2019 somalia micronutrient survey - unicef

THE FEDERAL REPUBLIC OF SOMALIA

Somali Micronutrient Survey 2019

Xog la helaa talo la helaa - Information for better decisions

SOMALIA MICRONUTRIENT SURVEY – 2019

SOMALIA MICRONUTRIENT SURVEY 2019

FINAL REPORT – AUGUST 2020

Recommended Citation: Ministry of Health FGS, FMS, Somaliland, UNICEF,

Brandpro, GroundWork. Somalia Micronutrient Survey 2019. Mogadishu,

Somalia; 2020.


i

Government Partner

Funding agencies

KINGDOM OF BELGIUM

Implementing agency


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TABLE OF CONTENTS

INVESTIGATORS AND INSTITUTIONAL AFFILIATIONS ......................................................... x

ACKNOWLEDGEMENTS .................................................................................................... xi

ABBREVIATIONS ............................................................................................................. xii

EXECUTIVE SUMMARY ...................................................................................................... 1

1. INTRODUCTION ........................................................................................................... 9

1.1. Nutritional situation of young children and women in Somalia ............................... 9

1.2. Rationale for the survey ............................................................................................ 9

1.3. Research goal .......................................................................................................... 10

1.4. Primary objectives ................................................................................................... 10

1.5. Secondary objectives ............................................................................................... 11

1.6. Implementation of the SMS 2019 ........................................................................... 11

2. METHODOLOGY ........................................................................................................ 12

2.1. Survey design ........................................................................................................... 12

2.2. Survey participants .................................................................................................. 13

2.3. Sample size determination ...................................................................................... 13

2.4. Primary outcomes ................................................................................................... 15

2.5. Ethical considerations ............................................................................................. 15

2.6. Field work and data collection ................................................................................ 16

2.6.1. Instrument pre-testing, training of survey teams, and field testing ........ 16

2.6.2. Community mobilization and sensitization .............................................. 18

2.6.3. Selection of PSUs ...................................................................................... 18

2.6.4. Changing security status and replacement PSUs ..................................... 19

2.6.5. Household listing and random selection of households .......................... 19

2.6.6. Field work (interviews) ............................................................................. 19

2.6.7. Field work (anthropometry and phlebotomy) .......................................... 21

2.6.8. Cold chain and processing of blood samples ............................................ 22

2.6.9. Supervision of fieldwork ........................................................................... 22

2.7. Definitions of indicators and specimen analysis ..................................................... 22

2.7.1. Anthropometric indicators ....................................................................... 22

2.7.2. Urinary iodine and drinking water iodine concentrations ....................... 23

2.7.3. Blood specimens ....................................................................................... 24

2.7.4. Analysis of iodine in salt ........................................................................... 26

2.8. Data management and analysis .............................................................................. 26

2.8.1. Data entry ................................................................................................. 26

2.8.2. Data monitoring ........................................................................................ 26

2.8.3. Data analysis ............................................................................................. 26

2.8.4. Case definitions of key indicators and nutritional deficiencies ................ 27


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3. RESULTS .................................................................................................................... 29

3.1. Response rates for households, children, and women ........................................... 29

3.2. Household Characteristics ....................................................................................... 31

3.2.1. Demographic characteristics .................................................................... 31

3.2.2. Displacement and relocation .................................................................... 33

3.2.3. Agricultural activities and livestock ownership ........................................ 36

3.2.4. Household financial access and remittances ............................................ 36

3.2.5. Cooking fuel and household lighting ........................................................ 38

3.2.6. Water and sanitation ................................................................................ 38

3.2.7. Bednet ownership and use ....................................................................... 40

3.2.8. Food insecurity ......................................................................................... 42

3.2.9. Salt iodine concentration .......................................................................... 44

3.2.10. Drinking water iodine concentration ........................................................ 45

3.2.11. Bouillon cube use and consumption ........................................................ 48

3.3. Children ................................................................................................................... 50

3.3.1. Characteristics .......................................................................................... 50

3.3.2. Low birthweight ........................................................................................ 51

3.3.3. Recent illness and treatment .................................................................... 53

3.3.4. Infant and young child feeding indicators ................................................ 53

3.3.5. Consumption of vitamin and mineral supplements ................................. 55

3.3.6. Stunting ..................................................................................................... 59

3.3.7. Wasting ..................................................................................................... 62

3.3.8. Underweight ............................................................................................. 65

3.3.9. Mid-upper arm circumference ................................................................. 68

3.3.10. Malaria ...................................................................................................... 71

3.3.11. Anemia, iron deficiency, and iron deficiency anemia .............................. 71

3.3.12. Vitamin A deficiency ................................................................................. 78

3.3.13. Zinc deficiency .......................................................................................... 80

3.3.14. Hemoglobinopathies ................................................................................ 81

3.4. All Women ............................................................................................................... 83

3.4.1. Characteristics .......................................................................................... 83

3.4.2. Educational attainment ............................................................................ 84

3.4.3. Supplement consumption ........................................................................ 88

3.4.1. Coffee and tea consumption .................................................................... 89

3.4.2. Dietary diversity ........................................................................................ 89

3.5. Non-pregnant women ............................................................................................. 91

3.5.1. Anthropometry ......................................................................................... 91

3.5.2. Malaria ...................................................................................................... 95

3.5.3. Anemia, iron deficiency, and iron deficiency anemia .............................. 95

3.5.4. Vitamin A deficiency ............................................................................... 102

3.5.5. Folate and Vitamin B12 deficiencies....................................................... 102

3.5.6. Median urinary iodine concentration ..................................................... 104


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3.6. Pregnant women ................................................................................................... 106

3.6.1. Characteristics ........................................................................................ 106

3.6.2. Mid-upper arm circumference ............................................................... 107

3.6.3. Malaria .................................................................................................... 107

3.6.4. Anemia .................................................................................................... 107

3.6.5. Median urinary iodine concentration ..................................................... 109

4. COMPARISON BETWEEN THE 2009 AND THE 2019 SURVEYS ..................................... 111

5. DISCUSSION ............................................................................................................ 115

5.1. Strength and limitations ........................................................................................ 115

5.2. Household level findings ....................................................................................... 116

5.3. Child stunting, wasting, underweight and overweight ......................................... 117

5.4. Underweight, overweight and obesity in women ................................................. 118

5.5. Anemia and micronutrient status ......................................................................... 118

5.5.1. Anemia .................................................................................................... 118

5.5.2. Iron deficiency ........................................................................................ 119

5.5.3. Vitamin A deficiency ............................................................................... 120

5.5.4. Iodine deficiency ..................................................................................... 120

6. Recommendations .................................................................................................. 121

6.1. Carry out situation analysis to increase iodine intake in Somaliland ................... 121

6.2. Reduce undernutrition in children and women .................................................... 121

6.3. Reduce overweight and obesity in women ........................................................... 122

6.4. Reduce anemia and iron deficiency in children and women ................................ 122

6.5. Reduce vitamin A deficiency in children ............................................................... 123

7. REFERENCES ............................................................................................................ 124

8. APPENDICES ............................................................................................................ 130

8.1. LIST OF SELECTED ENUMERATION AREAS ............................................................. 130

8.2. A PRIORI SAMPLE SIZE CALCULATIONS ................................................................. 136

8.3. ETHICAL APPROVALS ............................................................................................. 137

8.4. INFORMATION SHEET ............................................................................................ 140

8.5. CONSENT FORMS IN ENGLISH AND SOMALI ......................................................... 143

8.6. REFERAL FORM ...................................................................................................... 145

8.7. TEAMS, TEAM MEMBERS, AND SUPERVISORS ...................................................... 146

8.8. SURVEY QUESTIONNAIRES .................................................................................... 148

8.9. ADDITIONAL HOUSEHOLD TABLES ........................................................................ 149

8.10. COMPARISON OF SERUM RETINOL AND RETINOL BINDING PROTEIN ................. 156

8.11. CHILD ANTHROPOMETRY QUALITY SUMMARY .................................................... 158


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LIST OF FIGURES

Figure 1. States of Somalia and geographic strata of SMS, Somalia 2019 ............................... 12

Figure 2. Participation diagram of households, women and children, Somalia 2019 .............. 30

Figure 3. Main reason households that relocated in past 30 years moved from their

previous residence, by state, Somalia 2019 .............................................................. 36

Figure 4. Mean amount of remittances received in past 3 months, Somalia 2019 ................. 37

Figure 5. Proportion of water and sanitation variables, by State, Somalia 2019 ..................... 39

Figure 6. Proportion of bednets used the previous night amount households owning

bednets, by state, Somalia 2019 ................................................................................ 41

Figure 7. Proportion of iodine in salt at different levels, Somalia 2019 ................................... 45

Figure 8. Distribution of iodine concentration in salt samples, Somalia 2019 ......................... 45

Figure 9. Proportion of iodine in water at different levels (µg/L), Somalia 2019 ..................... 47

Figure 10. Distribution of water iodine concentration, Somalia 2019 ....................................... 48

Figure 11. Brands of bouillon cube usually consumed by households, by state, Somalia

2019............................................................................................................................ 49

Figure 12. Proportion of children weighed at birth, by state and residence, Somalia 2019 ...... 51

Figure 13. Proportion of children 6-59 months registered in a feeding program, by

residence, state, and wealth quintile, Somalia 2019 ................................................. 57

Figure 14. Histogram of height-for-age z-scores of the SMS 2019 compared to the WHO

growth curve, preschool-age children, Somalia 2019 ............................................... 62

Figure 15. Histogram of weight-for-height z-scores of the SMS 2019 compared to the WHO


Figure 16. Histogram of weight-for-age z-scores of the SMS 2019 compared to the WHO


Figure 17. Histogram of mid upper arm circumference (MUAC) in children 6-59 months of

age, Somalia 2019 ...................................................................................................... 69

Figure 18. Venn diagram showing overlap between anemia and iron deficiency in children

6-59 months of age, Somalia 2019 ............................................................................ 76

Figure 19. Histogram of hemoglobin concentration (g/L) in children 6-59 months of age,

Somalia 2019 .............................................................................................................. 76

Figure 20. Distribution of retinol binding protein concentration, adjusted for inflammation

using the BRINDA approach, children 6-59 months of age, Somalia 2019 ................ 79

Figure 21. Distribution of serum zinc concentrations in children 6-59 months of age,

Somalia 2019 .............................................................................................................. 80

Figure 22. Educational attendance of pregnant and non-pregnant women, by state,

Somalia 2019 .............................................................................................................. 85


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Figure 23. Proportion of pregnant and non-pregnant women that consumed various

vitamin and mineral supplements in past 6 months, by state, Somalia 2019 ........... 88

Figure 24. Frequency of coffee and tea consumption by pregnant and non-pregnant

women, by state, Somalia 2019 ................................................................................. 89

Figure 25. Prevalence of underweight, normal weight, and overweight and obesity in non-

pregnant women, Somalia 2019 ................................................................................ 91

Figure 26. Prevalence of normal weight, overweight, and obesity in non-pregnant women

by age group, Somalia 2019 ....................................................................................... 91

Figure 27. Venn diagram showing overlap between anemia and iron deficiency in non-

pregnant women of reproductive age, Somalia 2019 ............................................... 99

Figure 28. Histogram of hemoglobin concentration (g/L) in non-pregnant women of

reproductive age, Somalia 2019 .............................................................................. 100

Figure 29. Histogram of hemoglobin concentration (g/L) in pregnant women, Somalia

2019.......................................................................................................................... 109


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LIST OF TABLES

Table 1. Summary results of the Somalia Micronutrient Survey 2019...................................... 4

Table 2. Inclusion criteria by targeted population group ........................................................ 13

Table 3. Overview of key biomarker indicators, by population group* .................................. 16

Table 4. Clinical cut-off points and classifications for biomarker indicators ........................... 28

Table 5. Distribution of various demographic variables for participating households,

Somalia 2019 .............................................................................................................. 31

Table 6. Distribution of household composition of participating households, Somalia

2019............................................................................................................................ 32

Table 7. Educational level of household head for participating households, Somalia 2019

.................................................................................................................................... 33

Table 8. Proportion of households hosting of internally displaced persons and arrival of

IDPs in past 6 months, Somalia 2019 a ....................................................................... 34

Table 9. Proportion or mean number of years at current residence, Somalia 2019 .............. 35

Table 10. Proportion of agriculture and livestock variables for participating households,

Somalia 2019 .............................................................................................................. 37

Table 11. Distribution of cooking fuel and lighting variables for participating households,

Somalia, 2019 ............................................................................................................. 38

Table 12. Distribution of water and sanitation variables for participating households,

Somalia 2019 .............................................................................................................. 39

Table 13. Distribution of handwashing variables for participating households, Somalia

2019............................................................................................................................ 40

Table 14. Proportion of households owning at least one bednet, Somalia 2019 ..................... 41

Table 15. Household food insecurity score (HFIAS) categories, by residence, state, and

wealth quintile, Somalia 2019 ................................................................................... 43

Table 16. Proportion of salt specimens with an iodine concentration ≥15 ppm and mean

salt iodine concentration in participating households, Somalia 2019 ...................... 44

Table 17. Median water iodine concentration in participating households, Somalia 2019 ..... 46

Table 18. Proportion of households that use bouillon cubes, Somalia 2019 ............................ 48

Table 19. Description of sampled children (0 - 59 months), Somalia 2019 .............................. 50

Table 20. Proportion of preschool age children born with low birthweight, with data from

health card or recall, Somalia 2019 ........................................................................... 52

Table 21. Proportion of preschool age children with caregiver-reported diarrhea, fever,

cough and measured inflammation, Somalia 2019 ................................................... 54

Table 22. Proportion of children with various infant and young child feeding indicators in

children less than 2 years of age, Somalia 2019 ........................................................ 55


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Table 23. Proportion of children 6-59 months of age consuming micronutrient powders,

RUTF/RUSF, and iron-fortified foods in past 24 hours, Somalia 2019 ....................... 56

Table 24. Percentage of children (6-59 months) that received vitamin A supplements in

the past 6 months, Somalia 2019 .............................................................................. 58

Table 25. Percentage of children (0-59 months) with stunting, Somalia 2019 ......................... 60

Table 26. Percentage of children (0-59 months) with wasting, Somalia 2019 .......................... 63

Table 27. Percentage of children (0-59 months) underweight, Somalia 2019.......................... 66

Table 28. Severe Acute Malnutrition (SAM) as measured by MUAC, by various

characteristics in children 6-59 months of age, Somalia 2019 .................................. 70

Table 29. Proportion of mild, moderate and severe anemia in children 6-59 months of

age, Somalia 2019 ...................................................................................................... 72

Table 30. Anemia, iron deficiency, and iron deficiency anemia in children 6-59 months of

age, Somalia 2019 ...................................................................................................... 74

Table 31. Anemia in children 6-59 months of age, by infection-related characteristics and

vitamin A and iron status, Somalia 2019 ................................................................... 77

Table 32. Proportion of children 6-59 months of age with vitamin A deficiency, by various

characteristics, Somalia 2019 .................................................................................... 78

Table 33. Sickle cell trait/disease, α-thalassemia (heterozygote and homozygote) in

children 6-59 months of age, Somalia 2019 .............................................................. 82

Table 34. Description of all sampled pregnant and non-pregnant women, Somalia 2019 ....... 83

Table 35. Proportion of women that completed primary school or higher, Somalia 2019 ...... 86

Table 36. Proportion of literate women, Somalia 2019 ............................................................ 87

Table 37. Proportion of women that consumed various vitamin and mineral supplements

in past 6 months, Somalia .......................................................................................... 88

Table 38. Proportion of pregnant and non-pregnant women with minimum dietary

diversity, Somalia 2019 .............................................................................................. 90

Table 39. Percentage of specific Body Mass Index (BMI) levels in non-pregnant women

(15-49 years), Somalia 2019 ....................................................................................... 93

Table 40. Proportion of mild, moderate and severe anemia in non-pregnant women,

Somalia 2019 .............................................................................................................. 96

Table 41. Anemia, iron deficiency, and iron deficiency anemia in non-pregnant women

(15-49 years), Somalia 2019 ....................................................................................... 97

Table 42. Anemia, iron deficiency, and iron deficiency anemia in non-pregnant women

(15-49 years) by micronutrient status, inflammation and nutritional status,

Somalia 2019 ............................................................................................................ 101

Table 43. Vitamin A deficiency in non-pregnant women (15-49 years), Somalia 2019 .......... 103


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Table 44. Median urinary iodine concentration in non-pregnant women (15-49 years),

Somalia 2019 ............................................................................................................ 104

Table 45. Description of pregnant women, Somalia 2019 ...................................................... 106

Table 46. Percentage undernourished by various characteristics in pregnant women,

Somalia 2019 ............................................................................................................ 107

Table 47. Anemia in pregnant women, Somalia 2019 ............................................................. 108

Table 48. Median urinary iodine concentration in pregnant women, Somalia 2019 ............. 110

Table 49. Comparison of analytic methods, biomarkers, and cutoffs using the 2009 and

2019 surveys, Somalia .............................................................................................. 112

Table 50. Comparison of key results between 2009 and 2019, Somalia ................................ 114


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INVESTIGATORS AND INSTITUTIONAL AFFILIATIONS

Principal Investigators (PI) Organization

Dr. James P Wirth GroundWork

Mr. Joshua Mbai Brandpro

Field Coordinators

Ms. Sundus Ibrahim Ali Brandpro

Mr. Hassan Abukar Brandpro

Ms. Zahra Hashi Brandpro

Mr. Abdi Dimbil Brandpro

Mr. Hassan Sacad Brandpro

Mr. Shucayb Muhamud Ministry of Health – Somaliland & Brandpro

Governmental partners

Ms. Kheyriya Mohamed Mohamud Ministry of Health & Human Services – Federal

Government of Somalia

Dr. Ahmed Muse Ministry of Health Development – Somaliland

Dr. Warsame Said Mohamed Ministry of Health Development – Puntland

Laboratory Coordinators

Dr. Ahmed Hashi Ministry of Health Development – Somaliland

Co-Investigators

Dr. Bradley Woodruff GroundWork

Mr. William Donkor GroundWork

Dr. Nicolai Petry GroundWork

Dr. Fabian Rohner GroundWork

Dr. Andrew Seal University College London (GroundWork consultant)

Ms. Yvonne Katambo BrandPro

Ms. Fatmata Sesay UNICEF

Ms. Melanie Galvin UNICEF

Ms. Hamda Omar Yussuf UNICEF

Mr. Mohamed Mohamoud Isse UNICEF

Mr. Abdirizak Osman Hussien UNICEF

Ms. Dorothy Nabiwemba UNICEF

Dr. Ezatullah Majeed UNICEF

Dr. John Ntambi UNICEF

Partners:

Federal Ministry of Health Development & Federal Ministry of Planning – Somalia

Ministry of Health Development & Ministry of Planning – Somaliland

Ministry of Health Development & Ministry of Planning – Puntland

Ministry of Health Development & Ministry of Planning – South West State

Ministry of Health Development & Ministry of Planning – Jubaland

Ministry of Health Development & Ministry of Planning – Galmadug

Ministry of Health Development & Ministry of Planning – Hirshabelle


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ACKNOWLEDGEMENTS

The Federal Ministry of Health and UNICEF Somalia would like to thank the governments of

United States of America (through the United States Agency for International Development) and

Belgium as well as other donors for the financial support in enabling the implementation of the

first national nutrition survey in Somalia over a decade.

The invaluable participation of the following institutions is also recognized. The Ministries of

Health Somaliland, and the Federal member States for providing data collectors, temporary

laboratory spaces, survey coordinators and their strong leadership and oversight in all aspects of

survey planning and implementation to survey planning which led to the successful completion

of the survey.

Special thanks to Ministry of Planning and Development for their invaluable contribution in

providing survey maps for primary sampling units areas and technical Geographic Information

Specialists for mapping clusters.

Special thanks to the survey consultancy firms (GroundWork and BrandPro) for the technical and

operational support in planning and implementing the survey, as well as data analysis and for

drafting of this report.

We would also like to thank the Laboratory of Human Nutrition at ETH (Zurich) for providing

Nanopure™ water samples and for temporary provision of freezer space, and all other

laboratories for analyzing the survey biomarkers.


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ABBREVIATIONS

AGP α-1-acid glycoprotein

BMI Body mass index

CRP C-reactive protein

DHS Demographic and Health Survey

EA Enumeration area

ELISA Enzyme linked immunosorbent assay

EU European Union

FANTA Food and Nutrition Technical Assistance project

HAZ Height-for-age z-score

Hb Hemoglobin

LRI Lower respiratory infection

ID Iron deficiency

IDA Iron deficiency anemia

MICS Multiple Indicator Cluster Survey

MDG Millennium Development Goal

MoH Ministry of Health

MUAC Mid-upper arm circumference

NCD Non-communicable disease

NPW Non-pregnant women (15-49 years)

ppm Parts per million

PSC Preschool-age children (0-59 months)

PW Pregnant women

RBP Retinol-binding protein

RDT Rapid diagnostic test

sTfR Soluble Transferrin receptor

SMS Somalia Micronutrient Survey

UNHCR United Nations High Commissioner for Refugees

UNICEF United Nations Children’s Fund

VAD Vitamin A deficiency

WAZ Weight-for-age z-score

WHO World Health Organization

WHZ Weight-for-height z-score


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EXECUTIVE SUMMARY

Introduction

For the past three decades, Somalia has suffered from multiple recurring natural and

manmade emergencies, including political instability, drought, famine, flood and others.

These events have adversely affected household food security and results in the internal

displacement and external migrations of hundreds of thousands of people. Chronic food

insecurity, poor-access to sanitation, diseases outbreak, sub optimal-feeding practices and

other factors have resulted in high levels of malnutrition.

A National Micronutrient Survey, conducted in 2009 [1], collected data on anemia, iron,

vitamin A and iodine status among women of reproductive age, preschool-age and school-age

children, along with markers of inflammation and of malarial parasitemia. Additionally,

anthropometric measurements were taken and information collected on infant and young

child feeding practices. The survey reported high prevalence of anemia in children (59%) and

non-pregnant women (47%). The prevalence rates of iron deficiency (ID; children = 59%; non-

pregnant women = 42%) and vitamin A deficiency (VAD; children = 33%; non-pregnant women

= 54%) were also high. Interestingly, very high median urinary iodine concentrations were

found in all groups (school-age children = 417.1μg/L; non-pregnant women = 325.1 μg/L),

despite the low proportion of household salt being adequately iodized. The survey revealed

very poor infant and young child feeding practices, with only 5.3% of children less than 6

months of age exclusive breastfeeding and one quarter of newborns experiencing early

initiation of breastfeeding.

The 2009 survey [1] found that just over 20% of women received postpartum vitamin A

supplementation and that around 45% of children under 5 years of age had received vitamin

A supplementation in the prior 6 months. While from a single study, the available data on

micronutrient status of women of reproductive age, preschool-aged (6-59 months of age) and

school-aged children (6-11 years of age) indicates that micronutrient deficiencies are a public

health concern.

Rationale & Objectives

Since 2010, UNICEF and other stakeholders operating in Somalia have established and

brought to scale several programs, in particular the semi-annual vitamin A supplementation

for children 6-59 months of age, micronutrient supplementation to pregnant and lactating

women, efforts to facilitate access to diarrhea management (zinc tablets, ORS), and delivery

of micronutrient powders to households with children aged 6-59 months.

These recently established initiatives, together with a need to better understand the

seemingly-contradictory iodine nutrition indicators in Somalia, justify renewed efforts for


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data collection. Thus, the Somalia Micronutrient Survey (SMS) 2019 aimed to provide a

comprehensive picture of micronutrient deficiencies in Somalia and to expand upon the

information collected as part of the 2009 survey. Data collected as part of the SMS 2019

provides up-to date information for future programme planning.

The target population groups of the SMS 2019 were children 0-59 months of age (6-59 months

for blood biomarkers), non-pregnant women of reproductive age (15-49 years), and pregnant

women. Data was collected from pregnant women when encountered in selected households,

but their small number precluded stratum-specific conclusions.

The 2019 Somalia Micronutrient Survey (SMS 2019) assessed numerous factors related to

micronutrient status in children 6-59 months, non-pregnant women 15-49 years of age, and

pregnant women. In particular, the SMS 2019 measured hemoglobin concentration and

malaria status in all groups, and assessed iron, vitamin A, and inflammation status in children

and non-pregnant women. In addition, the prevalence of thalassemias and sickle cell disease

or trait was measured in children, and iodine status was measured in non-pregnant and

pregnant women. Among children 0-59 months of age, the prevalence of chronic malnutrition

(stunting), acute malnutrition (wasting), underweight, and overweight and obesity was

measured using standard anthropometric indicators. The prevalence of chronic energy

deficiency and overweight and obesity in non-pregnant women by calculating body mass

index (BMI), and undernutrition assessed in pregnant women by measuring mid-upper arm

circumference (MUAC). Further, the proportion of households consuming adequately iodized

salt was measured. Lastly, the iodine concentration of drinking water was assessed on

household level.

In addition to these aforementioned indicators, the SMS 2019 evaluated infant and young

child feeding practices, and assessed the relative importance of the likely causes of anemia.

Further, the SMS 2019 collected data on other factors that are frequently associated with

health and nutrition status, such as household wealth, household-level water, sanitation, and

hygiene practices, women's education, and other household-level and individual-level factors.

In a subsample of non-pregnant women, vitamin B12 and folate was assessed, and in a

subsample of children, serum zinc was measured. The number of samples measured was not

large enough to yield stratum-specific precision, but provided some idea about the magnitude

of these deficiencies.

Design

The SMS 2019 was a national cross-sectional survey using six independent strata: 1)

Somaliland, 2) Puntland, 3) Hirshabelle and Galmudug states together; 4) Jubaland and South-

West states together, 5) Banaadir state, and 6) internally displaced persons (IDPs) in all five

aforementioned strata combined. Each of the six strata had 25 clusters, and each cluster

consisted of 16 randomly-selected households. In settled strata (i.e. strata 1-5), systematic


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random sampling with probability proportional to population size was used based on recently

established household lists. To select IDP camps, UNHCR’s IDP site assessment served as the

sampling frame, as it contained all IDP settlements throughout Somalia and contained

information on the number of households residing in each settlement. For the selected settled

clusters, the teams attempted to obtain up to date lists from the village authorities; if not

available, teams proceeded to create such lists by conducting a rapid cluster-census. From

these lists, 16 households in each settled cluster were randomly selected.

The SMS 2019 collected individual information during interviews, anthropometric

measurements, blood specimens, urine and water samples. For children, selection of 2400

households was expected to result in the recruitment of about 1400 survey subjects for the

entire survey sample, of whom 1250 were expected to have biologic specimens collected. In

addition, selection of 2400 households was expected to result in the recruitment of 2150 non-

pregnant women, which was more than twice that needed to reach the desired precision for

most indicators measured in this subgroup. Therefore, data was only obtained from non-

pregnant women in a randomly selected subset of ½ of selected households. This was

expected to result in the collection of 960 blood specimens from women.

Results

In this executive summary, only national estimates are presented in Table 1. However, this

table refers readers to the corresponding tables in the report that contain more detailed

results. In addition, readers can also examine the Table 49 & Table 50 as these compare the

methods by the 2009 survey and SMS 2019 and compare the national-level results from the

two surveys.


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Table 1. Summary results of the Somalia Micronutrient Survey 2019

Target group Indicator a Result Table b

Households

Adequately iodized salt (>15ppm) 7.0% Table 16

Median iodine concentration in salt (ppm) 5.1

Median iodine concentration in water (µg/L) 58.8 Table 17

Children c

Anemia 43.4% Table 30

Mild anemia 21.4%

Table 29 Moderate anemia 20.5%

Severe anemia 1.5%

Iron deficiency 47.2% Table 30

Iron deficiency anemia 28.6%

Vitamin A deficiency 34.4% Table 32

Zinc deficiency 5.0% --

Malaria infection (RDT) 0.5% --

Stunting (HAZ≤ -2.0) 17.2% Table 25

Wasting (WHZ≤ -2.0) 11.0% Table 26

SAM (MUAC<115mm) 1.7% Table 28

Underweight 12.6% Table 27

Non-pregnant women (15-49 years of age)


Mild anemia 19.7%

Table 40 Moderate anemia 18.0%

Severe anemia 2.6%

Iron deficiency 49.7% Table 41

Iron deficiency anemia 26.3%

Vitamin A deficiency 10.7% Table 43

Folate deficiency 35.1% --

Vitamin B12 deficiency 36.9% --

Median urinary iodine concentration (µg/ L) 261.3 Table 44


Underweight (BMI<18.5) 10.9%

Table 39 Overweight (BMI≥25<30) 24.4%

Obesity (BMI≥30) 15.1%

Pregnant women (any age)


Median urinary iodine concentration (µg/ L) 369.4 Table 48


Underweight (MUAC<23cm) 11.3% Table 46 a See text of method section for case definitions; b Refer to the table indicated for more detailed analysis of the outcome, including group-specific results by age, region, wealth quintiles

and other analyses. C Anthropometric indicators measured in children 0-59 months of age and biological indicators is children 6-59 months


5

Discussion While only a small proportion of the salt in Somalia is adequately iodized, the median urinary

iodine concentrations in non-pregnant and pregnant women indicate iodine sufficiency. Sub-

group analyses show that in some states of Somalia, particularly in Hirshabelle and Puntland,

women are at risk for iodine excess (median urinary iodine concentration >500µg/L), which

may result in transient iodine-induced hyperthyroidism (goiter). The high urinary iodine

concentration found in women in most states can be ascribed to the high iodine

concentration found in drinking water. Although the water can provide large amounts of

iodine to women and prevent iodine deficiency, the iodine concentration in the water varies

widely (0-750 µg/L) even within the same state, which renders it nearly impossible to

geographically target interventions to reach women with insufficient iodine intake. Only in

Somaliland does the median urinary iodine concentration indicate iodine insufficiency. This

relatively low iodine intake likely stems from differences in geology that resulted in less iodine

in Somaliland’s drinking water.

Overall, the nutritional situation in Somali children poses a public health problem with

moderate to high significance. The situation improved compared to the Micronutrient Survey

conducted in 2009; specifically stunting declined by about 6 percentage points, wasting by

about 3 percentage points and underweight by about 7 percentage points. However, the

situation is more severe in certain sub-groups. The SMS results show that the situation in

South West is of particular concern; this state is classified with the highest public health

importance for all assessed nutritional indicators. South West is also the state with the highest

food insecurity and is among the states with the highest prevalence of anemia and iron and

vitamin A deficiencies. These results may indicate a lack of nutritious food. In addition, South

West has been hit by drought, which caused considerable damage in the agricultural sector

and led to increased movement of people from rural areas to urban and peri-urban centers.

Sub-group analyses indicate that poor feeding in early childhood as well as poor

complementary feeding contribute to the high stunting prevalence in young children.

The SMS estimates that nearly 40% of non-pregnant women 15-49 years of age are

overweight or obese. The prevalence increases with age and more than 80% of women 40-44

years of age are either overweight or obese. Overweight and obesity are key risk factors of

type-2 diabetes mellitus as well as cardiovascular diseases and various cancers. As a result,

Somalia may expect a rise in the incidence of several chronic diseases associated with

overweight. At the same time about every tenth woman in Somalia is underweight, clearly

demonstrating the presence of the double burden of malnutrition in Somalia.

According to WHO classification, the prevalence of anemia is very high and denotes a severe

public health problem in Somali children and women. Risk factor analyses show that in both

population groups, anemia is mainly driven by nutritional factors, such as iron and vitamin A

deficiencies. About 65% of the anemic children and women also have ID, which is a larger

proportion than reported from most countries in Sub-Saharan Africa. For children, sub-clinical

inflammation was also identified as one of the risk factors of anemia. Bivariate analysis also


6

showed that neither diarrhea, fever, nor cough in the past 2 weeks were associated with

anemia. Although recent diarrhea, fever, and cough could result in inflammation, no

significant associations were found between inflammation category and these three recent

illnesses, or any inflammation and the three recent illnesses. . Based on its low prevalence,

and malaria is not a primary risk factor of anemia during the dry seasons in Somalia. As the

incidence of malaria can rise quickly during rainy seasons, malaria may be risk factor of

anemia at other times of the year. Further research is required to estimate the prevalence

and severity of malaria during rainy periods and the strength of the association between

malaria and anemia.

The national prevalence of ID in children (47.2%) and non-pregnant women (49.7%) is

relatively high. Compared to the survey in 2009, the prevalence is more than 20% percentage

points lower in children and almost 20% percentage points higher in women. However,

comparison of the results of the two surveys should be done with caution since different

biomarkers were used to assess ID. Moreover, the prevalence of VAD denotes a severe public

health problem in children (>30%) and a moderate public health problem in women (>10%).

In children the prevalence remained almost unchanged compared to 2009 (34.5% vs. 33.3%).

Though the same biomarker (i.e. RBP) was used in both surveys, the deficiency cut-offs used

in women were not comparable, and thus the VAD prevalence comparisons for women were

not conducted. With the exception of Galmudug, where VAD can be classified as moderate

public health significance, VAD in children is severe in all states. The main reasons for

micronutrient malnutrition are low micronutrient intakes. This is often the case in poor

populations, which subsist on a monotonous diet which is low in minerals and vitamins. Less

than 20% of the surveyed children 6-23 months of age had a diet with minimum diversity. The

low consumption of fortified foods probably also contributes to the high prevalence of ID.

Recommendations Carry out situation analysis to increase iodine intake in Somaliland:

Households and individuals in most states (i.e. Puntland, Galmudug, Hirshabelle, Banaadir,

South-West, Jubaland) get sufficient amounts of iodine through drinking water. As such, itis

thus not recommended to increase the coverage of iodized salt in these states as the

additional iodine intake from iodized salt may lead to excessive intake and potential health

consequences. Unlike other states, the iodine status of women in Somaliland showed

insufficient intake of iodine. However, some women surveyed in Somaliland had excessive

intake of iodine. To understand the situation more thoroughly, it is recommended that a

spatial and situational analyses of iodine nutrition be conducted in Somaliland to identify (if

possible) a) areas where iodine intake is insufficient, and b) determine if improving expanding

the coverage of iodine-fortified foods (e.g. salt, bouillon cubes) would be feasible.

Reduce undernutrition in children and women:

While national prevalence of stunting and wasting denote mild and serious public health

problems, respectively, the situation is more severe in some states of the country and


7

warrants immediate attention. Inappropriate feeding practices of infants and young children

may contribute to high prevalence of undernutrition, particularly wasting. Exclusive

breastfeeding in the first 6 months of life should be promoted and supported to ensure

adequate nutrition and ensure that overall immune status of infants and young children is

improved and children are better able to prevent and recover from diarrhea, pneumonia, and

other infections. The consumption of healthy, diversified diets in the complementary feeding

period (6-23 months) should also be promoted to consistently improve the diversity and

quality of diet for young children. Adequate feeding habits and good hygiene and sanitation

practices can be promoted via nutrition education.

Reduce overweight and obesity in women:

There is a clear need to educate urban women about approaches to maintaining healthy

weight to prevent the prevalence of overweight and obesity from rising further. Preventing

and reducing overweight and obesity in women is included in the 2020-2025 National

Nutrition Strategy, which calls for the “provision of counseling for increased physical activity

(protective from overweight) and for reduction of sedentary lifestyles (causative for over-

weight)” and the “promoting the shift of social norms on food taboos preventing adequate

nutrition for pregnant and lactating women.” Behavior change communication programs and

outreach initiatives designed to meet this objective should be conducted in the near future,

and the prevalence of overweight and obesity monitored. Future national health policies

should also recommend the measurement of other diet-related non-communicable diseases,

such as hypertension and diabetes, to understand if these diseases are a public health

concern.

Reduce anemia and iron deficiency in children and women:

The results of the SMS 2019 found that anemia in children and women is mainly associated

with iron and vitamin A deficiencies, with child anemia also associated with inflammation. In

order to reduce nutritional anemia in children, we suggest implementing interventions such

as the promotion of age-appropriate infant and young child feeding practices, including the

promotion of foods (fortified or unfortified) rich in iron and vitamin A. In women, programs

to promote the consumption of iron-rich foods and iron supplements can be considered.

Moreover, to decrease the prevalence of ID, mandatory iron fortification programs should be

considered, such as iron fortification of rice and/or wheat flour. These programs should be

implemented in combination with behavior change communication strategies in order to

increase awareness of and knowledge about fortified foods. In order to reduce anemia

chronic disease, it is recommended to further elucidate which risk factors, both assessed and

not assessed by the SMS 2019, that contribute to inflammation and chronic disease.


8

Reduce vitamin A deficiency in children:

VAD affects about one-third of children in Somalia. Its prevalence reaches more than 40% of

children in certain sub-groups. To address this severe public health problem, multiple

approaches should be used. First, the implementation of Somalia's vitamin A

supplementation program should be improved to reduce the risk of the direct consequences

of VAD as well as mortality due to measles, diarrhea, and other illnesses. Specifically, the

national vitamin A supplementation protocols and guidelines should be followed by state

governments, health care workers, and other stakeholders. Secondly, to increase the vitamin

A body stores, vitamin A fortification programs, such as vitamin A fortification of vegetable

oil, should be considered. Thirdly, programs to improve consumption of vitamin A-rich foods,

other than fortified products, should be pursued. This is particularly relevant in rural areas

where vitamin A deficiency is high. This type of intervention could include promoting local

food products rich in vitamin A, or introducing vitamin A-biofortified staple foods that could

be readily cultivated in Somalia.


9

1. INTRODUCTION

1.1. Nutritional situation of young children and women in Somalia

The African continent, in particular the sub-Saharan region, continues to be highly affected

by undernutrition, including micronutrient deficiencies [2]. Undernutrition is a major factor

to be overcome to achieve progress in human and economic development. Women of

reproductive age, particularly during pregnancy and lactation, exhibit increased nutrient

needs and are therefore vulnerable population groups and public health efforts have turned

to ensuring adequate child nutrition during the first 1,000 days: from conception to a child’s

second birthday.

In Somalia, a National Micronutrient Survey, conducted in 2009 [1], collected data on anemia,

iron, vitamin A and iodine status among women of reproductive age, preschool-age and

school-age children, along with markers of inflammation and of malarial parasitemia.

Additionally, anthropometric measures were conducted and information on infant and young

child feeding practices collected. The survey reported high prevalence of anemia, iron

deficiency and vitamin A deficiency in most groups assessed. The survey revealed very poor

infant and young child feeding practices, with exclusive breastfeeding rates as low as 5.3%

and early initiation of breastfeeding among only a quarter of all newborns. The survey also

found very high median urinary iodine concentrations in women despite low proportions of

adequately iodized household salt[3]. Coverage with postpartum vitamin A supplements was

reported to be very low (just over 20%) in the 2009 survey, whereas vitamin A

supplementation in the past 6 months for children under 5 years of age was approximately

45%.

While data exists only from a single survey, the data on micronutrient status of women of

reproductive age, preschool-aged and school-aged children indicates that micronutrient

deficiencies are a public health concern.

1.2. Rationale for the survey

Since 2010, UNICEF and other stakeholders operating in Somalia have established and

brought to scale several programs, in particular the semi-annual vitamin A supplementation

for children 6-59 months of age, micronutrient supplementation to pregnant and lactating

women, efforts to facilitate access to diarrhea management (zinc tablets, ORS), and delivery

of micronutrient powders to households with children aged 6-59 months.

These recently established initiatives, together with a need to better understand the

seemingly-contradictory iodine nutrition indicators in Somalia, justify renewed efforts for

data collection. Thus, the Somalia Micronutrient Survey (SMS) 2019 aimed to provide a

comprehensive picture of micronutrient deficiencies in Somalia and to expand upon the

information collected as part of the 2009 survey. Data collected as part of the SMS 2019

provides up-to date information for future programme planning.


10

1.3. Research goal

The SMS 2019 did not have an a priori hypotheses related to the nutrition status of children

and women. The overall goal of this survey was to obtain updated and reliable information

on the current micronutrient status of women of reproductive age and children 6-59 months

of age. This up-to-date information will be used to develop evidence-based policies to

improve the nutritional status of these target groups in Somalia.

Although there are no hypotheses regarding severity and prevalence of various micronutrient

deficiencies, it was speculated that high concentrations of iodine in groundwater may

contribute to high iodine intake and the lack of apparent iodine deficiency [3]. The SMS 2019

has examined this hypothesis by analyzing iodine concentrations in both urine and water

samples.

Also, the timing of the survey was established to ensure that data collection did not occur at

the same time as nationwide vitamin A supplementation campaigns of child under 5 years of

age. Circulating retinol and RBP concentrations return to baseline levels approximately 2

months following receipt of vitamin A supplements. As such, the timing of the SMS 2019 field

work captured the baseline levels of vitamin A status in children.

The SMS 2019 was nationwide in scope to the extent that teams were able to access the areas

to be included; some areas were inaccessible, largely due to insecurity. Inaccessible areas

were excluded from the sample frame prior to selection of the primary sampling units (PSUs).

The SMS 2019 collected data from five target groups: 1) households, 2) children 0-59 months

of age (6-59 months of age for phlebotomy), 3) non-pregnant women of child-bearing age

(15-49 years of age), and 4) pregnant women.

1.4. Primary objectives

The 10 primary objectives of the SMS 2019 were as follows:

1. To measure hemoglobin concentration and estimate the prevalence and severity of

anemia in children 6-59 months of age, non-pregnant women 15-49 years of age, and

pregnant women.

2. To assess the prevalence of malaria in children 6-59 months of age, non-pregnant and

pregnant women using a rapid antigen test for at least the presence of Plasmodium

falciparum and other malaria parasites.

3. To assess the prevalence and severity of iron deficiency in children 6-59 months of age and

non-pregnant women of child-bearing age by measuring serum ferritin concentration to

calculate what proportion of anemia is iron deficiency anemia (IDA). Serum ferritin in both

children and women will be adjusted for the presence of inflammation as indicated by

elevated levels of C-reactive protein (CRP) and/or alpha-1-acid glycoprotein (AGP). Iron

deficiency was also measured using soluble transferrin receptor (sTfR) so that results from

the 2019 SMS could be compared to previous surveys.

4. To assess the vitamin A status of children 6-59 months of age and non-pregnant women

of child-bearing age by measuring retinol binding protein (RBP) in serum. RBP in children


11

will be adjusted for the presence of inflammation as indicated by elevated levels of C-

reactive protein (CRP) and/or alpha-1-acid glycoprotein (AGP).

5. To estimate the prevalence of alpha-thalassemia and sickle cell disease/trait among

children 6-59 months of age.

6. To assess the iodine status of non-pregnant women 15-49 year of age and pregnant

women using spot urinary iodine concentrations.

7. To estimate the current prevalence of chronic malnutrition (stunting), acute malnutrition

(wasting), underweight, and overweight and obesity in children 0-59 months old by

calculation height-for-age and weight-for-height z-scores.

8. To estimate the current prevalence of chronic energy deficiency and overweight and

obesity in non-pregnant women by calculating body mass index (BMI).

9. To estimate the current prevalence of undernutrition in pregnant women by measuring

mid-upper arm circumference (MUAC).

10. To estimate the proportion of households consuming adequately iodized salt.

1.5. Secondary objectives

Additional variables that may influence various types of malnutrition or play a contributory

role were also collected. Questionnaire tools were used to collect data to calculate relevant

demographic-, health-, and nutrition-related variables. Such additional variables include the

assessment of socio-economic status, household food insecurity, infant and young child

feeding (IYCF) practices, intake of micronutrient supplements, household water source and

sanitation facilities, and recent child illness. Standard questionnaire modules (e.g. WHO IYCF

module; WHO/UNICEF questions) were used to ensure that the results produced will be

comparable to other large-scale surveys.

Additional micronutrient deficiencies were measured on a sub-sample of children 6-59

months of age and non-pregnant women. This included the assessment of zinc status in a sub-

group of children 6-59 months of age and folate and vitamin B12 status in a sub-group of non-

pregnant women of reproductive age.

Lastly, as intrinsic iodine in drinking water may result in a sizable contribution to iodine intake,

the iodine concentration of drinking water was measured in water samples collected from a

sub-sample of households.

1.6. Implementation of the SMS 2019

To implement the SMS 2019, UNICEF and governmental partners retained the services of

GroundWork and Brandpro Research. GroundWork has extensive experience conducting

national micronutrient and nutrition surveys, and Brandpro Research had extensive

experience conducting large-scale surveys in Somalia. Together with UNICEF, the Federal

Ministry of Health and Human Services, and other government partners, GroundWork and

Brandpro Research led the design and the day-to-day field implementation of the SMS 2019.


12

2. METHODOLOGY

2.1. Survey design

Separate samples were selected in each of the six following strata: 1) Somaliland, 2) Puntland,

3) Hirshabelle and Galmudug states; 4) Jubaland and South-West states, 5) Banaadir state,

and 6) recently internally displaced persons (IDPs) in all five aforementioned geographic strata

combined. Figure 1 shows how Somalia’s seven states (Map A) were merged into five

geographic strata for the SMS 2019 (Map B). Due to its relatively small geographic size,

Banaadir, the state that encompasses Mogadishu, is only just visible in the maps below.

Figure 1. States of Somalia and geographic strata of SMS, Somalia 2019

Somalia’s 2014 Population Estimation Survey of Somalia (PESS) [4], which divided urban and

rural areas into enumeration areas with approximately 100-200 households, was used as the

sampling frame for the urban and rural areas of the SMS 2019. The block lists from the 2014

survey were updated by state governments where required, and were used in the first stage

of sampling. In these five strata, EAs were selected using systematic random sampling with

the probability of selection being proportional to size. For the IDP stratum, population data

on IDPs, including lists of camps with populations, was obtained from the United Nations High

Commissioner for Refugees. Camps were selected using systematic random sampling with the

probability of selection being proportional to size.

Importantly, the 25 clusters in each stratum were drawn after excluding areas that were non-

accessible due to security reasons. While the EAs were selected a couple months prior to the

survey field work after a thorough security review, security reviews were repeated during the

fieldwork, and replacement clusters were randomly selected in cases when the originally-


13

selected clusters became inaccessible. Details of the selected EAs are presented in Appendix

8.1, with “replacement” clusters identified.

In each selected EA, survey teams made a list of all households by walking through the EA and

asking basic identifying information from someone in each household or a knowledgeable

community informant. Following the listing of all the households in each EA, 16 households

were selected using simple random sampling by using a random number table.

This resulted in an a priori sample size of 2400 households. As non-response was included in

a priori sample size calculations, no replacement of non-responding or non-available

households, children, or women was done.

2.2. Survey participants

Table 2 shows the inclusion criteria applied at each selected household to determine eligible

households and individuals. To restrict the number of non-pregnant women recruited by the

survey, non-pregnant women were only enrolled from a random sample of 50% of households

by selecting non-pregnant women only from households with an even number on the cluster

control form used by the teams in each cluster. There are no specific exclusion criteria other

than the negation of the inclusion criteria.

Table 2. Inclusion criteria by targeted population group

Target population Inclusion criteria

Households • Household head or spouse or other adult household member gives signed or thumb printed consent for survey data collection

• Members currently reside in one of the 5 strata included in the sampling universe

Children 0-59

months

• Age between 0-59 months of age at the time of survey data collection (6-59 months for blood sampling)

• Considered a household member by adults living in the household

• Mother or caretaker gives oral consent for survey data collection

Non-pregnant

women

• Resides in a household that is randomly-selected to included non-pregnant women

• Age 15-49 years at the time of survey data collection

• Currently non-pregnant by self-report

• Gives verbal/oral consent for survey data collection

• Considered a household member by other adults living in the household

Pregnant women • Currently pregnant by self-report

• Gives verbal/oral consent for survey data collection

• Considered a household member by other adults living in the household

2.3. Sample size determination

The number of participants in each stratum was projected to be equal; therefore, the total

sample size for the survey was the stratum-specific sample size multiplied times six. Sample

sizes were calculated to achieve the desired precision for this single survey, not to compare

the results of this survey to any previous or future surveys. The SMS 2019 measured many


14

nutritional indicators in several target groups, including households, preschool-age children,

non-pregnant women, and pregnant women; however, sample sizes were calculated for only

the most important indicators in each target group.

The formula used to calculate all sample sizes is shown below. Because the population from

which the sample of households will be drawn exceeds 10000 in each stratum, no finite

population correction factor was applied.

RRDEFF

d

PPZn

1**

)1(2

2/2 −

=

Where:

Zα/2 = Standard error corresponding to the 95% confidence level

P = Assumed prevalence

d = Desired ½ confidence interval

DEFF = Design effect

RR = Total response rate (household and individual combined) expressed as a decimal

Intra-cluster correlation coefficients were back-calculated from the design effects seen in the

2009 micronutrient survey. These intra-cluster correlation coefficients were then used to

calculate the expected design effects for the sample size calculation of the SMS 2019. These

data are shown in Appendix 8.2

Estimated household and individual response rates were slightly underestimated to ensure

adequate sample size upon completion of data collection. Further, the estimated response

rates for those indicators which required blood collection was lower than those of indicators

requiring only interview responses or anthropometric measurements.

The basic sampling unit was households, but for many indicators, the unit of analysis was

woman or child; therefore, the number of women and children required for each indicator

was translated into a number of households in which this number of women and children

could be found. The number of women and children, on average, to be found in each selected

household was calculated from the average household size and the proportion of the

population made up of each target group. The estimates for children 6-59 months of age

(12.2% of the population) and women 15-49 years of age (23.4% of the population) were

taken from a recent survey from which census-like population estimates were derived [4]. To

determine what proportion of the population was non-pregnant women 15-49 years of age

and what proportion was pregnant women, it was assumed that all pregnant women are 15-

49 years of age. The 2009 micronutrient survey found that 19.8% of women 15-49 years of

age were pregnant at the time of data collection; therefore, 19.8% of 23.4%, or 3.7% of the

population, was pregnant women and 80.2% of 23.4%, or 18.8% of the population, was non-

pregnant women 15-49 years of age.


15

Sample size was not calculated for estimates of iodine deficiency because the prevalence of

iodine deficiency cannot be calculated; a single spot urine specimen cannot be used to

indicate an individual's iodine status. According to current WHO recommendations [5], the

median urinary iodine concentration and accompanying confidence intervals were calculated

for non-pregnant and pregnant women separately and compared to cut-off points defining

population iodine deficiency and excess. A urine specimen for testing iodine concentration

was solicited from each selected pregnant and non-pregnant woman, which substantially

exceeded the sample size of 300 for assessment of urinary iodine recommended by WHO[6].

The assumptions and desired sample size for each of the important indicators for which

sample size was calculated is presented in Appendix 8.2. The SMS 2019 selected a total sample

size of 2,400 households, or 400 households per stratum. This sample size produced

sufficient precision for most indicators, and due to great number of children enrolled than

expected (see Section 3.1), the desired precision for each indicator used for sample size

calculations was achieved.

2.4. Primary outcomes

Table 3 below shows the primary nutrition outcomes measured in each target group and the

specific indicator for each of these outcomes. Details on methodologies are provided in

Section 2.7.

2.5. Ethical considerations

In order to ensure that the survey followed principles to protect respondents and prevent

unnecessary risk to survey respondents, ethical approval was obtained by the ethical review

committees of the Federal Ministry of Health and the Ministries of Health in Somaliland and

Puntland (Appendix 8.3).

Prior to inclusion in the survey, informed written consent for interviews was sought from the

head of each household (or spouse or other adult household member in case of absence) on

behalf of the household. If the household head was unable to read and write, the consent

form was read out to them and a thumb or fingerprint was be taken as consent in lieu of a

signature.

Additionally, verbal informed consent was obtained from selected women themselves for

interview, anthropometric measurement, and blood collection and from mothers or

caretakers for anthropometry and blood collection in children.

Urine and blood were collected in the survey. Collection of urine samples is non-invasive, and

collection of blood samples was done via finger prick in women and children 12-59 months or

heel prick children 6-11 months. Finger and heel prick are considered minimally invasive, and

only causes momentary discomfort. To protect small children and avoid undue stress, no

blood samples were collected from children less than 6 months of age.


16

Table 3. Overview of key biomarker indicators, by population group*

Condition measured Biomarker Indicators Children NPW PW HH

Anemia Hemoglobin concentration + + +

Malaria Malaria parasitemia/HRP-antibodies + + +

Iron deficiency Ferritin + +

Vitamin A deficiency Retinol binding protein + +

Inflammation Alpha1-acid glycoprotein (AGP),

C-reactive protein (CRP) + +

Zinc deficiency Zinc +

Folate deficiency Folate +

Vitamin B12 deficiency Vitamin B12 +

Iodine status

Urinary iodine concentration + +

Blood disorders α-thalassemia, sickle cell disorders +

Stunting Height-for-age z-score +

Wasting Weight-for-height z-score +

Severe acute

malnutrition MUAC in children +

Underweight Weight-for-age z-score +

MUAC MUAC + +

HH salt iodine

concentration

(salt iodization)

HH salt iodine concentration

(salt iodization) +

HH water iodine

concentration

HH water iodine concentration +

NPW = non-pregnant women; PW = pregnant women; HH = households

Individuals identified with malaria, severe anemia or severe acute malnutrition (children only)

according to WHO criteria [7,8] were referred for treatment at the nearest health facility. The

referral form is presented in Appendix 8.6.

Identifying records, in both electronic and paper formats, were stored under lock and key (or

password) at all times and access granted only to specifically identified survey personnel.

Specific identifying information was stripped from all electronic databases used by the survey

management team prior to data analysis.

2.6. Field work and data collection

2.6.1. Instrument pre-testing, training of survey teams, and field testing

Prior to providing full training to the team members, all questionnaires and survey

instruments were translated into two Somalia dialects: 1) Somali as spoken in Central South

Zone (CSZ), and 2) Somali as spoken in Somaliland. Translations were conducted by the


17

Federal Ministry of Health (MOH) in Mogadishu and the Somaliland MOH in Hargeisa,

respectively. Back translation of questionnaires into English was done to ensure that all

questions were correctly translated. The questionnaires and survey instruments were pre-

tested by the survey management team to check the flow and the different response options

as well as the quality of translation.

Two trainings were conducted as part of the SMS. The first training was conducted in

December 2018 in Hargeisa, Somaliland. Team members from Somaliland, Banaadir,

Hirshabelle, Galmudug, and South-West attended this training. Team members from

Puntland and Jubaland were unable to attend this training due to travel restrictions. As such,

a second training for survey teams assigned to data collection in Puntland and Jubaland was

conducted in July 2019 in Garowe, Puntland.

The classroom training of interviewers included procedures to list and randomly select

households, instruction about how to use the interview device (tablet computer), and

discussions of each question in the household, child, and women questionnaires. The training

on each questionnaire consisted of practice reading each question and labelling procedures,

linking the individuals questionnaire and biomarker information to the respective household.

Interviewers also conduct mock interviews in the local languages and provided input on the

translation of the questions into different languages to ensure the correctness of the

translated questions with the questions originally-formulated in English.

For large parts of the training, the interviewers underwent separate training from that of the

anthropometrists and phlebotomists. Anthropometrists and phlebotomists were trained on

anthropometric techniques and underwent a rigorous standardization exercise, where the

phlebotomist acted as the anthropometrists’ assistant. Also, blood sampling procedures were

practiced at length, including training on maintenance of the cold chain to transport blood

specimens. Anthropometrists were trained to assist the phlebotomist during blood taking.

For the training, more survey workers than required were recruited and at the end of the

training, a post-test was administered to assess their understanding of field procedures. This,

along with observations from the trainers and the results of the anthropometry

standardization exercise, was used to select the best performing team members and appoint

a team leader for each team. Those members not included for the field work were released

but kept on retainer in case other team members dropped out.

Following the classroom training, two practice enumeration areas were visited. The purpose

of the field testing was to give the teams a chance to practice all survey procedures in a cluster

under very close supervision. For this, survey teams practiced all data collection steps in

households selected from communities in the vicinity of the training site but not included in

the survey sample. These communities were selected to have comparable characteristics to

the finally selected communities. The teams conducted the community sensitization,

household listing and selection, interviewing and anthropometry and phlebotomy, and

transportation of specimens to a blood processing point. The team leaders coordinated water


18

sampling activities from the households. Data collected in these practice clusters was not

included in the final SMS data files.

2.6.2. Community mobilization and sensitization

Stakeholders from the MOH in each state led the sensitization activities ahead of the survey

data collection. The sensitization activities included appearances on local television and radio

programs, sending letters to the district representatives and the respective health authorities

in charge of the selected PSU’s, and other activities deemed necessary. The implementing

agency (Brandpro) supported the MOH with these sensitization activities, and these activities

were done in close collaboration with representatives from UNICEF in each state.

Shortly before the team's arrival in a given PSU, the pre-sensitized local authorities were

informed by telephone, whenever possible. Upon arrival of a team in a cluster, the team met

with the relevant authorities to inform them again about the work and seek their support.

Because of the security situation in certain areas, security escorts were contracted when

required. When utilized, these escorts accompanied the teams throughout the PSU to ensure

that all survey staff and equipment were protected at all times. When the security of a

selected PSU was considered untenable, team leaders informed the survey management

staff, and replacement PSU’s were randomly selected from accessible areas in the sample

frame (see Section 2.6.4).

2.6.3. Selection of PSUs

Prior to selecting PSUs, Somalia was divided into 6 strata (see Section 2.1). For geographic

strata, PSUs lists from the 2014 PESS survey were obtained [4]. For certain areas, the state-

level governments had established additional PSUs since the 2014 PESS. These additional

PSUs were developed to either a) segment PSUs from the PESS that contained a large number

of households, or b) identify new population settlements. For the IDP strata, a list of IDP

camps was obtained from UNHCR’s Somalia Camp Coordination and Camp Management

(CCCM) cluster database. The list of IDP settlements had been developed constructed as part

of a Detailed Site Assessment undertaken between September 2017 to March 2018, and

comprised of 1890 IDP settlements [9].

Following the compilation of the list of PSUs in each stratum, the accessibility of each area

was determined in collaboration with state-level governmental partners. This accessibility

assessment was conducted in August and September 2018 to identify areas that could be

safely accessed and areas that could not be safely accessed due to poor security. Areas that

were deemed inaccessible were excluded from the lists of PSUs in each stratum.

Prior to PSU selection, PSUs were ordered by district based in a serpentine pattern to ensure

geographic distribution throughout the sampling universe. Subsequently, 25 PSUs per strata

were selected using systematic random sampling using standard techniques. Subsequently,

In total, this resulted in the random selection of 150 PSUs in accessible areas.


19

2.6.4. Changing security status and replacement PSUs

Because the security situation in Somalia is dynamic, the accessibly of PSUs changed between

the accessibility assessment and data collection. As such, just prior to initiating the field work

in each state, the list of selected PSUs was reviewed with state-level governmental staff and

field workers to determine if the PSUs were accessible at the time. In addition, field workers

also contacted local government officials and the survey management staff if the security

situation changed during the field work.

Selected PSUs that were deemed inaccessible were replaced by randomly-selected PSUs from

within the same district if possible. If the entire district was inaccessible, inaccessible PSUs

were replaced by randomly-selected PSUs in a nearby district. A detailed list of all selected

PSUs is presented in Appendix 8.1, with replacement PSUs identified.

2.6.5. Household listing and random selection of households

Because the most recent population estimates date from 2014, the household list in each

selected PSU was updated by conducting a household listing. At each household in the PSU,

survey teams collected information about the head of the household (name, phone number

if available) along with information on the location of the household, which helped

streamlining survey work later on. The household listing was conducted by the field workers

just prior to beginning survey data collection and was recorded on paper to facilitate random

selection of households. To assist in the exercise, the local guides were trained by the teams

to lead in this activity.

The household listing exercise consisted of delimiting the PSU and drawing sketch maps for

each of the selected PSUs. These maps were cross-checked against detailed satellite imagery

maps provided by the various Ministries of Planning and Development. The maps were used

to locate and visit each household within the boundaries of the PSU. Each household was

listed on a separate line in the household listing form.

Once completed, the team leader selected the required number of households at random

from the household listing form using random number tables. After selection, the different

households were assigned to interviewers. If phone numbers and network coverage were

available, the interviewers or the team leader contacted the heads of the household to

schedule an interview. If phone calls could not be done, household visits were made to

schedule interviews.

2.6.6. Field work (interviews)

In total, 16 teams were recruited to collect data throughout Somalia. Each team was

comprised of one team leader, two interviewers, one phlebotomist, one anthropometrist,

and one driver. The anthropometrist and phlebotomist worked together as a pair, each

assisting the other with anthropometry and phlebotomy, respectively. The composition of

each team is provided in Appendix 8.7.


20

Data collection was conducted between 24 December 2018 and 8 September 2019. No field

work was conducted between 5th May 2019 and 3rd June 2019 as this time period

encompassed the 2019 Ramadan and Eid holidays. In addition, there was a gap between the

field work conducted in states completed following the first training (i.e. Somaliland,

Banaadir, Hirshabelle, Galmudug, and South-West) and the states completed following the

second training (i.e. Puntland, Jubaland). The specific start and end dates for field work in

each area are as follows:

• Somaliland – 24th Dec 2018 to 19th Feb 2019

• Banaadir – 20th Jan 2019 to 27th Feb 2019

• Hirshabelle – 23rd Feb 2019 to 24th March 2019

• Galmudug – 25th March 2019 to 15th April 2019

• South-West State – 16th Feb 2019 to 25th Feb 2019

• Jubaland – 5th Aug 2019 to 8th Sep 2019

• Puntland – 22nd June 2019 to 13th July 2019

All reasonable attempts were made to recruit selected households. At least two repeat visits

(total 3 visits) were made before dismissing a household as non-responding. No substitution

of non-responding households was done as this had been taken into account during the

sample size calculation.

Tablet computers were used for direct data entry for data collection at the household level.

Skip patterns were built into the electronic questionnaires, which increased the speed of the

interviewing process as well as minimized inappropriate entries. Interviewers administered

the household interview first, followed by women interviews if the household had eligible

women and interviews of mothers about their children. Household and individual

questionnaires were administered mainly in the local languages (e.g. Somalia) depending on

the interviewee’s preference. Links to all questionnaires (in English) are provided in Appendix

8.8.

Nearly all questions were subjective, requiring the respondent to directly provide a response.

However, some questions were objective and verified with supporting documentation or

visual inspection by the interviewer. For questions in the child questionnaire, interviewers

started the interviews asking if the child had a health card, and if so, this health card was used

to answer some questions. For example, if a child had a health card, his/her birth date was

taken directly from the card. In addition, when asking for a child’s weight at birth, would note

the birthweight on the child’s health card if present.

In addition to the questionnaires, a series of supporting instruments were loaded on the

tablet computers to facilitate field work. Specifically, regarding the quality of the household’s

dwelling, pictures of latrine types, wall materials, roof materials, and floor materials were

loaded on each tablet computer so that interviewers could consult these pictures when

completing these observational questions in the household questionnaire. Pictures of


21

selected nutrition supplements (i.e. vitamin A supplements for children, MNPs, lipid-based

nutrient supplements, RUTF, and maternal multivitamin supplements) were also loaded on

each interviewers tablet, and were shown to interviewees to prevent any confusion.

After the interviews, a labeled urine beaker was handed out to each woman and a sample of

salt was collected from each consenting household. For selected women and children,

interviewers prepared and labeled a woman and/or child paper biological form and directed

those participants (or their mothers) to a central location in the EA where the

anthropometrists and phlebotomist were stationed.

2.6.7. Field work (anthropometry and phlebotomy)

Before conducting anthropometric measurements, anthropometrists received urine

specimens from women and stored them in a cold box at +4 to +8°C. Anthropometric

measurements from eligible children and women using standard methods [10]. For weight

measurements of children who could not stand by themselves, the mother or caregiver was

first measured alone, the scale (SECA™ #S0141021, California, USA) was tared, then the child

was handed to the mother. Children’s height or length was measured by using a standard

wooden height board (UNICEF, #S0114540). In addition, mid-upper arm circumference

(MUAC) was measured in children 6-59 months of age by using MUAC tapes (UniMUAC tapes,

Medicins sans Frontières UK). For non-pregnant women, weight was measured using the

same scale as used for children. Height was measured using the same standard wooden

height board as used for the children. For pregnant women, only their MUAC was measured

by using the same MUAC tape as used for children.

Capillary blood was collected from all children 6-59 months of age as well as in non- pregnant

and pregnant women. For the finger prick or heel-prick (children 6-11 months of age), the site

was cleaned with an alcohol pad and wiped dry with a sterile gauze pad. Following the lancet

puncture (Becton Dickinson, Franklin Lakes, NJ, USA), the first drop was wiped away with

sterile gauze. The second and third drops of blood were collected to measure hemoglobin

concentration (HemoCue™ 301) and malaria status (SD Bioline™, Malaria Ag P.f/Pan).

Thereafter, approximately 300-400 µl of blood was collected from children and non-pregnant

women into a silica-coated Microtainer™. Filled Microtainers were placed in a cool box

containing cold packs to ensure they are stored cold but not frozen at ~4°C and in the dark

until further processing later the same day.

Participants found to have severe acute malnutrition, severe anemia or malaria parasitemia

were referred for treatment at the nearest health facility. Blood collection did not require

fasting; none of the biomarkers collected were sensitive to fasting.

At the end of each day the team leader collated all paper forms, including the cluster control

forms, biological forms for children and women. For questionnaire data that was only

collected electronically, team leaders examined to make sure that all questions were

answered. In addition, the team leader compared the data on the paper biological forms with

https://supply.unicef.org/s0141021.html

https://supply.unicef.org/s0114540.html


22

the data entered into ODK to ensure that there was no transcription errors. Interviewers were

notified of any errors and/ or omissions, for which they were instructed to make the necessary

corrections.

All contaminated sharp materials (e.g. lancets, hemocuvettes, etc) were placed in sharps

disposal boxes directly following blood collection to prevent any injury. Teams collected all

non-sharp waste in biohazard bags, which were brought to government laboratories for

appropriate biomedical disposal.

2.6.8. Cold chain and processing of blood samples

Following collection, all blood samples were placed on cold packs (approximately 4°C) until

processing in the laboratories. The phlebotomists recorded the temperature inside of the cold

box containing the cold packs every two hours.

The phlebotomists processed blood samples each evening. Blood was centrifuged at 3,000

rpm for 10 minutes. Serum and red blood cells (RBCs) were subsequently aliquoted into

appropriately-labeled micro tubes. Urine sample were also aliquoted into appropriately

labeled micro tubes. The micro tubes were kept in portable freezers (PrimeTech) at -20°C

purchased for the survey, or transported in a portable freezer to a stationary freezer (where

possible) for the duration of the field data collection. Thereafter, all blood and urine samples

were transported to a central freezer (-20°C) at Hargeisa, before being shipped on dry ice to

international laboratories for analyses. All samples arrived frozen at their respective

destinations.

2.6.9. Supervision of fieldwork

During the field work, intense supervision was conducted to prevent potential flaws in

operation. In addition to team leaders, supervisors were assigned to the different strata and

teams to ensure that the correct survey procedures were followed. Furthermore, spot-check

visits to teams were conducted by management staff from the various governments, UNICEF,

and Brandpro. The central laboratories used to store samples were also frequently visited by

supervisors to ensure that samples received from the field are properly processed and stored.

2.7. Definitions of indicators and specimen analysis

2.7.1. Anthropometric indicators

Children 0-59

Undernutrition (including wasting, stunting, underweight and microcephaly) and

overnutrition in children was defined using WHO Child Growth Standards [11]. Children with

z-scores of ≤ -2.0 for weight-for-height, height-for-age, and weight-for-age were defined as

wasted, stunted, or underweight, respectively [11,12]. Moderate wasting, stunting, and

underweight were defined as a z-score less than -2.0 but greater than or equal to -3.0, and

severe wasting or stunting, or underweight were denoted by z-scores less than -3.0.

Overnutrition was defined as a weight-for-height z-score greater than +2.0. Overweight was


23

defined as a weight-for-height z-score of greater than +2.0 but less than or equal to +3.0 and

obesity as a weight-for-height z-score greater than +3.0.

Mid-upper arm circumference (MUAC) was also taken for all children 6-59 months, and those

with a MUAC<115 millimeters were classified as having severe acute malnutrition (SAM) [13].

As SAM can also be calculated using as both weight-for-height z-score <-3.0, the SMS 2019

contains two measurements of SAM. Although having two measurements of the same

indicator may result in some discrepancies, it was deemed necessary to include MUAC

results— nationally and at the state level — as they may help program planners estimate the

number of children who might be detected in a MUAC screening program.

Non-pregnant women

Chronic energy deficiency and overnutrition in non-pregnant women were assessed by using

BMI. Various levels of undernutrition were defined as following: <16.0 severe undernutrition,

16.0-16.9 moderate undernutrition, 17.0-18.4 at risk of undernutrition, 18.5-24.9 normal,

25.0-29.9 overweight and >30 obese.

Pregnant women

Because body weight in pregnancy is increased by the products of conception and extra body

fluid, BMI is not a valid indicator of nutritional status. MUAC was used instead to measure the

nutritional status of pregnant women. A MUAC of less than 23 cm was used to define a

pregnant woman as undernourished [14].

2.7.2. Urinary iodine and drinking water iodine concentrations

Urinary iodine and drinking water iodine concentrations were determined using the

ammonium persulfate/Sandell-Kolthoff reaction method [15]. Carry 60 Uv/Visible

Spectrophotometer™ (Agilent technologies) was used by the Technicians to assess the

concentration of each specimen in duplicate using a standard dilution assuming iodine

concentrations <300 ug/L at 420 nm wavelength. Urine or water samples with iodine

concentrations ≥300 ug/L were diluted with deionized distilled water using an appropriate

dilution factor, and reanalyzed in duplicate so that the values yielded were within

the calibration range. Final results were calculated and adjusted by the dilution factor. The

mean of the final duplicate run for each specimen was calculated and used as the final

concentration for data analysis.

In addition, internal quality control materials labelled as low, medium and high were run

concurrently with specimens. Results from an analytical run were rejected if the value from

the internal quality control material was not within the acceptable targeted range.

Analysis of iodine in urine and water samples was conducted at the Tanzania Food and

Nutrition Center in Dar es Salaam, Tanzania.


24

2.7.3. Blood specimens

Malaria measurement

The qualitative assessment of malaria infection was done on whole blood by using the SD

BIOLINE Malaria Ag P.f/Pan rapid diagnostic test (RDT) produced by Standard Diagnostics Inc

(Gyeonggi-do, Republic of Korea), which detects P. falciparum and other Plasmodium species

(e.g. P. vivax, P. malariae or P. ovale).

Anemia

Blood hemoglobin concentration was measured by using a HemoCue™ portable

hemoglobinometer (Hb301, HemoCue, Ängelholm, Sweden). Quality control of the Hemocue

devices was done daily using low and medium concentration liquid control blood

commercially available from the device supplier. Control blood was kept in cold boxes (2-8°C)

for the duration of the field work to prevent degradation.

Iron (serum ferritin and sTfR), acute phase proteins (CRP, AGP), and vitamin A (RBP)

Serum ferritin was used to assess iron status of all young children and non-pregnant women.

Ferritin concentration has been recommended by the World Health Organization (WHO) as

the marker of first choice for the assessment of iron deficiency in population based surveys

[16]. Because serum ferritin levels can be elevated in the presence of infection or other causes

of inflammation, the acute phase proteins AGP and CRP were used to detect the presence of

inflammation in survey subjects. For children and women, ferritin values were adjusted for

inflammation using the correction algorithm developed by the BRINDA project [17], and these

adjusted ferritin values were used to calculate the prevalence of iron deficiency. sTfR was

measured in serum as a biomarker of iron status to enable a comparison with the 2009

micronutrient survey.

Retinol Binding Protein (RBP) was used to assess vitamin A status of young children and non-

pregnant women. RBP instead of serum retinol was used because it requires smaller

quantities of serum and is much cheaper to measure than measuring retinol using high

performance liquid chromatography (HPLC). RBP concentration is highly correlated with

serum retinol [18], which is the biomarker for vitamin A status recommended by WHO. The

concentration of RBP is suppressed during acute inflammation and therefore an adjustment

algorithm developed by the BRINDA project, which is similar to the one proposed for ferritin,

was applied for children only. No adjustment to RBP concentrations is recommended for

women.

In addition to inflammation adjustments made to ferritin and RBP, acute phase proteins were

used to categorize the various inflammation stages of individuals. Concentrations of CRP >5

mg/L and AGP >1 g/L denote acute and chronic inflammation, respectively. Using the

approach developed by Thurnham [19,20], each individual was assigned to one of the

following four inflammation categories: no inflammation, incubation (elevated CRP only),

early convalescence (elevated CRP and AGP), and late convalescence (elevated AGP only).


25

These categories illustrated the various stages of inflammation that can occur. In addition,

“any inflammation”, defined as elevated CRP and/or elevated AGP, was used for sub-group

analyses to illustrate the potential risk factors of anemia and other indicators.

Serum ferritin, sTfR, CRP, AGP, and RBP were analyzed using an enzyme linked

immunosorbent assay (ELISA) technique [21]. The VitMin Laboratory in Germany, where

these analyses were conducted, participates regularly in inter-laboratory quality assurance

programs, such as the VITAL-EQA from the CDC.

Hemoglobinopathies

Testing for hemoglobinopathies (for all children 6-59 months of age and non-pregnant

women) was conducted by using polymerase chain reaction (PCR) to genotype HbA and HbS

alleles [22–24]. Hemoglobinopathies that were measured included sickle cell disease and trait

(HbSS and HbS) and α-thalassemia. Hemoglobinopathies were assessed at the KEMRI-

Wellcome Trust Institute in Kilifi, Kenya.

Retinol

Serum retinol was analyzed in a sub-sample of children using HPLC at the Swiss Vitamin

Institute in Lausanne, Switzerland.

Zinc

Zinc concentrations were measured in a 10% a sub-sample of children serum samples and the

by ICP-MS at UC Davis. Importantly, as zinc contamination is a potential risk when collecting

biological specimens as an individual’s zinc measurements can be compromised by contamination

from blood collection materials or environment. As such, standard procedure are recommended

to safeguard samples from zinc contamination [25] (e.g. use of trace-element free materials,

pipetting samples under ventilated hood). These procedures, however, could not be followed for

this survey due to logistical challenges. Thus, to determine if zinc contamination occurred

during the collection and processing of blood samples, Nanopure™ water samples (aka “water

controls”) were added to microtainers in the field, and all processing procedures (e.g.

pipetting, aliquoting) were conducted along with other samples to capture any contamination

during all procedures conducted in the field. As with the serum samples, the zinc

concentrations of these water controls were measured using ICP-MS at UC Davis.

Folate and vitamin B12

Serum folate and vitamin B12 concentrations were measured in a sub-sample of women using

a microbiological assay method using Lactobacillus caseii (ATCC 7469) as test organism [26]

following the turbidimetric reference method [27]. Serum vitamin B12 concentrations were

assessed following the reference method, AOAC Official Method 952.20 Cobalamin (Vitamin

B12 Activity). This method uses Lactobacillus leichmanii as test organism and turbidimetry.


26

Folate and vitamin B12 were measured by the Swiss Vitamin Institute in Lausanne,

Switzerland.

2.7.4. Analysis of iodine in salt

Iodine in salt was analyzed quantitatively using the standard iodometric titration method.

Samples were analyzed in Dar Es Salam, Tanzania, at the Tanzanian Food and Nutrition Center,

an institution with longstanding experience in measuring iodine in salt.

Following international recommendations [28], salt iodine concentrations of 15 ppm were

classified as adequately fortified.

2.8. Data management and analysis

2.8.1. Data entry

Direct electronic data entry was done by using Open Data Kit (ODK) during the household,

child, and women interviews. Data related to anthropometry and blood collection were

completed by the anthropometrists and phlebotomist using a paper form, and the

interviewers subsequently entered the data into ODK on the same day.

2.8.2. Data monitoring

Interview data was uploaded from the tablets to a Dropbox on a daily basis. Data were

monitored routinely and in case of systematic errors made by several teams, all team leaders

were immediately informed about the problem, so the problem was not repeated; sporadic

errors were directly reported to the respective team leaders. For errors that the teams could

address, they were requested to do so immediately, while still in a given EA. For errors that

could not be addressed by the teams immediately, the national coordinator was informed

about the problem and visited the respective teams to correct errors.

2.8.3. Data analysis

Data analyses were done using Stata/IC version 14.2. All analyses of data that included units

of more than one stratum were conducted using a weighted analysis to account for the

unequal probability of selection in the 6 strata. Analyses that included units from a single

stratum used unweighted analysis. Importantly, both analysis approaches accounted for

cluster sampling.

Means with 95% confidence intervals were calculated for normally-distributed continuous

variables. For non-normally-distributed variables, and medians with interquartile ranges (IQR)

were calculated. Regarding urinary iodine concentrations (UIC), median UICs were calculated

for each target group overall and for subgroups in order to judge population iodine status

against WHO criteria [5]. However, in order to judge the statistical precision of apparent

differences among subgroups, a square root transformation of the UIC values created a

variable which was normally distributed [29]. Linear regression was then used to calculate p

values for apparent association between the geometric UIC means and each characteristic.


27

The statistical precisions of all prevalence estimates were assessed by using 95% confidence

limits which were calculated accounting for the complex sampling used in this survey,

including the cluster and stratified sampling. All measures of precision, including confidence

limits and chi square p values for differences, were calculated accounting for the complex

cluster and stratified sampling used by the SMS 2019.

Descriptive statistics were calculated for all children together and all women together (i.e.,

across all strata), for each stratum separately, and by sex (for children). The results are also

presented by specific age sub-groups for non-pregnant women (15-19 years, 20-29 years, 30-

39 years, and 40-49 years) and children (6-11 months, 12-23 months, 24-35 months, 36-47

months, and 48-59 months of age). For pregnant women, only national estimates for all ages

were generated.

2.8.4. Case definitions of key indicators and nutritional deficiencies

A wealth index was calculated by using the World Bank method [30]. This method utilizes data

on each household’s dwelling (e.g. material of wall, floor, roof), drinking water source,

sanitation facility used, livestock ownership, and ownership of durable goods, to generate a

composite index using principal component analysis. The wealth index was subsequently

separated into quintiles to permit the sub-group analysis of various key indicators by a

household’s wealth quintile group.

Each household’s latrine type was classified as “improved” or “unimproved” based on the

methodology developed by the WHO/UNICEF Joint Monitoring Programme for Water Supply,

Sanitation and Hygiene (JMP) Sanitation adequacy is a composite measure, based on the

household’s toilet or latrine type, and the household’s exclusive use or shared use of the toilet

or latrine. In addition, the water source of each household and water treatment method were

used to classify a household’s drinking water as “safe” or “unsafe” based on the JMP

methodology [31]. Two water sources that were not present on the JMP list, but are used in

Somalia, were berkad and balley: rainwater-harvesting approaches that capture and retain

surface water after rains. Berkads are reservoirs lined with concrete that are 30 to 400 m3

[32], and balleys are “natural depressions on flattened silt soils that collect surface runoff with

water-holding capacity ranging from less than 1000 m3 to more than 100,000 m3 [32]. Berkads

are considered by the UN WASH cluster in Somalia as an un-improved water source [33], and

balleys were also considered un-improved water sources as they are effectively “surface

water”, which is classified by UNICEF and WHO as an un-improved drinking water source [34].

To determine each households access for food in the past 30 days, household questionnaires

included the Household Food Insecurity Access Scale (HFIAS) module with questions modified

slightly for the context in Somalia. Specifically, nine separate yes/no questions were asked to

gauge if different aspects of food insecurity existed in the past 30 days, and if so, a follow up

question was asked to determine the frequency of the occurrence. Question responses were

summed to produces a food insecurity score for each household, which in turn was classified


28

into a food insecurity category according to guidelines developed by the Food and Nutrition

Technical Assistance (FANTA) project [35].

In children, low birthweight was defined as a birthweight less than 2.5 kg. In addition, the cut-

off values for each biomarker indicator used to determine deficiency are presented in Table

4. For hemoglobin concentration multiple cut-offs were used to classify the severity of

anemia. For other indicators, a single cut-off was used to identify deficiency or abnormality.

Table 4. Clinical cut-off points and classifications for biomarker indicators

Indicator Cut-offs defining deficiency or abnormality

Hemoglobin [7] Severe Moderate Mild

Preschool children 6-59 months of age

(PSC)

<70 g/L 70-99 g/L 100-109 g/L

Pregnant women (PW) <70 g/L 70-99 g/L 100-109 g/L

Non-pregnant women (NPW) <80 g/L 80-109 g/L 110-119 g/L

Ferritin † [16]

PSC

NPW

< 12 µg/L

< 15 µg/L

Soluble transferrin receptor (sTfR)

PSC and NPW

>8.3mg/L‡

α1-acid-glycoprotein (AGP) [19,20]

PSC and NPW

>1 g/L

C-reactive protein (CRP) [19,20]

PSC and NPW

>5 mg/L

Retinol-binding protein (RBP) †, § and retinol

PSC and NPW

<0.7 µmol/L§

Serum zinc [36]

PSC

Morning, non-fasting: 65 µg/dL, afternoon, non-fasting: 57 µg/dL

Folate [37]

NPW

<10nmol/L

Vitamin B12 [37]

NPW

<150pmol/L

Urinary iodine concentration [28]

NPW

PW

< 100 µg/L

<150 µg/L * The cut-off defining normal hemoglobin concentrations is typically adjusted for the number of cigarettes smoked per day following WHO

guidelines.

† Ferritin and RBP values were adjusted for inflammation using appropriate algorithms [17,18]. ‡ There is no generally agreed upon threshold for serum sTfR, but the most commonly used commercial assay (Ramco) suggests the above

threshold § There is no general consensus on the cut-off point for RBP to be used to define vitamin A deficiency. Because of the 1:1 molar ratio of

retinol and RBP in blood, many investigators use 0.70 µmol/L (the same cut-off used for serum retinol) although other cut-offs have been

proposed [38].


29

3. RESULTS

3.1. Response rates for households, children, and women

Figure 2 below provides an overview of the number of respondents at the different survey

stages, and illustrates that few households, women and children refused to participate in the

survey.

The SMS aimed to enroll 2400 households, and in total, successfully recruited 2172

households, or 90.5% of the planned sample. Of the households that were not enrolled, 27

households (0.9%) refused to participate in the survey, and 201 households (8.5%) were

either absent for an extended period of time or not available after three consecutive visits.

Overall, 1961 children 0-59 months of age resided in the enrolled households. Nearly all

children identified (99.3%, n=1947) participated in the questionnaire portion of the SMS. Of

participating children, 93.0% (n=1811) were 6-59 months of age, and thus eligible for blood

collection. Of these children, 92.5% (n=1675) of participating children 6-59 months of age had

valid hemoglobin values. Blood samples with sufficient volume to measure micronutrient

deficiencies were available in 1,487 (82.1%) children 6-59 months of age (see Section 3.3.11).

Overall, 914 eligible non-pregnant women lived in the ½ sample of households selected for

woman recruitment; 92.1% (n=842) of these women were available and accepted to

participate. Of participating non-pregnant women, 95.5% had complete anthropometry data,

and 92.3% had valid hemoglobin data.

Households consenting to survey participation included 280 pregnant women, none of whom

refused to participate. Anthropometry and hemoglobin data was available for 96.1% of

pregnant women.


30

Figure 2. Participation diagram of households, women and children, Somalia 2019


31

3.2. Household Characteristics

3.2.1. Demographic characteristics

The characteristics of participating households in the SMS 2019 are summarized in Table 5

below. In total 2172 households were included; about one-half were from urban areas, about

one-third from rural areas, and about 15% were IDP households. The distribution of the

population selected by the SMS varies from population estimates from the 2014 PESS [4].

Most notably, the SMS 2019 survey did not include nomadic households, which accounted for

one-quarter of Somalia’s population in 2014. In addition, the SMS 2019 had a higher

proportion of urban households than the 2014 PESS, which is due to a higher proportion of

rural areas being inaccessible to SMS data collection.

Table 5. Distribution of various demographic variables for participating households,

Somalia 2019

Survey Sample Somalia Population c

Characteristic n % a (95% CI) b %

Residence Rural 700 34.8 (27.1, 43.5) 42.4 Urban 1129 49.5 (41.3, 57.7) 22.8 IDP 343 15.7 (15.2, 16.2) 9.0 Nomadic -- -- -- 25.9

State Somaliland 455 26.4 (23.9, 29.2) 28.5 Puntland 343 9.4 (7.5, 11.6) 9.0 Hirshabelle 163 4.5 (2.8, 7.1) 8.4 Galmudug 249 7.5 (5.0, 10.9) 10.5 South-West 217 16.0 (10.3, 24.0) 19.2 Jubaland 287 21.4 (15.5, 28.7) 11.0 Banaadir 458 14.9 (12.0, 18.3) 13.4

Wealth quintile

Lowest 287 20.3 (16.4, 24.9) -- Second 335 19.7 (16.1, 23.8) -- Middle 485 20.0 (16.8, 23.6) -- Fourth 573 20.0 (17.5, 22.8) -- Highest 492 20.0 (16.3, 24.1) --

TOTAL RESPONDING HOUSEHOLDS 2172 100 --

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing

data. a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Population figures from Somalia’s 2014 Population Estimation Survey [4]

On average, households have about 4.1 members, and more than 60% of households had four

members or fewer (see Table 6). About one quarter of households had no women of

reproductive age living in the households, and most of the remaining households had only

one woman of reproductive age living in the household. About half of the households had no


32

child currently aged 0-59 months living in the household. Information about household

composition is presented by state in Appendix 8.9.

Table 6. Distribution of household composition of participating households, Somalia

2019

Characteristic n % a (95% CI) b

Average household size Mean 2085 4.1 (3.9, 4.4)

Number of household members 1 252 13.2 (10.1, 16.9) 2 387 19.6 (16.8, 22.8) 3 395 17.2 (15.2, 19.3) 4 307 13.2 (11.3, 15.4) 5 256 10.8 (9.3, 12.5) 6 181 8.0 (6.7, 9.5) 7 139 6.3 (5.2, 7.5) 8 98 4.5 (3.6, 5.6) 9 65 3.1 (2.3, 4.2) 10+ 92 4.2 (3.4, 5.2)

Number of women 15-49 years of age in households 0 506 24.3 (22.2, 26.6) 1 1355 62.5 (60.1, 64.9) 2 213 8.9 (7.6, 10.5) 3 74 3.2 (2.4, 4.3) 4 20 0.8 (0.5, 1.3) 5 3 0.2 (0.0, 0.8) 6 1 0.0 (0.0, 0.2)

Number of children 0-59 months in households 0 1002 47.9 (45.2, 50.6) 1 586 27.1 (24.7, 29.5) 2 391 17.3 (15.7, 19.0) 3 152 6.3 (5.2, 7.6) 4 37 1.4 (1.0, 2.2) 5 4 0.1 (0.0, 0.3) TOTAL RESPONDING HOUSEHOLDS 2172 100

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design.

As presented in Table 7 just about three quarters of household heads never attended school.

Of those 25% of household heads who ever attended school, about half had secondary or

lower education and just over 40% had only Koranic education. Addition information about

education of the household head is presented by state and residence in Appendix 8.9.


33

Table 7. Educational level of household head for participating households, Somalia 2019


Head of household ever attended school or preschool No 1547 74.3 (70.8, 77.5) Yes 616 25.5 (22.3, 29.0) Don’t know 9 0.3 (0.1, 0.5)

Highest level of school attended by household head Preschool 22 3.7 (2.3, 6.0) Primary 187 31.6 (26.8, 36.9) Secondary 100 15.2 (12.5, 18.3) Higher 47 7.9 (5.5, 11.1) Koranic 257 41.2 (35.4, 47.2) Don´t know 3 0.4 (0.1, 1.5) TOTAL RESPONDING HOUSEHOLDS 2172 100 --


3.2.2. Displacement and relocation

As shown in Table 8, in total 7.5% of households in Somalia hosted IDPs, with a slightly higher

proportion of urban households being hosts. The largest proportion of hosting households

were found in Galmudug and Banaadir, whereas no households in Hirshabelle hosted IDPs.

For most of the regions, the vast majority of IDPs had arrived within the last six months.

As shown in Table 9, almost two-thirds of households had relocated in the past 30 years, and

almost 45% of households relocated in the past 5 years, whereas almost one third have lived

for at least 30 years at the current residence. On average urban households lived longer at

their current residence compared to rural or IDP households. Households relocate most

frequently in Jubaland; on average about every six years. In contrast, households located in

Hirshabelle stay on average for more than 75 years at the same place. Also, wealthier

households tend to stay longer at the current residence compared to poorer households.

The reasons for household relocation strongly differed by state (see Figure 3). In all states less

than 20% of all households relocated in the last 30 years because of security reasons. In

Somaliland, Puntland, Hirshabelle and Banaadir the prospect of better economic

opportunities was the main driver of relocation, whereas in South-West, Galmudug and

Jubaland the main reason for relocation was drought. Many households in Galmudug state

relocated because of flooding.


34

Table 8. Proportion of households hosting of internally displaced persons and arrival of

IDPs in past 6 months, Somalia 2019 a

Characteristic n % b (95% CI) c

Household currently hosting IDPs c No 1491 92.5 (90.2, 94.3) Yes 132 7.5 (5.7, 9.8)

Proportion of household currently hosting IDPs d Rural 47 6.6 (4.0, 10.6) Urban 85 8.2 (6.0, 11.1)

Proportion of household currently hosting IDPs d Somaliland 24 6.2 (3.6, 10.4) Puntland 19 6.3 (3.6, 10.6) Hirshabelle 0 -- Galmudug 31 14.3 (8.3, 23.5) South-West 3 4.5 (1.5, 12.4) Jubaland 12 7.1 (2.9, 16.8) Banaadir 43 13.0 (8.9, 18.5)

Proportion of household with IDPs that arrived in the past six months d

Somaliland 19 79.2 (56.0, 91.9) Puntland 14 73.7 (53.4, 87.2) Galmudug 11 35.5 (17.9, 58.2) South-West 0 -- Jubaland 8 66.7 (18.7, 94.5) Banaadir 36 83.7 (60.7, 94.5)

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data. a Analysis excludes households from IDP settlements b Percentages weighted for unequal probability of selection. c CI=confidence interval, calculated taking into account the complex sampling design.

d Excludes households locate in IDP communities and only includes households located in urban or rural areas


35

Table 9. Proportion or mean number of years at current residence, Somalia 2019

Characteristic n % or mean a

(95% CI) b

Household relocated in past 30 years No 867 36.8 (32.1, 41.8) Yes 1305 63.2 (58.2, 67.9)

Proportion of households that relocated by year <1 year 130 6.3 (4.6, 8.5) 1-2 years 269 12.8 (10.9, 15.0) 3-5 years 493 26.1 (22.2, 30.4) 6-9 years 231 10.6 (8.7, 12.9) 10-29 years 312 13.6 (11.2, 16.4) ≥ 30 years 737 30.6 (26.5, 35.0)

Residence, mean years Rural 700 26.5 (19.7, 33.3) Urban 1129 37.1 (32.3, 41.7) IDP 343 27.6 (19.2, 35.9)

State, mean years Somaliland 455 36.3 (29., 43.2) Puntland 343 23.1 (18.3, 27.) Hirshabelle 163 76.8 (60.4, 93.) Galmudug 249 28.0 (19.6, 36.3) South-West 217 40.9 (33.0, 48.8) Jubaland 287 6.2 (5.0, 7.5) Banaadir 458 45.0 (40.0, 50.0)

Wealth quintile, mean years Lowest 287 26.3 (17., 35.3) Second 335 19.6 (12.4, 26.7) Middle 485 31.7 (26.3, 37.0) Fourth 573 40.5 (34.0, 47.0) Highest 492 41.3 (34.6, 47.9)

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data. a Means are weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design.


36

Figure 3. Main reason households that relocated in past 30 years moved from their

previous residence, by state, Somalia 2019

3.2.3. Agricultural activities and livestock ownership

As shown in Table 10, only few households owned some agricultural or grazing land.

Approximately 15% of households owned some livestock. Of those households, more than

80% owned goats, one-third owned sheep, one-fourth poultry and one-fifth cattle. About 16%

owned donkeys. Camels and bees were less commonly owned. Livestock ownership is

presented by state, residence, and sex of the household head in Appendix 8.9.

3.2.4. Household financial access and remittances

Only 2.4% (95% CI: 1.7, 3.3) households reported having a bank account. This proportion

varied significantly (p<0.01) by wealth quintile, with 6.6% (95% CI: 4.3, 10.0) of households in

the highest wealth quintile having a bank account compared to 0.9% (95% CI: 0.2, 3.1) of

households in the lowest wealth quintile. Having a bank account was not significantly

associated with state (data not shown).

In total, 6.2% (95% CI: 4.8, 8.1) of households received remittances within three months of

being interviewed. The receipt of remittances differed statistically by household wealth

(p<0.001): 17.4% (95% CI: 12.9, 22.9) of households in the highest wealth quintile received

remittances, whereas only 0.3% (95% CI: 0.0, 2.3) of households in the lowest wealth quintile

received remittances. In addition, the proportion of households receiving remittances varied

by state (p=0.05), with the highest proportions found in Banaadir (13.6%; 95% CI: 9.4, 19.3),

Southwest (9.1%; 95% CI: 4.9, 16.6), and Somaliland (8.5%; 95% CI: 5.6, 12.5). Overall, the

mean remittance amount received in the past three months was approximately 370 US

Dollars, with wealthier households receiving higher remittance amounts than poorer

households (Figure 4).

11 166

17 13 18 14

20 12 33

41

7147

10

0 0

0

34

1

1

14 9

9

17

5

12

40 38

36

6 5

18

59

14 15

151 2 7

411 100 1 2 3 1

0%

20%

40%

60%

80%

100%

Somaliland Puntland Hirshabelle Galmudug South-West Jubaland Banaadir

Other

Lack of access to services

Better economic opportities

Total loss of livelihood

Floods

Drought

Insecurity


37

Table 10. Proportion of agriculture and livestock variables for participating households,

Somalia 2019


Member of household owns any agricultural land No 2056 94.3 (91.1, 96.4) Yes 111 5.5 (3.5, 8.7)

Member of household owns any grazing land No 2133 98.4 (97.2, 99.1) Yes 36 1.6 (0.9, 2.8)

Household owns any livestock No 1841 85.3 (81.1, 88.7) Yes 324 14.4 (11.0, 18.6)

Household owns livestock, specific c Camels 21 7.3 (4.1, 12.7) Cattle 60 19.5 (9.8, 35.1) Goats 261 80.4 (68.8, 88.4) Sheep 112 33.3 (24.8, 43.0) Donkeys 53 16.0 (9.6, 25.4) Poultry (chicken, ducks, etc.) 83 23.9 (16.0, 34.1) Bees 1 0.6 (0.1, 3.6)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Question only asked to households responding “Yes” to livestock ownership

Figure 4. Mean amount of remittances received in past 3 months, Somalia 2019

100138.9 140.4

232.4

569.2

0

100

200

300

400

500

600

Lowest Second Middle Fourth Highest

Mea

n a

mo

un

t (U

SD)

Wealth quintiles


38

3.2.5. Cooking fuel and household lighting

Cooking was done in virtually all households, and most households use natural fuels (see

Table 11). Lighting is provided by a torch or flashlight in about one-half of households and by

mains electricity in about one-third of households. Fuel type and electricity status is displayed

by residence in Appendix 8.9.

Table 11. Distribution of cooking fuel and lighting variables for participating households,

Somalia, 2019


Type of fuel used for cooking Mains electricity 267 11.0 (8.2, 14.6) Liquefied petroleum gas (LPG) 55 2.1 (1.3, 3.3) Kerosene 17 1.0 (0.4, 2.2) Charcoal 1000 37.8 (33.4, 42.3) Firewood 820 47.6 (41.9, 53.4) Straw, shrubs, or grass 4 0.2 (0.0, 0.9) No food cooked in the household 3 0.2 (0.1, 0.6) Other 6 0.2 (0.1, 0.6)

How household is lit at night Mains electricity 1004 36.9 (31.9, 42.2) Solar energy 135 6.4 (4.5, 9.1) Kerosene 74 3.6 (2.3, 5.5) Firewood 70 3.9 (2.4, 6.1) Torch/flashlight 887 49.0 (44.1, 54.0) Other 2 0.1 (0.0, 0.5)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design.

3.2.6. Water and sanitation

As shown in Table 12, eight out of ten households had an improved source of water for

drinking. About 20% of households reported treating their water to make it safe to drink.

However, because the majority of these households already consume water from an

improved source about eight out of ten households actually drank safe water. About half of

the households had improved sanitation facilities. More than 90% of households residing in

Hirshabelle, Banaadir, South-West and Galmudug had improved water source and access to

safe drinking water; this proportion was much smaller in Somaliland, Puntland and Jubaland

(Figure 5). A smaller proportion of households residing in South-West and Hirshabelle than in

other states had improved sanitation.


39

Table 12. Distribution of water and sanitation variables for participating households,

Somalia 2019


Main source of water for drinking c Unimproved source 375 19.4 (14.8, 25.1) Improved source 1797 80.6 (74.9, 85.2)

Treat water to make safe to drink No 1775 79.2 (75.6, 82.4) Yes 397 20.8 (17.6, 24.4)

Drink safe water d No 319 17.2 (12.8, 22.6) Yes 1853 82.8 (77.4, 87.2)

Household sanitation e Inadequate 1047 51.3 (46.3, 56.3) Adequate 1118 48.7 (43.7, 53.7)

TOTAL RESPONDING HOUSEHOLDS 2172 100 -- Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Improved source = water from piped system, tube well or borehole, protected well, protected spring, rainwater collection,

bottled water or sachet water. Unimproved source = water from unprotected well, unprotected spring, tanker truck or cart, surface water or other.

d Composite variable of main source of drinking water and treating water to make safe for drinking e Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation =

flush or pour flush toilet or pit latrine with slab not shared with another household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field.

Figure 5. Proportion of water and sanitation variables, by State, Somalia 2019

74

63

98 10093

64

100

81

4

16

15

31

43

2621

7471

98 9993

70

100

83

44

66

31

66

19

65

5249

0

10

20

30

40

50

60

70

80

90

100

Somaliland Puntland Hirshabelle Galmudug South-West Jubaland Banaadir Total

Pro

po

rtio

n (

%)

Improved water source Treats water Drinks safe water Improved sanitation


40

About half of the households had a fixed sink or basin for handwashing, and the remaining

household washed hands elsewhere outside the house or compound. Almost two-thirds of

the households had water available at the handwashing site, and two-thirds had some kind

of soap at the handwashing site. A larger proportion of households had soap elsewhere in the

house.

Table 13. Distribution of handwashing variables for participating households, Somalia

2019


Location of handwashing site Sink or fixed basin (observed) 1046 51.7 (46.4, 56.9) Hands washed anywhere around dwelling (observed) 742 28.6 (24.9, 32.6) Not in dwelling / plot / yard (not observed) 223 11.8 (8.9, 15.4) Permission to see handwashing area not given 134 6.5 (4.7, 9.0) Other 27 1.4 (0.6, 3.6)

Water is available at observed handwashing place c No 519 37.1 (32.4, 42.0) Yes 1266 62.9 (58.0, 67.6)

Soap seen only at handwashing site c, d Bar soap 1100 45.5 (39.8, 51.3) Detergent 542 20.5 (18.2, 23.0) Liquid soap 236 7.1 (5.9, 8.5) Ash / mud / sand 470 23.9 (18.8, 29.8)

Soap anywhere in dwelling d, e Bar soap 1278 53.8 (48.0, 59.6) Detergent 696 27.6 (25.0, 30.5) Liquid soap 306 9.4 (8.1, 10.9) Ash / mud / sand 556 28.6 (23.4, 34.5)

Any type of soap in household for handwashing f No 801 42.4 (36.7, 48.3) Yes 1371 57.6 (51.7, 63.3)

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Results based on households where handwashing site was observed, only

d N’s do not sum to total as multiple responses were permitted e Results based on all surveyed households

f “Any Soap” includes bar soap / detergent / liquid soap either seen at handwashing site or soap possessed by the household.

3.2.7. Bednet ownership and use

Overall more than 50% of households owned at least 1 bednet (Table 14). Bednet ownership

was more common in urban households than in rural and IDP households. Bednet ownership

varied by state, with the highest proportion of households owning a bednet in Galmudug state

and the lowest proportion in Jubaland.


41

Among households owning bednets, 90.3% (95% CI: 88.3, 91.9) households owned between

1 and 3 bednets (data not shown). The relatively small number of bednets owned and high

usage helps explain the high proportion of bednets being used.

Use of bednets the night before data collection was near universal among households

possessing one bednet or more. Among these households, nearly all households used all

bednets owned (Figure 6).

Table 14. Proportion of households owning at least one bednet, Somalia 2019


Total No 818 42.7 (38.3, 47.3) Yes 1290 57.3 (52.7, 61.7)

Residence Rural 374 47.0 (37.8, 56.4) Urban 763 67.8 (63.3, 72.0) IDP 153 45.8 (35.4, 56.6)

State Somaliland 267 68.5 (57.7, 77.5) Puntland 224 63.8 (55.6, 71.3) Hirshabelle 113 69.3 (59.4, 77.7) Galmudug 209 83.0 (64.4, 92.9) South-West 96 45.9 (37.5, 54.5) Jubaland 123 41.9 (29.5, 55.4) Banaadir 258 53.7 (48.2, 59.1)


Figure 6. Proportion of bednets used the previous night amount households owning

bednets, by state, Somalia 2019

97.6 97.3 97.990.8 92.5

97.2 97.4

0

10

20

30

40

50

60

70

80

90

100


Pro

po

rtio

n (

%)


42

3.2.8. Food insecurity

Table 15 presents the prevalence of food insecurity categories for residence, state, and

wealth quintile. Most notably, most households either described themselves as food secure

(49%) or severely food insecure (38%); few households considered themselves mildly or

moderately food insecure. Food insecurity category was significantly associated with

residence, state, and wealth quintile. Regarding residence, the more than half of the

households in urban areas and IDP settlements were classified as either moderately or

severely food insecure.

Regarding state, nearly three-quarters of households in Galmudug were food secure, which

was the highest proportion of any state. Conversely, South-West state had the highest

proportion of households classified as severely food insecure. Food insecurity was also

prominent in Somaliland and Banaadir, were approximately 52% and 43% of households were

either moderately or severely food insecure.

Wealth quintile was also significantly associated with food insecurity status, illustrating a

positive dose-response relationship, with the proportion of food secure households

increasing as wealth quintile increases. Among the lowest wealth quintile, less than 40% of

households were considered food secure, compared to nearly 70% of households in the

highest quintile.

SOMALIA MICRONUTRIENT SURVEY - 2019

43

Table 15. Household food insecurity score (HFIAS) categories, by residence, state, and wealth quintile, Somalia 2019

Characteristic n Food secure, %

(95% CI) b Mild food insecurity,

%

(95% CI) b Moderate food

insecurity, %

(95% CI) b Severe food

insecurity,%

(95% CI) b P value c

Residence Rural 700 55.5 (47.7, 63.1) 7.0 (4.8, 10.0) 12.0 (8.8, 16.2) 25.5 (18.1, 34.6) 0.0000 Urban 1129 45.7 (40.5, 51.0) 2.9 (1.9, 4.5) 5.9 (4.3, 8.1) 45.4 (40.4, 50.6) IDP 345 45.2 (36.2, 54.6) 3.5 (2.0, 5.9) 9.0 (6.0, 13.3) 42.3 (32.6, 52.7)

State Somaliland 455 42.3 (35.7, 49.1) 5.9 (3.7, 9.1) 6.7 (4.0, 10.8) 45.2 (40.3, 50.2) 0.0000 Puntland 343 60.8 (53.9, 67.2) 3.7 (2, 6.6) 9.7 (6.0, 15.3) 25.8 (21.1, 31.1) Hirshabelle 163 50.9 (38.7, 63.1) 6.1 (2.8, 13.0) 17.8 (12.9, 24.0) 25.2 (16.7, 36.0) Galmudug 249 74.5 (67.9, 80.1) 2.8 (1.3, 6.2) 4.8 (2.7, 8.5) 17.9 (12.1, 25.6) South-West 217 19.3 (14.3, 25.4) 0.0 -- 0.5 (0.1, 3.4) 80.2 (74.5, 84.9) Jubaland 287 61.9 (51.4, 71.4) 7.2 (4.4, 11.6) 13.8 (9.4, 19.9) 17.1 (10.0, 27.6) Banaadir 458 53.8 (48.1, 59.4) 3.3 (3.4, 5.8) 11.2 (8.8, 14.0) 31.7 (26.7, 37.1)

Wealth Quintile Lowest 287 37.2 (25.4, 50.7) 4.8 (2.5, 9.1) 8.3 (4.6, 14.7) 49.6 (33.6, 65.6) 0.0001 Second 335 42.6 (33.1, 52.7) 4.0 (2.2, 7.3) 9.6 (6.5, 14.1) 43.7 (33.9, 54.1) Middle 485 41.3 (35.8, 47.1) 4.9 (3.0, 8.0) 9.7 (6.6, 14.1) 44.1 (38.7, 49.6) Fourth 573 55.8 (50.2, 61.2) 4.7 (2.8, 7.9) 10.0 (7.3, 13.6) 29.4 (24.8, 34.5) Highest 492 68.4 (61.2, 74.7) 3.6 (2.2, 6.0) 5.0 (3.2, 7.7) 23.1 (18.0, 29.1)

Total 2174 49.0 (44.7, 53.4) 4.4 (3.4, 5.8) 8.5 (6.9, 10.5) 38.0 (33.2, 43.1) Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection.

b CI=confidence interval, calculated taking into account the complex sampling design.

c Chi-square p-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups


44

3.2.9. Salt iodine concentration

About 94% of households had salt in their house at the time of the survey, and a salt sample

was collected from almost all of those households. Nationally, the mean iodine salt

concentration (5.1 mg/Kg) and the median iodine salt concentration (2.1 mg/kg; data not

shown) were low, and few households had adequately iodized salt (see Table 16). Salt in rural

households has statistically significantly more iodine than that found in urban and IDP

households. Salt from households in Jubaland has much more iodine that salt in households

in other states. Unexpectedly, salt from poorer households has more iodine than salt from

wealthier households.

As shown in Figure 7, most salt samples contain little or no iodine, a small minority is

inadequately iodized, and a smaller proportion is adequately iodized. Figure 8 shows the

distribution of salt iodine concentrations.

Table 16. Proportion of salt specimens with an iodine concentration ≥15 ppm and mean

salt iodine concentration in participating households, Somalia 2019

Characteristic n

Adequately

iodized (%) a

(95% CI)b P value c

Mean iodine

concentration

(ppm) (95% CI)b P value c

Residence

Rural 69 17.2 (8.8, 30.8) 0.0000 9.47 (5.42, 13.5) 0.0042

Urban 14 1.1 (0.6, 1.9) 2.61 (2.37, 2.86)

IDP 6 2.0 (0.7, 5.8) 2.90 (1.89, 3.91)

State

Somaliland 5 1.1 (0.4, 3.1) 0.0000 2.85 (2.23, 3.45) 0.0001

Puntland 2 0.6 (0.1, 2.2) 1.97 (1.76, 2.19)

Hirshabelle 2 1.5 (0.2, 9.5) 3.24 (2.27, 4.20)

Galmudug 2 1.1 (0.3, 3.9) 2.14 (1.72, 2.57)

South-West 1 0.3 (0.0, 2.2) 2.87 (2.26, 3.47)

Jubaland 69 36.2 (18.7, 58.4) 17.40 (9.85, 25.1)

Banaadir 8 1.6 (0.8, 3.4) 2.19 (1.76, 2.61)

Wealth Quintile

Lowest 23 9.4 (4.2, 19.6) 0.0000 6.69 (4.16, 9.22) 0.0011

Second 45 20.9 (9.7, 39.4) 11.22 (5.49, 16.9)

Middle 8 2.8 (1.2, 6.5) 3.17 (2.21, 4.13)

Fourth 7 1.3 (0.6, 2.7) 2.16 (1.86, 2.46)

Highest 6 1.2 (0.5, 2.8) 2.56 (2.20, 2.92)

ALL HOUSEHOLDS 89 7.0 (3.8, 12.5) 5.09 (3.50, 6.69)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.


c Chi-square p-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the

other subgroups


45

Figure 7. Proportion of iodine in salt at different levels, Somalia 2019

Figure 8. Distribution of iodine concentration in salt samples, Somalia 2019

3.2.10. Drinking water iodine concentration

The median iodine concentration in drinking water is nearly 60 µg/L, and Table 17 presents

the unweighted median iodine concentrations in drinking water for different sub-groups.

Because the distribution of iodine concentrations in drinking water is not normally distributed

(see Figure 9), the calculation of p-values used to compare the drinking water iodine

79.6%

13.4%

7.0%

0.0 - 4.9 ppm 5.0 - 14.9 ppm ≥ 15.0 ppm

0

10

20

30

40

50

60

70

80

Pro

po

rtio

n (

%)

Iodine content in salt (ppm)


46

concentrations in sub-groups in Table 17 was done using log transformed values, which

yielded a normal distribution and facilitate sub-group comparisons.

Table 17. Median water iodine concentration in participating households, Somalia 2019

Characteristic n Median iodine

concentration

(µg/L)

(IQR) b P value c

Residence

Rural 375 38.2 (5.0, 116.3) 0.3157

Urban 572 66.1 (11.8, 113.6)

IDP 173 84.9 (29.8, 112.6)

State

Somaliland 240 43.3 (17.8, 71.5) 0.0000

Puntland 176 72.3 (10.4, 108.3)

Hirshabelle 93 0.0 (0.0, 151.5)

Galmudug 129 79.4 (0.0, 124.1)

South-West 114 34.3 (14.6, 62.0)

Jubaland 117 34.5 (14.7, 63.4)

Banaadir 251 106.7 (79.7, 134.5)

Wealth Quintile

Lowest 142 39.7 (20.2, 105.3) 0.0018

Second 171 36.9 (11.9, 82.7)

Middle 249 54.4 (8.5, 114.1)

Fourth 301 79.6 (6.6, 125.7)

Highest 257 70.9 (16.7, 116.5)

Drinking water source

Piped water 542 81.5 (0.0, 126.1) 0.0000

Public tap / standpipe 96 39.7 (15.3, 105.1)

Tube well / borehole 64 46.4 (17.2, 141.6)

Well spring 100 53.3 (15.7, 111.7)

Berkad or Balley 146 41.1 (14.5, 81.3)

Tanker truck 165 37.2 (13.5, 79.8)

Other 7 63.3 (35.2, 66.8)

ALL HOUSEHOLDS 1120 58.8 (11.8, 113.5)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because

of missing data. a Median concentration is calculated using unweighted data.

b IQR = Interquartile range, calculated using unweighted data.

c ANOVA p-value <0.05 indicates that the mean of the natural log of water iodine concentration in at least one

subgroup is statistically significantly different from the values in the other subgroups.


47

No significant differences were observed by household residence, but significant differences

were observed by state, wealth quintile, and type of water source. The highest drinking water

iodine concentrations were found in households in Banaadir, Galmudug, and Puntland and

the lowest in Hirshabelle. The highest median iodine concentrations were found among the

two higher wealth groups. The highest iodine concentrations were found in piped water, and

the lowest concentrations found in water delivered by tanker trucks.

As shown in Figure 9, only one fifth of households are consuming water with less than 10 µg/L

of iodine. For reference, the median concentration of iodine in drinking water in the United

States is 4 µg/L [39]. Despite the relatively high mean water iodine concentrations, Figure 10

illustrates that a substantial proportion of households drink water containing no detectable

iodine. Nonetheless, the majority of households are consuming water containing high levels

of iodine, with a small proportion of households consuming water with >350 µg/L of iodine.

Figure 9. Proportion of iodine in water at different levels (µg/L), Somalia 2019

20.0%

31.7%21.1%

20.5%

6.6%

<10.0 10-49.9 50-99.9 100-199.9 ≥200


48

Figure 10. Distribution of water iodine concentration, Somalia 2019

3.2.11. Bouillon cube use and consumption

Bouillon cube use and consumption is shown in Table 18 and Figure 11. More than one-

quarter of all households reported using bouillon cubes. No significant differences were found

between urban, rural and IDP households, but a significant difference was observed by state.

A large proportion of households in Somaliland and Banaadir consumed bouillon cubes at the

time of the survey while only few of the households in Hirshabelle consumed bouillon cubes.

Table 18. Proportion of households that use bouillon cubes, Somalia 2019

Characteristic n % a (95% CI) b P value c

Residence

Rural 183 25.5 (17.6, 35.3) 0.3958

Urban 352 31.8 (27.3, 36.7)

IDP 89 25.9 (17.1, 37.3)

State

Somaliland 203 45.2 (35.4, 55.4) 0.0000

Puntland 91 25.1 (17.9, 34.0)

Hirshabelle 9 5.5 (2.2, 13.1)

Galmudug 64 24.7 (15.5, 37.0)

South-West 38 19.1 (13.6, 26.1)

Jubaland 43 15.3 (8.2, 26.7)

Banaadir 176 40.2 (33.0, 47.8)

Table cont. on next page


49

Characteristic n % a (95% CI) b P value c

Wealth Quintile

Lowest 18 6.3 (3.8, 10.2) 0.0000

Second 88 27.1 (19.5, 36.3)

Middle 147 34.5 (26.8, 43.1)

Fourth 179 34.4 (28.3, 41.0)

Highest 192 41.6 (35.3, 48.2)

ALL HOUSEHOLDS 624 28.7 (24.9, 32.8) --

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of

missing data. a Percentages weighted for unequal probability of selection.


c Chi-square p-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from

the values in the other subgroups

Among household that reported consuming bouillon cubes, Jumbo™ was the most commonly

consumed brand in nearly all states (see Figure 11). In total, 67.1% (95% CI: 58.5, 74.7) of

households consumed Jumbo™, with approximately 40% to 100% of households consuming

this brand by state. The second most common brand of bouillon cube was Maggi™, consumed

by 16.5% (95% CI: 11.7, 22.8) of households overall, but with highest proportions found in

Somaliland, Puntland, and Jubaland. Importantly, results from Jubaland should be interpreted

with caution as nearly half of household did not know the brand of bouillon cube regularly

consumed.

Figure 11. Brands of bouillon cube usually consumed by households, by state, Somalia

2019

55.462.2

100.0

85.5 89.4

41.3

90.6

67.6

28.6 11.9

11.510.6

7.4

2.9

16.2

15.124.8

3.0

6.4

0.89.4

1.0 1.1

44.9

5.76.8

0

20

40

60

80

100


Pro

po

rtio

n (

%)

Jumbo Maggi Other Don't know


50

3.3. Children

3.3.1. Characteristics

Table 19 presents the demographic characteristics of children participating in the SMS 2019.

Children between 12-59 months of age were almost equally distributed among the different

age groups, whereas younger children are slightly underrepresented

Table 19. Description of sampled children (0 - 59 months), Somalia 2019

Survey Sample


Age Group (in months)

0-5 136 7.3 (5.8, 9.1)

6-11 165 8.5 (7.3, 9.8)

12-23 386 19.7 (17.7, 21.8)

24-35 434 22.6 (20.8, 24.5)

36-47 436 22.3 (20.3, 24.3)

48-59 390 19.8 (17.9, 21.8)

Sex

Male 979 50.9 (48.5, 53.4)

Female 968 49.1 (46.6, 51.5)

Residence

Rural 626 31.9 (23.9, 41.0)

Urban 1042 53.2 (44.4, 61.8)

IDP 279 15.0 (12.7, 17.6)

State

Somaliland 550 37.4 (32.8, 42.2)

Puntland 346 10.7 (8.5, 13.4)

Hirshabelle 217 7.0 (4.3, 11.3)

Galmudug 150 5.3 (3.4, 8.0)

South-West 124 10.5 (6.6, 16.3)

Jubaland 158 13.7 (9.5, 19.4)

Banaadir 402 15.3 (12.1, 19.2)

Wealth quintile

Lowest 146 11.4 (8.9, 14.6)

Second 264 17.3 (13.1, 22.4)

Middle 503 23.8 (19.4, 28.8)

Fourth 543 22.5 (19.2, 26.2)

Highest 481 25.0 (19.9, 30.9)

ALL CHILDREN 1947 100.0 --

Note: The n’s are un-weighted numbers of subjects in each subgroup; the sum of subgroups may

not equal the total because of missing data.

a Percentages are un-weighted and do not account for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design


51

3.3.2. Low birthweight

In total, 425 children (24.8%;95% CI: 20.1, 30.1) of children were weighed at birth. As shown

in Figure 12, the proportion of children weighed at birth is highest in Somaliland (44.0%) and

Banaadir (21.4%). Also, a greater proportion of urban children were weighed birth compared

to children from rural areas and IDP settlements

Figure 12. Proportion of children weighed at birth, by state and residence, Somalia 2019

Of the 425 children weighed at birth, caretakers of 69 children could not recall the birthweight

and did not have any documentation showing the weight. In total, 356 children had data on

their birthweight, with 293 children (83.5%; 95% CI: 72.2, 90.8) having birthweights from

recall and 63 children (16.5%; 95% CI: 9.2, 27.8) having birthweights from a health card or

other document.

44.0

6.9

11.7

4.3 5.4

16.3

21.4

17.1

32.1

14.7

24.8

0

5

10

15

20

25

30

35

40

45

50

Pro

po

rtio

n (

%)


52

Table 20. Proportion of preschool age children born with low birthweight, with data from

health card or recall, Somalia 2019

From Card From Recall From Card and Recall

Characteristic n % a n % a n % a (95% CI) b

Sex

Male 8 31.6 35 22.6 43 24.0 (17.2, 32.5)

Female 20 64.0 22 16.1 42 24.6 (16.4, 35.1)

Residence

Rural 2 52.0 17 27.9 19 29.2 (13.6, 52.1)

Urban 21 45.7 28 13.6 49 19.8 (13.7, 27.6)

IDP 5 62.5 12 44.4 17 48.6 (33.9, 63.5)

State

Somaliland 6 31.6 39 17.4 45 18.6 (12.1, 27.4)

Puntland 1 25.0 3 50.0 4 40.0 (17.0, 68.4)

Hirshabelle - - 0 0.0 0 0.0 -

Galmudug - - 2 28.6 2 28.6 (5.9, 71.9)

South-West - - 1 100.0 1 100.0 -

Jubaland 13 72.4 4 40.0 17 59.4 (32.4, 81.7)

Banaadir 8 35.3 8 20.8 16 25.3 (17.3, 35.3)

Wealth quintile

Lowest 1 100.0 1 14.4 2 25.2 (3.6, 75.2)

Second 4 82.7 16 28.8 20 33.1 (19.8, 49.8)

Middle 8 59.1 11 42.5 19 49.2 (30.9, 67.7)

Fourth 3 37.6 12 22.2 15 24.4 (15.0, 37.2)

Highest 12 34.1 17 11.5 29 14.7 (9.3, 22.5)

ALL CHILDREN 28 48.1 57 19.6 85 24.3 (18.1, 31.8)

Note: The n’s are un-weighted numbers of subjects in each subgroup; the sum of subgroups may not equal the total because of

missing data.

a Percentages are un-weighted and do not account for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design

For Galmudug, South-West, and Hirshabelle states, very few children had usable birthweight

data. To illustrate, only 25 children from Galmudug and 6 children from South-West were

reported to be weighed at birth, and of these children, caretakers could only provide

birthweights for 7 children in Galmudug and 1 child in South-West. In Hirshabelle, only 7

children were reported to be weighed at birth, and all had birthweights.

As shown in Table 20, low birthweight was higher among female children for those with data

from health cards, but was similar when low birthweight was calculated for all children.

Regarding residence, low birthweight was consistently higher among children from IDP

settlements, regardless of how the birthweight was provided. Low birthweight was highest in

Jubaland and Puntland, but due to the large number of sub-groups, the confidence intervals

for all states are quite wide, showing that there is poor precision for state-level results. Lastly,


53

the prevalence of low birthweight was considerably lower among children from the highest

wealth quintile.

3.3.3. Recent illness and treatment

Table 21 below shows various health indicators for children less than 5 years of age. Illnesses

during the past 2 weeks reported by the caregiver are relatively common, especially illnesses

with cough and fever. Diarrhea and lower respiratory infection are less common. About one

quarter of children have elevated CRP or AGP, indicating some measure of inflammation.

3.3.4. Infant and young child feeding indicators

Table 22 presents infant and young child feeding indicators. The number of children in the

denominator for each indicator is often small because of the restricted age range of children

for whom the indicator is calculated. Therefore, these indicators are presented only at the

national level. Among all children under two years of age, more than half have ever been

breastfed. Early initiation of breastfeeding (in the first hour after birth) is practiced by the

caretakers of almost nine out of ten children. The prevalence of exclusive breastfeeding is

very poor in children less than 6 months of age. Further, continued breastfeeding at 1 year is

practiced by the caretakers of about one-third of children.

Indicators of complementary feeding, including introduction of solid foods and minimum

dietary diversity are mostly suboptimal. More than two-thirds of children 6-8 months of age

were eating complementary foods the day before the interview and less than every fifth child

has an adequate dietary diversity.


54

Table 21. Proportion of preschool age children with caregiver-reported diarrhea, fever,

cough and measured inflammation, Somalia 2019


Diarrhea in the past 2 weeks

No 1691 88.2 (85.8, 90.2)

Yes 254 11.7 (9.7, 14.1)

Feeding pattern when child had diarrhea

Less than normal 148 50.6 (40.7, 60.5)

The same amount as normal 58 27.7 (20.0, 36.9)

More than normal 36 18.7 (12.6, 26.8)

No food given 12 3.1 (1.3, 6.9)

Diarrhea with blood in the past 2 weeks

No 1916 98.7 (98.0, 99.1)

Yes 23 0.9 (0.6, 1.6)

Fever the past 2 weeks

No 1512 78.4 (75.0, 81.4)

Yes 432 21.5 (18.4, 24.9)

Illness with a cough in the past 2 weeks

No 1413 72.5 (68.6, 76.0)

Yes 527 27.2 (23.6, 31.1)

Lower respiratory infection C

No 1833 94.5 (92.8, 95.8)

Yes 114 5.5 (4.2, 7.2)

Inflammation d

None 1114 74.6 (71.6, 77.5)

Incubation (elevated CRP only) 47 2.9 (1.9, 4.5)

Early convalescence (elevated CRP and AGP) 108 7.3 (5.9, 9.1)

Late convalescence (elevated AGP only) 226 15.1 (12.8, 17.8)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Lower respiratory infection defined as fever, cough, and difficulty breathing due to problem in chest d CRP=C-reactive protein, AGP=alpha1-acid-glucoprotein


55

Table 22. Proportion of children with various infant and young child feeding indicators in

children less than 2 years of age, Somalia 2019


Ever breastfed (children <24 months) c

Ever given breastmilk in any form 395 53.9 (49.1, 58.6)

Early initiation of breastfeeding c,d

Initiated breastfeeding in first hour after birth 338 86.0 (80.7, 90.1)

Initiated breastfeeding in 1-23 hours after birth 39 9.1 (5.9, 13.6)

Initiated breastfeeding in≥24 hours after birth 11 3.2 (1.4, 7.1)

Don’t know 7 1.7 (0.7, 3.9)

Exclusive breastfeeding (children <6 months)

Only consumed breast milk 24 hours before interview 21 15.6 (9.2, 25.3)

Continued breastfeeding at 1 year (children 12-15 months)

Breastfed the day before the interview 62 37.0 (28.7, 46.0)

Introduction of solid, semi-solid or soft foods (children 6-8 months)

Eating complementary food the day before the interview 58 71.2 (59.0, 81.0)

Minimum dietary diversity (children 6-23 months)

Adequate dietary diversity the day before the interview 79 17.6 (12.9, 23.5)

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Results presented for all children <24 months of age d Early initiation of breastfeeding only asked for children reported to have ever breastfed e According to WHO’s IYCF guidelines [40], children 6-23 months of age who received foods from ≥ 4 food groups (out of 7 total

food groups) during the previous day

3.3.5. Consumption of vitamin and mineral supplements

The consumption of selected micronutrient supplements and fortified foods among children

6-59 months and 6-23 months of age is presented in Table 23. Age group specific coverage

was calculated because supplements and fortified products are sometimes marketed and

targeted specifically at younger children. However, among all indicators examined, there are

only very minor differences in the coverage by age group. Regardless of age group,

consumption of micronutrient supplements and fortified foods is not common.

Approximately 15% of children are registered in any type of feeding programs, such as

inpatient or outpatient supplementary or therapeutic feeding. While no significant

differences were observed by age group or sex, significant and marked differences were

found by residence, state, and wealth quintile (Figure 13). Participation is highest in rural

areas (26.3%), and nearly two-thirds of children in Jubaland reported to be registered in a

feeding program. In addition, the proportion of children registered in feeding programs is

highest in poorer households.


56

Table 23. Proportion of children 6-59 months of age consuming micronutrient powders, RUTF/RUSF, and iron-fortified foods in past 24

hours, Somalia 2019

Characteristic Children 6-59 months Children 6-23 months

n % a (95% CI) b n % a (95% CI) b

Consumed micronutrient powders

No 1636 91.5 (89.4, 93.2) 501 92.4 (89.9, 94.4)

Yes 140 7.1 (5.5, 9.0) 41 6.1 (4.4, 8.5)

Don’t know 35 1.5 (0.9, 2.4) 9 1.4 (0.7, 2.8)

Consumed RUTF or RUSF c

No 1590 87.7 (84.4, 90.4) 487 87.8 (83.6, 91.1)

Yes 185 10.9 (8.4, 14.2) 56 11.2 (8.1, 15.4)

Don’t know 36 1.3 (0.7, 2.5) 8 1.0 (0.4, 2.0)

Consumed infant formula with added iron

No 1523 83.3 (79.0, 86.9) 464 83.0 (77.6, 87.3)

Yes 250 15.3 (11.8, 19.6) 77 15.7 (11.5, 21.0)

Don’t know 38 1.4 (0.8, 2.4) 10 1.3 (0.7, 2.5)

Consumed fortified baby cereal

No 1629 89.7 (86.6, 92.2) 506 91.4 (87.3, 94.2)

Yes 143 8.8 (6.5, 11.8) 37 7.7 (5.0, 11.8)

Don’t know 39 1.5 (0.8, 2.5) 8 0.9 (0.4, 2.0)

Is registered in any feeding programs

No 1547 83.2 (79.2, 86.6) 479 84.3 (78.6, 88.6)

Yes 228 15.5 (12.2, 19.4) 64 14.8 (10.5, 20.4)

Don’t know 36 1.3 (0.7, 2.4) 8 0.9 (0.4, 2.0)

Note: The n’s are un-weighted denominators for each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.


c RUTF = Ready-to-use Therapeutic Foods; RUSF = Ready-to-use Supplementary Food.


57

Figure 13. Proportion of children 6-59 months registered in a feeding program, by residence, state, and wealth quintile, Somalia 2019

26.3

9.3

14.8

9.1

4.8

17.3

6.0 5.0

66.1

7.2

38.4

24.6

7.7

13.2

7.7

0

10

20

30

40

50

60

70

Residence State Wealth quintile

Pro

po

rtio

n (

%)


58

Vitamin A supplementation coverage does not differ by child’s age or sex (Table 24). Vitamin

A supplementation coverage is higher in children living in urban centers compared to children

living in rural or IDP households. Large differences in vitamin A supplementation coverage

were found between states. Somaliland and Puntland had much higher vitamin A

supplementation coverage, whereas few children living in the other states had received

vitamin A. Vitamin A supplementation coverage is also somewhat higher in wealthier

households.

Table 24. Percentage of children (6-59 months) that received vitamin A supplements in

the past 6 months, Somalia 2019

Characteristic n % a (95% CI) b P-value c

Age Group (in months) 6-11 35 19.1 (12.5, 19.1) 0.7720 12-23 60 15.6 (11.1, 21.6) 24-35 69 16.5 (12.1, 21.9) 36-47 66 15.3 (10.0, 22.7) 48-59 64 17.6 (12.3, 24.6)

Sex Male 144 15.5 (11.4, 20.9) 0.2092 Female 150 17.5 (13.0, 23.2)

Residence Rural 52 9.0 (4.8, 16.4) 0.0166 Urban 216 22.7 (15.8, 31.4) IDP 26 10.3 (4.0, 24.2)

State Somaliland 150 30.6 (20.6, 42.7) 0.0000 Puntland 125 40.8 (28.3, 54.6) Hirshabelle 0 0.0 -- Galmudug 1 0.7 (0.1, 5.0) South-West 2 1.4 (0.4, 5.6) Jubaland 1 0.8 (0.1, 5.6) Banaadir 15 4.0 (1.8, 8.3)

Wealth Quintile Lowest 7 4.4 (1.6, 11.8) 0.0000 Second 28 11.3 (5.5, 21.7) Middle 54 10.5 (6.3, 17.0) Fourth 86 17.7 (12.2, 24.9) Highest 118 31.0 (20.9, 43.4)

ALL CHILDREN 294 15.9 (11.9, 21.0)

Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Percentages weighted for unequal probability of selection

b CI=confidence interval, calculated taking into account the complex sampling design c P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups.


59

3.3.6. Stunting

Estimates of stunting prevalence and severity do not include Galmudug due to serious

problems with the quality of height measurements in that state, as described more fully in

Appendix 8.11.

The stunting prevalence and distribution in Somali pre-school children is presented in Table

25 and Figure 14, respectively. The prevalence of stunting is considered a medium level in

pre-school children in Somalia, according to recently released updated recommendations

[41]. Nonetheless, stunting would be considered at a high or very high level in certain sub-

groups. The prevalence of stunting rises to a peak in children 24 to 35 months of age, then

declines in older children (Table 25). Also, the substantially higher prevalence of stunting

among IDP children makes this a high-level public health problem in this subgroup. Stunting

prevalence progressively declines with increasing household wealth. The highest stunting

prevalence was found in South West. No statistically significant differences were found

between stunting prevalence and child’s sex or household sanitation.


60

Table 25. Percentage of children (0-59 months) with stunting, Somalia 2019

Characteristic

Severe and Moderate Stunting Any Stunting

n % a

Severe c (95% CI) b n % a

Moderate d (95% CI) b n % a

Any e (95% CI) b P-value f Age Group (in months)

0-5 3 1.8 (0.6, 5.7) 8 9.1 (4.7, 16.6) 11 10.9 (6.2, 18.4) 0.0004 6-11 7 5.5 (2.4, 12.2) 8 6.0 (3.0, 11.8) 15 11.5 (7.0, 18.5) 12-23 21 6.1 (3.8, 9.6) 41 14.1 (10.4, 18.8) 62 20.2 (15.9, 25.3) 24-35 34 10.7 (7.5, 15.0) 42 12.3 (8.6, 17.5) 76 23.0 (18.0, 29.0) 36-47 29 9.3 (6.1, 13.8) 30 9.5 (6.4, 13.9) 59 18.8 (14.4, 24.2) 48-59 10 2.8 (1.5, 5.0) 32 8.7 (6.3, 11.8) 42 11.5 (8.9, 14.7)

Sex Male 60 7.5 (5.7, 9.8) 90 11.8 (9.4, 14.8) 150 19.3 (16.1, 23.0) 0.0554 Female 44 5.8 (4.3, 7.9) 71 9.1 (7.0, 11.8) 115 15.0 (12.3, 18.1)

Residence Rural 19 4.6 (3.0, 7.0) 39 9.0 (6.3, 12.6) 58 13.6 (10.6, 17.1) 0.0049 Urban 59 6.3 (4.6, 8.5) 89 10.0 (7.9, 12.5) 148 16.2 (13.4, 19.4) IDP 26 12.0 (7.3, 19.1) 33 15.2 (10.8, 21.0) 59 27.2 (18.8, 37.6)

State Somaliland 19 3.4 (2.3, 5.1) 49 9.0 (6.6, 12.0) 68 12.3 (9.5, 15.8) 0.0000

Puntland 14 4.3 (2.5, 7.2) 31 9.7 (6.8, 13.6) 45 13.9 (10.4, 18.5)

Hirshabelle 11 5.3 (2.9, 9.6) 15 7.3 (4.6, 11.3) 26 12.6 (8.6, 18.1)

South-West 16 25.7 (14.6, 41.1) 8 13.3 (7.6, 22.0) 24 38.9 (25.0, 55.0)

Jubaland 2 5.0 (1.4, 16.4) 7 15.4 (8.4, 26.5) 9 20.4 (12.7, 31.2)

Banaadir 42 11.5 (8.6, 15.3) 51 14.3 (10.6, 19.0) 93 25.8 (20.3, 32.3)

Wealth Quintile Lowest 14 19.8 (11.2, 32.6) 10 13.7 (7.9, 22.6) 24 33.5 (21.7, 47.8) 0.0015 Second 15 10.1 (5.2, 18.7) 16 11.3 (7.0, 17.7) 31 21.4 (14.2, 30.8) Middle 25 5.6 (3.8, 8.2) 57 14.4 (10.7, 19.1) 82 20.0 (15.2, 25.8) Fourth 32 5.9 (4.2, 8.3) 44 8.4 (5.9, 11.7) 76 14.3 (11.1, 18.1) Highest 15 3.4 (1.9, 5.8) 34 8.1 (5.2, 12.6) 49 11.5 (7.5, 17.4)



61

Characteristic

Severe and Moderate Stunting Any Stunting

n % a

Severe c (95% CI) b n % a

Moderate d (95% CI) b n % a

Any e (95% CI) b P-value f Household sanitation g

Inadequate 66 8.4 (6.3, 11.0) 84 10.4 (8.3, 13.0) 150 18.8 (15.5, 22.5) 0.3424 Adequate 35 4.4 (3.1, 6.3) 77 10.7 (8.4, 13.6) 112 15.1 (12.2, 18.7)

ALL CHILDREN 104 6.7 (5.4, 8.3) 161 10.5 (8.9, 12.3) 265 17.2 (15.0, 19.6) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Percentages weighted for unequal probability of selection

b CI=confidence interval, calculated taking into account the complex sampling design c Severe stunting represents children who are below -3 standard deviations (SD; z-scores) from the WHO Child Growth Standards population median d Moderate stunting includes children who are equal to or above -3 standard deviations (SD) and below-2 SD from the WHO Child Growth Standards population median c Any stunting includes both severely and moderately stunted children f P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups. Results are based on any stunting. g Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not shared with another household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field


62

The distribution of height-for-age z-scores is shifted to the left, as shown in Figure 14. The

mean height-for-age z-score was -0.41, with a standard deviation of 1.76.

Figure 14. Histogram of height-for-age z-scores of the SMS 2019 compared to the WHO

growth curve, preschool-age children, Somalia 2019

3.3.7. Wasting

As with stunting, estimates of wasting prevalence and severity do not include Galmudug due

to serious problems with the quality of height measurements in that state, as described more

fully in appendix 8.11.

The overall prevalence of wasting, also referred to as Global Acute Malnutrition (GAM),

among children 0-59 months of age in Somalia is 11.0% (Table 26). This prevalence is classified

as high. [41]. Although the prevalence of wasting differs by age, there is no clear trend with

increasing age. As with age, there are statistically significant differences in wasting prevalence

by household wealth; however, there is no clear trend. Similar to stunting, significant

differences were found between the different states; the prevalence rates in Jubaland,

Puntland and South West are classified as very high, in Hirshabelle and Banaadir the rates are

high, and in Somaliland medium. No statistically significant differences were found between

wasting prevalence and child’s sex, residence or household sanitation.

050

10

015

020

0

Fre

qu

en

cy

-5 -4 -3 -2 -1 0 1 2 3 4 5

Height-for-age Z score (HAZ)

Frequency

WHO Growth standard


63

Table 26. Percentage of children (0-59 months) with wasting, Somalia 2019

Characteristic

Severe and Moderate Wasting Any Wasting

n

% a

Severe c (95% CI) b n

% a

Moderate d (95% CI) b n

% a

Any e (95% CI) b P-value f

Age Group (in months) 0-5 8 5.7 (2.7, 11.5) 11 11.6 (5.4, 22.9) 19 17.2 (9.7, 28.7) 0.0195 6-11 7 3.0 (1.5, 6.0) 9 5.2 (2.6, 10.2) 16 8.2 (5.1, 12.9) 12-23 12 3.0 (1.6, 5.5) 23 7.2 (4.7, 10.8) 35 10.2 (7.3, 14.3) 24-35 5 1.9 (0.8, 4.8) 20 5.5 (3.5, 8.6) 25 7.4 (5.0, 10.9) 36-47 5 1.7 (0.7, 4.1) 28 8.8 (5.7, 13.4) 33 10.5 (7.2, 15.1) 48-59 5 1.5 (0.6, 3.5) 48 13.5 (10.0, 18.0) 53 15.0 (11.4, 19.5)

Sex

Male 25 2.7 (1.8, 3.9) 78 9.5 (7.4, 12.1) 103 12.2 (9.8, 15.1) 0.1176 Female 17 2.0 (1.2, 3.4) 61 7.7 (5.8, 10.0) 78 9.7 (7.7, 12.1)

Residence

Rural 14 2.8 (1.6, 4.9) 49 10.8 (7.9, 14.6) 63 13.6 (10.4, 17.7) 0.2397 Urban 22 2.0 (1.3, 3.2) 71 7.6 (5.7, 10.0) 93 9.6 (7.7, 12.0)

IDP 6 2.8 (1.1, 6.6) 19 8.8 (5.1, 14.6) 25 11.6 (6.8, 19.1)

State

Somaliland 5 0.9 (0.4, 2.0) 36 6.6 (4.7, 9.1) 41 7.5 (5.6, 10.0) 0.0016

Puntland 15 4.8 (2.9, 7.7) 39 12.4 (9.6, 15.8) 54 17.2 (13.2, 22.1)

Hirshabelle 5 2.4 (1.0, 5.4) 22 10.6 (6.7, 16.3) 27 13.0 (7.9, 20.5)

South-West 2 3.1 (0.9, 10.5) 6 11.9 (6.5, 20.8) 8 15.0 (9.1, 23.8)

Jubaland 2 5.0 (1.5, 15.6) 7 14.3 (4.6, 36.8) 9 19.3 (10.4, 33.2)

Banaadir 13 3.4 (1.8, 6.2) 29 7.8 (5.0, 11.8) 42 11.2 (7.4, 16.6)

Wealth Quintile

Lowest 5 5.1 (2.0, 12.1) 4 6.3 (2.4, 15.6) 9 11.3 (6.2, 19.9) 0.0177

Second 4 3.1 (1.2, 8.2) 20 13.5 (8.6, 20.6) 24 16.6 (11.1, 24.3)

Middle 8 1.6 (0.8, 3.3) 53 11.5 (8.7, 15.2) 61 13.1 (10.0, 17.0)

Fourth 14 2.6 (1.4, 4.7) 35 7.5 (5.5, 10.3) 49 10.1 (7.4, 13.6)

Highest 11 1.9 (1.0, 3.5) 25 5.4 (3.3, 8.6) 36 7.3 (5.0, 10.5)



64

Characteristic

Severe and Moderate Wasting Any Wasting

n

% a


% a


% a


Household sanitation g Inadequate 22 2.2 (1.4, 3.4) 70 8.7 (6.7, 11.1) 92 10.8 (8.6, 13.5) 0.9446

Adequate 20 2.6 (1.6, 4.2) 66 8.3 (6.2, 11.0) 86 10.9 (8.7, 13.6)

ALL CHILDREN 42 2.3 (1.7, 3.2) 139 8.6 (7.1, 10.4) 181 11.0 (9.3, 12.9)


b CI=confidence, calculated taking into interval account the complex sampling design c Severe wasting represents children who are below -3 standard deviations (SD; z-scores) from the WHO Child Growth Standards population median d Moderate wasting includes children who are equal to or above -3 standard deviations (SD) and below-2 SD from the WHO Child Growth Standards population median e Any wasting includes both severely and moderately wasted children f P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups. Results are based on any wasting. g Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not shared with

another household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field


65

Figure 15 shows the distribution of the weight for height z-score (WHZ) in the surveyed

population of children 0-59 months of age. The distribution of WHZ is normal, and is shifted

to the left of the WHO standard growth curve. The mean weight-for-height z-score was -0.48,

with a standard deviation of 1.35.

The prevalence of overweight and obesity in children 0-59 months of age is 3.2% (95% CI: 2.3,

4.4). Because of this low prevalence, no subgroup analysis was done.

Figure 15. Histogram of weight-for-height z-scores of the SMS 2019 compared to the WHO


3.3.8. Underweight

The prevalence of underweight in children 0-59 months of age is presented in Table 27. The

overall prevalence of underweight is classified as medium [8]. However, the prevalence is

classified as high in certain sub-groups, including children living in IDP camps, children in

Jubaland and South-West state, and children in poorer households.

050

10

015

020

0

Fre

qu

en

cy

-5 -4 -3 -2 -1 0 1 2 3 4 5

Weight-for-height Z score (WHZ)

Frequency

WHO Growth standard


66

Table 27. Percentage of children (0-59 months) underweight, Somalia 2019

Characteristic

Severe and Moderate Underweight Any Underweight

n

% a


% a


% a



0-5 4 3.4 (1.3, 8.8) 9 7.5 (3.6, 15.0) 13 10.9 (6.3, 18.4) 0.7148

6-11 5 2.9 (1.2, 7.0) 11 7.1 (3.3, 14.8) 16 10.0 (5.5, 17.7) 12-23 13 3.2 (1.8, 5.7) 38 10.8 (7.6, 15.0) 51 13.9 (10.4, 18.4) 24-35 10 2.6 (1.3, 5.0) 35 9.4 (6.2, 13.8) 45 12.0 (8.5, 16.5) 36-47 13 3.9 (2.2, 6.8) 34 10.6 (7.2, 15.4) 47 14.5 (10.7, 19.5) 48-59 8 1.9 (1.0, 3.8) 35 9.6 (6.7, 13.8) 43 11.6 (8.4, 15.7)

Sex

Male 30 3.1 (2.1, 4.5) 86 10.5 (8.3, 13.2) 116 13.6 (11.0, 16.6) 0.2083 Female 23 2.8 (1.8, 4.2) 76 8.8 (7.0, 11.0) 99 11.5 (9.4, 14.1)

Residence

Rural 13 2.7 (1.5, 4.8) 42 7.9 (5.8, 10.6) 55 10.6 (7.8, 14.3) 0.0010

Urban 30 2.7 (1.8, 4.1) 76 8.1 (6.2, 10.6) 106 10.8 (8.5, 13.6) IDP 10 4.2 (2.3, 7.5) 44 18.4 (12.0, 27.2) 54 22.6 (15.3, 32.0)

State

Somaliland 9 1.6 (0.8, 3.3) 37 6.6 (4.7, 9.1) 46 8.2 (5.8, 11.5) 0.0001

Puntland 11 3.6 (1.9, 6.5) 35 11.8 (8.7, 15.9) 46 15.4 (11.6, 20.1)

Hirshabelle 6 2.9 (1.4, 5.6) 12 5.7 (3.3, 9.8) 18 8.6 (4.9, 14.7)

Galmudug 4 2.9 (1.1, 7.2) 16 11.6 (5.8, 21.9) 20 14.5 (7.4, 26.3)

South-West 5 8.2 (3.2, 19.5) 11 20.1 (11.0, 33.7) 16 28.3 (18.9, 40.0)

Jubaland 1 2.3 (0.3, 14.3) 9 15.7 (7.9, 29.0) 10 18.1 (9.4, 32.0)

Banaadir 17 4.3 (2.8, 6.4) 42 11.9 (7.5, 18.2) 59 16.2 (11.3, 22.6)

Wealth Quintile

Lowest 7 8.4 (4.0, 16.8) 9 11.5 (5.9, 21.3) 16 20.0 (12.9, 29.5) 0.0000 Second 9 4.9 (2.5, 9.5) 31 18.2 (11.4, 27.6) 40 23.1 (15.3, 33.2) Middle 13 2.5 (1.5, 4.2) 58 12.1 (8.7, 16.5) 71 14.6 (11.0, 19.2) Fourth 18 2.7 (1.7, 4.4) 37 6.8 (4.8, 9.6) 55 9.5 (7.0, 12.8) Highest 3 0.8 (0.3, 2.3) 26 5.7 (3.8, 8.4) 29 6.5 (4.3, 9.6)



67

Characteristic

Severe and Moderate Underweight Any Underweight

n

% a


% a


% a


Household sanitation g

Inadequate 28 3.0 (2.0, 4.5) 85 10.0 (7.7, 13.0) 113 13.1 (10.3, 16.5) 0.7143 Adequate 22 2.6 (1.6, 4.0) 76 9.2 (7.1, 11.8) 98 11.7 (9.4, 14.5)

ALL CHILDREN 53 2.9 (2.2, 3.9) 162 9.6 (8.0, 11.5) 215 12.6 (10.7, 14.7)


b CI=confidence interval, calculated taking into account the complex sampling design c Severe underweight represents children who are below -3 standard deviations (SD; z-scores) from the WHO Child Growth Standards population median d Moderate underweight includes children who are equal to or above -3 standard deviations (SD) and below-2 SD from the WHO Child Growth Standards population median e Any underweight includes both severely and moderately underweight children f P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups. Results are based on any underweight. g Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not shared with

another household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field


68

As shown in Figure 16, the distribution of WAZ is shifted to the left of the WHO standard

growth curve, similar to the distributions of HAZ and WHZ. Mean WAZ was -0.68, and WAZ

had a standard deviation of 1.4.

Figure 16. Histogram of weight-for-age z-scores of the SMS 2019 compared to the WHO


3.3.9. Mid-upper arm circumference

Figure 9 below shows the distribution of MUAC measurements in children 6-59 months of age

and the cut-off of 115 mm for severe acute malnutrition (SAM). Only 25 children were

classified with SAM. Despite this small number, statistically significant differences were

observed by state, with a higher proportion of children with SAM found in Jubaland than

found in other states. (Table 28). No significant associations were observed by age category,

sex, residence, or household sanitation.

050

10

015

020

025

0

Fre

qu

en

cy

-5 -4 -3 -2 -1 0 1 2 3 4 5

Weight-for-age Z score (WAZ)

Frequency

WHO Growth standard


69

Figure 17. Histogram of mid upper arm circumference (MUAC) in children 6-59 months of

age, Somalia 2019

0

10

020

030

040

0

Fre

qu

en

cy

10 11 12 13 14 15 16 17 18 19 20 21 22 23

Mid upper arm circumference (cm)

Frequency

11.5 cm


70

Table 28. Severe Acute Malnutrition (SAM) as measured by MUAC, by various

characteristics in children 6-59 months of age, Somalia 2019

Characteristic n % SAM a, b (95% CI) c P Value d


Sex Male 12 1.5 (0.7, 3.1) 0.6125 Female 13 1.8 (0.9, 3.4)


State Somaliland 4 0.8 (0.2, 2.5) 0.0115 Puntland 7 2.3 (1.1, 4.7) Hirshabelle 0 0.0 Galmudug 0 0.0 South-West 1 0.5 (0.1, 3.5) Jubaland 11 8.0 (3.4, 17.7) Banaadir 2 0.4 (0.1, 1.8)


Household sanitation f Inadequate 10 1.5 (0.6, 3.8) 0.1204 Adequate 14 1.7 (0.9, 3.3)

ALL CHILDREN 25 1.7 (0.9, 3.0) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a SAM= Severe Acute Malnutrition; children with mid upper arm circumference (MUAC) < 11.5 cm are classified with SAM b Percentages weighted for unequal probability of selection

c CI=confidence interval, calculated taking into account the complex sampling design d P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups.


71

3.3.10. Malaria

Of the 1668 children with results from malaria rapid diagnostic tests, only 7 (0.5%; 95% CI:

0.2, 1.1) were found to be malaria positive.

3.3.11. Anemia, iron deficiency, and iron deficiency anemia

Anemia is very common in pre-school children, and about one half of anemia is mild and one-

half moderate. Very few children have severe anemia (1.5%). The overall anemia prevalence,

as well as the prevalence of anemia in most states, constitutes a severe public health problem

according to WHO (47). The prevalence of anemia peaks in children 12-23 months and then,

gradually decreased with age. Further, children living in poorer households are more likely to

have anemia compared to children in wealthier households (Table 30). A significantly larger

proportion of children with inflammation had anemia compared to those children without any

inflammation. Other demographic variables such as child’s sex, residence and household

sanitation were not significantly associated with anemia.

About half of children 6-59 months old are iron deficient. Similar to anemia, the prevalence

of ID is highest among children 12-23 month of age and gradually decreases with increasing

age. Also similar to anemia, iron deficiency is less prevalent in children living in Galmudug

compared to the other states. Other demographic variables investigated here were not

significantly associated with iron deficiency.

About two-thirds of the children who have anemia also have iron deficiency. Similar to anemia

and ID highest prevalence of iron deficiency anemia was found in children 12-23 months of

age. Also, IDA was strongly associated with the state; by far the smallest proportion of children

with IDA was found in Galmudug. Other demographic variables investigated here were not

significantly associated with IDA.


72

Table 29. Proportion of mild, moderate and severe anemia in children 6-59 months of age, Somalia 2019

Mild anemia c Moderate anemia c Severe anemia c

Characteristic n % a (95% CI) b n % a (95% CI) b n % a (95% CI) b

Age Group (in months) 6-11 35 25.0 (17.9, 33.8) 39 26.4 (19.2, 35.1) 3 1.3 (0.4, 3.9)

12-23 93 25.6 (21.1, 30.6) 120 32.6 (26.5, 39.4) 6 1.5 (0.6, 3.7)

24-35 88 22.0 (18.5, 26.0) 93 23.0 (18.7, 28.0) 9 1.8 (0.8, 3.9)

36-47 94 24.1 (19.8, 29.0) 62 16.6 (12.7, 21.4) 6 1.4 (0.6, 3.4)

48-59 51 12.0 (9.0, 15.8) 28 8.0 (5.1, 12.4) 5 1.4 (0.5, 3.6)

Sex

Male 168 19.4 (16.9, 22.2) 203 24.0 (20.4, 28.0) 18 1.9 (1.0, 3.5)

Female 193 23.4 (20.5, 26.7) 139 16.9 (14.1, 20.0) 11 1.1 (0.6, 2.0)

Residence

Rural 115 21.1 (17.4, 25.3) 110 21.8 (17.5, 26.7) 5 0.7 (0.3, 1.7)

Urban 182 20.1 (17.4, 23.1) 177 19.1 (16.0, 22.8) 17 1.6 (0.8, 3.2)

IDP 64 26.6 (19.9, 34.4) 55 22.8 (15.8, 31.8) 7 2.9 (1.1, 7.3)

State

Somaliland 77 15.6 (12.6, 19.2) 65 13.3 (10.1, 17.3) 2 0.4 (0.1, 1.4)

Puntland 66 24.8 (19.4, 31.1) 49 17.7 (12.8, 23.8) 2 0.7 (0.2, 2.6)

Hirshabelle 36 19.1 (14.1, 25.4) 49 26.1 (20.8, 32.1) 6 3.2 (1.5, 6.5)

Galmudug 31 21.6 (16.1, 28.3) 12 7.6 (4.5, 12.4) 0 --

South-West 38 30.5 (22.7, 39.7) 36 30.3 (21.6, 40.6) 1 0.5 (0.1, 3.6)

Jubaland 30 24.9 (19.6, 31.1) 30 26.1 (17.5, 37.1) 3 2.8 (0.7, 10.9)

Banaadir 83 25.2 (19.1, 32.4) 101 31.8 (26.8, 37.2) 15 4.7 (2.4, 8.9)

Wealth Quintile

Lowest 39 29.4 (23.4, 36.2) 35 26.9 (19.1, 36.5) 1 0.7 (0.1, 4.5)

Second 46 21.1 (16.1, 27.3) 55 26.1 (19.9, 33.3) 6 1.7 (0.8, 3.7)

Middle 90 20.8 (16.2, 26.2) 91 19.1 (15.2, 23.8) 5 1.4 (0.4, 5.4)

Fourth 102 20.9 (17.6, 24.5) 100 22.7 (18.3, 27.7) 7 1.2 (0.5, 2.9)

Highest 80 18.3 (14.1, 23.4) 57 12.4 (8.7, 17.4) 10 2.2 (0.9, 5.3)


Inadequate 175 21.0 (18.0, 24.3) 184 21.4 (18.1, 25.1) 18 2.0 (1.0, 3.6)

Adequate 180 21.5 (18.5, 24.7) 153 19.2 (15.5, 23.4) 11 1.1 (0.5, 2.2)



73

Mild anemia c Moderate anemia c Severe anemia c

Characteristic n % a (95% CI) b n % a (95% CI) b n % a (95% CI) b

Inflammation

None 237 21.4 (18.8, 24.2) 190 16.9 (14.1, 20.3) 8 0.6 (0.2, 1.3)

Any inflammation 83 21.7 (17.4, 26.9) 99 28.4 (23.2, 34.3) 7 1.9 (0.9, 4.2)

ALL CHILDREN 361 21.4 (19.3, 23.6) 342 20.5 (18.1, 23.2) 29 1.5 (0.9, 2.5)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.


c Mild, moderate, and severe anemia defined as hemoglobin 100-109 g/L, 70-99 g/L, and <70 g/L, respectively. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups. e Any inflammation includes incubation=CRP only; early convalescence=CRP and AGP; late convalescence=AGP only.


74

Table 30. Anemia, iron deficiency, and iron deficiency anemia in children 6-59 months of age, Somalia 2019

Anemia b Iron deficiency e Iron deficiency anemia f

Characteristic n % a (95% CI) c P value d n % a (95% CI) c P value d n % a (95% CI) c P value d


6-11 77 52.7 (43.4, 61.8) 0.0000 57 49.8 (39.9, 59.7) 0.0000 38 33.0 (24.8, 42.4) 0.0000

12-23 219 59.7 (52.4, 66.6) 196 62.0 (56.0, 67.6) 135 41.4 (34.4, 48.8)

24-35 190 46.9 (41.5, 52.3) 195 54.5 (47.5, 61.3) 114 31.5 (26.0, 37.5)

36-47 162 42.1 (36.2, 48.2) 158 43.4 (37.4, 49.6) 93 27.5 (21.8, 34.0)

48-59 84 21.4 (16.6, 27.1) 99 29.1 (23.1, 36.0) 48 13.1 (9.0, 18.6)

Sex

Male 389 45.3 (41.2, 49.5) 0.1142 367 49.8 (44.9, 54.6) 0.0801 226 30.9 (26.9, 35.2) 0.1051

Female 343 41.4 (37.2, 45.7) 338 44.6 (40.0, 49.3) 202 26.2 (22.1, 30.8)

Residence

Rural 230 43.6 (37.2, 50.2) 0.1731 240 48.2 (41.5, 55.0) 0.8545 147 30.2 (24.2, 36.9) 0.3812

Urban 376 40.8 (36.5, 45.3) 362 46.2 (41.1, 51.4) 213 26.4 (23.1, 29.9)

IDP 126 52.3 (40.3, 64.0) 103 48.6 (39.1, 58.2) 68 32.9 (23.2, 44.2)

State

Somaliland 144 29.3 (23.7, 35.5) 0.0000 214 44.9 (39.3, 50.5) 0.0087 113 23.9 (19.5, 28.9) 0.0000

Puntland 117 43.2 (35.8, 50.9) 110 46.1 (37.3, 55.1) 73 31.0 (23.4, 39.7)

Hirshabelle 91 48.4 (40.0, 56.9) 92 55.1 (43.9, 65.8) 63 38.2 (29.6, 47.5)

Galmudug 43 29.2 (22.5, 36.9) 36 23.3 (12.1, 40.2) 10 6.6 (3.1, 13.5)

South-West 75 61.3 (49.5, 71.9) 44 53.0 (36.2, 69.1) 29 33.6 (24.9, 43.5)

Jubaland 63 53.9 (46.9, 60.7) 47 46.3 (35.1, 57.9) 29 29.9 (20.7, 41.2)

Banaadir 199 61.7 (53.7, 69.0) 162 59.3 (52.9, 65.5) 111 42.3 (33.2, 52.0)

Wealth Quintile

Lowest 75 57.0 (47.2, 66.2) 0.0004 53 48.5 (39.2, 58.0) 0.3673 31 28.1 (19.3, 39.1) 0.2276

Second 107 48.9 (41.5, 56.4) 96 48.1 (38.2, 58.1) 63 33.0 (24.7, 42.4)

Middle 186 41.3 (35.2, 47.7) 181 45.0 (38.5, 51.6) 117 28.1 (22.8, 34.2)

Fourth 209 44.7 (39.6, 50.0) 207 52.3 (45.9, 58.6) 124 31.4 (26.6, 36.6)

Highest 147 32.9 (26.0, 40.5) 162 42.9 (36.6, 49.6) 88 22.8 (17.3, 29.3)



75


Characteristic n % a (95% CI) c P value d n % a (95% CI) c P value d n % a (95% CI) c P value d


Inadequate 377 44.3 (39.7, 48.9) 0.4579 357 47.9 (43.2, 52.7) 0.3923 207 27.6 (23.6, 31.9) 0.6883

Adequate 344 41.7 (37.0, 46.6) 338 45.9 (40.8, 51.0) 213 28.8 (24.3, 33.8)

Inflammation

None 435 38.9 (34.6, 43.4) 0.0000 532 48.1 (43.8, 52.5) 0.3041 308 27.3 (23.5, 31.4) 0.1436

Any inflammation 189 52.1 (47.1, 57.0) 173 44.5 (38.7, 50.5) 120 32.3 (27.1, 38.1)

ALL CHILDREN 732 43.4 (40.0, 46.9) 705 47.2 (43.4, 51.1) 428 28.6 (25.5, 31.9)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b Anemia defined as hemoglobin < 110 g/L. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups e ID= Iron deficiency defined as serum ferritin < 12 µg/l, values are adjusted for inflammation according to BRINDA. f IDA= Iron deficiency anemia, defined as low Hb (< 110 g/L) with low serum ferritin (< 12.0 μg/L). g Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not

shared with another household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field. h Any inflammation includes incubation=CRP only; early convalescence=CRP and AGP; late convalescence=AGP only.


76

Figure 18. Venn diagram showing overlap between anemia and iron deficiency in children

6-59 months of age, Somalia 2019

The distribution of hemoglobin values for children is shown in Figure 19. The hemoglobin

distribution is essentially normal between the ranges of 50 to 150 g/L, however a small

number of children with extremely low hemoglobin levels were found. The mean hemoglobin

concentration among all children 6-59 months old was 110.4 g/L.

Figure 19. Histogram of hemoglobin concentration (g/L) in children 6-59 months of age,

Somalia 2019

050

10

015

020

0

Fre

qu

en

cy

30 40 50 60 70 80 90 100 110 120 130 140 150 160

Hemoglobin concentration (g/L)

Frequency

110 g/L

Anemia 43.4%

Iron deficiency

47.4%

Iron deficiency

anemia

28.5%


77

Table 31 shows that anemia prevalence does not significantly differ between children with a

recent illness compared to those without a recent illness; specifically, anemia prevalence does

not differ significantly between children who had fever, cough or diarrhea the past 2 weeks

prior to the survey compared to apparently healthy children. However, significant differences

were found by inflammation status. Anemia is more common in children with elevated AGP

or AGP and CRP compared to children with elevated CRP only or without elevated

inflammation markers. Although recent diarrhea, fever, and cough could result in

inflammation, no significant associations were found between inflammation category and

these three recent illnesses, or any inflammation and the three recent illnesses (data not

shown).

Table 31. Anemia in children 6-59 months of age, by infection-related characteristics and

vitamin A and iron status, Somalia 2019

Characteristic n Anemia b

% a (95% CI) c P value d

Child had any type of diarrhea in past 2 weeks No 641 43.8 (40.4, 47.2) 0.5872 Yes 90 40.9 (33.7, 48.4)

Child had a cough in past 2 weeks No 543 44.7 (40.9, 48.5) 0.3431 Yes 186 39.9 (34.2, 45.9)

Child had fever in past 2 weeks No 577 44.4 (40.8, 47.9) 0.2108 Yes 153 39.6 (33.3, 46.2)

Inflammation f None 435 38.9 (34.6, 43.4) 0.0009 Incubation (elevated CRP only) 15 38.2 (22.7, 56.6) Early convalescence (elevated CRP and AGP) 57 53.1 (41.6, 64.3) Late convalescence (elevated AGP only) 117 54.2 (47.5, 60.8)

Any Inflammation f None 435 38.9 (34.6, 43.4) 0.0000 Elevated CRP and/or AGP 189 52.1 (47.1, 57.0)

Vitamin A deficient g No (RBP≥0.7 μmol/L) 381 38.6 (34.8, 42.5) 0.0007 Yes (RBP<0.7 μmol/L) 243 49.3 (43.4, 55.2)

Iron deficient h No (ferrtin≥12 μg/L) 196 25.8 (22.1, 30.0) 0.0000 Yes (ferritin<12 μg/L) 428 60.7 (55.9, 65.3)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b Anemia defined as hemoglobin < 110 g/L adjusted for altitude. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly e Positive malaria status identified using rapid diagnostic tests results f Incubation=CRP only; early convalescence=CRP and AGP; late convalescence=AGP only. g Vitamin A deficiency (VAD) defined as retinol binding protein (RBP) <0.70 μmol/L; RBP concentrations adjusted for inflammation using the BRINDA approach h Iron deficiency defined as serum ferritin < 12 µg/l, values are adjusted for inflammation according to BRINDA


78

Anemia prevalence is highly associated with micronutrient deficiencies. A larger proportion

of children with vitamin A deficiency or iron deficiency have anemia compared to children

without.

3.3.12. Vitamin A deficiency

Nationally, more than one-third of children have vitamin A deficiency using RBP as the

indicator (see Table 32), denoting a severe public health problem according to WHO

classifications [42]. Statistically significant differences were found by age, with children less

than 2 years of age being less likely to have vitamin A deficiency compared to young children.

In addition, the prevalence of vitamin A deficiency is statistically significantly different in

different states. Significant differences were also observed by household wealth, although no

clear trend is apparent. Similarly, the prevalence of vitamin A deficiency differs between

children with different stage of inflammation but there is no clear trend.

Vitamin A status in Somali children did not differ significantly by sex, residence, household

sanitation and vitamin A supplementation.

Table 32. Proportion of children 6-59 months of age with vitamin A deficiency, by various

characteristics, Somalia 2019

Characteristic n Vitamin A deficiency

% a, b (95% CI) c P value d


Sex Male 256 33.8 (29.8, 38.1) 0.6662 Female 251 34.9 (31.4, 38.7)


State Somaliland 146 30.7 (25.7, 36.2) 0.0046 Puntland 82 34.2 (28.4, 40.5) Hirshabelle 64 38.3 (32.8, 44.2) Galmudug 30 19.4 (11.1, 31.8) South-West 35 43.6 (35.7, 51.7) Jubaland 37 38.2 (28.7, 48.6) Banaadir 113 41.2 (34.0, 48.9)




79

Characteristic n Vitamin A deficiency

% a, b (95% CI) c P value d

Received vitamin A supplementation in past 6 months No 395 35.0 (31.6, 38.6) 0.4246 Yes 93 33.6 (26.1, 41.9) Don’t know / not sure if was vit A 19 24.5 (13.9, 39.5)

Received vitamin A supplementation in past 3 months No 468 34.8 (31.8, 38.1) 0.3839 Yes 19 33.8 (20.8, 49.8) Don’t know / not sure if was vit A 19 24.5 (13.9, 39.5)

Inflammation e None 355 32.4 (29.0, 35.9) 0.0157 Incubation 18 39.2 (25.4, 55.0) Early convalescence 50 48.4 (38.5, 58.4) Late convalescence 84 36.5 (29.8, 43.9)

Household sanitation Inadequate 264 35.8 (31.3, 40.5) 0.3621 Adequate 239 32.9 (29.0, 37.1)

ALL CHILDREN 507 34.4 (31.4, 37.5) Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data.

a Percentages weighted for unequal probability of selection.

b Vitamin A deficiency (VAD) defined as retinol binding protein (RBP) <0.70 μmol/L; RBP concentrations adjusted for inflammation using BRINDA approach

c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other

subgroups e Incubation=CRP only; early convalescence=CRP and AGP; late convalescence=AGP only.

Figure 20. Distribution of retinol binding protein concentration, adjusted for inflammation

using the BRINDA approach, children 6-59 months of age, Somalia 2019

050

10

015

020

025

030

0

Fre

qu

en

cy

0 .5 1 1.5 2 2.5 3

Retinol binding protein concentration (umol/L)

Percent

0.7 µmol/L


80

3.3.13. Zinc deficiency

Figure 21 presents the distribution of serum zinc in children 6-59 months of age. To control

for contamination, Nanopure™ water samples were treated like blood samples and zinc was

measured in the central laboratory along with blood specimens (see Section 2.7.3). Results

show only minor zinc contamination, with a median zinc concentration in the control

specimens of 1.4 µg/dL. As this level of contamination was consistently observed in most

control specimens, it likely reflects zinc contamination of the blood collection supplies and

equipment. As such, 1.4 µg/dL was subtracted from each zinc blood testing result.

The resulting zinc concentration values range from 46.7 - 328.9 µg/dL, and the mean zinc

concentration is 102.7 µg/dL (95% CI: 95.2, 110.3). Only 5.0% (95% CI: 2.2, 11.2) of children

are classified as zinc deficient based on non-fasting cut-offs based on collection of blood

samples in the morning (65 µg/dL) and afternoon/evening (57 µg/dL)(see Figure 21). This level

of deficiency characterizes a “moderate” level of zinc deficiency [43]. Due to the small sample

size and the low prevalence of deficiency, no sub-group analyses are presented.

Figure 21. Distribution of serum zinc concentrations in children 6-59 months of age,

Somalia 2019

05

10

15

20

25

Fre

qu

en

cy

0 50 100 150 200 250 300 350

Serum zinc concentration (ug/dL)

Frequency

57 µg/dL

65 µg/dL


81

3.3.14. Hemoglobinopathies

Blood pellets from children produced 1492 and 1441 valid results for sickle cell and alpha-

thalassemia status, respectively. As shown in Table 33, the prevalence of sickle cell trait

(HbAS) or disease (HbSS) is low, affecting less than 1% of children. Ten children have sickle

cell trait, and only one child found sickle cell disease (data not shown). Statistically significant

differences were found only by residence, where the prevalence of sickle cell is slightly higher

among children in residing in IDP settlements. No significant differences were found by age

category, residence, state, or wealth quintile.

Contrary to sickle cell, the prevalence of alpha-thalassemia is almost 8%. Alpha-thalassemia

is more common in the states in southern Somalia and in poorer households. No statistically

significant differences were found by child’s age category or residence.


82

Table 33. Sickle cell trait/disease, α-thalassemia (heterozygote and homozygote) in

children 6-59 months of age, Somalia 2019

Sickle cell trait or disease a α-thalassemia b

Characteristic n % c (95% CI) d P value e

n % c (95% CI) d P value e


6-11 0 0.0 - 0.8327 10 7.2 (3.4, 14.6) 0.5235

12-23 3 1.0 (0.3, 3.0) 25 9.6 (6, 15.1)

24-35 2 0.7 (0.1, 3.2) 26 7.6 (5.1, 11.4)

36-47 3 0.6 (0.2, 2.1) 27 7.5 (4.9, 11.3)

48-59 3 0.8 (0.3, 2.5) 22 5.7 (3.5, 9)

Residence

Rural 1 0.4 (0.1, 2.7) 0.0098 25 6.3 (3.1, 12.3) 0.7161

Urban 4 0.3 (0.1, 1.6) 65 7.8 (5.4, 11.1)

IDP 6 2.7 (1.1, 6.3) 20 9.1 (4.8, 16.5)

State

Somaliland 1 0.2 (0.0, 1.2) 0.2108 4 0.8 (0.3, 2.4) 0.0000

Puntland 0 0.0 - 18 7.6 (3.3, 16.6)

Hirshabelle 1 0.5 (0.1, 3.8) 7 3.8 (1.1, 12.6)

Galmudug 0 0.0 - 16 11.6 (5.1, 24.5)

South-West 1 0.6 (0.1, 3.9) 23 21.4 (13.8, 31.7)

Jubaland 2 1.7 (0.4, 7.0) 11 11.3 (5.3, 22.5)

Banaadir 6 2.3 (0.7, 7.2) 31 11 (6.1, 18.9)

Wealth Quintile

Lowest 3 2.1 (0.7, 6.5) 0.3486 18 16.2 (9.6, 26.1) 0.0003

Second 0 0.0 - 19 10.4 (5.7, 18.2)

Middle 2 0.5 (0.1, 2.0) 27 6.4 (3.6, 11.1)

Fourth 6 1.5 (0.5, 4.6) 37 7.6 (4.7, 11.9)

Highest 0 0.0 - 9 1.9 (0.9, 4.1)

TOTAL 11 0.7 (0.3, 1.5) 110 7.5 (5.7, 9.9) Note: The n’s are un-weighted denominators for each subgroup; subgroups that do not sum to the total have missing data. a Includes all children with sickle cell trait and disease. b Includes all children with homozygous and heterozygous α- thalassemia. c Percentages weighted for non-response and survey design. d CI=confidence interval, adjusted for cluster sampling design. e Chi-square p-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly


83

3.4. All Women


Characteristics of women randomly selected for the SMS are presented in Table 34. The SMS

identified 1194 eligible women (280 pregnant, 914 non-pregnant), from which 1122 women

(280 pregnant, 842 non-pregnant) were present during the time of the interview, agreed to

participate, and had complete data related to their age in years. There are more women aged

15-39 years than the other ages, and in accordance with the household sample selected, more

than half are residing in urban settlements, almost one-third in rural areas and about 15% in

IDP camps. The largest proportion of women are from Somaliland. More than 70% were

currently married, and approximately three-quarters of the surveyed women were non-

pregnant at the time of the survey. Of non-pregnant women, almost one-third were

breastfeeding a child.

Table 34. Description of all sampled pregnant and non-pregnant women, Somalia 2019

Survey Sample


Age Group (in years)

15-19 194 17.6 (14.9, 20.5)

20-24 230 20.4 (17.5, 23.5)

25-29 267 23.5 (20.7, 26.6)

30-34 168 14.4 (12.2, 16.8)

35-39 155 15.2 (12.5, 18.3)

40-44 74 6.0 (4.8, 7.6)

45-49 34 3.0 (2.0, 4.4)

Woman pregnant

No 842 75.0 (71.2, 78.4)

Yes 280 25.0 (21.6, 28.8)

Woman currently breastfeeding

No 588 71.8 (68.4, 75.0)

Yes 223 28.2 (25.0, 31.6)

Marital status

Never married 207 18.0 (14.7, 21.9)

Currently married 799 72.3 (68.0, 76.2)

Divorced 104 8.6 (7.0, 10.6)

Widowed 12 1.1 (0.6, 1.9)

Residence

Rural 304 28.9 (21.4, 37.7)

Urban 656 56.7 (47.9, 65.1)

IDP 162 14.4 (12.2, 17.0)



84

Survey Sample


State

Somaliland 305 34.7 (30.4, 39.3)

Puntland 164 8.6 (6.6, 11.1)

Hirshabelle 84 4.5 (2.8, 7.2)

Galmudug 82 4.9 (3.0, 8.0)

South-West 82 12.2 (7.6, 18.9)

Jubaland 118 17.1 (11.7, 24.3)

Banaadir 287 17.9 (14.3, 22.2)

ALL WOMEN 1122 100.0

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because

of missing data. a Percentages are un-weighted and do not account for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design.

3.4.2. Educational attainment

Overall, 61.7% of the women in Somalia never attended school or only informal school, 18.1%

attended Koranic, 10.6% primary and 9.6% secondary school. The largest proportion of those

not attending school or informal school was found in Jubaland (85.6%) and the smallest in

Hirshabelle with 47.6%, whereas the largest proportion of those who went to a Koranic school

was found in Galmudug (42.4%) and the lowest in Jubaland (5.5%, Figure 22).

The completion of primary school or higher is significantly associated with age, residence,

state and household wealth. A larger proportion of younger women and women living in

urban centers compared to older women and women residing in rural areas or IDP camps

completed primary school or higher. Completion of primary school or higher is more common

in women residing in Somaliland, Puntland Hirshabelle and Banaadir compared to the other

states and in women living in wealthy households compared to women residing in households

of the lowest wealth quintiles (Table 35).

Overall, only about one quarter of women included in the SMS are literate, the proportion

declining with age. Literacy rate significantly differed by residence, state and household

wealth. More women living in urban centers are literate compared to women residing in rural

areas or IDP camps. Banaadir, Hirshabelle and Somaliland have highest women literacy rates,

with more than one-third of surveyed women being able to read, whereas in Galmudug and

South West only about every tenth woman is literate. Also, literacy is more common in wealthy

households.


85

Figure 22. Educational attendance of pregnant and non-pregnant women, by state,

Somalia 2019

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


No school/ informal school Attended Koranic Attended Primary Attended Secondary


86

Table 35. Proportion of women that completed primary school or higher, Somalia 2019

Characteristic

Completed primary school or higher a

n % b (95% CI) c P-value d


15-19 74 33.9 (26.6, 42.2) 0.0000

20-24 63 26.9 (20.5, 34.4)

25-29 49 16.9 (12.2, 22.9)

30-34 22 11.9 (7.3, 18.9)

35-39 15 9.1 (5.5, 14.9)

40-44 5 6.6 (1.9, 20.6)

45-49 5 12.4 (5.0, 27.8)

Residence

Rural 35 10.3 (6.2, 16.7) 0.0042

Urban 173 24.8 (20.8, 29.4)

IDP 25 15.4 (7.8, 28.3)

State

Somaliland 87 28.7 (22.7, 35.5) 0.0000

Puntland 32 18.9 (12.4, 27.6)

Hirshabelle 19 22.6 (13.8, 34.8)

Galmudug 3 3.3 (1.1, 9.0)

South-West 2 2.0 (0.5, 7.8)

Jubaland 9 7.9 (3.0, 19.3)

Banaadir 81 27.3 (20.0, 36.1)

Wealth Quintile

Lowest 1 0.7 (0.1, 4.9) 0.0000

Second 4 2.2 (0.7, 6.5)

Middle 26 11.1 (7.4, 16.3)

Fourth 59 19.9 (15.3, 25.5)

Highest 143 44.1 (38.5, 49.8)

ALL WOMEN 233 19.3 (16.3, 22.6) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Self-report of educational attainment b Percentages weighted for unequal probability of selection

c CI=confidence interval, calculated taking into account the complex sampling design d P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups


87

Table 36. Proportion of literate women, Somalia 2019

Characteristic

Literacy a



15-19 87 43.2 (35.9, 50.7) 0.0000

20-24 76 31.8 (25.1, 39.3)

25-29 74 25.1 (19.7, 31.5)

30-34 39 22.4 (15.6, 31.1)

35-39 33 19.9 (13.5, 28.3)

40-44 14 16.8 (8.9, 29.6)

45-49 7 18.2 (6.8, 40.3)

Residence

Rural 54 16.4 (11.1, 23.4) 0.0007

Urban 239 34.3 (28.6, 40.4)

IDP 37 24.3 (15.7, 35.8)

State

Somaliland 99 34.1 (25.9, 43.3) 0.0000

Puntland 43 25.5 (17.4, 35.7)

Hirshabelle 32 38.1 (27.6, 49.9)

Galmudug 8 12.9 (6.7, 23.3)

South-West 8 9.2 (4.5, 18.0)

Jubaland 16 14.8 (6.6, 29.8)

Banaadir 124 42.8 (34.7, 51.3)

Wealth Quintile

Lowest 8 8.8 (3.6, 20.1) 0.0000

Second 10 7.9 (3.5, 16.7)

Middle 56 22.3 (17.4, 28.0)

Fourth 88 30.1 (24.3, 36.7)

Highest 167 51.2 (42.9, 59.3)

ALL WOMEN 330 27.8 (23.8, 32.2) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Respondent was able to read whole sentence in Somali, Arabic, or English presented by interviewer. The analysis excludes women that

were visually impaired, blind, mute, or spoke a language not on the card presented by the interviewer b Percentages weighted for unequal probability of selection



88

3.4.3. Supplement consumption

The consumption of oral vitamin and mineral supplements is rather low, particularly in Galmudug (Figure 23). As shown in

Table 37 the consumption of supplements significantly differs by pregnancy status. More

pregnant women consumed iron, folic acid and multivitamin supplements in the past 6

months prior to the survey compared to non-pregnant women.

Figure 23. Proportion of pregnant and non-pregnant women that consumed various

vitamin and mineral supplements in past 6 months, by state, Somalia 2019

Table 37. Proportion of women that consumed various vitamin and mineral supplements

in past 6 months, Somalia

Characteristic n % b (95% CI) c P-value d

Woman consumed iron tablet/ syrup in past 6 months

Woman is pregnant 59 21.3 (16.3, 27.3) 0.0000

Woman is not pregnant 91 10.9 (8.1, 14.5)

All women 150 13.5 (10.7, 16.8)

Woman consumed folic acid tablets/syrup in past 6 months



All women 123 11.3 (8.8, 14.5)


14% 14% 14%

4%

16%

12%

15%

13%13%13%

7%6%

14%

8%

10%11%

18%

14%

12%

3%

13%

7%

15%

13%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%


Iron Folic acid Multi-vitamin


89

Characteristic n % b (95% CI) c P-value d

Woman consumed multivitamin tablets in past 6 months



All women 151 13.2 (10.5, 16.5) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Percentages weighted for unequal probability of selection

b CI=confidence interval, calculated taking into account the complex sampling design c P-value <0.05 indicates that the variation in the values of the subgroup are significantly different from all other subgroups

3.4.1. Coffee and tea consumption

Consumption of coffee and tea are widespread throughout Somalia, 76.1% of women

reported consuming coffee or tea at least a few times per week. However, 43.8% of women

reported consuming coffee or tea 2-3 times per day. The frequency of consumption is highest

in Hirshabelle and Galmudug, where only few women reported consuming coffee or tea never

or rarely (Figure 24).

Figure 24. Frequency of coffee and tea consumption by pregnant and non-pregnant

women, by state, Somalia 2019

3.4.2. Dietary diversity

As shown in Table 38 over one-third of women meet the minimum dietary diversity by eating

at least 5 food groups. Minimum dietary diversity is significantly associated with pregnancy

status, residence, state and household wealth, but not with age. Almost half of the women

living in urban centers consume a diet with minimum diversity, compared to only every fourth

woman in rural areas and IDP camps. The largest proportion of women meeting minimum

dietary diversity was found in Puntland and Banaadir, by far the lowest in Hirshabelle. A

diverse diet is more common in women living in wealthy households compared to those of

poorer households.

17 20 1 0 923

11 15

15 11

2 2 9

2

89

3 2

0 1

810

24

2616

13 11

9

18

2620

37

31

76 84 38

4646 44

2 207

127

27 8

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Don't drink Rarely drink A few times a week

Once a day 2-3 times a day 4 or more times per day


90

Table 38. Proportion of pregnant and non-pregnant women with minimum dietary

diversity, Somalia 2019

Characteristic

Minimum dietary diversity a



15-19 90 45.0 (36.0, 54.4) 0.3569

20-24 88 35.1 (27.6, 43.3)

25-29 104 34.0 (27.4, 41.2)

30-34 67 35.7 (27.4, 44.9)

35-39 58 34.6 (26.0, 44.4)

40-44 31 38.0 (24.8, 53.3)

45-49 16 46.0 (25.9, 67.5)

Pregnancy

No 357 39.2 (33.3, 45.4) 0.0280

Yes 97 30.9 (25.0, 37.5)

Residence

Rural 90 25.6 (19.4, 33.0) 0.0002

Urban 322 45.8 (38.2, 53.5)

IDP 42 25.9 (16.4, 38.5)

State

Somaliland 101 33.4 (23.1, 45.7) 0.0001

Puntland 101 59.1 (47.7, 69.6)

Hirshabelle 8 9.5 (3.2, 25.2)

Galmudug 21 22.9 (11.0, 41.5)

South-West 20 23.6 (14.5, 36.1)

Jubaland 40 36.1 (23.1, 51.5)

Banaadir 163 54.6 (46.9, 62.1)

Wealth Quintile

Lowest 16 18.2 (11.6, 27.6) 0.0000

Second 27 18.0 (11.7, 26.7)

Middle 77 27.5 (21.3, 34.7)

Fourth 144 50.8 (42.7, 58.9)

Highest 185 53.6 (43.6, 63.2)

ALL WOMEN 454 37.1 (31.9, 42.5) Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Minimum dietary diversity calculated using FANTA W-MDD method [44] b Percentages weighted for unequal probability of selection



91

3.5. Non-pregnant women

3.5.1. Anthropometry

As is shown in Table 39, undernutrition affects more than every tenth woman. Of those, only

1.9% are severely undernourished, and 2.5% moderately malnourished. The big remainder

(6.5%) is ‘at risk’ for undernutrition (Figure 25). Overweight and obesity is relatively common

in non-pregnant women: about one out of four is overweight and about 15% obese.

The results indicate that BMI is age depended: Underweight almost gradually decreases and

obesity gradually increases with age (Figure 26).

Figure 25. Prevalence of underweight, normal weight, and overweight and obesity in non-

pregnant women, Somalia 2019

Figure 26. Prevalence of normal weight, overweight, and obesity in non-pregnant women

by age group, Somalia 2019

1.9 2.56.5

49.7

24.3

15.1

0

10

20

30

40

50

60

Severe chronicenergy

deficiency

Moderatechronic energy

deficiency

At risk Normal Overweight Obese

Pro

po

rtio

n (

%)

Nutrition status category based on BMI

2511 8 7 6 4 6

59

5946 48 49

18

42

13

18

30 29 22

53

26

3

12 16 17 24 26 26

0

20

40

60

80

100

15-19 20-24 25-29 30-34 35-39 40-44 45-49

Pro

po

rtio

n (

%)

Age group (years)

Underweight Normal Overweight Obese


92

As shown in Table 39 large differences in the prevalence of underweight, overweight and

obesity were observed between the states. The survey found no woman with underweight in

Galmudug and only few in Banaadir, whereas more than every fifth woman residing in

Puntland was found to be underweight. Overweight affects only about every tenth woman

living in South West and is at least twice as high in all other states of Somalia. Obesity is not

very common in Galmudug and Jubaland compared to Banaadir, Hirshabelle, Puntland and

Somaliland. Not surprisingly the prevalence of underweight is highest in women living in the

poorest households and overweight and obesity in women living in the wealthiest

households.


93

Table 39. Percentage of specific Body Mass Index (BMI) levels in non-pregnant women (15-49 years), Somalia 2019

Any underweight

(BMI<18.5)

Normal

(BMI: 18.5-24.9)

Overweight

(BMI: 25.0-29.9)

Obese

(BMI: ≥30.0)

Characteristic n % a 95 CI b n % a 95 CI b n % a 95 CI b n % a 95 CI b

Age in years

15-19 37 25.3 (18.6, 33.5) 95 59.1 (50.9, 66.9) 19 13.1 (7.8, 21.1) 4 2.5 (0.9, 6.6)

20-24 17 10.5 (6.6, 16.2) 90 59.3 (50.1, 67.9) 27 18.3 (13.0, 25.1) 22 12.0 (7.6, 18.4)

25-29 13 7.8 (4.6, 13.2) 74 45.8 (37.7, 54.0) 50 30.0 (23.1, 37.9) 27 16.4 (11.4, 23.1)

30-34 8 6.9 (3.4, 13.3) 52 47.6 (37.7, 57.7) 35 28.5 (20.5, 38.1) 21 17.1 (10.9, 25.7)

35-39 6 5.5 (2.0, 13.8) 53 49.4 (37.7, 61.2) 27 21.5 (14.1, 31.4) 29 23.6 (15.9, 33.7)

40-44 2 3.5 (0.9, 12.7) 12 17.6 (10.0, 29.1) 33 53.4 (41.7, 64.8) 17 25.5 (15.8, 38.6)

45-49 2 5.9 (1.4, 21.9) 12 42.2 (25.2, 61.4) 9 25.5 (12.4, 45.5) 10 26.3 (13.8, 44.4)

Residence

Rural 28 13.8 (9.3, 19.9) 113 55.5 (46.2, 64.4) 50 21.9 (16.6, 28.2) 21 8.9 (5.5, 14.0)

Urban 44 9.4 (6.7, 13.1) 222 47.7 (42.7, 52.8) 122 25.4 (21.6, 29.7) 89 17.5 (13.8, 21.9)

IDP 13 11.4 (7.0, 17.9) 53 46.5 (35.8, 57.5) 28 24.6 (17.8, 32.9) 20 17.5 (11.1, 26.6)

State

Somaliland 34 14.0 (10.4, 18.6) 107 44.5 (37.4, 52.0) 63 25.8 (21.1, 31.2) 38 15.7 (11.2, 21.5)

Puntland 24 20.8 (14.6, 28.9) 47 40.2 (31.7, 49.4) 29 23.6 (16.2, 33.1) 18 15.3 (10.7, 21.4)

Hirshabelle 4 8.2 (2.8, 21.5) 28 57.1 (36.7, 75.4) 9 18.4 (9.2, 33.3) 8 16.3 (6.7, 34.8)

Galmudug 0 46 74.5 (65.6, 81.8) 15 24.1 (17.0, 32.9) 1 1.4 (0.2, 10.3)

South-West 6 12.8 (5.8, 26.1) 29 62.8 (52.4, 72.2) 6 12.8 (6.6, 23.5) 5 11.5 (4.8, 25.4)

Jubaland 9 9.2 (4.3, 18.5) 51 58.9 (48.4, 68.7) 21 24.9 (17.8, 33.6) 6 7.0 (3.2, 14.6)

Banaadir 8 3.8 (1.6, 8.7) 80 40.1 (32.6, 48.0) 57 28.4 (21.7, 36.3) 54 27.7 (22.3, 33.7)

Woman's Education

No school /informal school 48 10.9 (8.1, 14.5) 237 52.2 (46.7, 57.5) 108 21.7 (17.8, 26.1) 79 15.3 (12.0, 19.2)

Koranic school 9 5.8 (2.9, 11.5) 67 46.9 (38.2, 55.7) 46 32.0 (24.0, 41.2) 24 15.3 (10.7, 21.5)

Primary school 12 14.7 (7.4, 27.1) 34 41.9 (29.6, 55.4) 26 32.8 (19.2, 50.1) 10 10.5 (5.4, 19.5)

Secondary school 16 15.8 (9.2, 25.9) 50 47.8 (36.3, 59.5) 20 18.8 (12.4, 27.6) 17 17.5 (11.7, 25.4)



94

Any underweight

(BMI<18.5)

Normal

(BMI: 18.5-24.9)

Overweight

(BMI: 25.0-29.9)

Obese

(BMI: ≥30.0)

Characteristic n % a 95 CI b n % a 95 CI b n % a 95 CI b n % a 95 CI b

Wealth Quintile

Lowest 14 20.5 (11.9, 32.9) 35 55.7 (44.7, 66.2) 12 18.5 (10.2, 31.2) 3 5.3 (1.7, 15.5)

Second 8 8.1 (4.0, 15.7) 62 62.9 (49.0, 75.0) 21 20.6 (13.9, 29.5) 8 8.4 (3.7, 18.0)

Middle 13 8.0 (4.5, 14.0) 96 58.6 (49.5, 67.2) 30 18.4 (12.4, 26.4) 25 14.9 (9.8, 22.1)

Fourth 21 9.0 (5.4, 14.7) 94 47.9 (41.4, 54.5) 58 28.3 (23.4, 33.7) 32 14.7 (10.2, 20.8)

Highest 28 11.5 (7.8, 16.6) 98 37.1 (31.5, 43.1) 77 29.0 (24.1, 34.5) 61 22.4 (16.7, 29.2)

Minimal dietary diversity

No (0-4) 59 12.8 (9.9, 16.4) 240 52.5 (46.7, 58.1) 99 20.2 (16.7, 24.1) 70 14.5 (11.3, 18.4)

Yes (≥5) 26 7.9 (5.1, 12.0) 148 45.3 (39.9, 50.9) 101 30.9 (26.1, 36.1) 60 15.9 (12.1, 20.6)

Household sanitation d

Inadequate 33 9.5 (6.5, 13.8) 192 52.6 (46.1, 58.9) 93 23.5 (19.2, 28.3) 58 14.4 (11.1, 18.5)

Adequate 51 12.3 (9.3, 16.2) 191 46.8 (41.0, 52.6) 105 25.0 (21.0, 29.5) 71 15.8 (12.4, 20.0)

ALL WOMEN 85 10.9 (8.7, 13.6) 388 49.7 (45.6, 53.9) 200 24.4 (21.3, 27.5) 130 15.1 (12.5, 18.1)

Note: The n’s are un-weighted numerators for each subgroup; subgroups that do not sum to the total have missing data. a Percentages weighted for non-response and survey design. b CI=confidence interval, adjusted for cluster sampling design. c Chi-square p-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly d Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not shared with another

household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field


95

3.5.2. Malaria

Only 2 women were diagnosed with malaria, which precluded any sub-group analyses. One

woman resided in an urban area of Jubaland, and other woman resided in Banaadir.

3.5.3. Anemia, iron deficiency, and iron deficiency anemia

Table 41 shows the prevalence of anemia, iron deficiency and iron deficiency anemia for non-

pregnant women. Nationally, about 40% of these women are anemic and half are iron

deficient. Overall, the anemia situation in non-pregnant women is classified of ‘severe’ public

health importance as per WHO criteria [45]. The majority of the anemia can be classified as

mild and moderate, only a small proportion of women were found to have severe anemia

(Table 40). Significant differences in anemia prevalence were found by state: Anemia affects

every fourth woman living in Galmudug and Somaliland compared to about half of the women

in the other states; highest prevalence was found in Hirshabelle. Further, anemia is less

common in women residing in wealthy households compared to poorer women. Our results

show no significant difference in anemia prevalence by woman’s age, residence and

education (Table 41).

About half of the women in Somalia are iron deficient. Also here Galmudug stands out: Only

just about 16% of women are iron deficient compared to more than 65% in Puntland and

about 50% in all other states. No significant differences in IDA prevalence were found for any

of the other investigated demographic indicators.

IDA was found in about every fourth woman, thus slightly more than half of the women with

anemia also have ID (Figure 27). Similar to anemia IDA prevalence significantly differs by state

and is lowest in Galmudug. No significant differences in IDA prevalence were found for any of

the other investigated demographic indicators.


96

Table 40. Proportion of mild, moderate and severe anemia in non-pregnant women,

Somalia 2019

Mild anemia b Moderate anemia b Severe anemia b

Characteristic n %a (95% CI)c n %a (95% CI)c n %a (95% CI)c

Age Group (in years) 15-19 30 18.0 (11.9, 26.2) 24 13.4 (8.7, 19.9) 4 3.1 (1.0, 8.7) 20-24 31 19.4 (13.3, 27.5) 29 16.4 (11.2, 23.5) 3 2.7 (0.8, 8.5) 25-29 34 21.4 (15.0, 29.5) 32 17.9 (12.5, 24.9) 6 4.3 (1.9, 9.6) 30-34 26 22.3 (14.8, 32.2) 26 22.1 (14.6, 31.9) 3 1.8 (0.6, 5.5) 35-39 20 21.5 (14.1, 31.2) 28 22.6 (15.9, 31.1) 3 2.0 (0.6, 6.7) 40-44 9 14.1 (7.2, 25.7) 17 24.1 (14.6, 37.1) 0 -- 45-49 5 15.3 (5.7, 35.1) 3 6.2 (2.0, 17.5) 0 --

Residence Rural 37 17.6 (12.4, 24.5) 46 19.1 (14.5, 24.6) 10 4.7 (2.5, 8.6) Urban 91 19.4 (16.0, 23.3) 97 18.1 (14.6, 22.3) 8 1.9 (0.9, 4.0) IDP 27 25.5 (15.6, 38.8) 16 15.1 (9.2, 23.7) 1 0.9 (0.1, 6.7)

State Somaliland 32 13.7 (9.7, 18.9) 30 12.6 (9.1, 17.2) 2 0.9 (0.2, 3.4) Puntland 24 22.2 (15.7, 30.5) 34 29.3 (22.1, 37.7) 4 3.4 (1.3, 8.4) Hirshabelle 11 22.4 (10.8, 40.9) 15 30.6 (19.8, 44.1) 3 6.1 (2.2, 15.8) Galmudug 8 11.0 (5.4, 21.3) 10 13.8 (6.8, 26.0) 0 -- South-West 12 25.0 (14.8, 39.0) 8 18.7 (7.8, 38.4) 3 7.0 (2.4, 18.9) Jubaland 23 25.6 (18.8, 33.8) 13 15.0 (10.3, 21.4) 3 3.8 (1.3, 10.9) Banaadir 45 25.7 (19.2, 33.5) 49 26.4 (20.9, 32.8) 4 2.3 (0.8, 6.0)

Woman's Education No school /informal

school 93 19.1 (15.4, 23.5) 97 18.4 (14.8, 22.7) 13 2.8 (1.5, 5.0)

Koranic school 27 22.5 (15.4, 31.8) 30 21.2 (15.3, 28.7) 5 4.4 (1.8, 10.6) Primary school 19 23.0 (14.2, 35.1) 14 13.2 (7.7, 21.6) 0 -- Secondary school 16 15.5 (9.5, 24.2) 18 14.6 (9.2, 22.2) 1 0.7 (0.1, 5.2)

Wealth quintile Lowest 14 21.9 (13.6, 33.4) 12 19.5 (11.6, 30.7) 2 3.1 (0.8, 12.0) Second 22 22.6 (14.8, 32.9) 15 14.3 (8.3, 23.4) 6 6.6 (3.0, 13.9) Middle 33 21.0 (15.0, 28.7) 30 17.3 (12.3, 23.7) 4 2.4 (0.7, 7.3) Fourth 41 21.0 (15.5, 27.8) 47 23.0 (17.0, 30.5) 6 3.1 (1.2, 7.6) Highest 44 16.0 (12.0, 21.1) 53 16.3 (13.0, 20.3) 1 0.2 (0.0, 1.6)

ALL WOMEN 155 19.7 (16.8, 23.0) 159 18.0 (15.4, 20.9) 19 2.6 (1.6, 4.1) Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b Mild, moderate, and severe anemia defined as hemoglobin 110-119 g/L, 80-109 g/L, and <80 g/L, respectively; after adjusting hemoglobin for smoking. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups


97

Table 41. Anemia, iron deficiency, and iron deficiency anemia in non-pregnant women (15-49 years), Somalia 2019


Characteristic n %a (95% CI)c P valued n %a (95% CI)c P valued n %a (95% CI)c P valued


15-19 58 34.4 (27.3, 42.3) 0.1746 65 44.8 (35.2, 54.8) 0.5572 35 22.1 (15.9, 29.9) 0.4809

20-24 63 38.5 (30.2, 47.5) 73 52.2 (44.0, 60.3) 40 25.3 (18.3, 33.9)

25-29 72 43.6 (35.3, 52.2) 69 49.1 (40.6, 57.7) 44 29.5 (21.5, 39.0)

30-34 55 46.2 (35.6, 57.1) 55 57.4 (46.0, 68.2) 34 31.4 (21.9, 42.8)

35-39 51 46.1 (36.2, 56.3) 48 49.9 (39.1, 60.7) 30 28.4 (19.7, 39.0)

40-44 26 38.1 (24.9, 53.4) 26 50.0 (35.6, 64.4) 15 26.4 (15.7, 40.9)

45-49 8 21.4 (9.4, 41.8) 9 37.4 (21.7, 56.3) 3 11.6 (3.3, 33.4)

Residence

Rural 93 41.4 (33.7, 49.5) 0.9041 89 51.1 (43.1, 58.9) 0.7885 48 25.0 (18.6, 32.8) 0.6708

Urban 196 39.4 (34.3, 44.6) 210 49.9 (44.6, 55.3) 122 25.9 (21.6, 30.8)

IDP 44 41.5 (28.7, 55.5) 46 46.0 (33.2, 59.4) 31 30.7 (20.3, 43.5)

State

Somaliland 64 27.1 (21.9, 33.0) 0.0000 112 49.5 (42.0, 57.1) 0.0005 46 19.6 (15.3, 24.9) 0.0001

Puntland 62 54.9 (45.5, 64.0) 64 65.2 (54.6, 74.5) 45 42.8 (32.9, 53.3)

Hirshabelle 29 59.2 (46.5, 70.8) 15 48.4 (32.5, 64.6) 13 33.3 (20.4, 49.4)

Galmudug 18 24.8 (13.5, 41.2) 10 16.4 (8.4, 29.7) 1 1.5 (0.2, 10.6)

South-West 23 50.7 (31.1, 70.1) 23 52.1 (40.0, 63.9) 15 32.7 (19.1, 50.0)

Jubaland 39 44.4 (36.1, 53.1) 40 51.6 (42.1, 61.0) 25 30.1 (21.6, 40.3)

Banaadir 98 54.3 (47.3, 61.2) 81 50.5 (42.4, 58.6) 56 34.2 (26.4, 42.9)

Woman's Education

No school /informal school 203 40.3 (35.1, 45.7) 0.1218 217 52.9 (47.4, 58.3) 0.1092 122 26.2 (21.6, 31.4) 0.2928

Koranic school 62 48.2 (38.0, 58.5) 55 49.3 (39.8, 58.9) 37 32.2 (24.2, 41.4)

Primary school 33 36.2 (25.6, 48.2) 35 44.0 (32.7, 56.0) 21 24.3 (16.2, 34.9)

Secondary school 35 30.7 (21.8, 41.4) 38 37.6 (26.4, 50.3) 21 19.6 (11.6, 31.2)



98



Wealth Quintiles

Lowest 28 44.5 (33.1, 56.6) 0.0381 37 61.3 (47.0, 73.8) 0.0608 22 33.8 (22.4, 47.4) 0.1823

Second 43 43.4 (33.5, 53.9) 34 38.0 (26.5, 51.1) 22 24.4 (15.6, 35.9)

Middle 67 40.7 (33.2, 48.6) 67 52.7 (41.0, 64.1) 36 24.1 (16.8, 33.2)

Fourth 94 47.1 (40.1, 54.2) 91 55.6 (47.6, 63.2) 57 32.2 (25.2, 40.1)

Highest 98 32.6 (27.7, 37.9) 111 45.7 (38.2, 53.4) 61 21.7 (17.0, 27.4)

ALL WOMEN 333 40.2 (36.2, 44.4) 345 49.7 (45.4, 54.0) 201 26.3 (22.9, 30.1) a Percentages weighted for unequal probability of selection. b Anemia defined as hemoglobin < 120 g/L adjusted for smoking. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups. e Iron deficiency defined as serum ferritin < 12 µg/L, values are adjusted for inflammation according to BRINDA f Iron deficiency anemia, defined as low Hb (< 110 g/L) with low serum ferritin (< 15.0 μg/L). g Composite variable of toilet type and if toilet facilities are shared with non-household members; Adequate Sanitation = flush or pour flush toilet or pit latrine with slab not shared with another

household. Inadequate sanitation= open pit, bucket latrine, hanging toilet/latrine, no facility, bush, field


99

Figure 27. Venn diagram showing overlap between anemia and iron deficiency in non-pregnant

women of reproductive age, Somalia 2019

The distribution of hemoglobin values for women is shown in Figure 28. The hemoglobin distribution is

essentially normal between the ranges of 50 to 150 g/L, however a small number of women with

extremely low and high hemoglobin levels were found. The mean hemoglobin concentration among all

non-pregnant women was 121.4, with haemoglobin values ranging from 30-164 g/L (Figure 28).

Anemia 40.2%

Iron deficiency

49.7%

Iron deficiency

anemia

26.3%


100

Figure 28. Histogram of hemoglobin concentration (g/L) in non-pregnant women of reproductive

age, Somalia 2019

Table 42 shows that iron status has an impact on anemia prevalence; specifically, anemia is almost 3-

times higher in women with ID compared to women without ID. Also anemia prevalence is significantly

higher in women with VAD compared to vitamin A replete women. Anemia prevalence does not differ

significantly by inflammation, nutritional status, minimal dietary diversity, or coffee/tea consumption.

025

50

75

10

0

Fre

qu

en

cy

0 25 50 75 100 125 150 175 200


Frequency

120 g/L


101

Table 42. Anemia, iron deficiency, and iron deficiency anemia in non-pregnant women (15-49 years) by micronutrient status, inflammation

and nutritional status, Somalia 2019



Iron status e

Deficient (<15 µg/l) 201 56.0 (50.3, 61.6) 0.0000

Sufficient (≥15µg/L) 80 21.7 (17.4, 26.8)

Vitamin A status g

Deficient (<0.7nmol/L) 41 55.2 (42.5, 67.2) 0.0039 46 58.1 (46.8, 68.7) 0.1421 30 37.6 (26.4, 50.2) 0.0745

Sufficient (≥0.70nmol/L) 240 36.8 (32.6, 41.2) 299 48.7 (44.0, 53.4) 171 26.7 (22.9, 30.8)

Inflammation h

None 195 40.4 (35.5, 45.5) 0.6911 237 51.2 (46.1, 56.3) 0.0230 139 28.7 (24.3, 33.4) 0.4920

Incubation 40 36.0 (26.6, 46.7) 50 45.7 (34.7, 57.1) 31 28.9 (20.2, 39.5)

Early convalescence 32 33.7 (22.6, 46.8) 34 37.5 (26.8, 49.5) 19 20.3 (11.5, 33.1)

Late convalescence 14 38.3 (23.6, 55.5) 24 70.2 (50.8, 84.3) 12 31.9 (19.2, 48.0)

Nutritional status

Underweight (BMI< 18.5) 33 38.7 (28.9, 49.6) 0.8553 43 58.4 (44.4, 71.2) 0.3499 24 30.3 (20.1, 42.9) 0.4375

Normal weight (BMI 18.5-24.9) 162 40.9 (35.1, 46.9) 155 48.5 (42.6, 54.3) 87 23.9 (19.0, 29.5)

Overweight/Obesity (BMI >25) 131 38.8 (32.9, 45.1) 144 48.9 (43.0, 54.9) 88 27.8 (22.8, 33.4)

Minimal dietary diversity

No (0-4) 192 39.8 (34.4, 45.5) 0.7546 188 47.3 (41.9, 52.8) 0.1162 114 26.1 (21.7, 31.1) 0.8825

Yes (≥5) 141 40.9 (36.1, 46.0) 157 53.7 (47.4, 59.8) 87 26.6 (22.0, 31.8)

Coffee/Tea consumption

Less than daily 83 39.8 (32.1, 48.1) 0.8978 94 49.0 (41.0, 57.0) 0.8387 50 25.7 (18.7, 34.1) 0.5116

Daily or more frequently 250 40.4 (35.8, 45.2) 251 50.0 (44.7, 55.3) 151 28.7 (24.6, 33.2)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b Anemia defined as hemoglobin < 120 g/L adjusted for smoking. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups. e Iron deficiency defined as serum ferritin < 12 µg/l, values are adjusted for inflammation according to BRINDA f Iron deficiency anemia, defined as low Hb (< 110 g/L) with low serum ferritin (< 15.0 μg/L). g VAD= Vitamin A deficiency, defined as low retinol binding protein (<0.7 nmol/L), was not adjusted for inflammation according to BRINDA recommendations h Incubation=CRP only; early convalescence=CRP and AGP; late convalescence=AGP only.


102


Nationally, about every tenth woman has vitamin A deficiency using RBP as the indicator (see

Table 43), denoting a moderate public health problem according to WHO classifications [42].

Statistically significant differences were found by woman’s education, but not for age,

residence, state and household wealth. The prevalence of VAD is twice as high in woman who

went to Koranic school compared to women who attended no school/ informal school or

secondary school and thrice as high as compared to women who attended primary school.

3.5.5. Folate and Vitamin B12 deficiencies

In total, folate status was measured in 141 women, approximately 18% of women that

provided blood samples. Folate concentrations were normally distributed, and mean folate

concentrations were 13.5 nmol/L (95% CI: 12.4, 14.7). Folate deficiency was found in 35.1%

(95% CI: 26.7, 44.7) of non-pregnant women. Due to the small sample size, no sub-group

analyses were conducted.

Vitamin B12 status was measured in 128 non-pregnant women, or about 16% of women that

provided blood samples. Vitamin B12 concentrations were not normally distributed, and

median concentrations were 180.3 pmol/L (95% CI: 139.3, 221.2). Vitamin B12 deficiency was

found in 36.9% (95% CI: 25.4, 50.1) of non-pregnant women. Similar to folate deficiency, due

to the small sample size, no sub-national analysis was conducted.

Concurrent folate and B12 deficiency was found in 13.0% (95% CI: 6.6, 24.1) of the non-

pregnant women (n= 122) with both folate and B12 results.


103

Table 43. Vitamin A deficiency in non-pregnant women (15-49 years), Somalia 2019

Characteristic n %a, b (95% CI)c P valued


15-19 24 16.3 (11.2, 23.1) 0.0541

20-24 13 8.6 (5.1, 14.1)

25-29 8 5.6 (2.5, 11.9)

30-34 14 15.6 (9.4, 24.9)

35-39 11 11.6 (6.2, 20.8)

40-44 5 6.1 (2.2, 16.1)

45-49 2 7.0 (1.7, 24.8)

Residence

Rural 22 11.7 (7.9, 16.8) 0.4853

Urban 48 11.1 (8.0, 15.1)

IDP 7 7.0 (3.0, 15.5)

State

Somaliland 26 11.6 (8.3, 15.8) 0.1831

Puntland 18 17.7 (10.5, 28.2)

Hirshabelle 3 9.7 (3.4, 24.4)

Galmudug 5 7.7 (2.7, 20.1)

South-West 7 16.1 (6.4, 34.8)

Jubaland 4 5.1 (2.0, 12.2)

Banaadir 14 8.6 (4.9, 14.6)

Woman's Education

No school /informal school 44 9.7 (7.2, 13.0) 0.0090

Koranic school 19 18.3 (12.2, 26.6)

Primary school 5 5.0 (2.2, 10.9)

Secondary school 9 9.8 (5.0, 18.5)

Wealth quintile

Lowest 9 14.2 (7.1, 26.4) 0.3365

Second 9 9.4 (4.9, 17.4)

Middle 13 9.8 (4.8, 19.1)

Fourth 25 15.2 (10.2, 22.1)

Highest 21 8.0 (5.1, 12.2)

ALL WOMEN 77 10.7 (8.4, 13.5)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b Vitamin A deficiency defined as retinol binding protein (RBP) <0.70 μmol/L; RBP concentrations adjusted for inflammation. c CI=confidence interval, calculated taking into account the complex sampling design. d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in the other subgroups


104

3.5.6. Median urinary iodine concentration

As shown in Table 44, median urinary iodine concentration is 263.3 ug/L., with enormous

differences between the states: the concentration found in Somaliland indicates iodine

insufficiency, whereas median UIC in the other states is high, especially in women living in

Hirshabelle. UIC also significantly differs by household wealth and woman’s education.

Lowest median UIC was found in women who attended primary school and highest in those

who attended Koranic school. Further, women living in households of the highest wealth

quintile have the lowest medium UIC.

Although not significant there is a trend towards higher median UIC in women living in urban

centers, who have 2-3 times higher median UIC compared to women living in rural areas or

IDP camps. UIC does not significantly differ in women consuming adequately iodized salt

compared to those consuming inadequately or not iodized salt. Further, no statistically

significant differences in iodine concentrations were found by women’s age. Not surprisingly,

median UIC increases with increasing iodine drinking water concentration and is highest in

the fourth and fifth quintile. Why median UIC in women of the lowest quintile is higher

compared to those of the second and middle quintile is unclear.

Table 44. Median urinary iodine concentration in non-pregnant women (15-49 years),

Somalia 2019

Characteristic n

Median UIC

(µg/L) a, b (IQR)c P Valued


15-19 125 273.3 (98.7, 606.0) 0.2355

20-24 132 209.8 (90.4, 593.5)

25-29 149 262.8 (112.9, 729.8)

30-34 92 259.1 (89.5, 594.4)

35-39 100 190.4 (65.8, 453.2)

40-44 58 334.6 (119.5, 678.0)

45-49 30 312.9 (171.2, 618.0)

Residence

Rural 184 331.6 (136.6, 843.0) 0.0740

Urban 401 224.5 (85.6, 537.8)

IDP 101 242.5 (112.9, 485.8)

State

Somaliland 221 70.8 (41.7, 138.9) 0.0000

Puntland 110 522.8 (272.3, 874.0)

Hirshabelle 29 1379.8 (842.2, 2064.9)

Galmudug 59 490.8 (212.3, 927.5)

South-West 44 286.6 (200.7, 446.4)

Jubaland 85 212.8 (104.1, 399.0)

Banaadir 138 461.5 (263.3, 692.3)



105

Characteristic n

Median UIC

(µg/L) a, b (IQR)c P Valued

Woman's Education

No school /informal school 430 249.0 (100.8, 576.75) 0.0417

Koranic school 113 318.5 (127.0, 684.5)

Primary school 65 177.5 (54.8, 483.0)

Secondary school 78 274.0 (99.3, 770.3)

Wealth quintile

Lowest 62 261.1 (153.3, 423.0) 0.0025

Second 94 208.6 (93.1, 472.5)

Middle 130 287.4 (88.6, 745.0)

Fourth 169 371.3 (168.8, 807.8)

Highest 223 173.0 (59.8, 519.3)

Adequately iodized salt in

householdd

No 587 269.3 (93.1, 621.8) 0.2950

Yes 23 305.1 (136.8, 737.0)

Iodine drinking water concentration

quintiles

Lowest 116 365.5 (122.3, 1200.4) 0.0000

Second 105 140.5 (71.4, 373.3)

Middle 161 148.9 (47.7, 299.5)

Fourth 107 383.5 (203.8, 611.3)

Highest 107 526.3 (211.7, 845.3)

ALL WOMEN a 686 261.3 (94.6, 3123.5)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of

missing data. a Median’s unweighted; UIC = urinary iodine concentration b IQR = Interquartile range, calculated using unweighted data. c ANOVA p-value <0.05 indicates that the mean of the natural log of urinary iodine concentration in at least one subgroup

is statistically significantly different from the values in the other subgroups. d Adequately iodized salt > 15 ppm


106

3.6. Pregnant women


A total of 280 pregnant women were enrolled into the survey. Women 15-20 years of age

represent a smaller proportion of pregnant women that other age groups. As expected, the

majority of pregnant women reside in urban dwellings, as this matches the household profiles

of this survey. Similarly, more women from Somaliland and South West made it into the

sample. The number of pregnant women is almost equally distributed between the wealth

quintiles.

Table 45. Description of pregnant women, Somalia 2019

Survey Sample



15-19 29 10.7 (7.4, 15.2)

20-29 163 55.3 (48.8, 61.6)

30-44 88 34.0 (28.1, 40.4)

Residence

Rural 81 30.0 (20.7, 41.1)

Urban 161 56.5 (45.6, 66.9)

IDP 38 13.5 (10.2, 17.7)

State

Somaliland 60 27.4 (22.8, 32.6)

Puntland 38 8.3 (5.7, 12.0)

Hirshabelle 33 7.1 (4.3, 11.5)

Galmudug 18 4.2 (2.2, 7.6)

South-West 35 20.7 (12.0, 33.3)

Jubaland 24 14.3 (8.3, 23.6)

Banaadir 72 18.0 (13.3, 24.0)

Wealth quintile

Lowest 27 15.2 (10.8, 21.1)

Second 39 18.7 (13.4, 25.5)

Middle 81 27.3 (21.5, 34.1)

Fourth 80 22.6 (17.4, 28.8)

Highest 48 16.1 (11.6, 22.1)

ALL WOMEN 280 100.0 --

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the

total because of missing data. a Percentages are un-weighted and do not account for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design


107

3.6.2. Mid-upper arm circumference

As shown in Table 46, more than every tenth woman had a MUAC <23 cm and is therefore

considered undernourished. Due to the relatively small sample size, large confidence intervals

accompany the point estimates and as such, no demographic factors appear as significant

modifiers. Although not statistically significant, the proportion of undernourished women

tends to be higher in pregnant women 15-19 years of age and lower in urban centers.

Similarly, there is a trend towards a higher prevalence of undernutrition in poorer households.

Table 46. Percentage undernourished by various characteristics in pregnant women,

Somalia 2019

Characteristic

n

% under-

nourished a, b (95% CI) b P value c


15-19 6 21.8 (9.3, 43.3) 0.2097

20-29 17 10.8 (6.2, 18.1)

30-44 8 8.9 (4.3, 17.2)

Residence

Rural 13 15.4 (8.1, 27.3) 0.0860

Urban 11 7.3 (3.5, 14.5)

IDP 7 19.4 (10.2, 33.9)

Wealth quintile

Lowest 5 20.0 (8.9, 38.9) 0.1654

Second 7 16.9 (8.3, 31.3)

Middle 7 7.9 (3.2, 17.9)

Fourth 8 9.5 (4.6, 18.6)

Highest 4 6.4 (2.3, 16.5)

ALL WOMEN 31 11.3 (7.6, 16.5)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because

of missing data. a Percentages weighted for unequal probability of selection. b %Undernourished= % of women with MUAC < 23 cm

c P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the

values in the other subgroups

3.6.3. Malaria

Only three pregnant women or 1.3% of pregnant women had malaria at the time of the survey.

3.6.4. Anemia

As shown in Table 47, anemia was found in almost half of the pregnant women, which

classifies anemia in pregnant women as a severe public health problem according to WHO.

Due to the relatively small sample size, large confidence intervals accompany the point

estimates and as such, not many demographic factors appear as significant modifiers.

However, there are significant differences by residence, with pregnant women living in rural

areas being more often anemic compared to those living in urban centers and IDP camps.


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Table 47. Anemia in pregnant women, Somalia 2019

Characteristic n Anemia %a, b (95% CI)c P Valued


15-19 15 50.2 (30.4, 69.8) 0.7986

20-29 74 45.4 (35.3, 55.8)

30-44 44 49.9 (38.5, 61.3)

Residence

Rural 51 62.9 (48.4, 75.4) 0.0131

Urban 69 41.4 (32.3, 51.2)

IDP 13 37.1 (24.1, 52.4)

State

Somaliland 30 50.0 (36.3, 63.7) 0.3159

Puntland 22 62.6 (45.7, 76.9)

Hirshabelle 21 63.6 (42.0, 80.9)

Galmudug 8 44.8 (22.3, 69.7)

South-West 13 35.6 (21.2, 53.2)

Jubaland 12 52.9 (28.5, 75.9)

Banaadir 27 39.7 (27.1, 53.7)

Wealth quintile

Lowest 11 45.4 (25.6, 66.7) 0.6210

Second 19 46.3 (33.4, 59.8)

Middle 43 56.0 (43.8, 67.4)

Fourth 37 43.4 (30.7, 57.1)

Highest 22 44.3 (30.9, 58.6)

ALL WOMEN 133 47.4 (39.8, 55.2)

Note: The n’s are un-weighted numerators in each subgroup; the sum of subgroups may not equal the total because of missing

data. a Percentages weighted for unequal probability of selection.

b Anemia defined as hemoglobin < 110 g/L adjusted for smoking.

c CI=confidence interval, calculated taking into account the complex sampling design.

d P-value <0.05 indicates that the proportion in at least one subgroup is statistically significantly different from the values in

the other subgroups

The mean hemoglobin concentration among all pregnant women was 109.7 g/L, with

haemoglobin value observed between 47-157 g/L (Figure 29).


109

Figure 29. Histogram of hemoglobin concentration (g/L) in pregnant women, Somalia

2019

3.6.5. Median urinary iodine concentration

As shown in Table 48 median UIC among all pregnant women is 369.4 ug/L. Although not

statistically significant, median UIC tends to be higher in pregnant women 20-29 years of age

and those living in urban centers. Similar to no-pregnant women the consumption of

adequately iodized salt does not lead to significant differences in median UIC.

We found that iodine drinking water is a predictor of median UIC. However, results are

different to what was expected. Highest median UIC was found in women who consumed

water with the lowest iodine concentration and only second highest median UIC was found

in women consuming drinking water with the highest iodine concentration.

010

20

30

40

50

Fre

qu

en

cy

0 25 50 75 100 125 150 175


Frequency

110 g/L


110

Table 48. Median urinary iodine concentration in pregnant women, Somalia 2019

Urinary iodine concentration (ug/L)

Characteristic n Median a IQR b P Value c


15-19 29 377.8 (166.1, 630.5) 0.2154

20-29 163 440.1 (169.5, 806.5)

30-44 88 226.0 (77.7, 518.5)

Residence

Rural 69 286.0 (109.1, 636.0) 0.8840

Urban 135 436.8 (153.2, 817.8)

IDP 32 345.9 (147.4, 445.4)

Adequately iodized salt in household d

No 203 373.0 (153.3, 731.6) 0.9741

Yes 8 336.0 (116.2, 678.8)

Iodine drinking water concentration

quintiles

Lowest 36 786.5 (175.6, 1878.7) 0.0253

Second 37 301.5 (154.4,524.0

Middle 41 205.8 (68.7, 440.0)

Fourth 32 382.0 (222.5, 567.5)

Highest 36 531.4 (531.4, 806.6)

ALL WOMEN a 236 369.4 (142.9, 732.3) Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.

b IQR=interquartile range

c ANOVA p-value <0.05 indicates that the mean of the natural log of urinary iodine concentration in at least one subgroup is

statistically significantly different from the values in the other subgroups.

d Adequately iodized salt > 15 ppm


111

4. COMPARISON BETWEEN THE 2009 AND THE 2019 SURVEYS

To identify potential changes in the nutrition status of children and women over the past 10 years, we

have compared the results from Somalia’s 2009 National Micronutrient and Anthropometric Nutrition

Survey [1] and the SMS 2019. Due to differences in the data collection procedures, variable definitions,

and cutoffs used in both surveys, we have adapted the data analysis approach for the SMS 2019 to

match to the approaches/methods used in the 2009 survey. Moreover, comparisons between the

surveys should be made with caution, as both surveys excluded areas of Somalia due to insecurity,

and the areas excluded changed between the surveys. In addition, the sampling methodology of both

surveys varied slightly, with clusters in 2009 “defined generically as Urban, Rural and IDP”, whereas

the 2019 SMS had a separate stratum including households residing in IDP settlements. As such, the

2019 results present in this chapter are different from those results reported in Chapter 3 of this

report. Thus, this analysis should only be used for the comparison with the 2009 survey; these results

do not indicate current nutrition or health status of children or women in Somalia.

To highlight the methodological differences in the two surveys, a detailed comparison of the

biomarkers, analytical methods, and deficiency cut-offs used to compare the 2009 and 2019 surveys

are presented in Table 49. As school-age children were not included as a target group in the 2019 SMS,

this age group was not included in the comparison of survey results presented below.

As shown in Table 50, the proportion of households with adequately iodized salt increased slightly

between 2009 and 2019, but the confidence intervals of both surveys suggest that the difference is

not statistically significant.

In children and women, there is an apparent decrease in the prevalence of anemia, iron deficiency,

and iron deficiency anemia since 2009. The decrease is in children is more notable, with reductions of

approximately 15 percentage points for these three indicators.

A decrease in the stunting, wasting, and underweight prevalence was also observed in children, with

percentage point reductions between approximately 3 and 7 percent. Anthropometric data in non-

pregnant women, which was measured using the same indicator and cutoffs in both surveys, showed

a reduction in the prevalence of undernutrition by more than 10 percentage points. The prevalence

of obesity increased by approximately 8 percentage points, illustrating that the obesity prevalence

more than doubled since 2009.

In children, vitamin A deficiency remained constant between the two surveys. Due to considerable

methodological differences in the cutoffs used to measure vitamin A deficiency in women, no

comparison could be made between the 2009 and 2019 surveys.

Lastly, median urinary iodine concentrations decreased by about 65 ug/L, which represented a change

in the overall iodine status from levels considered to be “excessive” to “above requirements” [46].


112

Table 49. Comparison of analytic methods, biomarkers, and cutoffs using the 2009 and 2019 surveys, Somalia

Biomarker Target group 2009 methodology 2019 comparison methodology Remarks

Adequately

iodized salt

≥ 15 ppm

Households Analytic method: Rapid test kits Analytic method: Iodometric titration Iodometric titration provides more accurate determination

for iodization adequacy [47]

Anemia Children and

women

Biomarkers: Hb from capillary blood

Analytic method: Hemocue™ 201+ device

Cutoff (children): Hb<110 g/L

Cutoff (non-pregnant women): Hb<120 g/L

Cutoff (pregnant women): Hb<110 g/L

Biomarkers: Hb from capillary blood

Analytic method: Hemocue™ 301 device

Cutoff (children): Hb<110 g/L

Cutoff (non-pregnant women): Hb<120 g/L

Cutoff (pregnant women): Hb<110 g/L

Same indicator, same cut-offs, but different device for

measuring Hb; some differences in device performance have

been noted [48]

Iron deficiency Children and

women

Biomarkers: sTfR from dried blood spot

Analytic method: Sandwich ELISA

Cutoff (children and women): sTfR > 8.3

mg/L

Biomarkers: sTfR from serum

Analytic method: Sandwich ELISA

Cutoff (children and women): sTfR > 8.3

mg/L

Serum ferritin, adjusted for CRP and AGP, it the iron

biomarkers recommended by the WHO [49] for population

based surveys, but the 2009 did not measure ferrtin. As such,

sTfR was compared for both surveys using the same cut-off

values. The only difference is the type of sample collected

(i.e. 2009 used dried blood spot, 2019 used serum).

Iron deficiency

anemia c

Children and

women

Definition/cutoff: Concurrent anemia and

iron deficiency (i.e. sTfR > 8.3 mg/L)

Definition/cutoff: Concurrent anemia and

iron deficiency (i.e. sTfR > 8.3 mg/L)

Same indicator, same cut-offs, but sTfR measured on

different samples

Vitamin A

deficiency

(RBP) d

Children and

women

Biomarkers: RBP from dried blood spot

Inflammation-adjustment: Exclusion of

children with CRP < 5 mg/L

Cutoff (children): RBP < 0.825 µmol/L

Cutoff (women): No comparable methods

between surveys

Biomarkers: RBP from serum

Inflammation-adjustment: Exclusion of

children with CRP < 5 mg/L

Cutoff (children): RBP < 0.7 µmol/L

Cutoff (women): No comparable methods

between surveys

RBP, adjusted for CRP and AGP, is routinely used to measure

vitamin A status in population based survey [18]. However,

the 2009 survey only measured CRP, and calculated vitamin

A deficiency by excluding children and women with elevated

CRP >5 mg/L level. Though this is not the currently

recommended approach, we have applied the 2009

approach to the 2019 data for this comparison. While the

2009 survey utilized a cut-off of 0.825 µmol/L, the 2019

survey showed a strong correlation between retinol and RBP,

and thus, a cut-off of 0.7 µmol/L was used.


113

Biomarker Target group 2009 methodology 2019 comparison methodology Remarks

Stunting Children Biomarkers: length/height & age

Cutoff: HAZ < -2

Biomarkers: length/height & age

Definition/Cutoff: HAZ < -2

Same methodology used in both surveys; 2009 only included

children 6-59 months of age, so the same age group was

used for 2019

Wasting Children Biomarkers: weight & length/height

Cutoff: WHZ < -2

Biomarkers: weight & length/height

Definition/Cutoff: WHZ < -2



used for 2019

Underweight Children Biomarkers: weight & age

Cutoff: WAZ < -2

Biomarkers: weight & age

Definition/Cutoff: WAZ < -2



used for 2019

Urinary Iodine

Concentration

(ug/L)e

Women Biomarkers: urinary iodine concentration

(UIC)

Analytic method: Sandel Kolthoff reaction

Definition: median UIC ug/L

Biomarkers: urinary iodine concentration

(UIC)

Analytic method: Sandel Kolthoff reaction

Definition: median UIC ug/L

Same methodology used in both surveys.

Undernutrition Women Biomarkers: weight & height; used to

calculate BMI

Cutoff: BMI <18.5

Biomarkers: weight & height

Cutoff: BMI <18.5


Obesity Women Biomarkers: weight & height; used to

calculate BMI

Cutoff: BMI >30

Biomarkers: weight & height

Cutoff: BMI >30



114

Table 50. Comparison of key results between 2009 and 2019, Somalia

Indicator a 2009 2019

N % (95%CI) N % (95% CI)

Households

Adequately iodized salt ≥ 15 ppm b 2411 3.9% (1.3, 11.3) 1956 7.0% (3.8, 12.5)

Children 6-59 months

Anemiac 784 59.3% (54.8, 63.6) 1675 43.4% (40.0, 46.9)

Iron deficiencyc 691 58.9% (53.5, 64.1) 1487 43.3% (39.7, 46.9)

Iron deficiency anemiac 664 42.6% 1470 28.4% (25.2, 31.9)

Vitamin A deficiency (retinol)d 691 36.2% (30.6, 42.1) 1335 36.4 (32.9, 40.2)

Stunting (HAZ < -2) 2890 23.2% (21.0, 25.4) 1448 17.8% (15.5, 20.3)

Wasting (WHZ (< -2) 2955 13.9% (11.9, 16.0) 1449 10.5% (8.9, 12.3)

Underweight (WAZ (< -2) 2965 19.5% (17.1, 22.0) 1619 12.7% (10.8, 14.9)

Non-pregnant women 15-49 years

Anemia c 685 46.6% (41.3, 51.9) 777 40.2% (36.2, 44.4)

Iron deficiency c 585 41.5% (36.5, 46.7) 689 32.5% (28.3, 37.1)

Iron deficiency anemia c 550 29.8% 682 20.8% (17.4, 24.7)

Urinary Iodine Concentration (ug/L)e 604 325.1 686 261.3

Undernutrition (BMI<18.5) 1929 21.5% (19.4, 25.8) 806 10.9% (8.7, 13.6)

Obesity (BMI>30) 1929 6.7% 806 15.1% (12.5, 18.1) a As school-age children were not included as a target group in the 2019 SMS, this age group was not included in this table. N’s represent un-weighted denominators of all indicators; b Salt iodine concentrations were measured using qualitative test kits in the 2009 survey, and iodimetric titration in the 2019 SMS. c Hemoglobin concentrations and anemia status were determined using portable hemoglobinometers; the 2009 survey used the Hemocue ™ 201+ device whereas the 2019 SMS used the Hemocue™ 301 devices. d Iron deficiency defined as soluble transferrin receptor concentrations >8.3 mg/L, and iron deficiency anemia defined as concurrent anemia and iron deficiency; e Vitamin A deficiency in children defined as RBP <0.825 µmol/L in 2009, and <0.825 µmol/L in 2019 after excluding those with acute inflammation (i.e. CRP >5 mg/L). Vitamin A deficiency in women could not be compared as the vitamin A deficiency cut-off used in 2009 was not compatible with currently-recommended cutoffs for vitamin A deficiency in women.


115

5. DISCUSSION

5.1. Strength and limitations

The SMS 2019 was the first micronutrient survey conducted in Somalia in the past 10 years.

As such, this survey provides nutrition policy makers with up-to-date data upon which to

develop new programs and reinforce existing programs. In addition to collecting key nutrition

indicators from questionnaire data and anthropometric measurements, as has been done in

prior surveys, the SMS 2019 also implemented an extensive cold chain system to permit the

collection and analysis of liquid serum samples from children and women. This enabled the

analysis of two inflammatory biomarkers (CRP, AGP) which are used to adjust the

concentrations of ferritin and retinol binding protein. This inflammation adjustment enables

the SMS 2019 to calculate more accurate estimates of iron and vitamin A deficiency

prevalence.

A key strength of the SMS was the attention given to collecting quality data and the use of

multiple quality-control systems in order to optimize the quality of data collected. Quality

control approaches utilized included extensive training, close field supervision of the teams,

utilization of Nanopure™ water as control specimens to identify any zinc contamination, and

remote real-time tracking of data collection.

The major limitation of this survey is the potentially biased sample derived from the Somalia

population. Numerous areas had to be excluded from the sample frame due to security

concerns. Large areas of Jubaland, South-West State, Hirshabelle, and Galmudug could not

be accessed by the teams, and were thus not included in the sampling frame used to select

PSUs in the first stage of sampling (see Section 2.6.3). In addition, the SMS 2019 did not

attempt to collect data from nomadic populations. Such sampling is more technically

demanding and requires substantially more training and supervision than standard household

sampling in settled populations. Because of the limited resources and constantly shifting

zones of insecurity, this could not be done in the SMS 2019. As a result of these constraints,

most excluded areas were rural. Thus, the sample of households selected by the SMS 2019

contains a disproportionate number of urban households (49.5% urban households in the

SMS 2019 after statistical weighing vs. 22.8% of households included in the 2014 PESS [4]).

Because the SMS 2019 finds that deficiency prevalences are slightly lower in urban areas

compared to rural areas, national and stratum-specific estimates derived from this survey

may underestimate the actual national and stratum-specific levels of deficiencies.

A second limitation is the poor quality of anthropometric measurements in children 0-59

months of age. The data collected by 3 of the 16 teams did not produce feasible results and

had to be excluded from the analysis. Specifically, length/height measurements from

Galmudug and both length/height and weight measurements taken by one of the two teams

working in South-West State and one of the two teams working in Jubaland were of

unacceptable quality.


116

Despite this subset of poor anthropometric measurements, the overall quality of the

anthropometric data collected by other teams was acceptable. The standard deviation of

WAZ and WHZ were 1.29 and 1.35, respectively, denoting minimal random error in

measurements. On the other hand, the standard deviation of HAZ was 1.73, which is higher

than recommended by international anthropometric guidelines [50]. This likely stems for the

most part from inaccurate reporting of child age by the respondent or inaccurate estimates

of child age by the interviewer. There was also some digit heaping in length/height

measurements, with a greater proportion of measurements ending in .0 and .5 than other

digits. Nonetheless, relative to inaccurate age estimation, this probably introduced relatively

little random error (Appendix 8.11).

Data quality checks comparing child age collected in the household roster (produced by

calculating the number of months between the child’s birthday and the interview date) and

the child questionnaire (written in completed months by the interviewer, with date in

complete months automatically calculated by ODK from household roster data) show few

transcription errors. Moreover, z-score calculations made using the age from the household

roster — rather than the age from the child questionnaire — produced nearly identical means

and standard deviations for HAZ, WHZ, and WAZ.

Nonetheless, distributions of child age (see Appendix 8.11) show a considerable amount of

age heaping, with a disproportionate number of children reported to be 12, 24, 36, 48, and

59 months of age. The lack of calendar literacy in Somalia likely explains how such age heaping

can occur. While the SMS teams used local event calendars to try help estimate the age of

children <5 years of age, team supervisors reported that some respondents had difficulty

recalling the events that coincided with the birth of their children.

5.2. Household level findings

The high proportion of households having access to safe drinking water presents a markedly

better situation that observed by the 2014-2015 Somalia Nutrition Analysis report [51].

However, this may be a result of the biased sampling frame used in the SMS, which has more

urban households and excluded insecure areas that were mostly rural. In contrast, the large

proportion of households without access to adequate sanitation facilities presents an ongoing

public health problem. Access to safe drinking water alone is insufficient to reduce diarrhea

in children.

The large proportion of households which had relocated in the past five years indicates that

many households are recent residents of the current dwelling. The reasons given for

relocation varied considerably by state. This finding is not surprising due to the political and

agro-ecologic differences found in Somalia. The transient nature of households in some states

has implications for health and nutrition programs, and planners should take this into account

when designing and implementing programs.


117

The low prevalence of adequately iodized household salt is in agreement with the findings

from the National Micronutrient survey in 2009 [1] which reported a household coverage of

adequately iodized salt of only 3.9%; however, this survey used rapid test kits. The largest

proportion of households with adequately iodized salt was located in Jubaland. Other states

in Somalia have much lower coverage. Few households in Somaliland and Puntland had

adequately iodized salt. The higher coverage in Jubaland could be due to salt imports from

Kenya, which borders Jubaland and has a well-functioning mandatory salt iodization program

[52]. This finding is similar to that found by the 2009 survey [1], which also found the highest

proportion of adequately iodized salt samples in areas bordering Kenya.

As hypothesized, the SMS 2019 found high concentrations of iodine in household drinking

water samples; however, the mean concentration of iodine was lower than reported from a

previous study conducted in 2010/11, which collected samples from different sources all over

Somalia [53]. However, the SMS 2019 results for Somaliland showed similar drinking water

iodine concentrations as a study comprised of households in Hargeisa only [54]. The median

iodine concentration in drinking water measured by the SMS 2019 was considerably lower

than that found in the 2009 micronutrient survey, but the 2009 survey excluded from the

calculation of the median concentration about 50% of samples that were below the detection

limit (<10 µg/L) of the method used. If all samples in the 2009 survey were included, the

median iodine content of water samples would certainly have decreased, but would still be

higher than observed in 2019.

5.3. Child stunting, wasting, underweight and overweight

The prevalence of stunting in Somalia can be categorized as medium according to the newly

established WHO thresholds [41], but as high or very high level for certain sub-groups.

Stunting prevalence is approximately 7 percentage points higher than the prevalence report

the 2014 FSNAU nutrition assessment [51] and slightly lower than reported by the

micronutrient survey in 2009 [1]. The peak in stunting prevalence is among children 12-35

months of age, which may reflect poor feeding during periods of great caloric need in early

childhood. This relatively high level of stunting does not decrease until the age of 48-59

months, which indicates that also complementary feeding later in childhood is rather poor.

South West state has the highest state-specific prevalence of stunting and is classified with

the highest public health importance for all assessed nutritional indicators. South West also

has the highest food insecurity and is among the states with the highest prevalence of anemia

and iron and vitamin A deficiencies. South West state has been hit by drought, which caused

considerable damage in the agricultural sector and led to increased movement of people from

rural areas to urban and peri-urban centers [55].

The overall prevalence of wasting in Somalia can be classified as high according to WHO and

as very high in certain sub-groups. Compared to last assessment in 2014 [56], which estimated

a wasting prevalence of about 12%, the decline in wasting prevalence is small. Nationally,


118

wasting is most common in children 0-5 months of age, which may be due to poor

breastfeeding practices, especially the low proportion of children being breast fed and the

lack of exclusive breastfeeding. Unlike in other countries where the average WHZ begins to

fall later in the first year of life and remains low throughout the second year of life due, in

part, to poor complementary feeding [57], the prevalence of wasting drops by 50% in children

6-11 and remains on the same level until the age of 48-59 months.

Jubaland has the highest prevalence of wasting as well as SAM. One explanation might be the

high prevalence of low birth weight in Jubaland.

5.4. Underweight, overweight and obesity in women

The prevalence of overweight and obesity in non-pregnant women 15-49 years of age is quite

high in Somalia. This mirrors the steady increase seen in Sub- Saharan Africa over the past few

decades [58]. Overweight and obesity is a key risk factor of type-2 diabetes mellitus. In

addition to diabetes, overweight and obesity is a risk factor of cardiovascular diseases and

various cancers [58]. As a result, Somalia may expect a rise in the near future in the incidence

of several chronic diseases associated with overweight. At the same time about every tenth

woman in Somalia is underweight, clearly demonstrating the presence of the double burden

of malnutrition in Somalia.

5.5. Anemia and micronutrient status

5.5.1. Anemia

The prevalence of anemia in children poses a severe public health problem according to WHO

classification [45]. Nonetheless, in certain areas, such as Somaliland and Galmudug, anemia

constitutes only a moderate public health problem. The prevalence of anemia has decreased

by about 15 percentage points since the 2009 survey. The observed difference is surprisingly

large considering the relatively short time interval between the two surveys. The difference

cannot be ascribed to a decline in the prevalence of malaria parasitemia which was low in

both surveys. The decline in ID from 2009 to 2019 could be a major contributor to the decline

in anemia prevalence. However, because different biomarkers were used to assess ID in the

two surveys, the results are not completely comparable. The SMS 2019 used serum ferritin,

which measures the bodies iron stores. This biomarker is recommended by the World Health

Organization for population-based assessment of iron status [59]. On the other hand, the

2009 survey used serum transferrin receptor (sTfR) which is a measure of systemic or

functional iron deficiency.

There are some additional technical differences that may partly explain the difference in

anemia prevalence between 2009 and 2019. The SMS 2019 used the Hemocue Hb 301 device,

which may yield slightly higher hemoglobin readings than the Hb 201+, the machine used in

the 2009 survey [60], although such a bias is not described consistently [61].


119

Anemia is multifactorial and can have different causes even in the same individual. The SMS

2019 was not designed to evaluate a comprehensive set of possible risk factors for anemia;

however, the associations of iron deficiency and vitamin A deficiency with anemia in both

children and women demonstrate that other micronutrient deficiencies contribute to anemia

in Somalia. In addition, the increase in the prevalence of anemia with early convalescence and

late convalescent inflammation demonstrates that inflammatory processes also contribute to

anemia in children. Anemia of infection (chronic or acute) or anemia of chronic disease has

been shown to be one of the main types of anemia in Sub-Saharan Africa [62]. One of the

main causes of anemia of infection is malaria; however, the very low prevalence of malaria

parasitemia found by the SMS 2019 provides some evidence against malaria as an important

cause of anemia in Somalia, at least in the season in which data were collected for this survey.

Alpha thalassemia trait may also contribute to anemia in the states of Somalia’s Central-South

Zone, where the prevalence is substantially higher than that found in Somaliland and

Puntland. Other factors which were not assessed in the SMS 2019, such as certain chronic

diseases (e.g. tuberculosis) could have contributed to anemia of infection/ chronic disease.

The role of such chronic diseases should be investigated with appropriate studies.

Additionally, inflammation and infectious diseases are often related to poor sanitary

conditions and hygiene [63]. Long term exposure to poor sanitary conditions, in particular

constant fecal oral contamination, can damage intestinal villi and cause chronic inflammation

[64]. Household sanitation was inadequate in about half of the surveyed households,

particularly in poor households, which also have a larger proportion of children with anemia.

In women, programs to promote the consumption of iron-rich foods and iron supplements

can be considered.

As it may be difficult for many households to afford a diet that is nutritionally sufficient, food

aid and targeted food and supplement delivery platforms are likely the most appropriate

near-term programmatic strategies in Somalia. Micronutrient powders could be a potential

approach for delivering sufficient levels of iron and vitamin A to women, the micronutrient

powders for pregnant women have been developed. However, current WHO

recommendations suggest that iron and folic acid supplementation using tablets is preferable

to MNPs for pregnant women [65]. Coverage of vitamin and mineral supplements in Somalia

should be expanded to both non-pregnant and pregnant women.

5.5.2. Iron deficiency

The national prevalence of ID in children and women is high. Compared to the survey in 2009

[1], the prevalence of ID decreased in children but increased in women. As mentioned above,

such a comparison must be done with caution since different biomarkers were used to assess

ID in survey subjects. Where the survey in 2009 reported only marginal differences in ID

between the strata (North West, North East and South Central) the SMS found considerable

variation. As in case of anemia, Galmudug has the lowest prevalence of ID and IDA in children

and women. The main reasons for ID are low intake of iron and/or low bioavailability of iron


120

in the diet. This is often the case in poor populations, who consume a monotonous diet that

is low in iron or a plant-based diet, which is low in bioavailable iron and high in iron absorption

inhibitors. As shown above, few Somali children 6-23 months consume a diet with minimum

diversity. For women, only about one-third have a minimum diverse diet. The low

consumption of fortified foods probably also contributes to the high prevalence of ID in

children.


The national prevalence of VAD denotes a severe public health problem in children (>30%)

and a moderate public health problem in women (10%-20%) according to WHO criteria [42].

In children the prevalence remained almost unchanged compared to 2009. In women, it was

difficult to make comparisons between the two surveys as the 2009 survey utilized a

deficiency cutoff that is infrequently used; no suitable cutoff could be determined for women

in 2019. Of note, although RBP was used to determine VAD in both surveys, the method used

to account for inflammation differs. In the 2009 survey RBP values were excluded from

analysis if the survey subject had a CRP concentration >5mg/L. Recommendations from

studies done as a part of Biomarkers Reflecting Inflammation and Nutritional Determinants

of Anemia (BRINDA), RBP should be corrected for inflammation in children, but not in women.

Thus, the SMS 2019 did not adjust RBP values for inflammation in women. This difference in

inflammation adjustment may have influenced the comparison of vitamin A results in

children. In 2009, children with inflammation were excluded from analysis where in 2019

children’s RBP values were adjusted using CRP and AGP levels according to the BRINDA

methodology.

Surprisingly, VAD is not significantly associated with intake of vitamin A supplements in the

past 3 months, although retinol levels are typically elevated for 2-3 months following vitamin

A supplementation [66]. However, only 17 of the surveyed children received vitamin A

supplements within the 3 months prior to the survey, thus the results have little relevance.

More importantly, overall, only about 15% of children received vitamin A supplementation in

the past 6 months. With such a low coverage there is most likely a large proportion of children

with higher risk of mortality due to compromised immune function [67].

5.5.4. Iodine deficiency

While only a small proportion of the salt in Somalia is adequately iodized, the median urinary

iodine concentrations in non-pregnant and pregnant women indicate iodine sufficiency. Sub-

group analyses show that in some states of Somalia, particularly in Hirshabelle and Puntland,

women are at risk for iodine excess [5] (median urinary iodine concentration >500µg/L) with

serious health consequences, such as hypothyroidism and hyperthyroidism [68]. Only in

Somaliland does the median urinary iodine concentration indicate iodine insufficiency. The

high urinary iodine concentration found in women can be ascribed to the high iodine

concentration found in drinking water.


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Although in theory water can provide large amounts of dietary iodine and prevent iodine

deficiency, the iodine concentration in water varies widely (0-750 µg/L) even within the same

state, which renders it impossible to target women with inadequate iodine intakes for

additional intervention. While one can surmise that excess urinary iodine levels in Puntland

are largely attributable to high levels of iodine in drinking water, Hirshabelle have the highest

median urinary iodine concentration, but with the lowest drinking water iodine

concentration. While the drinking water for many households in Hirshabelle did not contain

any iodine, there are some households in which water iodine concentrations are very high.

This may suggest that Hirshabelle contains multiple aquifers, and that the soluble iodine

concentrations vary considerably. Due to the considerable amount of iodine excess, further

research is required to determine if other dietary sources besides drinking water are

contributing to excessive iodine intake in Hirshabelle and Puntland.

6. Recommendations

6.1. Carry out situation analysis to increase iodine intake in Somaliland

Most households get sufficient amounts of dietary iodine through drinking water. It is thus

not recommended to increase the coverage of iodized salt throughout Somalia. Additional

iodine intake from iodized salt may lead to excessive intake and serious health consequences.

Unlike in other states, the iodine status of women in Somaliland shows an overall insufficient

intake of iodine, even though some women had excessive intake. To understand the situation

more thoroughly, it is recommended that a study of iodine drinking water concentration be

conducted in Somaliland to identify (if possible) a) areas where iodine intake is insufficient,

and b) determine if expanding the coverage of iodized salt could be limited to Somaliland, and

prevented to enter other states. The situation analysis should also consider the feasibility of

delivering iodine via bouillon cubes in Somaliland, as they may serve as an alternate vehicle

for delivering iodine.

6.2. Reduce undernutrition in children and women

While national undernutrition (stunting and wasting) prevalence is considered a mild and

serious public health problem, respectively, the situation is more severe in some parts of the

country and warrants immediate attention. Inappropriate feeding practices of infants and

young children may contribute to high prevalence of undernutrition. Exclusive breastfeeding

in the first 6 months of life should be promoted and supported to ensure that overall immune

status of infants and young children is improved and children are better able to prevent and

recover from diarrhea, pneumonia, and other infections. The consumption of healthy,

diversified diets in the complementary feeding period (6-23 months) should also be promoted

to consistently improve the diversity and quality of diet for young children. Adequate feeding

habits and good hygiene and sanitation practices can be promoted via nutrition education.


122

The SMS 2019 did not show an association between water and sanitation indicators and

nutritional status. This may be due to the lack of precision in the expedient WASH indicators

used in cross-sectional surveys. Other studies employing different methods of data collection

which may more accurately measure WASH indicators have demonstrated a clear association

between multiple nutrition outcomes and drinking safe water and having adequate sanitation

facilities. In addition, safe drinking water and adequate nutrition can have a substantial effect

on many non-nutritional health conditions. Therefore, given the relatively high proportion of

Somali households drinking unsafe water and the very high proportion with inadequate

sanitation facilities, WASH programs should be strengthened to include the provision of safe

water, expansion of adequate water treatment and storage in the home, improved

handwashing facilities and practices, and the use of adequate sanitation facilities.

6.3. Reduce overweight and obesity in women

Preventing and reducing overweight and obesity in women is included in the 2020-2025

National Nutrition Strategy, which calls for the “provision of counseling for increased physical

activity (protective from overweight) and for reduction of sedentary lifestyles (causative for

over-weight)” and the “promoting the shift of social norms on food taboos preventing

adequate nutrition for pregnant and lactating women.” Behavior change communication

programs and outreach initiatives designed to meet this objective should be conducted in

the near future, and the prevalence of overweight and obesity monitored.

As overweight and obesity are risk factors of type-2 diabetes mellitus and are associated with

hypertension and some forms of cancer, there is an imperative need to educate urban women

about approaches to maintaining healthy weight to prevent the prevalence of overweight and

obesity from rising further. As breastfeeding behaviors are inadequate in Somalia, and

improper breastfeeding is associated with postpartum weight retention [69], messages

encouraging exclusive and continued breastfeeding should be stressed in behavior change

materials and during training of medical professionals who should in turn encourage, support

and protect breastfeeding. Determining the causal factors of overweight and obesity in Somali

women is needed and can be used to inform the design of an overweight/obesity reduction

and prevention program. Further, operational research should be conducted to ensure that

specific messages lead to significant improvements in postpartum weight retention.

6.4. Reduce anemia and iron deficiency in children and women

In order to reduce nutritional anemia in children, we suggest implementing interventions such

as the promotion of age-appropriate infant and young child feeding practices, including the

promotion of foods (fortified or unfortified) which are rich in iron and vitamin A. In women,

programs to promote the consumption of iron-rich foods and iron supplements can be

considered. Moreover, to decrease the prevalence of ID, mandatory iron fortification

programs should be considered, such as iron fortification of rice and/or wheat flour. The

feasibility and appropriateness of these interventions should be explored to determine if they

would increase iron intake in a meaningful proportion of the population. In order to reduce


123

anemia chronic disease, it is recommended to further elucidate which risk factors, both

assessed and not assessed by the SMS 2019, that contribute to inflammation and chronic

disease.

6.5. Reduce vitamin A deficiency in children

VAD affects about one-third of children in Somalia. Its prevalence reaches more than 40% of

children in certain sub-groups. To address this severe public health problem, multiple

approaches should be used. First, Somalia's vitamin A supplementation program should be

improved to reduce the risk of the direct consequences of VAD as well as mortality due to

measles, diarrhea, and other illnesses. Secondly, to increase the vitamin A body stores,

vitamin A fortification programs, such as vitamin A fortification of vegetable oil, should be

considered. Thirdly, programs to improve consumption of vitamin A-rich foods, other than

fortified products, should be pursued. This is particularly relevant in rural areas where vitamin

A deficiency is high. This type of intervention could include promoting local food products rich

in vitamin A, or introducing vitamin A-biofortified staple foods that could be readily cultivated

in Somalia.


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8. APPENDICES

8.1. LIST OF SELECTED ENUMERATION AREAS

Geographic Strata Survey Strata Region name District name Name of settlement

or town Cluster number

Somaliland Somaliland Awdal Zeila Wadajir 1

Somaliland Somaliland Awdal Borama Borama 2

Somaliland Somaliland Awdal Borama Borama 3

Somaliland IDP Awdal Borama Xero dhiigta 4

Somaliland Somaliland Woqooyi Galbeed Gebiley Ugaadhyahanka 5

Somaliland Somaliland Woqooyi Galbeed Hargeisa Baliga Cas 6

Somaliland Somaliland Woqooyi Galbeed Hargeisa Hargeysa 7






Somaliland IDP Woqooyi Galbeed Hargeisa Digaale 13

Somaliland Somaliland Woqooyi

Galbeed Berbera Berbera 14

Somaliland Somaliland Togdheer Oodweyne Jameecada Caynaashe

15

Somaliland Somaliland Togdheer Burao Dabaqabad 16

Somaliland Somaliland Togdheer Burco Burco 17



Somaliland IDP Togdheer Burco October 1 20

Somaliland Somaliland Sool Caynabo Caynabo 21

Somaliland Somaliland Togdheer Buuhoodle Buuhoodle 22

Somaliland Somaliland Sool Laas Caanood Laas Caanood 23

Somaliland IDP Sool Laas Caanood Wadajir 24

Somaliland Somaliland Sanaag Ceerigaabo Ardaa 25


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Somaliland Somaliland Sanaag Ceerigaabo Madar Moge 26

Somaliland Somaliland Sanaag Ceerigabo Ceerigabo 27

Somaliland Somaliland Sanaag Badhan Hadaaftimo 28

Somaliland Somaliland Sanaag Lasqorey Anfoolax 29

Puntland Puntland Bari Caluula Dhurbo 991

Puntland Puntland Bari Caluula Taraxo 992

Puntland Puntland Bari Bosasso Geeso Qabad 30

Puntland Puntland Bari Bossaso Bossaso 31












Puntland IDP Bari Bossaso Boqolka buush 42

Puntland IDP Bari Bossaso Xaadoole 43

Puntland Puntland Bari Qardho Yakayaka 44

Puntland Puntland Bari Qardho Qardho 45

Puntland Puntland Bari Qardho Qardho 46

Puntland Puntland Nugaal Garoowe Baq Baq 47

Puntland Puntland Nugaal Garowe Garowe 48

Puntland Puntland Nugaal Garowe Garowe 49

Puntland Puntland Mudug Jariiban Sallax 50


132



Puntland Puntland Mudug Jariiban Dhinowda 51

Puntland Puntland Mudug Galkacyo Beer-Dhagaxtur 52

Puntland Puntland Mudug Galkacyo Xarfo 53

Puntland IDP Mudug Galkacyo - North Madina 994

Puntland IDP Mudug Galgodob Danwadaag 54

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Dhuusamarreeb Jooraanshe 55

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Cabudwaaq Barkada Doonyaale 56

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Cabudwaaq Guriceel ( Town ) 57

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Cadaado Docolay 58

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Dhuusamarreeb Dhuusamarreeb 59

Hirshabelle/Galmudug Hirshabelle/Galmudug Galgaduud Cabudwaaq Cabudwaaq 60

Hirshabelle/Galmudug Hirshabelle/Galmudug Mudug Hobyo Ceeldibir 61

Hirshabelle/Galmudug Hirshabelle/Galmudug Mudug Hobyo Wisil 62

Hirshabelle/Galmudug Hirshabelle/Galmudug Mudug Gaalkacyo Gaalkacyo 63

Hirshabelle/Galmudug Hirshabelle/Galmudug Mudug Gaalkacyo Gaalkacyo 64

Hirshabelle/Galmudug Hirshabelle/Galmudug Mudug Galdogob Galdogob 65

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Jowhar Banaaney 66

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Jowhar Cali-Gaabow 67

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Jowhar Maagey 68

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Balcad Bakad-Jeex 69

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Balcad Farsooley 70

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Balcad Mandheere 71

Hirshabelle/Galmudug IDP Hiraan Beledwein Xalane 2 72

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Cadale Bagdaad 73

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Balcad Balcad 74

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Jowhar Jowhar 75

Hirshabelle/Galmudug Hirshabelle/Galmudug Hiraan Beledwein Berdaaley 76

Hirshabelle/Galmudug Hirshabelle/Galmudug Hiraan Beledwein Harqaboobe 77


133



Hirshabelle/Galmudug Hirshabelle/Galmudug Hiraan Beledwein Takaraale 78

Hirshabelle/Galmudug Hirshabelle/Galmudug Hiraan Beledwein Belet Weyne 79

Hirshabelle/Galmudug Hirshabelle/Galmudug Hiraan Beledwein Belet Weyne 80

Hirshabelle/Galmudug IDP Hiraan Beledwein Beerey 81

Hirshabelle/Galmudug Hirshabelle/Galmudug Middle Shabelle Jowhar Cali-Gaabow 82

Southwest/Jubaland Southwest/Jubaland Bakool Xudur Waneey 83

Southwest/Jubaland Southwest/Jubaland Bay Baidoa Baidoa 84










Southwest/Jubaland IDP Bay Baidoa Dooy 94

Southwest/Jubaland IDP Bay Baidoa Mogor iyo maayow 95

Southwest/Jubaland IDP Bay Baidoa Bay iyo bakool 96

Southwest/Jubaland IDP Gedo Luuq Jaziira 97

Southwest/Jubaland IDP Gedo Doolow Qansaxley 98

Southwest/Jubaland IDP Gedo Doolow Buulo-Qalooc Camp 99

Southwest/Jubaland Southwest/Jubaland Gedo Luuq Hoodey/Hoobishow 100

Southwest/Jubaland Southwest/Jubaland Gedo Luuq Raaxaale 101

Southwest/Jubaland Southwest/Jubaland Gedo Doolow Laamaloodshe 102

Southwest/Jubaland Southwest/Jubaland Gedo Belet Xaawo Qoriyow 103

Southwest/Jubaland Southwest/Jubaland Gedo Belet Xaawo Wecella 104

Southwest/Jubaland Southwest/Jubaland Gedo Luuq Bar Baar Ees 105


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Southwest/Jubaland Southwest/Jubaland Gedo Luuq Laba Naasyo 106

Southwest/Jubaland Southwest/Jubaland Gedo Doolow Koorey 107

Southwest/Jubaland Southwest/Jubaland Gedo Doolow Unaa 108

Southwest/Jubaland Southwest/Jubaland Gedo Belet Xaawo Libi Buulle 109

Southwest/Jubaland Southwest/Jubaland Gedo Belet Xaawo Qalaf 110

Southwest/Jubaland Southwest/Jubaland Gedo Belet Xaawo Shirko1 111

Southwest/Jubaland Southwest/Jubaland Lower Juba Goobweyn Goobweyn 112

Southwest/Jubaland Southwest/Jubaland Lower Juba Kismayo Kismayo 113

Southwest/Jubaland IDP Lower Juba Kismayo-Barwaaqo

Kismayo 114



Banaadir Banaadir Banadir Heliwaa Mogadishu 117



Banaadir Banaadir Banadir Kaaraan Mogadishu 120

Banaadir Banaadir Banadir Kaaraan Mogadishu 121

Banaadir Banaadir Banadir Yaaqshiid Mogadishu 122



Banaadir Banaadir Banadir Boondheere Mogadishu 125

Banaadir IDP Banadir Boondheere Wasaaradda Caafimaadka

126

Banaadir Banaadir Banadir Xamar Jajab Mogadishu 127

Banaadir Banaadir Banadir Hawl Wadaag Mogadishu 128

Banaadir Banaadir Banadir Hodan Mogadishu 129

Banaadir Banaadir Banadir Hodan Mogadishu 130

Banaadir Banaadir Banadir Dayniile Mogadishu 131




135




Banaadir IDP Banadir Dayniile Geed weyne 135

Banaadir IDP Banadir Dayniile Roboow 136

Banaadir IDP Banadir Dayniile Dalmar 137

Banaadir Banaadir Banadir Dharkenley Mogadishu 138



Banaadir Banaadir Banadir Wadajir Mogadishu 141

Banaadir Banaadir Banadir Wadajir Mogadishu 142

Banaadir Banaadir Banadir Kaxda Mogadishu 143

Banaadir Banaadir Banadir Kaxda Mogadishu 144

Banaadir IDP Banadir Kaxda Dulqaad 2 145

Banaadir IDP Banadir Kaxda Janaale 3 146

Banaadir IDP Banadir Kaxda Najuun 147

Banaadir IDP Banadir Kaxda Wanaag Center 148

Banaadir Banaadir Banadir Warta Nabada Mogadishu 149


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8.2. A PRIORI SAMPLE SIZE CALCULATIONS

Sample size for children (0-59 months of age), non-pregnant women (15-49 years of age) and pregnant women per stratum and in the two

strata (urban/ rural) taking into account desired precision and assumed design effect and individual response rate

Target group

and indicator

Estimated

prevalence

Desired

precision for urban/

rural estimates

(percentage points)

Assumed

design

effect

Assumed household

/ individual

response

Number of

households /

persons to

select in one

stratum

Number of

households /

persons to select in

six strata

Households

Adequately iodized salt 10% 50 4.0 90% 615 3,690

Children

Anemia 50% 10 1.5 80% 181 1,086

Iron deficiency 50% 10 1.5 80% 181 1,086

Vitamin A deficiency 35% 7 1.5 80% 335 2,010

Wasting 15% 5 1.5 90% 327 1,962

Stunting 25% 5 1.5 90% 481 2,886

Non-pregnant women

Anemia 50% 10 1.5 80% 181 1,086

Iron deficiency 50% 10 1.5 80% 181 1,086

Vitamin A deficiency 50% 10 1.5 80% 181 1,086

BMI <18.5 22% 5 2.0 90% 586 3,516

Minimum dietary diversity 50% 10 2.5 90% 267 1,602

Pregnant women

Anemia 50% 10 1.3 80% 151 906


137

8.3. ETHICAL APPROVALS


138


139


140

8.4. INFORMATION SHEET

INFORMATION SHEET (FOR THE PARTICIPANT TO KEEP)

Title of Study: Somalia Micronutrient Survey - 2019

Principal Investigators:

Dr. James P. Wirth

Dr. Joshua Mbai

What is informed consent?

You are invited to take part in a survey. Participating in a survey is not the same as getting

regular medical care. The purpose of normal medical care is to improve one’s health. The

purpose of a survey is to gather information that may be useful in the future for the whole

population. It is your decision to take part and you can stop at any time without giving any

reason.

Before you decide you need to understand why the survey is being done and what will happen

in it. Please take time to read the following information or get the information explained to

you in your language. Listen carefully. You can ask questions if there is anything that you do

not understand. Ask for it to be explained until you are satisfied. You may also wish to speak

your spouse, family members or others before deciding to take part in the study.

If you decide to join the study, you will need to sign or thumbprint a consent form saying you

agree to be in the study. You will receive a copy of the consent form.

Why is this study being done?

The Somalia Micronutrient Survey 2019 is conducted to understand the severity of various

nutritional deficiencies, such as anemia, iron deficiency, vitamin A deficiency, and

malnutrition in women and children. The survey is being implemented with support from

UNICEF and the governments of South-Central Somalia, Puntland, and Somaliland. The survey

is being implemented by Brandpro (Kenya) and GroundWork (Switzerland).

What does this study involve?

We will ask some questions about your household, and if there are selected women or

children living in the household, we will ask individual questions to better understand their

person and their food habits. The household questionnaire usually takes about 20 minutes to

complete, and the individual questionnaires take about 30 minutes to complete.

Following the completion of the questionnaire, we will measure height and weight from all

women 15 to 49 years of age and children 0 to 59 months of age. After this, we will request

that non-pregnant and pregnant women 15 to 49 years of age will provide a urine sample.

We will then request to draw a small amount of blood by pricking the finger from all women


141

15 to 49 years of age, and pricking the heel of children 6-11 months of age and the finger of

children 12 to 59 months of age.

This small blood sample will be used to test if you have anemia or malaria, and these results

will be provided to you directly. In addition, a small portion of blood will be collected to test

for micronutrient deficiencies (i.e. iron, vitamin A, zinc, folate, vitamin B12)

Benefits/Risk of the study

For all non-pregnant women and children 6-59 months of age a small sample of blood (0.4

mL) will be collected from the finger or heel (children 6-11 months of age) via a small lancet

by trained technicians. This might cause some pain, but is otherwise harmless. The blood draw

should take less than 5 minutes, and the anemia and malaria results will be provided in less

than 15 minutes following the taking of blood, and should you be diagnosed with severe

anemia or malaria, we will provide you with a referral to a nearby health facility for further

testing and treatment. This survey poses no serious risks to you or other participating family

members.

Other than the information about your hemoglobin levels or malaria parasitemia and referral

in case of diagnosis of severe anemia, we cannot promise that the survey will help you

directly. But the information we get will help the Government to evaluate its nutrition and

health services and if needed, adapt them.

Will you be compensated for participating in the study?

You will not get paid by the study.

Confidentiality

All information which is collected about you and your household during the course of the

interview will be kept strictly confidential, and any information about you and the household

address will not be included in the final report so that you cannot be recognized. Only the

personnel doing the interview and the principal researchers will have access to identifiable

information and by providing your signature/thumbprint, you allow the research team in

doing so.

Withdrawal from Study

Participation in this survey is voluntary, and if we should come to any question you do not

want to answer, just let me know and I will go on to the next question; or you can stop the

interview at any time, without any consequences to you or your household. However, we

hope that you will participate in this survey since your views are important. There will not be

any negative effects on you, if you decide that you no longer want to continue with the

interview.

If you are younger than 18 years, your legal parent will have to give signed consent for your

participation. This information sheet will be for you/your caretaker to keep. If you have any

question, do not hesitate to contact the principal researchers.


142

Contact for additional Information

If you have any questions about the study, you are welcome to call Joshua Mbais from

Brandpro, who is in charge of implementing this study, on telephone* and he will be happy to

answer your questions. You can also call Ms. Fatmata Sesay from UNICEF's Somalia Support

center, on telephone* if you have further concerns.

*Telephone numbers removed in report


143

8.5. CONSENT FORMS IN ENGLISH AND SOMALI


144


145

8.6. REFERAL FORM


146

8.7. TEAMS, TEAM MEMBERS, AND SUPERVISORS

Team #

State Region Team Supervisor (Leader)

Interviewers Anthropometrist Phlebotomist

1 Somaliland M.jeh/Awdal Nura Ibrahim Mohamed

1) Ilyas Abdirahman Hussein 2) Asma Mohamed Jama

Asha Abdirahman Aw-Ali

Shuayb Mohamud Hussein

2 Somaliland Togdheer Ayan Mohamud Jama

1) Hana Ismail Ali 2) Samira Mohamed Issa

Said Hussein Yusuf Ali Abdisalan Musa

3 Somaliland Sool Hassan Ahmed Salah

1) Ifrah Omar Muuse 2) Faisa Abdinasir Ahmed

Abdikarim Abdalla Abdirahman Ahmed Hassan

4 Somaliland Sanaag Hussein Jama Salah

1) Fadumo Ali Farah 2) Umayma Mohamed Mohamud

Mohamud Jama Mohamed

Abdirahman Saleban Jama

5 Puntland Bari Said Mohamed Said

1) Mohamed Jama A/Rahman 2) AbdiRahman Abdi Aziz Mohamed

Qali Hassan Hirsi Muhubo Abdiaziz Mohamed

6 Puntland Bari Abdiwali Mohamed Aden

1) Abdirahman Ismail Suleiman 2) Mohamed Abdisalan Gulled

Fahima Abdillahi Ismail

Yahye Abdirahman Farah

7 Puntland Nugaal Mohamud Abdikadir Mohamed

1) Mohamed Dahir Ali 2) Hawa Ahmed Omar

Fadumo Mohamed Ali Abdirahman Yusuf Aynab

8 Puntland Mudug Abdihakim Barre Nuh

1) Maria Shire Ahmed 2) Mohamud Muse Khalif

Ifrax Mohamed Ahmed

Mahdi Abdulle Abdulahi

9 Galmudug Mudug Bashir Abdi 1) Shafie Adan Farah Farah Hashi Adan Shukri Sheikh Mire

10 Galmudug Mudug Dr. Adno Abdi Osman

1) Amal Abdulahi Ali Hodan Hirsi Mohamed Abdi Hassan ALI

11 Hirshabelle Shabelle Abdullahi Mohamed Jim’ale

1) Ismail Mohamed Hafso Mohamed Khaliif

Mahad Ibrahim Hur

12 Hirshabelle Hiraan Dr. Ahmed Mohamed Shire

1) Dr. Abdirahman Khaliif 2) Ali Mohamud Ahmed

Abdirashiid Mohamed Gashin

Abdikadir Gashin Ahmed


147

Team #

State Region Team Supervisor (Leader)

Interviewers Anthropometrist Phlebotomist

13 SouthWest Bay Adan Abdirahman Ahmed

1) Ahmed Mohamed Ali 2) Na’ima Ahmed Adow

Deqa Mohamud Hassan

Hassan Mohammed

14 SouthWest Bakol Mustaf Mohamed Ahmed

1) Muhidin Adan Ibrahim 2) Abubakar Mohamed

Fozia Mohamed Abdi Abdurrahman Hussein Mohamed

15 Jubaland Gedo Hassan Abdi Mohamed

1) Abdullahi Aden Hassan 2) Alinoor Mohamed Hussein

Hussein Abdulle Muhumed

Abdirizak Mohamoud Hassan

16 Jubaland Lower Juba Ahmed Asayr Haji Mohamed

1) Zakariye Abdirahman Mohamed 2) Deeqa Abdinasir Abdule

Khalif Mahad Muhumed

Ahmed Issack Hussien

17 Banaadir Banaadir Dr. Ayan Mohamed Osman

1) Abdikadir Adam 2) Hawa Mohamud Omad

Istar Hirsi Dabel Amino Sharif Syiaad

18 Banaadir Banaadir Mohamed Abdi Isse

1) Mariyo Mahad Ahmad 2) Deko Ali Guled

Sacda Dahir Adan Asho Ali Mohamud


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8.8. SURVEY QUESTIONNAIRES

PDF versions of all questionnaires can be download from GroundWork's website using the URL

hyperlinks provided below:

English versions:

Household questionnaire:

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_household_questionnaire-

English.pdf

Child questionnaire:

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_children_questionnaire-

English.pdf

Woman questionnaire:

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_women_questionnaire-

English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_household_questionnaire-English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_household_questionnaire-English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_children_questionnaire-English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_children_questionnaire-English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_women_questionnaire-English.pdf

http://groundworkhealth.org/wp-content/uploads/2020/02/SMS_women_questionnaire-English.pdf


149

8.9. ADDITIONAL HOUSEHOLD TABLES

Table 8.9-1. Distribution of household composition of participating households, by state, Somalia 2019


Characteristic n Mean,

% a n Mean,

% a n Mean,

% a n Mean,

% a n Mean,

% a n Mean,

% a n Mean,

% a p-value

Average household size Mean 455 5.6 343 3.8 163 4.3 249 4.1 217 4.4 287 1.9 458 4.6 0.0000

Number of household members, % 1 19 4.0 58 17.7 1 0.6 18 6.9 6 2.4 114 39.1 36 7.8 0.0000 2 36 7.9 65 19.8 22 13.5 47 18.6 33 15.2 125 43.8 59 12.6 3 57 12.6 70 20.9 41 25.2 60 24.2 51 23.0 35 12.6 81 17.3 4 62 13.7 35 9.9 34 20.9 42 17.0 42 19.6 9 3.3 83 17.8 5 63 13.7 38 10.3 31 19.0 27 11.2 29 13.8 2 0.4 66 14.8 6 59 12.9 23 6.5 20 12.3 19 7.5 22 10.1 0 0.0 38 8.3 7 47 10.2 19 5.4 6 3.7 11 4.3 16 7.2 2 0.8 38 8.3 8 38 8.5 12 3.3 2 1.2 13 5.3 9 4.1 0 0.0 24 5.6 9 27 5.9 10 2.7 4 2.5 7 3.1 7 3.6 0 0.0 10 2.5 10+ 47 10.5 13 3.5 2 1.2 5 1.9 2 1.0 0 0.0 23 4.9

Number of women 15-49 years of age in households, %

0 70 15.1 76 23.2 37 22.7 83 32.9 65 29.7 101 35.0 74 16.3 0.0055 1 272 59.7 215 62.5 99 60.7 139 55.4 141 65.2 183 64.0 306 66.9 2 73 16.3 41 11.3 22 13.5 15 6.3 10 4.6 2 0.6 50 11.0 3 31 6.9 8 2.2 3 1.8 8 3.7 1 0.5 1 0.4 22 4.5 4 6 1.4 2 0.5 2 1.2 4 1.7 0 0.0 0 0.0 6 1.4 5 3 0.7 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 6 0 0.0 1 0.3 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

Number of children 0-59 months in households,% 0 162 35.6 154 45.9 60 36.8 138 55.5 129 60.6 167 58.1 192 42.1 0.0006 1 104 22.9 82 24.0 34 20.9 75 29.7 60 26.0 96 33.3 135 29.1 2 123 27.1 63 18.2 33 20.2 34 14.0 25 11.8 19 7.0 94 20.4 3 54 11.8 32 8.7 24 14.7 1 0.4 2 1.0 5 1.6 34 7.7 4 12 2.6 11 3.0 9 5.5 1 0.4 1 0.5 0 0.0 3 0.7 5 0 0.0 1 0.3 3 1.8 0 0.0 0 0.0 0 0.0 0 0.0

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.



150

Table 8.9-2. Household head education level, by state, Somalia, 2019

Somaliland Puntland Hirshabelle Galmudug South-West Jubaland Banaadir p-value Characteristic n % a n % a n % a n % a n % a n % a n % a

Head of household ever attended school or preschool

No 320 70.1 234 69.3 99 60.7 189 76.6 156 71.5 275 95.1 276 61.0 0.0001 Yes 135 29.9 102 28.8 64 39.3 60 23.4 60 28.3 13 4.9 182 38.8 Don’t know 0 0.0 7 1.9 0 0.0 0 0.0 1 0.3 0 0.0 1 0.2

Highest level of school attended by household head

Preschool 7 5.1 6 6.4 4 6.3 0 0.0 2 3.6 0 0.0 3 1.8 0.0000 Primary 64 47.3 42 40.4 14 21.9 6 11.6 9 13.0 8 61.5 44 24.0 Secondary 24 17.7 20 20.4 7 10.9 4 7.4 2 3.6 2 15.4 41 22.0 Higher 15 11.4 9 9.2 2 3.1 1 1.6 0 0.0 3 23.1 17 9.1 Koranic 24 17.7 23 21.7 37 57.8 49 79.5 47 79.8 0 0.0 77 43.1 Don´t know 1 0.8 2 1.9 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0




151

Table 8.9-3. Household head education level, by residence, Somalia, 2019

Rural Urban IDPs p-value Characteristic n % a n % a n % a

Head of household ever attended school or preschool No 542 83.1 736 66.7 271 78.6 0.0000 Yes 153 16.5 390 33.1 73 21.2 Don’t know 5 0.4 3 0.2 1 0.3

Highest level of school attended by household head Preschool 7 4.4 12 3.4 3 4.1 0.3991 Primary 48 37.4 116 29.6 23 31.5 Secondary 12 9.7 75 16.6 13 17.8 Higher 5 2.9 38 10.1 4 5.5 Koranic 81 45.6 146 39.6 30 41.1 Don´t know 0 0.0 3 0.7 0 0.0




152

Table 8.9-4. Distribution of agricultural, grazing, and livestock variables, by state, Somalia, 2019

Somaliland Puntland Hirshabelle Galmudug South-West Jubaland Banaadir p-value Characteristic n % a n % a n % a n % a n % a n % a n % a

Member of household owns any agricultural land No 430 94.4 332 97.0 163 100.0 205 82.7 193 88.3 279 96.9 456 99.3 0.0732 Yes 25 5.6 8 2.2 0 0.0 44 17.3 24 11.7 7 2.7 3 0.7

Member of household owns any grazing land

No 454 99.8 339 98.9 163 100.0 224 89.5 208 95.7 288 100.0 459 100.0 0.0400 Yes 1 0.2 1 0.3 0 0.0 25 10.5 9 4.3 0 0.0 0 0.0

Household owns any livestock No 320 70.1 304 89.4 102 62.6 198 80.6 199 91.1 280 97.3 440 95.5 0.0000 Yes 135 29.9 36 9.8 61 37.4 50 18.8 17 8.4 8 2.7 17 3.9

Household owns livestock, specific c Camels 5 3.8 4 11.1 2 3.3 3 5.9 5 30.2 1 14.0 1 4.8 0.0001 Cattle 16 12.2 1 2.8 2 3.3 28 55.3 10 60.4 2 28.1 1 4.8 0.0020 Goats 106 79.1 33 91.7 55 90.2 37 74.3 16 94.0 5 64.0 9 52.4 0.1698 Sheep 38 28.1 10 27.8 15 24.6 36 72.4 6 36.2 4 56.1 3 14.4 0.0016 Donkeys 11 8.2 1 2.8 1 1.6 31 62.5 7 42.3 2 28.1 0 0.0 0.0000 Poultry (chicken, ducks, etc.) 24 17.7 5 13.9 4 6.6 32 63.2 5 30.2 2 21.9 11 68.1 0.0002 Bees 0 0.0 0 0.0 0 0.0 0 0.0 1 6.0 0 0.0 0 0.0 0.2168



c Question only asked to households responding “Yes” to livestock ownership


153

Table 8.9-5. Distribution of agricultural, grazing, and livestock variables, by residence, Somalia, 2019

Rural Urban IDPs p-value Characteristic n % a n % a n % a

Member of household owns any agricultural land No 630 91.0 1093 95.7 335 97.1 0.0333 Yes 70 9.0 33 4.1 8 2.3

Member of household owns any grazing land No 676 97.5 1120 99.0 339 98.3 0.3179 Yes 24 2.5 6 0.8 6 1.7

Household owns any livestock No 494 73.6 1031 91.3 318 92.2 0.0000 Yes 206 26.4 94 8.4 24 7.0

Household owns livestock, specific c Camels 13 7.9 8 8.1 0 0.0 0.5342 Cattle 55 27.7 5 6.7 0 0.0 0.0283 Goats 177 85.8 70 74.4 14 58.3 0.1242 Sheep 79 36.7 28 29.2 5 20.8 0.4170 Donkeys 45 20.8 6 7.3 2 8.3 0.0486 Poultry (chicken, ducks, etc.) 46 20.5 28 28.0 9 37.5 0.3586 Bees 1 0.9 0 0.0 0 0.0 0.7219



c Question only asked to households responding “Yes” to livestock ownership

s


154

Table 8.9-6. Distribution of agricultural, grazing, and livestock variables, by sex of household head, Somalia, 2019

Male Female p-value Characteristic n % a n % a

Member of household owns any agricultural land

No 246 94.4 1812 93.4 0.8307 Yes 16 5.6 95 5.5

Member of household owns any grazing land No 257 98.1 1878 98.4 0.7406 Yes 5 1.9 31 1.5

Household owns any livestock No 229 87.2 1614 85.0 0.5485 Yes 33 12.8 291 14.7

Household owns livestock, specific c Camels 6 17.3 15 6.0 0.0924 Cattle 10 28.6 50 18.4 0.2675 Goats 29 89.4 232 79.2 0.2140 Sheep 10 20.7 102 34.9 0.1411 Donkeys 10 28.6 43 14.4 0.0985 Poultry (chicken, ducks, etc.) 10 28.0 73 23.4 0.6476 Bees 0 0.0 1 0.6 0.7170

Note: The n’s are un-weighted numbers in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection. b CI=confidence interval, calculated taking into account the complex sampling design. c Question only asked to households responding “Yes” to livestock ownership


155

Table 8.9-7. Distribution of cooking fuel and lighting variables for participating households, Somalia, 2019

Rural Urban IDP p-value Characteristic n % a (95% CI) b n % a (95% CI) b n % a (95% CI) b

Type of fuel used for cooking Mains electricity 27 2.6 (1.1, 5.8) 197 16.4 (12.2, 21.8) 44 12.8 (4.9, 29.2) 0.0000 Liquefied petroleum gas (LPG) 0 0.0 - 55 4.2 (2.6, 6.6) 0 0.0 - Kerosene 5 1.2 (0.4, 3.5) 12 1.2 (0.4, 3.6) 0 0.0 - Charcoal 208 19.2 (13.7, 26.1) 645 49.2 (42.7, 55.8) 147 42.6 (28.5, 58.0) Firewood 459 77.0 (69.1, 83.4) 213 28.3 (20.7, 37.5) 149 43.2 (28.3, 59.4) Straw, shrubs, or grass 1 0.1 (0.0, 0.6) 0 0.0 - 3 0.9 (0.1, 6.0) No food cooked in the household 0 0.0 - 3 0.4 (0.1, 1.2) 0 0.0 - Other 0 0.0 - 4 0.3 (0.1, 1.1) 2 0.6 (0.1, 2.3)

How household is lit at night Mains electricity 112 9.0 (5.4, 14.6) 788 58.7 (50.7, 66.2) 105 30.4 (17.8, 47.0) 0.0000 Solar energy 94 11.4 (6.9, 18.3) 33 4.2 (2.5, 6.9) 8 2.3 (1.1, 4.9) Kerosene 23 2.5 (1.2, 5.3) 39 4.3 (2.4, 7.8) 12 3.5 (1.2, 9.9) Firewood 43 7.6 (4.2, 13.4) 22 2.1 (1.0, 4.2) 5 1.4 (0.5, 3.8) Torch/flashlight 428 69.5 (60.5, 77.2) 246 30.5 (23.8, 38.2) 214 62.0 (46.6, 75.3) Other 0 0.0 - 1 0.2 (0.0, 1.2) 1 0.3 (0.0, 2.1)

Note: The n’s are un-weighted denominators in each subgroup; the sum of subgroups may not equal the total because of missing data. a Percentages weighted for unequal probability of selection.



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8.10. COMPARISON OF SERUM RETINOL AND RETINOL BINDING PROTEIN

Correlation of retinol and retinol-binding protein concentrations, Somalia

2019

Because RBP is not a WHO-recommended biomarker for the assessment of vitamin A status

[70], extra serum specimens from children and non-pregnant women were analyzed for

serum retinol as a comparison and validations of RBP measurements. RBP was measured from

the main serum aliquots prepared (aliquot A), and serum retinol was measured from excess

serum from the capillary blood samples (aliquot B) collected as part of the SMS. Serum retinol

was analyzed using HPLC at the Swiss Vitamin Institute, Switzerland, and RBP was measured

using the ELISA technique at the VitMin Lab, Freiburg, Germany.

Figure 7.10-1 below presents the correlation plot and regression equation comparing retinol

and RBP for both children and women combined. Using 103 cases (58 children, 45 women),

we found an acceptable correlation between RBP and serum retinol values (R2=0.7). The

estimated slope was 0.97, showing that RBP values were only slightly lower than their serum

retinol counterparts.

Figure 7.10-1. Combined comparison of retinol and retinol binding protein concentrations in children and

non-pregnant women, Somalia 2019

Figure 7.10-2 (panels A and B) shows the correlation and regression equations separately for

children and women, and shows similarly acceptable correlations between retinol and RBP

concentrations, with R2≈0.7 for both children and women. However, the estimated slope was

0.78, showing that RBP values were markedly lower than serum retinol concentrations. For

women, the estimated slope was 1.07, showing that RBP concentrations were slightly higher

than corresponding serum retinol concentrations.

Despite the sub-optimal correlation, when comparing RBP and retinol concentrations, a

vitamin A deficiency cut-off of 0.7 µmol/L was utilized, as this corresponds with the fact that

“in response to tissue demand, [retinol] is released from the liver in a 1:1 ratio with its carrier


157

protein, retinol-binding protein” [70,71]. Moreover, a comparison of RBP and retinol in 26

child samples, conducted by the VitMin Laboratory, found a near perfect correlation

(R2=0.9735; y=0.982x + 0.0402). As this comparison was done using the same aliquots, there

was not possibility of labelling errors between the A and B aliquots.

A B

Figure 7.10-2. Separate comparison of retinol and retinol binding protein concentrations in children (A) and

non-pregnant women (B), Somalia 2019


158

8.11. CHILD ANTHROPOMETRY QUALITY SUMMARY

Age heaping

The table below shows ages in completed years. Age in completed years was produced by round dropping the decimals in the age variable that

was calculated by subtracting the interview date from the child’s birth date. The table below shows that there was considerable age heaping on

ages 12, 24, 36, 48, and 59 months. Albeit sub-optimal, this finding is not surprising as not all survey respondents were calendar literate, and

most children did not possess a health card with their date of birth. Only minor age heaping was found for other age groups.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58

Pro

po

rtio

n (

%)

Age in completed months


159

Digit heaping for height measurements

The length/height measurements show a clear and strong preference for the decimals .0 and .5, particularly for the first length/height

measurement. This may indicate that anthropometrists either rounded or truncated measurements. The preference for decimals 6,7,8, and 9

were consistently the lowest in both the first and second measurement. While this may decrease precision somewhat, length/height

measurements which are inaccurate by only a few millimeters probably do not inject a substantial systematic bias.

0

10

020

030

040

0

Fre

qu

en

cy

0 1 2 3 4 5 6 7 8 9Height measurement 1 decimals in children 0-59 months of age

0

10

020

030

040

0

Fre

qu

en

cy

0 1 2 3 4 5 6 7 8 9Height measurement 2 decimals in children 0-59 months of age


160

Digit preference for weight measurements

The digit preference results for child weight were generally evenly distributed, except for a slightly higher prevalence for 0. It is unclear how this

digit prevalence could have resulted, as anthropometrists were instructed to directly record the weight in kilograms — measured to the nearest

100 grams — directly form the Seca scale.

050

10

015

020

025

0

Fre

qu

en

cy

0 1 2 3 4 5 6 7 8 9Weight measurement 1 decimals in children 0-59 months of age

050

10

015

020

025

0

Fre

qu

en

cy

0 1 2 3 4 5 6 7 8 9Weight measurement 2 decimals in children 0-59 months of age


161

Comparison of anthropometric results for all teams

The table below presents as summary of the child anthropometry results by team, including stunting, wasting, underweight, and overweight

prevalence estimates, mean height and weight measurements, and mean HAZ, WHZ, and WAZ z-scores. Importantly, the results present in this

table differ slightly from those presented in the body of the report, as these results were calculated separately to account for age, residence,

and household wealth quintile. The table also contains the percentage of flagged values for HAZ, WHZ, and WAZ. HAZ and WAZ values < -6 SD

or >+6 SD were flagged, and WHZ values < -5 SD or >+5 SD were flagged.

The table below uses a red and green color coding to indicate when results are above and below the mean by certain standard deviation

thresholds. Dark red and dark green denote values that are furthest from the mean for all teams. While there is naturally some variability in the

various measurements by each team, we found three team had consistently poorer performance: Team 10, Team 11, and Team 13.

For Team 10, which was responsible for data collection in Galmudug, nearly all measured were at least 1 SD away from the mean. This resulted

in a stunting prevalence that was higher than average, and an overweight prevalence that was nearly 30%, a result that is most certainly

improbable. On the other hand, and despite weight measurements that were between 1-1.5 SDs higher than the mean, the underweight

prevalence for Galmudug was close to the national mean prevalence. Due to these findings, the child height measurement for Team 10 was

excluded from the analysis, which in turn results in missing observations for HAZ and WHZ calculation, and missing stunting and wasting

prevalence estimates in Galmudug.

For Team 11, which was working in South-West State, both height and weight measurements were less than -1.5 SDs below the national mean.

This resulted in stunting and underweight estimates that were both substantially higher than the national average. More telling, is the difference

of the results between Team 11 and Team 12, as these teams collected data in the same clusters at the same time. Thus, as Team 11 undoubtedly

had incorrectly measured or recorded the height and weight measurements of children, both measurements were excluded from the data

analysis. However, since Team 11 and Team 12 were working simultaneously in the same clusters, we were able to estimate the stunting, wasting,

and underweight prevalences in South-West using the data collected by Team 12, only.

For Team 13, which was working in Jubaland, there was a very high proportion of flagged values for HAZ, WHZ, and WAZ. These likely influenced

the resulting stunting, wasting and underweight estimates, which were all substantially higher than the national average. Measurements from


162

Team 13 also produced an overweight prevalence of about 10%, which is considered unlikely. Team 13 and Team 14 collected data in the same

clusters in Jubaland. Thought the performance of Team 14 was not ideal due to a high proportion of flagged z-score values, their measures

results in HAZ, WHZ, and WAZ values that were within 1 SD of the mean, and stunting and underweight prevalences were also close to the mean.

As the performance of Team 13 was considered unacceptable, both height and weight measurements for Team 13 were excluded from the

analysis. However, since Team 13 and Team 14 were working simultaneously in the same clusters, we were able to estimate the stunting, wasting,

and underweight prevalences in Jubaland using the data collected by Team 14, only.


163

Somaliland 1 30.3 12.0% 9.6% 8.9% 3.0% 86.3 11.4 -0.83 -0.53 -0.86 0.6% 0.0% 0.6%

2 32.2 13.2% 5.9% 8.8% 2.0% 87.5 11.9 -0.69 -0.39 -0.64 0.0% 0.0% 0.5%

3 33.7 13.4% 6.2% 9.3% 0.0% 88.2 12.0 -0.41 -0.57 -0.60 0.0% 0.0%

4 32.1 11.5% 8.9% 5.1% 5.1% 87.5 11.8 -0.73 -0.42 -0.69 2.5% 1.3%

Puntland 5 28.1 16.5% 21.6% 16.2% 2.1% 87.6 11.2 -0.46 -1.04 -1.14 1.9% 0.0% 0.9%

6 30.6 12.0% 9.2% 6.7% 0.0% 88.4 11.7 -0.50 -0.72 -0.87 1.0% 1.0%

7 27.3 5.9% 14.9% 13.2% 3.0% 89.1 11.8 -0.29 -0.88 -0.76 0.0% 0.0% 1.4%

8 28.8 21.1% 21.1% 21.1% 0.0% 88.0 11.5 -0.57 -1.03 -1.02 0.0% 0.0%

Hirshabelle 9 29.3 13.0% 12.4% 7.8% 3.7% 88.8 12.0 -0.27 -0.52 -0.56 2.0% 0.0% 0.8%

Galmudug 10 32.9 39.0% 3.8% 12.6% 28.3% 83.5 12.4 -1.69 1.05 -0.49 7.6% 0.0% 4.7%

Southwest 11 32.6 61.4% 9.7% 51.6% 7.5% 80.0 10.4 -2.53 -0.31 -1.89 12.5% 0.0% 2.1%

12 28.7 35.8% 11.1% 22.2% 6.2% 86.3 11.9 -1.13 -0.21 -0.71 0.0% 0.0%

Jubaland 13 29.3 37.1% 25.7% 31.5% 10.8% 83.0 11.7 -1.03 -0.69 -1.00 22.4% 6.1% 12.2%

14 24.2 20.0% 20.0% 20.8% 0.0% 90.4 12.2 -0.59 -0.93 -1.01 9.7% 6.5% 6.5%

Banaadir 15 30.3 20.5% 9.8% 9.8% 9.8% 87.9 12.4 -0.68 -0.06 -0.39 4.7% 0.0% 2.0%

16 30.0 27.0% 13.7% 20.1% 2.7% 86.3 11.4 -1.23 -0.67 -1.19 0.7% 0.0% 2.0%

Mean: 30.03 22.5% 12.7% 16.6% 5.3% 86.80 11.73 -0.85 -0.50 -0.86 4.1% 0.9% 3.1%

Stnd Dev: 2.34 14.0% 6.1% 11.4% 6.8% 2.54 0.48 0.57 0.48 0.35 6% 2% 3%

Mean + 1 SD: 32.36 36.4% 18.9% 28.0% 12.1% 89.34 12.21 -0.29 -0.01 -0.52 0.10 0.03 0.06

Mean + 1.5xSD: 33.5 43.4% 21.9% 33.7% 15.5% 90.61 12.45 0.00 0.23 -0.34 13.12% 4.03% 8.15%

Mean - 1 SD: 27.69 8.5% 6.6% 5.2% -1.5% 84.26 11.25 -1.42 -0.98 -1.21 -0.02 -0.01 0.00

Mean - 1.5xSD: 26.52 1.5% 3.5% -0.5% -4.9% 82.99 11.01 -1.70 -1.22 -1.39 -4.92% -2.17% -2.02%

Estimate 1.0-1.5 standard deviations above the mean

Estimate more than 1.5 standard deviations above the mean

Estimate 1.0-1.5 standard deviations below the mean

Estimate more than 1.5 standard deviations below the mean

Percent flags exceeds SMART's threshold of acceptability

* Result of biivariate analysis

** Result of multivariable regression including age, urban/rural, and HH wealth quintile

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Somaliland 1 30.3 12.0% 9.6% 8.9% 3.0% 86.3 11.4 -0.83 -0.53 -0.86 0.6% 0.0% 0.6%

2 32.2 13.2% 5.9% 8.8% 2.0% 87.5 11.9 -0.69 -0.39 -0.64 0.0% 0.0% 0.5%

3 33.7 13.4% 6.2% 9.3% 0.0% 88.2 12.0 -0.41 -0.57 -0.60 0.0% 0.0%

4 32.1 11.5% 8.9% 5.1% 5.1% 87.5 11.8 -0.73 -0.42 -0.69 2.5% 1.3%

Puntland 5 28.1 16.5% 21.6% 16.2% 2.1% 87.6 11.2 -0.46 -1.04 -1.14 1.9% 0.0% 0.9%

6 30.6 12.0% 9.2% 6.7% 0.0% 88.4 11.7 -0.50 -0.72 -0.87 1.0% 1.0%

7 27.3 5.9% 14.9% 13.2% 3.0% 89.1 11.8 -0.29 -0.88 -0.76 0.0% 0.0% 1.4%

8 28.8 21.1% 21.1% 21.1% 0.0% 88.0 11.5 -0.57 -1.03 -1.02 0.0% 0.0%

Hirshabelle 9 29.3 13.0% 12.4% 7.8% 3.7% 88.8 12.0 -0.27 -0.52 -0.56 2.0% 0.0% 0.8%

Galmudug 10 32.9 39.0% 3.8% 12.6% 28.3% 83.5 12.4 -1.69 1.05 -0.49 7.6% 0.0% 4.7%

Southwest 11 32.6 61.4% 9.7% 51.6% 7.5% 80.0 10.4 -2.53 -0.31 -1.89 12.5% 0.0% 2.1%

12 28.7 35.8% 11.1% 22.2% 6.2% 86.3 11.9 -1.13 -0.21 -0.71 0.0% 0.0%

Jubaland 13 29.3 37.1% 25.7% 31.5% 10.8% 83.0 11.7 -1.03 -0.69 -1.00 22.4% 6.1% 12.2%

14 24.2 20.0% 20.0% 20.8% 0.0% 90.4 12.2 -0.59 -0.93 -1.01 9.7% 6.5% 6.5%

Banaadir 15 30.3 20.5% 9.8% 9.8% 9.8% 87.9 12.4 -0.68 -0.06 -0.39 4.7% 0.0% 2.0%

16 30.0 27.0% 13.7% 20.1% 2.7% 86.3 11.4 -1.23 -0.67 -1.19 0.7% 0.0% 2.0%

Mean: 30.03 22.5% 12.7% 16.6% 5.3% 86.80 11.73 -0.85 -0.50 -0.86 4.1% 0.9% 3.1%

Stnd Dev: 2.34 14.0% 6.1% 11.4% 6.8% 2.54 0.48 0.57 0.48 0.35 6% 2% 3%

Mean + 1 SD: 32.36 36.4% 18.9% 28.0% 12.1% 89.34 12.21 -0.29 -0.01 -0.52 0.10 0.03 0.06

Mean + 1.5xSD: 33.5 43.4% 21.9% 33.7% 15.5% 90.61 12.45 0.00 0.23 -0.34 13.12% 4.03% 8.15%

Mean - 1 SD: 27.69 8.5% 6.6% 5.2% -1.5% 84.26 11.25 -1.42 -0.98 -1.21 -0.02 -0.01 0.00

Mean - 1.5xSD: 26.52 1.5% 3.5% -0.5% -4.9% 82.99 11.01 -1.70 -1.22 -1.39 -4.92% -2.17% -2.02%

Estimate 1.0-1.5 standard deviations above the mean

Estimate more than 1.5 standard deviations above the mean

Estimate 1.0-1.5 standard deviations below the mean

Estimate more than 1.5 standard deviations below the mean

Percent flags exceeds SMART's threshold of acceptability

* Result of biivariate analysis

** Result of multivariable regression including age, urban/rural, and HH wealth quintile

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2019 somalia micronutrient survey - unicef

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