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TANA RIVER COUNTY SMART SURVEY REPORT
FEBRUARY 2015
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ACKNOWLEDGEMENT The Tana River 2015 SMART survey was carried out through collaborative efforts of a number of
partners. We take this opportunity to hail their commitments which led to the successful
completion.
Special appreciation goes to the County Department of Health Team led by the County Nutrition
Coordinator Mr. Omari Makopa and IMC program manager by Mr John Nderi. We would also wish to
extend our special thanks to the County government of Tana River for permission to carry out this
assessment within its jurisdiction, and the Tana River community especially the caregiver of children
under-fives who were instrumental in providing us with information.
We also recognise the financial support by UNICEF as well as the technical guidance by the National
Nutrition Information Working Group.
Lastly, we thank all the survey teams (supervisors, team leaders, enumerators, and data clerks) who
worked tirelessly to ensure the results were available on time.
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LIST OF ABBREVIATIONS ARI Acute Respiratory Infection BCG Bacille Calmette Guerin CIDP County Integrated Development Plan CNTF County Nutrition Technical Forum CSI Coping Strategy Index ENA Emergency Nutrition Assessment FCS Food Consumption Score FeFo Iron Folic acid (Folate) IDP Internally Displaced Persons IPC Integrated Phase Classification IMAM Integrated Management of Acute Malnutrition IMC International Medical Corps IYCF Infant and Young Child Feeding ITN Insecticide Treated Nets GAM Global Acute Malnutrition HFA Height for Age Ksh Kenya Shillings MAM Moderate Acute Malnutrition MOH Ministry of Health MUAC Mid Upper Arm Circumference NCHS National Centre for Health Statistics NGO Non Governmental Organisation ODF Open defaecation Free OPV Oral Polio vaccine PLW Pregnant and Lactating Women PPS Probability to proportion size SAM Severe Acute Malnutrition SD Standard Deviation SMART Standardized Monitoring Assessment on Relief and Transition UNICEF United Nations Children Fund WFA Weight for Age WFH Weight for Height WHO World Health Organisation
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TABLE OF CONTENTS
Contents ACKNOWLEDGEMENT ............................................................................................................................................ 2 LIST OF ABBREVIATIONS ......................................................................................................................................... 3 TABLE OF CONTENTS .............................................................................................................................................. 4 LIST OF TABLES ....................................................................................................................................................... 5 LIST OF FIGURES ..................................................................................................................................................... 5 EXECUTIVE SUMMARY ............................................................................................................................................ 6 1. INTRODUCTION .............................................................................................................................................. 9 1.1 Background Information ................................................................................................................................... 9 1.2 Survey Rationale ............................................................................................................................................. 10 1.3 Survey Objectives ........................................................................................................................................... 10 1.4. Specific Objectives ......................................................................................................................................... 10 1.5. Survey Timing ................................................................................................................................................ 10 1.6. Survey Geographical Coverage ...................................................................................................................... 11 2. METHODOLOGY ................................................................................................................................................ 12 2.1. SURVEY TYPE .................................................................................................................................................. 12 2.2. SAMPLING PLAN ............................................................................................................................................ 12 2.2.1 Sampling Frame ........................................................................................................................................... 12 2.2.2 Sampling Method and Sample Size Calculation ........................................................................................... 12 2.2.3. Description of Sampling .............................................................................................................................. 12 2.3. SURVEY TEAMS TRAINING AND ORGANIZATION ........................................................................................... 12 2.4. DATA COLLECTION ......................................................................................................................................... 13 2.4.1. DATA COLLECTION TOOLS AND INDICATORS MEASURED .......................................................................... 13 2.6. DATA ENTRY AND ANALYSIS .......................................................................................................................... 13 2.7. DATA QUALITY CONTROL............................................................................................................................... 14 2.8 SURVEY LIMITATION ....................................................................................................................................... 14 3.0 RESULTS .......................................................................................................................................................... 15 3.1 GENERAL CHARACTERISTICS OF STUDY POPULATION AND HOUSEHOLDS .................................................... 15 3.2 DISTRIBUTION OF AGE AND SEX (UNDERFIVES) ............................................................................................. 15 3.3. UNDERFIVES NUTRITION STATUS .................................................................................................................. 16 3.3.1 OVERVIEW OF MALNUTRITION AND WHO GROWTH STANDARDS ............................................................. 16 3.3.1 ACUTE MALNUTRITION (WASTING) ............................................................................................................. 16 3.3.1.1. Analysis of Acute malnutrition in relation to age .................................................................................... 17 3.3.1.2. Analysis of Acute malnutrition based on presence of oedema ............................................................... 18 3.3.1.3 Prevalence of acute malnutrition based on MUAC .................................................................................. 18 3.3.2 Prevalence of Underweight Based on Weight for Age (WHO Standards) ................................................... 19 3.3.4 Prevalence of Chronic Malnutrition (Stunting) Based on HFA ..................................................................... 19 3.4 CHILDREN’S MORBIDITY AND HEALTH SEEKING BEHAVIOR ........................................................................... 21 3.4.1. Therapeutic Zinc Supplementation during Watery Diarrhoea Episodes .................................................... 21 3.4.2. Health Seeking Behavior ............................................................................................................................. 21 3.5. CHILDHOOD IMMUNISATION, VITAMIN A SUPPLEMENTATION AND DEWORMING .................................... 22 3.6 MATERNAL NUTRITION ................................................................................................................................... 24 3.7 MOSQUITO NETS OWNERSHIP AND UTILISATION .......................................................................................... 25 3.8 WATER SANITATION AND HYNGIENE ............................................................................................................. 25 3.8.1 Main Sources of Water, Distance and Time to Water Sources .................................................................... 25 3.8.2 Water Treatment ......................................................................................................................................... 26 3.8.3 Water Consumption Storage and Payment ................................................................................................. 26 3.8.4 Hand washing .............................................................................................................................................. 27 3.8.5 Sanitation Facility Ownership and Accessibility ........................................................................................... 28 3.9 FOOD SECURITY AND LIVELIHOODS ................................................................................................................ 29 3.9.1. Households Income Sources ....................................................................................................................... 29 3.9.2. Household Dietary Diversity ....................................................................................................................... 29 3.9.3 Food Consumption Score ............................................................................................................................. 30
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Household Coping Strategy .................................................................................................................................. 31 4.0 DISCUSSION ..................................................................................................... Error! Bookmark not defined. 5.0 CONCLUSIONS RECOMMEDATIONS ............................................................................................................... 32 5.1 CONCLUSION .................................................................................................................................................. 32 5.2. RECOMMEDATIONS ....................................................................................................................................... 32 6.0 APPEDICIES ..................................................................................................................................................... 34 6.1. APPENDIX 1: ANTHROPOMETRIC PLAUSIBILITY REPORT SUMMARY ............................................................ 34 6.2. APPENDIX 2 SAMPLED VILLAGES ................................................................................................................... 35 6.3. APPENDIX 3: SURVEY TEAMS ......................................................................................................................... 36 6.4. APPENDIX 4: STANDARDISATION TEST FORMS ............................................................................................. 37 6.5 APPEDIX 5: AGE CALCULATION CHART ........................................................................................................... 38 6.6 APPENDIX 6: QUESTIONNAIRE ........................................................................................................................ 39
LIST OF TABLES Table 1: Results Summary Table --------------------------------------------------------------------------------------------------------- 6 Table 2: Tana River County Seasonal Calendar ------------------------------------------------------------------------------------ 11 Table 3: Main occupation of household heads ------------------------------------------------------------------------------------ 15 Table 4: Age/sex distribution ----------------------------------------------------------------------------------------------------------- 15 Table 5: Prevalence of acute malnutrition based on weight for height z- score (WHO 2006 Standards) ---------- 17 Table 6 : Prevalence of acute malnutrition by age based on WFH z-score ------------------------------------------------- 18 Table 7: Distribution of acute malnutrition and oedema based on WFH z-score ----------------------------------------- 18 Table 8: Prevalence of acute malnutrition based on MUAC -------------------------------------------------------------------- 19 Table 9: Prevalence of Underweight based on WFA ----------------------------------------------------------------------------- 19 Table 10: Prevalence of stunting based on height-for-age z-scores and by sex------------------------------------------- 20 Table 11: Vitamin A and Deworming ------------------------------------------------------------------------------------------------- 23 Table 12: Main Water Sources --------------------------------------------------------------------------------------------------------- 25 Table 13: Cost of water Ksh/20 liter Jerrican --------------------------------------------------------------------------------------- 26 Table 14: Cost of water in Ksh/Month ----------------------------------------------------------------------------------------------- 27 Table 15: Household Sources of Income --------------------------------------------------------------------------------------------- 29 Table 16: Food Consumption Score --------------------------------------------------------------------------------------------------- 30 Table 17: Household Dietary Diversity ----------------------------------------------------------------------------------------------- 31 Table 18: Coping Strategy Index ------------------------------------------------------------------------------------------------------- 31
LIST OF FIGURES Figure 1: Tana River County Livelihood Zones --------------------------------------------------------------------------------------- 9 Figure 2: Age Sex Pyramid--------------------------------------------------------------------------------------------------------------- 16 Figure 3: Graphical Representation of WFH distribution of children assessed -------------------------------------------- 17 Figure 4: Graphical Representation of HFA distribution in reference to WHO standards ------------------------------ 20 Figure 5: Health Seeking Points -------------------------------------------------------------------------------------------------------- 22 Figure 6: Immunisation Coverage ----------------------------------------------------------------------------------------------------- 23 Figure 7: Length of Iron Folate Consumption in days ---------------------------------------------------------------------------- 24 Figure 8: Water Treatment Methods ------------------------------------------------------------------------------------------------- 26 Figure 9: Hand washing in the 4 critical moments -------------------------------------------------------------------------------- 27 Figure 10: Use of Soap for Hand washing ------------------------------------------------------------------------------------------- 28 Figure 11: Relieving Points ------------------------------------------------------------------------------------------------------------- 28 Figure 12: Food Consumed at Household level ------------------------------------------------------------------------------------ 30
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EXECUTIVE SUMMARY
International Medical Corps and Tana River County Department of Health jointly carried out a
SMART survey in Tana River County in February 2015 (Data Collection from 2nd and 6th February
2015).Tana River County is one of the Counties in the Coastal region and experiences erratic rainfall
leading to the occurrence of recurring drought episodes.
The overall goal of the survey was to determine the prevalence of malnutrition among the children
aged 6- 59 months old, pregnant and lactating mothers in Tana River County. This was a cross
sectional survey that applied the SMART methodology. Two stage cluster sampling was adopted
whereby clusters were sampled during the first stage and households in the second stage. All
accessible villages in Tana River County were used as the survey’s sampling frame. Villages were
selected based on proportion to population size (PPS) principle. The second stage involved selection
of house-holds from the selected clusters using simple random sampling method. Household was
used as a basic sampling unit.
In calculating the sample size, ENA software was used. A sample of 397 children and 458 households
was obtained upon which 31 clusters were sampled. A standard questionnaire was used in data
collection. The questionnaire had 5 sections namely; identification, demographic, anthropometric,
maternal, water sanitation and hygiene and food security. Data collection took place for 5 days.
Prior, training was done at a central point (Hola town). A total of seven teams participated in the
survey. Each team comprised of a team leader and three enumerators. All team leaders were MOH
personnel. A team of four people was also engaged for data entry as data clerks. Table 1 summarises
the key survey findings.
Table 1: Results Summary Table
ANTHROPOMETRIC RESULTS
WHO 2006 Standards 95% CI 95% CI
Design Effect= 1.52 N June 2014 N February 2015
Prevalence of Global Acute Malnutrition (<-2 z-
score)
561 (42) 7.5(5.3-10.4) 415 (41) 9.9 % (6.8 -14.2)
Prevalence of Severe Acute Malnutrition (<-3 z-
score and/or oedema
(5) 0.9 % (0.4 - 2.0)
(4) 1.0 % (0.4 - 2.5 )
Prevalence of stunting (<-2 z-score)
549 28.6% (23.4 -34.4) 393 (94) 23.9 % (18.2 - 30.8)
Prevalence of severe stunting (<-3 z-score) (50) 9.1 % (6.5 -
12.7)
(25) 6.4 % (3.6 - 11.1)
Prevalence of Underweight (<-2 z-score 560 20.2 % (16.2 – 24.9) 412 (78) 18.9 % (14.1 - 24.9)
Prevalence of severe underweight (<-3 z-score 4.6 %(2.9 - 7.3) (15) 3.6 % (1.9 - 6.8)
IMMUNISATION
Measles Coverage (Children ≥9 months by 88.3% 89.9%
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card and recall
BCG(Scar present and card 92.6% 91.2%
OPV 1 (By card and Recall) 97.8% 98.5%
OPV 3(By Card and Recall) 92.8% 96.5%
VITAMIN A SUPPLEMENTATION AND DEWORMING
Children aged 6- 11 months who were supplemented with
vitamin A once
43 78.2% 52 80%
Children aged 12 – 59 who were supplemented with
vitamin A at least once
376 65.7% 355 89.2%
Children aged 12- 59 who were supplemented with
vitamin A twice
211 40.9% 253 63.6%
Children 12 month old and above who were dewormed at
least once
199 23.0% 207 52.0%
Children 12 months old and above who were de-wormed
twice
76 14.7% 80 20.1%
CHILD MORBIDITY
Indicator % June
2014
N % Feb. 2015
Illness in the last 2 weeks (6- 59 months All 58.9% 273 56.8%
Fever like Malaria 42.1% 94 34.4%
ARI 52.8% 146 53.5%
Watery diarrhoea 12.8% 38 13.9%
Bloody diarrhoea 0.9% 1 0.4%
Therapeutic zinc supplementation during
diarrhoea cases
62.8% 57.9%
MATERNAL NUTRITION
Iron folate supplementation for pregnant
women
67.3% 168 66.9%
Iron folate supplementation for at least
270 days
0% 0 0%
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PLW with MUAC less than 21 cm 3.3% 8 3.4%
People who slept under mosquito nets Under fives 78.1% 72%
PLW 78.4% 75%
WATER SANITATION AND HYGIENE PRACTICES
Access to Sanitation Facilities 48.4% 193 42.9%
FOOD SECURITY
Low Dietary Diversity (3 Food Groups) 1.4% 41 9.1
Medium Dietary Diversity (4-5 Food
Groups)
17.6% 155 34.4%
High Dietary Diversity (>6 Food groups 81.0% 254 56.4%
Food Consumption Score 62
Households that were food insecure in the
last 7 day
38.6% 190 42.2%
Coping Strategy Index 8.3 15
Recommendations:
To address the gaps the following recommendations were proposed:
Conduct active case finding for acutely malnourished children and appropriate referral
The CNTF needs to review the recommendations from the previous surveys for further
follow up
Biannual deworming campaigns to increase coverage- combine with Vitamin A campaign
Increased sensitization on ANC attendance and consumption of iron/folate supplements
Improve the supply chain for essential drugs e.g. Iron, folate.
Increase toilet coverage in the county and establishment of ODF villages.
A KAP survey needs to be done in the county to give in depth information on child care
practices
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·
1. INTRODUCTION
1.1 Background Information
Tana River County is located in the Coastal region of Kenya. The County which occupies an area of
approximately 38,437 km2 has an estimated population of 276,965 people1. Tana River County
borders Kitui County to the West, Garissa County to the North East, Isiolo County to the North, Lamu
County to the South East and Kilifi County to the South. The County is divided in to 3 sub counties
namely; Bura, Galore and Garsen.
Most of the County consists of low lying plains with the highest points being Minjila and Bilbil. The
River Tana traverses the County from Tharaka nithi County in the North to the Indian Ocean in the
South passing through Tana Delta and covering a stretch of approximately 500km. It is situated in
the Eastern side of the county and provides livelihood opportunity to resident population through
flood receded crop farming. Generally the county experiences bimodal rainfall pattern which is
mostly erratic with long rains falling between April and June and short rains being experienced
between October and December.
As indicated in figure 1 below, the county has 4 main livelihood zones namely; Pastoral, Marginal mixed farming, Mixed farming and National park.
Figure 1: Tana River County Livelihood Zones
1 2014, DHIS Population Estimates
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The pastoral and marginal mixed farming livelihood zones rely on the short rains while the mixed farming areas are dependent on the long rains (April – June). The mean annual rainfall ranges between 220mm and 500mm except the mixed farming zone which receives rainfall ranging between 750mm and 1250mm. The County is generally hot and dry with temperatures ranging between 21°C and 38°C with the coldest month being experienced in July and hottest months being September and January. It therefore experiences two dry spells every year occurring in December to March and July to October. Early warning assessment (January 2015), indicated the County as alert state of drought cycle. The situation however was deteriorating in all livelihood zones apart from mixed farming livelihoods where the situation was stable. During the December January, Tana River County only received 7.6mm compared to the normal 80- 120mm. The poor state of infrastructure and road network in the county affects access to health services and to markets especially during episodes of flooding. 92% of the health facilities in the County are currently functional. The main relief programmes in the county are food aid (GFD and Food for Assets), nutrition (Supplementary and therapeutic feeding programmes), Water sanitation and hygiene programmes as well as food security interventions.
1.2 Survey Rationale
The purpose of this survey was to find out the nutrition situation in Tana River County. The results
will form a solid basis for planning appropriate future interventions.
1.3 Survey Objectives
The main objective of the survey was to determine the prevalence of malnutrition among the
children aged 6- 59 months old, pregnant and lactating mothers in Tana River County.
1.4. Specific Objectives
1. To determine the nutritional status of women of reproductive age (15-49) years based on
maternal mid upper arm circumference (MUAC).
2. To determine immunization coverage; measles (9-59 months), OPV1/3 and Vitamin A for
children aged 6-59months.
3. To determine deworming coverage for children aged 12 to 59 months.
4. To determine the prevalence of common illnesses (diarrhea, measles and ARI).
5. To assess maternal and child health care practices.
6. To assess water, sanitation and hygiene practices.
7. To assess the prevailing situation of household food security in the County.
1.5. Survey Timing
This survey is planned for at the short dry spell. At this period there is usually the harvesting of
produce (short rain harvest) by the farming community. For pastoralists, it is usually a lean period
characterized by drop in milk yields, livestock movements towards the dry grazing areas, water
stress in traditional grazing areas as well as decline in livestock prices. This survey will help to assess
the effects of short rain assessment (SRA 2014).
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Table 2: Tana River County Seasonal Calendar Short dry spell Long rains Long dry spell Short rains
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec
Short rains harvest Land
Preparation
Planting/weeding
Lean period for
farmers
Crops at
green maturity
Long rains harvest Land
Preparation.
Planting/Weeding
Lean period for farmers
Crops at
green
maturity
1.6. Survey Geographical Coverage
This survey took place in the entire Tana River County. The County comprises of 3 Sub- Counties
namely; Bura, Galole and Garsen.
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2. METHODOLOGY
2.1. SURVEY TYPE
The survey was cross- section and applied Standardized Monitoring and Assessment for Relief and Transition (SMART) methodology. Data collected during the exercise included household, anthropometric, morbidity, child immunization and food security data.
2.2. SAMPLING PLAN
2.2.1 Sampling Frame
The sampling frame constituted of the entire population of Tana River County (276,965 people). All villages (clusters/sampling units) in Tana River County which were accessible, secure or not deserted were included in the sampling frame.
2.2.2 Sampling Method and Sample Size Calculation
Two stages Cluster sampling was used during the survey. The first stage involved random selection of clusters from the sampling frame based on probability proportion to population size (PPS)2.Emergency Nutrition Assessment (ENA) for Standardized Monitoring for Assessment for Relief and Transition (SMART) November 2014 was be used in calculation of sample size. In calculating the sample size, an estimated prevalence of 7.5% which was the prevalence for the immediate previous survey (June 2014) was entered in the ENA software. This was informed by the fact that there was no suspected change in prevalence. Other variables used included a desired precision of 3% informed by the previous survey prevalence, a design effect of 1.23 obtained from the June 2014 survey. From this a total of 397 Children was obtained as the under-five sample size. In order to obtain the number of household required; 3 more variables were used. These variables included; the average house size. According to Tana River County CIDP, the average household size is 6. The DHIS/AWP estimate for under-five population of 16.2% and the non-response rate of 1% based on June 2014 survey were also used. A total of 458 households were used as the sample size. A household was used as sampling unit in the second stage sampling.
2.2.3. Description of Sampling
The number of households obtained from ENA planning (458) was used for the survey. After taking into account the time to be spent to and from the survey sites, introduction at sites, breaks, survey and introduction time in each household, and time spent from one household to the next, 15 households were visited in every cluster. This translated into 31 clusters which were covered during the survey. Simple random sampling method was used in household selection. Household selection was based on an updated list of households provided by chiefs or respective village elders for the sampled clusters. Questionnaires were administered in each of the selected household.
2.3. SURVEY TEAMS TRAINING AND ORGANIZATION
Six teams participated in the survey. Each team comprised of a team leader and three enumerators (2 data collectors/ assistants and 1 measurer). All team leaders were MOH personnel. A team of four people was engaged for data entry as data clerks. The County Nutrition Coordinator and IMC program officers were tasked with supervisory roles. The survey team leaders, data clerks and enumerators were first recruited through a process which included; advertisement of positions, shortlisting of candidates and rigorous interviews conducted in a central place in Tana River County headquarters (Hola)
2 In this method villages with more population are likely to be selected as compared to those with low
population
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The survey teams were rigorously trained together for 5 days in Hola town from 28th January 2015 to 1st February 2015. The training topics included; introduction to SMART methodology, malnutrition, anthropometric measurements, sampling methods, data collection tools, accurate measurements and recording, interviewing techniques and administration of the questionnaires. On the fourth day, a standardization test was done to ascertain the accuracy and precision of taking anthropometric measurement by the enumerators. On the the fifth day, pre-test was done to a nearby village (Laza) which had not been sampled for the survey.
2.4. DATA COLLECTION
The data collection exercise took place in the sampled villages between 2nd and 6th February 2015.
2.4.1. DATA COLLECTION TOOLS AND INDICATORS MEASURED One questionnaire was used in data collection. The questionnaire had the following sections: Identifying information: This included data collector’s name, team leader’s name, survey date, County, Sub- County name. Divisions, location, sub- location and village names also included were cluster number, Household and team numbers. Demographic Information: Household demographics, namely; household composition by age groups, sexes, school enrollment, attendance and completion, residency status, household income, possession and utilization of mosquito nets was collected under this section. Anthropometric information: Children aged 6- 59 months anthropometric data was collected under this section. Such information included, Child’s date of birth, sex, weight, height, MUAC and presence or absence of edema. Health seeking Behavior: Mothers/Caregivers were requested to provide morbidity (sickness) status of their children in the past 2 weeks, the type of illness and in case of watery diarrhea, whether therapeutic zinc supplementation was given. They were also asked to provide information on whether they sought any assistance during this period and in case they did where they got it from. Vitamin A Supplementation, Deworming and Child Immunization: Under this section, information on vitamin A supplementation for children aged 6 to 59 months, the number of times the child was supplemented in the last one year and whether supplementation was done at the health facility/outreach or during a mass campaign. Deworming information for children aged 12 to 59 months was also captured under this session. Other information collected included immunization for BCG, OPV1 and OPV3 and also measles. Maternal Nutrition: The nutrition status of women of reproductive age (15 to 49 years) was assessed using MUAC. The physiological status of this group was also assessed. This was done in order to establish whether there were differences in nutrition status of normal women, pregnant and lactating women (who are normally under nutrition stress). Iron- folate supplementation and the number of days it was done was also assessed. Water Hygiene and Sanitation: The main sources for drinking water of each household were assessed. Trekking distances, queuing time was assessed based on sphere standards. Also assessed were water treatment and storage practices and also water payment rates. On hygiene and sanitation hand washing during the four critical moments and human waste disposal practices were interrogated. Food security Information: Household food security was assessed using food frequency and household dietary diversity (HDD) based on a 7 days recall. The information on the main source of dominant food items in each of the 16 food groups was also collected. The last part under this section was coping strategies through which coping strategy index was computed.
2.6. DATA ENTRY AND ANALYSIS
Anthropometric data entry and processing was done using ENA software version 2014 (November). 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
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measurements from anthropometric analysis. The ENA software generated weight-for-height, height-for-age and weight-for-age Z scores to classify them into various nutritional status categories using WHO standards and cut-off points and exported back to SPSS for further analysis. All the other quantitative data was entered and analysed using Excel.
2.7. DATA QUALITY CONTROL
To ensure data collected was valid and reliable for decision making, a number of measures were taken. These measures included;
1. Thorough training of teams was done in 5 days for all survey participants, the training dwelt on SMART methodology, survey objectives, interviewing techniques and data collection tools.
2. Ensuring all anthropometric equipments were functional and standardized. On daily basis each team was required to calibrate the tools
3. During the training exercise, standardization was done, in addition piloting of tools was done to ensure all the information was collected with uniformity
4. Review of data collection tools during training and after the pilot test was conducted.
5. All the survey teams were assigned a supervisor during data collection.
6. The anthropometric data collected was entered daily on ENA software and plausibility check was run. Any issues noted were communicated to the teams before they proceeded to the field the next day.
7. Teams were followed up by the supervisors to ensure all errors were rectified on time. More attention was given to the teams with notable weaknesses.
8. Adequate logistical planning beforehand and ensuring the assigned households per clusters can be comfortably surveyed.
2.8 SURVEY LIMITATION
The Previous survey was done in a different season and it was therefore difficult to compare the results with the immediate previous survey.
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3.0 RESULTS
3.1 GENERAL CHARACTERISTICS OF STUDY POPULATION AND HOUSEHOLDS
Data collection was done in 450 household in 30 clusters out of the sampled 458 households representing a response rate of 98.3%. The non-response rate was attributed to migration of household members to another area where they could not be traced. The average household size was 4.9%. Majority (98.7%) of the respondents were residents of their respective villages with 0.9% and 0.4% indicating IDP and refugees as their residency status respectively. The main occupation of heads of households visited was working on their own farm, livestock herding as well as waged casual labour at 26.4%, 24.0% and 21.6% respectively. Other household heads occupations are as indicated in table 3 below. In terms of household incomes, majority of households sourced their income from casual labour (23.3%), sale of crops at 20.5% while petty trade and sale of livestock accounted for 20.3% each as indicated in table 13. In terms of marital status majority of the respondents (88.2%) were married. The rest were windowed (7.8%), divorced (2.2%), separated (1.1%) and single (0.7% Table 3: Main occupation of household heads
Household head occupation n Percent
Livestock herding 108 24.0%
Own farm labor 119 26.4%
Employed(salaried) 30 6.7%
Waged labor casual 97 21.6%
Petty trade 39 8.7%
Merchant/trader 10 2.2%
Firewood/Charcoal 32 7.1%
Fishing 1 0.2%
Others 14 3.1%
3.2 DISTRIBUTION OF AGE AND SEX (UNDERFIVES)
A total of 421 children under five years were assessed during the survey. They included 194 boys and 227 girls. The boys: girls ratio was 0.85 (p= 0.108) meaning that, overall, boys and girls were equally represented. Table 4 below shows the overall age and sex distribution. Figure 2 show the age/sex pyramid.
Table 4: Age/sex distribution
Boys Girls Total Ratio
Age (months no. % no. % no. % Boy:girl
6-17 57 47.5 63 52.5 120 28.5 0.9
18-29 58 51.3 55 48.7 113 26.8 1.1
30-41 39 42.4 53 57.6 92 21.9 0.7
42-53 26 36.6 45 63.4 71 16.9 0.6
54-59 14 56.0 11 44.0 25 5.9 1.3
Total 194 46.1 227 53.9 421 100.0 0.9
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-80 -60 -40 -20 0 20 40 60 80
6 to 17 m
18 to 29 m
30 to 41 m
42 to 53m
54 to 59 m
Girls
Boys
Figure 2: Age Sex Pyramid
The overall Age distribution was problematic with a score of 10.Younger children (6-29) were found to be more than the older ones (30-59 months) (1.24).This could possibly be attributed to campaign by local politicians to increase births (though with some reservations). The increasing trend of facility-based delivery from DHIS is used as a proxy- from 142 in 2010, to 1813 in 2011, to 2019 in 2012. In as much as 31% of children did not have a document to verify age, it may not be a significant issue since the distribution is not skewed. Error! Reference source not found.
3.3. UNDERFIVES NUTRITION STATUS
3.3.1 OVERVIEW OF MALNUTRITION AND WHO GROWTH STANDARDS
According to Kenya National Guideline on Integrated Management of Acute Malnutrition (2009), malnutrition is defined as “a state when the body does not have enough of the required nutrients (under-nutrition) or has excess of the required nutrients (over-nutrition). One of the effective and commonly used methods of assessment of nutrition status is use of anthropometric measurements. For analysis of under-fives malnutrition, WHO in 2006 came up with reference standards to replace the 1977 NCHS child growth reference standards.
3.3.1 ACUTE MALNUTRITION (WASTING)
Acute malnutrition is defined as low weight for height in reference to a standard child of a given age based on WHO growth standard. Acute malnutrition can be categorised as severe or moderate (SMART methodology 2006). It reflects the current form of malnutrition. Severe acute malnutrition is defined as weight for height <-3 standard deviation in comparison to a reference child of the same age. It also includes those with bilateral oedema as well as those, whose MUAC is less than 11.5 cm3 Moderate acute malnutrition is defined as weight for height less than -2SD but more than -3SD or MUAC below 12.5 cm and more or equal to 11.5cm. Global Acute Malnutrition (GAM) refers to the sum prevalence of malnutrition (all SAM and MAM cases/ <-2 z-score). Analysis of acute malnutrition involved the assessment of 415 children (192 boys and 223 girls), with exclusion of 6 cases which were flagged. From the assessment, the GAM was 9.9 %( 6.8 -14.2, 95% CI) whereas the SAM rate was 1.0 %( 0.4 – 2.5, 95% CI) as indicated in table 5 below.
3 WHO growth Standards and identification of severe acute malnutrition
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Table 5: Prevalence of acute malnutrition based on weight for height z- score (WHO 2006 Standards)
All n = 415
Boys n = 192
Girls n = 223
Prevalence of global malnutrition (<-2 z-score and/or oedema)
(41) 9.9 % (6.8 - 14.2 95% C.I.)
(19) 9.9 % (5.8 - 16.3 95% C.I.)
(22) 9.9 % (6.5 - 14.8 95%
C.I.)
Prevalence of moderate malnutrition (<-2 z-score and >=-3 z-score, no oedema)
(37) 8.9 % (5.9 - 13.2 95% C.I.)
(17) 8.9 % (5.0 - 15.2 95% C.I.)
(20) 9.0 % (5.8 - 13.6 95%
C.I.)
Prevalence of severe malnutrition (<-3 z-score and/or oedema)
(4) 1.0 % (0.4 - 2.5 95%
C.I.)
(2) 1.0 % (0.2 - 4.3 95%
C.I.)
(2) 0.9 % (0.2 - 3.6 95% C.I.)
There were pockets of high rates of malnutrition specifically in cluster 6 (Konoramadha) and Cluster 7 (Gubatu) which gave a Poisson of 1.These are the pastoralists areas in the northern parts of the county, and have also consistently shown higher IMAM admission rates. Figure 3 below is a graphical representation of distribution of WFH of children surveyed in relation to WHO 2006 standard curve. The curve slightly shifts to the left (0.65±1.01) an indication of under nutrition in comparison to reference children.
Figure 3: Graphical Representation of WFH distribution of children assessed
3.3.1.1. Analysis of Acute malnutrition in relation to age
Further analysis was done on the prevalence of acute malnutrition based on age as indicated in table 6 below. Whereas younger children (below 30 months) are affected by severe malnutrition it is older children (over 30 months) that are affected by moderate malnutrition.
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Table 6 : Prevalence of acute malnutrition by age based on WFH z-score
Severe wasting (<-3 z-score)
Moderate wasting
(>= -3 and <-2 z-score )
Normal (> = -2 z score)
Oedema
Age (mo)
Total no.
No. % No. % No. % No. %
6-17 119 2 1.7 7 5.9 110 92.4 0 0.0
18-29 112 1 0.9 9 8.0 102 91.1 0 0.0
30-41 91 1 1.1 8 8.8 82 90.1 0 0.0
42-53 69 0 0.0 11 15.9 58 84.1 0 0.0
54-59 24 0 0.0 2 8.3 22 91.7 0 0.0
Total 415 4 1.0 37 8.9 374 90.1 0 0.0
3.3.1.2. Analysis of Acute malnutrition based on presence of oedema
Presence of oedema is a sign of severe acute malnutrition. Analysis for acute malnutrition was also based on the presence of oedema. As indicated in table 7, there was no oedema case that was recorded in the survey. Table 7: Distribution of acute malnutrition and oedema based on WFH z-score
<-3 z-score >=-3 z-score
Oedema present Marasmic kwashiorkor No. 0
(0.0 %)
Kwashiorkor No. 0
(0.0 %)
Oedema absent Marasmic No. 5
(1.2 %)
Not severely malnourished No. 413 (98.8 %)
3.3.1.3 Prevalence of acute malnutrition based on MUAC
Apart from the WHO standards 2006, MUAC is used for screening to determine malnutrition in children 6- 59 months. According to Kenya guideline on Integrated Management of Acute Malnutrition (2009), A very low MUAC (<11.5cm for children under five years) is considered a high mortality risk and is a criteria for admission with severe acute malnutrition. MUAC reading of 11.5 cm to < 12.5 cm is an indicator of MAM. Analysis of nutrition status for children 6 to 59 months based on MUAC of less than 12.5 cm and presence or absence of edema resulted to a GAM rate of 1.4 % (0.7- 3.0 95% C.I.) and a SAM rate of (based on MUAC less than 11.5 cm and/or oedema) of 0.5 % (0.1 – 1.9 95% C.I.) as indicated in table 8 below.
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Table 8: Prevalence of acute malnutrition based on MUAC
All n = 419
Boys n = 193
Girls n = 226
Prevalence of global malnutrition (< 125 mm and/or oedema)
(6) 1.4 % (0.7 - 3.0 95% C.I.)
(1) 0.5 % (0.1 - 4.0 95%
C.I.)
(5) 2.2 % (0.9 - 5.1 95%
C.I.)
Prevalence of moderate malnutrition (< 125 mm and >= 115 mm, no oedema)
(4) 1.0 % (0.4 - 2.5 95% C.I.)
(1) 0.5 % (0.1 - 4.0 95%
C.I.)
(3) 1.3 % (0.4 - 4.1 95%
C.I.)
Prevalence of severe malnutrition (< 115 mm and/or oedema)
(2) 0.5 % (0.1 - 1.9 95% C.I.)
(0) 0.0 % (0.0 - 0.0 95%
C.I.)
(2) 0.9 % (0.2 - 3.6 95%
C.I.)
3.3.2 Prevalence of Underweight Based on Weight for Age (WHO Standards)
Underweight is a composite form of under nutrition that includes elements of stunting and wasting. It is defined as the percentage of children aged 0 to 59 months whose weight for age is below minus two standard deviations (moderate and severe underweight) and minus three standard deviations (severe underweight) from the median of the WHO Child Growth Standards (UNICEF 2013)4. The prevalence of underweight in Tana River County for Children 6- 59 months based on this assessment was 18.9% (14.1- 24.9, 95% CI), while that of severe underweight was 3.6%(1.9- 6.8, 95% CI) as shown in table 9 below. Table 9: Prevalence of Underweight based on WFA
All n = 412
Boys n = 190
Girls n = 222
Prevalence of underweight (<-2 z-score)
(78) 18.9 % (14.1 - 24.9
95% C.I.)
(41) 21.6 % (15.2 - 29.7 95%
C.I.)
(37) 16.7 % (11.2 - 24.2 95%
C.I.)
Prevalence of moderate underweight (<-2 z-score and >=-3 z-score)
(63) 15.3 % (11.8 - 19.6
95% C.I.)
(36) 18.9 % (13.6 - 25.9 95%
C.I.)
(27) 12.2 % (8.2 - 17.6 95%
C.I.)
Prevalence of severe underweight (<-3 z-score)
(15) 3.6 % (1.9 - 6.8 95%
C.I.)
(5) 2.6 % (1.1 - 5.9 95%
C.I.)
(10) 4.5 % (2.3 - 8.7 95%
C.I.)
3.3.4 Prevalence of Chronic Malnutrition (Stunting) Based on HFA
Stunting means low height for age of a reference child. Childhood stunting is an outcome of maternal under nutrition and inadequate infant and young child feeding (IYCF), a correlate of impaired neurocognitive development, and a risk marker for non-communicable diseases and reduced productivity in later life(WHO 2013)5 Stunting results from causes that extend beyond hunger and food availability, and have wide ranging consequences that prevent communities and nations from achieving their social and economic development aspirations. Because stunting is not treatable it calls for preventive measures nested in multiple development sectors and requires a response that draws from a cross-section of disciplines.
4 Improving Child Nutrition: The achievable imperative for global progress
5 Childhood Stunting: Challenges and Opportunities
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Analysis of stunting revealed an overall stunting rate of 23.9 %( 18.2- 30.8, 95% CI). Boys were significantly stunted compared to girls as indicated in table 10 below. Table 10: Prevalence of stunting based on height-for-age z-scores and by sex
All n = 393
Boys n = 183
Girls n = 210
Prevalence of stunting (<-2 z-score)
(94) 23.9 % (18.2 - 30.8
95% C.I.)
(55) 30.1 % (22.3 - 39.1
95% C.I.)
(39) 18.6 % (12.6 - 26.5 95%
C.I.)
Prevalence of moderate stunting (<-2 z-score and >=-3 z-score)
(69) 17.6 % (13.3 - 22.8
95% C.I.)
(40) 21.9 % (15.9 - 29.2
95% C.I.)
(29) 13.8 % (9.1 - 20.4 95%
C.I.)
Prevalence of severe stunting (<-3 z-score)
(25) 6.4 % (3.6 - 11.1 95%
C.I.)
(15) 8.2 % (4.3 - 15.1 95%
C.I.)
(10) 4.8 % (2.2 - 10.2 95%
C.I.) Figure 4 below is a graphical representation of HFA distribution in relation to WHO standard curve. There is a left shift from the standard curve with a mean -1.61±1.18, an indication of overall stunting in relation to the reference children.
Figure 4: Graphical Representation of HFA distribution in reference to WHO standards
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3.4 CHILDREN’S MORBIDITY AND HEALTH SEEKING BEHAVIOR
According to UNICEF conceptual framework on causes of malnutrition, disease is categorised as an immediate cause of malnutrition. It also affect food intake which is also categorised as an immediate cause. It is important therefore to assess morbidity and whether it had some effect on malnutrition. 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 diarrhoea were probed on the kind of treatment that was given to them. From the assessment, 56.8% of the assessed children were reportedly sick in the past two weeks prior to the survey. Among those who were sick, majority (53.5%) were affected by acute respiratory infection (ARI). Fever like malaria affected 34.4%, while 13.9% suffered from watery diarrhoea.
3.4.1. Therapeutic Zinc Supplementation during Watery Diarrhoea 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 diarrhea6. 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 recommended supplementation dosage is 20 milligrams per day for children older than 6 months or 10 mg per day in those below the age six months, for 10–14 days during episodes of diarrhea. The survey also sought to establish the number of children who suffered from watery diarrhea and supplemented with zinc. Slightly than half (57.9%) of those who suffered from watery diarrhea were supplemented with zinc.
3.4.2. Health Seeking Behavior
Mothers and caregivers whose children were sick in the past 2 weeks were further asked where they sought assistance. Majority (88.9%) sought assistance from appropriate service delivery points namely, public hospital (70.9%), private clinic/pharmacy (17.1%). 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 (10.2%) 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. Another 14.3% never sought any assistance. Figure 5 below summarizes the health seeking behavior in Tana River County.
6 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.
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0.0%
0.4%
0.4%
0.9%
0.9%
0.9%
8.5%
17.1%
70.9%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Mobile Clinic
Traditional Healer
Relative or Friend
CHW
Local Herb
NGO/FBO
Shop
Private Clinic/Pharmacy
Public Clinic
Places assistance was sought when the child was sick
Figure 5: Health Seeking Points
3.5. CHILDHOOD IMMUNISATION, VITAMIN A SUPPLEMENTATION AND DEWORMING
3.5.1 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 define a fully immunized child is one who has received all the prescribed antigens and at least one Vitamin A dose under the national immunization schedule before the first birthday. According to the MOH child survival and development strategy 2008- 2015, there was a significant in-crease in immunization coverage between 2001 and 2008 attributed to successful supplemental immunization activities for polio, measles, maternal and neonatal tetanus which ultimately reduced the incidences of EPI targeted diseases. This survey assessed the coverage of 4 vaccines namely, BCG, OPV1, OPV3, and measles at 9 and 18 months. From this assessment, 91.2% of children were confirmed to have been immunised by BCG7. Those who were immunised by OPV18 and OPV3 were 98.5% and 96.5% respectively while 89.9% had been immunised for measles. However quite a small number (partly 25%) would confirm to have been immunised with the second dose of measles antigen at 18 months as indicated in figure 6 below.
7 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. 8 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
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62.8%
60.8%
54.8%
11.3%
35.7%
35.7%
35.1%
13.6%
1%
3%
9%
72%
0.7%
0.7%
0.7%
3.0%
0% 20% 40% 60% 80% 100% 120%
OPV1
OPV3
Measles at 9
Measles at 18
Yes by Card Yes by Recall No Do not Know
Figure 6: Immunisation Coverage
3.5.2 Vitamin A supplementation and deworming According to Kenya Demo-graphic and Health Survey 2008/2009, vitamin A coverage among 6-11 months in Kenya was estimated to be 81.8%. For 12-59 months, the coverage was estimated to be 14.3%, with an average coverage of 6-59 months being at 30%. 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 has 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 the number of times the child had received vitamin A 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. Among those who were supplemented, 85% was confirmed by the use of health cards with only 14% who were confirmed by recall. Majorly vitamin A supplementation in the County is done at the health facility or at an outreach site as 75.6% answered affirmatively to this question. Analysis of vitamin A supplementation for children aged 6months to 1 year indicates that 80% of this age group had been supplemented with vitamin A. Among those aged 12 to 59 months, 63.6% had been supplemented with vitamin A for 2 times in the past one year. Assessment on deworming for children aged 12 to 59 months indicates a small uptake of deworming drugs; only 20.1% had taken de-wormers twice in the past one year as indicated in table 11 below. Table 11: Vitamin A and Deworming
Factor Number Percentage
Vitamin A Supplementation
6 to 11 months 1 time 52 80
12 to 59 months At least 1 time 355 89.2
At least 2 times 253 63.6
Deworming (12 to 59 months)
1 time 207 52.0
2 times 80 20.1
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3.6 MATERNAL NUTRITION During pregnancy, women need to consume additional iron to ensure they have sufficient iron stores to prevent iron deficiency. Therefore, in most low- and middle-income countries, iron supplements are used extensively by pregnant women to prevent and correct iron deficiency and anemia during gestation. WHO recommends daily consumption of 60mg elemental iron as well as 0.4mg folic acid throughout the pregnancy (WHO 2012)9. These recommendations have since been adopted by Kenya government in its 2013 policy guidelines on supplementation of FEFO during pregnancy Maternal nutrition was assessed by measuring MUAC of all women of reproductive age (15 to 49) in all sampled household. Analysis was further done for pregnant and lactating women. Mothers of children below 2 years were also asked if they consumed iron folate in their most recent pregnancy. From the analysis, 2.9% of all women of women of reproductive age were malnourished (MUAC≤ 21.0 cm). Further analysis of pregnant and lactating women indicated that 3.4% of them were malnourished based on the same criteria. Majority (66.9%) of women with children below 2 years had been supplemented with iron folate supplements during their most recent pregnancy. However none of the interviewed mother had taken the supplements in the recommended 270 days, with only 9.5% having taken the supplement in 90 days and over, while the rest (90.5%) took the supplement in less than 90 days as indicated in figure 7 below.
22.6%
67.9%
9.5%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Less than 30 days 30- 60 days ≥90 days
Iron Folate Consumption in days
Figure 7: Length of Iron Folate Consumption in days
9 WHO. Guideline: Daily iron and folic acid supplementation in pregnant women.
Geneva, World Health Organization, 2012.
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3.7 MOSQUITO NETS OWNERSHIP AND UTILISATION Malaria is one of the health issues affecting the County. It is one of the biggest 5 diseases affecting the County. One of the preventive measures put in place is the use of ITN. Overall 72.9% of the households surveyed have at least one mosquito net. Among the under-fives, 72% slept under a mosquito net in the night prior to the survey, while among the pregnant women 75% slept under the mosquito nets in the same period.
3.8 WATER SANITATION AND HYNGIENE
3.8.1 Main Sources of Water, Distance and Time to Water Sources
Everyone has the right to water. This right is recognized in international legal instruments and provides for sufficient, safe, acceptable, physically accessible and affordable water for personal and domestic uses. An adequate amount of safe water is necessary to prevent deaths due to dehydration, to reduce the risk of water-related disease and to provide for consumption, cooking, and personal and domestic hygienic requirements10. 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 litres per person per day. 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. Water sources and systems should be maintained such that appropriate quantities of water are available consistently or on a regular basis. Majority of the households obtained their drinking water from piped systems boreholes, protected springs or protected shallow wells. These are safe sources of drinking water compared to the rest (38.9%) who obtained their drinking water from other sources whose safety could be compromised. Such sources include, unprotected shallow wells, River/spring, earthpan/dam, earthpan/dam with infiltration, water trucking and vendors are as indicated in table 12 below. Table 12: Main Water Sources
Water Source n Percent
Piped System/borehole/ protected spring/protected
shallow well 275 61.1%
Unprotected Shallow well 27 6.0%
River/Spring 78 17.3%
Earthpan/dam 33 7.3%
Earthpan/dam with infiltration well 6 1.3%
Water tracking/water vendor 29 6.4%
Others 2 0.4%
Analysis of distances to water sources indicated that, majority of the households (71.5%) obtained their water from sources not more than 500m or less than 15 minutes walking distance. 25.3% took between 15 min to 1 hour or a distance of approximately 500m to 2km. The rest (2.4%) walked as far as more than 2Km (1- 2hrs) to their water sources. Regarding the queuing time at the water sources, 37.3% of the respondents queued for water. For
10
The Sphere project- Humanitarian Charter and Minimum Standards in disaster response 2004
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those who queued, 64.8% queued for less than 30 minutes, 29.2% between 30 minutes and 1 hour. Only 6% queued for water for more than 1 hour.
3.8.2 Water Treatment
Majority of the households (82.7%) did nothing to their drinking water. Among those who treated their drinking water, use of chemicals (chlorine, Pur or water guard) was the most applied method. 65.4% used this method. Boiling was used by 17.9%, this is due to the fact that the respondents feel that the water changes it taste after boiling. 14.1% used traditional herbs while 2.6% used pot filters as shown in figure 8 below.
17.9%
65.4%
14.1%
2.6% 2.6%
0%
10%
20%
30%
40%
50%
60%
70%
Boiling Chemicals(Chlorine,Pur, Waterguard)
Traditional Herbs Pot filters Others
Water Treatment Methods
Figure 8: Water Treatment Methods
3.8.3 Water Consumption Storage and Payment
Despite the fact that majority of Tana River residents do not treat their water, it is apparent that majority of them(92.9%) store their drinking water properly in closed containers/jerry cans where it is less likely to have physical water contamination. The rest 7.1% indicated that they stored their water in open containers/jerry cans exposing it to physical contamination. Less than 0.5% of the households consumed 15 liters of water per day which is the minimum average household water use for drinking cooking and personal hygiene (SPHERE Hand book 2004). Approximately 46% of the households pay for water. Among those who pay for water 79.7% do so on Ksh/20 liter jerrican while the rest, pay on monthly basis. For those who pay water per 20 litre jerrican approximately 70 % pay Ksh 5 or less as indicated in table 13 below. Table 13: Cost of water Ksh/20 liter Jerrican
Payment(Ksh/20 liter jerrican n Percent
1-5 115 69.7
6-10 7 4.2
11- 20 3 1.8
21-30 26 15.8
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31-40 0 0
41-50 14 8.4
Total 165 100%
For those who paid on monthly basis, majority paid over Ksh 500 per month. Table 14 summarizes the payment of water per month. Table 14: Cost of water in Ksh/Month
Payment Ksh/Month n Percent
1 to 50 7 16.3
51 to 100 1 2.3
101 to 200 3 7.0
201 to 300 7 16.3
301 to 400 1 2.3
401 to 500 9 20.9
Over 500 15 34.9
Total 43 100
3.8.4 Hand washing
The importance of hand washing after defecation and before eating and preparing food, to prevent the spread of disease, cannot be over-estimated. Users should have the means to wash their hands after defecation with soap or an alternative (such as ash), and should be encouraged to do so. There should be a constant source of water near the toilet for this purpose. (SPHERE Handbook 2004). Assessment of hand washing in the 4 critical times in Tana River county indicated that majority of them (93.1%) washed their hands before eating. Quite a number (77.1%) indicated that they washed their hands before cooking while 71.1% mentioned after visiting the toilet. Only 40.7% indicated that they did so after taking children to toilet as indicated in figure 9 below.
71.1%
77.1%
93.1%
40.7%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
After Toilet Before Cooking Before Eating After taking childrento toilet
Handwashing Moments
Figure 9: Hand washing in the 4 critical moments
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Majority of respondents (58%) use water and soap to wash their hands compared to 33.7% who used water only. Very few said they used soap when they could afford it as indicated in figure 9 below.
0%
10%
20%
30%
40%
50%
60%
Water only Water and Soap Soap whe I canafford
33.3%
58.0%
8.7%
Figure 10: Use of Soap for Hand washing
3.8.5 Sanitation Facility Ownership and Accessibility
If organic solid waste is not disposed of well, major risks are incurred due to fly breeding and surface water pollution which is a major cause of diarrheal diseases. Solid waste often blocks drainage channels and leads to environmental health problems associated with stagnant and polluted surface water. Analysis of relieving points revealed that, most household are still relieving themselves in bushes and other open places. Open defaecation was practiced by 56.9% of the respondents. Toilet ownership remained low at 24.0% while 18.9% shared sanitary facilities or used neighbours toilets to relieve themselves as indicated in figure 11 below.
56.9%18.9%
24.0%
In bushes, Open defeacation
Neighbors or shared traditionalpit/improved latrine
Own traditional pit/improvedlatrine
Figure 11: Relieving Points
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3.9 FOOD SECURITY AND LIVELIHOODS
3.9.1. Households Income Sources
Tana River County residents used a number of activities to meet their household’s needs. Respondents were asked what the main source of their household income was. As indicated in table 15 below, casual labour, sale of crops, sale of livestock as well as petty trading were the main source of most households in the County. Table 15: Household Sources of Income
Income Source No of Households Percent
Casual labour 104 23.2%
Sale of crops 92 20.5%
Sale of Livestock 91 20.3%
Petty Trading 91 20.3%
Sale of livestock products 25 5.6%
Permanent Job 23 5.1%
Remittances 11 2.4%
Others 7 1.6%
No income 5 1.1%
3.9.2. Household Dietary Diversity
Food security is met when “all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life” (FAO, 2002). Household dietary diversity is one way in which household food security is measured. Dietary Diversity is the number of different foods or food groups over a reference period of time not regarding the frequency of consumption (WFP 2008)11. The assessment of HDD was based on 7 days food consumption in regard to 12 food groups. The assessment indicated a higher consumption rates for cereals (98.7%), oils and fats (95.6%), sweets and sugars (91.6%). Other foods with higher consumption rates included, milk and milk products, legumes and pulses and vegetables. However there was low consumption of eggs, fruits, white tubers and roots, fish and meats as indicated in figure 12 below.
11
Food consumption analysis Calculation and use of the food consumption score in food security analysis.
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98.7%95.6%
91.6%
82.0%
76.4% 74.4%
62.7%
38.7%34.2%
30.2%26.2%
9.6%
0%
20%
40%
60%
80%
100%
120%
Household Food Consumption
Figure 12: Food Consumed at Household level
3.9.3 Food Consumption Score
Food Consumption Score is a proxy for household food security and is designed to reflect the quality of people diets. It is a composite score based on dietary diversity, food frequency and relative nutrition importance of different food groups. FCS is considered as an outcome measure of household food security. The FCS was first created in Southern Africa in 1996, and has been in use there as part of the CHS (Community Household Surveillance) for 4 years and several rounds of data collection. Extensive testing and application of the FCS has validated its use in this region and context. Additionally, the FCS is now being tested and applied in other countries and regions (WFP 2008)12. FCS= (Total cereals* 2) + (Total pulses* 3) + (Total vegetables) + (Total Fruits) + (Total_meat_fish_eggs * 4) + (Total dairies* 4) + (sugar* 0.5) + (oil * 0.5).
The Food Consumption Score for the County was computed as indicated in table 16 below.
Table 16: Food Consumption Score Food Type Frequency of
Consumption Weighting Factor
Total
Cereals 7 2 14
Pulses 3 3 9
Vegetables 4 1 4
Fruits 1 1 1
12
Food consumption analysis Calculation and use of the food consumption score in food security analysis.
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Meat/Fish/eggs 2 4 8
Total Dairies 5 4 20
Sugars 6 0.5 3
Oils 6 0.5 3
Total 62
The Food Consumption Score was above 35.5. This means it was Acceptable. Analysis of dietary diversity indicated that, majority of Tana River County residents consumed diets with six or more food groups (High Dietary Diversity) compared to those who consumed diets with 4 to 5 or those who consumed foods with 3 or less food groups as indicated in table 17 below. Table 17: Household Dietary Diversity
HHD (N= 450) n %
Low dietary diversity (3 food groups or less) 41 9.1%
Medium dietary diversity (4 to 5 food groups) 155 34.4%
High dietary diversity (More than 6 food groups 254 56.4%
Household Coping Strategy
The Coping Strategies Index (CSI) is a simple and easy-to-use indicator of household stress due to a lack of food or money to buy food. The CSI is based on a series of responses (strategies) to a single question: “What do you do when you don’t have adequate food, and don’t have the money to buy food?” The CSI combines the frequency of each strategy (how many times was each strategy was adopted?) and their severity (how serious is each strategy?). Analysis indicated that 42.2% were food insecure in the last 7 days compared to 38.6% in June 2014. Table18 below summarises the coping strategies adopted by the households affected by food insecurity. Overall the coping strategy index was 15. Table 18: Coping Strategy Index
Coping Strategy No. of HHDs employing the strategy
Frequency score (0-7)
Severity score (1-3)
Weighted Score = Frequency x Weight
Rely on less preferred and less expensive foods?
87 3 1 3
Borrow food, or rely on help from a friend or relative?
102 2 2 4
Limit portion size at mealtimes? 78 3 1 3
Restrict consumption by adults in order for small children to eat?
35 1 2 2
Reduce number of meals eaten in a day?
108 3 1 3
TOTAL HOUSEHOLD SCORE 15
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4.0 CONCLUSIONS AND RECOMMEDATIONS
4.1 CONCLUSION
The GAM rate in Tana River County at 9.9% is classified as poor according to WHO. The trend
appears to be stable since 2013, although direct comparison cannot be made with 2014 due to
difference in seasonality. SAM prevalence was 1.0% which was below the WHO classification of <2%
for a critical situation. Chronic malnutrition (stunting) was 23.9% against a national target of
15%.The trend appears to be reducing since 2013 although the difference is not statistically
significant. Stunting is also relatively higher in boys than in girls. The prevalence of underweight was
18.9% and there was no significant difference with previous years.
The poor nutrition situation can be attributed to increased child illness and inadequate diet (due to
compromised food security) as immediate causes. 56.8% of children had been sick within two weeks
prior to the survey. The survey also indicated that 42.2% of households were food insecure in the
last 7 days with a coping strategy index of 15, as compared to 38.6% in June 2014. The short rains
assessment conducted in January/February 2015 characterized Tana River County based on different
vulnerabilities. The overall food security situation in the northern parts of the County was classified
as Stressed (IPC Phase 2).The southern parts of the county were classified as Minimal (Phase 1).
Food insecurity is expected to remain Stressed (IPC Phase 2) through March.
Some of the underlying causes of under nutrition could be attributed to poor WASH practices and
poor maternal and child care practices. Toilet ownership remains very low in the county at 28 % in
2014 and 24 % in 2015. The open defaecation rates have also remained significantly high (56.9%)
due to cultural issues. There is a need to scale up social mobilisation in order to change these trends.
Deworming rates in the county have remained relatively low over time as compared to the national
target of 80% from 14.7% in 2014 to 20.1% in 2015.Iron folate supplementation throughout
pregnancy (270 days) remains low due to late attendance to the ANC clinic. Lack of awareness at the
community level may also have contributed to low uptake.
Even though the malnutrition rates in Tana River are not currently alarming, factors like failure of the
short rains may increase food insecurity in the region and hence aggravate the situation.
The County government and partners working in the region have through concerted efforts
managed to maintain the GAM rates at below critical levels. However, these efforts will need to be
sustained in order to bring the levels down to acceptable levels of <5%. Immediate/Short term
measures should be taken by all stakeholders to arrest the situation and prevent deterioration.
4.2. RECOMMEDATIONS
Short Term
Action By Whom By When Conduct active case finding for acutely malnourished children and appropriate referral
MOH/NGOs Ongoing during the outreaches
Strengthen and scale up HiNi MOH/NGOs Programs aimed at scaling up HiNi are ongoing. IMC is supporting MOH in Scaling up High Impact Nutrition
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Interventions.
The CNTF needs to review the recommendations from the previous surveys for further follow up
MOH/NGOs/All stakeholders The CNTF are held on quarterly basis. Survey recommendations are frequently discussed in these forums
Medium term
Action By Whom By When
Biannual deworming campaigns to increase coverage- combine with Vitamin A campaign
MOH/NGOs Planned to start during 2015 malezi bora campaigns
Increased sensitization on ANC attendance and consumption of iron/folate supplements
MOH/NGOs/All stakeholders Ongoing at the County
Improve the supply chain for essential drugs- Iron, folate, etc
MOH/Partners Ongoing
Increase toilet coverage in the county and establishment of ODF villages.
MOH/NGOs/All stakeholders Ongoing. 4 villages have been certified as ODF
A KAP survey needs to be done in the county to give in depth information on child care practices
MOH/NGOs 2015
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5.0 APPEDICIES
5.1. APPENDIX 1: ANTHROPOMETRIC PLAUSIBILITY REPORT SUMMARY Indicator Acceptable
range/Values Survey Value Comment
Digit Preference- Weight <10 4 Excellent
Digit Preference- Height <10 7 Excellent
Digit Preference MUAC <10 6 Excellent
WFH (Standard Deviation) 0.8-1.2 1.01 Excellent
WFH (Skewness) -1 to+1 -0.09 Excellent
WFH (Kurtosis) -1 to +1 -0.07 Excellent
Percentage of flags WFH <3% 0.7 Excellent
Percentage of flags HFA <5% 6.4
Percentage of flags WFA <5% 1.9 Excellent
Age Distribution %
G1 6 to 17 months 20%-25% 28.5 Over represented
G2 18 to 29 months 20%- 25% 26.8 Over represented
G3 30 to 41 Months 20-25% 21.9 Good
G4 42 to 53 months 20- 25% 16.9 Under represented
G5 54 to 59 months Around 10% 5.9 Under-represented
Age Ratio= G1+G2/G3+G4+G5 Around 0.85 1.24
Sex Ratio 0.8- 1.2 0.85 Good (p= 0.108)
Overall Score 11% Good
Design Effect 1.52
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5.2. APPENDIX 2 SAMPLED VILLAGES
Sub County
Division Location Sub-location Cluster Name Population
BURA BANGALE BANGALI BANGALI BASAHARGESA 725
KAMAGUR BOKA KAMUNYO 539
MBALAMBALA ASAKO USHAD/MEYE 248
MADOGO MADOGO MADOGO MADOGO 'B' 2060
MORORO MORORO CARLFONIA 'A' 1399
SAKA KONORAMADHA KONORAMADHA 547
SALA SOMBO GUBATU/MALKAREA 389
BURA BURA METI TUMAINI NORTH/MANYATA
1237
BULA CHIFIRI/BULA KOLOSHO
332
HIRIMANI HIRIMANI SABUKIYE 564
NANIGHI NANIGHI MASIBABO 424
GALOLE GALOLE CHEWANI CHEWANI CHEWANI 'A' 441
KALKACHA KALKACHA MAKUTANO 258
MIKINDUNI MIKINDUNI KONE B 578
SUBAKI HOLA SOKONI 516
WAYU WAYUDUKA Golecha 610
WENJE JAMHURI BUBUBU KILINDINI 'B' 637
MUZUNI GAFURU GAFURU 'A' 413
GARSEN GARSEN NDERA MNAZINI BAHATI 3000
KINYADU 2000
BILISA GARSEN HAMESA C 1000
GALILI DANISA DANISA B 600
SHIRIKISHO IDSOWE IDSOWE 1650
TARASAA WACHU ODA TARASAA TARASAA MAIN 990
KURAWA HURARA 410
CHARA CHAMWANAMUMA CHAMWANAMUMA 1700
KONE MASA ODOLE MWANJA 840
KIPINI KIPINI MATANGENI BORA IMANI 197
KIPINI COAST 300
KILILENGWANI BODENI 240
RESERVE CLUSTERS
GALOLE MILALULU LAINI OVO 397
GARSEN GALILI DUMI PEPONI 270
GARSEN ASSA ASSA ASSA 398
KIPINI KIPINI KIPINI BULANAZI 796
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5.3. APPENDIX 3: SURVEY TEAMS TEAM LEADERS AND ENUMERATORS
No Team Leader Enumerators
1 Phedis Thinga Fatuma Haboya Musa
Fafi Ali Kassim
Priscilla Chepkemoi
2 Osman Iddi Maro Ibrahim Bile Adhan
Hassan Mohammed Santuri
Sadia Adan Ali
3 Flora Abio Pauline Wanjiru Kamotho
Mwandie Omar
Jeslov Wayu Galgalo
4 Julius Maluki Violet Kinara
Mohammed Abdi
Guyo Jamal Ali
5 Kahindi Tuva Guyo Fangare
Amina Galgalo
Abdul Fatah Ibrahim
6 Tumaini Charo Harry Muteti
Rachel Hajeri Bada
Nelly Ojwang
DATA ENTRY CLERKS
Esha Chudi Madawa
Vincent Odhiambo
Abbas Galgalo Shambaro
Paul Devaz Villa
SUPERVISION TEAM
1 Omar Makopa County Nutrition Coordinator
2 John Nderi IMC Program Manager
2 Gloria Kisia IMC M&E Officer
3 Mark Murage IMC M& E Officer
4 Salim Athman IMC Program Officer
5 Janet Gatimu IMC Program officer
5 Doris Kawuor IMC Program Officer
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5.4. APPENDIX 4: STANDARDISATION TEST FORMS
Enumerator #.................................................. Name…………………………………………………………………. 1st Measure:
Child # Weight (kg) Height (cm) MUAC (mm)
1
2
3
4
5
6
7
8
9
10
Enumerator #.................................................. Name…………………………………………………………………. 2nd Measure:
Child # Weight (kg) Height (cm) MUAC (mm)
1
2
3
4
5
6
7
8
9
10
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5.5 APPEDIX 5: AGE CALCULATION CHART TANA RIVER SMART SURVEY FEBRUARY 2015
AGE CALCULATION CHART FOR UNDER 5 (record Age in Months)
Adequately Verify the age of the child. Accurate as at February 2015 :Please cross- check against date of birth of child and date of survey to establish actual age)
DATE OF BIRTH AGE IN MONTHS DATE OF BIRTH AGE IN MONTHS
March 2010 59 June 2013 20
April 2010 58 July 2013 19
May 2010 57 August 2013 18
June 2010 56 September 2013 17
July 2010 55 October 2013 16
August 2010 54 November 2013 15
September 2010 53 December 2013 14
October 2010 52 January 2014 13
November 2010 51 February 2014 12
December 2010 50 March 2014 11
January 2010 49 April 2014 10
February 2011 48 May 2014 9
March 2011 47 June 2014 8
April 2011 46 July 2014 7
May 2011 45 August 2014 6
June 2011 44 September 2014 5
July 2011 43 October 2014 4
August 2011 42 November 2014 3
September 2011 41 December 2014 2
October 2011 40 Jan 2015 1
November 2011 39 Feb 2015 0
December 2011 38
January 2012 37
February 2012 36
March 2012 35
April 2012 34
May 2012 33
June 2012 32
July 2012 31
Aug 2012 30
September 2012 29
October 2012 28
November 2012 27
December 2012 26
January 2013 25
February 2013 24
March 2013 23
April 2013 22
May 2013 21
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5.6 APPENDIX 6: QUESTIONNAIRE
`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 cluster name
1.10 Cluster No 1.11 HH No 1.12 Team No.
2. Household Demographics
2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 Age Group
Please give me the names of the persons who usually live in your household.
Age (months for children <5yrs and years for over 5’s)
Childs age verified by 1=Health card 2=Birth certificate/ notification 3=Baptism card 4=Recall
Sex 1= Male 2= Female
If 3 yrs and under 18 Is child enrolled in school? 1 = Yes 2 = No
Main Reason for not attending School (Enter one code from list) 1=chronic Sickness 2=Weather (rain, floods, storms) 3=Family labour responsibilities 4=Working outside home 5=Teacher absenteeism 6=Too poor to buy school items e.t.c 7=Household doesn’t see value of schooling 8 =No food in the schools 9 = Migrated/ moved from school area 10=Insecurity 11-No school Near by 12=Married 13=others (specify)…………………..
What is the highest level of education attained?(level completed) adults only 1 = pre primary 2= Primary 3=Secondary 4=Tertiary 5= None 6=others(specify)
If the household owns mosquito net/s, who slept under the mosquito net last night? (Probe-enter all responses mentioned (Use 1 if “Yes” 2 if “No and 3 if not applicable)
YRS MTH
< 5 YRS 1 2 3 4
>5 TO 18 YRS
5 6 7 8 9 10 11 12
ADULT 13(HH) 14) 15 16
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2.10 How many mosquito nets does this household have? ____________________ (Indicate no.)
2.11 Main Occupation of the Household Head – HH. (enter code from list) 1=Livestock herding 2=Own farm labour 3=Employed (salaried) 4=Waged labour (Casual) 5=Petty trade 6=Merchant/trader 7=Firewood/charcoal 8=Fishing
9=Others (Specify) |____|
2.12. What is your main current source of income
1. =No income
2. = Sale of livestock
3. = Sale of livestock products
4. = Sale of crops
5. = Petty trading e.g. sale of firewood
6. =Casual labor
7. =Permanent job
8. = Sale of personal assets
9. = Remittance
10. Other-Specify |____|
2.13 Marital status of the respondent 1. = Married 2. = Single 3. = Widowed 4. = separated
5. = Divorced. |____|
2.14. What is the residency status of the household? 1. IDP 2.Refugee 3. Resident |____|
Fever with Malaria: High temperature with shivering
Cough/ARI: Any episode with severe, persistent cough or difficulty breathing
Watery diarrhoea: Any episode of three or more watery stools per day
Bloody diarrhoea: Any episode of three or more stools with blood per day
3. CHILD HEALTH AND NUTRITION (ONLY FOR CHILDREN 6-59 MONTHS OF AGE; IF N/A SKIP TO SECTION 3.6) Instructions: The caregiver of the child should be the main respondent for this section
3.1 CHILD ANTHROPOMETRY (Please fill in ALL REQUIRED details below. Kindly maintain the same child number as part 2)
A Child No.
B C D E F G H I J K L 3.2 3.3
what is the relationship of the respondent with the child/children 1=Mother 2=Father 3=Sibling 4=Grandmother 5=Other (specify)
SEX F/m
Exact Birth Date
Age in months (To be filled only if the exact date cannot be obtained)
Weight (KG) XX.X
Height (CM) XX.X
Oedema Y= Yes N= No
MUAC (cm) XX.X
Has your child (NAME) been ill in the past two weeks? If No, please skip part K and proceed to 3.4) 1.Yes 2. No
If YES, what type of illness (multiple responses possible) 1 = Fever with chills like malaria 2 = ARI /Cough 3 = Watery diarrhoea 4 = Bloody diarrhoea 5 = Other (specify) See case definitions below
If the child had watery diarrhoea in the last TWO (2) WEEKS, did the child get THERAPEUTIC zinc supplementation? Show sample and probe further for this component check the remaining drugs(confirm from mother child booklet)
1 = Yes 2 = No 3 = Do not know
When the child was sick did you seek assistance? 1.Yes 2. No
If the response is yes to question # 3.2 where did you seek assistance? (More than one response possible- 1. Traditional healer 2.Community health worker 3. Private clinic/ pharmacy 4. Shop/kiosk 5.Public clinic 6. Mobile clinic 7. Relative or friend 8. Local herbs 9.NGO/FBO
01
02
03
04
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3.4 Kindly maintain the same child number as part 2 and 3.1 above
MATERNAL NUTRITION FOR MOTHERS OF REPRODUCTIVE AGE (15-49 YEARS)(Please insert appropriate number in the box)
A B C D E F G H I 3.5 3.6 3.7 3.8 3.9
Child No.
How many times has child received Vitamin A in the past year? (show sample)
How
many times did
you
receive
vitamin A
capsules
from the facility or
out reach
If Vitamin A received how many times verified by Card?
How many times has child received drugs for worms in the past year? (12-59 Months) (show Sample)
Has the child received BCG vaccination? 1 = scar 2=No scar
Has child received OPV1 vaccination 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know
Has child received OPV3 vaccination? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know
Has child received measles vaccination at 9 months (On the upper right shoulder)? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know
Has child received the second measles vaccination (18 to 59 months ) (On the upper right shoulder)? 1=Yes, Card 2=Yes, Recall 3 = No 4 = Do not know
Woman ID. (all ladies in the HH aged 15-49 years from the demographics page)
What is the mother’s / caretaker’s physiological status
1. Pregnant
2. Lactating
3. None of the above
Mother/ caretaker’s MUAC reading: ____.__cm
During the pregnancy of the (name of child below 24 months) did you take iron pills, sprinkles with iron, iron syrup or iron-folate tablets? (name that appears in HH register)
1. Yes 2. No 3. Don’t
know 4. N/A
If Yes, for how many days? (approximate the number of days)
01 g
02
03
04