All Boys Girls n = 507 n = 257 n = 250 Prevalence of stunting (34) 6.7 % (24) 9.3 % (10) 4.0 % (<-2 z-score) (4.4 - 10.2 (5.6 - 15.1 (2.2 - 7.2 95% 95% C.I.) 95% C.I.) C.I.) Prevalence of moderate stunting (26) 5.1 % (17) 6.6 % (9) 3.6 % (<-2 z-score and >=-3 z-score) (3.5 - 7.5 95% (4.0 - 10.7 (1.9 - 6.8 95% C.I.) 95% C.I.) C.I.) Prevalence of severe stunting (8) 1.6 % (7) 2.7 % (1) 0.4 % (<-3 z-score) (0.7 - 3.8 95% (1.2 - 6.0 95% (0.1 - 3.0 95% C.I.) C.I.) C.I.)

Regarding the anlysis of stunting by age, children 18 to 29 months have the highest moderate (8.5%) and severe (2.8%) stunting, but the overall stunting level is low. Table 20 is showing the prevalence of stunting by age based on height for age z – scores.

Table 20: Prevalence of stunting by age based on height-for-age z-scores

Severe stunting Moderate Normal (<-3 z-score) stunting (> = -2 z score) (>= -3 and <-2 z- score ) Age Total No. % No. % No. % (mo) no. 6-17 118 1 0.8 4 3.4 113 95.8 18-29 141 4 2.8 12 8.5 125 88.7 30-41 101 2 2.0 7 6.9 92 91.1 42-53 89 1 1.1 2 2.2 86 96.6 54-59 58 0 0.0 1 1.7 57 98.3 Total 507 8 1.6 26 5.1 473 93.3

Table 21: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design Effect z-scores not z-scores out scores ± SD (z-score < -2) available* of range Weight-for-Height 508 -1.06±1.07 1.55 0 5 Weight-for-Age 509 -1.09±0.91 1.55 0 4 Height-for-Age 507 -0.67±0.92 1.58 0 6 * contains for WHZ and WAZ the children with edema.

3.4 Mortality results (retrospective over x months/days prior to interview)

Retrospective mortality rate survey of 96 days recall period has shown Crude death rate of 17

0.04/10,000/day with no households reported under five deaths during these 96 days, and this result is showing low/acceptable CDR and U5DR of <1 death/10,000/day and <2/deaths/10000/day respectively.

Table 22: Mortality Rates

CMR (0.04 deaths/10,000 people / day): (0.01 – 0.34 95% CI) U5MR (0.0 deaths in children under five/10,000 children under five / day): (0.0 – 0.0 95% CI)

3.5. Children’s morbidity

Figure 4: Reported symptoms break down

127 children 6 – 59 6 - 59 months children - reported syndromes of months (24.7) were two weeks prior the assessment reported sick two weeks Other 1.5% prior to the survey Blood in stool 1.5% based on mothers recall, with Fever Stomachache 6.2% (suspect malaria), Skin infection 8.6% Diarrhea and ARI, Eye infection 10.2% mentioned as the likely ARI/Cough 25.2% aggrivating factors of AWD 29.9% the observed high Fever 44.8% malnutrion.

Figure 4 and table 25 are both showing breakdown of sysmptoms reported two weeks prior the survey among the reported 127 sick children reported.

Table 23: Prevalence of reported illness in children in the two weeks prior to interview (n= 127)

6-59 months Prevalence of reported illness ( n= 127) 24.7% (18.6 – 30.8 95% C.I.)

Table 24: Symptom breakdown in the children in the two weeks prior to interview (n= 127)

6-59 months Diarrhoea (38) 29.9% (18.6 – 41.2 95% C.I.) Eye Infection (13) 10.2% (3.9 – 16.5 95% C.I.)

Cough (ARI) (32) 25.2% (15.4 – 34.9 95% C.I.) Fever (57) 44.8% ( 30.4 – 59. 95% C.I.) Skin Infection (11) 8.6% (2.4 – 14.8 95% C.I.) Stomachache (8) 6.2% (0.6 – 11.9 95% C.I.) Blood in Stool (2) 1.5% (0.0 – 3.8 95% C.I.) Toothache (1) 0.7% (0.0 – 2.7 95% C.I.) Other (2( 1.5% (0.0 – 3.7 95% C.I.)

3.6. Health Seeking Practices

Figure 5: Health seeking practices

Beside the high prevalence (24.7%) of 8.6% 16.5% 1.5% morbidities reported among the 6 – 59 months children in Elbarde, another issue of concern is that majority of care givers 37.8% (37.8%) do not seek any assisstant when 35.4% the child gets sick, and only 35.4% go to health facilities for treatment, while 16.5% use pharmacies/private facilities and 8.6% of the population use traditional medications. Pharmacy/private From Shop Health facility No where Traditional

3.7 Vaccination Results

Low coverage of immunization has been observed as only 26.3% of the assessed children received BCG vaccines and 49.5% were immunized against measles, showing very low imunization coverage status and can be as a result of access issues due to the remoteness of the villages , as majority of district population are pastoralists who move around alot.

Table 25: Vaccination coverage: BCG for 6-59 months and measles for 9-59 months

BCG= Measles Measles (with card)= (with card or confirmation from mother)= YES (No 135) 26.3 % (No. 196) 38.2% (No 254 ) 49.5% (14.8 – 37.7 95% C.I.) (25.4 – 50.9 95% C.I.) (36.2 – 62.7 95% C.I.)

3.8 Programme coverage

37 (7.2%) children among the assessed children were registered in supplementry and 15 (2.9%) children in Therapeutic feeding program. Table 27 is showing programme coverages.

Table 26: Programme coverage

Programme type Supplementary feeding programme coverage 7.2 % (3.0 – 11.3 95% C.I.) = 37 Therapeutic feeding programme coverage = 2.9% (0.9 – 4.9 95% C.I.) 15

3.9 Vitamin A Supplementation and Deworming Vitamin A is essential to support rapid growth and to help combat infections. Inadequate intakes of vitamin A may lead to vitamin A deficiency, which can cause visual impairment in the form of night blindness and may increase the risk of illness and death7. Based on the survey result, majority of the children did not receive recent vitamin supplementation, as only 40.9% has received supplementation during the past 6 months but showing improvement to the 13.5% reported in 2019. Proportion of children who received deworming is 46.0%, and is key as public health intervention for children 12 to 23 months8. This is showing also improvement from the 12.0% recorded in 2019 SMART survey in Elbarde. Table 28 is showing Vitamin A supplementation and deworming coverages.

Table 27: Vitamin A Supplementations and Deworming SN Vitamin A & Deworming Number Proportion (95% CI) 1 Vitamin A supplementations during 75 40.9% (25.5 – 56.3) last 6 months 2 Deworming 59 46.0% (29.0 – 63.1)

3.10. Maternal Malnutrition

3.10.1. Physical Status for Mothers The survey team took MUAC measurement for mothers aged 15 to 49 years at the sampled households. The physiological status of the 113 mothers were non-pregnant none lactating

7 WHO, 2011, Guideline: Vitamin A supplementation for infants 1–5 months of age. Geneva, World Health Organization.

8 WHO, https://www.who.int/elena/titles/deworming/en/

(32.6%), during the assessment, while nearly half of them were lactating (49.4%).

3.10.2. Maternal nutrition and Multiple Micronutrient supplementations Malnutrition among pregnant and lactating mothers with MUAC <21.0cm was 8.7% and MUAC <23cm is showing 25.0%, showing that high proportion of mothers are at risk of malnutrition Table 28: Maternal nutrition and Multiple Micronutrient supplementations Indicators Status Number Proportion (CI) Maternal Malnutrition MAUC <21.0cm 31 8.7% (2.6 – 14.7) MUAC <23.0cm 89 25.0% (17.6 – 32.3)

3.11. Water Sanitation and Hygiene (WASH) Access to safe source of drinking water is low (30.0%), and the majority of the households were getting water from unprotected sources, while access to latrine is also low (42.9%) indicting majority of the households are not using latrines. Majority of households were not practicing hand washing in most of critical times as shown in table 30. Table 29: WASH indicators SN Indicators Number Proportion (95% CI) 1 Access to safe drinking water sources 89 19.0% (7.8 – 30.) 2 Latrines at the HH 201 42.9% (30.3 – 55.5) 3 Hand washing after taking children to the toilet 141 30.1% ((16.6 – 43.5) 4 Hand washing after the toilet 183 60.8% (45.8 – 75.9) 5 Hand washings before cooking 128 27.3% (14.4 – 40.2) 6 Hand washings before eating 371 79.2% (66.8 – 91.6)

3.12. Food Security and Livelihood

3.12.1. Dietary diversity Households were asked about the foods consumed in last 24 hours. The findings indicated that the majority of households (86.3%) were consuming ≥4 food groups, especially cereals (93.8%), oil (93.4%) and sugar (82.5%). Figure 6 is showing on food consumed in 24 hour recall

Figure 6: Dietary Diversities

Dietary Diversity

97.9% 93.8% 93.4% 82.5%

44.2% 42.1%

24.8% 24.6% 11.1% 10.0% 10.0% 9.8% 3.8% 2.8% 2.6%

3.12.2. Reduced Coping Strategies Index (rCSI) and Household Hunger Scores (HHS)

Majority of the households are practicing either medium of (4 -9 (48.7%) or high9 (≥10 (29.7%) coping strategies, and this indicates the vulnerabilities among the population, however, regarding the household hunger scores (HHS), most of households have reported none or light hunger (89.3%). Table 23 is showing the HHS and rCSI results

Table 30: Reduced Coping Strategies and Household Hunger Scores

Indicator Category Number Proportion & 95% CI Low (1 -3) 101 21.5% (9.6 – 33.4) Reduced Copying Strategies Index Medium (4 -9) 228 48.7% (37.4 – 59.9) (rCSI) High ≥10 139 29.7% (19. – 39.6) None or light hunger 418 89.3% (83.9 – 94.7) Moderate hunger 27 5.7% (1.0 – 10.4) Household Hunger Scores (HHS Severe hunger 2 4.9% (2.1 – 7.7)

9 WFP, 2012,calculation of household food security outcome indicators, Afghanistan

4.0. DISCUSION

The survey findings indicated a very high prevalence of Global Acute Malnutrition (GAM) of 20.9% (16.7 – 25.8) with Severe Acute Malnutrition (SAM) rate of 4.1% (2.5 – 6.7) categorised as critical situation. The current result shows sustained critical nutrition situation compared to November 2019 ACF SMART survey with GAM of 20.4% and SAM of 5.0%. girls were more malnourished (22.8%) than boys (19.0%) but not statistically significant (P Value (>0.05). A further analysis of age cohort mostly affected by malnutrition indicates ages 6- 17 months which could be attributed to sub-optimal complementary feeding.

The sustained critical nutrition status is likely due to mixed factors that are negatively influencing the situation which includes the high morbidity levels; with 24.7% of children being sick two weeks prior to survey date. A further analysis also showed 44.8%, 29.9% and, 25.2% of sick children suffered from fever/malaria, diarrhoea and ARI respectively. The high morbidities of AWD and fever is likely because of the recent rains as use of unsafe water and increased malaria cases might happen during the rainy season. Majority of households are accessing water from unprotected (71%) source and there could be likelihood of increased diseases, especially AWD cases because of drinking water from unprotected source. The other possible cause of malnutrition is deteriorated/chronic food insecurity due to reduced market access because of insecurity and poor infrastructures. Regarding the dietary diversities, there is very limited use of fruits and vegetables, while the adaption of high rCSI is another indication of vulnerability as nearly 80% of households are practicing high or moderate rCSI. Low coverage of health services like immunizations, vitamin A supplementations and deworming, as well as, limited access to safe drinking water (19.0%) and household latrines (42.9%) might also attribute to worsening of the situation. Crude and under five death rates are within the acceptable ranges. Current CDR of 0.04/10,000 population/ day and no under five deaths were reported during the 96 days of recall period, showing almost similar to 2019 result (CRD 0.15).

5. CONCLUSIONS

Therefore, continuation of the current activities and upscaling further interventions are very crucial to mitigate and prevent further deteriorated situation. There is need to also have a detailed understanding of high malnutrition rates among ages 6-17 months to have targeted programing that promotes complementary feeding.

6. RECOMMENDATIONS AND PRIORITIES

To address the high malnutrition among children under five, continuation of current ongoing humanitarian interventions and further preventive measures are very crucial. The following recommendations have been put forward to complement the on-going interventions.

Table 31: Recommendations Findings Descriptions Recommendations Critical Acute malnutrition and high GAM 20.9% (16.7 – 25.8) • Continue the Ongoing management of Severe prevalence of Underweight MUAC <125mm 11.1% (8.2 – 14.8) and moderate acute malnutrition (OTP, SC and Underweight 16.1% (12.4 – 20.7) TSFPs). • Treatment/management of communicable diseases (especially AWD, Fever and ARI). Low health service coverages Measles immunizations (49.5%) • Demand generations to increase utilization of Immunization coverages are below BCG coverage (26.3%) health/immunization services. the international standards (sphere Vitamin A supplementations (40.9%) • Conduct immunization outreach and mobile >95%). Deworming (46.0%) activities to reach areas with no access to Very low vitamin A supplementations health services. Deworming • Vitamin A supplementations and deworming at facility level. Limited access to safe drinking water • Access to safe drinking water • Improve the access to safe drinking water and the need for improved WASH (19.0%) through availability of protected water practices • Households that have access to sources latrines (42.9%) • Construction of latrines • Hygiene promotions High proportion of the population are High rCSI (29.7%) • Food Security interventions (like cash still practicing high-reduced coping Medium rCSI (48.7%) voucher or cash for work). strategies. DD ≥ 4 Food groups (86.3%) • Improve road access through cash for work Low HH dietary diversity scores • Long term resilient plan on chronic food insecurity.

7. REFERENCES

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5983511/

2. World Health Organization 2018, reducing stunting in children

3. https://www.who.int/nutrition/team/prevalence-thresholds-wasting-overweight-stunting-

children-paper.pdf

4. WHO, 2011, Guideline: Vitamin A supplementation for infants 1–5 months of age. Geneva,

World Health Organization.

5. WHO, https://www.who.int/elena/titles/deworming/en/

6. WFP, 2012,calculation of household food security outcome indicators, Afghanistan

8. APPENDICES

Appendix 8.1: Plausibility Report for: Elbarde Deyr' 2020.as

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-7.5 >7.5 (% of out of range subjects) 0 5 10 20 0 (1.0 %)

Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 0 (p=0.691)

Age ratio(6-29 vs 30-59) Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 4 (p=0.012)

Dig pref score - weight Incl # 0-7 8-12 13-20 > 20 0 2 4 10 0 (3)

Dig pref score - height Incl # 0-7 8-12 13-20 > 20 0 2 4 10 0 (7)

Dig pref score - MUAC Incl # 0-7 8-12 13-20 > 20 0 2 4 10 0 (6)

Standard Dev WHZ Excl SD <1.1 <1.15 <1.20 >=1.20 . and and and or . Excl SD >0.9 >0.85 >0.80 <=0.80 0 5 10 20 0 (1.07)

Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 0 (0.00)

Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 3 (-0.52)

Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001 0 1 3 5 0 (p=0.077)

OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 7 %

The overall score of this survey is 7 %, this is excellent.

There were no duplicate entries detected.

Appendix 8.2. Assignment of Clusters

Geographical unit Population size Assigned cluster

Elbarde town 19800 1,2,3,4,RC,RC,5,6,7,RC,8,9,10,11,RC,12,13,14,15 Ato 3900 16,17,18,19 Gargar IDPs 900 20 Biyo fadhi 390 Salkudhooble 1200 21 Danshoot 660 22 Ondhere 570 Garidab 636 23 Figta 1500 24 Abesaale 2160 25,26,27 Higlole 570 Habarey 750 28 Elmagad 1410 29 Qurucjome 6300 30,31,32,33,34,35 Cimilow 360 36 Dharkeynley 330

Appendix 8.3. Evaluation of Enumerators

Weight:

Precision: Accuracy: No. +/- No. +/- Sum of Square Sum of Square Precision Accuracy [W1-W2] [Enum.(W1+W2)- (Superv.(W1+W2)]

Supervisor 0.49 4/5 Enumerator 1 0.21 OK 16.10 POOR 2/5 6/4 Enumerator 2 0.37 OK 0.62 OK 2/3 7/2 Enumerator 3 3.53 POOR 2.84 POOR 1/5 4/6 Enumerator 4 0.90 OK 54.71 POOR 3/1 3/7 Enumerator 5 0.29 OK 0.26 OK 2/8 6/3 Enumerator 6 13.34 POOR 16.75 POOR 2/6 3/6 Enumerator 7 0.27 OK 0.60 OK 0/6 6/2 Enumerator 8 0.06 OK 0.61 OK 1/5 5/3 Enumerator 9 0.13 OK 0.46 OK 1/6 4/5 Enumerator 10 0.36 OK 6.31 POOR 4/5 3/6 Enumerator 11 0.23 OK 52.88 POOR 3/6 4/5 Enumerator 12 2.99 POOR 3.28 POOR 2/6 4/6 Enumerator 13 0.79 OK 0.82 OK 2/5 5/5 Enumerator 14 0.08 OK 5.79 POOR 1/7 6/4 Enumerator 15 1.08 POOR 1.19 OK 1/5 6/3 Enumerator 16 0.08 OK 0.69 OK 1/4 5/3 Enumerator 17 0.13 OK 0.76 OK 1/9 5/4

Height:

Precision: Accuracy: No. +/- No. +/- Sum of Square Sum of Square Precision Accuracy [H1-H2] [Enum.(H1+H2)- Superv.(H1+H2)]

Supervisor 6.83 7/3 Enumerator 1 3.08 OK 35.01 POOR 1/9 7/3 Enumerator 2 2.53 OK 33.28 POOR 2/8 3/7 Enumerator 3 27.92 POOR 25.19 POOR 7/3 4/6 Enumerator 4 1.49 OK 41.70 POOR 2/2 6/4 4

Enumerator 5 0.15 OK 37.76 POOR 6/3 6/4 Enumerator 6 281.95 POOR 349.10 POOR 6/3 6/4 Enumerator 7 40.81 POOR 61.48 POOR 4/5 5/5 Enumerator 8 0.12 OK 38.01 POOR 2/2 4/6 Enumerator 9 3.87 OK 31.04 POOR 4/4 2/7 Enumerator 10 1.80 OK 21.77 POOR 1/9 4/5 Enumerator 11 1.56 OK 49.29 POOR 0/7 6/4 Enumerator 12 22.39 POOR 49.56 POOR 4/6 3/7 Enumerator 13 6.25 OK 31.08 POOR 6/4 4/6 Enumerator 14 0.93 OK 27.72 POOR 1/9 3/7 Enumerator 15 56.73 POOR 272.22 POOR 6/4 4/6 Enumerator 16 0.31 OK 39.10 POOR 3/1 4/6 Enumerator 17 0.83 OK 35.36 POOR 0/10 5/5

MUAC:

Precision: Accuracy: No. +/- No. +/- Sum of Square Sum of Square Precision Accuracy [MUAC1-MUAC2] [Enum.(MUAC1+MUAC2)- Superv.(MUAC1+MUAC2]

Supervisor 331.55 7/3 Enumerator 1 0.89 OK 305.44 OK 4/6 7/3 Enumerator 2 0.18 OK 336.93 OK 2/5 3/7 Enumerator 3 280.08 OK 635.33 OK 4/5 5/5 Enumerator 4 0.26 OK 309.31 OK 2/0 6/4 Enumerator 5 2.08 OK 321.23 OK 3/5 3/6 Enumerator 6 2.08 OK 320.37 OK 2/6 6/3 Enumerator 7 0.96 OK 337.69 OK 3/5 2/7 Enumerator 8 0.56 OK 317.73 OK 2/7 2/8 Enumerator 9 0.64 OK 337.57 OK 7/3 4/6 Enumerator 10 0.73 OK 311.70 OK 1/9 2/8 Enumerator 11 2.07 OK 311.70 OK 0/4 7/3 Enumerator 12 1.27 OK 320.46 OK 6/3 5/5 Enumerator 13 1.40 OK 344.61 OK 2/7 3/6 Enumerator 14 317.27 OK 10.98 OK 2/8 3/7 Enumerator 15 4.51 OK 342.44 OK 6/4 5/5 Enumerator 16 0.12 OK 316.81 OK 2/4 3/7 Enumerator 17 0.56 OK 334.89 OK 1/9 4/6

For evaluating the enumerators the precision and the accuracy of their measurements is calculated. For precision the sum of the square of the differences for the double measurements is calculated. This value should be less than two times the precision value of the supervisor. For the accuracy the sum of the square of the differences between the enumerator values (weight1+weight2) and the supervisor values (weight1+weight2) is calculated. This value should be less 5

than three times the precision value of the supervisor. To check for systematic errors of the enumerators the number of positive and negative deviations can be used.