Report on Integrated Health and Nutrition Survey

REPORT ON INTEGRATED HEALTH AND NUTRITION SURVEY IN LARGER MERU NORTH COUNTY OF KENYA.

FINAL REPORT

(April, 2012)

Anastacia Maluki, International Medical Corps Monitoring and Evaluation officer ACKNOWLEDGEMENTS I take this opportunity to thank UNICEF for the financial support they provided to conduct this survey. Special thanks are expressed to: the Survey co-ordinators (DNO), Team leaders, team members, data entry clerks, International Medical Corps staff members and drivers for their tireless efforts to ensure that the survey was a success.

I am also indebted to the district administrators, local leaders and community members who willingly participated in the survey and provided the information needed. TABLE OF CONTENTS

LIST OF TABLES Table 1: Anthropometric and mortality sample size calculation Table 2: Demographic information of target population Table 3: Distribution of age and sex of 6-59 months. Table 4: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex Table 5: Prevalence of acute malnutrition by age, based on weight-for-height z- scores and/or oedema Table 6: Distribution of acute malnutrition and oedema based on weight-for- height z-scores Table 7 Prevalence of acute malnutrition based on MUAC cut off's and/or oedema Table 8: Prevalence of underweight based on weight-for-age z-scores by sex Table 9: Prevalence of underweight by age, based on weight-for-age z-scores Table 10: Prevalence of stunting based on height-for-age z-scores and by sex Table 11: Prevalence of stunting by age based on height-for-age z-scores Table 12: Mean z-scores, Design Effects and excluded subjects Table 13: Vaccination coverage: OPV 1 & 3 for 6-59 months and measles at 9 months and deworming for 12-59 months Table 14 Vitamin A coverage Table 15: Symptom breakdown in the children in the two weeks prior to interview (n=311) Table 16: Main Sources of food consumed in 24 hr recall Table 17: proportion of food crops shared sold and stored after harvesting. Table 18: top ten coping strategies Table 19: Mortality rates Table 20: Causes of death among under/above 5 years

LIST OF FIGURES Figure 1: Population age and sex pyramid Figure 2: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema Figure 3. Nutrition Status of caregivers of < 5 year old children: Figure 4:Lacteals given in the first three days of birth Figure 5 food groups taken by children 6-23 months in the previous 24 hrs Figure 6 :House hold water sources for general and domestic use Figure 7 household water treatment methods. Figure 8 Sources of Income Figure 9 Frequency of meals taken in household Figure 10 Ratio of foods groups consumed in 24-hour recall

LIST OF APPENDICES Appendix 1: IYCN calculator Appendix 2:Household Questionnaire Appendix 3:Anthropometric Questionnaire Appendix 4:IYCN Questionnaire Appendix 5:Mortality Questionnaire Appendix 6:Focu Group Discussion guide Appendix 7: Plausibility checks. Appendix 8: Assignment of Clusters Appendix 9: Map Appendix 10. Summary of findings

ACRONYMS AND ABBREVIATIONS ACF - Action Against Hunger AOP - Annual operation Plan ARI - Acute Respiratory Infection BFHI - Baby friendly Hospital Initiative CED - Chronic Energy Deficiency CHNE - Community-based Health/Nutrition Education CI - Confidence Interval CMAM - Community-based management of Acute Malnutrition CMR - Crude Mortality Rate CSB - Corn Soya Blend DDS - Dietary Diversity Score EMOP - Emergency Operation Programme ENA - Emergency Nutrition Assessment EWAS - Early warning System FAO - Food and Agriculture Organization FANTA - Food and Nutrition Technical Assistance FFA - Food for Assets FGD - Focus Group Discussion GCM - Global Chronic Malnutrition GFD - General Food Distribution GAM - Global Acute Malnutrition GOK - Government of Kenya GS - Growth Standards HFA - Height-for-Age ICNP - Integrated Community Nutrition Programme IMAM - Integrated management of Acute Malnutrition IMC - International Medical Corps IMCI - Integrated Management of Childhood Diseases ITN - Insecticide Treated Nets IYCF - Infant and Young Child Feeding KCO - Kenya Country office KEPI - Kenya Expanded Programme on Immunization MMCG - Mother to Mother Care Groups MoMS - Ministry of Medical Services MoPHS -Ministry of Public Health and Sanitation MUAC - Mid-Upper Arm Circumference NCHS - National Centre for Health Statistics NGO - Non-Governmental Organization OJT -On-the-Job Training OPV - Oral Polio Vaccine PPS - Probability Proportional to Population Size PR - Protection Ration PRRO - Protracted Relief and Recovery Operation SAM - Severe Acute Malnutrition SCM - Severe Chronic Malnutrition SD - Standard Deviation SFP - Supplementary Feeding Programme SMART - Standardized Monitoring and Assessment of Relief and Transitions SMP - School Meals Programme SPSS - Statistical Package for Social Scientists SSS - Small Scale Survey TBA - Traditional Birth Attendant UFMR - Underfive Mortality Rate UK - United Kingdom UNICEF- United Nations Children’s Fund USAID - United States of America International Aid WFA - Weight-for-Age WFH - Weight-for-Height WHO - World Health Organization EXECUTIVE SUMMARY This survey covered the greater Meru North district (Igembe South, Igembe North, Tigania West and Tigania East Districts), which is inhabited by people from Igembe and Tigania origins. The population in Meru North District is relatively static and densely populated with an annual growth rate of 2.8%. The district has an estimated 146,567 households with an average of 5 persons per house hold. It has an estimated population of 740,035 people (Igembe 471,836 and Tigania 268,199) and 123,770 children below 5 years (Igembe 75,494 and Tigania 48,276).Giving an average proportion of 16.7% children under 5 years. The district comprises of six livelihood zones namely; marginal mixed farming, mixed farming food crops, mixed farming: Tea/dairy, rain fed cropping and rain fed tea/dairy. Majority of the population fall under marginal mixed farming.

In view of the need to gauge the performance of the Essential Nutrition Action (ENA) package and for informed future formulation and prioritization of appropriate interventions in the district, International Medical Corps in collaboration with the MoPHS and MoMS carried out a nutritional survey between 26th March and 6th April 2012.Training of enumerators took 4 days (26th-29th March, 2012) and data collection took place as from 30 th March, 2012– 6th, April, 2012. The main objective of this survey was to establish the extent and severity of malnutrition and to provide data for use in monitoring the progression of the situation. The survey utilized the Standardized Monitoring of Relief and Transitions (SMART) methodology and also in accordance with both the National Guidelines for Nutrition and Mortality assessments in Kenya and the UNICEF-recommended nutritional survey key indicators. Both anthropometric and mortality data were collected simultaneously during the survey. A two-stage cluster sampling with probability proportional to size (PPS) design was employed for the integrated nutrition survey. Sample size was determined on the basis of estimated prevalence rates of malnutrition (GAM), desired precision and design effect) using the ENA for SMART software.. IYCF multi survey sampling calculator was used to calculate IYCF and Qualitative data was collected through: focus group discussions (FGDs), key informant interviews and general observations.

Overall, the surveyed households had, on average, 5.7 (SD 2.3) members per household. The findings showed a global acute malnutrition (GAM) rate of 7.8 % (5.2-11.6 CI), a severe acute malnutrition (SAM) rate of 1.2 % (0.5-2.8 CI) by WHO-GS. The overall prevalence of GAM in Meru North County reveals risky situation with aggravating factors in the community according to WHO benchmarks. Notably was the measles break out in the district just before the survey and high morbidity cases in coughing 50% and diarrhoea disease 11.3% which are considered as aggravating factors. Tigania East division was most afflicted by acute malnutrition where the prevalence of GAM stood at 11.7% (7.2-18.4 CI), this was followed by Igembe north 8.5%, Igembe south 6.8% and finally Tigania west 4.3%. The findings though are not statistically valid as the sample size are too small to represent a division. Both the crude mortality rate (CMR) of 0.24 (0.11-0.56 CI) deaths/10,000/day and the under-five mortality rate (UFMR) of 0.48 (0.14- 1.59CI) deaths/10,000/day did not reach the threshold for ‘Alert’ status. The MUAC measurements of 715 eligible primary childcare givers (15-49 years old) were taken to assess their nutritional status. The survey findings showed that.2.2% (n=133) of caretakers had MUAC <21cm meaning that they are at risk of malnutrition or have chronic energy deficiency (CED). Overall, 87.7 % of mothers reported having attended MCH clinics and in spite of the clinic visit only 55.3 % of the women delivered in the hospital. The findings indicate that practically all children (98.1%) were reported to have breastfed but only 53.3% were exclusively breastfed for 6 months. On average the mean food diversity was 2.8 (SD 1.8) given to children > 6 months. The findings showed that 64.2% of the children samples consumed low dietary diversity of less than four groups, a threat to optimal child growth and development while only 35.8% of the households had children >6 months who consumed 4 or more of the food group. Survey revealed that majority of the used tap water as their main source of water with 67% not treating water before drinking. 85.4% of the HH had access to toilet facilities that they use. The overall prevalence of GAM (7.8%) in Meru North County reveals poor nutritional status in the community according to WHO benchmarks. The study identified aggravating factors that had a negative bearing on optimal under-five nutritional status and their caregivers:

 Poverty and issues of who controls family income have a heavy contribution to household food security. Income sources are not diversified and therefore there’s over reliance on farm produce both as an income source and family food. Poverty has also made it difficult to access food from markets due to insufficient financial resources. Lack of water supply in many parts of Meru North districts especially in Igembe North division has led to infectious diseases spreading, causing childhood diarrhea, which leads to major malnutrition and subsequent death due to diarrheal dehydration  Poor agricultural practices including cultivation of Miraa in most areas whose income does not translate into food security. This is further compounded by poor soil fertility as a result of poor farming practices and environmental degradation.  Lack of access to food.Most major food and nutrition crises do not occur because of a lack of food, but rather because people are too poor to obtain enough food.  Poor child and adult dietary profiles. Over-consumption of certain food group like cereals usually goes along with deficiencies in essential vitamins and minerals.  High child morbidity prevalence reported to have affected 44.6% of the under-fives which was found to significantly affect child nutritional status;  Poor IYCF practices including early weaning, low maintenance of breast feeding and poor feeding practices.  Poor access to medical facilities some are too far for household to access. On average most health facilities are located 3.2 (SD 2.6) km away.  Poor water sanitation status in the community with minimal treatment of unsafe drinking water at the household level increase vulnerability to infectious and water- borne diseases, which are direct causes of acute malnutrition.

Because malnutrition has many causes, only multiple and synergistic interventions embedded in true multisectoral programs can be effective. A variety of actions both immediate and long term solutions are needed:  Addressing the poor access to essential health and nutrition services by strengthening the integrated outreach component- primarily focusing on regular medical outreach camps/mobile clinic. This will help to intensify active case finding of malnourished children and manage them accordingly.  Strengthen programmes and strategies currently addressing infant and young child nutrition (IYCN) with a view to improving the protection, promotion, and support of optimal IYCF. Viable action points include:  As the HINI program is rolled out there is need for continual monitoring of both facility and community based interventions to track progress while also documenting the process to assess the trends in the outcomes as well as impact indicators. Particular attention should go to improved maternal nutrition, iron/folate supplementation during the prenatal period and ensuring ORS/zinc support for diarrhoea.  Strengthening of hygiene practices to reduce the incidence of diarrhoeal disease associated with contaminated water in the household including health education to educate the community on domestic treatment of drinking water and effective hand washing (soap/ash) after helping a child in the latrine, during food preparation and before child feeding. This should be backed-up with provision of free water treatment chemicals where feasible.  Continued water trucking to areas affected by water stress by Ministry of Water and Irrigation and Kenya Red Cross especially in Igembe north District.  Provision of water purification chemicals for water treatment at Household level  Advocacy/public health campaigns on domestic water treatment such as boiling of drinking water and use of purification chemical to minimize risks of water-borne diseases, should be carried out.  The Ministries of Public Health and sanitation and Medical services in collaboration with other stakeholders in the district to initiate and offer concrete support in the implementation of strong awareness campaigns and community based health and nutrition programs with special focus on infant and young child feeding practices, dietary diversification, food preparation and preservation, consumption of energy dense and micronutrient-rich foods and kitchen gardening. Women should be the prime targets of these. Nutrition messages should address strategies/ways of improving access to locally available and cheaper sources of fat, protein and micronutrients.  Focus on programmes by ministry of agriculture that improve and sustain dietary diversity and consumption of micronutrient.-rich foods. And advising farmers on good farming methods .By improving agricultural yields, farmers could reduce poverty by increasing income as well as open up area for diversification of crops for household use.  To address the issues of limited access to safe water, there is a need to establish water points in areas where water is inaccessible.  MOH should increase access to health facilities in the rural parts of kenya by adding more health facilities or increasing CHW. These will improve hospital deliveries and access to medical services for those who cannot access the health facilities 1.0 BACKGROUND INTRODUCTION Meru County is located in the Eastern province and constitutes 7 constituencies: Igembe, Ntonyiri, Tigania West, Tigania East, North Imenti, Central Imenti and South Imenti. The Larger Meru North District is made up of four districts namely: Igembe South, Igembe North, Tigania West and Tigania East Districts. Meru North covers an area of 4057 Km2 of which 833 Km2 is Meru National Park .It has an estimated population of 740,035 people (Igembe 471,836 and Tigania 268,199 )and 123,770 children below 5 years(Igembe 75,494and Tigania 48,276 ).Giving an average proportion of 16.7% children under 5 years 1. The people of the district are mainly of Igembe and Tigania origins. Borans, Somalis, and others are also residents of the district.

The district lies within latitudes 0º 00’ and 0º 40’ North, and longitudes 37º 50’ East, with the southern boundary lying along the equator .Altitude ranges from 2,145m above sea level in the higher regions to 600m in the lower parts which cover the greatest land area (3/4 of total area). These low lying areas were designated as the Northern Grazing Areas (NGA) and are characterized by low and erratic rainfall. The soils are predominantly volcanic clay loams with patches of rock and black cotton soils. Rainfall amounts range from 380mm p.a. in the lower areas to 2500mm p.a. in the higher areas. Its spatial distribution is highly dependent on elevation, with the high altitude areas receiving the most amounts compared to the low-lying areas. Rainfall is bimodal with long rains expected from mid-March to May and the short rains expected from mid-October to late November. Short rains are most reliable.

Agro-ecological zones in the district range from LH22 (tea and dairy) to L6 (lowland grazing zones). LH2 zones cover a very small area while L6 covers the greatest area of the district. The district comprises of six livelihood zones namely; marginal mixed farming, mixed farming food crops, mixed farming: Tea/dairy, rain fed cropping and rain fed tea/dairy. Majority of the population fall under marginal mixed farming .Miraa is a major cash crop being farmed and is harvested throughout the whole year.

International Medical Corps with financial support from UNOCHA is supporting Ministry of Public Health and Sanitation and Ministry of Medical Services in scaling up of High Impact Interventions for improved maternal and child health in Meru North Districts. These

1 2010 Population projection, Ministry of Health

2 Simple agro-ecological zones were established by FAO in 1981. They are suited to make decisions in order to give advice to farmers showing yield probabilities and risks: The zone groups are temperature belts defined according to the maximum temperature limits within the main crops in Kenya can flourish; cashew and coconuts for the lowlands, sugar cane and cotton for the lower midlands, Arabica coffee for the upper midlands, tea for the lower highlands, pyrethrum for the upper highlands interventions are in line with the priorities outlined in the nutrition sector partnership framework which supports scaling up of high impact interventions as well as supporting the Ministry of Health to deliver essential nutrition services. The overall strategy for International Medical Corps is to improve the technical and logistical capacity of the MoMS / MoPHS to deliver high-impact nutrition interventions through an integrated package and promotion of iron enriched food that includes: Promotion of exclusive breastfeeding for the first six months of life; promotion of optimal complementary feeding for infants after the age of six months; Vitamin A Supplementation (2 doses per year for children aged 6-59 months); Zinc supplementation for diarrhea management; multiple micronutrients for children under five years; deworming for children (2 doses per year for children aged 12-59 months); iron-folic acid supplementation for pregnant mothers; prevention or treatment of SAM and MAM; promotion of improved hygiene practices including hand washing and promotion of the utilization of iodized salt.

1.1 Rationale for conducting a survey In order to gauge the performance of the HINI package and inform future programming in the district, International Medical Corps in collaboration with MOMS/MOPHS carried out a nutritional survey in the greater Meru North district between 26th March and 6th April 2012, to evaluate the extent and severity of malnutrition among children aged 6-59 months and analyze the possible factors contributing to malnutrition and recommend appropriate interventions.

1.2 Objectives: The main objective of this survey was to establish the extent and severity of malnutrition and to provide data for use in monitoring the progression of the situation.

 To estimate the current prevalence of acute malnutrition in children aged 6-59 months and to compare the overall nutritional changes with previous GAM and SAM  To estimate the retrospective crude and under five death rates and morbidity among under five children and as well compare with previous CMR and U5MR.  To estimate Measles, BCG vaccination and Vitamin A supplementation for children 9-59 months and 6-59 months respectively  To assess the current food security situation of the surveyed population, prevalence of some common diseases (Diarrhea, Fever, and Cough) and to identify factors likely to have influenced malnutrition in young children  To assess child and infant care and feeding practices among caretakers with children 0- 23 months  To establish the situation of water and sanitation, appropriate hygiene practices including hand washing among caretakers

1.3 Timing of the survey (including seasonal calendar) The survey was scheduled to take place as from 26th March and 6th April 2012.Training of enumerators took 4 days (26th-29th March, 2012) and data collection took place as from 30th March, 2012– 6th, April, 2012. Seasonal Calendar

JAN FEB MAR APRIL MAY JUN JUL AUG SEPT OCT NOV DEC E Y Dry season LONG RAINS Dry season SHORT RAINS SR Land Planting weeding LR Land planting weeding SR harvesting preparation harvesting preparation harvesting Miraa harvesting

Key: SR: short Rain LR: Long Rain

2 SURVEY METHODOLOGIES

2.1 Sampling Methodology and Sample Size Three different sampling methodologies were applied. IYCF multi survey sampling calculator was used to calculate IYCF sample while Emergency Nutrition Assessment (ENA) for Standardised Monitoring of Relief and Transition (SMART) was used to calculate anthropometric and mortality data. This was guided both by the National Guidelines for Nutrition and Mortality assessments in Kenya and the recommended UNICEF nutritional survey key indicators. Qualitative data was collected through: focus group discussions (FGDs), key informant interviews and general observations. In calculating anthropometric sample size, a GAM prevalence of 7.2%3 (3.9-7.2 95% CI)4 , desired precision of 3%, a design effect of 1.5, an estimated household size of 5 persons, 17% < 5 years and non-response rate of 3% gave a sample size of 466 children (6-59 months) and a household sample of 628 households. The second sampling stage comprises of village and household selection. In order to select survey clusters, the names of villages/sub-locations, their respective population sizes and the required number of clusters was entered into the SMART software, which generated the actual list of the villages to survey (including reserve clusters). At the field level, the EPI method was employed to select the first household to be enumerated. This was because it was not possible to get the list of households to use random sampling and the villages were not arranged in a systematic manner to employ random sampling. A household was

3 Considered the upper limit of the GAM prevalence with a confidence Interval of 95%

4Integrated Health and Nutrition survey ,Meru North District, Kenya, June 2008.MOH defined as a group of people who lived together and shared a common cooking pot. In polygamous families with several structures within the same compound but with different wives having their own cooking pots, the structures were considered as separate households and assessed separately. In cases where there was no eligible child, a household was still considered part of the sample, where only household and mortality data were collected. If a respondent was absent during the time of household visit, the teams left a message and re-visited later to collect data for the missing person, with no substitution of households allowed. The teams visited the nearest adjacent village (not among those sampled) to make up for the required number of households if the selected village yielded a number below 20 households, following the methodology described above.

IYCF multi survey sampling calculator was used to obtain sample size for Infants and young children (0-23 months). Indicators calculated were: Timely initiation of breastfeeding (children 0-23 months), Exclusive breastfeeding under 6 months, Timely complementary feeding, and Continued breastfeeding at 1 year. Using information obtained from cluster survey5, the sample size for children between 0-23 months was 730 (annex 1).The number of children aged 0-23 months to be reached per cluster was given by dividing 730 by 37 giving 20 children per cluster .The number of households was given by dividing 146 by 1.5 (average number of children under 59 months in a household6) to give 98 households. Getting children below 6 months in a cluster was quite a challenge and therefore purposive sampling was used where no children of that age group were found in the cluster. Sample size for mortality will be based on survey conducted in Meru north in ,2008 with a crude death rate of 0.98 deaths/10,000/day a desired precision of 0.5, design effect of 1.5, household size of 5 with a recall period of 90 days and non-response rate of 3%. This gives a sample of 2732 people and 563 households.

Table1: Anthropometric and mortality sample size calculation Data entered on ENA software Anthropometric sample Retrospective Mortality sample Estimated prevalence 7.27 0.988 Desired precision 3 0.5 Design effect 1.5 1.5 Recall period 90 days Average household size 59 510 Percent of under five children 1711

5 Multiple Indicator Cluster Survey, Meru North District, 2008.

6 Integrated Health and Nutrition survey ,Meru North District, Kenya, June 2008.MOH

9 Integrated Health and Nutrition survey ,Meru North District, Kenya, June 2008.MOH Percent of non-respondent 3 3 Households to be included 628 563 Children to be included 466 Population to be included 2732

2.2 Description of sampling frame (including source of population data) The survey covered all areas of the Larger Meru North District which is made up of four districts namely: Igembe South, Igembe North, Tigania West and Tigania East Districts. Meru North covers an area of 4057 Km2 of which 833 Km2 is Meru National Park .It has an estimated population of 740,035 people (Igembe 471,836 and Tigania 268,199 )and 123,770 children below 5 years(Igembe 75,494 and Tigania 48,276 ).Giving an average proportion of 16.7% children under 5 years 12. The people of the district are mainly of Igembe and Tigania origins. Borans, Somalis, and others are also residents of the district.

2.3 Description of sampling methods Number of households surveyed were 628 given by ENA software added to 98 households calculated for IYCN (726) divided by number of Household reached per day (20) gave a total of 37 clusters that were surveyed. A total of 6 survey teams, each comprising of 1 team leader and 3 enumerators collected the data for 6 days. One team collected data for 7 days as they had an extra cluster to survey. Clusters were assigned randomly by the ENA FOR SMART SOFTWARE. Survey teams first reported to the area chief, who assigned them a local guide. With the assistance of the local guide, the teams went to the approximate centre of the village and span a pen to select a random direction to walk to the boundary of the village. While at the boundary of the village, the teams span the pen again to select a second direction. The first household visited were randomly selected by drawing a random number list between one and the number of households counted when walking to the periphery. The subsequent households were selected by proximity always selecting households to the right. In villages with more than one cluster, the village were subdivided and the centre of each subdivision was determined and households were selected as described above. In a cluster that was sparsely populated, all the households in the cluster were visited. All children aged 6-59 in every household visited were included in the anthropometric survey and 0-23 month category was included in IYCF.

11 2010 Population projection, Ministry of Health

12 2010 Population projection, Ministry of Health 2.4 Data to be collected, and data collection methods and tools To estimate malnutrition prevalence, mortality rates and IYCF the following information was collected:  Anthropometry (weight, height, oedema, MUAC, age, sex) for children aged 6-59 months and MUAC for caretakers  Vaccination information (measles, BCG, and Vitamin A supplementation)  Incidences of childhood illnesses in the last 2 weeks prior to the survey  Crude and Under 5 mortality rates over a recall period of the last 3 months  Other child care, food security and hygiene data at household level  For children aged below 23 months, IYCF data were equally be collected  HINI indicators were as well captured.  total number (of all ages) currently in the household  number who were in the household at the start of the recall period  number of deaths  number of births  number who left the household during the recall period  number who joined the household during the recall period

2.5 Data collection Tools and Variables Measured A total of 6 survey teams, each comprising of 1 team leader and 3 enumerators collected the data. 4 sets of questionnaires were used for data collection. These included 4 sets of structured questionnaires Questionnaire A (household)- all HH members; Questionnaire B(anthropometry and maternal)- 6-59 months, caregivers; Questionnaire C(IYCF)- 0-23months and Questionnaire D(mortality)-all HH members as well as a focus group discussion (FGD) guide to collect qualitative data. 2.5.1 The household questionnaire This was used to elicit general household information (demographic data, household water sources and consumption, household food consumption, maternal health care information, maternal dietary diversity, sanitation, food aid, food insecurity mitigation strategies, possession and utilization of insecticide-treated mosquito nets (ITNs), livestock condition and household socio-economic status indicators. (appendix 2) 2.5.2 Child (6-59 months old) questionnaire (Anthropometry) Using this questionnaire, the following data were collected: Child age: the age of the child was recorded based on a combination of information collected from the child health cards, the mothers’/caretakers’ knowledge of the birth date and use of a calendar of events for the district developed in collaboration with the survey team (Appendix 3). Child sex : was recorded whether a child was male or female. Bilateral oedema: normal thumb pressure was applied on the top part of both feet for 3 seconds. If pitting occurred on both feet upon release of the fingers, nutritional oedema was indicated. Child weight: the weights of children were taken with minimal light clothing on, using UNICEF Salter Scales with a threshold of 25kgs .The teams were trained to use the Salter scale .The scales were always first set at zero, with the weighing pants, before weighing the child. Child length/height: children were measured bareheaded and barefooted using wooden UNICEF height boards with a precision of 0.1cm. Children under the age of two years were measured while lying down (length) and those over two years while standing upright (height). If child age could not be accurately determined, proxy heights were used to determine cases where height would be taken in a supine position (between 65cm-<85cm) or in an upright position (heights greater ≥85cm). Height rods with a marking at 85cm were used to assist in determining measuring position. Child MUAC: the MUAC of children were taken using child tapes, respectively, and recorded to the nearest 0.1cm. Morbidity: a 2-week morbidity recall was conducted for all index children (6-59 months) to assess the prevalence of common diseases (e.g. malaria, acute respiratory infections (ARI), diarrhoea, measles, stomach-ache, eye and skin infections). Child immunization and Vitamin A supplementation: data on vitamin A supplementation, deworming, and immunization for polio and measles were collected to estimate their coverage. The coverage for measles immunization was only done for eligible children (≥ 9 months). Feeding programme enrolment: it was established if children 6-59 months old were enrolled in SFP or OTP and the duration in the feeding programme. 2.5.3 Under 6 months old child questionnaire This was used to collect infant and young child feeding (IYCF) practices data in the households visited. Information on breastfeeding, weaning and child feeding were collected. Dietary diversity information based on a 24-hour food intake recall was collected for the children to assess the number of food groups taken the previous day 2.5.4 Mortality questionnaire This elicited 3-month (90-day) retrospective recall information on whether there had been any deaths in households and the probable causes of death through verbal autopsy. 2.5.5 Focus group discussion (FGD) guide A FGD guide was used to collect qualitative data to complement quantitative data. Each team implemented 2 FGDs, one for men and another for women in one of the assigned clusters. The FGD clusters were selected from the targeted villages in a manner that ensured adequate representation of socio-economic, ecological and livelihood differentials

2.6 Training and Supervision The survey was coordinated and supervised by International Medical Corps staffs and Meru north district Nutrition Officer (DNO) as the Survey Supervisor. For data collection, a total of 6 teams were recruited and trained for the survey. Each team comprised of a team leader and two enumerators. The local events calendar was developed jointly with the survey team and the questionnaires translated The anthropometric standardization exercise13, as recommended by the SMART methodology, was used as an assessment of the team members’ anthropometry techniques. Each team member was given a score of competence based on performing measurements with accuracy and precision. The results of the training exercise were analyzed by entering the data in the ENA computer package and training report generated. After the class room training, a Practical field experience was conducted on the last day of training, in one of the unselected clusters to take anthropometric measurements of children and caretakers, conduct interviews and fill questionnaires. The pre-testing exercise was performed on 5 households. Each team was supervised at least once a day throughout the data collection by either International Medical Corps staffs or DNO. At the end of each day at base, there was a de- briefing session and review of questionnaires. The survey, including the training, lasted for a period of 11 days. The following topics were covered during training: survey objectives, types and causes of malnutrition ,SMART survey and sampling methodologies verbal interpretation of the questions into the local languages during training for uniform contextual understanding by all the teams ,household, child and mortality questionnaire interviewing techniques, anthropometric measurement procedures ,practical on conducting interviews and anthropometric measurements ,interview techniques ,duties and responsibilities ,research ethics ,community entry behaviour and survey logistics.

2.7 Data Entry and Analysis Anthropometric and mortality data entry and processing was done using the SMART/ENA software where the World Health Organization Growth Standards (WHO-GS) data cleaning and flagging procedures were used to identify outliers which enabled data cleaning as well as exclusion of discordant measurements from anthropometric analysis. The SMART/ENA software generated weight-for-height, height-for-age and weight-for-age Z scores to classify them into various nutritional status categories using WHO9 standards and cut-off points and exported back to SPSS for further analysis. IYCF and all the other quantitative data were entered and analysed in the SPSS Statistics 15.

2.8 Nutritional Status Cut-off Points The following nutritional indices and cut-off points were used in this survey:

13 SMART Regional Training Kit for Capacity-Building and Methodology (ACF Canada) 2010

99 WHO 2006 Weight-for-height (WFH) and MUAC – Wasting among Children The prevalence of wasting (a reflection of the current health/nutritional status of an individual) are presented as global acute malnutrition (GAM) and severe acute malnutrition (SAM) using weight-for-height (WFH) z-scores, WFH percentage of median and MUAC indices. The results on wasting are presented as global acute malnutrition (GAM) and severe acute malnutrition (SAM): Children whose WFH z-scores fell below -2 standard deviations from the median of the WHO standards (WHO-GS) or had bilateral oedema were classified as wasted (to reflect GAM) Children whose WFH z-scores fell below -3 standard deviations from the median of the WHO- GS or had bilateral oedema were classified as severely wasted (to reflect SAM) A cut-off point of <12.5cm MUAC was used to denote GAM among the under-fives. Weight-for-age (WFA) – Underweight The measure of underweight gives a mixed reflection of both the current and past nutritional experience by a population and is a very useful tool in growth monitoring. Children whose WFA z-scores fell below -2 standard deviations from the median of the WHO- GS or had bilateral oedema were classified as underweight Children whose WFA z-scores fell below -3 standard deviations from the median of the WHO- GS or had bilateral oedema were classified as severely underweight. Children whose WFH indices were <80% of the National Centre for Health Statistics (NCHS) median or had bilateral oedema were classified as wasted (GAM) Children whose WFH indices were <70% of the NCHS median or had bilateral oedema were classified as severely wasted (SAM) Height-for-age (HFA) – Stunting Height-for-age is a measure of linear growth and therefore an unequivocal reflection of the cumulative effects of past nutritional inadequacy and/or illness episodes. Children whose HFA z-scores fell below -2 standard deviations from the median of the WHO-GS were classified as stunted (to reflect Global Stunting) Children whose HFA z-scores fell below -3 standard deviations from the median of the WHO-GS were classified as severely stunted. To determine the nutritional status the following variables were considered for analysis: sex, age, weight, height or length and oedema. The cluster number was also included for segregation purposes and to allow for smooth merging up of data with the other household variables in EPI and the SPSS software. During the z-score calculations the following facts were taken into consideration: Table 2: Definition of boundaries for exclusion 1. If Sex is missing the observation is excluded from analysis. 2. If Weight is missing, no WHZ and WAZ are calculated, and the programme derives only HAZ. 3. If Height is missing, no WHZ and HAZ are calculated, and the programme derives only WAZ. 5. For any child records with missing age (age in months) only WHZ will be calculated. 6. If a child has oedema only his/her HAZ is calculated. Additional analyses for frequencies, descriptive, correlations, cross–tabulations and regressions were conducted using SPSS and excel. Indices were expressed both in terms of z scores that represent the difference between observed weight and median weight of the reference population expressed in standard deviation. The result of this survey was compared to WHO standard cut-off points. The IYCF data was analysed to yield data for key indicators in SPSS and excel.

2.9 Survey data validation process Data quality was ensured through:  approval of the methodology by Nutrition Working Group  Thorough training of all team members for four daysthe majority of the enumerators and team leaders had prior experience in carrying out nutrition surveys  standardization of interviewing procedures through verbal translation of questions by survey team members into the local languages spoken in the district during training  standardization of anthropometric measurement procedures  practical sessions on interviewing and anthropometric measurements taking  daily supervision of the teams by IMC staff and Nutrition Coordinator  review of questionnaires on a daily basis for completeness and consistency  plausibility checks from SMART/ENA software specific to each team during daily data entry  on-the-spot correction/feedback of any mistakes noted during data collection to avoid mistake carry-overs  review of questionnaires by teams before leaving the household to ensure questionnaire completeness and consistency  frequencies for all variables were first run and the data cleaned by cross-checking any aberrant values observed on the respective questionnaire before analysis  triangulation of quantitative data using qualitative information-KIIs, secondary data and observation  Age of children verified by EPI health cards- in the absence of cards, use of height sticks and the local calendar of events formulated was used to give estimates of the birth month and year.

2.10 Survey Limitations 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 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. There was poor recording of vitamin A 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.

2.11 Good Practice Community mobilization which incorporated a significant part of administrative authority’s interaction and prior identification of cluster guides by DNO would assist in enhancing ownership of the outcome results of the survey. Working closely with a cluster guide that was respected by community members, yielded better quality data especially on sensitive topics e.g. infant mortality data. Crosschecking the date of birth with both health card and calendar of local events enhanced the age verification process 3. RESULTS

3.1 TARGET POPULATION DEMOGRAPHIC CHARACTERISTICS Overall, the surveyed households had, on average, 5.7 (SD 2.3) members (with a range of 1-15 persons). The mean number of children below 6 months in the households was 0.2 (SD 0.4), those aged 6-59 months 1.1 (SD 0.8). Polygamy was practised in 8.7% of the households and; while 8.4% households were single parents the rest 82.9 % practised monogamy. Majority (80.4%) of the households were being male-headed, 13.3% female-headed and 4.7% of the respondents reporting that their parents were heading the household.

Table 2: Demographic information of target population DEMOGRAPHY Number Number of HH surveyed 740 Number of children 6-59 months surveyed 709 Number of children 0-23 months surveyed for IYCN 731 Number of children 0-5 months surveyed for IYCN 152 Average number of persons per HH 5.7 S.D = 2.3 Average number of children (0-5 months ) per HH 0.2 S.D=0.4 Average number of children (6-59 months ) per HH 1.1 S.D = 0.8

Table 3: Distribution of age and sex of 6-59 months. Boys Girls Total Ratio AGE (months) no. % no. % no. % Boy:gir l 6-17 131 54.1 111 45.9 242 34.1 1.2 18-29 112 49.1 116 50.9 228 32.2 1.0 30-41 47 47.5 52 52.5 99 14.0 0.9 42-53 43 49.4 44 50.6 87 12.3 1.0 54-59 27 50.9 26 49.1 53 7.5 1.0 Total 360 50.8 349 49.2 709 100.0 1.0

The distribution of index children (6-59 months old) by age group and sex was as shown in Table 3 and figure 1, where both the age group and overall male: female ratios were within the expected range of 0.8 – 1.214 which is demonstrative of an unbiased under five survey sample. Of the children measured, 50.8% were boys and 49.2% were girls. Most of the children aged 6- 29 months for IYCN were purposively sampled and this explains why they are many children between these age groups.

Figure 1: Population age and sex pyramid

Child sex f m

50 50 s h t C n h o

40 40 i l d m

a n i g

e e 30 30

g i n a

m d l i o

h 20 20 n C t h s

40 30 20 10 0 10 20 30 40 Frequency

Numbers

3.2 ANTHROPOMETRIC RESULTS (BASED ON WHO STANDARDS 2006) The use of the National Centre for Health Statistics (NCHS) references has been phased out and replaced with the WHO growth standards (WHO-GS). The WHO-GS are structured as a standard rather than a reference, and are therefore better in the assessment of the nutritional status of under-fives regardless of child feeding differentials that characterize children in the community.

3.2.1 Overall Prevalence of Global Acute Malnutrition by WFH Z-scores (WHO Standards) The WFH index is the most appropriate index to quantify wasting in a population and reflects the current nutrition/health status of the community. Other than having a true statistical

14 Assessment and Treatment of Malnutrition in Emergency Situations, Claudine Prudhon, Action Contre la Faim (Action Against Hunger), 2002. meaning, the use of z-scores (standard deviation scores) conveys malnutrition rates very precisely and allows for inter-study comparisons. The WHO Global Database on Child Growth and Malnutrition uses a Z-score cut-off point of <-2 SD to classify low weight-for-age, low height-for-age and low weight-for-height as moderate and severe under nutrition, and <-3 SD to define severe under nutrition. The cut-off point of >+2 SD classifies high weight-for-height as overweight in children. The information presented below is based on the analyzable sample of 709 eligible children whose plausible anthropometric data were collected. 1.4% of the children were flagged off the WFH analysis according to the WHO-GS flagging procedures due to aberrant values In the complete sample, the prevalence of global acute malnutrition i.e. GAM (z-scores <-2 standard deviations and/or oedema) by WHO-GS (Table 4) was 7.8% (5.2-11.6 CI) while the prevalence of severe acute malnutrition (SAM) was 1.2% (0.5-2.8 CI). Although the prevalence of both GAM and MAM was higher among boys than girls, the differences, however, were not significant as indicated by the p.value of 0.208. According to age distribution, SAM was highest 2.4 % among age group 42-53 months, while MAM was highest among age group 54-59 months (table 5). The overall prevalence of GAM in Meru North County reveals risky situation with aggravating factors in the community according to WHO benchmarks11. Notably was the measles break out in the district just before the survey and high morbidity cases in coughing 50% and diarrhoea disease 11.3% which are considered as aggravating factors15. The major causes of malnutrition reported from FGD were: poverty, lack of water supply for irrigation and no land to cultivate.

Table 4: Prevalence of acute malnutrition based on weight-for-height z-scores (and/or oedema) and by sex Boys Girls Total Ratio AGE (months) no. % no. % no. % Boy:gir l 6-17 131 54.1 111 45.9 242 34.1 1.2 18-29 112 49.1 116 50.9 228 32.2 1.0 30-41 47 47.5 52 52.5 99 14.0 0.9 42-53 43 49.4 44 50.6 87 12.3 1.0 54-59 27 50.9 26 49.1 53 7.5 1.0 Total 360 50.8 349 49.2 709 100.0 1.0

The prevalence of oedema is 0.3 % Table 5: Prevalence of acute malnutrition by age, based on weight-for-height z-scores and/or

1111 ACUTE MALNUTRITION BENCHMARKING SYSTEM FOR GLOBAL HUMANITARIAN RESPONSE ,WHO (2000):

15 WHO (2002). The management of nutrition in major emergencies oedema

Severe wasting Moderate Normal Oedema (<-3 z-score) wasting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (months no. ) 6-17 244 3 1.2 19 7.8 222 91.0 0 0.0 18-29 232 1 0.4 15 6.5 214 92.2 2 0.9

Table 6: Distribution of acute malnutrition and oedema based on weight-for-height z-scores <-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 (0.0 %) No. 2 (0.3 %) Oedema absent Marasmic Not severely malnourished No. 6 (0.9 %) No. 684 (98.8 %)

This table shows that 6 children (0.9%) are severely wasted (marasmus) with no oedema. While 2 children 0.3% had kwashiorkor with oedema present. 3.2.2Prevalence of Acute Malnutrition by MUAC Another measurement used to determine a child’s nutritional status is the mid-upper arm circumference (MUAC) measurement. Because MUAC measurements require a simple, colour- coded measuring band rather than weighing scales and height boards, they are often used during crisis situations and as a rapid screening tool for admission into nutrition intervention programmes. Useful for children between six months and five years of age, a MUAC measurement of less than 12.5 cm indicates that a child is suffering from moderate acute malnutrition. If the MUAC measurement is under 11.0 cm, however, the under-five child’s life may be in danger as he or she is suffering from severe acute malnutrition. Compared to WFH z- scores, the mid-upper arm circumference (MUAC) is not a very sensitive indicator of acute malnutrition and tends to overestimate acute malnutrition for children below one year of age. However, used, Overall, MUAC usually tends to indicate lower GAM levels compared to WFH z- scores. The findings (Table 7) indicate that overall, 9.9% suffered from GAM (MUAC <12.5cm), 9.6 % suffered from MAM (MUAC >=11.5 and <12.5cm ) and 3% (0.3-1.8 CI) suffered from SAM (MUAC <11.5cm), with 27.3% of the under 5 years being at risk of malnutrition (MUAC >=12.5 and <13.5cm ).Analysis by age group shows that a high number of children between the age of 6-11 months are at risk of malnutrition. Table 7 Prevalence of acute malnutrition based on MUAC cut off's and/or oedema <-3 z-score >=-3 z-score Oedema present Marasmic kwashiorkor Kwashiorkor No. 0 (0.0 %) No. 2 (0.3 %) Oedema absent Marasmic Not severely malnourished No. 6 (0.9 %) No. 684 (98.8 %)

Figure 2: Prevalence of acute malnutrition by age, based on MUAC cut off's and/or oedema

3.2.3 Prevalence of Underweight by Weight-for-age Z-scores (WHO-GS) 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 EPI health cards 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. The prevalence of underweight among the children was 14.2% (11.5-17.4 CI) while 2.1% (1.3-3.6 CI) were severely underweight as shown in Table 8 More boys than girls suffered from global underweight as well as severe underweight. The difference is extremely significant as indicated by p.Value of 0.004. AS shown in table 9, children in age group 42-53 months were more affected as compared from the other age groups.

Table 8: Prevalence of underweight based on weight-for-age z-scores by sex All Boys Girls n = 705 n = 358 n = 347 Prevalence of underweight (100) 14.2 % (65) 18.2 % (35) 10.1 % (<-2 z-score) (11.5 - 17.4 (13.9 - 23.4 (7.0 - 14.4 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate underweight (85) 12.1 % (54) 15.1 % (31) 8.9 % (<-2 z-score and >=-3 z-score) (9.7 - 14.9 (11.4 - 19.7 (5.8 - 13.5 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe underweight (15) 2.1 % (11) 3.1 % (4) 1.2 % (<-3 z-score) (1.3 - 3.6 (1.5 - 6.1 95% (0.4 - 3.0 95% 95% C.I.) C.I.) C.I.)

Table 9: Prevalence of underweight by age, based on weight-for-age z-scores Severe Moderate Normal Oedema underweight underweight (> = -2 z score) (<-3 z-score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % No. % (months no. ) 6-17 242 7 2.9 32 13.2 203 83.9 0 0.0 18-29 224 3 1.3 20 8.9 201 89.7 0 0.0 30-41 99 1 1.0 6 6.1 92 92.9 0 0.0 42-53 87 3 3.4 17 19.5 67 77.0 0 0.0 54-59 53 1 1.9 10 18.9 42 79.2 0 0.0 Total 705 15 2.1 85 12.1 605 85.8 0 0.0

3.2.4 Prevalence of Stunting by Height-for-age (HFA) Z-scores (WHO-GS) The height-for-age (HFA) index is a measure of long-standing (chronic) malnutrition. It measures linear growth and is thus reflective of the cumulative effects of long-standing nutritional inadequacy and/or recurrent chronic illness episodes. 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. The findings (Table 10) indicated an overall global chronic malnutrition (GCM) rate of 29.5 % (26.1-33.1 CI) and a severe chronic malnutrition (SCM) rate of 7.8 % (6.2-9.9 CI). The results showed that the differences were significantly (p. value 0.009), more boys than girls suffered from both GCM and SCM.

Table 10: Prevalence of stunting based on height-for-age z-scores and by sex All Boys Girls n = 702 n = 357 n = 345 Prevalence of stunting (207) 29.5 % (120) 33.6 % (87) 25.2 % (<-2 z-score) (26.1 - 33.1 (28.3 - 39.4 (20.7 - 30.3 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of moderate stunting (152) 21.7 % (82) 23.0 % (70) 20.3 % (<-2 z-score and >=-3 z-score) (19.0 - 24.6 (18.4 - 28.3 (16.7 - 24.4 95% C.I.) 95% C.I.) 95% C.I.) Prevalence of severe stunting (55) 7.8 % (38) 10.6 % (17) 4.9 % (<-3 z-score) (6.2 - 9.9 (7.9 - 14.2 (3.1 - 7.9 95% 95% C.I.) 95% C.I.) C.I.)

Table 11: Prevalence of stunting by age based on height-for-age z-scores Severe stunting Moderate Normal (<-3 z-score) stunting (> = -2 z score) (>= -3 and <-2 z-score ) Age Total No. % No. % No. % (months no. ) 6-17 239 21 8.8 65 27.2 153 64.0 18-29 224 21 9.4 42 18.8 161 71.9 30-41 99 5 5.1 13 13.1 81 81.8 42-53 87 5 5.7 23 26.4 59 67.8 54-59 53 3 5.7 9 17.0 41 77.4 Total 702 55 7.8 152 21.7 495 70.5

Table 12: Mean z-scores, Design Effects and excluded subjects Indicator n Mean z- Design Effect z-scores not z-scores out scores ± SD (z-score < available* of range -2) Weight-for- 690 -0.31±1.13 1.18 2 17 Height Weight-for-Age 705 -0.93±0.98 1.00 2 2 Height-for-Age 702 -1.38±1.14 1.00 0 7

* contains for WHZ and WAZ the children with edema. The table above indicates the flagged values due to aberrant values for WFH, WFA and HFA.

3.3 ADULT NUTRITIONAL STATUS The MUAC measurements of 697 eligible primary childcare givers (15-49 years old) were taken to assess their nutritional status. The survey findings showed that of the 697 total, 4.3% were pregnant, 63. % were lactating, and 32.7% were neither lactating nor pregnant .6.9% (n=48) of caretakers had MUAC <21cm meaning that they are at risk of malnutrition/have chronic energy deficiency (CED). Among the pregnant and lactating sub-group, 3.3% and 4.8% of subgroup have MUAC<21.0 and therefore have severe chronic energy deficiency. 11.4% of women not pregnant or lactating had CED (MUAC<21.0). The admission criteria into SFP in adult is MUAC<21.0 for pregnant and lactating mothers of children <6 months old. The magnitude of under-nutrition was high among non-pregnant women compared to those who were pregnant. 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. The figures above indicate that pregnant women and lactating mothers in the district are relatively more vulnerable to malnutrition compared to their non-pregnant counterparts. Poor adult nutritional status is a key indicator to household food insecurity. High figures of malnourished pregnant and lactating mother carry a risk of growth retardation of the foetus and consequently low birth weight. Figure 3. Nutrition Status of caregivers of < 5 year old children:

3.4 MATERNAL HEALTH CARE INFORMATION. Overall, 87.7 % of mothers reported having attended MCH clinics and received the necessary care and advice including iron folate supplementation during their last pregnancy with a mean frequency of 3.2 (SD 1.2) clinic visits. 6.8% never attended MCH clinics and the rest 5.6% never delivered. Despite the high ANC uptake only 55.3 % of the women delivered in the hospital, 28.3% delivered at home with assistance from traditional birth attendants (TBAs), 8.0% delivered at home without assistance, and 1.8% delivered at home with assistance from nurse. Some of the Major reason as to why they did not attend ANC clinics were : they were not aware of the importance of ANC (32.0%),TBA services are adequate (14%) ,cultural barriers (8%) ,health facility was too far ( 6%) and unfriendly health worker (4%) .

The participation of TBAs in child deliveries is currently discouraged by WHO because the services they offer fall short of the minimum care that delivering mothers should receive. However, in many remote areas where there is limited access to conventional health care, they might be the only practical care that delivering mothers have access to. It is, therefore, recommended that children who are born outside a health facility setup should be taken to a health facility within 2 weeks of birth to allow for optimal health check-up and administration of the zero dose polio antigens. On the whole, only (36%) of the children delivered at home were taken for medical attention within the recommended 2-week period.

Maternal vitamin A supplementation within 2 weeks after birth is crucial and recommended by WHO as a means to boost its content in breast milk as well as promote maternal recovery following delivery. 42 % of mothers reported having received vitamin A supplementation following their last delivery. 34.7% and 34.6% of the mothers were given de-wormers and iron/folate tablets respectively during their last pregnancy. On average most health facilities are located 3.2 (SD 2.6) km away . The amount of time spent outside the home by mothers has a direct influence on both the quality and quantity of care that mothers are able to give to their children, which influences child health, growth and development. It also influences willingness and ability to access and utilize medical care services.

3.5 CHILD FEEDING, CARE AND HEALTH 3.5.1 Infant and Young Child Feeding Practices The findings indicate that practically all children (98.1%) were reported to have breastfed. The main reason for children who were not breastfed was that there was no milk from the mothers breast. Benefits of timely initiation of breastfeeding and therefore provision of colostrum include stimulation of the onset and maintenance of lactation as well as provision of necessary maternal antigens to the infant through colostrum. The proportion of infants reportedly put on the breast within the first hour of birth was 86.3%, with 66.4% of them reported having received colostrum during the first 3 days of birth. 14.4% of the infants were reportedly given pre-lacteals. Giving pre-lacteals interferes with optimal establishment of breastfeeding and may also give rise to infections such as diarrhoea in infants. Among infants given pre-lacteals, the most frequently given item (Figure 4) was plain water (88 %), followed by infant formula 5%, sugar/glucose water or honey 3%, Gripe water 3% and finally by animal milk and its products (1%) and Hospital delivery would help in curbing the practice of giving pre-lacteals to infants.

Figure 4:Pre-Lacteals given in the first three days of birth

Practically all (99.3%) of the children less than 6 months were reported to have been breastfeeding during the survey. However, the frequency of breastfeeding fell short of the ‘on- demand’ rule only 13.2% were breastfed more than 12 times during the preceding day. The WHO recommends that infants should be breastfed at least twice every 2 hours, which translates to 12 times a day. Exclusive breastfeeding was computed among infants who had not received pre-lacteals and were not on other foods. The findings revealed that 53% were exclusively breastfed compared to a national average of 31.9%16 according to the Kenya Demographic and Health Survey (KDHS) report. Out of those who were not exclusively breastfed, the average age when the weaning started was 99day (SD 58 days) with only 16.9% weaned at the WHO recommendation of 6 months. The findings of the FGDs revealed that the main reason cited for early weaning was to ‘prevent child from crying as the mothers seek casual labour, this is because children are left at home without breast milk’ .It was also revealed that the main type of food babies are introduces to is porridge because it is cheap and easy to swallow. Early weaning

1616 Kenya National Bureau of Statistics (June 2010): Kenya Demographic and Health Survey. increases the risk of infections in young children, with the foods given being nutritionally inferior to breast milk, which ultimately aggravates malnutrition. After 6 months children should receive other foods in addition to breast milk since the nutrients from breast milk alone cannot meet all the needs for accelerated growth and development. The findings indicated that 10.6% of the children aged 6-24 months had stopped breastfeeding, indicating that maintenance of breast feeding up to 2 years was maintained only for 89.7% of the children. The foodstuffs given to children between the ages of 6-24 months are referred to as complementary foods. Assessment of complementary feeding was therefore computed for children above 6 months. Dietary diversity is a qualitative measure of food consumption that reflects household access to a wide variety of foods, and is also a proxy of the nutrient intake adequacy of the diet for individuals. Dietary diversity scores (DDS), were created by summing up the number of food groups consumed the previous day to aid in understanding if and how the diets are diversified. According to the Food and Agricultural Organization (FAO), dietary diversity scores are meant to reflect, in a snap shot, the economic ability of a household to consume a variety of foods17. A score of 1 was allocated to each of the 8 food groups that were consumed by the child and a score of 0 for each of the food groups not consumed and thus the highest possible score was 8. Children who had consumed less than four food groups were classified as the low dietary diversity group and those with a score of 4 or more as high dietary diversity group. The dietary diversity questionnaire tool was based on the 24-hour food intake recall.

On average the mean food diversity was 3.2 (SD 1.6) given to children > 6 months. The findings showed that 64.2% of the children samples consumed low dietary diversity of less than four groups, a threat to optimal child growth and development while only 35.8% of the households had children >6 months who consumed 4 or more of the food group. 63.7% of children > 6 month who were still breastfeeding at the time of the survey consumed 3 or more food groups while 31.9% of non-breastfeeding children consumed 4 or more food groups. The mean frequency of feeding children between age 6-8 months was 3.2 (SD 1.2) and between age 9-23 months was also 3.2 (SD 0.9). An analysis of the food groups taken by children (Figure 5) indicates that relatively low proportions took eggs (4%). and meat products (5%). The food group taken by the highest proportion of children was carbohydrates (30%), Vitamin A rich foods (18%), Dairy products (16 %%), legumes (14%), and fruits and vegetables (13%).The reason given from FGD for low intake of dairy and meat products was that there were no enough dairy animals in the area and most of the households lacked enough money to buy milk and meat.

1717 Guidelines for measuring household and individual dietary diversity. Version 2, June 2007. Prepared by FAO Nutrition and Consumer Protection Division with the support from EC/FAO Food Security Information for Action Programme and the Food and Nutrition Technical Assistance (FANTA) Project. Rome, Italy Figure 5 food groups taken by children 6-23 months in the previous 24 hrs

3.5.2 Child Immunization, Vitamin A Supplementation and Deworming Child immunization is crucial as it prevents and/or reduces the severity of certain diseases in young children. The immunization coverage rates for polio 1 was (97.8%), polio 3 (95.8%) and measles (85.1 %) were commendably high and above the Kenya Expanded Programme on Immunization (KEPI) recommendation of 80%. Vitamin A supplementation is carried out as part of routine disease treatment in health facilities in Kenya. From the survey, an overall 65.7% of the under 5 years were reported to have received vitamin A supplementation. 58.5 % of those who received Vitamin A were aged 6-11 months while 66.7 % where in the age group 12-59 months. Deworming is crucial in warding off the debilitating effects that helminthic infections cause among growing children.53.1 % of children reported to have been dewormed. The high measles coverage can also be explained as a fact that there was an on –going measles campaign in Meru north district at the time of the survey and therefore most of the children had been immunized at the time. Table 13: Vaccination coverage: OPV 1 & 3 for 6-59 months and measles at 9 months and deworming for 12-59 months Measles OPV 1 OPV 3 Deworming n=651 n=697 n=697 (12-59 Months) N=603 YES with With with With with With with With card Recall card Recall card Recall card Recall n=279 from n=360 from n=347 from n=91 from mother mother mother mother n=275 n=322 n=321 n=229 % 42.9 42.2 51.6 46.2 49.8 46.1 15.1 28 Table 14 Vitamin A coverage

Vitamin A Vitamin A Vitamin A 6-59 months 6-11 months 12-59 months ( N=697 N=94 received twice in the last 1 year) N=603

65.6% 58.5% 66.7 %

3.5.3 Child Morbidity A 2-week child morbidity recall (inclusive of the day of survey) was assessed to establish the prevalence of common illnesses among the children. A good proportion (44.6%) of the under- fives was reported to have been sick. The most prevalent illness affecting the under-fives were cough 50% , followed by malaria (21.9% )then diarrhoea (11.3%) and finally by measles (2.3%). The health seeking behaviour by mothers of sick children was assessed by asking the respondents what they did the last time their under-five child was sick. During the last episode of illness, the majority (56.9%) of mothers reportedly took the children to public health facilities, 25.4% sought assistance from private clinics or pharmacies, 13.4% did not get any assistance, and 2.9 % bought medicine from shops/kiosks while the rest 0.3% each sorts assistance through CHW and mobile outreaches.

Of the children who had diarrhoea 30.8% were given Oralite, 15.4 % zinc, 5.1% both zinc and ORS,12.8% home- made salt/sugar and 35.9% did nothing to the child .

Table 15: Symptom breakdown in the children in the two weeks prior to interview (n=309)

Symptoms 6-59 months Cough 50.0 % Malaria 21.9% Diarrhoea 11.3 % Measles 2.3 % Other 14.5 %

3.6 SUPPLEMENTARY AND THERAPEUTIC FEEDING PROGRAMME COVERAGE The point coverage for both SFP and OTP was estimated using WFH z-scores, which are currently recommended for screening and admitting children into the programmes. In the survey sample and according to the WFH z-scores index, out of the 46 children who were moderately malnourished, 39 of them were reported to have been enrolled in the SFP programme. The point coverage for the SFP (estimated as the number of moderately malnourished children (39) in the programme) divided by the total number of moderately malnourished children found during the survey (46) is therefore 84.8 % with a mean duration of 23 days (SD 13) Likewise, the point coverage for OTP is estimated as the total number of children suffering from SAM (8 ) found during the survey divided by number of children suffering from SAM enrolled into the programme (4), which translates to 50% coverage with mean duration of 10.1 days (SD 18.5). SPHERE18 recommends a minimum 60% coverage for community nutrition intervention programmes.

SFP coverage = Children registered in SFP x 100 Children registered in SFP+ children with WHZ<-2 z-score and >=-3z-score that are not enrolled

OTP coverage = Children registered in OTP x 100 Children registered in OTP + children with WHM=<-3 z-score that are not enrolled

1818 The SPHERE Project Handbook (2004). Humanitarian Charter and Minimum Standards in Disaster Response. 3.7 INSECTICIDE TREATED MOSQUITO NETS (ITN) HOLDING RATES AND UTILIZATION The MoMS provides free insecticide treated mosquito nets (ITNs) to expectant mothers attending MCH clinics. 58.8 % of the households reported having mosquito nets, most (89.6%) of which had been sourced from the MoMS or Mission hospitals, while 7.8 % and 0.9 % had obtained the nets from shops and non-governmental organizations (NGOs), respectively. The nets obtained from hospitals and NGOs are treated with long-term insect-repelling chemicals while the ones obtained from shops or vendors may not be treated, which makes it necessary to wash them in the chemicals to repel mosquitoes and other insects. The proportion of households that reported treating nets they had obtained from shops was 38.9%. The reported utilization of the nets during the night preceding the survey was highest (84.6%) among the under-fives followed by fathers of children (73.7%), mothers (72.8%), children above five years (46.8%) and pregnant women (6.7%).

3.8 WATER, SANITATION AND HYGIENE PRACTICES There were several sources of water for household use reported by the survey respondents. 33.2% and 31.7% of the households got water from tap for general use and drinking respectively. Other sources of water reported for general use and drinking were; Rivers, unprotected well, boreholes. Only 0.3 % used rain water for drinking purposes. The findings show very minimal treatment of drinking water at the household level with 67% did not treat water before drinking, 30 % boiled drinking water while 3% used chemicals to treat their water. Clearly the role of untreated water as the main cause of childhood diarrhoea and subsequent levels of acute malnutrition cannot be underestimated.On average, it takes a caregiver about 41.75 minutes (SD 45) to access their main source of water and use 97.8 (SD 55.5) litres of water per day (which translates to about five 20-litre jerricans). Households buying water in jerricans paid on average Kshs 7.49 (SD 14.2) per 20-litre jerrican. Communities should be encouraged to boil their drinking water at the household level, being the most viable and cheap method. 85.4% of the HH had access to toilet facilities. Of those households with toilet facilities, majority 74.1% used traditional pit latrines, 10% used ventilated improved latrines, and 0.9% used buckets while 1.1% used other types of toilets. For the households with no toilet facility, bush 50% and open field 17%) were the major alternative methods used while 3% went behind their houses. It was also confirmed through observation that a significant proportion of children’s feaces are also disposed of hygienically (65.7%), and 63.1% of the compounds were clean. 64.9% of the mothers reported washing their hands with soap, 29.0% said they wash hands but without soap while the rest 6.1% don’t wash hands. Of those who wash hands; majority 38.2% washed hands before eating, 35.9% after visiting the toilet, 20.7% before food preparation while the rest 5.2 % after changing baby’s diapers. This makes it necessary to educate the community on the health implications of unhygienic faecal disposal. Washing of hands before handling food should also be given greater attention for example through health education messages since 20.7% of the mothers reported not washing hands before handling food. Figure 6: House hold water sources for general and domestic use

Figure 7 household water treatment methods.

3.9 HOUSEHOLD FOOD SECURITY 3.9.1 Sources of Income Overall, the three main source of income during the previous 3 months preceding the survey in Meru North were: wage labour 48%, sale of own crop 17% and petty trade 11%. Majority of inhabitants in Meru north plant miraa as a cash crop which is harvested throughout the year. Other sources of income included: Salaried employment, sale of charcoal/firewood, Sale of livestock, Sale of livestock products, Brewing, Remittances and Weaving/basketry which accounted for 10.8%.The mix of income sources is reflective of the varied livelihood activities in the larger district including crop farming and pastoralist activities. The survey also showed on average each household has one person (SD 0.7) who earned money that directly benefited the household. Within the wealth ranking system, the bigger majority is ranked as medium 49.3% and poor 46.9%. The findings from FGD showed that the ranking criteria were based on if the household has cattle, tea estate or assets in terms of rental houses. FGD findings also revealed Majority 75% of the community was poor with only 25% categorized as rich.

Majority 58.3% of the inhabitants owned livestock. Of those who reported they had livestock 40% reported that the number of livestock had not changed, 30% had reduced and 27.5% had increased. The main reason given as to why the number of livestock had reduced and increased were: the livestock were sold and animals gave birth respectively. Findings from FGD revealed that most of the livestock were thin and emaciated due to lack pasture. The lack of pasture was due lack of enough rain and no land to cultivate pasture Figure 8 Sources of Income

3.9.2 Household Dietary Diversity and Food Sources, food Aid The mean of meal frequency usually taken by household was 2.7 (SD of 0.6) times while the one reported for the previous day prior to survey was 2.6 (SD 0.7).71.4 % of the household usually have meal frequency of >3 meals a day while 66.1% households reported having had >3 meals a day the previous day of the survey. From the survey 99.2 % of the household use iodized salts.

Figure 9 Frequency of meals taken in household

At the household level, the dietary diversity score (DDS) is indicative of the ability to acquire a variety of foods, including foods that may not have high nutrient value such as beverages and condiments. The previous 24-hours’ food intake by mothers was used as a proxy to assess household dietary diversity in this survey. Food intake by caretakers is a good estimation of the variety of what other members of the households took (excluding the U5s). The 12 major food groups inquired about are cereals, fish and sea food, roots and tubers, vegetables, fruits, meat and poultry , eggs, legumes, milk and milk products, fats and oils, sugar and sweets, condiments and miscellaneous(spices, sweets, unsweetened beverages). A diverse diet was indicated by consumption of four or more food groups On average the mean Individual Diet Diversity Score was 4.1 (SD 1.5) for the number of food groups consumed. Overall, majority (66.1%) of the caregivers consumed at least four food groups in the previous 24 hours (above the threshold for a diverse diet). However, 33.9% HH had a low diet diversity score of <=3, which highlights serious food insecurity. A high proportion (78.0%) of households reported that all members took the meals prepared the previous day, with the main reason given for those who did not take meals at home being that they had taken their meals elsewhere (69.7%), lack of adequate food in the household (18.3%) and lastly 11.3% said food was not suitable. Figure 9 shows that the most common foods consumed by the households were Cereals and cereal products, Pulses / legumes / nuts and seeds and Fats and oils. The least consumed foods groups were fruits, meat and poultry, eggs and fish which each contributed to 1%. The uncharacteristic low consumption of meat, Vegetables and fruits was explained in FGDs by that fact that they were inaccessible because of increased prices. In addition, lack of sufficient nutritional knowledge on the importance of the consumption of food groups such as vegetables, fruits, eggs and pulses due to cultural reasons that shape food selection habits, also attributed to the low consumption of these food groups.

Figure 10 Ratio of foods groups consumed in 24-hour recall

Table 16 shows that 70.3% of the households purchase food and 28.35 got it from their own production while only 0.4% borrowed food. This shows that sharing between households is not such a common occurrence. Within the last three months only 7% of the households received general food aids which lasted them a mean of 2.4 (SD 3.4) days. Of those who had received food aids , the biggest amount of foodstuff received was maize 65.4 %, with households reporting receiving, on average, 2.8 (SD 4.8) kgs, followed by beans 44.2% receiving an average of 0.7( SD 0.95) Kgs, rice 34.6% receiving an average of 0.63 (SD 0.98) kgs, and finally received vegetable oil on average 0.17 (SD 0.3 litres). Table 16: Main Sources of food consumed in 24 hr recall Food source percentage Purchase 70.3 Own production 28.3 Gift from relatives 0.9 Borrowed/credit 0.4 Totals 100

81% of responded reported that they cultivated land, on average 0.9 (SD 1.2) acres was cultivated. Of those who reported having cultivated land the major food crop harvested was maize 88.7%, followed by Beans 68.4%, cowpeas 5.8 and finally green grams 4.3% .The table below shows the percentage of food crop shared, sold and available in the stores. According to the responses given during FGD, food availability was scarce due to the drought, lack of water for irrigation the scarcity of land to be cultivated. Food was available in the market but the prices were high and therefore hindering them to purchase.

Table 17:proportion of food crops shared sold and stored after harvesting. Food crop %shared %sold %Available in store Maize 39 19.7 50 Beans 25.4 12.9 25.4 Cow peas 1.7 0.7 1.3 Green grams 2.8 1.3 2.5

During the previous 2-month period, close to half (45.8%) of the sampled households reported having experienced a food shortage. The sample population reported having various coping strategies. The table belows shows top 10 coping strategies reported by the respondent. It was reported from FGD that sell of livestock as a coping strategy was mainly done when there was severe food shortage. Table 18: top ten coping strategies Coping Strategies Coping Strategies % Reduction in size of meals 14.8 Reduction in the number of meals per day 13.3 Borrow food from friends/beg 8.8 Skip food consumption for an entire day 8.6 Purchase food on credit 6.8 Sale of charcoal 2.4 Restrict consumption of adults to allow more 1.8 for children Swapped consumption to less foods/preferred 1.5 Individual migration out of the area 0.6 Consume wild foods 0.3

3.10 MORTALITY RESULTS The recall period for questions relating to the mortality questionnaire was 90 days (3 months) from the start date of the survey. Both the crude mortality rate (CMR) of 0.24 deaths/10,000/day and the under-five mortality rate (UFMR) of 0.48 deaths/10,000/day were within the acceptable levels for emergency situations16; (table 19).The major cause of death among the under 5 years was not known the second major been ARI (25%).The major cause of death above the 5 years of age was accident/ the person was killed (25%).

Table 19: Mortality rates CMR (total deaths/10,000 people / day 0.24 (0.11-0.56) (95% CI) U5MR (deaths in children under five/10,000 0.48 (0.14-1.59) (95% CI) children under five / day

Table 20: Causes of death among under/above 5 years Causes of Death >5 years <5 years Waterly diarrhoea 12.50% 0.00% ARI 12.50% 25.00% Malaria 12.50% 0.00% Killed/accident 25.00% 0.00% Not known 12.50% 75.00% Old age 12.50% 0.00% Others 12.50% 0.00% Totals 100.00% 100.00%

4. CONCLUSION Malnutrition is a major health problem, especially in developing countries. Water supply, sanitation and hygiene, given their direct impact on infectious disease, especially diarrhoea, are important for preventing malnutrition. Both malnutrition and inadequate water supply and sanitation are linked to poverty. Other underlying factors causing malnutrition are morbidity,

16 The Sphere Standards, 2004. Under Five Mortality Rate (U5MR): emergency threshold is 2.3/10,000/day, Alert 1.0/10000/day inadequate health and nutrition programme coverage and poor IYCF practices (breastfeeding, food frequency and dietary diversity). The study identified aggravating factors that had a negative bearing on optimal under-five nutritional status and their caregivers:

 Poverty and issues of who controls family income have a heavy contribution to household food security. Income sources are not diversified and therefore there’s over reliance on farm produce both as an income source and family food. Poverty has also made it difficult to access food from markets due to insufficient financial resources. Lack of water supply in many parts of Meru North districts especially in Igembe North division has led to infectious diseases spreading, causing childhood diarrhea, which leads to major malnutrition and subsequent death due to diarrheal dehydration  Poor agricultural practices including cultivation of Miraa in most areas whose income does not translate into food security. This is further compounded by poor soil fertility as a result of poor farming practices and environmental degradation.  Lack of access to food.Most major food and nutrition crises do not occur because of a lack of food, but rather because people are too poor to obtain enough food.  Poor child and adult dietary profiles. Over-consumption of certain food group like cereals usually goes along with deficiencies in essential vitamins and minerals.  High child morbidity prevalence reported to have affected 44.6% of the under-fives which was found to significantly affect child nutritional status;  Poor IYCF practices including early weaning, low maintenance of breast feeding and poor feeding practices.  Poor access to medical facilities some are too far for household to access. On average most health facilities are located 3.2 (SD 2.6) km away.  Poor water sanitation status in the community with minimal treatment of unsafe drinking water at the household level increase vulnerability to infectious and water- borne diseases, which are direct causes of acute malnutrition.

5 .RECOMMENDATIONS Because malnutrition has many causes, only multiple and synergistic interventions embedded in true multi-sectoral programs can be effective. A variety of actions both immediate and long term solutions are needed: Immediate Interventions  Addressing the poor access to essential health and nutrition services by strengthening the integrated outreach component- primarily focusing on regular medical outreach camps/mobile clinic. This will help to intensify active case finding of malnourished children and manage them accordingly.  Strengthen programmes and strategies currently addressing infant and young child nutrition (IYCN) with a view to improving the protection, promotion, and support of optimal IYCF. Viable action points include:  As the HINI program is rolled out there is need for continual monitoring of both facility and community based interventions to track progress while also documenting the process to assess the trends in the outcomes as well as impact indicators. Particular attention should go to improved maternal nutrition, iron/folate supplementation during the prenatal period and ensuring ORS/zinc support for diarrhoea.  Strengthening of hygiene practices to reduce the incidence of diarrhoeal disease associated with contaminated water in the household including health education to educate the community on domestic treatment of drinking water and effective hand washing (soap/ash) after helping a child in the latrine, during food preparation and before child feeding. This should be backed-up with provision of free water treatment chemicals where feasible.  Continued water trucking to areas affected by water stress by Ministry of Water and Irrigation and Kenya Red Cross especially in Igembe north District.  Provision of water purification chemicals for water treatment at Household level  Advocacy/public health campaigns on domestic water treatment such as boiling of drinking water and use of purification chemical to minimise risks of water-borne diseases, should be carried out.  The Ministries of Public Health and sanitation and Medical services in collaboration with other stakeholders in the district to initiate and offer concrete support in the implementation of strong awareness campaigns and community based health and nutrition programs with special focus on infant and young child feeding practices, dietary diversification, food preparation and preservation, consumption of energy dense and micronutrient-rich foods and kitchen gardening. Women should be the prime targets of these. Nutrition messages should address strategies/ways of improving access to locally available and cheaper sources of fat, protein and micronutrients.

Long-Term Interventions  Focus on programmes by ministry of agriculture that improve and sustain dietary diversity and consumption of micronutrient.-rich foods. And advising farmers on good farming methods .By improving agricultural yields, farmers could reduce poverty by increasing income as well as open up area for diversification of crops for household use.  To address the issues of limited access to safe water, there is a need to establish water points in areas where water is inaccessible.  MOH should increase access to health facilities in the rural parts of kenya by adding more health facilities or increasing CHW. These will improve hospital deliveries and access to medical services for those who cannot access the health facilities

6. APPENDICIES Appendix 1: IYCN calculator

Appendix 2: Household Questionnaire Appendix 3: Anthropometric Questionnaire

Appendix 4: IYCN Questionnaire

Appendix 5: Mortality Questionnaire

Appendix 6: Focus Group Discussion guide

Appendix 7: Plausibility checks......

Appendix 8: Assignment of Clusters

Appendix 9: Map Appendix 10. Summary of findings Characteristic % ( 95% CI) Overall GAM (WFH <-2 Z score or presence of oedema) - WHO 2006 7.8% [5.2 – 11.6] Overall SAM (WFH <-3 Z score or presence of oedema) - WHO 2006 1.2% [0.5 – 2.8] Overall underweight (WFA <-2 Z score or presence of oedema) - WHO 14.2% [11.5 – 17.4] Overall Severe underweight (WFA <-3 Z score or presence of oedema)-WHO 2.1% [1.3 – 3.6] Overall stunting (HFA <-2 Z score)- WHO 29.5% [26.1 - 33.1] Overall Severe stunting (Height for age <-3 Z score) -WHO 7.8% [6.2 – 9.9] Prevalence of GAM by MUAC (<12.5cm) 12.6% SFP Programme Coverage (Period Prevalence Estimate) 84.8% OTP Programme Coverage (Period Prevalence Estimate) 50% Proportion of children sick two weeks prior to survey 44.6% Measles* immunization (card and confirmation) 85.1% OPV1 immunization (card and confirmation) 97.8% OPV3 immunization (card and confirmation) 95.8% Vitamin A supplementation coverage 65.6% Proportion of children dewormed 52.6 Proportion of malnourished women (MUAC<21.0cm) 2.2% Proportion of malnourished pregnant and Lactating women (MUAC <21.0cm) 7.3% Hospital Delivery 55.3% Timely initiation of breastfeeding (children 0-23 months) 86.3 Exclusive breastfeeding under 6 months 53.3 Continued breastfeeding at 1 year 89.4 Minimum dietary diversity 2.8 Minimum meal frequency 3.2 Toilet coverage 85.4 Household that treat water before drinking 33% % of caregivers wash hands with soap 64.9% Under-five mortality rate (deaths/10000/day) 0.24[0.11-0.56] Crude mortality rate (deaths/10000/day) 0.48 [0.14-0.59]