INTEGRATED SMART SURVEY THARAKA NITHI COUNTY KENYA ...

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INTEGRATED SMART SURVEY, THARAKA NITHI COUNTY, KENYA, SEPTEMBER 2016

INTEGRATED SMART SURVEY

THARAKA NITHI COUNTY

KENYA

SEPTEMBER 2016

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ACKNOWLEDGEMENT

The ministry of health, Tharaka Nithi County, in conjunction with the Kenya Nutrition and

Health plus (NHP) program from FHI 360 would wish to express sincere gratitude to the

following partners who made the survey to be successful;

➢ The county health management team lead by the chief officer of health and the nutrition

coordinator for their overall supervision

➢ The Nutrition information technical working group for their technical inputs and

coordination

➢ The NHP staff for their supervision and coordination

➢ The survey participants for their effort in collecting quality and accurate data

➢ The survey respondents for taking time to give their responses and allowing their children

to be measured

➢ The local administration from the county commissioner to village elders for their role in

community mobilization

➢ USAID for financial support

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Table of Contents

EXECUTIVE SUMMARY .......................................................................................................... vii

OBJECTIVES ........................................................................................................................... vii

METHODOLOGY .................................................................................................................... vii

SUMMARY RECOMMENDATIONS..................................................................................... xii

1.0 INTRODUCTION .................................................................................................................... 1

1.1 Background information ....................................................................................................... 1

1.2 Survey Justification ............................................................................................................... 2

1.3 Survey objectives .................................................................................................................. 2

2.0 SURVEY METHODOLOGY .................................................................................................. 3

2.1 Type of the survey ................................................................................................................. 3

2.2 Sample size and sampling procedures ................................................................................... 3

2.3 Training Framework .............................................................................................................. 4

2.4 Survey teams composition and supervision .......................................................................... 4

2.5 Case Definitions and Inclusion Criteria ................................................................................ 4

2.6 Data Entry and Analysis ........................................................................................................ 6

2.7 Indicators, Guidelines and Formulas Used In Acute Malnutrition ....................................... 6

2.8 Referrals ................................................................................................................................ 7

3.0 SURVEY FINDINGS ............................................................................................................... 8

3.1 General Characteristics of Study Population and Households .............................................. 8

3.2 ANTHROPOMETRY ........................................................................................................... 9

3.2.1 Distribution by Age and Sex .......................................................................................... 9

3.2.2 Nutritional Status of Children 6-59 Months ................................................................... 9

3.3 Child Morbidity and health seeking behaviours ................................................................. 15

3.4 Child Immunization, Vitamin A Supplementation and Deworming................................... 17

3.4.1 Immunization ................................................................................................................ 17

3.4.2 Vitamin A supplementation .......................................................................................... 17

3.4.3 Deworming ................................................................................................................... 18

3.5 MATERNAL HEALTH AND NUTRITION ..................................................................... 18

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3.5.1 Physiological status of women of reproductive age (15-49years) ................................ 18

3.5.2 Maternal nutrition status based on MUAC ................................................................... 18

3.5.3 Consumption of Iron Folic Acid Supplements (IFAS) ................................................. 19

3.6 Water, Saniatation and Hygiene .......................................................................................... 19

3.6.1 Water ............................................................................................................................ 19

3.6.2 Hygiene ......................................................................................................................... 20

3.6.3 Sanitation ...................................................................................................................... 21

3.7 Food security and livelihood ............................................................................................... 21

3.7.1 Food Security information ............................................................................................ 21

3.7.2 Household dietary diversity .......................................................................................... 22

3.7.3 Food consumption score ............................................................................................... 23

3.7.4 Coping strategy Index ................................................................................................... 24

3.7.5 Household hunger scale ................................................................................................ 25

Discussion and Conclusion ........................................................................................................... 26

ANNEXES .................................................................................................................................... 28

Annex 1: Sample size calculation ............................................................................................. 28

Annex 2: Tharaka North and South Clusters ............................................................................ 28

Annex 3: Maara and Chuka Igambang’ombe clusters .............................................................. 29

Annex 4: Anthropometric data quality ...................................................................................... 30

List of figures

Figure 1: Gaussian curve for Tharaka North & South ....................................................... 10

Figure 2: Gaussian curve for Maara and Chuka Igambang'ombe ...................................... 11

Figure 3: Child Morbidity .................................................................................................. 16

Figure 4: Source of health assistance ................................................................................. 16

Figure 5: Source of drinking water .................................................................................... 19

Figure 6: Micronutrient food consumption ........................................................................ 23

Figure 7: Household Hunger Scale ................................................................................... 26

List of tables

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Table 1: Summary of Results, Tharaka Nithi County Integrated SMART survey 2016 .. viii

Table 2: Sub-County Population and Area .......................................................................... 1

Table 3: Sample size calculation.......................................................................................... 3

Table 4: MUAC guidelines .................................................................................................. 6

Table 5: Summary of demographic characteristics .............................................................. 8

Table 6: Distribution by age and sex ................................................................................... 9

Table 7: Prevalence of global acute malnutrition based on Weight-for -Height Z score

(and/or oedema) and by sex in Tharaka North and South ................................................. 10

Table 8: Prevalence of global acute malnutrition based on Weight-for -Height Z score

(and/or oedema) and by sex in Maara and Chuka Igambang'ombe ................................... 11

Table 9: Prevalence of underweight based on weight-for-age z-scores by sex in Tharaka

North and South ................................................................................................................. 12

Table 10: Prevalence of underweight based on weight-for-age z-scores by sex in Maara

and Chuka Igambang'ombe ................................................................................................ 13

Table 11: Prevalence of stunting based on height-for-age z-scores and by sex in Tharaka

North and South ................................................................................................................. 13

Table 12: Prevalence of underweight by age, based on weight-for-age z-scores in Tharaka

North and South ................................................................................................................. 14

Table 13: Prevalence of stunting based on height-for-age z-scores and by sex in Maara


Table 14: Prevalence of underweight by age, based on weight-for-age z-scores in Maara


Table 15: Coverage of Vaccination ................................................................................... 17

Table 16: Coverage of Vitamin A supplementation .......................................................... 17

Table 17: Physiological status of WRA ............................................................................. 18

Table 18: Nutrition status of WRA .................................................................................... 18

Table 19: Trekking distance to water sources.................................................................... 20

Table 20: Hygiene practices............................................................................................... 20

Table 21: Sanitation and latrine coverage .......................................................................... 21

Table 22: Household dietary diversity ............................................................................... 22

Table 23: Coping strategy for Tharaka North and South ................................................... 24

Table 24: Coping strategy index for Maara and chuka Igambang'ombe ........................... 24

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ABBREVIATION AND ACRONYMNS

BCG Bacillus Calmette–Guérin

DPS Digit Preference Score

CI Confidence Interval

GAM Global Acute Malnutrition

HFA Height-for-Age

KDHS Kenya Demographic and Health Survey

MUAC Mid-Upper Arm Circumference

NHP Nutrition and Health Plus

NDMA National Drought Management Authority

NIWG Nutrition Information working group

OPV Oral Polio Vaccine

PPS Probability Proportional to Population Size

SAM Severe Acute Malnutrition

USAID United States Agency for International Development

SD Standard Deviation

WFA Weight for Age

WFH Weight-for-Height

WRA Women of Reproductive age

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EXECUTIVE SUMMARY

The Tharaka-Nithi County is approximately 175 Kilometers North East of Nairobi and at the

foothills of Mt. Kenya. The County is divided into four (4) administrative Sub-Counties namely

Tharaka-North, Tharaka-South, Meru South and Maara. There are eight (8) Divisions, fifteen

(15) Wards, fifty-two (52) locations and one hundred and thirty-two (132) Sub-Locations in the

County. Tharaka-North Sub- County is the largest covering an area of 843.9 Km2, followed by

Tharaka-South with 705.6 Km2. Chuka Igambang’ombe is third in size with an area of 624.0

Km2. Maara is the smallest Sub-County covering an area of 465.3 Km2.

OBJECTIVES

The overall objective of the survey to estimate the prevalence of acute malnutrition amongst

children aged 6-59 months. The specific objectives were:

➢ To determine the prevalence of under nutrition in children aged 6-59 months

➢ To determine the immunization coverage for measles, Oral Polio Vaccines (OPV type 1

and 3), and vitamin A supplementation in children aged 6-59 months

➢ To assess coverage and consumption of micronutrients powder in children aged 6-23

months

➢ To determine maternal nutritional status based on MUAC measurements

➢ To estimate coverage of iron / folic acid supplementation during pregnancy in women of

reproductive age;

➢ To determine maternal dietary diversity based on 24 hours recall

➢ To collect information on possible underlying causes of malnutrition such as household

food security, water, sanitation, and hygiene practices

➢ To build the capacity of the Ministry of Health staff and National Drought Management

Authority field monitors

METHODOLOGY

Two separate surveys were carried out due to differences in livelihood zones. One survey

covered Maara and Chuka Igambang’ombe counties which are mainly agricultural productive

areas and the other covering Tharaka North and South sub counties which are mainly agro-

pastoral. The survey was led by the ministry of health with financial and technical support from

Kenya Nutrition and health Plus (NHP) program. Participants were drawn from the ministry of

health, National Drought Monitoring authority, NHP and NIWG. The survey was a cross

sectional study using Standardized Monitoring of Relief and Transition (SMART) methodology.

Emergency Nutrition Assessment (ENA) for SMART software (Version 9th July 2015) was used

for sample size calculation yielding a sample size of 567 and 518 for first and second survey

respectively.

Two stage cluster sampling was used with first stage involving selection of clusters (villages)

whereby 42 and 36 clusters respectively were selected using probability proportionate to size

(PPS) through the ENA software. The second stage involved randomly selecting 16 and 15

households per cluster respectively. The target populations were children aged 6-59 months with

anthropometric measurement morbidity, immunization and supplementation information

collected from the mentioned group. Other information collected included the household

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demographics, nutritional status and iron folic acid supplementation for women 15-49 years,

WaSH, food security and livelihood information. Data collection was done using open data kit

(ODK) application installed in tablets phone. Data analysis was done using ENA for SMART

(Version 9th July 2015) for anthropometric measurements and Microsoft excel and SPSS1

version 20 for morbidity, Immunization, Supplementation, WaSH and food security.

SUMMARY OF KEY FINDINGS

The surveys achieved a sample size of 615 and 510 households in survey 1 (Maara and Chuka

Igambang’ombe) and survey 2 (Tharaka North and south) respectively. One of theclusters in

Tharaka south could not be completed due to a hostile community member who destroyed the

anthropometric equipment’s just before the team could start. The survey data quality was

checked and reviewed daily and the overall data quality of the anthropometric measurements is

as shown in table 2. The prevalence of global acute malnutrition (GAM) based on weight for

height z scores (WHZ) was at 2.8% (1.3- 5.8 95% CI) and 5.0% (2.6- 9.5 95% CI) respectively.

The difference was not however significant difference (p=0.250). There were pockets of

malnutrition in in Murinda and Kamwathu (in Tharaka North and South Sub Counties

respectively). Chronic malnutrition (stunting) based on height for age Z-scores was at 19.7%

(15.7-24.5 95% CI) and 27.8% (21.3-35.5 95% CI) which was significant different (p=0.041).

The performance of children nutrition status was then weighted and compared with those of

Kenya demographic and health survey 2014 (KDHS, 2014). There was a significant change in

stunting rates (p=0.009) but none in wasting (p=0.984).

Table 1: Summary of Results, Tharaka Nithi County Integrated SMART survey 2016

INDEX INDICATOR Results2

Survey 1 (Maara

and Chuka

Igambang’ombe

)

Survey 2

(Tharaka North

and South)

County Weighted

estimates

WHZ3-

scores

Global Acute

Malnutrition

Weight for height <-2

z and/or oedema

5.0% (2.6- 9.5) 2.8% (1.3- 5.8) 3.20% (1.9 - 5.4)

Severe Acute

Malnutrition

Weight for height <-3

z and/or oedema

0.7% (0.2- 3.0) 0.0% ( 0.0- 0.0) 0.00% (0.0 - 0.0)

1 Statistical package for social sciences

2 Results in brackets are expressed in 95% Confidence intervals

3 Weight for height Z scores

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HAZ4-scores Stunting

(<-2 z-score)

19.7% (15.7-

24.5)

27.8% (21.3-

35.5)

22.0%(18.6 -

25.8)

Severe stunting

(<-3 z-score) 4.4% (2.6- 7.4) 7.0% ( 4.9- 9.8) 5.1%(3.6 - 7.2)

WAZ5-

scores

Underweight

(<-2 z-score) 7.1% (4.6-11.0)

14.2% (11.3-

17.7) 9.1%(7.0 - 11.7)

Severe underweight

(<-3 z-score) 1.6% (0.7- 3.9) 2.1% (0.7- 6.2) 1.8%(0.9 - 3.6)

MUAC6 Global Acute

Malnutrition

MUAC <125 mm

and/or oedema

2.5% ( 1.2- 4.9) 2.1% ( 0.9- 5.0) 2.3% ( 0.4- 4.2)

Severe Acute

Malnutrition

MUAC <115 mm

and/or oedema

0.3% ( 0.0- 2.0) 0.7% ( 0.2- 2.9) 0.3% ( 0.0- 1.5)

Vaccination

coverage

BCG 86.4% 91.3% 87.8

OPV 1 by card 56.9% 73.8% 62%

OPV 1 by recall 41.4% 22.4% 35.7%

OPV 3 by card 56.1% 68.9% 60.0%

OPV 3 by recall 50.0% 5.9% 36.1%

Measles at 9 months

by card 52.3% 69.3% 57.3%

Measles at 9 months

by recall 44.0% 22.6% 37.6%

Measles at 18 months

by card 33.5% 39.0% 35.1%

Measles at 18 Months

by recall 43.3% 13.8% 34.3%

Vitamin A

coverage

6-11 months ; At least

once 85.7% 53.5% 72.2%

12-59 months; once 77.2% 65.1% 71.0%

12- 59 months; at least

twice 32.9% 39.6% 34.4%

Deworming 12-59 months

dewormed at least

once

55.3% 35.3% 46.9%

12-59 months

dewormed at least 19.1% 8.5% 14.9%

4 Height for age Z scores

5 Weight for age Z scores

6Mid upper arm circumference

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twice

Morbidity 6-59 Months ill 2

weeks prior to the

survey

39.2% 41.6% 40.0%

Type of

illness

Fever with chills 17.4% 29.4% 21.1

Upper respiratory tract

infections 57.6% 60.5% 58.5

Water diarrhea 9.7% 9.2% 9.6%

Bloody diarrhea 0.7% 4.2% 1.8%

Others (Burns,

abdominal pain, skin

rashes, septicemia)

21.5% 12.6% 18.7%

Seeking

health

assistance

Proportion of

caregivers who sought

health assistance

83.3% 78.4% 80.7%

Source of

health

assistance

Community health

workers (CHW) 0.8% 1.1% 0.9%

Private clinics 40% 36% 38.8%

Shops/Kiosk 3.3% 5.6% 4.0%

Public clinics/health

centre 57.5% 56.2% 57.1%

Mobile clinics 0.0% 2.2% 0.7%

Maternal

Nutritional

status by

MUAC (15-

49years)

All women

malnourished

(<21cm)

1.0%

1.4% 1.1

All women at risk 3.2% 5.1% 4.9%

Pregnant and lactating

women malnourished

1.4% 1.6% 1.4%

Pregnant and lactating

women at risk

3.4% 8.2% 4.9%

Iron folic

acid

supplementat

ion (IFAS)

Proportion of women

who took IFAS

71.8% 55.3% 64.7%

Duration

of IFAS

consump

tion

>90 days 75.6% 82.2% 78.1%

90-180

days

23.0% 14.1% 19.5%

>180 days 3.7% 0.0% 2.4%

Current

source of

drinking

water

Piped water system,

protected springs,

boreholes

80.6% 57.2% 73.1%

Unprotected shallow

wells 1.6% 4.3% 2.5%

Rivers/Springs 15.8% 15.8% 22.5%

Earth pan/dam 0.7% 0.7% 22.4%

Earth pan with

infiltration well 0.0% 0.2% 0.9%

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Others (bottled water,

water vendor) 1.3% 0.2% 0.1%

Trekking

distance to

water source

<500metres

(<15minutes) 90.5% 50.1% 77.7%

500m-2Km 8.0% 31.4% 15.4%

>2KM 1.5% 18.5% 6.9%

Queue for

water

Proportion of

households who queue

for water

1.8% 24.2% 8.9%

Queueing

time

Less than 30 minutes 84.6% 53.5% 57.5%

30-60 minutes 7.7% 20.9% 18.8%

More than 1 hour 7.7% 25.6% 23.7%

Water

treatment

Proportion of

households who treats

their water

38.3% 36.3% 38%

Methods

used to treat

water

Boiling 87.8 74.6% 83.8%

Use of chemicals 12.6% 31.9% 18.5%

Pot filters 0.4% 2.2% 0.9%

Others (modern

filters) 1% 0.0% 0.8%

Average

household

water used

Average water used

(exclusive of

livestock)

80 ± 55.05 77.96 ± 37.87 86.95 ± 50.58

Handwashing

times

After toilet 84.3% 91.9% 86.7%

Before cooking 55.0% 35.7% 49%

Before eating 69.2% 83.4% 73.6%

After taking children

to toilet/changing

nappies

15.9% 9.8% 14%

Handwashing in 4

critical times 9.1% 4.3% 7.1%

soap and

water in

handwashing

Proportion of

caregivers who used 76.4% 65.8% 73.1%

Human waste

disposal

Open defecation 0.2% 1.8% 0.7%

Shared latrine 15.8% 12.2% 14.8%

Own latrine 83.8% 86.1% 84.5%

Household

consumption

score

Household with poor

and borderline food

consumption

1.6% 4.5% 2.5%

Coping

strategy

index

Household who

reported to have

lacked food or money

for food 7 days prior

14.7% 15.1% 14.9

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to the survey

Household

hunger score

None/light hunger 86.8% 85.5% 86.4%

Moderate hunger 11.5% 12.5% 11.9%

Severe hunger 1.6% 2.0% 1.7%

Women

dietary

diversity

Proportion of women

consuming more than

four food groups

67.7% 60.1% 63.6%

SUMMARY RECOMMENDATIONS

The following short-term and long-term recommendations were suggested by County and

sub County stakeholders to be activated for action plan.

Problem/issue Probable

causes

Recommendation Performance

indicator

Timeline Responsibility

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1.0 INTRODUCTION

1.1 Background information

Tharaka-Nithi County is approximately 175 Kilometres North East of Nairobi and at the foothills

of Mt. Kenya. It is located between latitude 00° 3.67’ North and 00° 27.06’ South and between

longitudes 37° 19’ and 38° 18.3’ East and altitudes ranging from 500 m above sea level in the

lowlands of Tharaka to 5200 m above sea level at the peak of Mt Kenya highlands. Tharaka-

Nithi County borders Meru County to the North, Kitui to the East and South East and Embu to

the South. Its spatial expanse of 2638.8 Km2 makes it one of the smallest Counties in Kenya. Its

headquarters is Kathwana. The County has three Constituencies namely Maara, Chuka or

Igambang’ombe and Tharaka.

The County is divided into four (4) administrative Sub-Counties namely Tharaka-North,

Tharaka-South, Meru South and Maara. There are eight (8) Divisions, fifteen (15) Wards, fifty-

two (52) locations and one hundred and thirty-two (132) Sub-Locations in the County. Tharaka-

North Sub- County is the largest covering an area of 843.9 Km2, followed by Tharaka-South

with 705.6 Km2. Chuka Igambang’ombe is third in size with an area of 624.0 Km2. Maara is the

smallest Sub-County covering an area of 465.3 Km2.

Table 2: Sub-County Population and Area

Constituency Sub-County Area in

Sq. Km

No. of

Divisions

No. of

wards

No. of

Locations

No. of

Sub-

Locations

Tharaka Tharaka North 843.9 1 2 7 12

Tharaka South 705.6 2 3 14 32

Chuka/Igambang’

ombe

Meru-South 624.0 3 5 17 45

Maara Maara 465.3 2 5 14 43

TOTAL 2638.8 8 15 52 132

The County has two main ecological conditions, which are influenced by climatic features. The

highland (upper) zone comprising of Maara and Chuka Igambang’ombe which receive adequate

rainfall is mainly agricultural while the semi-arid (lower) zone covering Tharaka receive less

rainfall and is semi-arid suitable for livestock production. Poor methods of farming and soil

conservation, charcoal burning and overgrazing have left the earth bare and rocky. The sloping

areas have experienced uncontrolled soil erosion, which has resulted in deep gullies across the

landscape especially in Tharaka. The drainage pattern consists of rivers and streams that

ultimately drain into the Indian Ocean through Tana River.

The County has a bi-modal rainfall pattern with the long rains falling during the months of April

to June and the short rains in October to December. The short rains are more reliable than the

long rains. The rainfall ranges from 2,200 mm in Chogoria forest to 500 mm in Tharaka. The

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high altitude areas experience reliable rainfall while middle areas of the County receive medium

amount. The lower regions receive low, unreliable and poorly distributed rainfall. The rainfall is

favourable for cultivation of tea, coffee, maize, cowpeas, pigeon peas, tobacco and a variety of

other food crops.

Temperatures in the highland areas range between 14 °C to 30 °C while those of the lowland

area range between 22°C to 36°C. The lower altitude is classified as semi-arid. However, there

are unusual climate variability incidences arising from climatic change. Tharaka Constituency

which lies in the lower side experience temperatures as high as 40°C at certain periods.

1.2 Survey Justification

The purpose of this survey is to assess the nutrition situation of children below five years in

Tharaka-Nithi County. The survey results will assist in complementing relevant County

information to be used as a basis for planning appropriate future interventions.

1.3 Survey objectives

The main objective of the survey is to determine the prevalence of malnutrition among the

children aged 6- 59 months old, pregnant and lactating mothers in Tharaka-Nithi County with the

following specific objectives;

➢ To determine the nutrition status of children 6 to

59 months

➢ To determine the nutritional status of women of

reproductive age (15-49) years based on maternal mid upper arm circumference

(MUAC).

➢ To determine immunization coverage; measles

(9-59 months), OPV1/3 and Vitamin A for children aged 6-59months.

➢ To determine deworming coverage for children

aged 12 to 59 months.

➢ To determine the prevalence of common

illnesses (diarrhoea, measles and ARI).

➢ To assess woman dietary diversity based on 24

hours recall.

➢ To assess water, sanitation and hygiene

practices.

➢ To assess the prevailing situation of household

food security in the County.

➢ To enhance the capacity of the county health

team in conducting nutrition surveys

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2.0 SURVEY METHODOLOGY

2.1 Type of the survey

The survey was a cross sectional study using standardized monitoring and assessment of relief

and transition (SMART) methodology in planning, data collection and analysis of

anthropometric indicators. Data on household demographics, children anthropometrics, food

security, immunization, Vitamin A supplementation, maternal health and nutrition, morbidity

and water, sanitation and hygiene practices was collected. Secondary review of various existing

surveillance data and information to include; NDMA monthly bulletins that specifically covers

Tharaka North and south, Health Information System (DHIS) and previous assessments were

undertaken prior to the survey.

2.2 Sample size and sampling procedures

The sample size was calculated using ENA for SMART software (July 9th Version) based on

various criteria as shown in table below;

Table 3: Sample size calculation

Data entered on

ENA software

Survey 1

(Maara and

Chuka

Igambang’ombe)

Survey 2

(Tharaka

North and

South)

Rationale

Estimated prevalence

of GAM

3.3% 3.3% Estimated based on KDHS 2014

results

±Desired precision ± 2.6 ± 2.6 Based on low GAM prevalence and

to meet the survey objectives

Design effect 1.5 1.5 To cater for heterogeneity

Average household

size

4 5 Population statistics (Census)

Percent of <5 13.1% 13.1% Population statistics (Census)

Percent of non-

respondent

3% 3% To cater for unforeseen

circumstances

Households to be

included

647 518 As calculated using the ENA for

SMART software

Children to be

included


SMART software

The survey adopted two stage cluster sampling. The first stage involved selecting of villages

(cluster) based on probability proportionate to size (PPS) using the ENA software. Based on the

sample size above, 42 and 36 clusters were selected for survey 1 and 2 respectively. The second

stage involved selecting households from selected villages using simple random sampling. Each

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team in Maara/Chuka conducted 16 households per day while those in Tharaka North and South

did 15 households per day.

2.3 Training Framework

Training of all teams was done from 5th to 10th September 2016. The training covered all the

components for an integrated nutrition Survey; focusing on survey objectives, sampling, and data

collection tools, anthropometric measurements, interviewing techniques, field procedures and

questionnaire administration. The training sessions was facilitated by MoH (led by the County

nutrition officer) with technical support from national MoH staff. A standardization test was

done on 10 children with aim of testing the participants’ precision and accuracy in taking

anthropometric measurements. A pilot test of three households per team in nearby villages (not

sampled) was conducted on the final day of the training. The experiences and arising challenges

were shared and addressed.

2.4 Survey teams composition and supervision

A total of 13 teams were involved; 7 for Maara/Chuka and 6 for Tharaka North and South. Each

team composed of a team leader and 2 enumerators. The team leaders were obtained from

relevant county government ministries. Enumerators were NDMA field monitors, health workers

and community members. The coordination and supervision of the entire process was led by the

County Nutrition Officer under technical support from national MoH staffs (2) and NHP Staff

(4). Data quality assurance process was maintained by observing the following steps:

➢ Validation of the survey planning and

methodology at the Nutrition information working group

➢ Survey team training in adherence to SMART

standards to including undertaking of both standardization and pilot test

➢ Use of mobile phone based technology in data

collection (ODK) that limits errors through skip patterns and validation criteria.

➢ Daily support and supervision of teams at the

cluster level

➢ Daily feedback session through plausibility and

questionnaire checks

2.5 Case Definitions and Inclusion Criteria

Primary data was gathered from the sampled villages to make inferences with regard to the

survey objectives for a period of 6 days.

Anthropometric data was collected from all eligible children aged 6-59 months. The children

were targeted with the following information

▪ Age: The child’s immunization card, birth certificate or birth notification were the primary

source for this information. In the absence of these documents, a local calendar of events

developed from discussions with community members, enumerators and key informants. Age

calculation chart was used for ease of identifying age in months (see Annex).

▪ Child’s Sex: This was recorded as either ‘m’ for male or ‘f’ for female.

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▪ Weight: A seca7digital weighing scale was used to measure the children’s weight. The

electronic scales were calibrated on daily basis using a standard weight to confirm

measurements and any faulty scales were replaced. In order to enhance accuracy and hence

quality, of emphasis was placement of weight scale to a hard flat surface, minimal or no

movement of the child and accurate recording of measurements to the nearest 0.1kg

▪ Height: Recumbent length was taken for children less than 2 years of age while those

children above 2 years of age were measured standing up. A height board was used to

measure length/height. Of emphasis was ideal placement of cursor as per instructions on

height measurements (SMART/IMAM8 guidelines) ensuring minimal or no movement of the

child and maintaining height readings at eye level to the nearest 0.1cm.

▪ MUAC: Mid Upper Arm Circumference was measured on the left arm, at the middle point

between the tip of the elbow and the tip shoulder bone while the arm is at right-angle, then

followed MUAC measurements of the arm while it is relaxed and hanging by the body’s side.

MUAC was measured to the nearest mm. In the event of a disability on the left arm or a left-

handed child, the right arm was used. Of emphasis during the exercise was correct

identification of mid-point and correct tension upon placement of MUAC tape on arm.

Maternal MUAC tapes were used to measure MUAC in women of reproductive age.

▪ Bilateral Oedema: This was assessed by the application of moderate thumb pressure for at

least 3 seconds on both feet. If a depression formed on both feet upon pressure application,

then presence of bilateral oedema was confirmed.

▪ Measles vaccination: The child’s vaccination card was used as a source of verification. In

circumstances where this was not available, the caregiver was probed to determine whether

the child had been immunized against measles or not (done subcutaneously on the right upper

arm). All children with confirmed immunization (by date) on the vaccination card, the status

were recorded as “1” (Card) otherwise as “3” (Not immunized). Oral confirmation from the

caregiver without proof of card was recorded as “2” (Recall). Children between 9 to 18

months or greater were used to determine coverage of this in the final analysis.

▪ Oral Polio Vaccine (OPV) 1 (1st dose at 6 weeks) and OPV3 (3rd dose at 14 weeks) was

calculated for all children aged 6-59 months.

Other relevant information about the eligible child was also gathered as follows:

• De-worming: Determined by whether the child

had received drugs for intestinal worms in the past one year. This was recorded as “0” for

No, “1” for Yes by card, ‘’2’’ for Yes by recall and ‘’3’’ for Do not know.

• Vitamin A coverage: This was determined by

the number of times the eligible child had received vitamin A in the past year. The

response received (number of times) was probed (to determine where health-

facility/outreach sites or elsewhere and the number of times recorded in the card) and

eventually recorded on the anthropometric questionnaire.

7Electronic SECA scale manufactured by Secagmbh& co.kg. Hammer Steindamm 9-25.22089

Hamburg. Germany.

8 Integrated Management of Acute Malnutrition

6


• Morbidity: This was gathered over a two week

recall period by interviewing/probing the mothers/caretakers of the target child and

eventually determined based on the respondent’s recall. This information was however

not verified by a clinician.

• Other data sets: the Household questionnaire

was used to gather data on other variables related to HINI indicators, WaSH (Water

Sanitation and Hygiene) and FSL (Food Security and Livelihood).

• Micronutrient powders: The eligible children

for this information were 6-23 months. The respondent was asked whether the child was

enrolled in the program; recorded in the questionnaire as “0” for No and “1” for Yes.

Those who said no were probed for reasons as to why not enroll. Those enrolled were

probed on adherence.

Other data sets: The household questionnaire was used to gather data on health related

variables, HINI9 Indicators, water availability and accessibility, sanitation and hygiene

practices, food sources, dietary diversity and coping strategies.

2.6 Data Entry and Analysis

Daily data entry was undertaken for all data sets so as to ensure close supervision and quality of

data. Anthropometric data was analyzed in ENA for SMART software January 2015 version.

All other data sets were entered and analyzed using Microsoft Excel.

2.7 Indicators, Guidelines and Formulas Used In Acute Malnutrition

Weight for height (WFH) index

This was estimated from a combination of the weight for height (WFH) index values (and/or

oedema) and by sex based on WHO standards 2006. This index was expressed in WFH indices in

Z-scores, according to WHO 2006 reference standards.

Z-Score:

• Severe acute malnutrition is defined by WFH < -3 SD and/or existing bilateral oedema

• Moderate acute malnutrition is defined by WFH < -2 SD and >-3 SD and no oedema.

• Global acute malnutrition is defined by WFH < -2 SD and/or existing bilateral oedema.

Mid upper arm circumference (MUAC)

MUAC analysis was also undertaken to determine the nutrition status of sampled children and

women of reproductive age (15-49 years). The following MUAC criteria were applied.

Table 4: MUAC guidelines

MUAC Guideline Interpretation

9High Impact Nutrition Interventions

7


Children 6-59 months

MUAC <115mm and/or bilateral Oedema Severe acute malnutrition

MUAC >=115mm and <125mm (no bilateral

oedema)

Moderate acute malnutrition

MUAC >=125mm and <135mm (no bilateral

Oedema)

Risk of malnutrition

MUAC > 135mm (no bilateral Oedema) Adequate nutritional status

Women of Reproductive Age (15-49 years)

MUAC <21-23cm At Risk of malnutrition

MUAC <21cm Moderate Acute Malnutrition

2.8 Referrals

During the survey, all severe and moderately malnourished children as per MUAC and Weight-

for-Height cut offs were referred to the nearby health facilities. Pregnant and lactating women

with MUAC <21cm were also referred.

8


3.0 SURVEY FINDINGS

3.1 General Characteristics of Study Population and Households

The survey involved 1125 households (510 for Tharaka survey and 610 for Chuka survey). The

average household sizes were 4.24 and 4.22 for tharaka and Chuka respectively. In both surveys,

majority of adults had primary school education as shown in the table below. There was also a

considerable proportion with no formal education and it was highest in Tharaka north/South

(14.3%).

Table 5: Summary of demographic characteristics

Household demographics Tharaka Chuka County P Value10

Average household sizes 4.24 4.22 4.1 P=0.809

% OF UNDER FIVES 14.7% 16.1% 15.0% P=0.797

School enrolment 90.4% 87.7% 85.6% X2=3.46

p=0.065

Highest

education

level attained

for adult

pre-primary 10.6% 4.8% 6.6% X2=188.4

p=0.00 primary 50.2% 47.5% 46.8%

Secondary 14.1% 30.2% 23.6%

Tertiary 10.6% 13.3% 11.9%

None 14.3% 3.6% 7.2%

Others 0.3% 0.6% 0.5%

Main

occupation of

household

head

Livestock Herding 3.6% 0.8% 1.7% X2=103.74,

P=0.00 Own farm labour 71.4% 48.3% 55.7%

Employed 5.9% 11.9% 10.0%

Waged labour 9.5% 24.2% 19.9%

Merchant/trader 5.5 % 8.9% 7.9%

Charcoal burning 0.0% 0.3% 0.2%

Others 3.9% 5.5% 4.8%

Main source of

income

No income 1.2% 1.8% 1.5%

Sale of livestock 7.7% 1.1% 3.3%

Sale of livestock

products

4.9% 6.2% 5.5%

Sale of crops 51.1% 35.3% 39.5%

Petty trading e.g. Sale 3.9% 7.0% 5.7%

10 This uses the appropriate statistical test where necessary to assess significant difference between Tharaka

Survey and Chuka Survey

9


of firewoods

Casual labor 18.5% 31.1% 25.7%

Permanent job 6.1% 10.4% 8.6%

Remittance 1.8% 1.5% 1.5%

Others 4.9% 5.5% 5.1%

Marital status

of the

respondent

Married 81.7% 79.6% 77.6%

Single 7.3% 6.5% 6.6%

Widowed 7.5% 10.4% 9.1%

Separated 3.1% 2.1% 2.4%

Divorced 0.4% 1.3% 0.9%

3.2 ANTHROPOMETRY

3.2.1 Distribution by Age and Sex

The anthropometric data were first analyzed separately and then weighted based on survey

proportion and the sample size of children 6-59 months. The Tharaka North/South survey had

286 children while Chuka/Maara survey had 367 children aged 6-59 moths who were measured

and assessed. The overall score for the survey was 5% (interpreted as excellent as per the

plausibility check). The boys and girls involved in the anthropometric measurements were

equally distributed. The boy: girl ratio was 0.92 which is within the estimated range of 0.8-1.2

with a p value of 0.273.The table below shows distribution by age and sex of all children in both

surveys.

Table 6: Distribution by age and sex

Boys Girls Total Ratio

AGE (mo) no. % no. % no. % Boy:girl

6-17 69 42.3 94 57.7 163 25.0 0.7

18-29 72 47.7 79 52.3 151 23.1 0.9

30-41 88 49.7 89 50.3 177 27.1 1.0

42-53 58 52.3 53 47.7 111 17.0 1.1

54-59 25 49.0 26 51.0 51 7.8 1.0

Total 312 47.8 341 52.2 653 100.0 0.9

3.2.2 Nutritional Status of Children 6-59 Months

3.2.2.1 Prevalence of global acute malnutrition based on Weight-for -Height Z score

a) Tharaka North and South

286 aged 6-59 months children were assessed. Among these, 4 children had their WHZ out of

range and none had oedema. Global acute malnutrition prevalence (GAM) was 5.0% (2.6- 9.5

10


95% CI) while severe acute malnutrition prevalence (SAM) was 0.7% (0.2- 3.0 95% CI). There

were pockets of malnutrition in Murinda and Kamwathu (in Tharaka North and South Sub

Counties respectively). Boys were more malnourished though the difference was not significant

(P=0.158).

Table 7: Prevalence of global acute malnutrition based on Weight-for -Height Z score (and/or

oedema) and by sex in Tharaka North and South

All

n = 281

Boys

n = 138

Girls

n = 143

P value

(boys/Girls)

Prevalence of global malnutrition

(<-2 z-score and/or oedema)

(14) 5.0 %

(2.6 - 9.5

95% C.I.)

(10) 7.2 %

(3.2 - 15.4

95% C.I.)

(4) 2.8 %

(1.1 - 7.2

95% C.I.)

P=0.158

Prevalence of moderate

malnutrition

(<-2 z-score and >=-3 z-score, no

oedema)

(12) 4.3 %

(2.0 - 8.9

95% C.I.)

(8) 5.8 %

(2.2 - 14.5

95% C.I.)

(4) 2.8 %

(1.1 - 7.2

95% C.I.)

P=0.320

Prevalence of severe malnutrition


(2) 0.7 %

(0.2 - 3.0

95% C.I.)

(2) 1.4 %

(0.3 - 6.1

95% C.I.)

(0) 0.0 %

(0.0 - 0.0

95% C.I.)

P=0.101

The nutrition status was poor with a mean and standard deviation WHZ of -0.28±1.00 as

represented in the Gaussian curve below.

Figure 1: Gaussian curve for Tharaka North & South

11


b) Maara Igambang’ombe and Chuka

367 aged 6-59 months children were assessed. Among these, 6 children had their WHZ out of

range and none had oedema. Global acute malnutrition prevalence (GAM) was 2.8% (1.3- 5.8

95% CI) while severe acute malnutrition prevalence (SAM) was 0.0% (0.0- 0.0 95% CI).

Table 8: Prevalence of global acute malnutrition based on Weight-for -Height Z score (and/or

oedema) and by sex in Maara and Chuka Igambang'ombe

All

n = 361

Boys

n = 171

Girls

n = 190

P value

(boys/Girls)

Prevalence of global malnutrition


(10) 2.8 %

(1.3 - 5.8

95% C.I.)

(5) 2.9 %

(1.2 - 6.7

95% C.I.)

(5) 2.6 %

(0.9 - 7.7

95% C.I.)

P=0.871


malnutrition

(<-2 z-score and >=-3 z-score, no

oedema)

(10) 2.8 %

(1.3 - 5.8

95% C.I.)

(5) 2.9 %

(1.2 - 6.7

95% C.I.)

(5) 2.6 %

(0.9 - 7.7

95% C.I.)

P=0.871

Prevalence of severe malnutrition


(0) 0.0 %

(0.0 - 0.0

95% C.I.)

(0) 0.0 %

(0.0 - 0.0

95% C.I.)

(0) 0.0 %

(0.0 - 0.0

95% C.I.)

-

The nutrition status of children 6-59months were relatively good a mean and standard deviation

WHZ of 0.11±1.01 as represented by the curve below.

Figure 2: Gaussian curve for Maara and Chuka Igambang'ombe

12


c) County weighted GAM estimated

The weighted estimate for GAM was at 3.20% (1.9 - 5.4 95% C.I) whereas for SAM was at 0.0%

(0.0-0.0 95% CI). GAM in boys was at 3.80% (2.0 - 7.1 95% CI) and 2.70% (1.2 - 5.9 95% CI).

The difference was not however significant (P=0.501).

3.2.2.2. Prevalence of Acute Malnutrition by MUAC

The Middle upper arm circumference (MUAC) is used as an early detector for malnourished

children. Assessing children using MUAC is cheap and easy and enhances quick referral for

malnourished children. GAM by MUAC was at 2.1% (0.9- 5.0 95% CI) and 2.5% (1.2- 4.9 95%

CI) in Tharaka North/South and Maara/Chuka Igambang’ombe respectively. SAM by MUAC

was at 0.3% (0.0- 2.0 95% CI) and 0.7% (0.2- 2.9 95% CI) respectively. Weighted county

estimates for GAM and SMA based on MUAC was at 2.3(0.4-4.2 95% CI) and 0.3 (0.0-1.5 95%

CI) respectively.

3.2.2.3 Prevalence of underweight by Weight-for-age (WFA) Z-scores

Underweight is a composite indicators of both acute and chronic malnutrition. Underweight child

have low weight compared to average children of the same age.


The prevalence of underweight based on WAZ in Tharaka North and South was at 14.2% (11.3-

17.7 95% CI) as shown in the table below.

Table 9: Prevalence of underweight based on weight-for-age z-scores by sex in Tharaka North

and South

All

n = 282

Boys

n = 137

Girls

n = 145

P value

(boys/Girls)

Prevalence of underweight

(<-2 z-score)

(40) 14.2 %

(11.3 - 17.7

95% C.I.)

(18) 13.1 %

(8.4 - 20.0

95% C.I.)

(22) 15.2 %

(10.9 - 20.7

95% C.I.)

P=0.571


underweight

(<-2 z-score and >=-3 z-score)

(34) 12.1 %

(9.3 - 15.5

95% C.I.)

(16) 11.7 %

(7.4 - 17.9

95% C.I.)

(18) 12.4 %

(8.6 - 17.5

95% C.I.)

P=0.836

Prevalence of severe underweight

(<-3 z-score)

(6) 2.1 %

(0.7 - 6.2

95% C.I.)

(2) 1.5 %

(0.4 - 5.3

95% C.I.)

(4) 2.8 %

(0.8 - 8.9

95% C.I.)

P=0.510

13


b) Maara and Chuka Igambang’ombe

The prevalence of underweight in Maara and Chuka Igambang’ombe was 7.1% (4.6-11.0 95%

CI) as shown in the table below.

Table 10: Prevalence of underweight based on weight-for-age z-scores by sex in Maara and

Chuka Igambang'ombe

All

n = 364

Boys

n = 171

Girls

n = 193

P value

(boys/Girls)

Prevalence of underweight

(<-2 z-score)

(26) 7.1 %

(4.6 - 11.0

95% C.I.)

(14) 8.2 %

(4.9 - 13.3

95% C.I.)

(12) 6.2 %

(2.7 - 13.5

95% C.I.)

P=0.538


underweight


(20) 5.5 %

(3.4 - 8.8

95% C.I.)

(11) 6.4 %

(3.3 - 12.0

95% C.I.)

(9) 4.7 %

(2.3 - 9.2

95% C.I.)

P=0.521

Prevalence of severe underweight

(<-3 z-score)

(6) 1.6 %

(0.7 - 3.9

95% C.I.)

(3) 1.8 %

(0.6 - 5.3

95% C.I.)

(3) 1.6 %

(0.4 - 6.2

95% C.I.)

P=0.865

c) County weighted estimates

The weighted underweight prevalence was at 9.10% (7.0 - 11.7 95% CI) and severe underweight

at (1.80% 0.9 - 3.6 95% CI). Underweight in boys was at 10.0% (7.0 - 13.9 95%) and 8.30% (5.5

- 12.3 95% CI). The difference was not statistically different (p=0.487).

3.2.2.4 Prevalence of stunting based on height-for-age z-scores

Stunting in children is an indication of poor nutrition and growth. It’s also a manifestation of

recurrent illness, poor care practices including feeding. Stunted children are very short compared

to average height of children of the same age. Stunting also leads to greater risk for illness and

premature death, may result in delayed mental development and therefore poorer school

performance and later on reduced productivity in the work force, and reduced cognitive

capacity11.


The prevalence of stunting in Tharaka North and South was the highest among the two surveys

standing at 27.8% (21.3-35.5 95% CI) as shown in the table below.

Table 11: Prevalence of stunting based on height-for-age z-scores and by sex in Tharaka North

and South

All Boys Girls P value

11Kenya comprehensive multiyear plan for 2011-2015

14


n = 273 n = 134 n = 139 (boys/Girls)

Prevalence of stunting

(<-2 z-score)

(76) 27.8 %

(21.3 - 35.5

95% C.I.)

(40) 29.9 %

(19.0 - 43.6

95% C.I.)

(36) 25.9 %

(19.7 - 33.2

95% C.I.)

P=0.570

Prevalence of moderate stunting


(57) 20.9 %

(14.6 - 29.0

95% C.I.)

(31) 23.1 %

(13.2 - 37.4

95% C.I.)

(26) 18.7 %

(12.3 - 27.4

95% C.I.)

P=0.532

Prevalence of severe stunting

(<-3 z-score)

(19) 7.0 %

(4.9 - 9.8

95% C.I.)

(9) 6.7 %

(3.9 - 11.4

95% C.I.)

(10) 7.2 %

(4.4 - 11.7

95% C.I.)

P=0.839

Further analysis revealed that stunting occurred relatively across all the age groups as shown in

the table below.

Table 12: Prevalence of underweight by age, based on weight-for-age z-scores in Tharaka North

and South

Severe stunting

(<-3 z-score)

Moderate

stunting

(>= -3 and <-2

z-score )

Normal

(> = -2 z score)

Mean WAZ ±

Standard

deviations

Age

(mo)

Total

no.

No. % No. % No. %

6-17 64 6 9.4 13 20.3 45 70.3 -1.30 ± 1.14

18-29 55 4 7.3 17 30.9 34 61.8 -1.41 ± 1.27

30-41 84 5 6.0 15 17.9 64 76.2 -1.31 ± 1.10

42-53 46 3 6.5 8 17.4 35 76.1 -1.38 ± 1.08

54-59 24 1 4.2 4 16.7 19 79.2 -1.43 ± 0.92

Total 273 19 7.0 57 20.9 197 72.2

b) Maara and Chuka Igambang’ombe

The stunting prevalence based on HAZ in Maara and Chuka Igambang’ombe was 19.7% (15.7-

24.5 95% CI) as shown in the table below.

Table 13: Prevalence of stunting based on height-for-age z-scores and by sex in Maara and

Chuka Igambang'ombe

All

n = 360

Boys

n = 169

Girls

n = 191

P value

(boys/Girls)

Prevalence of stunting

(<-2 z-score)

(71) 19.7 %

(15.7 - 24.5

95% C.I.)

(36) 21.3 %

(16.2 - 27.5

95% C.I.)

(35) 18.3 %

(13.3 - 24.7

95% C.I.)

P=0.450

Prevalence of moderate stunting (55) 15.3 % (27) 16.0 % (28) 14.7 % P=0.679

15


(<-2 z-score and >=-3 z-score) (12.1 - 19.1

95% C.I.)

(12.2 - 20.6

95% C.I.)

(10.6 - 20.0

95% C.I.)

Prevalence of severe stunting

(<-3 z-score)

(16) 4.4 %

(2.6 - 7.4

95% C.I.)

(9) 5.3 %

(2.7 - 10.2

95% C.I.)

(7) 3.7 %

(1.7 - 7.7

95% C.I.)

P=0.477

Analysis by age revealed that stunting was mainly at age 18-29months followed by 30-

41Months. At this age (18-29Months), majority of children stops breastfeeding and if the

complementary feeding is poor, the growth of that child will be impaired.

Table 14: Prevalence of underweight by age, based on weight-for-age z-scores in Maara and

Chuka Igambang'ombe

Severe stunting

(<-3 z-score)

Moderate

stunting

(>= -3 and <-2

z-score )

Normal

(> = -2 z score)

Mean WAZ ±

Standard

deviations

Age

(mo)

Total

no.

No. % No. % No. %

6-17 92 3 3.3 11 12.0 78 84.8 -0.80 ± 1.07

18-29 92 9 9.8 16 17.4 67 72.8 -1.26 ± 1.21

30-41 85 3 3.5 15 17.6 67 78.8 -1.09 ± 1.17

42-53 64 1 1.6 7 10.9 56 87.5 -0.66 ± 1.07

54-59 27 0 0.0 6 22.2 21 77.8 -0.95 ± 0.93

Total 360 16 4.4 55 15.3 289 80.3

c. Weighted county estimates

Prevalence of stunting in Tharaka Nithi County stands at 22.0% (18.6 - 25.8 95% CI) whereas

severe stunting stands at 5.10% (3.6 - 7.2 95%). Compared to Kenya demographic and health

survey (KDHS 2014), stunting reduced from 32.9% in which it was statistically difference

(p=0.009). The reduction could be attributed to efforts by county government and other

stakeholders in addressing stunting through multistakeholders approach e.g NHP-MoH

partnership.

3.3 Child Morbidity and health seeking behaviours

41.6% and 39.2% of the children 6-59months were reported to be ill 2 weeks prior to the survey

in the Tharaka North/South and Maara/Chuka Igambang’ombe respectively. Most of these

children had respiratory infections as shown in the figure

16


below.

Figure 3: Child Morbidity

74.8% and 83.3% of caregivers for the children reported ill sought health assistance in Tharaka

North/South and Maara/Chuka Igambang’ombe respectively. Public clinics were the main source

of health assistance at 56.2% and 57.5 respectively.

Figure 4: Source of health assistance

17


3.4 Child Immunization, Vitamin A Supplementation and Deworming

3.4.1 Immunization

Immunization process helps the child to respond and fight infections. The ministry of health

under the expanded programme on immunization aims to increase access to immunization

services in order to reduce morbidity and mortality due to vaccine preventable diseases12.

Assessment of each vaccination was assessed as per Kenya national immunization scheduled.

Positive response was either yes by card or by recall. The table below summarizes the

immunization coverage in the separate surveys and weighted county estimates.

Table 15: Coverage of Vaccination

Vaccination Tharaka North

and South

Maara and Chuka

Igambang’ombe

COUNTY

N % N % N %

BCG Presence of scar 286 91.3% 367 86.4% 653 87.8

OPV 1 By card 286 73.8% 367 56.9% 653 62%

By recall 22.4% 41.4% 35.7%

OPV 3 By card 286 68.9% 367 56.1% 653 60.0%

By recall 23.1% 41.7% 36.1%

Measles

at 9

months

By card 270 69.3% 352 52.3% 622 57.3%

By recall 22.6% 44.0% 37.6%

Measles

at 18

months

By card 218 39.0% 275 33.5% 493 35.1%

By recall 13.8% 43.3% 34.3%

The coverage of immunization were all above the national target (>80%) except for second

measles dose.

3.4.2 Vitamin A supplementation

The Ministry of health recognizes Vitamin A supplementation as one of the high impact nutrition

interventions. Vitamin Supplementation help reduces child morbidity and mortality, enhance

vision and growth. Vitamin supplementation was assessed for the last one year and data

disaggregated by age category. The results are as shown below.

Table 16: Coverage of Vitamin A supplementation

12Kenya comprehensive multiyear plan for 2011-2015

18


Tharaka North and

South

Maara and Chuka

Igambang’ombe

COUNTY

6-11 months Once 53.5% 85.7% 72.2%

12-59 At least once 65.1% 77.2% 71.0%

12-59 at least twice 39.6% 32.9% 34.4%

Only coverage for 6-11months in Maara and Chuka reaches the national target of >80%

3.4.3 Deworming

Deworming with doses of antihelmenthes (mebendazole or albendazole) helps to combat worms

that worsen the health and nutrition status through poor food absorption and competition for

nutrients. The coverage of deworming among children 12-29 months is as show below

Tharaka North and

South

Maara and Chuka

Igambang’ombe

COUNTY

Dewormed Once 35.3% 55.3% 46.9%

Dewormed At least twice 8.5% 19.1% 14.9%

3.5 MATERNAL HEALTH AND NUTRITION

3.5.1 Physiological status of women of reproductive age (15-49years)

Majority of women of reproductive age (WRA) were neither pregnant nor lactating during the

survey data collection period as shown in the table below.

Table 17: Physiological status of WRA

Tharaka North and South(N=296) Highland (N=415) County

(N=711)

Pregnant 4.2% 1.2% 2.2%

Lactating 29.8% 33.7% 31.6%

No pregnant, no

lactating

66.0% 65.1% 63.9%

3.5.2 Maternal nutrition status based on MUAC

The nutritional status of WRA was assessed using maternal MUAC tape. The results was then

aggregated to assess the nutrition status; first of all women then for pregnant and lactating

women. The results are as follows.

Table 18: Nutrition status of WRA

Tharaka North

and South

Highland

County

All WRA Malnourished (<21cm) 1.4% 1.0% 1.1%

At risk (21-23cm) 5.1% 3.2% 4.9%

19


Well nourished (>23cm) 93.5% 95.9% 95.1%

Pregnant

and

lactating

women

Malnourished (<21cm) 1.6% 1.4% 1.4%

At risk (21-23cm) 8.2% 3.4% 4.9%

Well nourished (>23cm) 88.2% 95.2 90.7%

3.5.3 Consumption of Iron Folic Acid Supplements (IFAS)

To prevent anaemia, improve maternal and perinatal health, the World Health Organization

recommends that all pregnant women in should routinely receive iron and folate supplements

(IFAS) together with appropriate dietary advice. Consumption of IFAS was assessed on women

who had a complete pregnancy in the last 2 years. 55.3% and 71.8% of women with a complete

pregnancy in 2 years reported to have consumed IFAS in Tharaka North/South and Maara/Chuka

Igambang’ombe respectively. Adherence was low in Tharaka North/South with 82.2% and

14.1% consuming in >90days and 90-180 days respectively. In Maara/Chuka Igambang’ombe,

adherence was relatively good with 75.6%, 23.0% and 3.7% consuming IFAS in >90 days, 90-

180 days and >180 days respectively.

3.6 Water, Sanitation and Hygiene

3.6.1 Water

Majority of households got their drinking water from piped water system as shown in the figure

below. At county level, piped water accounts for 73.1% of household drinking water. Whereas

this source is protected, the survey was limited on protected versus unprotected sources and

could not assess “water safety”. An improved water source is defined as water that is supplied

through a household connection, public standpipe, borehole well, protected dug well, protected

spring, or rainwater collection.

Figure 5: Source of drinking water

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To get household water, most households do not travel for long as shown in the table below.

Some even get water from within their compounds.

Table 19: Trekking distance to water sources

Distance Tharaka North

& South

Maara & Chuka

Igambang’ombe

COUNTY

Less than 500m (less than 15 min) 50.1% 90.5% 77.7%

More than 500 m to less than 2km

(15min to 1 hour)

31.4% 8.0% 15.4%

More than 2 km (1hr to 2hrs) 18.5% 1.5% 6.9%

36.3% and 38.3% of the assessed households in Tharaka North/South and Maara/Chuka

Igambang’ombe respectively treated their water before consumption. At county level, boiling

was the most water treatment method used at 83.8%. Other methods used were use of chemicals

(18.5%) and pot filters (1%).

3.6.2 Hygiene

Poor hygiene practices are associated with increased vulnerability of illnesses such as diarrhoea

and respiratory infections13. Household hygiene practices assessed include water storage and

handwashing. 5.9%, 14.8% and 12% of household in Tharaka North/South, Maara/Chuka

Igambang’ombe and County level kept their water in open containers.

96.6% of caregivers were aware of handwashing practices. Assessment of critical events that

caregivers washed their hands revealed that most caregivers did not wash hands after taking their

children to toilets as shown in the table below. On average, only 7.1% of caregivers washed their

hands in all the four critical handwashing times. A positive note is that majority of the caregivers

used soap and water in handwashing.

Table 20: Hygiene practices

Tharaka North and

South

Maara and

Chuka

Igambang’ombe

County

HH Aware of hygiene practices 94.7% 97.6% 96.6%

After toilet 91.9% 84.3% 86.7%

Before cooking 35.7% 55.0% 49%

13WHO. Prüss-Ustün A, Bos R, Gore F, Bartram J. Safer water, better health. Geneva, World Health Organization;

2008. (http://www.who.int/quantifying_ehimpacts/publications/saferwater/en/, accessed 23 November 2009).

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Before eating 83.4% 69.2% 73.6%

After taking children to the toilet 9.8% 15.9% 14.0%

Hand washing in all 4 critical times 4.3% 9.1% 7.1%

Hand washing by soap and water 65.8% 76.4% 70.1%

3.6.3 Sanitation

Improper disposal of human waste is one of the leading causes of food and water borne diseases.

Eighty-eight percent of diarrhoea cases worldwide are linked to unsafe water, inadequate

sanitation or insufficient hygiene resulting to 1.5 million deaths each year, mostly in young

children. Majority of households had their own latrines. However, some households especially in

Tharaka North and South were practising open defecation as shown in the table below.

Table 21: Sanitation and latrine coverage

Tharaka North

and South

Maara and Chuka

Igambang’ombe County

Open defecation(bushes) 1.8% 0.2% 1.1%

Neighbour, shared

tradition/improved latrine 12.2% 15.8% 14.4%

Own traditional/improved latrine 86.1% 83.8% 84.5%

3.7 Food security and livelihood

3.7.1 Food Security information

Agriculture is the mainstay and livelihood of the Tharaka-Nithi people. The County’s physical

features and Climatic conditions favour agriculture. 92% of the households are engaged in

agricultural activities. The total area of the County is 2,662.1 km2 of which 1,449.63 km2 are

arable with 941.62 km2 non-arable. The upper part of the County produces mainly cash crops

such as coffee and tea while the lower part mainly produces food crops such as maize, beans,

cowpeas, bananas, sorghum, tomatoes, paw paws, avocadoes and citrus fruits. Other crops grown

in lower areas include: macadamia, oranges, mangoes, cotton and tobacco.

Livestock farming is also an important economic activity in the County. Dairy farming using

exotic cattle breeds is concentrated in the upper parts of the County while in the lower parts

indigenous breeds are reared for both dairy and beef. The main types of animals reared include

cattle, goats, sheep and chicken. Rabbit rearing has also become an attractive venture to the

farmers. Besides, fish farming is practiced. The main types of fish in the County include; trout,

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tilapia, and mud fish which are available mostly from the rivers and ponds. In addition, the

Government through the Economic Stimulus Programme has constructed more than 692

fishponds in the County and the fish harvest is usually sold locally.

3.7.2 Household dietary diversity

Household dietary diversity (HDDS) is used as a proxy indicator to measure the socio-economic

ability of households to access a variety of foods and food consumption can be triangulated with

other food-related information to contribute towards providing a holistic picture of the food and

nutrition security status in a community or across a broader area14. Household dietary diversity

was assessed based on 24 hours recall. Oils and cereals were the most consumed whereas organ

meats and fish were least consumed as shown in the table below.

Table 22: Household dietary diversity

Food group Tharaka North

and South

Maara And Chuka

Igambang'ombe

Oils/fats 96.0% 95.4% 95.5%

Cereals 83.9% 90.9% 88.7%

Sweets/Sugar 83.5% 83.1% 83.1%

Other vegetables 79.6% 91.7% 87.8%

Pulses/legumes 77.4% 83.0% 81.2%

Milk 70.0% 89.6% 83.3%

Condiments 40.2% 42.3% 41.6%

White tubers and roots 21.8% 41.5% 35.1%

Flesh meats and offals 19.4% 18.0% 18.5%

Dark green leafy

vegetables

19.2% 52.4% 41.7%

Vitamin A rich fruits 14.5% 35.1% 28.6%

Vitamin A rich vegetables

and tubers

13.5% 33.8% 27.3%

Eggs 10.4% 18.5% 16.0%

Other fruits 10.0% 37.9% 28.9%

14Guidelines for measuring household and individual dietary diversity, FAO (2011)

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Organ meat (iron rich) 2.7% 2.0% 2.2%

Fish 1.6% 1.0% 1.2%

Further analysis of food was done to assess micronutrient food consumption based on 7 days

recall. Consumption of iron rich food was low across the two livelihood zones as shown in the

figure below.

Figure 6: Micronutrient food consumption

3.7.3 Food consumption score

Food consumption score (FCS) is a proxy indicator of the current food security situation15 and

combines measurements of dietary diversity, the frequency with which different foods are

consumed and the relative nutritional importance of various food groups16. FCS was based on

consumption of food in the last 7 days prior to the survey. Majority of the household were within

good food consumption score attributed to consumption of Cereal, pulses/legumes and milk

(>5/week), or fruit or vegetable, oil and sugar as shown in the table below.

15Food Consumption Scores and IPC by World Food Programme, 2009

16Ruel, M.T., Is Dietary Diversity an Indicator of Food Security or Dietary Quality? A Review of

Measurement Issues and Research Needs. Discussion paper 140. Washington D.C, 2003

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Main

Threshold

Nomenclature Tharaka

North and

South

Maara and Chuka

Igambang’ombe

County

0-21.4 Poor food

consumption…mainly

cereal

0.40% 0.50% 0.40%

21.5-35.4 Borderline food

consumption {Cereal,

protein or milk (3-4/week),

oil, sugar

4.10% 1.10% 3.80%

>35.5 Good food consumption

Cereal, protein and milk

(>5/week), or fruit or

vegetable, oil, sugar

95.50% 98.40% 95.80%

3.7.4 Coping strategy Index

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. 15.1 And 14.7% of the household assessed reported to

have lacked food or money to buy food one week prior to the survey in Tharaka North/South and

Maara/Chuka Igambang’ombe respectively. The strategies coped were as shown below.

Table 23: Coping strategy for Tharaka North and South

Percentage

of HH

(510)

Frequency

score (0-7)

Severity

score (1-3)

Weighted

score=Freq*weight

Rely on less preferred and less

expensive foods?

13.1%

(n=67) 2.59 1 2.59

Borrow food, or rely on help from a

friend or relative?

9.6%

(n=49) 1.64 2 3.29

Limit portion size at mealtimes?

10.4%

(n=53) 1.77 1 1.77

Restrict consumption by adults in

order for small children to eat?

3.7%

(n=19) 1.62 3 4.87

Reduce number of meals eaten in a

day?

11.0%

(n=56) 2.10 1 2.10

14.65

Table 24: Coping strategy index for Maara and chuka Igambang'ombe

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Percentage

of HH

(615)

Frequency

score (0-7)

Severity

score (1-3)

Weighted

score=Freq*weight

Rely on less preferred and less

expensive foods?

12.7%

(n=78)

4.36 1 4.36

Borrow food, or rely on help from a

friend or relative?

7.2%

(n=44)

1.17 2 2.35

Limit portion size at mealtimes? 11.1%

(n=68)

3.21 1 3.21

Restrict consumption by adults in

order for small children to eat?

6.0%

(n=37)

0.35 3 1.05

Reduce number of meals eaten in a

day?

9.4%

(n=58)

2.54 1 2.55

13.52

3.7.5 Household hunger scale

HHS is a proxy indicator of food access. The HHS is built around 3 questions about perceptions

of a household on varying degrees of hunger by the number of times a household has

experienced hunger within past 30 days prior to the survey. Three questions are:

➢ In the past 30 days, was there ever no food of

any kind to eat in your house because of lack of resources to get food?

➢ In the past 30 days, did you or any household

member go to sleep at night hungry because there was not enough food?

➢ In the past 30 days did you or any household

member go a whole day and night without eating anything at all because there was not

enough food?

Household are then classified into three hunger classes; none/mild, moderate and severe.

Majority of households were classified as having none/mild hunger as shown in the figure below.

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Figure 7: Household Hunger Scale

Discussion and Conclusion

Malnutrition levels in Tharaka Nithi County are more chronic than acute. The Semi-arid region

of Tharaka (North and South) leads in both the acute (wasting) and Chronic (Stunting)

malnutrition. Pockets of malnutrition were also found in semi-arid livelihood zone. Tharaka is

more vulnerable to acute food shortage due to;

➢ Poor rainfall in amount and distribution

➢ Recurrence of livestock diseases

➢ Poor livestock prices

➢ Poor arming methods

There is need for a multi sectoral approach to address these issues. Child illness contributes to

acute malnutrition, but when they occur more often, they ultimately lead to chronic malnutrition.

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ANNEXES

Annex 1: Sample size calculation

Data entered on

ENA software

Survey 1

(Maara and

Chuka

Igambang’ombe)

Survey 2

(Tharaka

North and

South)

Rationale

Estimated prevalence

of GAM

3.3% 3.3% Estimated based on KDHS 2014

results

±Desired precision ± 2.6 ± 2.6 Based on low GAM prevalence and

to meet the survey objectives

Design effect 1.5 1.5 To cater for heterogeneity

Average household

size

4 5 Population statistics (Census)

Percent of <5 13.1% 13.1% Population statistics (Census)

Percent of non-

respondent

3% 3% To cater for unforeseen

circumstances

Households to be

included


SMART software

Children to be

included


SMART software

Annex 2: Tharaka North and South Clusters

Geographical Unit Population Size Cluster

Kaguma 1694 1

Ibote 3130 2

Karocho 4189 3

Kamatungu 2673 4

Kithigiri 2682 5

Marimanti 1812 6

Matakiri 2639 RC

Mwanyani 2968 7

Tumbura 2390 8

Nkondi 3104 9

Rukurini 2154 10

Rukenya 2482 11

Kathuura 1749 12

Turima 4005 13

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Gatunga 6790 14,15

Irunduni 6313 RC,16

Mukothima 4722 17

Kanjoro 4282 18,19

Mauthini 3010 20

Kathangachini 3808 21

Twanthanju 3850 RC

Kamaguna 3202 22

Kamwathu 3771 23

Ntoroni 4351 24

Kirundi 2696 RC

Thiiti 3181 25

Chiakariga 3960 26

Matiri 2470 27

Gakurungu 1761 28

Murinda 3080 29

Kamanyaki 1502 30

Kamarandi 2724 31

Gaceraka 2297 32

Nkarini 3496 33

Gakirwe 1642 34

Tubui 1490 35

Tunyai 4044 36

Annex 3: Maara and Chuka Igambang’ombe clusters

Geographical Unit Population size Cluster

Chamunga 5030 1

Kandungu 3753 2

Gatua 4940 3

Karimba 5046 4

Ndumbini 2260 5

Igamurathi 4151 6

Weru 2425 7

Chogoria 7812 8

Giachuku 2697 9

Kiraro 3907 10

Kariakomo 3219 11

Kirumi 4969 12

Mugumango 6186 13

Ndunguri 3113 14

Magutuni 4474 15

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Iruma 6016 16

Kiroo 3437 17

Gantaraki 2261 18

Kianjagi 4697 19

Kiriani 2525 20

Gitareni 3143 21

Kiamucii 3120 22

Karongoni 4734 23

Mariani 5007 24

Rukindu 2934 25

Mucwa 2732 26

Township 8126 27,RC

Kirege 5194 RC,28

Mugirirwa 5341 29

Nkuthika 2145 30

Kajuki 2635 31

Makanyanga 2601 32

Kamaindi 1862 33

Kiaritha 3136 34

Kathanje 3979 35

Mutino 3322 36

Kanyakini 1811 37

Nthambo 3922 38

Karamani 4255 39

Kagumo 1385 40

Mwonge 2796 41

Thuita 3094 42

Annex 4: Anthropometric data quality

Parameter ASAL Highland County (weighted)

No. Of children 367 286 653

Flagged data 1.6% (Excellent) 1.4% (Excellent) 1.8% (Excellent)

Sex ratio p=0.273 (Excellent) p=0.636 (Excellent) p=0.256 (Excellent)

Age ratio p=0.054 (Good) p=0.447 (Good) p=0.273 (Excellent)

DPS weight 6 (Excellent) 7 (Excellent) 5 (Excellent)

DPS height 9 (Good) 8 (Good) 8 (Good)

DPS MUAC 4 (Excellent) 7 (Excellent) 4 (Excellent)

SD WHZ 1.01 (Excellent) 1.00 (Excellent) 1.01 (Excellent)

Skewness -0.14 (Excellent) -0.18 (Excellent) -0.09 (Excellent)

Kurtosis 0.13 (Excellent) -0.03 (Excellent) -0.03 (Excellent)

Poisson 0.011 (Good) 0.017 (Good) 0.002 (Good)

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Overall 5 (Excellent) 3 (Excellent) 5 (Excellent)

INTEGRATED SMART SURVEY THARAKA NITHI COUNTY KENYA ...

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