TURKANA SMART NUTRITION SURVEYS FINAL REPORT

TURKANA SMART NUTRITION SURVEYS

FINAL REPORT

June 2016

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ACKNOWLEDGEMENT Turkana County June 2016 SMART survey was successfully conducted with support of various partners. The directorate

of Family Health would like to acknowledge effort and support of all those individuals and organizations that supported

and participated in the survey. Specifically, I would like to thank EU under the Maternal Child Nutrition programme,

UNICEF Kenya, Save the Children, Aphia Plus Imaarisha, AMREF, GIZ, Feed the Children, KRCS, ILRI, for their financial

and technical support.

On behalf of the team, I appreciate our County Executive Committee Member for Health- Hon. Jane Ajele, Chief Officer of

Health services and Sanitation- Agnes Mana for providing leadership and an enabling environment and Mr. Wycliffe

Machani, County Nutrition Coordinator for his tireless commitment in spearheading the SMART survey and members of

County and Sub county health management teams for their valuable contribution

I also extend my special thanks to the parents and caretakers for providing valuable information during the interviews and

allowing their children to be measured. Lastly, I thank all the survey teams (coordinators, team leaders, enumerators) and

all those who gave their precious time and worked tirelessly to ensure the results were available on time.

Alice Akalapatan

Deputy Director, Family Health Directorate

Turkana County Department of Health

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LIST OF ABBREVIATION

1 ARI Acute Respiratory Infections

2 ASAL Arid and Semi-Arid Lands

3 CHWs Community Health Workers

4 CI Confidence interval

5 CMAM Community Management of acute Malnutrition

6 CMR Crude Mortality Rate

7 CSB Corn Soy Blend

8 DD Dietary Diversity

9 DHMT District Health Management Team

10 DMB Drought Management Bulletin

11 SCNO Sub County Nutrition Officer

12 DoL Diocese of Lodwar

13 ENA Emergency Nutrition Assessment

14 EPI Expanded Program on Immunizations

15 EWS Early Warning System

16 FEWSNET Famine Early Warning Systems Network

17 FCS Food Consumption Score

18 FFA Food For Asset

19 GFD General Food Distribution

20 GoK Government of Kenya

21 HH Household

22 HiNi High Impact Nutrition Interventions

23 HNDU Human Nutrition and Dietetics Unit

24 IMAM Integrated Management of Acute Malnutrition

25 IPC Integrated Food Security Phase Classification

26 KEPI Kenya Expanded Programme of Immunisation

27 KFSSG Kenya Food Security Steering Group

28 NDMA National Drought Management Authority

29 OJT On The Job Training

30 OPV Oral polio Vaccine

31 ORS Oral Rehydration Solution

32 OTP Outpatient Therapeutic Programme

33 PLW Pregnant and Lactating Women

34 PPS Probability proportional to size

35 SFP Supplementary Feeding Programme

36 SMART Standardized Monitoring and Assessment of Relief and Transitions

37 U5 Under Five Years Old

38 UMR Under-five Mortality Rate

39 UNICEF United Nations Children’s Fund

40 WFP World Food Programme

41 WHO-GS World Health Organisation Growth Standards

42 WFH Weight for Height

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TABLE OF CONTENTS Acknowledgement .............................................................................................................................2 List of abbreviation ............................................................................................................................3 Table of contents ...............................................................................................................................4 List of tables ......................................................................................................................................6 List of figures .....................................................................................................................................7 LIST OF APPENDICES ...........................................................................................................................7 EXECUTIVE SUMMARY .......................................................................................................................8 CHAPTER 1 ....................................................................................................................................... 14 1.0 Background information ........................................................................................................... 14

1.1 Food security situation ............................................................................................................................................. 14

1.2 Humanitarian and Development partners ................................................................................................................. 15

1.3 Main Objective ......................................................................................................................................................... 15

1.4 Specific Objectives ................................................................................................................................................... 15

1.5 Timing of Turkana SMART surveys ......................................................................................................................... 15

1.6 Survey Area ............................................................................................................................................................. 15

CHAPTER TWO ................................................................................................................................. 16 2.0 METHODOLOGY ....................................................................................................................... 16

2.1 Sample size calculation ............................................................................................................................................ 16

2.2 Sampling method ..................................................................................................................................................... 17

2.2.1 Selection of the households ............................................................................................................. 17

2.2.2 Selection of children for anthropometry ......................................................................................... 17

2.2.3 Selection of women for determination of nutritional status .......................................................... 17

2.3 Survey team ............................................................................................................................................................. 17

2.4 Survey team training ................................................................................................................................................ 17

2.4.1 Supervisors training .......................................................................................................................... 17

2.4.2 Enumerators training ....................................................................................................................... 18

2.5 Data collection .......................................................................................................................................................... 18

2.6 Variables Measured ................................................................................................................................................. 18

2.7 Data analysis ............................................................................................................................................................ 20

2.8 Survey Limitations .................................................................................................................................................... 21

2.9 Ethical considerations .............................................................................................................................................. 21

CHAPTER THREE: RESULTS & Dicsussions .......................................................................................... 22 3.0 CHILD hEALTH & nUTRITION ..................................................................................................... 22

3.1 Demographic results ................................................................................................................................................ 22

3.1.1 Residency and marital Status ........................................................................................................... 22

3.1.2 Occupation of the household main provider ................................................................................... 23

3.2 Anthropometry .......................................................................................................................................................... 23

3.2.1 Age and sex distribution of the sampled children ........................................................................... 23

3.3 Prevalence of Acute Malnutrition .............................................................................................................................. 24

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3.3.1 Prevalence of acute malnutrition based on weight-for-height z-scores (and/or edema) and by sex

................................................................................................................................................................... 25

3.3.2 Prevalence of acute malnutrition (wasting) by age based on weight-for-height Z-scores and or

edema (WHO Standards 2006) ................................................................................................................. 26

3.3.3 Prevalence of acute malnutrition based on MUAC .......................................................................... 27

3.4 Prevalence of underweight ....................................................................................................................................... 28

3.5 Prevalence of stunting .............................................................................................................................................. 28

3.6 Children’s Morbidity and Health Seeking Behavior .................................................................................................. 29

3.6.1 Child Morbidity ................................................................................................................................. 29

3.6.2 Therapeutic Zinc Supplementation during Watery Diarrhea Episodes ............................................ 30

3.6.3 Health Seeking Behavior .................................................................................................................. 30

3.7 Childhood Immunization, Vitamin A Supplementation and Deworming.................................................................... 31

3.7.1 Childhood Immunization .................................................................................................................. 31

3.7.2 Vitamin A supplementation ............................................................................................................. 32

3.7.3 De-worming ........................................................................................................................................................... 34

4.0 MATERNAL NUTRITION............................................................................................................. 35 4.1 Women physiological status ..................................................................................................................................... 35

4.2 Acute Malnutrition..................................................................................................................................................... 35

4.3 Iron and Folic Acid Supplementation (IFAS) ............................................................................................................ 36

4.4 Mosquito Nets Ownership and Utilization ................................................................................................................. 37

5.0 WATER SANITATION & HYGIENE ............................................................................................... 38 5.1 Main Source of Water ............................................................................................................................................... 38

5.2 Distance to Water Source and Queuing Time .......................................................................................................... 39

5.3 Methods of drinking water treatment and storage .................................................................................................... 40

5.4 Water Utilization and Payment ................................................................................................................................. 40

5.5 Hand washing .......................................................................................................................................................... 42

5.6 Latrine Ownership and Utilization ............................................................................................................................. 43

6.0 Food Security ........................................................................................................................... 44 6.1 Household’s Source of Income ................................................................................................................................ 44

6.2 Source of Dominant Foods ....................................................................................................................................... 44

6.3 Foods Groups Consumed by Households................................................................................................................ 45

6.4 Household Food Consumption Frequency ............................................................................................................... 46

6.5 Household Food consumption score (FCS) ............................................................................................................. 47

6.6 Household Consumption of Micronutrient Rich Foods ............................................................................................. 47

6.7 Household Consumption of Protein, Vitamin A and Heme Iron Rich Food Groups by Poor/Borderline and Acceptable Food Consumption Score Groups in Turkana County ................................................................................. 48

6.8 Minimum Dietary Diversity -Women Score (MDD-W) ............................................................................................... 49

6.9 Household Coping Strategy Index (Reduced CSI) ................................................................................................... 50

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7.0 CONCLUSION ............................................................................................................................ 51 8.0 RECOMMENDATIONs .......................................................................................................... 52 9.0 APPENDICES ............................................................................................................................. 55 LIST OF TABLES TABLE 1:SURVEY FINDINGS SUMMARY ........................................................................................................................................... 9

TABLE 2: RECOMMENDATIONS .................................................................................................................................................. 11

TABLE 3: TURKANA SEASONAL CALENDAR .................................................................................................................................... 15

TABLE 4:TURKANA COUNTY SURVEY ZONES .................................................................................................................................. 16

TABLE 5:SAMPLE SIZE CALCULATION ........................................................................................................................................... 16

TABLE 6:SAMPLED NUMBER OF CLUSTERS, HOUSEHOLDS AND CHILDREN ........................................................................................... 17

TABLE 7: WFP CORPORATE FCS THRESHOLDS .............................................................................................................................. 19

TABLE 8: DEFINITIONS OF ACUTE MALNUTRITION USING WFH AND/OR EDEMA IN CHILDREN AGED 6–59 MONTHS .................................... 20

TABLE 9:DEFINITION OF BOUNDARIES FOR EXCLUSION .................................................................................................................... 21

TABLE 10: HOUSEHOLD DEMOGRAPHY PER SURVEY ........................................................................................................................ 22

TABLE 11: RESIDENCY .............................................................................................................................................................. 22

TABLE 12: SUMMARY OF CARETAKERS’ MARITAL STATUS ................................................................................................................. 22

TABLE 13: SUMMARY OF HOUSEHOLD’S MAIN PROVIDER OCCUPATION .............................................................................................. 23

TABLE 14:SUMMARY OF CHILDREN AGE VERIFICATION MEANS ......................................................................................................... 23

TABLE 15: DISTRIBUTION OF AGE AND SEX OF SAMPLE .................................................................................................................... 24

TABLE 16: PREVALENCE OF MALNUTRITION WEIGHT-FOR-HEIGHT Z-SCORES (WHO STANDARDS 2006) ................................................... 24

TABLE 17: PREVALENCE OF ACUTE MALNUTRITION BASED ON WEIGHT-FOR-HEIGHT Z-SCORES (AND/OR EDEMA) AND BY SEX (95% CONFIDENCE

INTERVAL) ..................................................................................................................................................................... 25

TABLE 18: PREVALENCE OF ACUTE MALNUTRITION BY AGE, BASED ON WEIGHT-FOR-HEIGHT Z-SCORES AND/OR OEDEMA ............................. 26

TABLE 19: DISTRIBUTION OF ACUTE MALNUTRITION AND OEDEMA BASED ON WEIGHT-FOR-HEIGHT Z-SCORE ............................................. 27

TABLE 20:PREVALENCE OF MALNUTRITION BASED ON MUAC PER SURVEY ........................................................................................ 27

TABLE 21: PREVALENCE OF UNDERWEIGHT .................................................................................................................................. 28

TABLE 22:PREVALENCE OF STUNTING ......................................................................................................................................... 29

TABLE 23: CHILDREN ILL ........................................................................................................................................................... 29

TABLE 24:PREVALENCE OF CHILD MORBIDITY 2 WEEKS PRIOR TO THE SURVEY ...................................................................................... 30

TABLE 25: THERAPEUTIC ZINC SUPPLEMENTATION ......................................................................................................................... 30

TABLE 26:POINT OF SEEKING HEALTH ASSISTANCE ......................................................................................................................... 31

TABLE 27: CHILD BCG IMMUNIZATION COVERAGE ...................................................................................................................... 31

TABLE 28: CHILD OPV 1 AND 2 COVERAGE .................................................................................................................................. 32

TABLE 29: CHILD MEASLES 9 AND 18 MONTHS COVERAGE .............................................................................................................. 32

TABLE 30: CARETAKERS WITH CHILDREN AGED 24 MONTHS AND BELOW WHO WERE SUPPLEMENTED WITH IRON FOLIC ACID IN THEIR LAST

PREGNANCY ................................................................................................................................................................... 36

TABLE 31: NUMBER OF DAYS CARETAKERS WITH CHILDREN AGED 24 MONTHS AND BELOW CONSUMED IFAS IN THEIR LAST PREGNANCY ........ 37

TABLE 32: CURRENT MAIN SOURCES OF WATER ............................................................................................................................ 39

TABLE 33: QUEUING TIME AT WATER SOURCE ............................................................................................................................... 40

TABLE 34: METHODS USED FOR TREATING DRINKING WATER ........................................................................................................... 40

TABLE 35: PAYMENT FOR WATER ............................................................................................................................................... 41

TABLE 36: COST OF WATER PER 20 LITER JERRICAN ....................................................................................................................... 41

TABLE 37: COST OF WATER PER MONTH ...................................................................................................................................... 42

TABLE 38: HANDWASHING AT CRITICAL TIMES .............................................................................................................................. 42

TABLE 39: SOURCE OF DOMINANT FOODS .................................................................................................................................... 45

TABLE 40:COPING STRATEIES APPLIED ......................................................................................................................................... 51

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TABLE 41: MEAN HOUSEHOLD COPING STRATEGY INDEX(CSI) ........................................................................................................ 51

TABLE 42:RECCOMENDATIONS ............................................................................................................................................ 52

LIST OF FIGURES FIGURE 1: MAP OF TURKANA COUNTY ........................................................................................................................................ 14

FIGURE 2: TRENDS OF GLOBAL ACUTE MALNUTRITION IN TURKANA COUNTY (2010-2016) ................................................................. 25

FIGURE 3: VITAMIN A SUPPLEMENTATION COVERAGE .................................................................................................................... 33

FIGURE 4: PLACES OF VITAMIN A SUPPLEMENTATION ..................................................................................................................... 34

FIGURE 5:DE-WORMING COVERAGE AMONG CHILDREN 12-59 MONTHS OLD...................................................................................... 34

FIGURE 6: WOMEN PHYSIOLOGICAL STATUS ................................................................................................................................. 35

FIGURE 7: NUTRITION STATUS OF WOMEN OF REPRODUCTIVE AGE AND NUTRITION STATUS OF PREGNANT AND LACTATING WOMEN ............ 35

FIGURE 8: MOSQUITO NETS OWNERSHIP AND UTILIZATION .............................................................................................................. 37

FIGURE 9: PATHWAY TO REDUCTION OF STUNTING ........................................................................................................................ 38

FIGURE 10: DISTANCE TO WATER SOURCES .................................................................................................................................. 39

FIGURE 11: WATER UTILIZATION (LITERS/PERSON/DAY) ................................................................................................................. 41

FIGURE 12: WHAT IS USED FOR HANDWASHING ............................................................................................................................ 42

FIGURE 13: LATRINE OWNERSHIP AND UTILIZATION ....................................................................................................................... 43

FIGURE 14: HOUSEHOLD’S SOURCE OF INCOME ............................................................................................................................ 44

FIGURE 15: FOOD GROUPS CONSUMED BY HOUSEHOLDS FROM 24 HOUR RECALL ................................................................................ 45

FIGURE 16: FOOD CONSUMPTION FREQUENCY BY HOUSEHOLDS BASED ON A 7 DAY RECALL ................................................................... 46

FIGURE 17: HOUSEHOLD FOOD CONSUMPTION SCORE .................................................................................................................... 47

FIGURE 18: HOUSEHOLD CONSUMPTION OF MICRONUTRIENT RICH FOODS ......................................................................................... 47

FIGURE 19: CONSUMPTION OF PROTEIN, VITAMIN A AND HEME IRON RICH FOOD GROUPS BY POOR/BORDERLINE AND ACCEPTABLE FOOD

CONSUMPTION SCORE GROUPS IN TURKANA COUNTY ............................................................................................................ 48

FIGURE 20: MDD-W SCORE TURKANA COUNTY ........................................................................................................................... 49

FIGURE 21: PROPORTION OF HOUSEHOLD APPLYING A COPING STRATEGY ........................................................................................... 50

LIST OF APPENDICES APPENDIX 1: IPC FOR ACUTE MALNUTRITION MAPS ...................................................................................................................... 55

APPENDIX 2:SUMMARY OF PLAUSIBILITY REPORT ........................................................................................................................... 55

APPENDIX 3:TURKANA CENTRAL SURVEY ZONE SAMPLED CLUSTERS .................................................................................................. 56

APPENDIX 4:TURKANA NORTHSURVEY ZONE SAMPLED CLUSTERS ..................................................................................................... 58

APPENDIX 5:TURKANA SOUTHSURVEY ZONE SAMPLED CLUSTERS ...................................................................................................... 60

APPENDIX 6:TURKANA WEST SURVEY ZONE SAMPLED CLUSTERS ...................................................................................................... 63

APPENDIX 7:WEIGHT FOR HEIGHT Z SCORES ± SD-MALNUTRITION POCKETS IN RED FONT COLOUR ......................................................... 65

APPENDIX 8: SMART SURVEY QUESTIONNAIRE ............................................................................................................................. 68

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EXECUTIVE SUMMARY Turkana County department of health in collaboration with nutrition partners (UNICEF, Save the Children International, APHIA Plus Imarisha, AMREF, ILRI, Feed the Children, GIZ, and GAIN) successfully conducted Four independent SMART surveys concurrently in June 2016 covering the entire county. This ensured all the livelihood zones in the county (pastoral, agro-pastoral and formal employment/business/petty trade) were covered. The survey zones included Turkana Central (Central and Loima sub counties), Turkana North (North and Kibish sub counties), Turkana South (South and East sub counties) and Turkana West (West Sub County).

The main goal of the survey was to determine the prevalence of malnutrition among the children aged 6-59 months old and women of reproductive age (WRA) in Turkana County. The specific objectives of the survey were;

1. To determine the prevalence of acute malnutrition among under five year old children and women of reproductive age

2. To determine the immunization coverage for measles, Oral Polio Vaccines (OPV 1 and 3), and vitamin A supplementation in children aged 6-59 months;

3. To estimate coverage of iron / folic acid supplementation during pregnancy in women of reproductive age 4. To determine de-worming coverage for children aged 12 to 59 months; 5. To determine the prevalence of common illnesses; 6. To collect information on possible underlying causes of malnutrition such as household food security, water,

sanitation, and hygiene practices.

Standardized Monitoring Assessment for Relief and Transition Method (SMART) was used to conduct the surveys. The

methodology is a cross sectional design. A three stage sampling process was used in this survey. The first stage involved

sampling of sub locations (clusters) from a sampling frame using ENA for SMART software (July 9 , 2015 version).The

second stage sampling involved segmentation of the sampled sub locations to identify the villages to be sampled. In the

third stage, households were selected randomly upon getting the updated list of households in the village. Household was

used as the basic sampling unit. Standard SMART questionnaire in Open Data Kit (ODK) collect installed in android

tablets was used to collect data. The data was uploaded in ODK aggregate servers (courtesy of Save the Children) from

the tablets and downloaded daily for plausibility checks and at the end of the survey for data analysis. The data collection

teams were provided with daily feedback on the quality of data collected the previous day .Table 1 below shows the

summary of the survey findings.

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Table 1:Survey findings summary

Indicator Turkana Central

Turkana North

Turkana South

Turkana West

Turkana County

Wasting (WHO 2006)-2016 n=720 n=661 n=831 n=567 n=2718

Wasting (WHO 2006)-2015 n=744 n=781 n=824 n=587 n=2974

Global Acute Malnutrition

(GAM)-June 2016

24.5%

(20.2-29.4)

23.4%

(19.4-28.1)

30.3%

(26.7-34.1)

14.4%

(11.1-18.5)

23.3%

(21.1 – 25.5)


(GAM)-June 2015

20.9%

(17.9 – 24.4)

22.9%

(19.6 – 26.6)

24.5%

(21.1 – 28.2)

16.7%

(13.8 – 20.1)

21.2 %

(19.7 – 22.9)

Severe Acute Malnutrition

(SAM)-June 2016

5.6%

(4.2-7.5)

4.1%

(2.5-6.7)

8.9%

(7.1-11.0)

1.8 %

(1.0-3.3)

5.3%

(4.5-6.3)


(SAM)-June 2015

4.8 %

(3.4 – 6.6)

3.8 %

(2.4 – 6.1)

6.1 %

(4.3 – 8.5)

4.8 %

(3.3 – 6.9)

5.0%

(4.2 – 6.0)

Mean z-scores ± SD-2016 -1.36±0.92 -1.40±0.83 -1.54±0.88 -1.03±0.93 -1.27±1.03

Mean z-scores ± SD-2015 -1.24±1.01 -1.30±1.05 -1.27±10.98 -1.04±1.05 -1.22±1.03

Design Effect -2016 2.0 1.47 1.23 1.49 1.9

Design Effect -2015 1.36 1.38 1.18 1.08 1.12

Underweight (WHO 2006) n=720 n=831 n= 661 n=567 n=2771

Prevalence of global

underweight-June 2016

33.9%

(29.6 – 38.4)

30.8%

(25.6-36.5)

44.6%

(40.4 – 48.8)

27.7%

(23.1 – 33.2)

34.7%

(32.1-37.4)



30.5 %

(26.8 – 34.6)

29.4 %

(24.4 – 34.9)

38.3 %

(33.9 – 43.0)

24.0 %

(20.4 – 28.0)

31%

(28.8 – 33.3)

Prevalence of severe


10.0%

(7.3 – 13.5)

9.0%

(6.7-12.1)

17.8%

(14.6-21.4)

6.0%

(3.8– 9.5)

10.9%

(9.5-12.4)



8.8%

(7.1 – 10.9.)

8.3 %

(6.0 – 11.4)

12.0 %

(9.0 – 15.9 )

7.3 %

(5.5 – 9.6)

9.4%

(8.1-10.8)

Stunting (WHO 2006)-2015 n = 720 n =831 n =661 n =567 n=2771


stunting –June 2016

27.2%

(22.4-32.5)

25.1%

(20.9 – 29.9)

33.6% (29.3-38.1)

25.9%

(21.3-31.0)

28.2%

(25.7-30.9)


stunting –June 2015

24.6 %

(20.9 – 28.6)

21.0 %

(16.9 – 25.7)

32.7 % (28.6 – 37.0)

21.7 %

(18.4 – 25.5)

25.6%

(24.0-27.3)


stunting-June 2016

8.1%

(5.4-12.1)

10.5%

(7.8-13.9)

10.5%

(7.8-13.9)

6.5%

(4.5 – 9.1)

8.0%

(6.7-9.4)


stunting-June 2015

6.1%

(4.6 – 8.2)

5.4%

(3.8 – 7.7)

9.7%

(7.7 – 12.2)

5.3%

(3.7 – 7.6)

6.8%

(6.1-7.6)

Prevalence of acute

malnutrition by MUAC

n=720 n=831 n=661 n=567

Severe under nutrition

(< 115 mm)-July 2016

2.4%

(1.3-4.2)

1.5%

(0.8-2.8)

2.3%

(1.2-4.4)

1.2%

(0.5-3.3)


(< 115 mm)-July 2015

1.7 %

(0.7 – 3.7)

1.6 %

(0.9 – 2.9)

1.7 %

(1.0 – 2.8 9)

2.0 %

(0.8 – 4.9)

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Moderate undernutrition (≥115–<125 mm)-July 2016

6.3% (4.5-8.6)

9.1% (6.5-12.6)

8.2% (6.3-12.6)

7.2% (4.9-10.5)

Moderate undernutrition

(≥115–<125 mm)-July 2015

7.8 %

(5.9 – 10.1)

9.9 %

(7.4 – 13.0)

9.0 %

(6.7 – 12.0)

9.0 %

(6.0 – 13.5)


(≤125 mm) –June 2016

8.6%

(6.4-11.5)

10.6%

(7.6-14.6)

10.5%

(8.1-13.4)

8.5%

(5.8-12.2)


(≤125 mm) –June 2015

9.4 %

(7.3 – 12.1)

11.5 %

(8.9 – 14.7)

10.7 %

(8.0 – 14.1)

11.1 %

(7.4 – 16.2)

Maternal Malnutrition-

June 2016

n=455 n=455 n=550 n=412 n=1872

% of WRA with MUAC

<21cm 9.9% 9.0% 7.1% 5.8% 8.0%

PLW with MUAC<21 cm-

2016 9.2% 8.0% 6.5% 6.9% 7.5%

PLW with MUAC<21 cm-

2015 8.5% 10.4% 7.5% 7.8% 8.5%

Immunization-June 2016

BCG vaccination 96.9% 97.3% 98.7% 95.2% 97.1%

OPV1(Card and recall) 78.7% 65.8% 86.3% 63.3% 74.8%

OPV3 (Card and recall) 74.9% 62.5% 80.6% 52.6% 69.1%

Measles at 9 months 58.5% 57.4% 68.4% 43.7% 58.2%

Measles Vaccination at 18 months

9.7% 15.2% 7.7% 8.8% 10.2%

Indicator Turkana Central

Turkana North

Turkana South

Turkana West

Turkana County

Vitamin A supplementation and de-worming-June 2016

Children 6-59 months supplemented with vitamin A

n=467 n=487 n=671 n=395 n=2019

57.0% 48.5% 53.9% 42.9% 51.2%

Children 12-59 months supplemented with vitamin A at least once

n=412 n=428 n=580 n=355 n=1775

46.8% 53.5% 52.8% 62.0% 53.4%

Children 12-59 months supplemented with Vitamin A at least twice 51.7% 42.1% 46.7% 36.9% 44.8%

Children 12-59 months de-wormed at least once

24.5% 20.8% 24.6% 44.3% 27.7%

Children 12-59 months de-wormed at least twice

16.0% 8.4% 10.2% 10.8% 11.4%

Children 6-11 months supplemented with Vitamin A at least once

n=55 n=59 n=91 n=39 n=244

96.4% 94.9% 100.0% 97.4% 97.5%

Child Morbidity-June 2016

Ill in the last 2 weeks(children 6-59 months)

n=728 n=667 n=842 n=572 n=2809

51.24 % 54.27% 48.69% 56.47 % 52.26%

Fever with chill like malaria 38.3% 30.7% 34.9% 42.3% 36.2%

ARI/Cough 48.5% 52.8% 43.1% 39.1% 46.1%

Watery diarrhoea 8.7% 11.9% 14.9% 11.4% 11.8%

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Bloody diarrhoea 0.4% 0.4% 0.0% 0.9% 0.4%

Therapeutic Zinc Supplementation

n= 23 n=33 n= 44 n= 25 n= 125

68.3% 78.8% 85.1% 66.2% 75.5%

Maternal Nutrition-June 2015

Iron folate supplementation for pregnant women

n=323 n=203 n=393 n=227 n=1146

IFA supplementation for upto 90 days

(320)99.1% (187)92.7% (367)93.4% (202)89.0% (1076)93.9%

IFA supplementation for between 90 and 180days

(3) 0.9% (16)7.9% (26)6.6% (25)11.0% (70)6.1%

IFA supplementation >180 days

(0)0.0% (0)0.0% (0)0.0% (0)0.0% (0)0.0%

PLW with MUAC<21 cm n=455 n=455 n=550 n=412 n=1872

9.2% 8.0% 6.5% 6.8% 7.5%

% of WRA with MUAC

<21cm 9.9% 9.0% 7.1% 5.8% 8.0%

WASH practises-June 2016

Latrine/toilet utilization by HH

n=670 n=690 n=762 n=573 n=2695

Open defecation) 89.6% 90.7% 76.0% 84.1% 84.9%

Use latrine 10.4% 9.3% 24% 15.9% 15.1%

Food Security-June 2016

Household food consumption score 625 n=605 n=651 n=575 n=2456

Poor 3.5% 8.0% 2.5% 2.3% 3.4%

Borderline 25.4% 38.2% 29.0% 17.1% 26.6%

Acceptable 71.1% 53.3% 68.5% 80.6% 70.0%

Mean household Coping Strategy Index

19.3 23.2 22.4 23.2 21.9

Table 2: Recommendations

Action Activity By whom By when

1 Update and activate nutrition contingency and response plans in South, Central and North Survey zones.

Hold joint meeting to revise the contingency plans.

Ongoing quarterly review of the contingency plans.

MoH,NDMA and nutrition partners

Immediately

2 Scale up continuous active case

finding for malnutrition for the

expected caseload(U5) of 34,563

(severe 7,862 and moderate 26,701)

and 22,437 pregnant and lactating

women in the year 2016 and referral

for timely management

Sensitize the CHVs on rapid nutrition screening and referrals.

Conduct routine screening through the existing community health units.

Conduct rapid nutrition screening in the hot spot areas.

Conduct quarterly mass screening.

MoH (nutrition and community health strategy) and nutrition partners

Continuous

3 Increase access to life saving health and nutrition services through integrated outreaches for populations with limited access to these services.

Carry out mapping of communities with limited access to health and nutrition outreaches.

Conduct biweekly integrated health and nutrition outreaches in the in communities

MoH and nutrition partners

immediately

12

far from health facilities.

Monthly monitoring of the outreaches.

4 Develop simplified nutrition survey packs/briefs easily synthesized for nutrition advocacy and mobilization.

Hold community dialogue meetings, meetings with men, mother support Group to disseminate findings of SMART survey and generate community led actions.

Hold meetings with decision makers to disseminate the findings of the SMART survey and generate sector response plans.


immediately

5 Develop and implement nutrition service delivery score card at health facilities

On a quarterly basis populate the scorecard with nutrition data.

Quarterly meetings to deliberate on the output of nutrition score cards.


Quarterly.

6 Conduct comprehensive on the job training and mentorship targeting facility health workers, community health extension workers (CHEWs) and Community health workers(CHWs)

Sensitize all the HMTs, Health facility in charges and CHEWs on findings of SMART survey and rapid nutrition screening.

Conduct sensitization of the CHVs on rapid nutrition screening and referrals.

Train all the newly recruited nutritionists on IMAM including the SURGE model.


Immediately

7 Sensitize and link mother to mother support groups (MtMSGs) and households with malnourished children/pregnant and lactating women with other nutrition sensitive sectors to strengthen nutrition resilience

Hold meetings with MtMSGs to sensitize them on the current nutrition situation and generate community led actions.

Link MtMSGs to existing livelihoods interventions.


Continuous

8 Cconduct community dialogue sessions and sensitization meetings with caregivers, community leaders/key influencers on appropriate childcare practises including micronutrient supplementation, deworming, handwashing, drinking water treatment and latrine utilization.

Develop a brief pack for community dialogue meetings.

Hold meetings with community leaders, communities, MtMSGs and caretakers to sensitize them on recommended child care and health & nutrition practices.


Continuous

9 Advocate and create public awareness on micronutrient supplementation (micronutrient powders, IFA, Vitamin A), de-worming and dietary diversification.

Conduct community dialogues with focus on micronutrients supplementation and deworming.

Integrate micronutrient supplementation and deworming into the integrated outreaches and nutrition calendar events.

Conduct micronutrient supplementation and deworming at the ECDs.


Continuous

10 Continue capacity building of health care workers especially newly recruited staffs through OJT and joint support supervision on a quarterly basis

Mobilize resources for training of health workers.

Conduct IMAM training for the newly recruited 40 nutritionists.


Continuous

11 Scale up community led total sanitation approach to increase awareness on sanitation including latrine utilization

Conduct Health workers and CHVs sensitisation on CLTS,

Conduct community sensitisation on CLTS.

MoH (public health ) and nutrition and WASH partners

Continuous

13

Roll out CLTs jointly with the communities.

12 Institutionalize Vitamin A supplementation and de-worming at the Early Child Education Development(ECDE) centers and scale up during annual child health campaigns

Sensitize ECD teachers on vitamin A supplementation and deworming.

Provide supplies and reporting tools to the ECD teachers.

Monitor on quarterly basis VAS supplementation at the ECD Centers.

MoH (nutrition& public health), MoE (ECDEs) and nutrition partners

Quarterly

13 Procurement and timely distribution of essential nutrition commodities to health facilities

Provide health facilities with the required reporting tools.

Develop commodity consumption reports and requests.

MoH/UNICEF/WFP Quarterly

14 Train county, sub county health managers, health workers on behavior social change communication(BSCC)/communication for development(C4D)

Hold training on SBCC for HMTs, health workers and CHVs.


December 2016

15 Develop, disseminate and implement multi-sectoral nutrition social behavior change communication (SBCC) strategy to address maternal and child care knowledge, attitude, behavior and practices.

Validate and disseminate MIYCN SBCC strategy and messages.


February 2017

16 Pilot IMAM surge in select health facilities in the county. This will be scaled up upon successful pilot.

Sensitize health management team on IMAM surge.

Identify 7 health facilities (1 per Sub County) for the pilot of IMAM surge.

Conduct training of health workers in the pilot health facilities on IMAM surge.

Roll out IMAM surge in the pilot health facilities.

Monitor the implementation of the IMAM sure in the pilot health facilities.


October 2016

17 Train community health volunteers(CHVs) and community health extension workers(CHEWs) on nutrition module for community health strategy for improved active case finding, referral and nutrition education

Conduct training of the CHEWs and CHVs on community nutrition module.

MoH (nutrition, community strategy) and nutrition and health partners

October 2016

18 Scale up of Baby Friendly Community Initiatives(BFCI) in 20 MNCH centers of excellence

Conduct training of the HMTs ,Health workers and CHVs on BFCI.

Roll out BFCI in the pilot CHUs.

Monitor the roll out of BFCI.


November 2016

19 Conduct a Malnutrition Causal Link analysis to have in depth understanding of determinants of malnutrition

Develop and validate concept note and proposal for the study.

Mobilize resources for conducting the study.

MoH,MOAW and Partners

December 2017

14

CHAPTER 1

1.0 BACKGROUND INFORMATION

Turkana County is situated in the arid North-western region of the country. It shares international borders with Ethiopia, Sudan and Uganda and locally with Baringo, West Pokot and Samburu counties. The County has an estimated total population of 855,3991 and cover an area of 77,000km2

.The County is divided into seven sub counties namely; Turkana Central, Loima, South, East, North, Kibish and West

According to National Drought Management Authority (NDMA), the County has four main livelihood zones. Nearly 60% of the population is considered pastoral, 20% agro pastoral, 12% fisher folks and 8% are in the urban/peri-urban formal and informal employments. The county has poverty index of 94% which contributes 3.13% on national poverty index. Turkana is constrained by the harsh environment, remoteness coupled with the poor infrastructure and low access to essential services in addition to other underlying causes of poverty that are experienced elsewhere in Kenya. It is classified among the Arid and semi-arid lands (ASAL).

Figure 1: Map of Turkana County Being an ASAL county, Turkana is a drought prone area that experiences frequent, successive and prolonged drought and cattle rustling which leads to heavy losses of lives and livestock.

1.1 Food security situation

According to February 2016 Short Rains Assessment (SRA) report, Turkana County was classified as ‘Stressed’ according to the Integrated Food Security Phase Classification (IPC Phase 2) for all livelihood zones, the immediate factor affecting food security situation in the county was erratic performance of the rains. Other factors include spread of livestock diseases and cases of insecurity and conflict overgrazing reserves. In terms of food consumption score, 26, 42 and 32 percent of the households had poor, borderline and acceptable diets respectively. In the agro pastoral zones individuals were consuming 2 to 3 meals a day while in the pastoral zones they were consuming 1 to 2 meals a day which was normal. The main foods consumed consisted of cereals, legumes, milk/meat and vegetables. Nutrition status of children under 5 years had improved with the percentage of children less than five years at risk of malnutrition currently at 18 percent compared to the Long Term Average of 20.7 percent. The onset of short rains in the county was late by two dekads (10 day periods) during the third dekad of October and the amount varied between 75-90 percent in Turkana central, 90-125 percent in Turkana south (Loima, Turkwel, Kalokol, Lokori, Lomelo and Katilu) and 140-200 percent of normal to the North and North Western parts of the County. Temporal distribution was poor and spatial distribution was uneven Cessation was early during the second dekad of December compared to the normal first week of January (February 2016 SRA, report).

1 Kenya National Bureau of Statistics (KNBS) 2009 Census Report

15

1.2 Humanitarian and Development partners

Many agencies, UN and NGOs are working in collaboration with the Ministry of Health (MoH) in child survival

interventions. The main responsibility of MoH is quality assurance of the nutrition and health- related activities through

the coordination of all activities in Turkana County. The NGOs implementing health and nutrition programs include: Save

the Children International (SCI), APHIA PLUS IMARISHA, International Livestock Research Institute (ILRI), Global

Alliance in Nutrition (GAIN) and Elizabeth Glaser Pediatric Aids Foundation (EGPAF)

1. UNICEF supports Nutrition, Health, WASH, Communication for Development and Child Protection programs

2. World Food Programme (WFP) provides Food for Assets (FFA) and SFP food commodities.

3. Child fund, OXFAM and Turkana Relief program implement FFA and Cash transfer.

4. Kenya Red Cross support emergency response including Nutrition, WASH and livelihood project

5. Other agencies implementing resilience and livelihood projects are FAO, ADESO, DoL, APHIA PLUS Imarisha

and IOM

1.3 Main Objective

The overall goal of the survey was to determine the prevalence of malnutrition among the children aged 6- 59 months old and women of reproductive age in Turkana County.

1.4 Specific Objectives

1. To determine the prevalence of acute malnutrition among under five year old children and women of

reproductive age (WRA);

2. To determine the immunization coverage for measles, Oral Polio Vaccines (OPV 1 and 3), and vitamin A

supplementation in children aged 6-59 months;

3. To estimate coverage of Iron / Folic acid supplementation during pregnancy in women of reproductive age

4. To determine de-worming coverage for children aged 12 to 59 months;

5. To determine the prevalence of common illnesses;

6. To collect information on possible underlying causes of malnutrition such as household food security, water,

sanitation, and hygiene practices.

1.5 Timing of Turkana SMART surveys

The surveys were conducted in June 2016 towards the end of the long rains shortly before the Long Rains assessment

(LRA). The results of the survey will feed into the LRA.

Table 3: Turkana Seasonal Calendar

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Dry Season Long Rain Dry Cool Season Short Rains

1.6 Survey Area

Four independent surveys were conducted to cover all the livelihood zones (pastoral, agro-pastoral and formal employment/business/petty trade) and administrative boundaries of Turkana County. The survey zones are summarised in table 4 below;

16

Table 4:Turkana County survey zones

No Survey Zone Administrative Sub counties

1 Turkana Central Central and Loima

2 Turkana North North and Kibish

3 Turkana West West

4 Turkana South South and East

CHAPTER TWO

2.0 METHODOLOGY

The SMART Method was used to conduct the survey in planning, training, data entry and analysis. Other data sets

collected concurrently included data on Water Sanitation and Hygiene (WASH) and Food security and livelihood

(FSL).The entire exercise was done in consideration with all guidelines as stipulated by the MoH at county and national

level. The survey methodology was presented to the County Steering Group (CSG) and National Nutrition Information

Working Group (NIWG) for validation before commencement of data collection.

2.1 Sample size calculation

The Sample size was determined using ENA for SMART software (9th July 2015). The table below outlines factors considered when determining the sample size calculation Table 5:Sample size calculation

2 SMART survey 2015 - 20.9% (17.9 – 24.4 CI) 3 SMART survey 2015 -22.9% (19.6 – 26.6 CI) 4 SMART survey 2015 -24.5% (21.1 – 28.2 CI) 5 SMART survey 2015 -16.7% (13.8 – 20.1 CI)

6 Previous surveys values 7 Rule of thumb/Slight cluster variations and previous survey values 8 Due to the slight differences in the means of livelihood 9 Based on the heterogeneity of the villages(clusters) and previous survey values

Central North South West Rationale

Estimated prevalence of GAM

224.4% 326.6% 428.2% 520.1% NDMA march bulletin indicate an alert situation in all zones with a worsening trend in across the county, thus upper confidence level was used.

±Desired precision 5% 5% 5% 4% Limits of CI doesn’t influence decision making/control quality hence reduce bias and previous survey values

Design effect 61.6 71.5 81.6 91.5 Rule of thumb/slight variations among clusters and previous survey results

Average household size

6 6 6 6 KNBS Census report 2010 and previous survey results

Percent of under five children

15.2% 15.2% 15.2% 15.2% KNBS Census report 2010

Percent of non-respondent

2% 2% 2% 2% This is the anticipated non response based on the previous surveys experience

Households to be 614 609 674 501

17

2.2 Sampling method

A three stage sampling process was used in this survey. The first stage involved sampling of sub locations (clusters) from

a sampling frame using ENA for SMART software (9th July 2015 version).The second stage sampling involved

segmentation of the sampled sub locations using the estimated populations provided by the chief/sub chief to identify the

villages to be sampled. In the third stage, households were selected randomly upon getting the updated list of households

in the village provided by the village elder. Taking into account the time spent on travelling to each household,

introductions and breaks, 16 households were sampled per cluster. Table 6 shows a summary of the actual number of

sampled clusters, households and children per survey zone

Table 6:Sampled number of Clusters, Households and Children

Survey Zone Number of Clusters No of Households No. of children sampled

Turkana Central 41 670 720

Turkana North 41 690 661

Turkana South 45 762 831

Turkana West 34 567 567

2.2.1 Selection of the households

The definition of a household was a shelter or more whose residents ate from the same “cooking pot”. Households

to be surveyed were selected randomly using the updated list of households in the selected village/segment.

2.2.2 Selection of children for anthropometry

All children between 6-59 months of age staying in the selected household were included in the sample. The respondent

was the primary care giver of the index child/children. If a child and/or the caregiver were temporarily absent, then the

survey team re-visited the household to collect the data at an appropriate time.

2.2.3 Selection of women for determination of nutritional status

All women within the reproductive age (15-49 years) in the identified households were enlisted in the study and their

MUAC measurements taken.

2.3 Survey team

The survey was coordinated by the County Nutrition Coordinator and supervised by four Sub County Nutrition Officers.

The team was supported by officers from implementing partners and the Human Nutrition and Dietetics Unit-National

MoH)/UNICEF. The survey was undertaken by 5 teams in each survey zone. Each team comprised of 2 enumerators and

1 team leader.

2.4 Survey team training

2.4.1 Supervisors training

The survey core team [from Health Management Team (HMT) and nutrition partners] was sensitized on supervisor’s

module for SMART for a day. The training was supported by 1 UNICEF technical advisor and representatives from nutrition

implementing partners.

included

Children to be included

494 490 542 403

Number of clusters 41 41 45 34

18

2.4.2 Enumerators training

A four-days training was conducted before the commencement of the survey. The training focused on the objectives of

the survey, survey questionnaire, interviewing techniques, anthropometric measurements, cluster and household

selection. Role-plays on how to administer the questionnaire and record responses were conducted. Demonstrations on

how to take anthropometric measurements were also conducted. This was followed by practice to standardize

anthropometric measurements.

A half day of the training was allocated to pre-testing of the tablet questionnaire (in areas that had not been selected

for inclusion in the survey) and reviewing of the data collection tools based on the feedback from the field. The

anthropometric measurements from pre-testing were entered into the ENA for SMART software and a plausibility report

developed for each team and this information was used to correct the teams’ mistakes.

2.5 Data collection

Data collection took place concurrently in all the four survey zones. The data collection took 7 -9 days. Survey

zones coordinators with support from implementing partners’ officers supervised the teams throughout the data collection

period. Teams administered the standardized questionnaire to the mother or primary caregiver. Each survey team

explained the purpose of the survey and issues of confidentiality and obtained verbal consent before proceeding with the

interview. The teams used ODK questionnaire in tablets to record the responses. The data was uploaded to Save the

Children servers at the end of each day. Anthropometry data was downloaded daily, reviewed/analyzed for plausibility

and feedback provided to the teams. Feedback was provided through use of daily customized scorecards.

2.6 Variables Measured

Age: The exact age of the child was recorded in months. Calendar of events, health or baptismal cards and birth

certificates were used to determine age.

Weight: Children were measured using a digital weighing scale

Height: Recumbent length was taken for children less than 87 cm or less than 2 years of age while height measured for

those greater or equal to 87 cm or more than 2 years of age.

MUAC: Mid Upper Arm Circumference (MUAC) was measured on the left arm, at the middle point between the elbow and

the shoulder, while the arm was relaxed and hanging by the body’s side. MUAC was measured to the nearest Cm. MUAC

measurements were taken for children 6-59 months of age and for women in the reproductive age (1545 years of age).

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

same time. The presence of a pit or depression on both feet was recorded as oedema present and no pit or

depression as oedema absent.

Morbidity: Information on two-week morbidity prevalence was collected by asking the mothers or caregivers if the index

child had been ill in the two weeks preceding the survey and including the day of the survey. Illness was determined

based on respondent’s recall and was not verified by a clinician.

Immunization status: For all children 6-59 months, information on BCG, OPV1, OPV3 and measles vaccinations

status was collected using health cards and recall from caregivers. When estimating measles coverage, only children 9

months of age or older were taken into consideration as they are the ones who were eligible for the vaccination.

19

Vitamin A supplementation status: For all children 6-59 months of age, information on Vitamin A supplementation in

the 6 months prior to the survey date was collected using child health and immunization campaign cards and recall from

caregivers.

Iron-Folic Acid supplementation: For all female caregivers, information was collected on IFA supplementation and

number of days (period) they took IFA supplements in the pregnancy of the last birth that was within 24 months.

De-worming status: Information was solicited from the caregivers as to whether children 12-59 months of age had

received de-worming tablets or not in the previous one year. This information was verified by health card where

available.

Food security status of the households: Food consumption score, Minimum dietary diversity score women source of

predominant foods and coping strategies data was collected.

Household water consumption and utilization: The indicators used were main source of drinking and household

water, time taken to water source and back, cost of water per 20-litre jerry-can and treatment given to drinking water.

Sanitation: Data on household access and ownership to a toilet/latrine, occasions when the respondents wash their

hands were also obtained.

Mosquito nets ownership and utilization: Data on the household ownership of mosquito nets and their utilisation was

collected

Minimum dietary diversity score women (MDD-W): A 24 hour food consumption recall was administered to all women

of reproductive Age (15-49 years ).All foods consumed in the last 24 hours were enumerated for analysis. All food items

were combined to form 10 defined food groups and all women consuming more at least five of the ten food groups were

considered to meet the MDD-W.

Household food consumption score (FCS). Data on the frequency of consumption of different food groups consumed

by a household during 7 days before the survey was collected. The Table below shows WFP corporate thresholds for

FCS used to analyse the data.

Table 7: WFP corporate FCS thresholds

Food Consumption Score Profile

<21 Poor

21.5-35 Borderline

>35 Acceptable

Coping strategy index (CSI): Data on the frequency of the five reduced CSI individual coping behaviours was collected.

The five standard coping strategies and their severity weightings used in the calculation of Coping Strategy Index are:

1. eating less-preferred foods (1.0),

2. borrowing food/money from friends and relatives (2.0),

3. limiting portions at mealtime (1.0),

4. limiting adult intake (3.0), and

5. reducing the number of meals per day (1.0)

20

CSI index per household was calculated by summing the product of each coping strategy weight and the frequency of its

use in a week (no of days).

Nutrition Indicators

Nutritional Indicators for children 6-59 months of age

The following nutrition indicators were used to determine the nutritional status children under five years

Table 8: Definitions of acute malnutrition using WFH and/or edema in children aged 6–59 months

Acute malnutrition WFH Z-Score Oedema

Severe <-3 Z Score Yes/No

>-3 Z Score Yes

Moderate <-2 Z Scores to ≥ -3 Z scores No

Global <-2 Z scores Yes/No

Adapted from SMART Manual, Version 1, April 2006

MUAC

Guidelines for the results expressed as follows:

1. Severe malnutrition is defined by measurements <115mm

2. Moderate malnutrition is defined by measurements >=115mm to <125mm

3. At risk is defined by measurements >=125mm to <135mm

4. Normal >=135mm

MUAC cut off points for the women for pregnant and lactating women: Cut off <21 cm was used for under nutrition

2.7 Data analysis

During supervision in the field, and at the end of each day, supervisors manually checked the tablet questionnaires for

completeness, consistency and accuracy. This check was also used to provide feedback to the teams to improve data

collection as the survey progressed. At the end of each day, and once supervisors had completed their checks, the

tablets were each synchronized to the server and the data collected was uploaded, therefore there was no need for any

further data entry. The SMART plausibility report was generated daily in order to identify any problems with

anthropometric data collection such as flags and digit preference for age, height and weight, to improve the quality of the

anthropometric data collected as the survey was on-going. Feedback was given to the teams every morning before the

teams left for the field.

All data files were cleaned before analysis, although use of tablet reduced the amount of cleaning needed, as a number of

restrictions were programmed in order to reduce data entry errors. Anthropometric data for children 6-59 months was

cleaned and analysed using ENA for SMART software (9th July 2015) by the coordination team. The nutritional indices

were cleaned using SMART flags in the ENA for SMART software. Weighting of the sub county results was done in order

to obtain county data. Table 9 summarises other criterion that was used for exclusion.

21

Table 9:Definition of boundaries for exclusion

1. If sex is missing the observation was excluded from analysis.

2. If Weight is missing, no WHZ and WAZ were calculated, and the programme derived only HAZ.

3. If Height is missing, no WHZ and HAZ were calculated, and the programme derived only WAZ.

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

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

Additional data for children aged 6-59 months, women aged 15-49 years, WASH, and food security indicators were

cleaned and analysed using SPSS and Microsoft excel.

2.8 Survey Limitations

1. There were inherent difficulties in determining the exact age of some children (even with use of the local calendar

of events), as some health cards had erroneous information. This may have led to inaccuracies when analysing

chronic malnutrition. Although verification of age was done by use of health cards, in some cases no exact date of

birth was recorded on the card other than the date a child was first seen at the health facility or just the month of

birth. Recall bias may link to wrong age which then leads to wrong weight for age and height for age indices.

2. There was poor recording of vitamin A supplementation and de-worming in the health cards. Some of the mothers

indicated that their children had received Vitamin A and de-worming while it was not recorded in the health cards.

2.9 Ethical considerations

Sufficient information was provided to the local authorities about the survey including the purpose and objectives of the

survey, the nature of the data collection procedures, the target group, and survey procedures. Verbal consent was

obtained from all adult participants and parents/caregivers of all eligible children in the survey. The decision of caregiver

to participate or withdrawal was respected. Privacy and confidentiality of survey respondent and data was protected.

22

CHAPTER THREE: RESULTS & DICSUSSIONS 3.0 CHILD HEALTH & NUTRITION

3.1 Demographic results

Turkana coun ty mean household size was 5.89 and the mean number of children 6-59 months old per household

was 1.09.The the sex ratio of male to female was 1 .1 which is considered normal. Table 10 below shows a summary

of household demography per survey zone.

Table 10: household demography per survey

Attribute Central North South West County

Household Characteristics n=670 n=690 n=762 n=574 n=2696

Mean household size 5.79 5.64 6.05 6.1 5.89

Total population 3878 3890 4607 3503 15,878

Total children 6-59 months 728 667 842 572 2,809

Total males children under 5 385 363 443 309 1,500

Total female children U5 343 304 399 263 1,309

Children U5 sex ratio boy: girl 1.1 1.2 1.1 1.2 1.1

Mean Children 6-59 month

old

1.15 1.02 1.13 1.02 1.09

3.1.1 Residency and marital Status

98.1 % of the respondents were residents of Turkana County. Turkana North had the highest number of IDPs at

6.23%.This is due to a population that had been displaced from Todonyang and settled at Lowarengak due to

international boarder conflicts with Merile tribe in Ethiopia .In addition 88.4% of the respondents were married and the

Turkana central had the highest number of widowed caretakers at 9.4% of the respondent. Table 11 and 12 below shows

a summary of caretakers’ marital status per survey zone.

Table 11: Residency

Attribute Central North South west County

n 670 690 762 573 2695

Resident 100% (670) 93.6 % (646) 99.08%(755) 100%(573) 98.10% (2644)

Refugee 0%(0) 0.14%(1) 0%(0) 0%(0) 0.04% (1)

IDP 0%(0) 6.23% (43) 0.91% (7) 0% (0) 1.86 %(50)

Table 12: Summary of caretakers’ marital status

Attribute Central North South West County

n 670 690 762 573 2695

Married 86.7%(581) 87.4%(603) 91.5% (697) 87.4%(501) 88.4% (2382)

Single 2.5% (17) 3.8% (26) 2.1% (16) 2.6%(15) 2.7%(74)

Widowed 9.4%(63) 6.7% (46) 5% (38) 6.5%(37) 6.8% (184)

Separated 1% (7) 1% (7) 0.7% (5) 0.2% (1) 0.7%(20)

Divorced 0.3% (2) 1.2% (8) 0.8% (6) 3.3%(19) 1.3%(35)

23

3.1.2 Occupation of the household main provider

The main occupation for the main household provider were livestock herding (40.6%), petty trade (20.8%) and firewood

selling/charcoal burning (14.9%).Table 13 shows the household’s main provider occupation per survey zone.

Table 13: Summary of household’s main provider occupation

Occupation Central North South West County

n 670 690 762 573 2695

Livestock herding 36.0% (241) 71.7% (495) 31.5%(240) 20.4% (117) 40.6%(1093)

Own farm labour 4.8 % (32) 0.9%(6) 16.3% (124) 4.2% (24) 6.9% (186)

Employed (salaried 2.1% (14) 0.9% (6) 3.4%(26) 4.9% (28) 2.7% (74)

Waged labour (casual) 12.8% (86) 4.1%(28) 15.2% (116) 14.3% (82) 11.6% (312)

Petty trade 19.6% (131) 11.2% (77) 21.4% (163) 33% (189) 20.8% (560)

Merchant/trader 1.0% (7) 1.3%(9) 0.5% (4) 1.7% (10) 1.1% (30)

Firewood/charcoal 21.3% (143) 6.8% (47) 11.7% (89) 21.5% (123) 14.9% (402)

Fishing 2.4% (16) 3.2% (22) 0.0 % (0) 0.0% (0) 1.4% (38)

3.2 Anthropometry

Out of all sampled children in the County 77.7% of them had a health card, birth certificate/notification or baptism card

and these were used to verify their age. Age determination for 22.3% of the children was based on recall, hence prone to

bias. Turkana West (65.4%) and North (69.1%) had the least proportion of children with a health card, birth

certificate/notification or baptism card. This might have affected indices with age as a variable such as stunting and

underweight. Table 14 below show the age verification means per survey zone.

Table 14:Summary of Children age verification means

Means of verification Central North South West County

n 728 667 842 572 2809

Health card/Birth certificate/ notification / Baptism card

83.4 % (607)

69.1% (461)

88.0% (741) 65.4% (374 )

77.7% (2183)

Recall 16.6% (121)

30.9% (206)

12.0% (101)

34.6% (198)

22.3% (626)

3.2.1 Age and sex distribution of the sampled children

Generally there were younger children selected in the sample across all survey zones. For example in south and west

30.4% and 31.9% o f the ch i ld ren were in the age g roup o f 6 -17mon ths respec t i ve ly ins tead o f

t he expec ted propo r t ion o f 20 -25% . As shown in tab le 15 below, the overall sex ratio (boys: girls) was

within the acceptable range of 0.8-1.2.This means that both sexes were equally distributed, and the sample was

unbiased.

24

Table 15: Distribution of age and sex of sample

Turkana Central

n=720

Turkana North

n=661

Turkana south

n=831

Turkana West

n=567

County

n=2779

AGE

(mo)

Total

%

Ratio

Boy: girl

Total

%

Ratio

Boy: girl

Total

%

Ratio

Boy: girl

Total

%

Ratio

Boy: girl

Total

%

Ratio

Boy: girl

6-17 29.6 1.2 29.2 1.2 30.4 1.1 31.9 1.4 30.4 1.2

18-29 27.6 1.5 25.3 1.3 25.5 1.2 27.2 1.1 26.8 1.3

30-41 24.6 0.9 22.4 1.3 20.9 1.1 20.6 1.3 22.2 1.1

42-53 14.6 0.9 17.5 1.0 17.9 1.2 15.9 1.0 16.0 1.0

54-59 3.6 1.0 5.6 1.1 5.3 0.6 4.4 0.9 4.6 0.8

Total 100.0 1.1 100.0 1.2 100.0 1.1 100.0 1.2 100.0 1.1

3.3 Prevalence of Acute Malnutrition

Rates of acute malnutrition in Turkana Central/Loima, North/Kibish and South/East indicate a Very Critical nutrition

situation, while the nutrition situation in Turkana West is classified as serious. As shown in Table 16, there was no

significant change of the nutrition situation in Turkana County from the same time last year. The weighted Global Acute

Malnutrition (GAM) for Turkana County is 23.3% which is an increase from the same time last year. However, further

analysis reveals an overlap in the confidence intervals GAM rates for 2016 and 2015 SMART survey, thus there is

no significant deterioration of the situation. However, it is worthwhile to note both the 2015 and 2016 point

estimates of Acute Malnutrition have been on the rise over the last 3 years. These results estimate that about 1 in 4

children is acutely malnourished.

There were 0.2% (1 child) cases of edema in Turkana North with no cases report in the other three survey zones; this

case was verified by the zonal coordinators. The Weight for Height standard deviation of -1.07 to -0.93 to across as all the

survey zones was within the acceptable range of 0.8-1.2.The design effect ranged from 1.34 to 2.0 across all survey

zones. However in Turkana Central and Turkana west design effect of 2.0 and 1.65 respectively indicated heterogeneity

in the sample selected due to urban settlements in Lodwar, Kakuma and Lokichogio.

Table 16: Prevalence of malnutrition weight-for-height z-scores (WHO Standards 2006)

Turkana Central North South West County

Wasting (WHO 2006) N=710 N=657 N=823 N=561 N=2718


(GAM) -June 2016

(174)24.5%

(20.2- 29.4)

(154)23.4%

(19.4-28.1)

(249)30.3%

(26.7-34.1)

(81)14.4%

(11.1-18.5)

23.3%

(21.1 – 25.5)


(GAM)-June 2015)

(162) 20.9 %

(17.9 - 24.4)

(179) 22.9 %

(19.6 - 26.6)

(202) 24.5 %

(21.1 - 28.2)

(105) 16.7 %

(13.8 - 20.1)

21.2 %

(19.7 – 22.9)


(SAM)-June 2016

(40) 5.6%

(4.2-7.5)

(27)4.1%

(2.5-6.7)

(73)8.9%

(7.1-11.0)

(10)1.8

(1.0-3.3)

5.3%

(4.5-6.3)


(SAM) –June 2015

(37) 4.8 %

(3.4 - 6.6)

(30) 3.8 %

(2.4 - 6.1 9

(50) 6.1 %

(4.3 - 8.5)

(30) 4.8 %

(3.3 - 6.9 )

5.0%

(4.2 – 6.0)

25

The levels of acute malnutrition have varied in severity across the four survey zones of Turkana since the severe

drought in 2011. Figure 2 below illustrates the changes in acute malnutrition over time per survey cluster, this

further reveals persistently high GAM rates (exceeding WHO emergency thresholds of 15%) for over the last five

years. This again highlights no obvious recovery from the persistent shocks from drought, floods, and conflict that

the communities are faced with.

Figure 2: Trends of Global Acute Malnutrition in Turkana County (2010-2016)

NB: The results for 2009 which used a different methodology (LQAS) and 2013 Turkana North results were not validated

due data quality issues have not been captured.

3.3.1 Prevalence of acute malnutrition based on weight-for-height z-scores (and/or edema) and by sex

The proportion of boys malnourished was higher than girls in all the 4 surveys zones. Table 17 below shows the

prevalence of global acute malnutrition by sex per survey.

Table 17: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or edema) and by sex (95% Confidence interval)

Sex Central N=710

M =375,F=335

North N=657

M =359,F=298

South N= 823

M =429, F=394

West N=561

M =304,F =257

County N= 2571

M= 1447 F=1271

Prevalence of

global malnutrition

(<-2 z- score and/or

edema)

Boys (93) 24.8 %

(19.6 - 30.9)

(91) 25.3 %

(20.6 - 30.8)

(142) 33.1 %

(29.1 - 37.4)

(52) 17.1 %

(12.4 - 23.2)

(378)25.1%

(22.4 - 27.9)

Girls (81) 24.2 %

(18.8 - 30.5)

(63) 21.1 %

(15.5 - 28.2)

(107) 27.2 %

(22.2 - 32.8

(29) 11.3 %

(8.1 - 15.6)

( 280)21.2%

(18.6 - 24.0)

Prevalence of

moderate

Boys (73) 19.5 %

(15.1 - 24.7)

(73) 20.3 %

(16.3 - 25.1)

(91) 21.2 %

(18.6 - 24.1)

(44) 14.5 %

(10.0 - 20.4)

26

malnutrition (<-2

z-score and >=-3

z-score, no

oedema)

Girls (61) 18.2 %

(14.0 - 23.4)

(54) 18.1 %

(13.2 - 24.3)

(85) 21.6 %

(18.0 - 25.6)

(27) 10.5 %

(7.4 - 14.8)

Prevalence of

severe

malnutrition (<-

3 z-score and/or

oedema)

Boys (20) 5.3 %

(3.4 - 8.2)

(18) 5.0 %

(2.7 - 9.2)

(51) 11.9 %

(9.2 - 15.3)

(8) 2.6 %

(1.3 - 5.4)

6.4%

(5.2 - 7.9)

Girls (20) 6.0 %

(4.0 - 8.8)

(9) 3.0 %

(1.4 - 6.2)

(22) 5.6 %

(3.5 - 8.7)

(2) 0.8 %

(0.2 - 3.2)

4.1%

(3.1 - 5.5)

3.3.2 Prevalence of acute malnutrition (wasting) by age based on weight-for-height Z-scores and or edema (WHO Standards 2006)

As shown in table 18 below, Turkana south and Central had the highest cases of moderate malnutrition in the age group

54-59 months. The edema case identified was within the age of 6-17 months. Turkana central and south had highest

cases of acute malnutrition within the age group 6-17 months.

Table 18: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or oedema

Zone Age

(mths)

Total

no.

Severe wasting (<-3

z-score)

Moderate wasting (>= -3

and <-2 z-score )

Normal (> = -2 z

score)

Oedema

Central 6-17 211 (20) 9.5% (40) 19.0% (151)71.6% (0) 0.0%

18-29 196 (13) 6.6% (34) 17.3% (149) 76.0% (0)0.0%

30-41 173 (4) 2.3% (27) 15.6% (142) 82.1% (0)0.0%

42-53 104 (3) 2.9% (23) 22.1% (78) 75.0% (0)0.0%

54-59 26 (0 ) 0.0 (10) 38.5% (16) 61.5% (0)0.0%

Total 710 (40) 5.6% (134)18.9% 5 (36) 75.5% (0)0.0%

North 6-17 193 (6)3.1% (35)18.1% (151)78.2% (1)0.5%

18-29 163 (8) 4.9% (30) 18.4% (125)76.75 (0)0.0%

30-41 148 (7) 4.7% (28) 18.9% (113)76.4% (0)0.0%

42-53 116 (3)2.6% (25) 21.6% (88)75.9% (0)0.0%

54-59 37 (2)5.4% (9) 24.3% (26) 70.3% (0)0.0%

Total 657 (4.0) 26% (127)19.3% (503)76.6% (1)0.2%

South 6-17 256 (36)14.1% (54)21.1% (166)64.8% (0)0.0%

18-29 222 (17)7.7% (51) 23.0% (154) 69.4% (0)0.0%

30-41 173 (16)9.2% (22)12.7% (135)78.0% (0)0.0%

42-53 131 (2)1.5% (31) 23.7% (98)74.8% (0)0.0%

54-59 41 (2)4.9% (18)43.9% (21)51.2% (0)0.0%

Total 823 (73)8.9% (176)21.4% (574)69.7% (0)0.0%

West 6-17 176 (3)1.7% (30)17.0% (143)81.3% (0)0.0%

18-29 153 (3)2.0% (13)8.5% (137) 89.5% (0)0.0%

30-41 117 (1)0.9% (14) 12.0% (102) 87.2% (0)0.0%

42-53 90 (3)3.3% (11)12.2% (76) 84.4% (0)0.0%

54-59 25 (0)0.0% (3)12.0% (22) 88.0% (0)0.0%

Total 561 (10)1.8% (71)12.7% (480) 85.6% (0)0.0%

There was only one case of Marasmic- kwashiorkor in Turkana North

27

Table 19: Distribution of acute malnutrition and oedema based on weight-for-height z-score

3.3.3 Prevalence of acute malnutrition based on MUAC

Compared to weight for height Z-scores, the mid-upper arm circumference (MUAC) is not a very sensitive indicator of

acute malnutrition and tends to underestimate acute malnutrition for children below one year of age. It is, however, used

as a rapid screening tool for admission into nutrition intervention programmes.

Generally, MUAC usually tends to indicate lower GAM levels compared to WFH z-scores. The prevalence of malnutrition

using MUAC is significantly lower compared to using Weight for Height Z-scores. This could be associated with the

physiology of this population in Turkana, similar to the Somali and South Sudanese, with a high cormic index10.This

means, overall significantly lower cases of malnourished children are identified using MUAC when compared to weight

for height. Turkana North (10.6%) had the highest GAM rate followed by Turkana south (10.5%). While SAM was highest

in Turkana central (2.4%) followed by Turkana south (2.3%).The table 20 below summarizes prevalence of malnutrition by

MUAC.

Table 20:Prevalence of Malnutrition based on MUAC per survey

Prevalence of Acute

malnutrition MUAC

Central North South West County

2016 n n=720 n=661 n=831 n=567 n=2779

2015 n n=787 n=791 n=832 n=642 n=3052


(< 115 mm) -June 2016)

(17) 2.4 %

(1.3 - 4.2)

(10) 1.5 %

(0.8 - 2.8)

(19) 2.3 %

(1.2 - 4.4)

(7) 1.2 % (0.5

- 3.3)

(53) 1.9 %

(1.3 - 2.7)


(< 115 mm) -June 2015)

(13) 1.7 %

(0.7 - 3.7)

(13) 1.6 %

(0.9 - 2.9)

(14) 1.7 %

(1.0 - 2.8)

(13) 2.0 %

(0.8 - 4.9)

(53) 1.7 %

(1.3 - 2.4)


(≥115–<125 mm)-June 2016)

(45) 6.3 %

(4.5 - 8.6)

(60) 9.1 %

(6.5 - 12.6)

(68) 8.2 %

(6.3 - 10.6

(41) 7.2 %

(4.9 - 10.5)

(214) 7.7 %

(6.5 - 9.1)

10 The most common bivariate index of shape is the Cormic index, sitting height/ total height (SH/S). It is a measure of the relative length of the trunks or legs and

varies between individuals and groups. If sitting height is held constant and leg length varied it produce a range of ratios from 0.48 to 0.55 within and between populations. This demonstrates that variations in SH/S found in or between different population groups may be associated with variations in BMI of some 5kg/m2, with weight and composition being kept constant. The mean SH/S for European and Indo-Mediterranean populations is about 0.52. Africans have proportionally longer legs, in general, with ratios around 0.51 most notable Somali, Sudanese and Turkana populations with even higher ratios. Asian and Far Eastern populations have proportionally shorter legs and means of 0.53-0.54. However, there is considerable variation within populations and within these major groupings

Turkana Central Turkana North Turkana South Turkana west County

Z-score <-3 >=-3 <-3 >=-3 <-3 >=-3 <-3 >=-3 <-3 >=-3

Oedem

a

present

Maras

kwash

Kwash Maras

kwash

Kwash Maras

kwash

Kwash Maras

kwash

Kwash Maras

kwash

Kwash

. 0

(0.0 %)

0

(0.0%)

1

(0.2%)

0

(0.0 %)

0

(0.0 %)

0

(0.0%)

0

(0.0 %)

0

(0.0 %)

1

(0.0 %)

0 (0.0

%)

Oedem

a

absent

Maras Not

SAM

Maras Not

SAM

Maras Not SAM Maras Not

SAM

Maras Not

SAM

44

(6.1%)

676

(93.9 %)

28

(4.2 %)

632

(95.6 %)

75

(9.0 %)

756

(91.0 %)

12

(2.1 %)

555

(97.9 %)

159

(5.7 %)

2619

(94.2 %)

28


(≥115–<125 mm)-June 2015)

(61) 7.8%

(5.9 - 10.1)

(78) 9.9 %

(7.4 - 13.0)

(75) 9.0%

(6.7 - 12.0.)

(58) 9.0 %

(6.0 - 13.5 )

(272) 8.9 %

(7.7 - 10.3 )


(≤125 mm)-June 2016)

62) 8.6 %

(6.4 - 11.5)

(70) 10.6 %

(7.6 - 14.6)

(87) 10.5 %

(8.1 - 13.4

(48) 8.5 %

(5.8 - 12.2)

(267) 9.6 % (8.2 - 11.3)


(≤125 mm)-June 2015)

(74) 9.4 %

(7.3 -12.1)

(91) 11.5 %

(8.9 -14.7)

(89) 10.7 %

(8.0 -14.1)

(71) 11.1 %

(7.4 -16.2)

(325) 10.6 % (9.3 - 12.2)

3.4 Prevalence of underweight

The weight-for-age (WFA) index provides a composite measure of wasting and stunting and is commonly used to monitor

the growth of individual children in Mother-child booklet since it enables mothers to easily visualise the trend of their

children’s increase in weight against age. A low WFA is referred to as underweight. Turkana south had the highest

prevalence of underweight (44.6%) followed by Turkana Central (33.9%) and Turkana North (30.8%) respectively, as

illustrated in the table 21 below. There is a slight increase in the prevalence of underweight in June 2016 compared to

June 2015 in all the surveys zones.

Table 21: Prevalence of underweight

Underweight (WHO 2006) Central North South West County

2016 n=771 n=653 n=821 n=563 n=2718

2015 n=773 n=783 n= 824 n=629 n=3008


underweight -June 2016)

(241) 33.9 %

(29.6 - 38.4)

(201) 30.8 %

(25.6 - 36.5)

366) 44.6 %

(40.4 - 48.8)

(157) 27.9 %

(23.1 - 33.2)

(943) 34.7%

(32.1 - 37.4)



(236) 30.5 %

(26.8 - 34.6.)

(230) 29.4 %

(24.4 - 34.9)

(316) 38.3 %

(33.9 - 43.0)

(151) 24.0 %

(20.4 - 28.0)

(934) 31.1 %

(28.7 - 33.5.)



(71) 10.0 %

(7.3 - 13.5)

(59) 9.0 %

(6.7 - 12.1)

(146) 17.8 %

(14.6 - 21.4)

(34) 6.0 %

(3.8 - 9.5)

(297)10.9%

(9.5 - 12.4)


underweight (June 2015)

(68) 8.8 %

(7.1 - 10.9 )

(65) 8.3 %

(6.0 - 11.4)

(99) 12.0 %

(9.0 - 15.9 )

(46) 7.3 %

(5.5 - 9.6)

(279) 9.3 %

(8.0 - 10.8)

3.5 Prevalence of stunting

Height for age (stunting) is an indicator of chronic (long-term) malnutrition arising from deprivation related to

persistent/chronic poor food security situation, micronutrient deficiencies, recurrent illnesses and other factors which

interrupt normal growth. Unlike wasting, it is not affected by seasonality but is rather related to the long-term effects of

socio-economic development and long-standing food insecurity situation. A low height-for-age reflects deficits in linear

growth and is referred to as stunting.

Global stunting was highest in Turkana South (33.6%) followed by Turkana central (27.2%).There is a slight increase in

the prevalence of stunting in June 2016 compared to June 2015 across all the survey zones as shown in table 22 below.

Overall standing rates were above 25% across all the survey zones, this is indicative of minimal/no positive change in

addressing stunting context factors (community and societal) and causes.

29

Table 22:Prevalence of Stunting

Stunting (WHO 2006) Central North South West County

2016 n=688 n=633 n=813 n=557 n=2691

2015 n = 749 n =743 n =802 n=617 n=2913

Prevalence of global stunting

(<-2 z-score) June 2016

(187) 27.2 %

(22.4 - 32.5)

(159) 25.1 %

(20.9 - 29.9)

(273)33.6%

(29.3 -38.1)

(144) 25.9 %

(21.3 - 31.0)

(763)28.2%

(25.7 - 30.9)

Prevalence of global stunting

(<-2 z-score) June 2015

(184) 24.6 %

(20.9 - 28.6)

(156) 21.0 %

(16.9 - 25.7)

(262) 32.7 %

(28.6 - 37.0)

(134) 21.7 %

(18.4 - 25.5)

(736) 25.3 %

(23.6 - 27.1)

Prevalence of severe stunting

(<-3 z-score )-June 2016

(56) 8.1 %

(5.4 - 12.1)

(41) 6.5 %

(4.7 - 8.9)

(85) 10.5 %

(7.8 - 13.9)

(36) 6.5 %

(4.5 - 9.1)

(218)8.0%

(6.7 - 9.4)

Prevalence of severe stunting

(<-3 z-score )-June 2015

(46) 6.1 %

(4.6 - 8.2 )

(40) 5.4 %

(3.8 - 7.7)

(78) 9.7 %

(7.7 - 12.2)

(33) 5.3 %

(3.7 - 7.6)

(197) 6.8 %

(6.0 - 7.6)

3.6 Children’s Morbidity and Health Seeking Behavior

According to UNICEF conceptual framework on causes of malnutrition, disease is an immediate cause of malnutrition. It

also affects food intake which is also categorized as an immediate cause. It is important therefore to assess morbidity and

whether it had some effect on malnutrition.

3.6.1 Child Morbidity

To assess child morbidity mothers/caregivers of children aged 6 to 59 months were asked to recall whether their children

had been sick in the past 2 weeks. Those who gave an affirmative answer to this question were further probed on what

illness affected their children and whether and where they sought any assistance when their child/children were ill. Those

who indicated that their child/children suffered from watery diarrhea were probed on the kind of treatment that was given

to them.

From the assessment, slightly more than half (52.3%) of the assessed children were reportedly sick in the past two weeks

prior to the survey and 83.5% of these sought assistance. Figure 23 below summarizes the proportion of children sick and

those who sought assistance per survey zone.

Table 23: Children ill


n 728 667 842 572 2809

No 48.76% (355) 45.73% (305) 51.31%(432) 43.53%(249) 47.74% (1341)

Yes 51.24 % (373) (54.27%) 362 (48.69%)410 56.47 % (323) 52.26% (1468)

Among those who were sick in the county, majority (46.11%) were affected by acute respiratory infection (ARI)/Cough

especially in the North. Fever chills like malaria affected 36.1%, while 11.83% suffered from watery diarrhea. In depth

analysis indicated a positive correlation between child morbidity and malnutrition. Table 24 below summarizes prevalence

of child morbidity.

30

Table 24:Prevalence of child morbidity 2 weeks prior to the survey

Type of illness Central North South West County

n 373 362 410 323 1468

Fever with chill like

malaria 38.26% (101) 30.68% (85) 34.92% (103) 42.27% (93) 36.17% (382)

ARI/Cough 48.48% (128) 52.71%(146) 43.05% (127) 39.09% (86) 46.11% (487)

Watery Diarrhoea 8.7% (23) 11.91% (33) 14.92% (44) 11.36% (25) 11.83% (125) Bloody Diarrhoea 0.38% (1) 0.36% (1) 0%(0) 0.9% (2) 0.37% (4)

others (specify) 4.17 (11) 4.3% (12) 7.12%(21) 6.3% (14) 5.49% (58)

3.6.2 Therapeutic Zinc Supplementation during Watery Diarrhea Episodes

Based on compelling evidence from efficacy studies that zinc supplementation reduces the duration and severity of

diarrhea, in 2004 WHO and UNICEF recommended incorporating zinc supplementation (20 mg/day for 10-14 days for

children 6 months and older, 10 mg/day for children under 6 months of age) as an adjunct treatment to low osmolality oral

rehydration salts (ORS), and continuing child feeding for managing acute diarrhea11. Kenya has adopted these

recommendations. According to Kenyan policy guideline on control and management of diarrheal diseases in children

below five years in Kenya, all under-fives with diarrhea should be given zinc supplements as soon as possible.

The survey sought to establish the number of children who suffered from watery diarrhea and supplemented with zinc.

75.5% of those who suffered from watery diarrhea were supplemented with zinc as indicated in the table below.

Table 25: Therapeutic Zinc supplementation

Therapeutic Zinc Supplementation Central North South West County

n 23 33 44 25 125

Yes 68.4%(16) 78.9% (26) 85.1%(37) 66.2%(17) 75.5% (94)

No 28.3%(6) 19.2%(6) 14.9%(7) 33.8%(8) 23.4%(29)

Do not Know 3.3%(1) 1.9%(1) 0%(0) 0%(0) 1.1% (2)

3.6.3 Health Seeking Behavior

Mothers and caregivers whose children were sick in the past 2 weeks were further asked where they sought assistance.

Majority (91.4%) sought assistance from appropriate service delivery points namely, public hospital (80.6%), private

clinic/pharmacy (6.1%), mobile clinics (1.3%) and NGO/FBO clinics (3.4%). From such places they are likely to get

assistance from trained health personnel with proper diagnosis and treatment being done. Apparently a number of them

(4.1 %) sought assistance either from a shop/kiosk, relatives and friends, traditional healers or local herbs. In such places,

they were likely to be misdiagnosed and receive inappropriate treatment as the service providers lacked expertise and

knowledge of offering treatment services. Figure 26below summarizes the health seeking behavior per survey zone in

Turkana County.

11

Klemm RDW, Harvey PWJ, Wainwright E, Faillace S, Wasantwisut, E. Micronutrient Programs: What Works and What Needs More

Work? A Report of the 2008 Innocenti Process. August 2009, Micronutrient Forum, Washington, DC.

31

Table 26:Point of seeking health assistance

First Point of seeking health care Central North South West County

n 308 283 365 272 1228

Traditional healer 0.0%(0) 0.4%(1) 0.5%(2) 1.1% (3) 0.5%(6)

Community Health Worker 0.0% (0) 1.4% (4) 13.4% (49) 0.7%(2) 4.5% (55)

Private clinic/pharmacy 2.9% (9) 1.8% (5) 1.6% (6) 20.2% (55) 6.1% (75)

shop/kiosk 0.6% (2) 1.1.% (3) 0.5% (2) 4.0% (11) 1.5% (18)

Public clinic 94.2%(290) 82.3% (233) 82.7% (302) 60.7%(165) 80.6%(990)

mobile clinic 0.6%(2) 2.1% (6) 0.8% (3) 1.8% (5) 1.3% (16)

Relative or friend 0.6%(2) 1.4% (4) 0% (0) 0.4%(1) 0.6% (7)

Local herbs 0.7% (2) 0.4% (1) 0.3% (1) 5.5%(15) 1.5% (19)

NGO/FBO 0.3% (1) 9.2% (26) 0%(0) 5.5% (15) 3.4% (42)

3.7 Childhood Immunization, Vitamin A Supplementation and Deworming

3.7.1 Childhood Immunization

Kenya aims to achieve 90% under one immunization coverage by the end of second medium term plan (2013- 2017).

The Kenya guideline on immunization defines a fully immunized child as one who has received all the prescribed

antigens and at least one Vitamin A dose under the national immunization schedule before the first birthday. This

survey assessed the coverage of 4 vaccines namely, BCG, OPV1, OPV3, and measles at 9 and 18 months. From the

assessment, 97.1% of children were confirmed by scar to have been immunized by BCG12. Those who were immunized

(based on card and recall) by OPV113 and OPV3 were 95.6 % and 88.3% respectively while 78% had been immunized

for measles at 9 months. However, only 27.1% had been immunized (card and recall) with the second dose of measles

antigen at 18 months.

Table 27 -29: below summarizes the coverage of the assessed 4 vaccines per survey zone in Turkana County

Table 27: Child BCG immunization Coverage

Has child received BCG vaccination Confirmation of BCG vaccination

Survey zone N No Yes N Scar No scar

Central 728 2.9% (21) 97.1% (707) 707 96.9% (685) 3.1% (22)

North 664 6.5% (43) 93.5% (621) 621 97.3% (604) 2.7% (17)

South 842 0.7% (6) 99.3% (836) 836 98.4% (823) 1.6% (13)

west 571 5.4% (31) 94.6% (540) 540 95.2% (514) 4.8% (26)

County 2805 3.6% (101) 96.4% (2704) 2704 97.1%(2626) 2.9% (78)

12

The BCG vaccine has variable efficacy or protection against tuberculosis (TB) ranging from 60-80% for a period ranging from 10-15

years. It is known to be effective in reducing the likelihood and severity of military TB and TB meningitis especially in infants and young children. This is especially important in Kenya where TB is highly prevalent, and the chances of an infant or young child being exposed to an infectious case are high. 13

In Kenya infants receive 4 doses of trivalent OPV before one year of age 1st dose is given immediately at birth or within two weeks

of birth. This is known as the “birth dose” or “Zero dose” The other 3 doses should be given at 6 (OPV1) 10(OPV2) and 14 weeks (OPV3 of age

32

Table 28: Child OPV 1 and 2 coverage

OPV1 vaccination OPV3 vaccination

Survey zone

n

Yes by

card

Yes

by

recall

No Do

not

know

n Yes

by

card

Yes

by

recall

No Do not know

Central 728 78.7%

(573)

18.8%

(137)

1.8%

(13)

0.7%

(5)

728 74.9%

(545)

16.2%

(118)

7.1%

(52) 1.8% (13)

North 667 65.8%

(439)

28.2%

(188)

5.7%

(38)

0.3%

(2)

667 62.5%

(417)

27.1%

(181)

9.9%

(66) 0.4% (3)

South 842 86.3%

(727)

10.7%

(90)

1.7%

(14)

1.3%

(11)

842 80.6%

(679)

10.6%

(89)

7.5%

(63) 1.3% (11)

west 572 63.3%

(362)

29.5%

(169)

6.3%

(36)

0.7%

(5)

572 52.6%

(301)

26.6%

(152)

19.9%

(114) 0.7% (5)

County 2809 74.8%

(2101)

20.8%

(584)

3.6%

(101)

0.8%

(23)

2809 69.1%

(1942)

19.2%

(540)

10.5%

(295) 1.1% (32)

Table 29: Child measles 9 and 18 months coverage

Measles vaccination at 9 months Measles vaccination at 18 months

Survey

zone

n Yes by

card

Yes by

recall

No Do

not

know

N/A n Yes

by

card

Yes

by

recall

No Do

not

know

N/A

Central

728

58.5%

(426)

17.6%

(128)

21.8%

(159)

1.4%

(10)

0.7%

(5) 308

15.5%

(48)

9.7%

(30)

66.9%

(206)

3.9%

(12)

3.9%

(12)

North

667 57.4%

(383)

25.3%

(169)

16.3%

(109)

0.9%

(6)

0.0%

(0) 283

19.1%

(54)

15.2%

(43)

64.7%

(183)

1.1%

(3)

0.0%

(0)

South

842

68.4%

(576)

11.8%

(99)

17.6%

(148)

1.4%

(12)

0.8%

(7) 365

19.2%

(70)

7.7%

(28)

70.7%

(258)

(0.8%

) 3

1.6%

(6)

West

572 43.7%

(250)

28.1%

(161)

19.8%

(113)

1.0%

(6)

7.3%

(42) 272

11.8%

(32)

8.8%

(24)

48.5%

(132)

1.1%

(3)

29.8

%

(81)

County

2809

58.2%

(1635)

19.8%

(557)

18.8%

(529)

1.2%

(34)

1.9%

(54) 1228

16.6%

(204)

10.5%

(125)

63.4%

(779)

1.7%

(21)

8.1%

(99)

3.7.2 Vitamin A supplementation

Improving the vitamin A status of deficient children through supplementation enhances their resistance to disease and

can reduce mortality from all causes by approximately 23 per cent14. Therefore, vitamin A supplementation is critical, not

only for eliminating vitamin A deficiency as a public-health problem, but also as a central element for child survival.

14

Vitamin A Supplementation: A Decade of Progress, UNICEF 2007

33

Poor data management on vitamin A logistics, inadequate social mobilization to improve vitamin uptake and placement of

vitamin A at lower level of priority among other interventions have been cited as major challenges in achieving the

supplementation targets (MOH Vitamin A supplementation Operational Guidelines for Health Workers 2012).

To assess vitamin A supplementation, parents and caregivers were probed on whether children had been supplemented,

for how many times and the place of supplementation (whether it was done in a health facility, outreach site or during

mass campaigns) in the past one year. Reference was made to the child health card and in case the card was not

available recall method was applied.

According to the survey, 97.5% of the children aged 6- 11 months were supplemented with vitamin A at least once, and

only 53.4% children aged 12 to 59 months who had been at least supplemented once while only 44.8% received twice as

recommended by MOH policy. The performance of vitamin A supplementation especially among children 12-59 months is

poor compared to the ministry of health target of 80%.Figure 4 below shows vitamin A supplementation coverage per

survey zone in Turkana County.

Figure 3: Vitamin A supplementation coverage

Majority (75.5%) of vitamin A supplementation was done at the health facilities, 14.4% from outreaches, 8.6% from

outreaches and only 1.1% from ECDE centers. This indicates the need to integrate Vitamin A supplementation into other

existing points of care including ECDs and Outreaches. Figure 4 below shows of the vitamin A supplementation sites per

survey zone in Turkana County.


6-11 Months once(%) 96.4% 94.9% 100.0% 97.4% 97.5%

12-59 Months once(%) 46.8% 53.5% 52.8% 62.0% 53.4%

12-59 Months twice (%) 51.7% 42.1% 46.7% 36.9% 44.8%

6-59 months (%) 57.0% 48.5% 53.9% 42.9% 51.2%

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

% c

ove

rage

Vitamin A coverage (6-59 months)

34

Figure 4: places of vitamin A supplementation

3.7.3 De-worming

De-worming is important in controlling parasites such as helminthes, schistosomiasis (bilharzias) and prevention of anemia. WHO recommends that children in developing countries exposed to poor sanitation and poor availability of clean safe water to be de-wormed once every 6 months.

De-worming was assessed for children aged 12-59 months old. Based on the findings, only 11.4% of this category of children was de-wormed at least twice as per the WHO recommendation. 27.7% of the children were de-wormed at least once. This coverage is extremely low compared to the Country’s target of 80%. This could be attributed to low community awareness on the importance of deworming or low access to the service, thus the need for further research to confirm this. Figure 5 shows coverage of de-worming per survey zone in Turkana County.

Figure 5:De-worming coverage among children 12-59 months old

79.4%

74.8%

90.1%

49.4%

75.5%

14.5%

14.8%

1.3%

33.7%

14.4%

5.0%

7.1%

8.4%

15.2%

8.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Central

North

South

West

Turkana County

Service delivery point for Vitamin A supplementation(n=1768)

health facility Outreach ECDE Campaign Others

35

4.0 MATERNAL NUTRITION

Pregnancy imposes a big nutrient-need load on mothers, which in the absence of adequate extra nutrients leads to

utilization of body nutrient reserves leading to malnutrition.Gestational malnutrition leads to low birth weights and may

ultimately culminate in poor child growth and development, thus there is an urgent need to address high rates of

malnutrition among pregnant women. Household food insecurity is a key indicator/determinant for poor adult nutritional

status. A high number of malnourished PLWs increase the risk of growth retardation of the fetus and consequently an

increase in low birth weight and malnutrition burden spreads to both U5 children and caretakers from the same household

faced with food insecurity and related vulnerabilities, a common scenario during nutrition emergency levels .

4.1 Women physiological status

The figure 6 below indicates that majority of the surveyed women in the county were lactating (58.7%) and pregnant

(10.7%) respectively.

Figure 6: Women physiological status

4.2 Acute Malnutrition

Maternal nutrition was assessed by measuring MUAC of all women of reproductive age (15 to 49) in all sampled

households. Analysis was further focused on pregnant and lactating women. Based on the survey findings, 8.0% of all

assessed women of reproductive age in the county were malnourished with a MUAC≤ 21.0 cm. In particular, 7.5% of

pregnant and lactating women were malnourished using the same criteria. Figure 7 below show the prevalence of acute

malnutrition among pregnant and lactating women and women of reproductive age (WRA) respectively.

Figure 7: Nutrition status of Women of Reproductive age and Nutrition status of pregnant and lactating women

7.7%

12.3%

10.2%

13.1%

10.7%

56.5%

53.8%

62.7%

60.9%

58.7%

0.2%

0.0%

0.2%

1.0%

0.3%

35.6%

33.8%

26.9%

25.0%

30.3%

0% 20% 40% 60% 80% 100%

Central

North

South

West

County

Women Pysiological Status(n=1872)

Pregnant Lactating Pregnant and lactating None

36

4.3 Iron and Folic Acid Supplementation (IFAS)

During pregnancy, women have increased need for additional iron to ensure they have sufficient iron stores to prevent

iron deficiency. Iron supplementation is recommended in resource limited settings as strategy to prevent and correct iron

deficiency and anemia among pregnant women

WHO recommends daily consumption of 60mg elemental iron and 0.4mg folic acid throughout the pregnancy15.These

recommendations have since been adopted by Kenya government in its 2013 policy guidelines on supplementation of

iron folic acid supplementation (IFAS) during pregnancy. During the survey, iron folic supplementation was assessed by

asking mothers of children below 2 years if they consumed iron folate in their most recent pregnancy.

The assessment findings showed that 61.2% of women with children below 2 years had been supplemented with iron

folate supplements during their last pregnancy. Mean number of days IFAS was consumed by women was as

follows; Central 33.6,North 42.1,South 41.7,West 49.7,County41.1 days.

Table 30: Caretakers with children aged 24 months and below who were supplemented with Iron Folic acid in their last pregnancy

IFAS supplementation Central North South West County

n 455 455 550 412 1872

Yes 71.0%(323) 44.6(203) 71.5%(393) 55.1%(227) 61.2%(1146)

No 13.6% (62) 25.1%(114) 12.4%(68) 13.1%(54) 15.9%(298)

Do not Know 2.6%(12) 10.3%(47) 4.9%(27) 1.9%(8) 5.0%(94)

Not Applicable 12.75(58) 20.0%(91) 11.3%(62) 29.9%(123) 17.8%(334)

Only 6.1% of the interviewed mothers had taken iron folate supplement in 90 days and over, with no mother taking

supplements for more than 180 days as recommended, see table 31 below.

15

WHO. Guideline: Daily iron and folic acid supplementation in pregnant women. Geneva, World Health Organization, 2012.

37

Table 31: Number of days caretakers with children aged 24 months and below consumed IFAS in their last pregnancy

Zone Central North South West County

Categories of IFA

Consumption (In Days)

n % n % n % n % n %

< 90 Days 320 99.1 187 92.1 367 93.4 202 89 1076 93.9

90≥180 Days 3 0.9 16 7.9 26 6.6 25 11 70 6.1

> 180 Days 0 0 0 0 0 0 0 0 0 0

4.4 Mosquito Nets Ownership and Utilization

Overall, 24.9% of Turkana County residents own at least one mosquito net. 18.8% of children under five, 9.9% of

pregnant and lactating women and 5.2% of other family members slept under mosquito net.

Figure 8: Mosquito nets ownership and utilization

38

5.0 WATER SANITATION & HYGIENE

International human rights consider access to water and sanitation as a human right.16 This means that all individuals are

entitled to have access to an essential amount of safe drinking water and to basic sanitation facilities. The human right to

water entitles everyone to sufficient, safe, acceptable, physically accessible and affordable water for personal and

domestic use. Water and sanitation are deeply interrelated. Sanitation is essential for the conservation and sustainable

use of water resources, while access to water is required for sanitation and hygiene practices. Furthermore, the

realization of other human rights, such as the right to the highest attainable standard of health, the right to food, right to

education and the right to adequate housing, depends very substantially upon the implementation of the right to water and

sanitation. Increasingly current evidence on poor WASH indicators is being linked to under nutrition and more so on High

Stunting levels. Diarrhea, the leading killer of young children is closely linked to poor/inadequate WASH (Pruss-Ustun et

al, 2014), which often causes undernutrition, which in turn reduces a child’s resistance to subsequent infections, thus

creating a vicious circle. An estimated 25% of stunting is attributable to five or more episodes of diarrhea before 24

months of age (Checkley et al, 2008). Below is a pathway to reduce stunting among children 0-2years of age showing the

prominence of WASH interventions.

Figure 9: Pathway to reduction of stunting

5.1 Main Source of Water

Only 58.6% of Turkana County residents obtain their drinking water from safe sources namely; piped water, borehole,

protected spring or protected shallow wells. The rest (41.4%) obtained their water for drinking from sources whose safety

can be compromised hence need proper treatment before drinking. Such sources included; unprotected shallow well

(10.4%), river/spring (14.7%), unprotected dug well/ laga (10.5%) and earth pan/dam (3.0%). Table 32 below,

summarizes main sources of water per survey zone.

16

The UN committee on economic, Cultural and Social rights states in its General Comment of November 2002

39

Table 32: Current main sources of water

Current main source of

drinking water


n 670 690 762 573 2695

Piped System/borehole/ protected

spring/protected shallow well

49.7%(333) 55.4%(373) 63.8%(486) 67.5%(387) 58.6%(1579)

Unprotected shallow well 4.8%(32) 23.8%(164) 7.2%(55) 5.1%(29) 10.4%(280)

River/Spring 27.9%(187) 9.9%(68) 10.2%(78) 11.2%(64) 14.7%(397)

Unprotected dug well/ laga 10.1%(68) 7.8%(54) 13.1%(100) 10.6%(61) 10.5%(283)

Earth pan/dam 2.4%(16) 2.5%(17) 2.2%(17) 5.6%(32) 3.0%(82)

Earth pan/dam with infiltration well 4.9%(33) 0.6%(4) 0.0%(0) 0.0%(0) 1.4%(37)

Water trucking /Water vendor 0.1%(1) 0%(0) 3.1%(24) 0.0%(0) 0.9%(25)

Others 0.0%(0) 1.4%(10) 0.3%(2) 0.0%(0) 0.4%(12)

5.2 Distance to Water Source and Queuing Time

According to SPHERE handbook for minimum standards for WASH, the maximum distance from any household to the nearest water point should be 500 meters. It also gives the maximum queuing time at a water source which should be no more than 15 minutes and it should not take more than three minutes to fill a 20-litre container.

Analysis of distances to water sources indicated 54.8% of the households obtained their water from sources less than 500m (less than 15 minutes walking distance),35.8% took between 15 min to 1 hour (approximately 500m to 2km) while the rest (9.2%) walked as far as more than 2Km (1- 2hrs) to their water sources. Figure 10 below shows distance to water sources per survey zone in Turkana County

Figure 10: Distance to water sources

In the county only 38.0% of the respondents queued for water for less than 30 minutes, half of them queued for between 30 and 60 minutes and only 11.3% queued for more than one hour. Table 33 shows the percentage that queue and queuing time per survey zone

40

Table 33: Queuing time at water source

Turkana Central Turkana North Turkana South Turkana West

Turkana County

670 690 762 573 2695

Queue for water (150)22.4% (117)17.0% (251)32.9% (200)34.9% (718) 26.6%

Queuing Time/N 150 117 251 200 718

Less than 30 min (55)36.7% (58)49.6% (65)25.9% (95)47.5% (273)38.0%

30 to 60 min (75)50% (33)28.2% (179)71.3% (77)38.5% (364)50.7%

More than 1 hour (20)13.3% (26)22.2% (7)2.8% (28)14.0% (81)11.3%

5.3 Methods of drinking water treatment and storage

The survey showed that almost all (92.1%) Turkana County residents do not treat their drinking water despite the fact that

41.4% of the respondents obtain their water from unsafe sources. Majority of those who treat drinking water use

chemicals, which can be attributed to the distribution of pur that was done during the rainy season and in response to a

threat of cholera outbreak not only in Turkana County but in more than half of the entire country in 2016. This was

followed by use of traditional herbs (16.5%), thus a need for further study on the herbs used and their effectiveness in

water treatment. Only 13.7% used boiling as a method of water treatment. Out of the sampled households only 76.6%

stored drinking water in closed container/Jerri can, thus preventing it from physical contamination. This extremely low

proportion of households that treat drinking water, coupled with the low latrine coverage and high rates of open defecation

( as covered in section 5.6 below…) could be one of the main contributors of malnutrition in the County as already

explained above (relationship between undernutrition and poor WASH.

Table 34: Methods used for treating drinking water


Drinking water treatment

done/yes

n=670 n=690 n=762 n=573 n=2695

6.7%(45) 11.3%(78) 5.4%(41) 8.6%(49) 7.9%(213)

Water treatment methods n=44 n=78 n=41 n=49 n=212

Boiling 4.5%(2) 17.9%(14) 31.7%(13) 0.0%(0) 13.7%(29)

chemicals 88.6%(39) 65.4%(51) 31.7%(13) 67.3%(33) 64.2%(136)

Traditional herbs 2.3%(1) 10.3%(8) 26.8%(11) 30.6%(15) 16.5%(35)

Pot filters 2.3%(1) 6.4%(5) 9.8(4) 0.0%(0) 4.7%(10)

other 2.3%(1) 0.0%(0) 0.0%(0) 2.0%(1) 0.9%(2)

Drinking water storage n=670 n=690 n=762 n=573 N=2695

Closed container/jerrican 86.9%(582) 60.7%(419) 77.3%(589) 82.9%(475) 76.6%(2065)

5.4 Water Utilization and Payment

According to SPHERE handbook for minimum standards for WASH, the average water use for drinking, cooking and

personal hygiene in any household should be at least 15 liters per person per day. However, only 14.8 % of the

households used at least 15 liters of water per person per day which is the minimum per capita recommendation for

drinking cooking and personal hygiene (SPHERE Hand book 2004). Figure 11 below shows the water utilization in Liters

per person per day in all the survey zones in Turkana County.

41

Figure 11: Water utilization (Liters/person/day)

In the county 35.9 % of the surveyed household buy water for domestic use. Over two thirds of households (69.8%)

make a monthly payment and 30.2% pay per 20 liter jerrican. Turkana west had the highest proportion of population

paying for water per 20 litre jerrican mode while North had the highest proportion of households paying for water on

monthly basis.

Table 35: Payment for water


Do you pay for water n=670 n=690 n=762 n=573 n=2695

yes 31.6% (212) 40.7%(281) 40.2%(306) 29.5%(169) 35.9%(968)

Mode of payment n=212 n=281 n=306 n=169 n=968

Monthly 74.4%(158) 94%(264) 68%(208) 26.8%(46) 69.8%(676)

Per 20 L jerrican 25.6%(54) 6.0%(17) 32.0%(98) 73.2%(123) 30.2%(292)

About 83.6% of the households bought a 20 L Jerrican of water at least Ksh.10; Water costed more in Turkana Central

and Turkana North at above 10 Ksh /per 20 L jerrican. Table 36 shows the percentage of households paying for water

and cost of water per 20 litter jerican per survey zone.

Table 36: Cost of water per 20 Liter jerrican

Cost per 20L jerican Central North South West County

n n=35 n=21 n=95 n=106 n=292

<10 Ksh 40.0%(14) 47.6%(10) 95.8%(91) 93.4%(99) 83.6%(244)

>10-<20Ksh 11.4%(4) 0.0%(0) 0.0%(0) 0.0%(0) 2.1%(6)

>20 Ksh-<30ksh 45.7%(16) 52.4%(11) 1.1%(1) 6.6%(7) 12.30%(36)

>30Ksh 2.9%(1) 0.0%(0) 3.2%(3) 0.0%(0) 2.1%(6)

42

About three quarters(73%) of household paying a monthly bill spent about ksh 100.Monthly water bill was higher in Turkana central (10.2%) at above Ksh. 100 Table 37 below summarizes cost of water per month per survey zone. The above results on cost of water show that access to water is still a gap in the county. With nearly 4 in 10 households buying water, this could partially explain the fact that most of the households are not meeting the SPHERE standards on the average number of litres per person per day.

Table 37: Cost of water per month

Monthly cost of water Central North South West County

n n=157 n=264 n=208 n=45 n=674

<100ksh 82.2%(129) 67.4%(178) 76.9%(160) 55.6%(25) 73%(492) >100-<200ksh 5.7%(9) 17%(45) 7.2%(15) 17.8%(8) 11.4%(77) >200-<300ksh 1.9%(3) 14.8%(39) 7.2%(15) 24.4%(11) 10.1%(68) >300-<400ksh 1.3%(2) 0.4%(1) 2.9%(6) 0.0%(0) 1.3%(9)

>400ksh 8.9%(14) 0.4%(1) 5.8%(12) 2.20%(1) 4.2%(28)

5.5 Hand washing

Hand washing with soap is the single most cost-effective intervention in preventing diarrhea diseases17. The four critical

hand washing moments include; after visiting the toilet/latrine, before cooking, before eating and after taking children to

the toilet/latrine.

Assessment of hand washing in the 4 critical times in Turkana County indicated that while, 77.0% of the respondents

were practicing hand washing, only a mere 15% of respondents adhered to the recommendation for 4 critical times.

Majority (69.1%) of them washed their hands before eating, 41.6% before cooking and 40.3% after visiting toilet/latrine.

Table 38 below shows hand washing at critical times per survey zone in Turkana County

Table 38: Handwashing at critical times

Practice hand washing Central North South West County

n n=670 n=690 n=762 n=573 n=2695

% washing hands 80.0%(536) 52.0%(359) 93.0%(709) 82.4%(472) 77.0%(2076)

After visiting toilet 47.5%(318) 34.9%(241) 41.7%(318) 36.6%(210) 40.3%(1087)

Before cooking 58.1%(389) 36.4%(251) 32.4%(247) 40.7%(233) 41.6%(1120)

Before eating 71.3%(478) 45.4%(313) 88.6%(675) 69.1%(396) 69.1%(1862)

After taking the children to toilet 18.4%(123) 20.0%(138) 23.6%(114) 19.9%(114) 20.6%(555)

Other moments 0.6%(4) 0.1%(1) 0.3%(2) 1.4%(8) 0.6%(15)

Further, almost half (48.3%) of the respondents only used water only for handwashing, while just a third (32.7 %) always

used soap and water for handwashing. Handwashing without soap does not offer effective protection against germs.

Figure 12 below shows what is used for handwashing

Figure 12: What is used for handwashing

17

Borghi, J., Guinness, L., Ouedraogo, and J., Curtis, V. (2002): Is hygiene promotion cost-effective? A case study in Burkina Faso.

Tropical Medicine and International Health, 7(11), 960-969.

43

5.6 Latrine Ownership and Utilization

In 2016, little progress has been made in reducing Open Defecation .Overall, 84.9% of the respondents continue to

relieve themselves in the bushes (open defecation) while the rest use own latrine, neighbor’s or shared traditional

pit/improved latrines). Turkana North and Central recorded the highest Open defecation rate at 90.7% and 89.6%

respectively with Turkana south having the lowest but poor rate at 76.0%. Figure 13 below show latrine ownership and

utilization per survey zone.

Figure 13: Latrine ownership and utilization

44

6.0 FOOD SECURITY

6.1 Household’s Source of Income

Household income is critical to food availability at household level. In Turkana county majority (83.3%) of the

households had access to some form of income, with the main source income across the survey zone being petty

trading (37.0%), sale of livestock (16.6%) and waged labour (12.4%). In the respective survey zones, Turkana south

had the highest proportion with no source of income (20.2%) followed by west (18.0%) and north at 17.8%. In

comparison to other survey zones, Turkana south had the highest proportion of households’ income being waged

labour (15.7%). Petty trading was dominant in Turkana west largely due to the presence of the Kakuma refugee camp

that offers a ready market for host communities. Turkana North is dependent on livestock as a main source of income,

figure 14 shows the household’s source of income.

Figure 14: Household’s source of income

6.2 Source of Dominant Foods

In the entire county the main source of starches (77.4%), legume (83.2%), vegetables and fruits (67.3%) and milk (61.3%)

was purchase. It is important to note that majority of the household in Turkana North (40%) were consuming milk from

own production. Turkana south had the least proportion of households consuming milk from own production with the

majority of the residents (76.9%) accessing milk through purchase. This, coupled with fact that the South region has the

highest proportion of households with no source of income as already highlighted implies that the South residents could

be more vulnerable to shocks compared to the other geographic locations which could partially explain the high levels of

GAM in the region.Table 39 below summarizes the sources of dominant foods.

45

Table 39: Source of dominant foods

Own

production Purchase

Gifts

from

familes&

friends

Food aid Traded/

Battered

Borrowe

d Gathering Others

Sta

rch

Central 2.1%(14) 81.2%(544) 1.8%(12) 7%(47) 1.0%(7) 5.1%(34) 0.9%(6) 0.9%(6)

North 1.0%(7) 72.6%(488) 3.3%(22)

18.3%(12

3) 2.5%(17) 1.9%(13) 0.1%(1) 0.1%(1)

South 16.8%(128) 79.1%(602) 0.5%(4) 2.9%(22) 0.5%(4) 0.1%(1) 0.0%(0) 0.0%(0)

West 5.7%(33) 76.1(437) 2.6%(15) 10.5%(60) 2.3%(13) 1.6%(9) 0.1%(6) 0.2%(1)

County 6.8%(182) 77.4%(2071) 2.0%(53) 9.4%(252) 1.5%(41) 2.1%(57) 0.5%(13) 0.3%(8)

Pu

lses

, leg

um

es&

nu

ts

Central 1.2%(8) 83.1%(557) 1.2%(15) 6.3%(42) 0.4%(3) 3.9%(26) 1.2%(8) 1.6%(11)

North 0.6%(4) 68%(452) 2.9%(19) 13.5%(90) 4.1%(27) 5.3%(35) 0.3%(2) 5.4%(36)

South 0.5%(4) 95.2%(715) 0.8%(6) 1.3%(10) 0.3%(2) 0.0%(0) 0.7%(5) 1.2%(9)

West 1.1%(6) 84.4%(486) 2.5%(14) 3.5%(20) 2.3%(13) 2.5%(14) 0.9%(5) 1.9%(11)

County 0.8%(22) 83.2%(2210) 2.0%(54) 6.1%(162) 1.7%(45) 2.8%(75) 0.8%(20) 2.5%(67)

Veg

etab

les&

fru

its

Central 2.1%(14) 65.5%(437) 0.6%(4) 1.8%(12) 0.4%(3) 2.2%(15) 15.7%(105) 11.5%(77)

North 1.4%(8) 51.2%(301) 6.6%(39) 2.7%(16) 3.9%(23) 5.4%(32) 4.4%(32) 23.3%(137)

South 9.7%(69) 71.4%(508) 0.6%(4) 0.1%(1) 0.1%(1) 0.1%(1) 2.8%(20) 15.0%(107)

West 1.6%(9) 81.6%(447) 2.4%(13) 0.9%(5) 1.3%(7) 2.7%(15) 4.0%(22) 5.5%(30)

County 4.0%(100) 67.3%(1693) 2.4%(60) 1.4%(34) 1.4%(34) 2.5%(63) 7.1%(179) 14.0%(351)

Milk

Central 28.1%(188) 61.1%(408) 0.0%(0) 0.7%(5) 0.1%(1) 3.6%(24) 0.0%(0) 6.3%(42)

North 40.0%(271) 38.8%(263) 0.9%(6) 1.0%(7) 2.4%(16) 4.0%(27) 0.6%(4) 12.4%(84)

South 20.3%(146%) 76.9%(553) 1.8%(13) 0.0%(0) 0.3%(2) 0.7%(5) 0.0%(0) 0.0%(0)

West 24.1%(237) 68.5%(390) 2.1%(12) 0.9%(5) 1.4%(8) 1.2%(7) 0.9%(5) 0.9%(5)

County 28.2%(742) 61.3%(1614) 1.2%(31) 0.6%(17) 1.0%(27) 2.4%(63) 0.3%(9) 5.0%(131)

6.3 Foods Groups Consumed by Households

As illustrated in figure 15 below, consumption of vegetables, fruits, eggs, fish and organ meat were very low, with less

than 20% of the households consumed food items from these food groups. Foods mostly consumed are cereals, oils/fats

milk and pulses &legumes. Most of the households consumed a cereal based diet in the county (87.1%). Fish

consumption was higher in Turkana North and Central compared to other survey zones which might be associated with

access to the lake. It basically indicates that households are not consuming diversified diets .This is a proxy indicator of

insufficient nutrient intake further exposing the populations to deficiencies especially micronutrients.

Figure 15: Food groups consumed by households from 24 hour recall

46

6.4 Household Food Consumption Frequency

The diversity in food profile consumed in households remains poor. Cereals and cereal products were the main staple

food consumed for more than 5 days in the week, this was closely followed by oil and fats and pulses/legumes. Meat/offal

consumption was a bit low at 2 days per week .Consumption of dark green vegetables, eggs, fish and vitamin A rich

vegetables and tubers were least consumed for just around 2 day per week as indicated in the figure 16 below. This

further goes to demonstrate that general food availability in Turkana does not necessarily guarantee nutrition security

more so for children less than five years old who require nutrient dense diets which are seemingly lacking in the diets of

the population. This could also partially account for the persistently high rates of malnutrition in the county.

Figure 16: Food consumption frequency by households based on a 7 day recall

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Food groups consumed by the household -24 hr recal

Central North South West

0.00

1.00

2.00

3.00

4.00

5.00

6.00Cereals

Vitamin A richvegetables and tubers

White tubers androots

Dark green leafyvegetables

Othervegetables(tomatoe…

Vitamin A rich fruits

Other fruits

Iron rich(organ meat)foods

Flesh meats and offals

eggs

Fish

Pulses/legumes, nuts

Milk and milkploducts

Oils/fats

Sweets; sugar, honeyand other sugary…

Condiments, spicesand beverage

Average number of days households consumed food from 16 food groups, 7 days prior to the survey

Turkana Central

Turkana North

Turkana South

Turkana West

47

6.5 Household Food consumption score (FCS)

The FCS is used to identify the most food insecure households. The prevalence of households with poor and borderline

food consumption provides essential information on people’s current diets and is helpful in deciding the most appropriate

type and scale of food security intervention as well as the right target group for the assistance. In this survey, the

household food consumption score was poor in the entire county. Out of about half (56.4%) of all the sampled households

in the county, 3 in 10 households (30%) reported poor to borderline FCS, with Turkana North and South most affected at

46.3% and 31.6% respectively (figure 17).

Figure 17: Household food consumption score

6.6 Household Consumption of Micronutrient Rich Foods

As illustrated in the figures below consumption frequency of nutrient rich food groups in the four survey zones showed

that a higher proportion of households are not eating enough Iron-rich and fruits & vegetables with protein, staples or

vitamin A rich foods being prominent. However protein and vitamin A sources are largely from milk. However, Turkana

North and South fared poorly compared to the other areas which is in line with the poor FCS in these two livelihood

zones. As already captured above, these results portend a higher risk of undernutrition and micronutrient deficiencies in

Turkana further explaining the relatively high rates of chronic and acute undernutrition prevailing in the county. At the

same time, the widespread low consumption frequency of iron rich foods across all survey zones could indicate a higher

risk of iron deficiency anemia across the country.

Figure 18: Household consumption of micronutrient rich foods

48

6.7 Household Consumption of Protein, Vitamin A and Heme Iron Rich Food Groups by Poor/Borderline

and Acceptable Food Consumption Score Groups in Turkana County

Figure 19 below shows that despite most households consuming more of protein and Vitamin A rich foods, most of the

households with poor/borderline food consumption score have a low frequency of consumption of protein rich foods and

vitamin A rich foods and as such they are likely not to be consuming enough to meet their nutrient needs. Consumption of

iron rich food is even much worse among poor/borderline and acceptable food consumption groups with 7 out of 10

households reporting to never have consumed these foods in the last one week, thus further indicating a higher risk of

iron deficiency anemia across the county.

Figure 19: consumption of protein, vitamin A and heme iron rich food groups by poor/borderline and acceptable food consumption score groups in Turkana County

49

6.8 Minimum Dietary Diversity -Women Score (MDD-W)

Women of reproductive age (WRA) are often nutritionally vulnerable because of the physiological demands of pregnancy

and lactation. Requirements for most nutrients are higher for pregnant and lactating women than for adult men (National

Research Council, 2006; World Health Organization [WHO]/Food and Agriculture Organization of the United Nations

[FAO], 2004). Outside of pregnancy and lactation, other than for iron, requirements for WRA may be similar to or lower

than those of adult men, but because women may be smaller and eat less (fewer calories), they require a more nutrient-

dense diet (Torheim and Arimond, 2013). Insufficient nutrient intakes before and during pregnancy and lactation can

affect both women and their infants. Yet in many resource-poor environments, diet quality for WRA is very poor, and there

are gaps between intakes and requirements for a range of micronutrients (Arimond et al., 2010; Lee et al. 2013).

MDD-W is a dichotomous indicator of whether or not women 15–49 years of age have consumed at least five out of ten

defined food groups the previous day or night. The ten defined food groups include ;1) Grains, white roots and tubers and

plantains; 2) pulses (beans ,peas and lentils); 3)Nuts and seeds,4) Dairy; 5) Meat ,poultry and fish; 6) Eggs; 7) Dark

green Leafy vegetables; 8) Other vitamin A rich fruits and vegetables; 9) Other vegetables; 10) Other fruits. The

proportion of women 15–49 years of age who reach this minimum in a population can be used as a proxy indicator for

higher micronutrient adequacy, one important dimension of diet quality.

Figure 20: MDD-W score Turkana County

21.7%

72.1%

26.9% 44.7%

48.5%

18.4%

27.2%

54.3%

55.3%

94.5%

29.8%

78.0%

18.9%

0%10%20%30%40%50%60%70%80%90%

100%

Poor/Borderline Acceptable Poor/Borderline Acceptable Poor/Borderline Acceptable

Protein Vitamin A rich Groups Hem Iron Rich Groups

Per

cen

tag

e

Food groups

Consumption of ptotein, Vitamin A and Heam iron -rich food groups by category- Turkana County (n=1521)

Never Consumed sometimes (1-6 DAYS) Consumed atleast daily (7 and above days)

50

As illustrated in figure 20, only a paltry 2 out of 10 (19.7%) of the WRA in Turkana County were consuming at least five

food items from the 10 food groups in the MDD-W. Turkana North had the least proportion of WRA (9.7%) consuming at

least five food items from the 10 food groups in the MDD-W .The lower proportions of WRA consuming food items from at

least five of the ten food groups indicates lower proxy micronutrient adequacy among the WRA in the county. In summary

this is a proxy indicator of high micronutrient deficiency among WRA in Turkana County that is likely to affect birth

outcomes.

6.9 Household Coping Strategy Index (Reduced CSI)

As indicated in the figure 20 below, 80.7% of the households in the county reported facing food shortage and thus

adopting coping strategies. Again, in line with food consumption score and intake of micro-nutrient rich foods, Turkana

North and South had a slightly higher number of households applying a coping strategy

Figure 21: Proportion of household applying a coping strategy

The main adopted coping strategies in all the survey zones were; 1) consumption of less preferred and less expensive

foods 2) restricted consumption by adults in order for small children to eat as illustrated in the table 38 below.

51

Table 40:coping strateies applied

Coping

strategy

frequency by

Households

Household

relied on less

preferred and

less expensive

foods

Borrowed or

relied on help

from a friend or

relative

Household

limited portion

size at meal times

Household

restricted

consumption by

adults in order

for small

children to eat

Household

reduced the

number of meals

taken in a day

n Yes No Yes No Yes No Yes No Yes No

Central 670

71.3%

(478)

28.7%

(192)

51.2%

(343)

48.8%

(327)

51.8%

(347)

48.20%

(323)

65.8%

(441)

34.2%

(229)

0.0%

(0)

100%

(670)

North 690

48.6%

(335)

51.4%

(355)

52.3%

(361)

47.7

(329)

52.9%

(365)

47.1%

(325)

65.9%

(455)

34.1%

(235)

0.0%

(0)

100%

(690)

south 762

64.3%

(490)

35.7%

(272)

50.3%

(383)

49.7%

(379)

56.0%

(427)

44.0

(335)

67.8%

(517)

32.2%

(245)

0.0%

(0)

100%

(762)

West 574

56.3%

(323)

43.7%

(251)

52.6%

(302)

47.4%

(272)

53.03%

(306)

46.7%

(268)

58.7%

(337)

41.3%

(237)

0.0%

(0)

100%

(574)

County 2696

60.3%

(1626)

39.7%

(1070)

51.5%

(1389)

48.5%

(1307)

53.6%

(1445)

46.4%

(1251)

64.9%

(1750)

35.1%

(946)

0.0%

(0)

100%

(2696)

As shown in table 41 below, there was a slight increase (from 21.06 to 21.88) in the CSI from the same time last year

indicating an increase in proportion of food insecure households. Turkana North and West had the highest CSI followed

by Turkana south indicating more food insecure households as illustrated in the table below.

Table 41: Mean Household Coping Strategy Index(CSI)

Mean Household Coping Strategy Index(CSI)


2016 19.33 23.26 22.39 23.19 21.88

2015 18.28 17.31 26.01 22.6 21.06

2014 22.72 19.52 17.61 13.77

7.0 CONCLUSION

Results of nutrition surveys conducted in June 2016 in the four sub counties of Turkana as part of the routine surveillance

system reported a Very Critical Nutrition Situation (>20% Global Acute Malnutrition (GAM)) with Turkana South, Central

and North being the most affected. The nutrition situation is of great concern with the following rates of acute malnutrition

at: Turkana South - 30.3% GAM including 8.9% Severely Acutely Malnourished SAM) and Turkana Central - 24.5% GAM

including 5.4% SAM. Thus 1 in EVERY 3 children in Turkana South suffers from ACUTE malnutrition and is at increased

risk of mortality. While this is not a statistically significant deterioration of the nutrition situation from 2015, both the 2015

and 2016 point estimates of Acute Malnutrition have been on the rise over the last 3 seasons, it highlights no obvious

recovery from the persistent shocks including drought, floods, and conflict that the communities are faced regularly with,

thus illustrating very high levels of chronic vulnerability.

52

The major drivers of the high levels of acute malnutrition in the county remain chronic food insecurity, poor dietary diversity and hence nutrition security, suboptimal child care and feeding practices including poor hygiene and sanitation, low access to essential health and nutrition services and especially for the schedulable services, as well as insecurity which directly influence access to basic quality services. The June 2016 survey also again highlighted the specific vulnerabilities related to hygiene and sanitation with less than 15% of the respondents practicing hand washing at four critical times and over 75% practicing open defecation. The perennially high rates of Malnutrition County are a cause for concern and call for a change in tact in dealing with the problem by all actors, led by the County government of Turkana. Addressing the underlying factors has to become a priority with preventive measures being put in place to cushion the population from a further deterioration of the nutrition situation which has direct negative impact on their wellbeing and continues to fuel the cycle of poverty in the County from generation to generation. Resource allocation by County also needs to factor the unique nutrition needs of Turkana County which currently has the highest rates of malnutrition (wasting) in the country.

8.0 RECOMMENDATIONS

Table 42:Reccomendations

Action Activity By whom By when

1 Update and activate nutrition contingency and response plans in South, Central and North Survey zones.

Hold joint meeting to revise the contingency plans.

Ongoing quarterly review of the contingency plans.


Immediately

2 Scale up continuous active case

finding for malnutrition for the

expected caseload(U5) of 34,563

(severe 7,862 and moderate 26,701)

and 22,437 pregnant and lactating

women in the year 2016 and referral

for timely management

Sensitize the CHVs on rapid nutrition screening and referrals.

Conduct routine screening through the existing community health units.

Conduct rapid nutrition screening in the hot spot areas.

Conduct quarterly mass screening.


Continuous

3 Increase access to life saving health and nutrition services through integrated outreaches for populations with limited access to these services.

Carry out mapping of communities with limited access to health and nutrition outreaches.

Conduct biweekly integrated health and nutrition outreaches in the in communities far from health facilities.

Monthly monitoring of the outreaches.


immediately

4 Develop simplified nutrition survey packs/briefs easily synthesized for nutrition advocacy and mobilization.

Hold community dialogue meetings, meetings with men, mother support Group to disseminate findings of SMART survey and generate community led actions.

Hold meetings with decision makers to disseminate the findings of the SMART survey and generate sector response plans.


immediately

5 Develop and implement nutrition service delivery score card at health facilities

On a quarterly basis populate the scorecard with nutrition data.

Quarterly meetings to deliberate on the


Quarterly.

53

output of nutrition score cards.

6 Conduct comprehensive on the job training and mentorship targeting facility health workers, community health extension workers (CHEWs) and Community health workers(CHWs)

Sensitize all the HMTs, Health facility in charges and CHEWs on findings of SMART survey and rapid nutrition screening.

Conduct sensitization of the CHVs on rapid nutrition screening and referrals.

Train all the newly recruited nutritionists on IMAM including the SURGE model.


Immediately

7 Sensitize and link mother to mother support groups (MtMSGs) and households with malnourished children/pregnant and lactating women with other nutrition sensitive sectors to strengthen nutrition resilience

Hold meetings with MtMSGs to sensitize them on the current nutrition situation and generate community led actions.

Link MtMSGs to existing livelihoods interventions.


Continuous

8 Cconduct community dialogue sessions and sensitization meetings with caregivers, community leaders/key influencers on appropriate childcare practises including micronutrient supplementation, deworming, handwashing, drinking water treatment and latrine utilization.

Develop a brief pack for community dialogue meetings.

Hold meetings with community leaders, communities, MtMSGs and caretakers to sensitize them on recommended child care and health & nutrition practices.


Continuous

9 Advocate and create public awareness on micronutrient supplementation (micronutrient powders, IFA, Vitamin A), de-worming and dietary diversification.

Conduct community dialogues with focus on micronutrients supplementation and deworming.

Integrate micronutrient supplementation and deworming into the integrated outreaches and nutrition calendar events.

Conduct micronutrient supplementation and deworming at the ECDs.


Continuous

10 Continue capacity building of health care workers especially newly recruited staffs through OJT and joint support supervision on a quarterly basis

Mobilize resources for training of health workers.

Conduct IMAM training for the newly recruited 40 nutritionists.


Continuous

11 Scale up community led total sanitation approach to increase awareness on sanitation including latrine utilization

Conduct Health workers and CHVs sensitisation on CLTS,

Conduct community sensitisation on CLTS.

Roll out CLTs jointly with the communities.

MoH (public health ) and nutrition and WASH partners

Continuous

12 Institutionalize Vitamin A supplementation and de-worming at the Early Child Education Development(ECDE) centers and scale up during annual child health campaigns

Sensitize ECD teachers on vitamin A supplementation and deworming.

Provide supplies and reporting tools to the ECD teachers.

Monitor on quarterly basis VAS supplementation at the ECD Centers.

MoH (nutrition& public health), MoE (ECDEs) and nutrition partners

Quarterly

13 Procurement and timely distribution of essential nutrition commodities to health facilities

Provide health facilities with the required reporting tools.

Develop commodity consumption reports and requests.

MoH/UNICEF/WFP Quarterly

14 Train county, sub county health managers, health workers on behavior

Hold training on SBCC for HMTs, health workers and CHVs.


December 2016

54

social change communication(BSCC)/communication for development(C4D)

15 Develop, disseminate and implement multi-sectoral nutrition social behavior change communication (SBCC) strategy to address maternal and child care knowledge, attitude, behavior and practices.

Validate and disseminate MIYCN SBCC strategy and messages.


February 2017

16 Pilot IMAM surge in select health facilities in the county. This will be scaled up upon successful pilot.

Sensitize health management team on IMAM surge.

Identify 7 health facilities (1 per Sub County) for the pilot of IMAM surge.

Conduct training of health workers in the pilot health facilities on IMAM surge.

Roll out IMAM surge in the pilot health facilities.

Monitor the implementation of the IMAM sure in the pilot health facilities.


October 2016

17 Train community health volunteers(CHVs) and community health extension workers(CHEWs) on nutrition module for community health strategy for improved active case finding, referral and nutrition education

Conduct training of the CHEWs and CHVs on community nutrition module.

MoH (nutrition, community strategy) and nutrition and health partners

October 2016

18 Scale up of Baby Friendly Community Initiatives(BFCI) in 20 MNCH centers of excellence

Conduct training of the HMTs ,Health workers and CHVs on BFCI.

Roll out BFCI in the pilot CHUs.

Monitor the roll out of BFCI.


November 2016

19 Conduct a Malnutrition Causal Link analysis to have in depth understanding of determinants of malnutrition

Develop and validate concept note and proposal for the study.

Mobilize resources for conducting the study.

MoH,MOAW and Partners

December 2017

55

9.0 APPENDICES

Appendix 1: IPC for Acute malnutrition Maps

Appendix 2:Summary of plausibility report

Indicator Acceptable

values/range

CENTRAL SOUTH NORTH WEST

1 Flagged data

(% of out of range subjects)

<7.5 0 (1.4% Excel) 0 (1.0%

Excel)

0 (0.6%

Excel)

0 (1.1%

Excel)

2 Overall sex ratio (significant CHI

square)

>0.001 0 (0.101 Excel) 0(0.0176

Excel)

4 (0.022

Accep)

4 (0.04

Accept)

3 Age ratio (6-29vs 30-59) Significant

CHI square

>0.001 10 (0.000 Prob) 10 (0.000

Prob)

10 (0.000

Prob)

10 (0.000

Prob)

4 Dig. prevalence score-weight <20 0 (4 Excel) 0 (3 Excel) 0 (3 Excel) 0 (3 Excel)

5 Dig. prevalence score-height <20 2 (12 Good) 0 (6 Excel) 0 (7 Excel) 0 (5 Excel)

6 Dig. prevalence score-MUAC <20 0 (4 Excel) 0 (4 Excel) 0 (4 Excel) 0 (4 Excel)

7 Standard Dev..height WHZ >0.80 0 (1.07 Excel) 0 (1.06

Excel)

0 (0.98 Excel) 0 (0.93

Excel)

8 Skewness WHZ <±0.6 0 (-0.13 Excel) 0 (-0.09

Excel)

0 (-0.05

Excel)

0 (0.05 Excl)

9 Kurtosis WHZ <±0.6 1 (-0.33 Good) 0 (-0.10

Excel)

0 (-0.012

Excel)

0 (-0.160

Excl)

10 Poisson WHZ -2 >0.001 3 (0.006 accep) 0 (0.370

Excel)

1 (0.030

Good)

1.(0.038

good)

11 OVERALL <24 16(Accep) 10(Good) 15(Accep) 15(Accep)

56

Appendix 3:Turkana Central Survey Zone Sampled clusters

Geographical Unit Population Size Cluster Geographical Unit Population Size Cluster

Carlifonia 6600 Rc Naoros 757

Chukultom 2276 Polait 42

Lodwar Town 1457 Etitipu 432

Nayenaengikalali 3596 1 Kichada 1359 21

Township 137 Nakadukui 688

Asingila 122 Kalapata 963

Eluktoliasi 3463 2 Komuget 535

Legio Maria 122 Kospir 410

Namakat_B 35 Lochor Ekuyen C 464

Namuduket 70 Lomukusei 232

Naregae 630 Moruangikiliok 214

Ngasaja 3848 3 Nakinyanga 536

Redburner_A 718 Echwaa-Ilema 374 22

Redburner_B 1277 Locher-Edome 158

Lodopua 1002 Locher-Emeyan 632

Mission A 1002 Lochor Esekon 216

Nakwalele 635 Lomukusei 503

Natambusio 1970 4 Nayanae-Esajait 359

Natotol 6546 Rc Kachakachom 1321

Bondeni 2124 Kalelakol 1849

Hewan 10319 5,6 Lokwatuba 1981 23

Kailoseget 354 Naurendudung 2377

Naotin 245 Puch 2246 24

Napeyewoi 408 Kiwanjandege 1250

Natumaoi 408 Lobole 864

Ngirokipi 545 Rc Nabolocha 852

Atiirlulung 1558 Nakorimunyen 671

Chokochok 1506 Natanyakipi 841

Lokadwaran 1642 Lorengippi Centre 2459 25

Lomeyen 339 Kaemanik 1207

Nadipoe 440 7 Nakurio 1070

Nakosmae 965 Lodwat 1384 26

Narewa 576 Loya 1501

Nayuu 1355 Kekoroakwan 270

Loboitom 738 Lokiriama 1600

Nakwapoo 2213 8 Natapae 468

Napetanyang 204 Urum 1277

Ngatadei 942 Lochor- Alomala 5839 27,28

Ngimuriae 157 Atalokamusio 1538

Kairiama 1949 Didinga 995

Louwae 2620 9 Ngikorkipi 1448

Mugur 2452 Aurmosing 1488

Nairobi 733 Logogo 804 29

57

Kura 3925 10 Lolemgete 684

Nakoda 32 Lorugum Center 1761

Nangitony 3319 11 Kalomegur 2466 30

Nangolekuruk 1149 Kechemeri 1828

Kangirisae 2923 Natuntun 1849

Nakwaale 720 Turkwel 1996 31

Elupe 789 12 Blue Line 745

Kamariae 331 Kabulokor 1771

Loborok 76 Kangalita 2918 32

Nakoret 2442 Kawaret 134

Nariamawoi 560 Kimkoe 60

Lorengelup 1146 Koleleui 194

Nangorichoto 285 Kotela 2561

Lodwat 742 13 Nakorokoroite 283

Nariamao 187 Komera 1765

Kangagetei 378 Lobei Centre 2151 Rc

Nakechichok 755 Lomilo 1213

Nakwaperit 566 Lochodo 446

Kakimat 1039 Nagis 553

Kosikiria 840 Namoru 561

Atangi 289 Naurienpuu 1255 33

Dapal 2265 14 Ngikwakais 471

Lowoiangikeny 3484 Kaitese 1963

Napeikopo 1545 15 Tiya 960

Napetet 2275 Kapolokine 399

Nariamawoi 1622 Kodopa 639

Kaatamat 1500 16 Nachomin 253

Kalotum 98 Naoyawoi 3235 34

Kanugurmeri 401 Lokoyo 1473

Lokatukon 1973 Kakeem 1793 35

Nakigol 315 Lokorikipi 2586

Narukopo 1787 Lomeyan-Mobile 125

Ngikalalio -Aloru 1923 17 Nameyana 772

Abute 3094 Napeililim 709

Akwamekwi 2500 18 Nasiger 417

Lokitoeangikiliok 47 Ngakoriyek 3691 36

Lokwarin 641 Kangataruk 3176 Rc

Eporoto 291 Nachuro 693

Katongun 15 Nakitoekirion 1295

Moruapolon 2733 Kaekoroengorok 1788

Nakurio 1541 19 Lokatul 1267 37

Echilet 65 Nakwamunyen 4003

Kaachuna 1116 Kotaruk 9878 38,39

Kambi Fora 194 Kotaruk 280

Lokurumuka 1392 Moruongor 84

58

Longelech 421 Naipa Centre 2068 40

Lookwa 567 Nariamao 1174

Lore-Kaalany 130 Kakalel 982

Nakapelkuruk 907 20 Namwa 1207

Katula 1135 Naremit 2161 41

Nameresiae 2628

Appendix 4:Turkana NorthSurvey zone sampled clusters

Geographical unit Population size Cluster Geographical unit Population size Cluster

Jiriman 148 Kanamethe 398

Kalonyangkori 72 Nawoyadome 27

Kamatira 52 Ngikeridak 692

Kekongo 100 Nachotoi 610

Locher edome b 143 Nameju 671 20

Lokitaung town 10 Nasiritei 182

Lolupe 1172 Natodomeri mobi 1951

Lomeguro 505 Naita 2734 21

Mission 29 1 Koyasa 298

Mlango pesa 114 Kaemosia 2295 22

Nadokoro 133 Kambi safi 860 23

Nagis 129 Napak 1290

Nakalale 1 43 Karach 2444 24

Nakalale 2 33 Karach 1 2037 Rc

Nakeriau 57 Napak centre 1758

Nameturan 67 Ngikaturiakae 1055 25

Napetet 57 Nginyamakidioko 996

Naurukor 195 Ngipucho 586

Nawoyatir 105 Ngirikokwa 762

Ngakarearengak 43 Kangitulae 182 26

Ngatabab 153 Loitanit 393

Ngolan height 91 Nakitorong 847

Punipun 52 Nakululung 1271

Shabaa 24 Napak edung 636 27

Apokorit 135 Narikorikodapal 908

Kaabarait 302 Ekicheles 525

Kachoda center 300 Ekoopus 713

Locher edome 375 Loyopokou 600 28

Lomaareng 225 Ekicheles 524

Loporkou 585 2 Ekoopus 713

Mana longoria 375 Loyopokou 600

Nangitony 90 Akilodet 671 29

Kangarukia 669 Central 1302

Nachomin 656 Longolemwar 1024

Akatorogot 204 Maendeleo 1185 30

Atapar 341 Nakilinga 1726

Epur 613 3 Nakwamekwi 951 31

59

Eteere 204 Acumae 172

Kaitekapel 341 Ameritaaba/amug 189

Kambi safi 727 Apak 86

Lokitoangaber 592 Awoi 17

Narding 545 4 Elelea 378

Nasechabuin 318 Kaanyangalem 326

Ngipeikituk 318 Kangilenga 378

Kaitengiro 357 Kangilenga/loch 86

Lochorangidomo 453 Kasuguru 155

Nayanaeapuu 521 Lochoredapal 447 Rc

Topernawi 700 5 Lokaliban 773

Lomekwi 1147 Lotukutan 138

Lotirmoe 1380 Lowoyakasiwan 601

Kekoropus 1606 6 Milima tatu a 241

Lokwakipi 1070 Milimatatu b 326

Ngiburin 848 7 Nakitoekakumon 309

Ngirusio 1160 Abei 321

Turamoe 1026 Akai amana 161 Rc

Ata kalailae 194 8 Burnt forest 32

Kalomeu 449 Emoja 241

Kangararae 385 Emoru 482

Kobosan 278 Epeta 466

Loalany 557 Father robert li 48

Lokipetot 321 Karioreng 257

Lokirimo 107 Kopotea ii 305

Lokorimanik 171 Kopotea 1 370

Nakwasiro 235 Lokalale akwan 129

Nameriyek 235 Lokapelpus 370 Rc

Narisae/nakuleu 300 9 Lokidongo 370

Elelea 200 Lokumae 2 948

Emlango 401 Lonyamiile 64

Kambi miti 513 Morueris 514

Kambi mpya 476 Nakapelewoi 321

Kiwanjandege 325 Nanyangamunyen 321

Legio 150 Napeded 16

Lokapetemoe 325 Nasirite 145

Lokitoenyala 300 10 Nawoyatubwa 129 32

Mlango 238 River line 129

Nadopua 138 Apeikituk 418

Namorotot 263 Etelite 725

Nasia/nakitoe 275 Kamatira 222

Natoo 413 Napetain 669

Naupwala 801 Rukruk 474

Rukuruk 363 Loruth/esekon 2675 33

Uwanja ndege 300 11 Akoros 503 34

Kare-edome 2167 Alidat 567

Piringan 64 Emunyen 346

60

Asekon 631 12 Natomean 535

Kambi miti 1421 Rukruk 346

Kokiselei 293 Karach 1465 35

Nakwamekwi 1240 13 Kaituko 1441

Ngiburin 654 Kangamaleteny 251

Rukruk 1781 Kangamojoj 553 36

Yerusalem 68 14 Lotorob 369

Kaatongun 1210 Maatangikorio 251

Kainyanglup 528 Narole 587

Nakalale 1 102 Natebus 117

Nakalale 2 142 Nayanaeemeyan 335

Lotorongoruk 622 Nimwae 587

Nabulukok 549 15 Ejem 825 37

Lewan 2798 16 Kalapata 1866

Achukulmuria 226 Lodwarakipi 768 38

Highway 433 Nabulon 192

Kambi chafu 358 Nangomo 357

Kapolikine 339 Nawoitorong 521

Kodopa 94 Ngipidinga 823

Morutorong 1187 17 Soweto 192

Nalemsekon 94 Kaikit 602 39

Naoyawoi 57 Kakalepus 408

Kokuro 1246 Kambi mawe 350

Kokuro town 235 Kangibenyoi 835

Natete 505 Lochipua 661

Ngichwae a 799 18 Moru arengan 680 40

Ngichwae b 212 Ngauriendiria 738

Ngikui 846 Akalaliot 580

Akalale 683 Ikingol 374

Kitale 1428 19 Kangakipur town 706

Todonyang plain 2786 Rc Nakwei 249 41

Achukule 27 Naukomoru 498

Appendix 5:Turkana SouthSurvey zone sampled clusters

Geographical Unit Population Size

Cluster Geographical Unit Population Size

Cluster

A.P.Line 285 Lokwamosing 2919 13

Akatorongot 1455 Lomelo 1144

Anyangalim 416 Katir 1756 Rc

Anyangasekon 390 1 Napeitom 4203 14

Apetet 364 Echwaa 2102

Calvary 312 Nadome 2975 15

Elelea 1091 Ekipor 1597

Emanman 442 Kamuga 5104 16

Kalokume 208 Ngilukia 3547 17

61

Kambi Moi 701 Akatorongot 479

Laini Moja 779 Lodoketapolon 936

Morudapal 909 2 Eturo 168

Ngikuropua 909 Nakapotion 1427

Kakulit 224 Lokichar Centre 5093 18,19

Apachole 467 Idp 1684

Ekwar 389 Achukule 284

Kangitit 379 Anapukul 1091

Lobokoro 195 Lokordoyo 2021 Rc

Lodoupua_A 418 Tonyou 647

Lodoupua_B 1021 Kapese Centre 3805 20

Nabwelnyang 204 Lokaburu 1636

Nadoto 282 3 Lomokama 2530 21

Nakwakunyuk 360 Lowoiang 1807

Nakwamomwa 992 Nalemsek 2854 22

Nakwasinyen 253 Kaakalel 2060

Naputirio 720 Kaesamalit 850

Nawoyatira 243 Kaesam 369

Urban 253 Kangaki 257

Apetet 1602 Kangakipur 2585 23

Arumrum 708 4 Katiir 2820

Ayengyeng 686 Loperot 4427 24

Catholic Lotubae 1602 Nalemkal 1162 25

Lotubae Dispensary 1436 Lomelek 1795

Epetamuge 1956 5 Chokchok 1916

Juluk 645 Nakabosan 1756 26

Kambi Maji_Karen 916 Nakalalei 3512 27

Lereete 312 Kaakalel 495

Nakwakiru 812 Kimabur 2544

Nakwamekwi 916 6 Locheremoit 5419 28

Nakwasinyen 1124 Lochwaa 4948 29

Namorutunga 749 Lochwa-Kaakalel 377 30

Namukuse 874 Sopel 778

Naoyatiira 520 Kaekorisol 2526

Napetao 437 Kaikoit 408

Nayanaekatwan 812 7 Nakaititia 307

Totitinyo 1040 Namanatelem 190

Windmill 874 Kaab 292 31

62

Nakukulas 1427 Kengna 292

Kaaruko 612 Napusimoru 2205

Kaereng 781 8 Market 1763

Kaloporor 312 Nadapal 2241 32

Kariwo 156 Naregaekamar 1005

Lopii 468 Natorobwo 2142 33

Lotonguna 469 Kakong 1883

Loturerei 624 Loyapat 2094

Katiir 158 Aregae 1230 34

Edoot 299 Juluk 1174

Juluk Katilia 808 Aregae 1230

Kaao 211 Kaputir 1139 35

Kaekoromug 404 Lomerimudang 1518

Kaibole 474 Rc Nakwamoru 1609

Kanaan 105 Naoyaragae 4677 36

Kanakipe 53 Ngikwakes 137

Kangisaja 439 Lorogon 2405 Rc

Kidewa 211 Angarabat 4343 37

Kotoro 492 Katilu Centre 4823 Rc

Lokamusio 70 Kalopuricho 166

Lokorkor 457 Lomonyang 150

Lomunyenakwaan 422 Lopur Bethlehem 3291

Lopeduru 878 Lopur-Naperobei 466 38

Nabei 527 Lopur-Shanty 3080

Nagelesea 70 Ngabaakan 135

Nakatunga 404 Nyangaita 1232 39

Nakolobae 527 9 Kaareng 1365

Nakwachawa 264 Lokapel Arumarum 438

Nakwamekwi 123 Lokapel 3029 40

Nawouna 105 Naperobei 2643

Ngataparin 246 Arumrum-Alocha 1317

Atoot 657 Kalemungorok 1720 41

Ayanae Katwaan 464 Kapelo 1734

Echoke 351 Achukule 2136 42

Lokulubech 760 Nabeiye 464

Mlango Pesa 476 Nakabosan 232

Naaruma 498 Simailele 928

Ngikengoi 181 Kagitankori 1618

63

Riet 260 10 Kanaodon Centre 1096 43

Veterenary 260 Kanaodon 757

Parkati 9329 11,12 Kangakimak 3209

Kangisaja 352 Kangitankori 861 44

Nakabosan 18 Lotonguna 691

Nakwamekui 580 Nariamao 1174

Nanyangabei 422 Kakalel 982

Ngimeyana 194 Namwa 1207 45

Kakulit 2029 Naremit 2161

Appendix 6:Turkana West Survey zone sampled clusters

Geographical Unit Population Size

Cluster Geographical Unit Population Size

Cluster

Loreng 956 Ngimugetuk 59

Abune 845 Ngiremioto 103

Adipo 745 Pag 221

Akoros 367 Pokotom 649 Rc

Alemsekon 1001 1 Abulon 161

Atiir 1023 Akodos 377

Esanyanait 978 Kabilikeret 653

Kaameyan 434 Kambi Kaabuk 502

Kokorio 233 Komotogae 100

Lokoudekei 478 Lokichar 829

Lorengesinyen 967 Lokipetotakwaan 289

Nachakamor 500 Lorus 314

Tulubalany 4202 2 Loruth 25

Katelemot 4007 3 Nairobi 377

Lokipoto 15437 Rc,4 Naivasha 590

Loito 5388 5 Nakitoekirion 239

Nalapatui 4016 6 Nakitoikiron 138

Abaat 630 Nakodos 226

Natira 1374 Napeto 239

Kimukoe 1418 Nayanaakali 251

Lokitokin 664 Pelekech 653 20

Nakwasuguru 1297 7 Adome 399

Nawontos 1448 Agis 290

Lonyuduk 488 Akode 183

Nakoyo 2033 Kaepokongoria 341

Kikeunae 280 Kalopusia 150

Lochileta 315 Kamunyayep 532

Lokwamor 186 Kangatesiroi 1488

Market 164 Lobanga 133

64

Nakilekipus 300 Lodakach 440

Nakwajem 508 Lopusiki Centre 349

Nakwamunyen 686 8 Ngimugerega 116

Napasia 21 Ngimunyanakirino 449

Nawotom 214 Namon 2583 21

Ngapasia 336 Nakalale 3886

Kanginyangakipo 196 Losajait 168

Kapetajom 125 Lowoi 320

Kiiloroe 258 Naurukori 1145 22

Kikeunae 240 Ngakare Arengak 519

Ngariemeto 560 Kenyangalupu 2164

Ngimunyenakirion 391 Naduat 1323

Songot Emoru 124 Jerusalem 2070 23

Loreng 3210 9 Kabangakeny 5768 24

Namor-Kirionok 2491 Nachuchukait 3142

Lopur 21997 10,11,12 Locherakal 6459 Rc

Nalemsekon 16214 13,Rc,14 Nabangakeny 598

Tarach 2186 Nachuchukait 96

Lochorangereng 3750 15 Nadapal 2296 25

Agiis 336 Ngigoloki 1531

Lopededkit 270 Kanginyangakipo 374

Akwanga 1371 Kapetajom 238

Atirae 1439 16 Kiiloroe 493

Kiwanjandege 539 Kikeunae 459

Komudei 2091 Ngariemeto 1070

Lopacho 1236 Ngimunyenakirion 748

Nadapal 1034 Songot Emoru 238

Natiir I 1798 17 Lochor Ereng 725 26

Natiir 2 1259 Napeikar 1692

Ngikwakais 967 Ngmunyena 202

Ngikwakais 1574 Lomidat 704

Towokayeni 1956 18 Moruamekwi 876

Aic 352 Teremukus 359

America 148 Lokangae A 3608 27

Asekon 369 Lokangae B 5838 28

Ayanae-Angitiira 15 Nasinyono 3600

Ejore 989 Apong 3806 29

Ekipetot 236 Aritae 1388

Idps 856 Kanyangangiro 2488

Kabokorit 797 Kapetadie 4689 30

Kambi Forest 207 Lorus 5982 31

Kawarnaparan 133 Jie 1 334

65

Kipirikibari 280 Jie 2 98

Lego 1122 19 Kaachuro 1258 32

Loitakori 251 Lotoom 1 766

Lokiding 74 Lotoom 2 1867

Lokwasinyen 30 Tamil 885

Lomunyenpus 708 Amina 669

Lotaka 74 Ekisil 753

Mana-Anarue 162 Longor 1031

Market 221 Nalamacha 1533 33

Nadunga 89 Ngidocha 335

Naitakori 103 Ngimerisua 139

Nakure 605 Ngisali 530

Nakwamunyen 930 Ngiwoyasike 613

Napeibabat 266 Napeikar 573

Nateloi 89 Nasoo 1058

Nateniso 900 Ngisoowa 838

Nawoitorong 517 Rukruk 882 34

Nayanae Angitiira 30 Loremiet 2700

Ngaremeto 148

Appendix 7:Weight for Height Z scores ± SD-Malnutrition pockets in red font colour Turkana Central Weight for Height Z scores ± SD

Sublocation Z scores Sub location Z scores

Lodwwar township Cluster 1 : -0.87 ± 0.77 (n=13) lochor-edome Cluster 22 : -0.02 ± 0.11 (n=22)

Nakwamekwi Cluster 2 : -1.52 ± 1.05 (n=19) Puch Cluster 23 : -1.00 ± 0.91 (n=15)

Nakwamekwi Cluster 3 : -1.31 ± 1.05 (n=14) puch Cluster 24 : -1.49 ± 1.05 (n=20)

Napetet Cluster 4 : -1.85 ± 0.90 (n=18) lorengipi Cluster 25 : -0.78 ± 1.04 (n=24)

Kanamkemer Cluster 5 : -1.87 ± 0.77 (n=16) Lorengipi-lodwat Cluster 26 : -1.01 ± 0.66 (n=18)

Kanamkemer Cluster 6 : -1.28 ± 1.47 (n=15) Lorengipi-lochor alomala Cluster 27 : -0.59 ± 1.20 (n=12)

Nawaitorong Cluster 7 : -1.72 ± 0.68 (n=18) Lorengipi-lochor alomala Cluster 28 : -1.00 ± 0.95 (n=21)

Kerio Cluster 8 : -1.20 ± 1.20 (n=12) lorugum Cluster 29 : -1.89 ± 0.87 (n=17)

Nakurio Cluster 9 : -1.53 ± 1.20 (n=20) Turkwel Cluster 30 : -1.14 ± 1.15 (n=13)

Nadoto Cluster 10 : -1.76 ± 0.97 (n=16) Turkwel Cluster 31 : -1.03 ± 0.85 (n=20)

Kerio Cluster 11 : -1.85 ± 1.27 (n=15) Kalemnyang Cluster 32 : -1.38 ± 0.87 (n=16)

Nakoret Cluster 12 : -1.61 ± 1.19 (n=18) Nadapal Cluster 33 : -1.65 ± 1.27 (n=15)

Lorengelup Cluster 13 : -1.66 ± 1.11 (n=18) Napeikar Cluster 34 : -1.37 ± 0.98 (n=22)

Kalokol Cluster 14 : -1.49 ± 1.21 (n=16) Lomeyan-Turkwel Cluster 35 : -1.11 ± 1.37 (n=24)

Kalokol Cluster 15 : -1.72 ± 0.98 (n=17) Lomeyan-Turkwel Cluster 36 : -1.15 ± 0.99 (n=21)

Kapua Cluster 16 : -1.78 ± 1.03 (n=11) Lomeyan-Turkwel Cluster 37 : -0.83 ± 0.80 (n=12)

Namadak Cluster 17 : -1.20 ± 0.93 (n=12) kotaruk Cluster 38 : -0.84 ± 0.86 (n=18)

Kalolok Cluster 18 : -1.17 ± 0.98 (n=13) kotaruk Cluster 39 : -0.52 ± 0.91 (n=17)

Kalokol- Cluster 19 : -0.90 ± 1.07 (n=16) Naipa Cluster 40 : -1.64 ± 1.07 (n=19)

66

Lochereikeny

Elieye Cluster 20 : -1.61 ± 0.66 (n=19) Kalemnyamng Cluster 41 : 0.02 ± 0.30 (n=22)

Lomopus Cluster 21 : -1.46 ± 0.81 (n=26)

Turkana North weight-for-Height z-scores ± SD

Sub location weight-for-Height z-scores ± SD Sub location weight-for-Height z-scores ± SD

Nakalale-Gold Cluster 1 : -1.22 ± 0.99 (n=19) Natapar Cluster 22 : -1.55 ± 1.05 (n=14)

Kachoda Cluster 2 : -1.62 ± 1.18 (n=21) Natapar Cluster 23 : -0.91 ± 0.92 (n=13)

Natoo Cluster 3 : -1.49 ± 0.70 (n=17) Karach1 Cluster 24 : -1.54 ± 0.92 (n=15)

Kataboi Cluster 4 : -1.77 ± 1.23 (n=17) Natapar Cluster 25 : -1.12 ± 0.78 (n=15)

Katiko Cluster 5 : -1.06 ± 0.82 (n=17) Kaitende Cluster 26 : -0.99 ± 0.74 (n=13)

Lomekwi Cluster 6 : -1.39 ± 0.64 (n=15) Nalita Cluster 27 : -1.12 ± 0.89 (n=18)

riokomor Cluster 7 : -1.24 ± 0.78 (n=15) Nalita Cluster 28 : -1.23 ± 1.16 (n=16)

riokomor Cluster 8 : -1.02 ± 0.78 (n=18) lokolio Cluster 29 : -1.07 ± 0.81 (n=16)

Kokslei Cluster 9 : -1.37 ± 0.75 (n=14) lokolio Cluster 30 : -1.54 ± 0.83 (n=20)

lowarengak Cluster 10 : -1.90 ± 1.02 (n=18) Mlimatatu Cluster 31 : -1.15 ± 1.07 (n=15)

lowarengak Cluster 11 : -1.45 ± 1.07 (n=17) Mlimatatu Cluster 32 : -0.98 ± 0.67 (n=16)

kanamkuny Cluster 12 : -1.47 ± 1.39 (n=15) kaalem Cluster 33 : -1.57 ± 1.01 (n=17)

Kokslei Cluster 13 : -1.40 ± 0.89 (n=15) kaalem Cluster 34 : -0.96 ± 0.80 (n=18)

Kachoda Cluster 14 : -1.90 ± 1.11 (n=19) kotome Cluster 35 : -1.00 ± 1.20 (n=12)

Napeikar Cluster 15 : -1.31 ± 0.98 (n=16) Karach Cluster 36 : -0.99 ± 1.32 (n=17)

kokuro Cluster 16 : -0.96 ± 0.93 (n=16) kanakurudio Cluster 37 : -1.08 ± 1.10 (n=13)

sesame Cluster 17 : -1.50 ± 0.75 (n=17) kanakurudio Cluster 38 : -1.63 ± 0.89 (n=15)

lowarengak Cluster 18 : -1.58 ± 0.92 (n=16) Nadunga Cluster 39 : -0.53 ± 0.96 (n=16)

lokomarinyang Cluster 19 : -1.45 ± 0.82 (n=15) Kangakipur Cluster 40 : -0.69 ± 0.90 (n=17)

koyosa Cluster 20 : -1.18 ± 1.02 (n=14) Kangakipur Cluster 41 : -0.64 ± 1.09 (n=13)

Natapar Cluster 21 : -1.32 ± 0.54 (n=16)

Turkana South Weight-for-Height z-scores ± SD

Sub location Weight-for-Height z-scores ± SD Sub location Weight-for-Height z-scores ± SD

lokori Cluster 1 : -1.26 ± 1.29 (n=15) loperot Cluster 24 : -1.55 ± 1.13 (n=16)

lokori Cluster 2 : -2.19 ± 0.81 (n=21) loperot Cluster 25 : -1.57 ± 0.88 (n=21)

lotubae Cluster 3 : -1.39 ± 1.19 (n=13) Kalemngorok Cluster 26 : -1.56 ± 0.91 (n=25)

lutubae Cluster 4 : -1.52 ± 1.04 (n=24) Nakalale Cluster 27 : -1.54 ± 0.68 (n=16)

lotubae Cluster 5 : -1.90 ± 0.92 (n=23) locwangitamtak Cluster 28 : -1.53 ± 1.38 (n=18)



lopii Cluster 8 : -1.60 ± 0.94 (n=16) Napusmoru Cluster 31 : -1.36 ± 1.51 (n=13)

katilia Cluster 9 : -1.07 ± 0.76 (n=19) Kainuk Cluster 32 : -1.17 ± 0.99 (n=22)

Elelea Cluster 10 : -1.45 ± 0.87 (n=19) Kainuk Cluster 33 : -0.98 ± 0.89 (n=16)

parakati Cluster 11 : -1.97 ± 0.80 (n=16) Kaptir-Kalomwae Cluster 34 : -1.36 ± 1.18 (n=16)

parakati Cluster 12 : -1.83 ± 1.21 (n=21) Nakwamoru Cluster 35 : -1.42 ± 1.07 (n=15)

Kochodin-lochordin Cluster 13 : -1.99 ± 1.11 (n=11) Nakwamoru Cluster 36 : -1.16 ± 1.32 (n=20)

67

Napeitom Cluster 14 : -1.63 ± 1.16 (n=18) Katilu Cluster 37 : -1.46 ± 0.87 (n=14)

Nadome Cluster 15 : -1.78 ± 1.23 (n=18) Katilu Cluster 38 : -1.89 ± 1.22 (n=21)

Kamuge Cluster 16 : -1.25 ± 1.04 (n=19) Katilu Cluster 39 : -1.63 ± 1.11 (n=23)

ngilukia Cluster 17 : -0.97 ± 0.79 (n=19) lokapel Cluster 40 : -1.39 ± 0.97 (n=20)

lokichar Cluster 18 : -2.17 ± 1.09 (n=16) Kalmenngorok Cluster 41 : -1.44 ± 0.96 (n=22)

Kapese Cluster 19 : -1.59 ± 1.06 (n=16) Kalemngorok Cluster 42 : -1.51 ± 0.70 (n=21)

Kapese Cluster 20 : -1.49 ± 1.10 (n=19) kanaodon Cluster 43 : -1.80 ± 1.05 (n=20)

Kapese Cluster 21 : -0.87 ± 1.19 (n=10) kanaodon Cluster 44 : -1.45 ± 1.22 (n=20)

kapese Cluster 22 : -1.52 ± 1.21 (n=21) Kalemngorok Cluster 45 : -0.68 ± 0.78 (n=20)

Kalapata Cluster 23 : -1.55 ± 1.02 (n=16)

Turkana West Weight-for-Height z-scores ± SD

Sublocation Weight-for-Height z-scores ± SD Sublocation Weight-for-Height z-scores ± SD

Letea-Lorit Cluster 1 : -1.62 ± 0.99 (n=18) Nadapal Cluster 18 : -0.77 ± 0.97 (n=16)

letea-tulubany Cluster 2 : -1.22 ± 0.92 (n=17) Namorungole Cluster 19 : -1.00 ± 1.03 (n=22)

loreng Cluster 3 : -0.78 ± 0.70 (n=19) Pelekech-lokore Cluster 20 : -1.09 ± 0.98 (n=16)

lokipoto Cluster 4 : -0.99 ± 1.33 (n=15) pelekech Namon Cluster 21 : -0.97 ± 1.06 (n=16)

letea-loito Cluster 5 : -0.57 ± 0.88 (n=14) losajait Cluster 22 : -0.79 ± 0.70 (n=16)

kalaopeyei-Nalaputui Cluster 6 : -0.91 ± 1.14 (n=17) Lokichogio Cluster 23 : -1.58 ± 0.94 (n=20)

oropoi Cluster 7 : -1.21 ± 0.93 (n=20) Lokichogio Cluster 24 : -0.87 ± 0.96 (n=20)

Kalobeyei Cluster 8 : -1.42 ± 1.00 (n=13) Lokichogio Cluster 25 : -1.19 ± 1.01 (n=19)

Katelemot Cluster 9 : -0.62 ± 1.36 (n=13) songot-Lokundule Cluster 26 : -1.21 ± 0.76 (n=16)

Kakuma-Lopur Cluster 10 : -0.95 ± 0.95 (n=14) lokangae Cluster 27 : -1.49 ± 0.99 (n=16)

Kakuma-Lopur Cluster 11 : -0.92 ± 0.83 (n=18) lokangae Cluster 28 : -1.15 ± 0.96 (n=16)

pelekech-lopusiki Cluster 12 : -1.50 ± 0.88 (n=17) mogila Cluster 29 : -0.71 ± 0.70 (n=14)

Kakuma-Lopur Cluster 13 : -0.94 ± 0.74 (n=17) mogila Cluster 30 : -0.56 ± 0.50 (n=9)

Kakuma-lopur Cluster 14 : -1.01 ± 1.10 (n=21) mogila Cluster 31 : -0.87 ± 0.54 (n=15)

talach Cluster 15 : -1.34 ± 0.60 (n=20) mogila Cluster 32 : -0.77 ± 0.74 (n=16)

Nadapal Cluster 16 : -1.02 ± 0.77 (n=16) Nanam Cluster 33 : -0.75 ± 0.79 (n=13)

Nadapal Cluster 17 : -0.85 ± 0.75 (n=17) lokichogio Cluster 34 : -0.82 ± 0.98 (n=15)

68

Appendix 8: SMART survey questionnaire

Turkana Nutrition SMART Survey Questionnaire © June 2016

1.IDENTIFICATION 1.1 Data Collector___________________ 1.2 Team Leader_______________ 1.3 Survey date (dd/mm/yy)--------------------------

1.4 County 1.5 Sub County 1.6 Division 1.7 Location 1.8 Sub-Location 1.9 Village 1.10 Cluster No 1.11 HH No 1.12 Team No.

2. Household Demographics

2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9

Age Group

Please give me

the names of the

persons who

usually live in

your household.

Age (months

for children

<5yrs and

years for over

5’s)

Childs age

verified by

1=Health

card/Birth

certificate/

notification

/Baptism card

2=Recall

3= no

verification

Sex 1= Male 2=

Femal

e

If 3 yrs and under 18 Is child enrolled in school? 1 = Yes 2 = No

(If yes go

to 2.8; If no

go t o 2.7)

Main Reason for not attending School (Enter one code from list) 1=chronic Sickness 2=Weather (rain, floods, storms) 3=Family labour responsibilities 4=Working outside home 5=Teacher absenteeism 6=Too poor to buy school items e.t.c 7=Household doesn’t see value of schooling 8 =No food in the schools 9 = Migrated/ moved from school area 10=Insecurity 11-No school Near by 12=Married 13=others

(specify)…………………..

What is the highest level of education attained?(level completed) From 5 yrs and above 1 = pre primary 2= Primary 3=Secondary 4=Tertiary

5= None 6=others(specify)

If the household

owns mosquito

net/s, who slept

under the mosquito

net last night?

1= children under 5

2=Pregnant /lactating

mothers

3= other(_____)

YRS MTH

< 5 YRS

>5 TO 18

YRS

69

ADULT

2.10 How many mosquito nets does this household have? ____________________ (Indicate no.)

2.11 Main Occupation of the Household Head – HH.

(enter code from list) 1=Livestock herding 2=Own farm labour 3=Employed (salaried) 4=Waged labour (Casual) 5=Petty trade 6=Merchant/trader 7=Firewood/charcoal 8=Fishing

9=Others (Specify) |____|

2.12. What is your main current source of income

1. =No income

2. = Sale of livestock

3. = Sale of livestock products

4. = Sale of crops

5. = Petty trading e.g. sale of firewood

6. = waged labor

7. =Permanent job

8. = Sale of personal assets

9. = Remittance

10. Other-Specify |____|

2.13 Marital status of the respondent

1. = Married 2. = Single 3. = Widowed 4. = separated

5. = Divorced. |____|

2.14 What is the residency status of the household? 1. IDP

2. Refugee

|____|

3. Resident

70

Fever with Malaria: High temperature with shivering

Cough/ARI: Any episode with severe, persistent cough or difficulty breathing

Watery diarrhoea: Any episode of three or more watery stools per day

Bloody diarrhoea: Any episode of three or more stools with blood per day

3. CHILD HEALTH AND NUTRITION (ONLY FOR CHILDREN 6-59 MONTHS OF AGE; IF N/A SKIP TO SECTION 3.6) Instructions: The caregiver of the child should be the main respondent for this section

3.1 CHILD ANTHROPOMETRY

(Please fill in ALL REQUIRED details below. Kindly maintain the same child number as part 2)

A

Child No.

B C D E F G H I J K L 3.2 3.3

what is the

relationship of the

respondent with the

child/children

1=Mother

2=Father

3=Sibling

4= grandparent

5=Other (specify)

SEX

F/m

Exact

Birth

Date

Age in

month

s

Weigh

t

(KG)

XX.X

Height

(CM)

XX.X

Oedem

a

Y= Yes

N= No

MUAC

(cm)

XX.X

Has your child (NAME) been ill in the past two weeks? If No, please skip part K and proceed to 3.4) 1.Yes

2. No

If YES, what type of illness (multiple responses possible) 1 = Fever with chills like malaria 2 = ARI /Cough 3 = Watery diarrhoea 4 = Bloody diarrhoea 5 = Other (specify) See case definitions below

If the child had watery diarrhoea in the last TWO (2) WEEKS, did the child get THERAPEUTIC zinc supplementation? Show sample and probe further for this component check the remaining drugs(confirm from mother child booklet)

1 = Yes 2 = No 3 = Do not know

When the

child was

sick did

you seek

assistance

?

1.Yes 2. No

If the response is yes to

question # 3.2 where did

you first seek assistance?

1. Traditional healer

2.Community health worker

3. Private clinic/ pharmacy

4. Shop/kiosk 5.Public clinic

6. Mobile clinic

7. Relative or friend

8. Local herbs

9.NGO/FBO 10. other specify

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3.4 Kindly maintain the same child number as part 2 and 3.1 above

A B C D E F G H I

Child No.

How many

times has

child

received

Vitamin A

in the past

year?

(show

sample)

How many

times did

you receive

vitamin A

capsules

from the

facility or

out reach

1= health facility 2= outreach site 3= ECDE centres 4= campaigns

If Vitamin A

received

how many

times

verified by

Card?

How many

times has

child

received

drugs for

worms

in the past

year? (12-

59 Months)

(show Sample)

Has the child received BCG vaccination? 1 = scar 2=No scar

Has child received OPV1 vaccination 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know

Has child received OPV3 vaccination? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know

Has child received measles vaccination at 9 months (On the upper right shoulder)? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know

Has child received the second measles vaccination (18 to 59 months ) (On the upper right shoulder)? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know

01

02

03

01

02

03

04

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MATERNAL NUTRITION FOR WOMEN OF REPRODUCTIVE AGE (15-49 YEARS)(Please insert appropriate number in the box)

3.7 3.8 3.9 3.10 3.11

Woman NUmber. (all ladies in the HH aged 15-49 years from the demographics page)

What is the Woman’s physiological

status

1. Pregnant 2. Lactating 3. Pregnant and lactating 4. None of the above

Woman’s MUAC

reading: ____.__cm

During the pregnancy of the (name of

child below 24 months) did you take

IFASS (iron pills, sprinkles with iron,

iron syrup or iron-folate tablets? (name

that appears in HH register)

1. Yes 2. No 3. Don’t know 4. N/A

If Yes, for how many days? (approximate the number of days)

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4.0 WATER, SANITATION AND HYGIENE (WASH)/- Please ask the respondent and indicate the appropriate number in the space provided

4.1 What is the MAIN source of drinking water for the

household NOW?

1. Piped water system/ borehole/ protected spring/protected shallow wells

2. Unprotected shallow well 3. River/spring 4. Earth pan/dam 5. Earth pan/dam with infiltration well |____| 6. Water trucking /Water vendor 7. Other (Please specify)

4.2 What is the trekking distance to the current main water

source?

1=less than 500m (Less than 15 minutes) 2=more than 500m to less than 2km (15 to 1 hour) 3=more than 2 km (1 – 2 hrs) 4=Other(specify) |____|

4.2.2a Do you queue for water?

1. Yes 2. No (If No skip to question 4.3) |____|

4.2.2b. If yes how long?

1. Less than 30 minutes 2. 30-60 minutes 3. More than 1 hour

|____|

4.3a Is anything done to your water before drinking

(Use 1 if YES and 2 if NO). if No skip to 4.4

|____|

4.3b If yes what do you do? (MULTIPLE RESPONSES

POSSIBLE) (Use 1 if YES and 2 if NO).

1. Boiling………… ……………………………………. |____| 2. Chemicals (Chlorine,Pur,Waterguard)…………… |____| 3. Traditional herb……………………………………... |____| 4. Pot filters…………………………………………….. |____| 5. Other (specify_________)…………………………. |____|

4.4 Where do you store water for drinking?

1. Open container / Jerrican 2. Closed container / Jerrican |____|

4.5 How much water did your household use YESTERDAY

(excluding for animals)?

(Ask the question in the number of 20 liter Jerrican and convert to

liters & write down the total quantity used in liters)

|____|

4.6 Do you pay for water?

1. Yes 2. No (If No skip to Question 4.7.1)

|____|

4.6.1 If yes, how much per 20 liters

jerrican _________ KSh/20ltrs

4.6.2 If paid per month

how much |____|

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4.7.1 Yesterday (within last 24 hours) at what instances did you wash your hands? (MULTIPLE RESPONSE- (Use 1 if

“Yes” and 2 if “No”)

1. After toilet……………………………………………………………………………………………………………… 2. Before cooking………………………………………………………………………………………………………... 3. Before eating…………………………………………………………………………………………………………. 4. After taking children to the toilet……………………………………………………………………………………. 5. Others………………………………………………………………………………………………………………….

|____|

|____|

|____|

|____|

|____|

4.7.2 If the caregiver washes her hands, then probe further; what do you use to wash your hands?

1. Only water 2. Soap and water 3. Soap when I can afford it 4. traditional herb 5. water and ash 6. Any other specify |____|

4.8 Where do members of your household Mainly

relieve themselves?

1. In the bushes, open defecation 2. Neighbor or shared traditional pit/improved latrine 3. Own traditional pit/improved latrine 4. Others Specify

|____|

5.0: Food frequency and Household Dietary Diversity

Did members of

your household

consume any

food from these

food groups in

the last 7

days?(food must

have been

cooked/served at

the household)

1=Yes

0=No

If yes, mark days the food was consumed in the last 7 days? yes=1; no=2

What was the main

source of the dominant

food item consumed in

the HHD?

1.Own production

2.Purchase

3.Gifts from

friends/families

4.Food aid

5.Traded or Bartered

6.Borrowed

7.Gathering/wild fruits

8.Other (specify)

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*Type of food* D1 D2 D 3 D 4 D5 D 6 D7 TOTAL

5.1. Cereals and cereal products (e.g. sorghum,

maize, spaghetti, pasta, anjera, bread)?

5.2. Vitamin A rich vegetables and tubers: Pumpkins, carrots, orange sweet potatoes

5.3. White tubers and roots: White potatoes, white yams, cassava, or foods made from roots

5.4 Dark green leafy vegetables: Dark green leafy vegetables, including wild ones + locally available vitamin A rich leaves such as cassava leaves etc.

5.5 Other vegetables (e,g, tomatoes, egg plant, onions)?

5.6. Vitamin A rich fruits: + other locally available vitamin A rich fruits

5.7 Other fruits 5.8 Organ meat (iron rich): Liver, kidney, heart

or other organ meats or blood based foods

5.9. Flesh meats and offals: Meat, poultry, offal (e.g. goat/camel meat, beef; chicken/poultry)?

5.10 Eggs?

5.11 Fish: Fresh or dries fish or shellfish 5.12 Pulses/legumes, nuts (e.g. beans, lentils,

green grams, cowpeas)?

5.13 Milk and milk products (e.g. goat/camel/ fermented milk, milk powder)?

5.14 Oils/fats (e.g. cooking fat or oil, butter, ghee, margarine)?

5.15 Sweets: Sugar, honey, sweetened soda or sugary foods such as chocolates, sweets or candies

5.16 Condiments, spices and beverages:

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REFERENCES:

Bhutta, Z. A., Das, J. K., Walker, N., Rizvi, A., Campbell, H., Rudan, I. & Black, R. E. 2013. Interventions to

address deaths from childhood pneumonia and diarrhoea equitably: what works and at what cost? The

Lancet, 381, 1417-1429.

Checkley, W., Buckley, G., Gilman, R. H., Assis, A. M., Guerrant, R. L., Morris, S. S., MØlbak, K.,

Valentiner-Branth, P.,Lanata, C. F., Black, R. E., Malnutrition, A. T. C. & Network, I. (2008). Multi-country

analysis of the effects of diarrhoea on childhood stunting. International journal of epidemiology, 37(4), 816-

830.

FAO and FHI 360. 2016. Minimum Dietary Diversity for Women: A Guide for Measurement. Rome: FAO.

Prüss‐Ustün, A., Bartram, J., Clasen, T., Colford, J. M., Cumming, O., Curtis, V., ... & Cairncross, S. (2014).

Burden of disease from inadequate water, sanitation and hygiene in low‐and middle‐income settings: a

retrospective analysis of data from 145 countries. Tropical Medicine & International Health,19(8), 894-905.

United Nations World Food Programme, Food security analysis (VAM). Food Consumption Score Nutritional

Quality analysis (FCS-N): Technical Guidance Notes .Via Cesare Giulio Viola 68, Parco dei Medici, 00148 -

Rome - Italy

6. COPING STRATEGIES INDEX

Frequency score:

Number of days out of the

past seven (0 -7).

In the past 7 DAYS, have there been times when you did not have enough food or money to buy food?

If No; END THE INTERVIEW AND THANK THE RESPONDENT

If YES, how often has your household had to: (INDICATE THE SCORE IN THE SPACE PROVIDED)

1 Rely on less preferred and less expensive foods?

2 Borrow food, or rely on help from a friend or relative?

3 Limit portion size at mealtimes?

4 Restrict consumption by adults in order for small children to eat?

5 Reduce number of meals eaten in a day?

TOTAL HOUSEHOLD SCORE:

END THE INTERVIEW AND THANK THE RESPONDENT

TURKANA SMART NUTRITION SURVEYS FINAL REPORT

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