Turkana Smart Nutrition Surveys Final Report

TURKANA SMART NUTRITION SURVEYS

FINAL REPORT

June 2016 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

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

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 4

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 5

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

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

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.

Table 1:Survey findings summary Indicator Turkana Turkana Turkana Turkana Turkana Central North South West 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 24.5% 23.4% 30.3% 14.4% 23.3% (GAM)-June 2016 (20.2-29.4) (19.4-28.1) (26.7-34.1) (11.1-18.5) (21.1 – 25.5) Global Acute Malnutrition 20.9% 22.9% 24.5% 16.7% 21.2 % (GAM)-June 2015 (17.9 – 24.4) (19.6 – 26.6) (21.1 – 28.2) (13.8 – 20.1) (19.7 – 22.9) Severe Acute Malnutrition 5.6% 4.1% 8.9% 1.8 % 5.3% (SAM)-June 2016 (4.2-7.5) (2.5-6.7) (7.1-11.0) (1.0-3.3) (4.5-6.3) Severe Acute Malnutrition 4.8 % 3.8 % 6.1 % 4.8 % 5.0% (SAM)-June 2015 (3.4 – 6.6) (2.4 – 6.1) (4.3 – 8.5) (3.3 – 6.9) (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 33.9% 30.8% 44.6% 27.7% 34.7% underweight-June 2016 (29.6 – 38.4) (25.6-36.5) (40.4 – 48.8) (23.1 – 33.2) (32.1-37.4) Prevalence of global 30.5 % 29.4 % 38.3 % 24.0 % 31% underweight-June 2015 (26.8 – 34.6) (24.4 – 34.9) (33.9 – 43.0) (20.4 – 28.0) (28.8 – 33.3) Prevalence of severe 10.0% 9.0% 17.8% 6.0% 10.9% underweight-June 2016 (7.3 – 13.5) (6.7-12.1) (14.6-21.4) (3.8– 9.5) (9.5-12.4) Prevalence of severe 8.8% 8.3 % 12.0 % 7.3 % 9.4% underweight-June 2015 (7.1 – 10.9.) (6.0 – 11.4) (9.0 – 15.9 ) (5.5 – 9.6) (8.1-10.8) Stunting (WHO 2006)-2015 n = 720 n =831 n =661 n =567 n=2771 Prevalence of global 27.2% 25.1% 33.6% 25.9% 28.2% stunting –June 2016 (22.4-32.5) (20.9 – 29.9) (29.3-38.1) (21.3-31.0) (25.7-30.9) Prevalence of global 24.6 % 21.0 % 32.7 % 21.7 % 25.6% stunting –June 2015 (20.9 – 28.6) (16.9 – 25.7) (28.6 – 37.0) (18.4 – 25.5) (24.0-27.3) Prevalence of severe 8.1% 10.5% 10.5% 6.5% 8.0% stunting-June 2016 (5.4-12.1) (7.8-13.9) (7.8-13.9) (4.5 – 9.1) (6.7-9.4) Prevalence of severe 6.1% 5.4% 9.7% 5.3% 6.8% stunting-June 2015 (4.6 – 8.2) (3.8 – 7.7) (7.7 – 12.2) (3.7 – 7.6) (6.1-7.6) Prevalence of acute n=720 n=831 n=661 n=567 malnutrition by MUAC Severe under nutrition 2.4% 1.5% 2.3% 1.2% (< 115 mm)-July 2016 (1.3-4.2) (0.8-2.8) (1.2-4.4) (0.5-3.3) Severe under nutrition 1.7 % 1.6 % 1.7 % 2.0 % (< 115 mm)-July 2015 (0.7 – 3.7) (0.9 – 2.9) (1.0 – 2.8 9) (0.8 – 4.9)

Moderate undernutrition 6.3% 9.1% 8.2% 7.2% (≥115–<125 mm)-July 2016 (4.5-8.6) (6.5-12.6) (6.3-12.6) (4.9-10.5) Moderate undernutrition 7.8 % 9.9 % 9.0 % 9.0 % (≥115–<125 mm)-July 2015 (5.9 – 10.1) (7.4 – 13.0) (6.7 – 12.0) (6.0 – 13.5) Global Acute Malnutrition 8.6% 10.6% 10.5% 8.5% (≤125 mm) –June 2016 (6.4-11.5) (7.6-14.6) (8.1-13.4) (5.8-12.2) Global Acute Malnutrition 9.4 % 11.5 % 10.7 % 11.1 % (≤125 mm) –June 2015 (7.3 – 12.1) (8.9 – 14.7) (8.0 – 14.1) (7.4 – 16.2) Maternal Malnutrition- n=455 n=455 n=550 n=412 n=1872 June 2016 % of WRA with MUAC 9.9% 9.0% 7.1% 5.8% 8.0% <21cm PLW with MUAC<21 cm- 9.2% 8.0% 6.5% 6.9% 7.5% 2016 PLW with MUAC<21 cm- 8.5% 10.4% 7.5% 7.8% 8.5% 2015 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 9.7% 15.2% 7.7% 8.8% 10.2% months Indicator Turkana Turkana Turkana Turkana Turkana Central North South West County Vitamin A supplementation and de-worming-June 2016 Children 6-59 months n=467 n=487 n=671 n=395 n=2019 supplemented with vitamin A 57.0% 48.5% 53.9% 42.9% 51.2% Children 12-59 months n=412 n=428 n=580 n=355 n=1775 supplemented with vitamin A at least once 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 24.5% 20.8% 24.6% 44.3% 27.7% de-wormed at least once Children 12-59 months 16.0% 8.4% 10.2% 10.8% 11.4% de-wormed at least twice Children 6-11 months n=55 n=59 n=91 n=39 n=244 supplemented with Vitamin A at least once 96.4% 94.9% 100.0% 97.4% 97.5% Child Morbidity-June 2016 Ill in the last 2 n=728 n=667 n=842 n=572 n=2809 weeks(children 6-59 51.24 % 54.27% 48.69% 56.47 % 52.26% months) 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% 10

0.4% 0.4% 0.0% 0.9% 0.4% Bloody diarrhoea Therapeutic Zinc n= 23 n=33 n= 44 n= 25 n= 125 Supplementation 68.3% 78.8% 85.1% 66.2% 75.5% Maternal Nutrition-June 2015 Iron folate supplementation n=323 n=203 n=393 n=227 n=1146 for pregnant women IFA supplementation for (320)99.1% (187)92.7% (367)93.4% (202)89.0% (1076)93.9% upto 90 days IFA supplementation for (3) 0.9% (16)7.9% (26)6.6% (25)11.0% (70)6.1% between 90 and 180days IFA supplementation >180 (0)0.0% (0)0.0% (0)0.0% (0)0.0% (0)0.0% days n=455 n=455 n=550 n=412 n=1872 PLW with MUAC<21 cm 9.2% 8.0% 6.5% 6.8% 7.5% % of WRA with MUAC 9.9% 9.0% 7.1% 5.8% 8.0% <21cm WASH practises-June 2016 Latrine/toilet utilization by n=670 n=690 n=762 n=573 n=2695 HH 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 19.3 23.2 22.4 23.2 21.9 Strategy Index

Table 2: Recommendations Action Activity By whom By when 1 Update and activate nutrition  Hold joint meeting to revise the contingency MoH,NDMA and Immediately contingency and response plans in plans. nutrition partners South, Central and North Survey  Ongoing quarterly review of the zones. contingency plans. 2 Scale up continuous active case  Sensitize the CHVs on rapid nutrition MoH (nutrition and Continuous finding for malnutrition for the screening and referrals. community health expected caseload(U5) of 34,563  Conduct routine screening through the strategy) and nutrition partners (severe 7,862 and moderate 26,701) existing community health units. and 22,437 pregnant and lactating  Conduct rapid nutrition screening in the hot spot areas. women in the year 2016 and referral  Conduct quarterly mass screening. for timely management 3 Increase access to life saving health  Carry out mapping of communities with MoH and nutrition immediately and nutrition services through limited access to health and nutrition partners integrated outreaches for populations outreaches. with limited access to these services.  Conduct biweekly integrated health and nutrition outreaches in the in communities

far from health facilities.  Monthly monitoring of the outreaches. 4 Develop simplified nutrition survey  Hold community dialogue meetings, MoH and nutrition immediately packs/briefs easily synthesized for meetings with men, mother support Group partners nutrition advocacy and mobilization. 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. 5 Develop and implement nutrition  On a quarterly basis populate the MoH and nutrition Quarterly. service delivery score card at health scorecard with nutrition data. partners facilities  Quarterly meetings to deliberate on the output of nutrition score cards. 6 Conduct comprehensive on the job  Sensitize all the HMTs, Health facility in MoH and nutrition Immediately training and mentorship targeting charges and CHEWs on findings of SMART partners facility health workers, community survey and rapid nutrition screening. health extension workers (CHEWs)  Conduct sensitization of the CHVs on rapid and Community health workers(CHWs) nutrition screening and referrals.  Train all the newly recruited nutritionists on IMAM including the SURGE model. 7 Sensitize and link mother to mother  Hold meetings with MtMSGs to sensitize MoH,NDMA and Continuous support groups (MtMSGs) and them on the current nutrition situation and nutrition partners households with malnourished generate community led actions. children/pregnant and lactating women  Link MtMSGs to existing livelihoods with other nutrition sensitive sectors to interventions. strengthen nutrition resilience 8 Cconduct community dialogue  Develop a brief pack for community dialogue MoH and nutrition Continuous sessions and sensitization meetings meetings. partners with caregivers, community leaders/key  Hold meetings with community leaders, influencers on appropriate childcare communities, MtMSGs and caretakers to practises including micronutrient sensitize them on recommended child care supplementation, deworming, and health & nutrition practices. handwashing, drinking water treatment and latrine utilization. 9 Advocate and create public awareness  Conduct community dialogues with focus MoH and nutrition Continuous on micronutrient supplementation on micronutrients supplementation and partners (micronutrient powders, IFA, Vitamin deworming. A), de-worming and dietary  Integrate micronutrient supplementation diversification. and deworming into the integrated outreaches and nutrition calendar events.  Conduct micronutrient supplementation and deworming at the ECDs. 10 Continue capacity building of health  Mobilize resources for training of health MoH and nutrition Continuous care workers especially newly recruited workers. partners staffs through OJT and joint support  Conduct IMAM training for the newly supervision on a quarterly basis recruited 40 nutritionists.

 Roll out CLTs jointly with the communities. 12 Institutionalize Vitamin A  Sensitize ECD teachers on vitamin A MoH (nutrition& public Quarterly supplementation and de-worming at supplementation and deworming. health), MoE (ECDEs) the Early Child Education  Provide supplies and reporting tools to the and nutrition partners Development(ECDE) centers and scale ECD teachers. up during annual child health  Monitor on quarterly basis VAS campaigns supplementation at the ECD Centers. 13 Procurement and timely distribution of  Provide health facilities with the required MoH/UNICEF/WFP Quarterly essential nutrition commodities to reporting tools. health facilities  Develop commodity consumption reports and requests. 14 Train county, sub county health  Hold training on SBCC for HMTs, health MoH and nutrition December managers, health workers on behavior workers and CHVs. partners 2016 social change communication(BSCC)/communication for development(C4D) 15 Develop, disseminate and implement  Validate and disseminate MIYCN SBCC MoH and nutrition February multi-sectoral nutrition social behavior strategy and messages. partners 2017 change communication (SBCC) strategy to address maternal and child care knowledge, attitude, behavior and practices. 16 Pilot IMAM surge in select health  Sensitize health management team on MoH and nutrition October facilities in the county. This will be IMAM surge. partners 2016 scaled up upon successful pilot.  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. 17 Train community health  Conduct training of the CHEWs and CHVs MoH (nutrition, October volunteers(CHVs) and community on community nutrition module. community strategy) 2016 health extension workers(CHEWs) on and nutrition and nutrition module for community health health partners strategy for improved active case finding, referral and nutrition education 18 Scale up of Baby Friendly Community  Conduct training of the HMTs ,Health MoH (nutrition and November Initiatives(BFCI) in 20 MNCH centers workers and CHVs on BFCI. community health 2016 of excellence  Roll out BFCI in the pilot CHUs. strategy) and nutrition  Monitor the roll out of BFCI. partners 19 Conduct a Malnutrition Causal Link  Develop and validate concept note and MoH,MOAW and December analysis to have in depth proposal for the study. Partners 2017 understanding of determinants of  Mobilize resources for conducting the malnutrition study.

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

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;

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

Central North South West Rationale Estimated 224.4% 326.6% 428.2% 520.1% NDMA march bulletin indicate an alert prevalence of GAM 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 6 6 6 6 KNBS Census report 2010 and previous size survey results Percent of under 15.2% 15.2% 15.2% 15.2% KNBS Census report 2010 five children Percent of non- 2% 2% 2% 2% This is the anticipated non response based respondent on the previous surveys experience Households to be 614 609 674 501

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

included Children to be 494 490 542 403 included Number of clusters 41 41 45 34

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. 17

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.

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)

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.

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.

CHAPTER THREE: RESULTS & DICSUSSIONS 3.0 CHILD HEALTH & NUTRITION 3.1 Demographic results Turkana county 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 1.15 1.02 1.13 1.02 1.09 old 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)

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 83.4 % 69.1% 88.0% (741) 65.4% 77.7% / Baptism card (607) (461) (374 ) (2183) Recall 16.6% 30.9% 12.0% 34.6% 22.3% (121) (206) (101) (198) (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% of the children were in the age group of 6 -17months respectively instead of the expected proportion of 20 -25%. As shown in table 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.

Table 15: Distribution of age and sex of sample Turkana Central Turkana North Turkana south Turkana West County n=720 n=661 n=831 n=567 n=2779 AGE Total Ratio Total Ratio Total Ratio Total Ratio Total Ratio (mo) % Boy: girl % Boy: girl % Boy: girl % Boy: girl % 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 Global Acute Malnutrition (174)24.5% (154)23.4% (249)30.3% (81)14.4% 23.3% (GAM) -June 2016 (20.2- 29.4) (19.4-28.1) (26.7-34.1) (11.1-18.5) (21.1 – 25.5) Global Acute Malnutrition (162) 20.9 % (179) 22.9 % (202) 24.5 % (105) 16.7 % 21.2 % (GAM)-June 2015) (17.9 - 24.4) (19.6 - 26.6) (21.1 - 28.2) (13.8 - 20.1) (19.7 – 22.9) Severe Acute Malnutrition (40) 5.6% (27)4.1% (73)8.9% (10)1.8 5.3% (SAM)-June 2016 (4.2-7.5) (2.5-6.7) (7.1-11.0) (1.0-3.3) (4.5-6.3) Severe Acute Malnutrition (37) 4.8 % (30) 3.8 % (50) 6.1 % (30) 4.8 % 5.0% (SAM) –June 2015 (3.4 - 6.6) (2.4 - 6.1 9 (4.3 - 8.5) (3.3 - 6.9 ) (4.2 – 6.0)

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 North N=657 South N= 823 West N=561 County N= 2571 M =375,F=335 M =359,F=298 M =429, F=394 M =304,F =257 M= 1447 F=1271 Prevalence of Boys (93) 24.8 % (91) 25.3 % (142) 33.1 % (52) 17.1 % (378)25.1% global malnutrition (19.6 - 30.9) (20.6 - 30.8) (29.1 - 37.4) (12.4 - 23.2) (22.4 - 27.9) (<-2 z- score and/or Girls (81) 24.2 % (63) 21.1 % (107) 27.2 % (29) 11.3 % ( 280)21.2% edema) (18.8 - 30.5) (15.5 - 28.2) (22.2 - 32.8 (8.1 - 15.6) (18.6 - 24.0)

Prevalence of Boys (73) 19.5 % (73) 20.3 % (91) 21.2 % (44) 14.5 % moderate (15.1 - 24.7) (16.3 - 25.1) (18.6 - 24.1) (10.0 - 20.4)

25 malnutrition (<-2 Girls (61) 18.2 % (54) 18.1 % (85) 21.6 % (27) 10.5 % z-score and >=-3 (14.0 - 23.4) (13.2 - 24.3) (18.0 - 25.6) (7.4 - 14.8) z-score, no oedema) Prevalence of Boys (20) 5.3 % (18) 5.0 % (51) 11.9 % (8) 2.6 % 6.4% severe (3.4 - 8.2) (2.7 - 9.2) (9.2 - 15.3) (1.3 - 5.4) (5.2 - 7.9) malnutrition (<- Girls (20) 6.0 % (9) 3.0 % (22) 5.6 % (2) 0.8 % 4.1% 3 z-score and/or (4.0 - 8.8) (1.4 - 6.2) (3.5 - 8.7) (0.2 - 3.2) (3.1 - 5.5) oedema) 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 Total Severe wasting (<-3 Moderate wasting (>= -3 Normal (> = -2 z Oedema (mths) no. z-score) and <-2 z-score ) score) 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 26

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

Turkana Central Turkana North Turkana South Turkana west County Z-score <-3 >=-3 <-3 >=-3 <-3 >=-3 <-3 >=-3 <-3 >=-3 Oedem Maras Kwash Maras Kwash Maras Kwash Maras Kwash Maras Kwash a kwash kwash kwash kwash kwash present . 0 0 1 0 0 0 0 0 1 0 (0.0 (0.0 %) (0.0%) (0.2%) (0.0 %) (0.0 %) (0.0%) (0.0 %) (0.0 %) (0.0 %) %) Oedem Maras Not Maras Not Maras Not SAM Maras Not Maras Not a SAM SAM SAM SAM absent 44 676 28 632 75 756 12 555 159 2619 (6.1%) (93.9 %) (4.2 %) (95.6 %) (9.0 %) (91.0 %) (2.1 %) (97.9 %) (5.7 %) (94.2 %) 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 Central North South West County malnutrition MUAC 2016 n n=720 n=661 n=831 n=567 n=2779 2015 n n=787 n=791 n=832 n=642 n=3052 Severe under nutrition (17) 2.4 % (10) 1.5 % (19) 2.3 % (7) 1.2 % (0.5 (53) 1.9 % (< 115 mm) -June 2016) (1.3 - 4.2) (0.8 - 2.8) (1.2 - 4.4) - 3.3) (1.3 - 2.7) Severe under nutrition (13) 1.7 % (13) 1.6 % (14) 1.7 % (13) 2.0 % (53) 1.7 % (< 115 mm) -June 2015) (0.7 - 3.7) (0.9 - 2.9) (1.0 - 2.8) (0.8 - 4.9) (1.3 - 2.4) Moderate undernutrition (45) 6.3 % (60) 9.1 % (68) 8.2 % (41) 7.2 % (214) 7.7 % (≥115–<125 mm)-June 2016) (4.5 - 8.6) (6.5 - 12.6) (6.3 - 10.6 (4.9 - 10.5) (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

Moderate undernutrition (61) 7.8% (78) 9.9 % (75) 9.0% (58) 9.0 % (272) 8.9 % (≥115–<125 mm)-June 2015) (5.9 - 10.1) (7.4 - 13.0) (6.7 - 12.0.) (6.0 - 13.5 ) (7.7 - 10.3 ) Global Acute Malnutrition 62) 8.6 % (70) 10.6 % (87) 10.5 % (48) 8.5 % (267) 9.6 % (≤125 mm)-June 2016) (6.4 - 11.5) (7.6 - 14.6) (8.1 - 13.4 (5.8 - 12.2) (8.2 - 11.3) Global Acute Malnutrition (74) 9.4 % (91) 11.5 % (89) 10.7 % (71) 11.1 % (325) 10.6 % (≤125 mm)-June 2015) (7.3 -12.1) (8.9 -14.7) (8.0 -14.1) (7.4 -16.2) (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 Prevalence of global (241) 33.9 % (201) 30.8 % 366) 44.6 % (157) 27.9 % (943) 34.7% underweight -June 2016) (29.6 - 38.4) (25.6 - 36.5) (40.4 - 48.8) (23.1 - 33.2) (32.1 - 37.4) Prevalence of global (236) 30.5 % (230) 29.4 % (316) 38.3 % (151) 24.0 % (934) 31.1 % underweight -June 2015) (26.8 - 34.6.) (24.4 - 34.9) (33.9 - 43.0) (20.4 - 28.0) (28.7 - 33.5.) Prevalence of severe (71) 10.0 % (59) 9.0 % (146) 17.8 % (34) 6.0 % (297)10.9% underweight -June 2016) (7.3 - 13.5) (6.7 - 12.1) (14.6 - 21.4) (3.8 - 9.5) (9.5 - 12.4) Prevalence of severe (68) 8.8 % (65) 8.3 % (99) 12.0 % (46) 7.3 % (279) 9.3 % underweight (June 2015) (7.1 - 10.9 ) (6.0 - 11.4) (9.0 - 15.9 ) (5.5 - 9.6) (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.

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 (187) 27.2 % (159) 25.1 % (273)33.6% (144) 25.9 % (763)28.2% (<-2 z-score) June 2016 (22.4 - 32.5) (20.9 - 29.9) (29.3 -38.1) (21.3 - 31.0) (25.7 - 30.9) Prevalence of global stunting (184) 24.6 % (156) 21.0 % (262) 32.7 % (134) 21.7 % (736) 25.3 % (<-2 z-score) June 2015 (20.9 - 28.6) (16.9 - 25.7) (28.6 - 37.0) (18.4 - 25.5) (23.6 - 27.1) Prevalence of severe stunting (56) 8.1 % (41) 6.5 % (85) 10.5 % (36) 6.5 % (218)8.0% (<-3 z-score )-June 2016 (5.4 - 12.1) (4.7 - 8.9) (7.8 - 13.9) (4.5 - 9.1) (6.7 - 9.4) Prevalence of severe stunting (46) 6.1 % (40) 5.4 % (78) 9.7 % (33) 5.3 % (197) 6.8 % (<-3 z-score )-June 2015 (4.6 - 8.2 ) (3.8 - 7.7) (7.7 - 12.2) (3.7 - 7.6) (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 Central North South West County 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.

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

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

Table 28: Child OPV 1 and 2 coverage OPV1 vaccination OPV3 vaccination Survey zone Yes by Yes No Do n Yes Yes No Do not know card by not by by n recall know card recall Central 728 78.7% 18.8% 1.8% 0.7% 728 74.9% 16.2% 7.1% 1.8% (13) (573) (137) (13) (5) (545) (118) (52) North 667 65.8% 28.2% 5.7% 0.3% 667 62.5% 27.1% 9.9% 0.4% (3) (439) (188) (38) (2) (417) (181) (66) South 842 86.3% 10.7% 1.7% 1.3% 842 80.6% 10.6% 7.5% 1.3% (11) (727) (90) (14) (11) (679) (89) (63) west 572 63.3% 29.5% 6.3% 0.7% 572 52.6% 26.6% 19.9% 0.7% (5) (362) (169) (36) (5) (301) (152) (114) County 2809 74.8% 20.8% 3.6% 0.8% 2809 69.1% 19.2% 10.5% 1.1% (32) (2101) (584) (101) (23) (1942) (540) (295)

Table 29: Child measles 9 and 18 months coverage Measles vaccination at 9 months Measles vaccination at 18 months Survey n Yes by Yes by No Do N/A n Yes Yes No Do N/A zone card recall not by by not know card recall know Central 58.5% 17.6% 21.8% 1.4% 0.7% 15.5% 9.7% 66.9% 3.9% 3.9% 308 728 (426) (128) (159) (10) (5) (48) (30) (206) (12) (12) North 57.4% 25.3% 16.3% 0.9% 0.0% 19.1% 15.2% 64.7% 1.1% 0.0% 667 (383) (169) (109) (6) (0) (54) (43) (183) (3) (0) 283 South 68.4% 11.8% 17.6% 1.4% 0.8% 19.2% 7.7% 70.7% (0.8% 1.6% 365 842 (576) (99) (148) (12) (7) (70) (28) (258) ) 3 (6) West 29.8 43.7% 28.1% 19.8% 1.0% 7.3% 11.8% 8.8% 48.5% 1.1% 572 % (250) (161) (113) (6) (42) (32) (24) (132) (3) 272 (81) County 58.2% 19.8% 18.8% 1.2% 1.9% 16.6% 10.5% 63.4% 1.7% 8.1% 1228 2809 (1635) (557) (529) (34) (54) (204) (125) (779) (21) (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

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 Vitamin A coverage (6-59 months) 120.0%

100.0%

80.0%

60.0%

40.0%

20.0% % coverage % 0.0% Central North South West 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%

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.

Figure 4: places of vitamin A supplementation Service delivery point for Vitamin A supplementation(n=1768)

Turkana County 75.5% 14.4% 8.6%

West 49.4% 33.7% 15.2%

South 90.1% 1.3% 8.4%

North 74.8% 14.8% 7.1%

Central 79.4% 14.5% 5.0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% health facility Outreach ECDE Campaign Others

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

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 Women Pysiological Status(n=1872) County 10.7% 58.7% 0.3% 30.3%

West 13.1% 60.9% 1.0% 25.0%

South 10.2% 62.7% 0.2% 26.9%

North 12.3% 53.8% 0.0% 33.8%

Central 7.7% 56.5% 0.2% 35.6%

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

Pregnant Lactating Pregnant and lactating None

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

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.

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 n % n % n % n % n % Consumption (In Days)

< 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

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

Table 32: Current main sources of water Current main source of Central North South West County drinking water n 670 690 762 573 2695 Piped System/borehole/ protected 49.7%(333) 55.4%(373) 63.8%(486) 67.5%(387) 58.6%(1579) spring/protected shallow well 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

Table 33: Queuing time at water source Turkana Turkana Turkana Central Turkana North Turkana South West 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 Central North South West County n=670 n=690 n=762 n=573 n=2695 Drinking water treatment done/yes 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. 40

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

Central North South West County Do you pay for water n=670 n=690 n=762 n=573 n=2695 31.6% yes (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)

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.

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

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.

Table 39: Source of dominant foods Gifts Own from Traded/ Borrowe Purchase Food aid Gathering Others production familes& Battered d friends Central 2.1%(14) 81.2%(544) 1.8%(12) 7%(47) 1.0%(7) 5.1%(34) 0.9%(6) 0.9%(6) 18.3%(12 North 1.0%(7) 72.6%(488) 3.3%(22) 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)

Starch 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) 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)

nuts 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)

Pulses, legumes& Pulses, 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)

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)

Vegetables& fruits Vegetables& 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)

Milk 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

Food groups consumed by the household -24 hr recal

100.00%

80.00% 60.00% 40.00%

20.00%

0.00%

Central North South West

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

Average number of days households consumed food from 16 food groups, 7 days prior to the survey Cereals Condiments, spices 6.00 Vitamin A rich and beverage vegetables and tubers Sweets; sugar, honey 5.00 White tubers and and other sugary… 4.00 roots 3.00 Dark green leafy Oils/fats 2.00 vegetables 1.00 Turkana Central Milk and milk Other 0.00 ploducts vegetables(tomatoe…Turkana North Turkana South Pulses/legumes, nuts Vitamin A rich fruits Turkana West

Fish Other fruits Iron rich(organ meat) eggs foods Flesh meats and offals

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

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

Consumption of ptotein, Vitamin A and Heam iron -rich food groups by category- Turkana County (n=1521) 100% 90% 27.2% 18.9% 80% 29.8% 70% 55.3% 60% 78.0% 94.5% 54.3% 50% 48.5% 40% 72.1%

Percentage 30% 20% 44.7% 10% 21.7% 18.4% 26.9% 0% Poor/Borderline Acceptable Poor/Borderline Acceptable Poor/Borderline Acceptable Protein Vitamin A rich Groups Hem Iron Rich Groups

Food groups Never Consumed sometimes (1-6 DAYS) Consumed atleast daily (7 and above days)

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

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.

Table 40:coping strateies applied Household Borrowed or Household Household Household relied on less relied on help limited portion restricted reduced the Coping preferred and from a friend or size at meal times consumption by number of meals strategy less expensive relative adults in order taken in a day frequency by foods for small Households children to eat n Yes No Yes No Yes No Yes No Yes No

71.3% 28.7% 51.2% 48.8% 51.8% 48.20% 65.8% 34.2% 0.0% 100% Central 670 (478) (192) (343) (327) (347) (323) (441) (229) (0) (670) 48.6% 51.4% 52.3% 47.7 52.9% 47.1% 65.9% 34.1% 0.0% 100% North 690 (335) (355) (361) (329) (365) (325) (455) (235) (0) (690)

64.3% 35.7% 50.3% 49.7% 56.0% 44.0 67.8% 32.2% 0.0% 100% south 762 (490) (272) (383) (379) (427) (335) (517) (245) (0) (762) 56.3% 43.7% 52.6% 47.4% 53.03% 46.7% 58.7% 41.3% 0.0% 100% West 574 (323) (251) (302) (272) (306) (268) (337) (237) (0) (574)

60.3% 39.7% 51.5% 48.5% 53.6% 46.4% 64.9% 35.1% 0.0% 100% County 2696 (1626) (1070) (1389) (1307) (1445) (1251) (1750) (946) (0) (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) Central North South West County 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.

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  Hold joint meeting to revise the contingency MoH,NDMA and Immediately contingency and response plans in plans. nutrition partners South, Central and North Survey  Ongoing quarterly review of the zones. contingency plans. 2 Scale up continuous active case  Sensitize the CHVs on rapid nutrition MoH (nutrition and Continuous finding for malnutrition for the screening and referrals. community health expected caseload(U5) of 34,563  Conduct routine screening through the strategy) and nutrition partners (severe 7,862 and moderate 26,701) existing community health units. and 22,437 pregnant and lactating  Conduct rapid nutrition screening in the hot spot areas. women in the year 2016 and referral  Conduct quarterly mass screening. for timely management 3 Increase access to life saving health  Carry out mapping of communities with MoH and nutrition immediately and nutrition services through limited access to health and nutrition partners integrated outreaches for populations outreaches. with limited access to these services.  Conduct biweekly integrated health and nutrition outreaches in the in communities far from health facilities.  Monthly monitoring of the outreaches. 4 Develop simplified nutrition survey  Hold community dialogue meetings, MoH and nutrition immediately packs/briefs easily synthesized for meetings with men, mother support Group partners nutrition advocacy and mobilization. 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. 5 Develop and implement nutrition  On a quarterly basis populate the MoH and nutrition Quarterly. service delivery score card at health scorecard with nutrition data. partners facilities  Quarterly meetings to deliberate on the 52

output of nutrition score cards. 6 Conduct comprehensive on the job  Sensitize all the HMTs, Health facility in MoH and nutrition Immediately training and mentorship targeting charges and CHEWs on findings of SMART partners facility health workers, community survey and rapid nutrition screening. health extension workers (CHEWs)  Conduct sensitization of the CHVs on rapid and Community health workers(CHWs) nutrition screening and referrals.  Train all the newly recruited nutritionists on IMAM including the SURGE model. 7 Sensitize and link mother to mother  Hold meetings with MtMSGs to sensitize MoH,NDMA and Continuous support groups (MtMSGs) and them on the current nutrition situation and nutrition partners households with malnourished generate community led actions. children/pregnant and lactating women  Link MtMSGs to existing livelihoods with other nutrition sensitive sectors to interventions. strengthen nutrition resilience 8 Cconduct community dialogue  Develop a brief pack for community dialogue MoH and nutrition Continuous sessions and sensitization meetings meetings. partners with caregivers, community leaders/key  Hold meetings with community leaders, influencers on appropriate childcare communities, MtMSGs and caretakers to practises including micronutrient sensitize them on recommended child care supplementation, deworming, and health & nutrition practices. handwashing, drinking water treatment and latrine utilization. 9 Advocate and create public awareness  Conduct community dialogues with focus MoH and nutrition Continuous on micronutrient supplementation on micronutrients supplementation and partners (micronutrient powders, IFA, Vitamin deworming. A), de-worming and dietary  Integrate micronutrient supplementation diversification. and deworming into the integrated outreaches and nutrition calendar events.  Conduct micronutrient supplementation and deworming at the ECDs. 10 Continue capacity building of health  Mobilize resources for training of health MoH and nutrition Continuous care workers especially newly recruited workers. partners staffs through OJT and joint support  Conduct IMAM training for the newly supervision on a quarterly basis recruited 40 nutritionists.

11 Scale up community led total sanitation  Conduct Health workers and CHVs MoH (public health ) Continuous approach to increase awareness on sensitisation on CLTS, and nutrition and sanitation including latrine utilization  Conduct community sensitisation on CLTS. WASH partners  Roll out CLTs jointly with the communities. 12 Institutionalize Vitamin A  Sensitize ECD teachers on vitamin A MoH (nutrition& public Quarterly supplementation and de-worming at supplementation and deworming. health), MoE (ECDEs) the Early Child Education  Provide supplies and reporting tools to the and nutrition partners Development(ECDE) centers and scale ECD teachers. up during annual child health  Monitor on quarterly basis VAS campaigns supplementation at the ECD Centers. 13 Procurement and timely distribution of  Provide health facilities with the required MoH/UNICEF/WFP Quarterly essential nutrition commodities to reporting tools. health facilities  Develop commodity consumption reports and requests. 14 Train county, sub county health  Hold training on SBCC for HMTs, health MoH and nutrition December managers, health workers on behavior workers and CHVs. partners 2016 53

social change communication(BSCC)/communication for development(C4D) 15 Develop, disseminate and implement  Validate and disseminate MIYCN SBCC MoH and nutrition February multi-sectoral nutrition social behavior strategy and messages. partners 2017 change communication (SBCC) strategy to address maternal and child care knowledge, attitude, behavior and practices. 16 Pilot IMAM surge in select health  Sensitize health management team on MoH and nutrition October facilities in the county. This will be IMAM surge. partners 2016 scaled up upon successful pilot.  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. 17 Train community health  Conduct training of the CHEWs and CHVs MoH (nutrition, October volunteers(CHVs) and community on community nutrition module. community strategy) 2016 health extension workers(CHEWs) on and nutrition and nutrition module for community health health partners strategy for improved active case finding, referral and nutrition education 18 Scale up of Baby Friendly Community  Conduct training of the HMTs ,Health MoH (nutrition and November Initiatives(BFCI) in 20 MNCH centers workers and CHVs on BFCI. community health 2016 of excellence  Roll out BFCI in the pilot CHUs. strategy) and nutrition  Monitor the roll out of BFCI. partners 19 Conduct a Malnutrition Causal Link  Develop and validate concept note and MoH,MOAW and December analysis to have in depth proposal for the study. Partners 2017 understanding of determinants of  Mobilize resources for conducting the malnutrition study.

9.0 APPENDICES Appendix 1: IPC for Acute malnutrition Maps

Appendix 2:Summary of plausibility report

Indicator Acceptable CENTRAL SOUTH NORTH WEST values/range 1 Flagged data <7.5 0 (1.4% Excel) 0 (1.0% 0 (0.6% 0 (1.1% (% of out of range subjects) Excel) Excel) Excel) 2 Overall sex ratio (significant CHI >0.001 0 (0.101 Excel) 0(0.0176 4 (0.022 4 (0.04 square) Excel) Accep) Accept) 3 Age ratio (6-29vs 30-59) Significant >0.001 10 (0.000 Prob) 10 (0.000 10 (0.000 10 (0.000 CHI square Prob) Prob) 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 0 (0.98 Excel) 0 (0.93 Excel) Excel) 8 Skewness WHZ <±0.6 0 (-0.13 Excel) 0 (-0.09 0 (-0.05 0 (0.05 Excl) Excel) Excel) 9 Kurtosis WHZ <±0.6 1 (-0.33 Good) 0 (-0.10 0 (-0.012 0 (-0.160 Excel) Excel) Excl) 10 Poisson WHZ -2 >0.001 3 (0.006 accep) 0 (0.370 1 (0.030 1.(0.038 Excel) Good) good) 11 OVERALL <24 16(Accep) 10(Good) 15(Accep) 15(Accep)

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

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

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

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

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 Cluster Geographical Unit Population Cluster Size Size 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

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

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

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 Cluster Geographical Unit Population Cluster Size Size 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

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

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) 65

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) lotubae Cluster 6 : -1.74 ± 1.02 (n=17) locwangitamtak Cluster 29 : -1.73 ± 1.08 (n=19) lotubae Cluster 7 : -1.35 ± 1.10 (n=19) locwangitamtak Cluster 30 : -1.25 ± 0.91 (n=19) 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) Kaptir- parakati Cluster 11 : -1.97 ± 0.80 (n=16) 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)

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) Pelekech- loreng Cluster 3 : -0.78 ± 0.70 (n=19) lokore Cluster 20 : -1.09 ± 0.98 (n=16) pelekech lokipoto Cluster 4 : -0.99 ± 1.33 (n=15) 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) songot- Katelemot Cluster 9 : -0.62 ± 1.36 (n=13) 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)

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 Please give me Age (months Childs age Sex If 3 yrs Main Reason for not attending What is the If the household Group and under School highest level of the names of the for children verified by owns mosquito persons who <5yrs and 1= 18 Is child (Enter one code from list) education Male enrolled in 1=chronic Sickness attained?(level usually live in years for over net/s, who slept school? 2=Weather (rain, floods, storms) completed) From under the mosquito your household. 2= 3=Family labour responsibilities 5 yrs and above 5’s) 1=Health Femal 1 = Yes 4=Working outside home net last night? card/Birth e 2 = No 5=Teacher absenteeism 1 = pre primary certificate/ 6=Too poor to buy school items 2= Primary YRS MTH 1= children under 5 (If yes go e.t.c 3=Secondary notification to 2.8; If no 7=Household doesn’t see value of 4=Tertiary schooling 2=Pregnant /lactating go t o 2.7) /Baptism card 8 =No food in the schools 5= None mothers 9 = Migrated/ moved from school 6=others(specify) area 2=Recall 10=Insecurity 3= other(_____) 11-No school Near by 3= no 12=Married verification 13=others (specify)…………………..

< 5 YRS

>5 TO 18 YRS

ADULT

2.10 How many mosquito nets does this household have? ______(Indicate no.)

2.11 Main Occupation of the Household Head – HH. 2.12. What is your main current source of income 1. =No income (enter code from list) 2. = Sale of livestock 1=Livestock herding 3. = Sale of livestock products 2=Own farm labour 3=Employed (salaried) 4. = Sale of crops 4=Waged labour (Casual) 5. = Petty trading e.g. sale of firewood 5=Petty trade 6. = waged labor 6=Merchant/trader 7. =Permanent job 7=Firewood/charcoal 8. = Sale of personal assets 8=Fishing 9. = Remittance 9=Others (Specify) |____| 10. Other-Specify |____| 2.13 Marital status of the respondent

1. = Married 2.14 What is the residency status of the household? 1. IDP 2. = Single 3. = Widowed 4. = separated 2. Refugee |____| 5. = Divorced. |____| 3. Resident

Fever with Malaria: Cough/ARI: Any episode Watery diarrhoea: Any Bloody diarrhoea: Any High temperature with severe, persistent episode of three or more episode of three or more with shivering cough or difficulty watery stools per day stools with blood per day breathing

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 B C D E F G H I J K L 3.2 3.3

Child No.

what is the SEX Exact Age in Weigh Height Oedem MUAC Has your If YES, If the child had When the If the response is yes to relationship of the Birth month t a child what type watery child was question # 3.2 where did respondent with the F/m Date s (CM) (cm) (NAME) of illness diarrhoea in the sick did you first seek assistance? been ill in (multiple last TWO (2) child/children (KG) Y= Yes you seek XX.X XX.X the past two responses WEEKS, did the assistance 1. Traditional healer XX.X N= No weeks? possible) child get 1=Mother ? If No, please 1 = Fever THERAPEUTIC 2.Community health worker 2=Father skip part K with chills zinc 3=Sibling 1.Yes and proceed like malaria supplementation 3. Private clinic/ pharmacy to 3.4) 2 = ARI ? 2. No 4= grandparent /Cough Show sample 4. Shop/kiosk 1.Yes 3 = Watery and probe 5.Public clinic 5=Other (specify) diarrhoea further for this 6. Mobile clinic 2. No 4 = Bloody component diarrhoea check the remaining 5 = Other drugs(confirm from 7. Relative or friend mother child booklet) (specify) 8. Local herbs See case 1 = Yes definitions 2 = No below 9.NGO/FBO 3 = Do not know 10. other specify

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 How many How many If Vitamin A How many Has the child Has child Has child Has child Has child No. times has times did received times has received BCG received OPV1 received OPV3 received received the vaccination? vaccination vaccination? measles second you receive how many child child vaccination at measles vitamin A times received received 1 = scar 1=Yes, Card 1=Yes, Card 9 months vaccination (18 capsules verified by 2=No scar 2=Yes, Recall 2=Yes, Recall (On the upper to 59 months ) Vitamin A drugs for from the 3 = No 3 = No right (On the upper Card? worms 4 = Do not 4 = Do not shoulder)? right in the past facility or know know shoulder)? year? out reach in the past 1=Yes, Card year? (12- 2=Yes, Recall 1=Yes, Card (show 1= health 59 Months) 3 = No 2=Yes, Recall sample) facility 4 = Do not 3 = No 2= outreach (show know 4 = Do not site Sample) know 3= ECDE centres 4= campaigns 01

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. What is the Woman’s physiological Woman’s MUAC During the pregnancy of the (name of If Yes, for how many days? (all ladies in the HH aged 15- status reading: ____.__cm child below 24 months) did you take (approximate the number of 49 years from the IFASS (iron pills, sprinkles with iron, days) demographics page) 1. Pregnant iron syrup or iron - folate tablets? (name 2. Lactating that appears in HH register) 3. Pregnant and lactating 4. None of the above 1. Yes

2. No 3. Don’t know 4. N/A

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 4.2 What is the trekking distance to the current main water household NOW? source?

1. Piped water system/ borehole/ protected 1=less than 500m (Less than 15 minutes) spring/protected shallow wells 2=more than 500m to less than 2km (15 to 1 hour) 2. Unprotected shallow well 3=more than 2 km (1 – 2 hrs) 3. River/spring 4=Other(specify) |____| 4. Earth pan/dam 5. Earth pan/dam with infiltration well |____| 6. Water trucking /Water vendor 7. Other (Please specify)

4.2.2a Do you queue for water? 4.2.2b. If yes how long?

1. Yes 1. Less than 30 minutes |____| 2. No (If No skip to question 4.3) |____| 2. 30-60 minutes 3. More than 1 hour

4.3a Is anything done to your water before drinking 4.3b If yes what do you do? (MULTIPLE RESPONSES (Use 1 if YES and 2 if NO). if No skip to 4.4 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? 4.5 How much water did your household use YESTERDAY (excluding for animals)? 1. Open container / Jerrican 2. Closed container / Jerrican |____| (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? 4.6.1 If yes, how much per 20 liters 4.6.2 If paid per month jerrican ______KSh/20ltrs how much |____| 1. Yes 2. No (If No skip to Question 4.7.1) |____|

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; 4.8 Where do members of your household Mainly what do you use to wash your hands? relieve themselves? 1. Only water 2. Soap and water 1. In the bushes, open defecation 3. Soap when I can afford it 2. Neighbor or shared traditional pit/improved latrine 4. traditional herb 3. Own traditional pit/improved latrine 5. water and ash 4. Others Specify 6. Any other specify |____|

|____|

5.0: Food frequency and Household Dietary Diversity

Did members of If yes, What was the main your household mark source of the dominant consume any days food item consumed in the food from these the HHD? food food groups in was 1.Own production the last 7 consu days?(food must med in 2.Purchase have been the last 7 cooked/served at 3.Gifts from the household) days? friends/families yes=1; no=2 4.Food aid

1=Yes 5.Traded or Bartered

0=No 6.Borrowed

7.Gathering/wild fruits

8.Other (specify)

*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:

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

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