Smart Survey Draft Report for Baidoa District

SMART SURVEY DRAFT REPORT FOR BAIDOA DISTRICT

AUGUST 2019

1 Acknowledgements

Appreciation goes to SCI nutrition team for the support given during the entire exercise including review of technical aspects for the proposal, the community and local administration for cooperation in data collection and approval of the survey. The AIM working groups Somalia cluster for technical support in validating the protocol.

2 Acronyms

SCI Save the children International HH Household GAM Global Acute Malnutrition MAM Moderate Acute Malnutrition SAM Severe Acute Malnutrition CDR Crude Death Rate U5DR Under5 Death Rate WHO World Health Organization MoH Ministry of Health WASH Water Sanitation and Hygiene FSL Food Security and Livelihood SMART Standardized Monitoring and Assessment in Relief and Transition IDPs Internally Displaced Populations U5MR Under 5 Mortality Rate CMR Crude Mortality Rate ENA Emergency Nutrition Assessment MUAC Mid Upper Arm Circumference WHZ Weigh for Height z-score WAZ Weight for Age Z-score HAZ Height for Age z-score EPI Extended programme on Immunization BCG Bacillus Chalmette–Guerin C.I Confidence Interval FSNAU Food Security and Nutrition Assessment Unit AAH Action Against Hunger SNS Strengthening Nutrition in Somalia ARI Acute respiratory infection IYCF Infant and Young child feeding MDD Minimum Dietary Diversity MMF Minimum Meal frequency rCSI Reduced Coping Strategy Index HDD Household Dietary Diversity IDD Individual Dietary Diversity UNFPA United nations Population Fund FGS Federal government of Somalia PESS Population Estimation survey of Somalia VAS. Vitamin A supplementation

3 Table of Contents

EXECUTIVE SUMMARY ...... 6

1. INTRODUCTION ...... 8

1.1 SURVEY OBJECTIVES ...... 8

2. METHODOLOGY ...... 10

2.1 SAMPLE SIZE ...... 10 2.2 SAMPLING PROCEDURE: SELECTING CLUSTERS...... 10 3.3 SAMPLING PROCEDURE: SELECTING HOUSEHOLDS AND CHILDREN ...... 10 2.3 SAMPLING PROCEDURE: SELECTING HOUSEHOLDS AND CHILDREN ...... 11 2.4 CASE DEFINITIONS AND INCLUSION CRITERIA ...... 11 2.5 QUESTIONNAIRE, TRAINING AND SUPERVISION ...... 12 QUESTIONNAIRE ...... 12 2.6 DATA ANALYSIS ...... 13

3. RESULTS ...... 14

3.1 ANTHROPOMETRIC RESULTS (BASED ON WHO STANDARDS 2006): ...... 14 POPULATION PYRAMID ...... 14 3.2 MORTALITY RESULTS (RETROSPECTIVE OVER X MONTHS/DAYS PRIOR TO INTERVIEW) ...... 20 3.3 CHILDREN’S MORBIDITY ...... 21 3.3.1 SYMPTOMS BREAKDOWN ...... 21 3.3.2 HEALTH SEEKING BEHAVIOR ...... 22 3.5 VITAMIN A AND DEWORMING ...... 22 3.6 INFANT AND YOUNG CHILD FEEDING PRACTICES ...... 23 3.6.1 EARLY INITIATION BREASTFEEDING ...... 23 3.6.2 MINIMUM DIETARY DIVERSITY ...... 24 3.6.3 MINIMUM MEAL FREQUENCY ...... 25 3.7 MATERNAL NUTRITION ...... 25 3.8 FOOD SECURITY AND LIVELIHOODS ...... 25 3.9 WATER, SANITATION AND HYGIENE ...... 26 3.9.1 MAIN WATER SOURCE ...... 26 3.9.2 HAND WASHING PRACTICES ...... 26 3.9.3 DEFECATION PRACTICES ...... 27

4. DISCUSSION ...... 28

4.1 CAUSES OF MALNUTRITION ...... 28

5. CONCLUSION ...... 29

4 6. RECOMMENDATIONS ...... 30

7. REFERENCES ...... 31

8. APPENDICES ...... 32

8.1 APPENDICES 1: BAIDOA PLAUSIBILITY REPORT ...... 32

5 Executive summary

Background

Baidoa or “Baydhaba” as it is locally known is located in bay region. It is a strategic economic hub and is considered the seat of agro-pastoralism in Somalia given its reputation for the high potential sorghum production, the relative farming in the area as well as optimum livestock production. Over the past few years, there has been a steady influx of IDPs into Baidoa town from within and outside the region. The town is swelling with large number of IDP camps. As of August 2019, there were 435 IDP sites independently hosting a combined 51, 322 households according to CCM. Given their vulnerability combined with strained household resources, the humanitarian situation of most households in the IDP is precarious making households with the under 5 children the most affected. In a bid to alleviate the growing humanitarian situation, SCI is one of the leading INGOs that have operated in the district implementing key interventions in the areas of Nutrition prevention and treatment, FSL, protection and education.

Methodology

SMART methodology was used in the surveys. Two-stage cluster sampling was employed with clusters being selected at first stage and HH selection at the second stage. Random number generator was used to randomly select individual HH from a Data collection was conducted using ODK and daily checks were done to ensure that feedback on data quality was timely and also any challenges in data collection were addressed on time. A total of 36 clusters was surveyed in Baidoa district.

Survey Objectives:

The overall survey objective was to estimate the nutrition status of children under 5 years as well as Crude and under5 mortality rates for Baidoa district.

Results

Summary results for the districts are as shown below:

Baidoa GAM 10.4 %( 7.8 - 13.9 ) MAM 9.2 % (6.5 - 12.7) SAM 1.3 % (0.7 - 2.5) CDR 0.40/10000/day U5DR 0.36/10000/day Morbidity 22.56% (19.28-26.20, 95% C.I.) Vitamin A 53.5%(33.5%-73.5%)

Baidoa district has a prevalence of both serious acute malnutrition levels as well as high stunting rates.

6 Recommendations:

Finding Explanation Recommendation Priority Responsible

Serious levels of GAM rates in Baidoa Sustain existing Immediate SCI/Cluster GAM. indicates serious programs and improve levels sustained on recruitment over the past three years. High stunting The prevalence of Upscale IYCF practices rates stunting is high especially through above 30% and has increased breastfeeding remained so over and complementary the past three years feeding counselling at if data from this both household and at survey and other community level assessments are anything to go by Undertake an in-depth investigation on the causes of stunting in Baidoa and Bay region at large.

If possible, undertake the cost of the diet assessment in bay agro- pastoral in order to propose low-cost nutritious diets.

Low vitamin A Vitamin A Advocate for Mid SCI/Cluster supplementation supplementation is coordinated joint bi- Term recommended to be annual mass at least 80% o have supplementation given an effect in the that low Vitamin A population. In both supplementation is districts directly related to supplementation increased morbidity. rates are below 50%.

7 1. Introduction

Save the Children has been working in Somalia since 1951 when SCI set up a vocational school for orphaned boys in Somaliland. From the early 1970s save the children have provided emergency assistance when the needs demand. Each year, about 650,000 people benefit from our longer-term development work in Health, Nutrition, Water, Sanitation & Hygiene (WASH), Education, Food Security and Livelihoods (FSL), Child Protection and Child Rights Governance. We believe that the provision of basic social services in education, health and protection and the strengthening of government capacity to deliver these, along with a concerted effort to work with communities and children to promote their rights, are all central to what we call resilience. SCI (Save the children International) in support of generation of quality information for humanitarian actors is conducting a SMART survey with the aim of filling nutrition information gaps at district level.

Baidoa or “Baydhaba” as it is locally known is located in Bay region. It is a strategic economic hub and is considered the seat of agro-pastoralism in Somalia given its reputation for the high potential sorghum production, the relative farming in the area as well as optimum livestock production. Despite this, the district has over the years seen the influx of IDPs from within the region as well as from the neighboring regions such as Bakool- displaced as a result of prolonged droughts as well as the precarious security situation in most of the rural areas. As of August 2019, there were 435 IDP sites independently hosting a combined 51, 322 households according to CCM1. Given their vulnerability combined with strained household resources, the humanitarian situation of most households in the IDP is precarious making households with the under 5 children the most affected.

The prevalence of acute malnutrition situation in Baidoa has remained constant with rates over the past three years hovering between serious and critical levels. On the other hand, the district as well as the larger bay agro-pastoral livelihood zone is grappling with high rates of stunting, three times the national prevalence of 10%. IYCF KAP study undertaken by SCI in December 2017 showed a limited uptake of key IYCF practices and could be a reason behind the prevalence of both acute and chronic malnutrition in the district. Nonetheless, over the past few years, SCI has operated in Baidoa district leading in humanitarian work that has included Nutrition prevention and treatment, FSL, resilience, protection and education

1.1 Survey Objectives

Overall objectives Asses Nutrition status of children 6-59 months, retrospective mortality rate among under5 children (U5MR) and the Crude Mortality rates in the entire population (CMR). Specific Objectives: • To estimate the prevalence of acute malnutrition amongst children aged 6- 59 months.

1 https://data2.unhcr.org/en/documents/download/71269

8 • To estimate the retrospective crude and under five mortality rates in the selected locations. • To estimate the coverage of vitamin A supplementation amongst children aged 6-59 months. • To estimate measles immunization coverage amongst children aged 9-59 months. • To estimate the coverage of immunization of polio and BCG amongst children aged 6-59 months • To estimate the prevalence of common child illnesses in the target locations amongst children under 5 years two weeks prior to the survey. • To assess water, sanitation and hygiene practices at the household level. • To estimate levels of key food security and livelihood indicators of HDDS and rCSI. • To assess maternal nutritional status among women of reproductive age using MUAC

9 2. Methodology

2.1 Sample size

Sample size for Baidoa was calculated using ENA software. The following parameters were entered into the software and produced the following sample. . Parameter Baidoa

Population estimate 39,506 Average HH Size 6 Design effect 1.5

eneral eneral Non-Response % 3

G parameters

Estimated prevalence % 18.1 Desired precision % 5 Percentage of <5 20 Total Children 372

nthropometry Total HH 355 A Estimated prevalence % 0.5%

Desired precision % 0.5% Recall Period 107

Population to be included 1832 ortality ortality

M Household to be included 315 Final Number of clusters 36

2.2 Sampling procedure: selecting clusters.

All accessible villages in the district were listed in the sampling frame with corresponding population. The population numbers were verified through consultation with the local authorities and SCI teams who do actual program implementation and have the numbers from other projects.

The data was then entered into the ENA Planning tab. Under the same software (ENA 2015, 9th July), 36 clusters were randomly selected initially using probability proportion to population size (PPS) for both districts. ENA selected three additional reserve clusters, which were not visited during the survey because the survey teams were able to reach all the selected clusters without any one being dropped. 3.3 Sampling Procedure: Selecting Households and Children

With the support of community leaders, all households within a cluster were listed on arrival to the cluster. Simple Random Sampling was used to pick 10 HH and these were the HH that were visited within the cluster. The same procedure was repeated for all 36 clusters randomly selected for the survey.

Anthropometric measurement (weight, height, MUAC and oedema) was taken for all children 6-59 months within the selected Households. MUAC was also taken for WCBA. Data on HH

10 demographic was collected in all households. Deserted HH were not replaced, as this would have introduced bias. Using the cluster control form, the process and outcome of data collection at each HH was noted. The cluster control forms assisted the teams to identify HH with absent children and aided in planning or re-visits.

In each HH, a caretaker of the child was the respondent. This in most HH was found to be the mother of the child. In HH where the mother was away, the father was identified as the respondent. In approximately 2% of all HH, the respondent was the eldest child who had been left in charge of the HH or due to loss of both parents.

2.3 Sampling procedure: selecting households and children

With the support of community leaders, all households within a cluster were listed on arrival to the cluster. Simple Random Sampling was used to pick 10 HH and these were the HH that were visited within the cluster. Anthropometric measurement was taken on all children 6-59 months within a HH.

2.4 Case definitions and inclusion criteria

The following case definitions were used in the assessment: • Household: People who live together and eat from the same pot at the time of assessment. In case of a polygamous family, each mother and her children will be treated as a separate HH. • Head of household: One who controls and makes key decisions on household resources (livestock, assets, income, and food), health and social matters for and on behalf of the household members • Respondent: Caregiver of the child; in case caregiver is not available, the person responsible for the HH at the time of survey will be the respondent. • Diarrhoea: Having three or more loose or watery stools per day. • Malaria: Presence of periodic chills/shivering, fever, sweating and convulsions. • Measles: Having more than three of these signs: fever, skin rash, runny nose or red eyes, and/or mouth infection or chest infection. • Measles immunization: An injection (confirmed by card) in the upper arm given to children after 6 months of age at health clinics or by mobile health teams.

For the purposes of analysis, the different types of malnutrition were defined based on WHO (2006) growth standards and WHO was used to report main results from the survey. Oedema: Swollen limbs leaving depression 3 seconds after pressing on both feet (bilateral) Global Acute Malnutrition (GAM): Weight-for-height Z scores less than -2SD and/or presence of oedema WHZ<-2SD and/oedema) ▪ Severe Acute Malnutrition (SAM): weight-for-height Z scores less than -3SD and/or presence of oedema (WHZ<-3SD and/oedema) ▪ Global Acute Malnutrition based on MUAC (GAMMUAC): Mid Upper Arm Circumference less than 125mm and/or presence of oedema (MUAC<125 mm and/oedema); and severe acute malnutrition as MUAC<115 mm and/or oedema ▪ Wasting: Weight-for-height Z scores less than -2 SDWHZ<-2), and severe wasting as Z scores less than -3 standard deviations (WHZ<-3).

11 ▪ Underweight: weight-for-age Z scores less than -2SD (WAZ<-2), and severe underweight as Z scores less than -3 standard deviations (WAZ<-3). ▪ Stunting: height-for-age Z scores less than -2SD (HAZ<-2), and severe stunting as Z scores less than -3 standard deviations (HAZ<-3).

EPI (Measles, BCG and Polio) coverage was estimated using immunization cards. Although there was an option for recall, such responses were analyzed separately to indicate the difference of the source of information.

Mortality data was collected in all households. This included HH that had no eligible children for the anthropometric survey. Individual questionnaire was used for Mortality data collection and this means that deaths were categorized by age. Causes of deaths were categorized into three; traumatic, non-traumatic and unknown. Place of death was also categorized into four; current location, during migration, place of last residence and unknown.

2.5 Questionnaire, training and supervision

Questionnaire

The SMART questionnaire was built into ODK collect and covered Anthropometry, Mortality, Food security and WASH questions. Additional sections included micronutrient supplementation and immunization proxies. To facilitate comprehension among enumerators and respondents, the questionnaire was translated to Somalia. The quality of the translation was validated through a two-way translation where the first Somalia translation was translated back to English to check for any possible loss of meaning in translations.

During the training a section for questionnaire review was allocated where enumerators went through the questionnaire to understand what the question entailed and how to appropriately ask sensitive questions especially mortality section. The flow of questions and time taken to complete one questionnaire was also estimated for actual planning purposes.

Survey teams and supervision A total of 6 teams participated in the survey in Baidoa. Each team comprised of a Team Leader, two Measurement Assistants and a Community Guide. Team leaders was responsible for the quality of data collected, ensuring adherence to standard steps in anthropometry data collection, completeness of data, communication of any challenges and leading in HH selection process.

The overall supervisor for the survey was the consultant.

Training Standard SMART enumerator training was conducted focusing on proper (Accuracy and Precision) collection of anthropometric data and standardization. This included training on taking MUAC, weight, height and checking oedema, age calculation, field procedures and team roles.

The training also featured a practical demonstration for conducting a standardization test. Only the enumerators who passed the test were allowed to proceed to the field.

2.6 Data analysis

Analysis was done using ENA for the anthropometric data and mortality data. The results were compared to findings from earlier similar surveys, and the survey results were explained within the context of factors that have direct effect on nutrition based on Nutritional Causal Framework. Outliers for anthropometry data were analyzed with boundaries of exclusion set at +/- 3SD of WHZ, HAZ and WAZ from the observed mean. Data was analyzed using standard indicators of GAM, MAM and SAM.

Stata was used to analyze HH demographic data, WASH, Morbidity and EPI. Descriptive statistics were used to analyze patterns. Correlation between diseases, FSL indicators and WHZ was also conducted.

3. Results

3.1 Anthropometric results (based on WHO standards 2006):

Definitions of acute malnutrition should be given (for example, global acute malnutrition is defined as <-2 z scores weight-for-height and/or oedema, severe acute malnutrition is defined as <-3z scores weight-for-height and/or oedema)

Exclusion of z-scores from Observed mean SMART flags: WHZ -3 to 3; HAZ -3 to 3; WAZ -3 to 3

Table 3.1: Distribution of age and sex of sample

Boys Girls Total Ratio AGE (mo) no. % no. % no. % Boy: girl 6-17 82 51.9 76 48.1 158 28.0 1.1 18-29 52 43.0 69 57.0 121 21.4 0.8 30-41 59 48.0 64 52.0 123 21.8 0.9 42-53 67 51.1 64 48.9 131 23.2 1.0 54-59 11 34.4 21 65.6 32 5.7 0.5 Total 271 48.0 294 52.0 565 100.0 0.9

A total of 565 children underwent anthropometric measurements. This is up from the expected 372 children and could be attributed to an error in the population of Baidoa district. The district has an estimated population of 129,000 and the 39,506 used in the sample size calculation could be an anomaly. The overall sex ratio was 0.9 indicating that boys and girls were equally represented (p-value 0.333).

Population pyramid

Figure 3.1: Population age and sex pyramid

Error! Reference source not found.Table 3.2: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex

All Boys Girls n = 546 n = 262 n = 284 Prevalence of global (57) 10.4 % (27) 10.3 % (30) 10.6 % malnutrition (7.8 - 13.9 (5.8 - 17.8 (7.4 - 14.9 (<-2 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (50) 9.2 % (25) 9.5 % (25) 8.8 % malnutrition (6.5 - 12.7 (5.1 - 17.1 (6.1 - 12.6 (<-2 z-score and >=-3 z-score, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (7) 1.3 % (2) 0.8 % (5) 1.8 % malnutrition (0.7 - 2.5 (0.2 - 3.1 (0.8 - 4.0 (<-3 z-score and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) The prevalence of oedema is 0.0 %

Nutrition status trends

15 BAY AGRO PASTORAL GAM RATES_2017-2019 16 14 12 10 8 6 4 2 0 G U 2 0 1 7 D E Y R 2 0 1 7 G U 2 0 1 8 D E Y R 2 0 1 8 G U 2 0 1 9 S C I S M A R T A U G 2 0 1 9

*** Data courtesy of FSNAU

From the above results, it is evident that the GAM prevalence in Bay agro-pastoral has been at serious levels for the past five rainy seasons.

Table 3.3: 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. 6-17 155 3 1.9 21 13.5 131 84.5 0 0.0 18-29 115 1 0.9 2 1.7 112 97.4 0 0.0 30-41 118 2 1.7 12 10.2 104 88.1 0 0.0 42-53 128 1 0.8 15 11.7 112 87.5 0 0.0 54-59 30 0 0.0 0 0.0 30 100.0 0 0.0 Total 546 7 1.3 50 9.2 489 89.6 0 0.0

The prevalence of acute malnutrition (WHZ<-2 and/or oedema) was highest among the younger age group of children aged 6 – 17 months. (Table 3.2) This indicates more problems and a low level of IYCF practices in the area.

Table 3.4: Distribution of acute malnutrition and oedema based on weight-for-height z- scores

<-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor

16 No. 0 No. 0 (0.0 %) (0.0 %) Oedema absent Marasmic Not severely malnourished No. 17 No. 548 (3.0 %) (97.0 %)

No oedema cases but 3% were found to be Marasmic

Table 3.5: Prevalence of acute malnutrition based on MUAC cut off's (and/or oedema) and by sex

All Boys Girls n = 565 n = 271 n = 294 Prevalence of global (41) 7.3 % (13) 4.8 % (28) 9.5 % malnutrition (5.1 - 10.2 (2.8 - 8.2 (6.6 - 13.6 (< 125 mm and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (32) 5.7 % (10) 3.7 % (22) 7.5 % malnutrition (3.8 - 8.3 (1.9 - 6.9 (4.7 - 11.7 (< 125 mm and >= 115 mm, no 95% C.I.) 95% C.I.) 95% C.I.) oedema) Prevalence of severe (9) 1.6 % (3) 1.1 % (6) 2.0 % malnutrition (0.9 - 2.7 (0.4 - 3.4 (1.0 - 4.1 (< 115 mm and/or oedema) 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.6: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

Severe wasting Moderate Normal Oedema

17 (< 115 mm) wasting (> = 125 mm ) (>= 115 mm and < 125 mm) Age Total No. % No. % No. % No. % (mo) no. 6-17 158 7 4.4 21 13.3 130 82.3 0 0.0 18-29 121 1 0.8 5 4.1 115 95.0 0 0.0 30-41 123 1 0.8 4 3.3 118 95.9 0 0.0 42-53 131 0 0.0 2 1.5 129 98.5 0 0.0 54-59 32 0 0.0 0 0.0 32 100.0 0 0.0 Total 565 9 1.6 32 5.7 524 92.7 0 0.0

In Somalia MUAC is the main toll used to identify malnourished children at the community level due to the relative ease in their use. The prevalence of GAM by MUAC in Baidoa was found to be 7.3% while the SAM rate of MUAC defined as MUAC of (115mm and/or oedema was found to be 1.6%. The prevalence of GAM was higher among girls (9.5%) than in boys (4.5%). Incidentally, two-thirds of the malnourished children identified by MUAC were girls.

As shown in table 3.6 above, the 6-17 age group recorded the highest burden similar to the GAM rate by WHZ. This indicates a limited uptake of IYCF practices in Baidoa.

Table 3.7: Prevalence of underweight based on weight-for-age z-scores by sex

All Boys Girls n = 556 n = 265 n = 291 Prevalence of underweight (125) 22.5 % (54) 20.4 % (71) 24.4 % (<-2 z-score) (17.8 - 28.0 (16.4 - 25.1 (17.9 - 32.3 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate (95) 17.1 % (42) 15.8 % (53) 18.2 % underweight (14.0 - 20.6 (12.4 - 20.1 (14.0 - 23.3 (<-2 z-score and >=-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe (30) 5.4 % (12) 4.5 % (18) 6.2 % underweight (3.2 - 8.8 (2.5 - 8.0 (3.3 - 11.3 (<-3 z-score) 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.8: 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. 6-17 156 10 6.4 34 21.8 112 71.8 0 0.0

18 18-29 118 8 6.8 23 19.5 87 73.7 0 0.0 30-41 119 5 4.2 19 16.0 95 79.8 0 0.0 42-53 131 6 4.6 16 12.2 109 83.2 0 0.0 54-59 32 1 3.1 3 9.4 28 87.5 0 0.0 Total 556 30 5.4 95 17.1 431 77.5 0 0.0

Underweight is an average indicator of wasting and stunting. The current underweight prevalence as observed by this survey was 22.5% and is considered high as per WHO classification. The national underweight prevalence was 13.6% in 20162. Bay agro pastoral is among the areas where the underweight burden is high.

Table 3.9: Prevalence of stunting based on height-for-age z-scores and by sex

All Boys Girls n = 533 n = 260 n = 273 Prevalence of stunting (162) 30.4 % (82) 31.5 % (80) 29.3 % (<-2 z-score) (25.0 - 36.4 (23.9 - 40.3 (23.5 - 35.9 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate stunting (104) 19.5 % (55) 21.2 % (49) 17.9 % (<-2 z-score and >=-3 z-score) (15.2 - 24.8 (14.7 - 29.4 (13.8 - 23.0 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (58) 10.9 % (27) 10.4 % (31) 11.4 % (<-3 z-score) (8.2 - 14.3 (7.2 - 14.7 (7.7 - 16.5 95% C.I.) 95% C.I.) 95% C.I.)

Table 3.10: Prevalence of stunting by age based on height-for-age z-scores

Severe Moderate Normal stunting stunting (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (mo) no. 6-17 147 18 12.2 34 23.1 95 64.6 18-29 114 25 21.9 21 18.4 68 59.6 30-41 113 9 8.0 22 19.5 82 72.6 42-53 127 6 4.7 22 17.3 99 78.0 54-59 32 0 0.0 5 15.6 27 84.4 Total 533 58 10.9 104 19.5 371 69.6

Child stunting is the phenomenon of children being too short for their age, which is a measure of profound physical and cognitive underdevelopment (UNICEF). The stunting rate

2 FSNAU 2016 Post Gu’ Assessment

19 in Baidoa (30.4%) was found to be high as per WHO classification. As shown in the table above, severe degree of stunting (21.9%) was observed among the 18-29 age group. Boys are slightly more stunted than girls.

While the national stunting prevalence is 10% according to FSNAU’s 2016 Post Gu’ Assessment, Baidoa has consistently recorded high stunting rates as opposed to the rest of the country. For instance, in the same assessment, Bay agro pastoral-current livelihood zone-reported high (>30-39%) prevalence. Importantly, age determination is a major issue in Somalia and since age is one of the key parameters involved stunting, the high stunting in Baidoa could be attributed to possible errors in age determination. However, different stakeholders such as FSNAU and SCI have found almost the same high prevalence in Baidoa and Bay agro pastoral again and again. This does not negate the fact that stunting is a widespread public health problem in Baidoa.

Table 3.13: Mean z-scores, Design Effects and excluded subjects

Indicator n Mean z- Design z-scores z-scores scores ± Effect (z- not out of SD score < -2) available* range Weight-for- 546 -0.55±1.11 1.28 0 19 Height Weight-for-Age 556 -1.05±1.18 1.98 0 9 Height-for-Age 533 -1.25±1.33 1.96 0 32 * contains for WHZ and WAZ the children with edema.

3.2 Mortality results (retrospective over x months/days prior to interview) The mortality assessment was conducted in all the randomly selected households with or without under-five children using the mortality individual questionnaire. Analysis was done using ENA and the following presents the results.

Table 3.14: Mortality rates

Mortality rate Baidoa WHO emergency Threshold

CMR3(95% CI) 0.40/10000/day 1/10,000/day

U5MR4 (95% C 0.36/10000/day 2/10,000/day

The mortality rates are not significant for Baidoa district and are similar to FSNAU’s Post Gu’ Findings from Bay agro-pastoral which reported CMR and U5MR of 0.35 and 0.37 respectively

3 total deaths/10,000 people / day 4 deaths in children under five/10,000 children under five / day

Other Demographic Details Summary

Baidoa Average household size 6.1 Mid Interval Population Size 2167.5 Percentage of children under five 28.5 Birth Rate 0.99 In-migration Rate (Joined) 1.79 Out-migration Rate (Left) 1.04

3.3 Children’s morbidity

Table 3.15: Prevalence of reported illness in children in the two weeks prior to interview (n=)

6-59 months Prevalence of reported illness 22.56% (19.28-26.20, 95% C.I.)

The reported morbidity is 22.56% which is considered high Figure 3.16: Symptom breakdown in the children in the two weeks prior to interview (n=)

3.3.1 Symptoms breakdown

Symptoms Breakdown 50 45 40 35 30 25 20 15 10 5 0 ARI Diarrhoea Malaria Skin infection Measles Stomache

As highlighted in the figure above, ARI followed by Malaria and then Diarrhea were the symptoms most observed in under5 children in Baidoa district.

3.3.2 Health Seeking Behavior

Health Seeking Behaviour

Bought medicine from shop Bought meicine from Pharmacy Health Facility Nowhere Traditional Healer

From the figure above, it is clear that 51% sought medical help from the nearest health facility even though 11% bought medicine from a shop. Overall it appears the residents have a good health seeking behavior.

3.4 Vaccination Results

Table 3.17: Vaccination coverage: BCG for 6-59 months and measles for 9-59 months

Baidoa [95% Conf.Interval] No 33% 29% 37% Yes by card 4% 3% 6% Yes by recall 63% 59% 68%

Polio

Baidoa [95% Conf.Interval] No 13% 11% 17% Yes by card 4% 3% 6% Yes by recall 82% 79% 85%

3.5 Vitamin A and Deworming

VAS is estimated at 53.5% (n=200) but still falls below the recommended WHO Coverage of 80%. On the other hand, the deworming coverage rate is 45% which is similarly low

Baidoa [95% Conf. Interval]

22 No 46.5% 26.5% 66.5% Vitamin A Yes 53.5% 33.5% 73.5% No 55% 35% 75% Deworming Yes 45% 25% 65%

3.6 Infant and young child feeding practices

Optimal IYCF practices are critical to proper child growth particularly children less than 2 years of age. This survey assessed three critical indicators namely timely initiation of breastfeeding, minimum dietary diversity as well as minimum meal frequency. It is noteworthy that the following proxy indicators were used to estimate the level of IYCF practices in Baidoa and hence there was no need to do a separate sample size calculation for IYCF practices.

3.6.1 Early initiation breastfeeding

Both UNICEF and WHO recommend that infants be put to the breast within the first hour after birth. This practice has profound benefits that go beyond the baby. For instance, UNICEF notes that breastfeeding immediately after delivery helps reduce post-partum hemorrhage among other benefits.

The survey showed that only 66% of children aged below 2 years were introduced to the breastmilk within the first hour after birth which is below the 80% threshold recommended by WHO. However, this is a slight increase from the KAP Survey conducted in late 2017 by SCI in Baidoa which indicated a figure of 60% but below the estimation rate for both national (80%) and SCS(75%) as per FSNAU 2016 Survey as shown in the table below

Timely initiation

Don't know

Within 1st day

Within 2hours_birth

Within 1 hour a_birth

0 10 20 30 40 50 60 70

2016 FSNAU SURVEY 2017 Baidoa KAP 2019 Baidoa National SCS Survey SMART Early initiation of 80% 75% 60% 66% Breastfeeding

3.6.2 Minimum dietary diversity

Minimum dietary diversity is defined as proportion of children 6–23 months of age who receive foods from 4 or more food groups5. The survey indicates that 48% of the children aged 6-23 months(n=200) managed to meet the minimum dietary diversity. This figure is higher than the estimate in the 2017 IYCF KAP survey which was 30.55%. it is also higher than the national estimate which was 15% in the 2016 FSNAU Survey as shown in the table below.

2016 FSNAU SURVEY 2017 Baidoa KAP 2019 Baidoa National SCS Survey SMART Minimum dietary 15% 21% 30.55% 48% diversity

In terms of the specific food groups consumed, the survey found out that dairy products (81%), grains, roots and tubers (79%) and to a lesser extent legumes and nuts (53.5%) were the most consumed food groups. On the other hand, the least consumed food groups were vitamin A rich fruits and vegetables (27.5%) and eggs (17.5%).

DIETARY DIVERSITY AMONG

CHILDREN 6-23MONTHS

81%

79%

53.50%

45.50%

32.50%

27.50% 17.50%

5 Indicators for assessing infant and young child feeding practices-WHO

3.6.3 Minimum meal Frequency

Minimum meal frequency is defined as the proportion of breastfed and non-breastfed children 6–23 months of age who receive solid, semi-solid, or soft foods (but also including milk feeds for non-breastfed children) the minimum number of times or more.

The survey results show that only 30.5% met the minimum meal frequency. This figure is well below the national average of 69% and also slightly lower than the estimate (34.98%) in the 2017 KAP survey as shown in the table below

2016 FSNAU SURVEY 2017 Baidoa KAP 2019 Baidoa National SCS Survey SMART Minimum meal 69% 66% 34.98% 30.5% frequency

3.7 Maternal Nutrition

Maintaining good nutrition and healthy diet during and after pregnancy are critical for the health of the mother and the infant. Both pregnancy and lactation place high demands on maternal stores of energy, protein and other nutrients. Nonetheless, some nutrient requirements such as iron and folic are difficult to obtain from food sources thus their recommendation in the form of supplements.

In our current survey, we sought to find out the physiological status of the women in the reproductive age of between 15-49 years. The survey established 304 women of which, 51% were lactating while 13% were pregnant. The survey went further to assess maternal nutrition by way of MUAC. The results indicated that about 3% were malnourished while a further 9% were at risk of malnutrition. Overall, the overwhelming majority (88%) were normal and well nourished.

Baidoa Malnourished <21cm 2.63% at risk 21-23cm 9.21% well-nourished >23cm 88.16%

3.8 Food security and Livelihoods

25 Household Dietary Diversity_Baidoa

3.07%

22.63%

74.30%

Low_HDD Medium_HDD Acceptable_HDD

3.9 Water, Sanitation and Hygiene

3.9.1 Main water source

Borehole Hand-pump 3.07 Burqad 0.56 Shallow Well Hand-pump 1.4 Unprotected Shallow Well 49.44 Water Kiosk 9.78 Water Trucking 2.51 tap 33.24

From the above table, majority of the respondents (49.4%) appear to obtain their water from unprotected shallow wells which are susceptible to possible contamination. Only 33.74% appear to have access to tap water up from 25.7%, which had access to tap water as of December 20176

3.9.2 Hand washing practices

6 SCI IYCF KAP Survey December 2017

26 Handwashing times 60

50 50.43 40

30 29.51 20

16.62 10 3.44 0 1 time 2 times 3 times 4 times

3.9.3 Defecation practices

Chart Title

near house

bushopen

Latrine outside

Latrinei n house

0 10 20 30 40 50 60 70

27 4. Discussion

The GAM rate by WHZ in Baidoa indicates a serious prevalence of malnutrition. Data from FSNAU over the past five seasons also shows similar rates. However, what is of particular concern is the prevalence of stunting in Baidoa. The current rate (30.4%) is considered high according to WHO classification. The age group that was found to have severe degree of stunting was the 18-29. This could be as a result of poor or sub-optimal IYCF practices. In the same vein, key indicators for IYCF measured in our survey indicates poor uptake of IYCF practices. For instance, only 48% of the children aged 6-23 months (n=200) managed to meet the minimum dietary diversity. On the other hand, only 30.5% managed to meet the minimum meal frequency against the national average of 69%

Further, a substantial number of the respondents (49%) indicated that their main source of water was unprotected shallow wells, which are prone to contamination. Baidoa is a water scarce urban area with perennial water scarcity. This contributes to the outbreak of acute watery diarrhea (AWD). About 11% of the children assessed had symptoms of diarrhea in the two weeks preceding the survey.

4.1 Causes of malnutrition

While the Prevalence of Wasting has remained steady i.e. serious levels of GAM in Baidoa for the last five seasons or so, what is also apparent is the existence of chronic malnutrition, particularly stunting in the same district. The immediate causes may be related to inadequate dietary intake and disease, this survey showed no correlation between morbidity and malnutrition status. This then boils down to inadequate dietary intake and the absence of optimal IYCF practices.

The causes of stunting denote are largely proximal such as inadequate access to food, maternal nutritional status and to an extent micronutrient deficiencies as highlighted in the figure below. Limited and improper IYCF practices are also be part of the causes of stunting given that child stunting can happen in the first 1000 days after conception.

A simplified conceptual model on stunting7

7 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5406759/figure/czw184-F1/

28 5. Conclusion

Baidoa appears to have the prevalence of both acute and chronic malnutrition. The current rates are staggering and show long-term prevalence, in particular, for the last 3 years if data by FSNAU is anything to go by. The causes of malnutrition in this case may be more underlying and requires interventions that go beyond short-term strategies. For instance, the national stunting prevalence is 10% as per FSNAU’s 2016 Assessment and hence not considered an issue in Somalia. However, the rates are very high in Baidoa as captured repeatedly by different partners in assessments undertaken over the last three years. In this regard, an in-depth investigation on the causes of stunting in Baidoa and bay region at large may be necessary if interventions are to be tailored according to the community needs.

6. Recommendations

Finding Explanation Recommendation Priority Responsible

Serious levels of GAM rates in baidoa Sustain existing Immediate SCI/Cluster GAM. indicates serious programs and improve levels sustained on recruitment over the past three years. High stunting The prevalence of Upscale IYCF practices rates stunting is high especially through above 30% and has increased breastfeeding remained so over and complementary the past three years feeding counselling at if data from this both household and at survey and other community level assessments are anything to go by Undertake an in-depth investigation on the causes of stunting in Baidoa and bay region at large.

If possible, undertake the cost of the diet assessment in bay agro- pastoral in order to propose low-cost nutritious diets.

30 7. References

1. FSNAU: Somalia 2019 Post Gu FSNAU FEWS-NET-Technical Release 2. FSNAU 2016 Post Gu Somalia Food Security and Nutrition Analysis 3. FSNAU 2016 Somali Infant and Young child Nutrition Assessment. 4. http://smartmethodology.org/survey-planning-tools/ 5. Sphere handbook (2011). The sphere project – Humanitarian charter and minimum standards in humanitarian response. 6. SCI: IYCF KAP Survey in Baidoa and Beletweine Districts December 2017 7. UNFPA- Population-Estimation-Survey-of-Somalia-PESS-2013-2014 8. Emergency Nutrition Assessment-Save the Children- Guideline for field workers. 9. SMART Methodology Manual EN (2006)- Measuring Mortality, Nutritional Status, and Food Security in Crisis Situations 10. Sampling Methods and Sample Size Calculation for the SMART Methodology (2012) 11. Indicators for assessing infant and young child feeding practices Part 1, definitions who UNICEF 2007 12. Somalia: WHO and UNICEF estimates of immunization coverage: 2016 estimations. 13. Wash Cluster Somalia Guide to WASH Cluster Strategy and Standards Also known as Strategic Operational Framework (SOF) 2015

31 8. Appendices

8.1 Appendices 1: Baidoa Plausibility report

Plausibility check for: Baidoa_complete ENA and Mortality.as

Standard/Reference used for z-score calculation: WHO standards 2006 (If it is not mentioned, flagged data is included in the evaluation. Some parts of this plausibility report are more for advanced users and can be skipped for a standard evaluation)

Overall data quality

Criteria Flags* Unit Excel. Good Accept Problematic Score

Flagged data Incl % 0-2.5 >2.5-5.0 >5.0-7.5 >7.5 (% of out of range subjects) 0 5 10 20 5 (3.4 %)

Overall Sex ratio Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 0 (p=0.333)

Age ratio(6-29 vs 30-59) Incl p >0.1 >0.05 >0.001 <=0.001 (Significant chi square) 0 2 4 10 0 (p=0.101)

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 5 (1.11)

Skewness WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 0 (-0.16)

Kurtosis WHZ Excl # <±0.2 <±0.4 <±0.6 >=±0.6 0 1 3 5 1 (-0.26)

Poisson dist WHZ-2 Excl p >0.05 >0.01 >0.001 <=0.001 0 1 3 5 0 (p=0.246)

OVERALL SCORE WHZ = 0-9 10-14 15-24 >25 11 %

The overall score of this survey is 11 %, this is good8.

8 Age determination was an issue in Baidoa as is the case in the rest of Somalia. On the other hand, Baidoa district has significantly higher stunting rates and this coupled with the challenge in the age determination could be responsible for the flagged data. The SD is however within the range of 0.8-1.2.