WAJIR EAST SUB COUNTY , KENYA...Wajir East sub county is one of the 4 sub counties in the larger...

WAJIR EAST SUB COUNTY, KENYA

20th September to 3rd October 2013

Caroline Njeri KIMERE

Inés ZUZA SANTACILIA

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ACKNOWLEDGEMENTS

Save the Children International (SCI) and Coverage Monitoring Network extends its deep gratitude to all those who have contributed to this study including: the authorities in Wajir East Sub County, North Eastern Region in Kenya and to all the health personnel and village residents for your hospitality and cooperation. Very special thanks to the mothers and caregivers of severe acute malnourished children who willingly participated in the study and provided the information needed.

Thanks to the SCI team in Wajir East, the North Eastern Province and Nairobi. To the Nutrition M&E Specialist (Caroline Njeri KIMERE) for her support coordinating the SQUEAC at field level; to the Health and Nutrition PM (Rahab KIMANI) and to the Nutrition Specialist (Irene SOI) for their collaboration and support. Thanks to the Nutrition Coordinator in Wajir East (Adan ABDILLE) for his support. And thanks to all SCI staff involved in the SQEUAC investigation for their collaboration.

I thank as well Ministry of Health (MoH) for their support and commitment especially the County Nutrition Coordinator (Nuria Ibrahim ABDI). And the Mandera Central Sub County Nutrition Officer (Patrick M KAMUNDI)

I wish to thank Islamic Relief Kenya for their participation and collaboration in the SQUEAC.

This study would not have been possible without the hard work and commitment of everyone involved.

Lastly, thank you to the Humanitarian Aid and Civil Protection Department of the European Commission and the Department for International Development (DFID) for financing this project.

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EXECUTIVE SUMMARY

Wajir East sub county is one of the 4 sub counties in the larger Wajir County in the North Eastern Region of Kenya. The current estimated population living in this area is 226,0861

Save the Children International (SCI) has been running a programme to contribute to the reduction of morbidity and mortality related to acute malnutrition and to improve nutrition practices in Wajir east Sub County since 2009 to date responding to the emergency nutrition needs. Under this arrangement there are 37 OTP sites in the Sub County based at both the health facilities and outreach sites (areas without a health facility) and one stabilization center integrated in the pediatric ward at the Wajir Sub County Hospital.

Additionally SCI supports the Ministry of Health in providing monthly incentives and capacity building for 99 community health workers (CHWs).

Regarding the nutritional situation, the Global Acute Malnutrition and Severe Acute Malnutrition rates for Wajir East were 10.5 % (7.9 -13.9 95% C.I.) and of 1.6% (0.8 -3.1 95% C.I.) respectively as reported in a SMART nutrition survey conducted in May 2013.

The coverage assessment was conducted to evaluate access and coverage of the Community based Management of Acute Malnutrition programme for children ages 6 to 59 months with SAM. It conducted between September 18th and 4th October 2013 and it was the fourth Coverage survey to be conducted in Wajir East. It was conducted at the end of the Haggai dry season.

Year 2006 (March) 2010 (March) 2011

(March) 2011 (December)

Assessment SCAS SQUEAC SQUEAC SQUEAC

Coverage results Point: 30,0%

(95% IC: 17.8- 42.2)

Point: 63.7%

(95% IC: 46.6- 78.5)

Point: 62.7%

(95% IC: 49.0- 75.5)

Point 1) Central division 48.3% (95% IC:

33.5-63.5)

2) Other divisions : 67.6% (95% IC: 55.1-78.0)

The Semi Quantitative Evaluation of Access and Coverage (SQUEAC) methodology was used, .

The coverage investigation conducted in Wajir East Sub County: point coverage is 54.6% (95% IC: 40.6% - 67.6%). It is above the SPHERE standards for a rural area (>50%). But there are no significant changes since 2010 in terms of coverage.

The table below presents the main barriers on which the programme must act to improve coverage as well as specific recommendations how to do so:

1 Current estimates from the District Data Officer- Wajir town Based on 2009 Census

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Barriers Boosters

Migration/ Nomadism( animals looking for water and pasture) Good inter-linkage between community and

health facilities (by CHWs follow up)

Competing priorities disrupting OTP Services (Campaigns, trainings, meetings)

Awareness of OTP and MUAC by caretakers and the community

Shortage and staff turnover at OTP sites Good mobilization (Acceptance of program) Distance to health facilities/ OTP site

Good integration at health facilities in services provided (Allows SAM Screening)

Late seeking behavior (Sheikh, food) Referrals from CHWs Collaboration and referral from TBA

Recommendations

1 Increase access of nomadic population to the programme

2 Strengthen Joint planning with MOH and other partners

3 Advocacy on human resources importance

4 Increase community mobilization / sensitization

5 Increase quality of the OTP follow up

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CONTENTS 1. INTRODUCTION ......................................................................................................................................................... 7

1.1 CONTEXT ....................................................................................................................................................... 7

1.2 Results of previous SQUEACs in Wajir East. ......................................................................................... 11

2. OBJECTIVES ............................................................................................................................................................. 13

3. METHODOLOGY ..................................................................................................................................................... 14

3.1. GENERAL OVERVIEW ................................................................................................................................. 14

3.2. STAGES ......................................................................................................................................................... 15

Stage 1: Identification of potential areas of high and low coverage and access barriers ...................................... 15

Stage 2: Confirms the location of areas of high and low coverage......................................................................... 17

Stage 3: Wide area survey conducted to estimate overall coverage. .................................................................... 18

3.3. ORGANIZATION OF THE EVALUATION .................................................................................................. 20

3.3.1 CMN technical support .................................................................................................................................. 20

3.3.2 Team training, logistic organization and evaluation development ............................................................... 21

3.4. LIMITATIONS .............................................................................................................................................. 21

4. RESULTS ................................................................................................................................................................. 22

4.1. STAGE 1 ........................................................................................................................................................ 22

4.1.1. Recommendations follow up of SQUEAC December 2011 ....................................................................... 22

4.1.2. Quantitative data analysis ......................................................................................................................... 24

4.1.3. Qualitative data analysis ........................................................................................................................... 31

4.2. STAGE 2 ........................................................................................................................................................ 33

2 The prior ................................................................................................................................................. 35

3 The likelihood ........................................................................................................................................ 36

4 The posterior .......................................................................................................................................... 37

5. DISCUSION ............................................................................................................................................................. 39

6. RECOMMENDATIONS ........................................................................................................................................... 41

Annex 1 : Survey questionnaire for current SAM children NOT in the program .................................................... 45

Annex 2: Wajir East SQUEAC plan, September – October 2013 ............................................................................... 46

Annex 3 : SQUEAC Survey team .................................................................................................................................. 47

Annex 4 : Terminology in Somali (S) and Borana (B) used to describe malnutrition and RUTF. .......................... 48

Annex 5: Weighted BBQ, Wajir East SQUEAC, September-October 2013 ................................................................ 49

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ABBREVIATIONS

ARI Acute Respiratory Infections ASALS Arid &. Semi-Arid Lands BBQ Barriers, Boosters and Questions CI Credible Interval CMAM Community Management of Acute malnutrition CMN Coverage Monitoring Network ECHO European Commission - Humanitarian Aid & Civil Protection FGD Focus Group Discussion FP Family Planning GAM Global Acute Malnutrition HC Health Centers HF Health Facility IMAM Integrated Management of Acute Malnutrition INGO International Non-Governmental Organisation IRK Islamic Relief Kenya LoS Length of Stay MAM Moderate Acute Malnutrition MEAL Monitoring, Evaluation, Accountability and Learning MoH Ministry of Health MUAC Mid-Upper Arm Circumference OCHA Office for the Coordination of Humanitarian Affairs OS Outreach Site OTP Outpatient Therapeutic Programme PLW Pregnant and lactating women RUTF Ready to Use Therapeutic Food SAM Severe Acute Malnutrition SC Stabilization Centre SCI Save the Children International SFP Supplementary Feeding Program SQUEAC Semi Quantitative Evaluation of Access and Coverage TBA Traditional Birth Attendants UNDP United Nation Development Programme UNICEF United Nations Children’s Fund WFP World Food Program WHO World Health Organisation

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1. INTRODUCTION

1.1 CONTEXT

4.1.1. Overview of the area

Wajir East sub County is one of the 4 sub-counties within the larger Wajir County; Wajir County is part

of former North Eastern Province of Kenya (Figure 1). The Sub County currently comprises of 6 divisions namely Wajir Bor, Tarbaj, Kutulo, Central, Mansa and Khorof harar.

It measures approximately 14,471 km², is classified as arid and is characterized by long dry spells and short rainy seasons.

Figure 1: Kenya and Wajir county map2

Wajir town is the Sub County headquarters and is the largest urban town in Wajir County. The population is predominantly Muslim and of Somali ethnicity, and is divided into clans, with community elders being in charge of daily affairs. Fai is the predominant clan and other clans include Masare, Garre, Degodia, Murule, Ogaden and Ajuran.

The survey area covered all six divisions of the Sub County. The current estimated population living in this area is 226,0863

1.1. Geography, current climatic conditions and food security

Wajir East Sub County is a featureless plain, which is prone to flooding during the rainy season. The Sub County has some seasonal swamps and perennial river beds/drainage lines (‘laghas’) that flow in the rainy season. These serve as dry season grazing zones and also allow some cultivation when it

2 Available at URL: http://en.wikipedia.org/ [visited December 2013]

3 Current estimates from the District Data Officer- Wajir town Based on 2009 Census

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rains. The area receives bimodal rains with the onset of the long rains in April. The months succeeding the long rains, June to September, are very dry but vegetation continues to thrive because the lower temperatures reduce the rate of evaporation. However the rains have become increasingly unpredictable and erratic.

Persistent incidences of drought and their increasing unpredictability in the region in recent years have continued to threaten the livelihoods of many pastoralists subjecting them to food insecurity (due to the short recovery phase between droughts), high malnutrition rates (above the emergency thresholds of 15%) and increased disease burden. In 2011, the Sub County, including the rest of the Arid &. Semi-Arid Lands (ASALS) suffered severe drought conditions, which further eroded the already diminishing livelihoods causing critical food insecurity, lack of water and high malnutrition rates. From October 2011 to date however, the sub-county has been receiving near average rainfall during all the rainy seasons (both short and long) leading to an improvement of the Global acute malnutrition (GAM) levels reducing from above the emergency thresholds to 10.5% in May 2013.

1.2. Livelihoods

Wajir East Sub County 70% of the population solely depends on livestock for their livelihood. The main form of land use is nomadic pastoralism which is seen as the most efficient method of exploiting the rangelands hence pastoral activities are practiced all over the Sub County.

Most of the area covers the Pastoral Camel Zone (Eastern Bush land) where predominantly camel herding occurs. Small pockets of agro-pastoral activity are found in Tarbaj and Wajir Bor divisions. The crops cultivated include maize, sorghum, beans, cowpeas (kunde)4, tomatoes, sweet pepper and pawpaw. In addition, small-scale irrigated horticulture is emerging in peri-urban areas (kitchen gardens) with crops such as watermelon, pawpaw, lemons and vegetables thriving5.

4.1.2. Nutritional situation

Regarding the nutritional situation, Save the Children (SCI) has been conducting nutritional SMART

surveys6 in Wajir East Sub-County since 2009 to date. Figure 2 below shows the results of these nutritional surveys. GAM rates had surpassed the WHO alert threshold for a state of emergency (>15%) until 2012 but in 2013 the levels seems to be dropping.

4 Sub County Steering Group Combined Report for Wajir North, East, West and South Sub Countys-Rapid Assessment and Sectoral Report on the Impact of the Short Rains in the Sub County- January 2009 5 Ministry of Agriculture- Wajir East Food and Crop Situation Report-April 2009

6 2006 WHO standards

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Figure 2: Results of nutrition surveys in Wajir East Sub-County, KENYA. 2009-2013.

In Country, malnutrition rates have been chronically at emergency levels. These high rates of malnutrition can be attributed to poor health conditions, sub-optimal maternal and child feeding, care practices, and food insecurity. This has been compounded by high rates of poverty and illiteracy, marginalization, recurrent environmental shocks (floods and droughts) and displaced populations adding an additional strain to already weak health systems and communities7.

4.1.3. Health access in Wajir East Sub County

Services are delivered both at the sixteen outreach sites, which are closer to community level and deliver primary health care unity and at all the twenty one Health Centers (HC), which are located within the Sub County. There is also one stabilization center in Sub County hospital in Wajir Town.

Ministry of Health (MOH) with support from SCI supports Community Health Workers (CHWs) through provision of monthly incentives and training. CHWs are team of community-level volunteers engaged in screening and mobilizing children under 5 and pregnant and lactating women. They detect cases of some diseases (including malnutrition and diarrhea) and refer them to the health facilities. The community has chosen them with the participation of the MoH and save the children following the laid down MOH standards. They are equipped with a CHW kit and manage fever and mild diarrhoea and the community level while conducting screening and referral for malnutrition cases.

The cases then are referred to either outreach sites or health facilities where they are managed by nurses.

7 SMART survey report 2013, Kenya Demographic Health Survey 2008

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4.1.4. Nutrition services and SCI support in Wajir East Sub County

Since July 2009 SCI has been in operation in the Sub County offering Integrate Management of Acute Malnutrition (IMAM) services. This was done through direct implementation with SCI providing services parallel to the government. Before, from 2004 to 2008 it was Merlin who providing this support.

This however was changed in August 2012 where a new implementation strategy was agreed upon by the entire nutrition sector under the leadership of the division of nutrition from the MOH. The strategy was to provide a package of services shown to have the greatest impact on malnutrition and strengthen the system to provide them (so that partners did not provide services but supported the MOH to provide the services).

In Wajir East Sub County there are 37 Outpatient Therapeutic Programmes (OTP) for the treatment of SAM cases (21 HF based and 16 in outreach sites). And one Stabilization Center (SC) based in Wajir East Hospital for the SAM cases with complications.

Figure 3: Temporally line of the nutrition support in Wajir East Sub-County and the coverage assessments, KENYA. 2004-2013.

In this light, through ECHO/DFID funded grants, SCI supports the MOH logistically, with human resources, and technically in HINI implementation in the Sub County. Through this support these services are being provided in the 21 health facilities ,16 outreach sites across the Sub County and SC. In logistics, it involves transferring of health and nutrition supplies either from SC or from the main Wajir Sub County hospital to the rest of 21 health facilities within Sub County. 5 nurses have also been seconded to MOH and distributed in various health facilities in the district. Besides that a monthly incentive of Ksh 3000 is provided to the 99 CHWs in the Sub-County. Capacity building has been provided to the health workers through classroom training and monthly OJT to all the implementing staff pertaining to HINI activities and weekly site supervision/monitoring to enhance programme quality and adherence to programme protocol.

The MOH has a guideline for management of Acute malnutrition written in June 2009 but it is currently under review. From the guideline, the admission criteria for OTP is MUAC <115mm (with length >65cm), and/or WHZ <3 Z score and/or bilateral pitting edema following the WHO guidelines for 2006. Both at Health Facilities and outreach sites, the activities are implemented by nurse or health officer supported by community health workers.

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UNICEF provides the Ready to Use Therapeutic Food (RUTF) and anthropometric tools. Medicines for SAM treatment are provided through the Kenya Medical Services together with the other routine drugs. For Moderate Acute Malnutrition Management (MAM), MOH manages the cases with World Food Programme (WFP) providing the rations needed through the Supplementary Feeding Programme (SFP).

1.2 Results of previous SQUEACs in Wajir East.

In order to assess and improve programme performance in terms of access and coverage, four coverage assessments have been carried out. Using the Centric Systematic Area Sampling (CSAS) in 2006 and the Semi-quantitative evaluation of access and coverage (SQUEAC) methodology in 2010, March and December 2011 . Table 1 show a summary of the main results of these coverage assessments.

Table 1: Results of coverage assessments, 2006-2012, Wajir East Sub County, North Eastern Province in Kenya*.

Year 2006 (March) 2010 (March) 2011 (March) 2011 (December)

Assessment SCAS SQUEAC SQUEAC SQUEAC

Zone / OTP sites Wajir East Sub County

Wajir East Sub County



22 OS and supporting the MoH in 20 HF

MUAC admission

< 110 mm < 115 mm < 115 mm

< 115 mm

Coverage results8

Point: 30,0%

(95% IC: 17.8- 42.2)

Point: 63.7%

(95% IC: 46.6- 78.5)

Point: 62.7%

(95% IC: 49.0- 75.5)

Point 1) Central division 48.3% (95% IC: 33.5-63.5)

2) Other divisions : 67.6% (95% IC: 55.1-78.0)

Main barriers

- Problems of rejected referrals and case definition

- Relapsed cases (in SFP with MUAC <100mm)

- Distance of OTP locations

- Awareness of malnutrition

- Monitoring of movements of the targeted group

- Mobilization

- Lack of access to programme sites

- Migration

- Challenges associated with MoH managing malnutrition

- Minimal inclusion of key field sources of referral

- Stigma

- Lack of CSB

- Previous rejection

- Apathy in childcare

- Distance for a proportion of the community

- Insecurity

- Staffing

*Recommendations of the SQUEAC from December 2011 are available in the results part.

8 Period coverage: CSAS 2006: 49.1% (95% IC: 33.6- 64.6), SQUEAC 2010 80.6% (95% IC: 70.0- 88.9), March 2011 82.3% (95% IC: 74.1- 88.8), Dec 2012 Central 67.6% (95% IC: 55.1- 78.0), other 83.7% (95% IC: 74.0-90.3),

2. OBJECTIVES

Main objective

The main objective of this assessment was to evaluate access and coverage of the Integrated Management of Acute Malnutrition (IMAM) for children ages 6 to 59 months with SAM in Wajir East Sub County, North Eastern Province in Kenya, using the Semi-quantitative evaluation of access and coverage (SQUEAC) methodology.

Specific objectives

- To develop capacity of various stakeholders on undertaking programme coverage assessments using SQUEAC methodology

- To determine baseline coverage for IMAM

- To identify boosters and barriers influencing IMAM programme access and coverage

- To develop feasible recommendations to improve IMAM programme access and coverage

Photo 1 : SQUEAC Investigation team in Wajir East Sub County, North Eastern Province in Kenya. September 2013

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3. METHODOLOGY9

3.1. GENERAL OVERVIEW

The Semi-Quantitative Evaluation of Access and Coverage (SQUEAC) is a coverage assessment method developed by Valid International, FHI 360/FANTA, UNICEF, Concern Worldwide, World Vision International, Action Against Hunger, Tufts University, and Brixton Health.

The methodology is “semi-qualitative” in nature, meaning that it draws from a mixture of both quantitative data from routine programme monitoring activities as well as qualitative data collected on the field. This mixed methods approach combines data sources to estimate programme coverage and to develop practical measures that can improve access and coverage.

- Quantitative data came mainly from routine monitoring information that the programme already collected including: admissions, defaulting, recovery, middle upper arm circumference (MUAC). Routine programme data was coupled with “complementary data” like agriculture, labor, and disease calendars, anthropometric nutritional surveys, and agricultural and food security assessments.

- Qualitative data collected came from interviews, focus groups and questionnaires with various key informants.

Together, the data were triangulated by source and method to formulate hypotheses about coverage and access. Data triangulation is a powerful technique that helped validate our findings through cross verification. Hypotheses were then tested with small-area surveys and small sample surveys. Then, a wide area survey was conducted in the community to determine the point coverage estimate.

Lastly, the results from the quantitative and qualitative analyses and the wide-area likelihood survey were combined and the overall global coverage estimate was calculated using Bayesian statistical techniques.

9 2012. SQUEAC and SLEAC Technical Reference. FANTA. Available at

http://www.fantaproject.org/sites/default/files/resources/SQUEAC-SLEAC-Technical-Reference-Oct2012_0.pdf

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The coverage assessment was fourth coverage study in the area. It was conducted

between the 20th September to the 3rd October 2013. It was carried out at the end of the dry season and when food availability was apparently good.

The SQUEAC methodology used consisted of 3 stages, applying the principles of triangulation (by source and method) and sampling to redundancy.

3.2. STAGES

Stage 1: Identification of potential areas of high and low

coverage and access barriers

Identification of potential areas of high and low coverage using routine programme data; in this stage, triangulation of data is going to be done by various sources and methods as highlighted below.

1. Recommendations follow up of SQUEAC December 2011

Analysis of recommendations from the SQUEAC of December 2011 follows up. The evolution of the factors influencing coverage positively and negatively has been studied.

2. Quantitative data

Quantitative, routine programme data helped to evaluate the general quality of IMAM service, to identify admission and performance trends and to determine if the programme adequately responds to need. It also helped point out problems in screening and admission. Lastly, routine programme data analysis provided the first insights into variation in programme performance between OTPs.

Route programme data analysis (January 2012– June 2013) 37 OTP: 16 OS + 21 HF

- Global (OTP and SC) trends of admission and defaulters over time and compared to the agricultural calendar, the lean period, child epidemics and diseases, workload, weather patterns and key events

- Admission: admission by OTP and SC

- OTP and SC programme performance indicators over time (recovery, default, death, non-response).

- Stock break out data.

Complementary from children card (June – August 2013) for 33 from 37 OTP10: 14 OS + 19

HF

- MUAC at the time of admission 10 Krof Harar HF OTP, Halane OS OTP, Dambas HF OTP and Orgaralle OS OTP.

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- Discharged

o Cured: length of stay (LoS) and MUAC at discharge.

o Defaulters: length of stay (LoS) and MUAC at discharge.

- OTP admissions by category (MUAC, W/H and Oedema)

- The village lists populations belonging to each OTP and distance walking to OTP. Admissions per village

Not available

- Admissions and defaulters per village

- Source of referral to the OTP

3. Qualitative data

Qualitative data was collected to investigate programme operations, to unravel the opinions and experiences of actors involved in IMAM and to identify any potential barriers to access. The following methods were used: focus groups, semi-structured interviews, structured interviews, case studies, observation and information from previous coverage assessments.

Interviews and focus groups were conducted with key informants either directly or indirectly involved in the IMAM program. These included: women’s and men’s community, SCI programme staff, local authorities, OTP/SC nurses, CHW, caregivers of SAM children, Informal caregivers (traditional healers and traditional birth attendants), partners (WFP, Aldef, and UNICEF) and mother to mother support group and county HDA. Finally we couldn’t meet county or sub county health authorities.

The BBQ framework. Throughout the investigation, the data are going to be organized,

analyzed and triangulated using the Barriers, Boosters and Questions (BBQ11) framework.

It is a tool that facilitates iterative data collection that is then categorized into one of

three categories. The various data organized within the BBQ framework, when combined,

will help providing information about where coverage is likely to be satisfactory as well as

where it is likely to be unsatisfactory. Additionally, the BBQ provided information about

likely barriers to services access that exists within the IMAM program.

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‘Barriers’ are negative findings that deter from programme coverage and complicate access to service. Conversely, ‘boosters’ contribute to a higher coverage and facilitate access. Lastly, ‘questions,’ are those findings elements to be further investigated, and either become a barrier or booster or remain inconclusive

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Stage 2: Confirms the location of areas of high and low coverage

The goal of stage 2 is to test the hypotheses about coverage and access elaborated in stage 1. These hypotheses usually take the form of identifying areas where the combined data suggest that coverage is likely to be either high or low. The small-area surveys method was used to test the hypotheses for IMAM high and low coverage areas.

The active and adaptive case-finding methodology was used to find SAM cases. Data surveys will be analysed using simplified lot quality assurance sampling (LQAS). The LQAS classification technique analyses data using the following formula:

where

If the number of covered cases found (that is, those cases in the program) is greater

than then then the coverage of the surveyed area is classified as being greater than or

equal to the coverage standard .

If the number of covered cases found (that is, those cases in the program) is less than then then the coverage of the surveyed area is classified as being less than or equal to

the coverage standard

The threshold chosen was 58.0%. The middle point coverage SQUEAC (from central and other divisions) results in Wajir East in December 2011 was the guide to establish this threshold. If the number of covered cases found (that is, those cases in the program) is

less than then then the coverage of the surveyed area is classified as being less than

or equal to the coverage standard

Stage 3: Wide area survey conducted to estimate overall coverage.

The goal of stage three is to calculate the overall coverage estimate. This is done using a Bayesian statistical technique called “beta-binomial conjugate analysis.” Conjugate analysis begins with a beta distributed, probability density called the “prior.” The prior is then combined with a binomial distributed, likelihood function called the “likelihood.” The likelihood is going to be determined by a wide-area coverage survey that will be conducted across the entire programme catchment area; the mode of the likelihood was, in fact, the point coverage estimate from the survey. Because the prior and the likelihood are mathematically expressed in similar ways (as probability distributions) they can be combined through conjugate analysis, the result of which is the posterior probability density—the “posterior.” The mode of the posterior is the final coverage estimate.

1. The Prior

The prior was constructed by combining the results from stages 1 and 2, that is: routine programme data, qualitative data and all relevant findings from the small-area and small sample surveys. The prior was the result of combining four modes:

1) The Simple BBQ : The simple BBQ is the first and simplest approach to calculating the prior. A uniform score of 5 points was attributed to each element (either a barrier or booster). The total booster and total barrier scores were summed. The total booster score was then added to the minimum possible coverage (0%) and the total barrier score was subtracted from the maximal possible coverage (100%). The coverage estimate was calculated by taking the mean of these two percentages.

2) The weighted BBQ : a score from 1 to 5 was attributed to each element. The score reflected the relative importance or likely effect that the element had on coverage. The coverage estimate was calculated by the method explained above.

3) The concept map : is a graphical analysis technique that was used to organize the data. The final product, the concept-map, is a diagram that visualizes relationships between findings. It was elaborated within a context frame, which is defined by an explicit focus topic. Links were drawn between each concept, representing the relationship between them. The various relationships types traced included: results in, leads to, encourages, helps create, allows, etc. Two concept maps were created, for barriers and boosters. For each map, the total number of ‘linkages’ was counted. Like before the booster linkage sum was added to the minimum possible coverage value (0%) while the barrier linkage sum was subtracted from to the maximum possible coverage value (100%). The coverage estimate was calculated by taking the mean of these two percentages.

4) The histogram prior : During a participatory working group, the investigation team designed a histogram representing the prior mode. This was done realistically and democratically. The mode, minimum and maximum coverage values were chosen credibly.

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2. The likelihood

A wide-area “likelihood survey” was conducted over the entire programme catchment area to calculate the coverage estimate.

The active and adaptive case-finding methodology was used to identify the SAM cases.

The case definition used for coverage survey was defined as “a child matching the admission criteria of the programme”. The admission criteria of the Kenyan IMAM programme included children age between 6 and 59 months with at least one of the following criteria: 1) a MUAC of <115 mm and/or 2) W/H < - 3 Z-scores and / or 3) bilateral pitting oedema

A simple structured interview questionnaire was used to caregivers of non-covered cases for SAM Annex 1.

The sample size required was calculated by using the following equation:

1. Mode: prior value expressed as a proportion.

2. α et β: shape parameters of the prior.

3. Precision : desired precision. In the present case the precision used was 0.135 (13.5%).

4. SAM prevalence: 0.2% was chosen after stage 2 results. Initially the rates considered were 0.7%, the prevalence in the last SMART survey in May 2013 (for MUAC admission criteria) in Wajir East Sub County. But the prevalence was found inferior to these data and this was revised downwards following the results from stage 2.

5. Average village population: 2,759 population in Wajir East (based on Sub County health office data which is projected fom the 1999 census since the 2009 data was refuted)

6. Population between 6 and 59 months : approximately 20.0%

And the sample size will was into the minimum number of villages needing to be sampled to achieve the sample size using the following equation:

X

The number of village required was randomly selected with ENA for SMART software12.

12

Available at: http://www.nutrisurvey.de/ena/ena.html [Visited October 2013]

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3. Overall Coverage Estimate

The point or period coverage estimate was chosen for SAM coverage. By method of Bayesian beta-binomial conjugate analysis the prior probability density was combined with the coverage estimate from the likelihood survey to calculate the mode of posterior probability density.

The Posterior Probability is the estimate of the overall coverage: it represents the synthesis of the prior probability and likelihood generated by the calculator with Bayes credible interval (CI) of 95%.

Recommendations and Action Plan: A final important step is the development of an action plan that clearly identifies the actions to be undertaken, indicators, evaluation methods and deadlines.

3.3. ORGANIZATION OF THE EVALUATION

3.3.1 CMN technical support

The SCI team, the Kenyan MoH (from Wajir and Mandera province) and Islamic Relief Kenya received the technical support of the Coverage Monitoring Network (CMN). The CMN Project is a joint initiative by ACF, Save the Children, International Comitee, Concern Worldwide, Helen Keller International and Valid International. The programme is funded by ECHO and USAID. This project aims to increase and improve coverage monitoring of the Community Management of Acute malnutrition (CMAM) programme globally and build capacities of national and international nutrition professionals; in particular across the West, Central, East & Southern African countries where the CMAM approach is used to treat acute malnutrition. It also aims to identify, analyse and share lessons learned to improve the IMAM policy and practice across the areas with a high prevalence of acute malnutrition. The technical and methodological support was provided by a Regional Coverage Advisor (RECO) Inés ZUZA SANTACILIA. During the evaluation CMN support was conducted in three phases:

- 1st phase: remote technical support for the planning and preparation of the evaluation with the CMN RECO.

- 2nd phase: in field technical support in Wajir East Sub County. The CMN RECO was deployed to support training on the use of the SQUEAC methodology and the implementation of the evaluation until Stage 1.

- 3rd phase: remote support for the completion of the investigation, analysis of results and report writing.

The SQUEAC plan is in Annex 2.

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3.3.2 Team training, logistic organization and evaluation development

The investigation team (described in Annex 3) was composed of members of SCI from Wajir, Mandera and Nairobi, MoH staff (from Wajir and Mandera County) and cone partner (IRK).

The SQUEAC was conducted in the field by the CMN RECO in collaboration with the SCI Nutrition M&E Specialist (Caroline Njeri KIMERE).

A two days training in the SQUEAC methodology was made by the CMN RECO in Wajir town. This training targeted people that integrated the evaluation team and other people who might be interested in the methodology.

The RECO couldn’t travel to the field because of the security situation (it would have required army protection). For the three steps the investigation team was divided in three teams, composed by normally three people each.

3.4. LIMITATIONS

The evaluation was limited by the following elements:

- The security situation didn’t allow the RECO to travel into the field (apart from Wajir town)

- Some villages were not accessible due to the security situation e.g Gunana, Konton

- During the SQUEAC a polio campaign was being conducted in Wajir East. Because of this Outreach sites were not working along the Stage 1 data collection because the children had received double ration the week prior to make up for time during the campaign.

- On checking for the OTP admissions by category it was realized in some cases the admission was done by both MUAC and W/H but the information was not correctly captured in the registers.

- On the admission information it was noted that the village information of where the beneficiaries came from were not collected in many of the OTP/ the patients cards which would make it hard to trace the kids.

- There is not available a list of villages in small units (some villages are conglomerates of small villages) and some new villages were not included in the official list. SCI completed handly the list of villages.

- No updated map of Wajir East was available.

- The distance to the OTP sites wasn’t available

- Programme data for the years before 2012 was not available from both the SCI programme or the MoH for analysis for the SQUEAC survey.

- Low SAM prevalence at the moment of the SQUEAC which made the precision of the likelihood survey low.

4. RESULTS

4.1. STAGE 1

4.1.1. Recommendations follow up of SQUEAC December 2011

Table 2: Resume of the recommendations of the SQUEAC of December 2011 and the follow up, Wajir East, Kenya. September 2013

Recommendation Description Achieved

On-going Comments Fully Partial

ly Not

1 Community

sensitization

Community sensitization should in particular seek to address

Previous rejection √ This is being addressed with the health workers giving health education but we need to see if it has worked during this survey

Apathy in childcare and neglect √ Address any rumours or misinformation on the programme such as food contamination.

√

Benefits of other interventions such as family planning (FP)

√ SC has secured funding for FP component but it is a sensitive issue due to religion.

2 Community

mobilization

Purposely target all key field sources of referral namely the Traditional Birth Attendants (TBAs), traditional healers and Sheiks. And work closely with the local administration and other social services available to support in enhancing childcare.

√ TBA mainly have been used since they know the families Quarterly meetings with the local leaders

Seek to map out migratory patterns in the county to enhance linkage between programs in the Sub County’s during drought periods

√ This was started but with the improvement of malnutrition this has been lower. The GFD targeting takes malnutrition as a target.

Provide CHWs microphones and bicycles. √ There has not been funding for this

3 Strengthen support to MoH

SCI should continue offering technical support to MoH to build capacity in IMAM. In addition SCI should support in supervision of mobilization activities within the MoH facility catchment areas.

√ On the job training has been on-going monthly. There are organized mass screenings when this is done. Mobilization however need to be strengthened

4 Monitoring and evaluation

Map out all catchment villages in the site areas and indicate the village of origin on the admission cards

√

The admission cards should indicate the source of referral to assess the key referral sources in the community

√ This may not have happened due to high nurse workload.

Confirm data accuracy particularly in comparison to the context e.g. defaulters.

√ It is still thought to be important and agreed to be advocated for suggested to have data management meeting with facility staff.

5 Programming To address double registration that could be occurring in the Central division

√

6 Emergency programming

The programme should have emergency strategies in case of unplanned occurrences.

√

7 Staffing The programme should seek to:

Fast track recruitment of critical staff in emergencies or explore different ways of ensuring critical field positions do not stay vacant for very long.

N/A

The strategy changed and SC is no longer

doing direct implementation.

Address and support the community mobilizers and CHWs particularly with the reporting challenges.

√

Training for CHWs has been done. Supportive supervision and On the job training ongoing but there are still issues on reporting.

8

Advocacy

MoH ownership and facilitation to be able to comfortably manage IMAM.

√ Now the MOH implementing the program

Infrastructure to facilitate in programme implementation mainly roads.

√ Waiting to see this through the county government

4.1.2. Quantitative data analysis

a. Needs response : admissions and defaulters trends compared to seasonal and key events

calendar

Figure number 4 shows the OTP admission over an 18 -month period (January 2012 –June 2013). This graph is aligned with seasonal and Key event calendar developed by the investigation team (weather patterns, seasonal calendar of human diseases associated with SAM in children, food availability, and workload). Together these two figures helped evaluate to what extent the programme responds to seasonal needs. There were 54 defaulters along these months’.

Figure 4: OTP admission patterns over time compared with seasonal event calendar, Wajir East Sub-County, Kenya. Jan 2012-June 2013

x

0

50

100

150

200

250

300

Jan Fev March Apr May June July Aug Sept Oct Nov Dec Jan Fev March Apr May June

Num

ber

of c

ases

Total (SC + OTP) Total Defaulters

J F M A M J J A S O N D J F M A M JSeason

Kalaazar

Clan

Ramadhan

SCI Funding Gap

Mass Screening

Food prize

Measles

rainy dry

Hunger gap

dry rainy dry

ARI

Malaria

Diarrhea

Animal calving

BSFP

Gum collection

Insecurity

Nomadism

rainy

floods

Herding (animals)

25

For the period under review, (Jan 2012 to June 2013), a total of 2 666 SAM children were admitted to OTPs and SC with a mean of 148.1 children admitted per month. 54 defaulters were notified during the period. Data quality issues were detected in one OTP along the register revision in Stage 1 especially with the period after MOH took over management of IMAM (since August 2012) with very few numbers of defaulters reported (3).

The SAM admission trends are reflecting few months of the year trends. The hunger gap is from January to Mid-April with a peak March. The admission trends however are low during the hunger gap. One of the reasons could be that families are moving with the animals in search of water and pasture13. The admission peaks were usually after mass screening and during the rainy season which is also the peak period for most of the illnesses like diarrhoea, acute respiratory infections (ARIs) malaria and Kalazar.

b. OTP vs SC admissions

The percentage of children admitted to the SC could be an indicator of the timeliness of admissions. It is directly related to the percentage of SAM cases that arrive at the OTP with associated medical complications. Children remaining untreated for long periods with declining nutritional status develop medical complications and end up needing SC care.

A high percentage of SAM cases with medical complications could often the product of a late presentation and uptake of services.

In Wajir East Sub County proportion of programme admissions requiring inpatient care from January 2012 to June 2013 was 6.3%. This percentage is slightly above the 5% recommended for established programs but it is within acceptable limits. This could be partly because of referrals received from other Sub Countys like sub counties like Mandera West and Central and Wajir South

Figure 5: OTP admission compared with SC admissions. Wajir East Sub County, Kenya. January 2012 to June 2013

13 Nomadism or hearding: during the rainy season communities move to the places where there are

water (with no mosquitoes)

26

c. Admissions by OTP

Figure 6 below shows the number of SAM cases admitted per OTP over a 12-month period (July 2012 –June 2013). MCH OTP is the one that received more cases during the period (185 SAM admissions). This could be attributed to it being in an urban setting (Wajir town) and therefore has a high catchment population. The OTP sites with the least admissions were Sarman, Dunto and Hodhan which had been affected by insecurity during the clan clashes.

Figure 6: SAM admissions per OTP site. Wajir East Sub-County, Kenya. July 2012-June 2013

Figure 7 below shows the percentage of SAM cases admitted per OTP and the percentage of population of the catchment area per OTP over the 18-month period (January 2012 –June 2013).

Figure 7: Percentage of SAM admissions per OTP and percentage of population catchment area. Wajir East sub county, Kenya. January 2012 -June 2013

27

Dasheq, Elben, Hungai, Kutulo, AIC, Alimaow, Arbaqaramso,MCH, Wajir bor and Wagberi health facilities are the ones that received proportionally much more percentage of cases than expected for their catchment area compared to Dambas, Dunto Sarman, korof Harar and Makoror health facilities who admitted few compared to their catchment populations. This could be attributed to wrong catchment population calculations, admissions from nearest villages which were not factored in the catchment population and double registration as was the case for AIC. This problem was mainly noticed in the relatively urban centers compared to the rural ones.

d. Admissions MUAC

Admission MUAC is an indicator for late /early presentation and service uptake at the OTP level. It can be a measure of direct coverage failure because late admissions are those non-covered SAM cases that went untreated for a significant period of time. Late admissions almost always require inpatient care and are associated with prolonged treatment, defaulting and poor treatment outcomes.

Figure 8 reports the MUAC distribution for SAM cases admitted by MUAC from January 2012 to June 2013. The admission MUAC criterion is < 115 mm. The MUAC median at admission was 112 mm (in red). That means 50% or the children arrive with a MUAC less than 112 mm with some presenting with a MUAC as low as 90mm.

The median MUAC at admission in general was very similar in all OTP ranging from 110mm and

114mm. The OTP with the least median MUAC at admissions was Tarbaj (105 mm). And the one that had the highest median MUAC were Arbaqaramso, Elben, jowhar and Katote OTP at 114 mm.

During the analysis of MUAC data, an over-representation of rounded values (i.e. 105 mm, 100 mm, 90mm etc.) was observed, indicating imprecision in the MUAC measurement.

Figure 8: MUAC at OTP admission. Wajir East Sub County, Kenya. Jan 2012-June 2013

28

e. Admission by type

In the country, admissions for OTP are based on MUAC < 110 mm with (with length > 65 cm), and or WHZ score <-3 and or presence of bilateral pitting edema. In Wajir East 48.0% of the OTP admissions were based on MUAC and 47.8% were based on WHZ score as shown in the figure below.

Figure 9: Percentage of admissions by the different admission criterion Wajir East, Kenya. March to August 2013

f. Performance indicators

The performance indicators for the sub-county were within the acceptable SPHERE standards

from January 2012 to June 2013. There were higher defaulting and non-response rates between the months of January to April in the two years which could be attributed to migration of the families with animals in search for pasture since this are the driest months.

The performance indicators for the SC were 100% cure rates from July 2012 to June 2013 with the exception of January 2013 where there were 2 cases of defaulting.

Figure 10: OTP Performance Indicators Wajir East, Kenya. January 2012 to June 2013

29

Looking at the performance indicators per OTP site however some of the OTP sites had 100% cure rates while in some on the sites like AIC defaulting was reported at greater that 50%. On going to the site to find out why that was the case it was discovered that this had been due to double registration with the beneficiaries served at both the MCH site and AIC. To curb this the distribution dates were synchronized and for this reason some most of them defaulted. The data quality in the site at the time of the survey was good.

Figure 11: Performance Indicators per OTP site in Wajir East, Kenya. Jan 2012 to June 2013

g. Discharged cured

The length of stay before recovery provides helpful insight into the duration of the treatment episode (e.g. the time from admission to discharge).

In figure 12 below the OTP median length of stay (LoS) for children cured in the sub-county was 9 weeks however there was quite a no who stayed up to 15 weeks.

The international standards define typical LoS should be between 30-40 days (4 to 6 weeks) to a maximum of 8 weeks. In this case the maximum length of stay was >15 weeks. And the median indicates the LoS in the Sub County is large because half of the cases stay more than the recommended maximum of 8 weeks.

30

Figure 12: Length of stay for discharge cured. Wajir East Sub-County, Kenya. May-August 2013

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 >15

No of Weeks in program

No

Of

Ch

ild

ren

h. Defaulters

Figure 13 shows the median length of stay before defaulting in Wajir East (May to Aug 2013). A Short length of stay before default can suggest a poor reception or communication between beneficiary and health staff. On the other hand defaulters after several weeks of treatment could be related to long length stays (caretaker assuming the children is cured or tired of keeping on the treatment).

Figure 13: OTP Length of stay before defaulting, Wajir East, KENYA. January 2012 – May 2013.

0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 >15

No

of

Ch

ild

ren

No of weeks in the program

In Wajir East, the median week at defaulting was 5 weeks. There were defaulters both at the beginning of the treatment and after several weeks of treatment. Those who defaulted at the first and

31

second week were mostly from the sites that had been affected by the clan clashes (Dunto, Basanincha, Gunana). Those defaulters above 8 weeks could be related with the long LoS.

The median MUAC at the last visit for defaulters is 11.5 mm. This means that 50% of the children defaulted from the programme before being recovered.

i. Community mobilization

In total there are 99 Community health workers in Wajir East. This translates to 2 CHWs per site with some sites with large caseloads having three. They are managed by the MOH through the public health department.

For a duration of 2 months before the SQUEAC (July and August) the CHWs screened a total of 527 children (283 males, 244 females) and 355 Pregnant and lactating women (PLW) (181 pregnant, 174 lactating). Out of those screened a total of 106 children (54 males, 52 females) and 70 PLW (38 pregnant, 32 lactating).

4.1.3. Qualitative data analysis

The qualitative methods used included focus groups, semi-structured and structured interviews, cases studies and observations. Doing so revealed boosters and barriers. Interviews and focus groups were conducted in villages across the sub county. Questionnaire guides were adapted and oriented to facilitate the collection of data pertinent to programme coverage and access. The investigation team also elaborated a list of terminology in the local languages (Annex 4) related to malnutrition and the RUTF. Qualitative data was triangulated by both method and source.

All findings were indexed daily into the three-pane BBQ framework (complete BBQ can be found in Annex 5). Table 1 lists the sources and methods used during qualitative data collection. Questions ("Q") that appeared along stage one were analyzed and resolved within days.

32

Table 3:. SQUEAC BBQ framework legend. Wajir East Sub-County, KENYA. September 2013

Code Source Code Method Code Zone 1. SAM caretakers 2. Local authorities (religious,

chief villages/elders) 3. Mother to mother support

group (TBA) 4. Traditional healers/traditional

dentist, TBA 5. OTP/ SC Nurse 6. CHW 7. Community of Women 8. Community of Men 9. Partners (WFP, etc.) 10. SCI programme staff 11. County’/sub-county health

authorities

A. Group Discussion B. Semi Structured

Interview C. Case Study D. Observation E. Data Analysis F. Last SQUEAC Dec 2011-

jan 2012

C R

Central Rest

Table 2 details the principal factors that either negatively or positively influenced programme coverage and access during the qualitative data analysis in Wajir East; these are the main barriers and boosters.

Table 4: Main programme barriers and boosters after qualitative data analysis. Wajir East Sub-county, Kenya. July 2013

Barriers Boosters

Migration/ Nomadism( animals looking for water and pasture) Good inter-linkage between community and

health facilities (by CHWs follow up)

Competing priorities disrupting OTP Services (Campaigns, trainings, meetings)

Awareness of OTP and MUAC by caretakers and the community

Shortage and staff turnover at OTP sites Good mobilization (Acceptance of program) Distance to health facilities/ OTP site

Good integration at health facilities in services provided (Allows SAM Screening)

Late seeking behavior (Sheikh, food) Referrals from CHWs Collaboration and referral from TBA

33

4.2. STAGE 2

This stage confirms the location of areas of high and low coverage and the reasons for coverage failure identified in Stage 1 using small studies, small surveys or small-area surveys. The methodologies used in this assessment were the small area survey and small studies on nomadic population.

1) Small-area survey

The routine program, quantitative and qualitative data collected in stage one, when combined, helped identify areas within the intervention zone where coverage was likely to be either satisfactory or unsatisfactory. This information was used to formulate hypotheses about coverage that were tested. Small-area surveys methodology were used to test this hypotheses.

Areas with high coverage were agreed to be areas with an active CHW, where distance to the OTP site was near, not in central division, where there was a health facility or outreach site and where there had not been insecurity incidences in the past one year while low coverage would be the opposite of that..

Table 5: Small-area survey selected villages for, Wajir East Sub-County, KENYA. September 2013

Low coverage SAM areas Outreach site /

HF Zone CHW Distance Insecurity

Haragal No Rest Yes Yes No Majabow No Rest Yes Yes No Machine ben No Rest Yes 35 min No Afarshanlle No Central No 20 min No Basanicha Yes Rest Yes 30 min Before yes Lanbib Yes Central Yes 7 km No Jowhar Yes Rest Yes 20 min No

High coverage SAM areas Outreach site /

HF Zone CHW Distance Insecurity

Kotulo (Bulla) HF Rest Very active 20 min No Mansa HF Rest Very active No

Qarsa Outr Rest Very active 45 min max No

Ogoralle Outr Rest Very active 1 h No

Sitawario Outr Rest Very active 15 Minutes No

Hassan Yarrow Outr Rest Very active 15 minutes No

The LQAS classification technique was used to analyse the data. The threshold value « p » used was 58%, and the results have been the following.

34

- Low coverage: n=2 (two SAM cases were found); none of these cases was covered in OTP. d = (2 x (58/100) =1.2 ~ 1. Since 0 < 1 » there was confirmation of hypothesis of low coverage area.

- High coverage: n=5 (five SAM cases were found); 2 of these cases were in OTP while three cases were not covered. d = (5 x (58/100) =2.9 ~ 3. Since 2 is <3» the hypothesis of high

coverage area was not confirmed.

The investigation team considered the reasons that could have been beside the non confirmation of high coverage area. One reason was regarding the selection of some “urban” villages that not all the team agreed as high coverage areas (and they were finally not confirmed) as was the case for example for Kotulo. Also, due to the low SAM prevalence the team did not get the anticipated number of cases to confirm the hypothesis with no cases being found in some villages like Mansa, Ogaralle and Sitawario.

The evaluation team decided to continue to stage 3.

2) Small study on nomadic population

At this stage the investigation team realized during stage 1 we did not have enough information regarding the nomadic population. A small study on their awareness of the program, presence of CHW, patterns of migration and therapeutic itinerary was done to them. The results mostly showed they know a case that was on the programme but that no mobilization is done in their group. And they would appreciate outreach services especially during the rainy seasons as most of them fall ill at these times.

In the previous SQUEAC (December 2011) the coverage was calculated separately for central and the other divisions. The investigation team however decided not to do it this year. The reasons to do this were that during December 2011 the emergency programme was running with many outreach sites that increased coverage in distant areas. And even if there are outreach sites in 2013, there are not as many as they were in 2011. And the difference from central and other divisions seemed not to be as much as in 2011 to calculate coverage separately. Also, stage 2 had not enough sample to show any difference between the divisions. For this reasons in stage 3 the estimation was done for a global coverage.

35

STAGE 3

2 The prior

As explained in the methods, the prior mode for the SAM programme was calculated using the mean of the three coverage estimates: 1. The simple BBQ; 2. The weighted BBQ; and 3. The concept map. Table 6 details the calculation of the prior mode.

Table 6: SAM programme prior probability mode calculation, Wajir East Sub County. September 2013

Boosters Barriers Results (in%)

Simple BBQ*5 25 24 ((25*5))+ (100-(24*5)))/2 52.5

Weight BBQ 92 66 (92+ (100-66))/2 63.0

Concept Map 34 35 (34 + (100 – 35))/2 49.5

Histogram 50.0

Average prior 57.8

Next, using the equations presented in methodology 3, the shape parameters and were

calculated with a prior mode of 57.8% about which the range of uncertainty +/- of 25%. was

19.7 and was 14.4. The distribution of the prior probability density has a mode at 57.8% and a 95% “credible interval” (i.e. the Bayesian equivalent of the 95% confidence interval) 32.8% to 82.8% as shown in figure 14 below.

Figure 14: SAM prior coverage (binomial probability density), Wajir East, KENYA September 2013

36

3 The likelihood

Sample size

The sample size was calculated using the equation described in methodology (for the “n likelihood”). In the present SAM programme SQUEAC the sample size for the likelihood survey used a precision of 0.135 (13.5%). And the minimum number of children to be sampled was 19.3. The precision of 0.135 was chosen because of the low SAM prevalence. The investigation team preferred to continue with stage 3 even if the likelihood precision was going to be low.

The sample size was then translated into the minimum number of villages needing to be sampled to achieve the sample size using the equation of “n villages” described in the methodology part.

With a revised estimated SAM prevalence of 0.2% in Wajir East and a median village population of 2,759 inhabitants (20% of which approximately are between 6 and 59 months) the minimum number of villages to be sampled was 20. They were randomly selected (described in methodology).

Active case-finding

The 20 selected villages were divided up among the investigation team. Stage 3 lasted 4 days. All the 20 selected villages were accessed.

In total, 15 SAM cases were identified. Seven of these children were covered and in an OTP. Eight children were non-covered cases. In addition, fourteen recovering cases were found. To determine if there were cases both card and RUTF were used.

Table 7: Results of the SAM active case-finding Wajir East Sub-county KENYA.September 2013.

SAM cases SAM covered

cases

SAM not

covered cases

Recovering

cases

15 7 8 14

A questionnaire was administered to caregivers of the 8 non-covered cases to find out why their children were not in the programme (Annex 2). Of the 8 caregivers questioned, 75% (6) realized their children were malnourished and they knew of a programme to treat it. However 4 of them (50%) did not know the name. the reasons why they did not take their children are detailed in figure 15.

37

Figure 15: Barriers to SAM service uptake found by the likelihood survey, Wajir East Sub County, KENYA. September 2013.

One of the mothers had a child in the programme but it was SFP while there was one child who had been discharged from OTP but did not know she could go back.

The final precision from the likelihood of the 15 SAM children found was 14.1% and not 13.5% as earlier planned. This is still in the interval of the acceptable precision (between 10 and 15%).

4 The posterior

The point coverage estimate was selected as the most appropriate indicator for this investigation. One of the main reasons for this selection was 1) the large length of stay for SAM children in the OTP service, 2) the poor quality of data suspected in the health facilities.

By method of Bayesian beta-binomial conjugate analysis the prior probability density was combined with the likelihood function to calculate the posterior—the final coverage estimate: 54.6% (40.6%-

67.6% 95 CI)

Figure 16 is a graph of the three probability densities. It shows that both the prior (blue curve) and posterior (red curve) probability densities are very accurate; that is, their modes coincide with the mode of the likelihood survey (green curve). The narrow distribution of the prior indicates that is very strong. Moreover, the prior and the likelihood do not conflict, as there is considerable overlap between the two distributions (prior and likelihood).

The likelihoods precision is lower than the prior one. This was due to the reduced number of cases found in the active case finding of the likelihood. And related to the low prevalence of SAM cases in the area at the moment of the SQUEAC.

38

Figure 16: SAM programme posterior coverage, Wajir East, Kenya.September 2013.

39

5. DISCUSION

The SQUEAC investigation took place in September and the beginning of October 2013. It is the fourth coverage assessment in Wajir East Sub County since 2006. The evolution of the coverage assessment findings can be seen in Figure 17. No statistically significant difference can be found between 201014 (only between the CSAS result of 2006 and the SQUEAC for other Divisions of 2011) with the last.

Figure 17: Evolution of the SQUEAC point coverage findings in Wajir East Sub county , Kenya. 2006-2013.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

CSAS

2006

(March)

SQUEAC

2010

(avril)

SQUEAC

2011

(Dec)

Central

SQUEAC

2011

(Dec)

Other D.

SQUEAC

2013

(Sept)

Method and year

Pe

rce

nta

ge

Coverage

Upper interval l imit

Lower interval l imit

This SQUEAC investigation in Wajir East showed a point coverage estimate of 54.6% (40.6%-67.6% 95

CI). The coverage was slightly above the SPHERE standards for a rural population.

A number of positive factors identified during the SQUEAC allowed the coverage and access in the Sub County to be so:

- System strengthening strategy:

o The MOH has taken over the implementation of the High Impact Nutrition interventions (among them IMAM) which means even in the absence of NGO support the services will be provided at the static health facilities.

o There is very good coordination between the civil society and the division of nutrition especially at national level leading to agreement of strategies of implementation in the country.

- SCI support: at logistic, material, training, supervision level and human resources.

14

No statistically significant difference is appreciated because the credible intervals overlap.

40

- Awareness on malnutrition and the IMAM programme by the community members and caretakers

- Good mobilization and appreciatiation of the programme by the community members and caretakers.

The main negative factor influencing coverage identified has been the migration of the nomadic

population (looking for water and pasture). During stage 2 the investigation team did a small survey on this population to try to get more information about the patterns of migration and therapeutic itinerary in case of children disease. It was also a recognized barrier identified in previous SQUEACs. Some recommendations will continue to be focused on this issue, trying to increase the access of this population to the IMAM services. In relation with this factor the distance has been appearing as a barrier (also in previous SQUEACs).

The competing priorities disturbing the OTP services (immunization campaigns, meetings, trainings, etc.) has been identified and observed during the SQUEAC. Better plan and coordination of these activities with the MoH could help to reduce it impact (like providing double ration, changing the IMAM day, etc).

Shortage and staff turnover was identified during the last SQUEACs as a negative factor influencing coverage. Advocacy on human resources importance should be done especially with the new structure of the county government. Retention measures should also be looked at to ensure that once the staffs come to the sub-county they don’t leave en masse leaving gaps

The late seeking behavior has been identified also as a barrier during the SQUEACs. This was so since the mothers went to the Sheikh for prayers first and seeking health care only if the prayers did not work. Involving the sheikhs then as sources of referral should be done so that they can refer the children to OTP sites after praying for them

41

6. RECOMMENDATIONS Based on the results above there are some factors that could contribute negatively to programme coverage in Wajir East Sub County. The following recommendations were developed to address them.

RECCOMMENDATION RATIONALE / EVIDENCE


Late care seeking to SC admissions coming from baadia Stage 2: small study on Nomadic population Reduced number of Outreach Sites was also a reason cases were not covered Migration/ Nomadism (animals looking for water and pasture)


Competing priorities disrupting OTP Services (Campaigns, trainings, meetings) Skipped distribution during these activities especially since the nurses do not report to the nutrition office


6 facilities closed. Facilities closed during outreach days Leave for staff. Shortage and staff turnover at OTP sites Long length of stay

4 Increase community mobilization / sensitization

(late care seeking) SC admissions coming from baadia Defaulters Distance to HF or outreach sites


Data quality, long length of stay, poor anthropometric measurement (supervision)

6 Involvement of fathers and grandmothers in decision making

A case refused referral in Bulla Alimaow and in Kajaja 1 because the husband was not home to give a go ahead even though both children were severe.

7 Come up with a communication strategy on RUTF Model CVA on RUTF Advocacy for banning of RUTF sale. Have a ‘not for sale label’ and prosecute perpetrators.

RUTF on sale in the market Sharing of RUTF at home

It is important to share the results of the investigation SQUEAC with the MoH and partners involved in

IMAM. IF possible a presentation of the results should be presented to the IC staff, MoH and partners.

And giving a feedback to the HAD could help to improve their work.

The action plan defined for implementing the recommendations (with indicators) will help to improve

the coverage after this assessment. The proposed recommendations should be worked with the MoH and

other actors in the field.

ACTION PLAN OF SQUEAC RECOMENDATIONS

Recommendations Activities Time

Frame

Indicators Source of

verification

Responsible

Person


1.1. Chart out migration routes for Nomadic population and

1.2. Identify the nomadic population in the registers

1.3. Design mobile outreach activities according to need

November 2013-March 2014

Percentage of admissions of nomadic populations increased in 10% 2 mobile clinics

Chart of migration Activity reports of the mobile

County Nutrition Officer Sub county Nutrition Officer

Nutrition Coordinator


2.1. Strengthen Joint planning between the Sub county Nutrition Officers, District Public Health Nurse and Sub county Medical Officer of Health so as to follow the work plan. (Identify more competent CHWs/CHEW to step in I the absence of a nurse)

Monthly By January 2014

1 meeting per month

Minutes from the meetings

District Public Health Nurse District Public Health Officer County Nutrition Coordinator Sub county Nutrition Officer

3 Advocacy on stoppage of RUTF sale

Come up with a communication strategy on RUTF Model CVA on RUTF Advocacy for banning of RUTF sale. Have a ‘not for sale label’ and prosecute perpetrators.

By June 2014 By March 2014

Communication strategy Advocacy document Diffusion of the advocacy

Minutes on the communication strategy development

County Nutrition Technical Forum County Steering group


3.1. Advocacy at the county and national level to hire more nurses and as well come up with a retention plan so that the health workers are motivated to stay in the sub-county after reporting.

By June 2014

Hired 2 more nurses by the MOH Developed a retention package

Minutes of meeting with MoH

Sub county Nutrition Officer

County Nutrition Cordinator SCI Programme Manager Advocacy director 5 Increase community

mobilization/ sensitization

4.1 Provide means of transport for CHW to conduct ACF/Follow up and Health workers to provide the HINI package in hard to reach areas (Bicycle)

January to June 2014

90% of the CHW in the distant areas have a Bicycle

Photos of the materials Definition of the distance areas

Nutrition Advisor/ SCI Programme Manager / County Nutrition Cordinator

4.2. Involvement of fathers and grandmothers in decision making (community dialogue sessions)

By March 2014

SQUEAC 2014 results

Number of community dialogue sessions


SCI Nutrition Officers Community Liaison Officer


5.2. Reinforce the supervisions With the focus of detecting non respondents and reasons of long lengths of stay 5.3. Plan conjoint supervisions with the MoH

By September 2014

Decreased the median LoS Elimination of rounding MUAC

Monthly follow up of LoS Follow up MUAC median each 6 months


SCI Nutrition Officers

Annex 1 : Survey questionnaire for current SAM children NOT in the program

1. DO YOU THINK YOU CHILD IS SICK? ____ if yes: WHICH DISEASE?_________________________________________

2. DO YOU THINK YOUR CHILD IS MALNOURISHED (local word to describe it)?

YES NO (��STOP)

3. DO YOU KNOW OF A PROGRAMME THAT CAN HELP MALNOURISHED CHILDREN? YES NO (��STOP)

If yes, what is the name of the program? _____________________________________________

4. WHY YOUR CHILD IS CURRENTLY NOT ENROLLED IN THE PROGRAM? Do NOT prompt Ask “Anything else?” Several answers are possible

Answers Tick Notes

1. No time/ Too busy (what is the caretakers’s occupation? _______________)

2. OTP site too far away (how long does it take to walk? _____________)

3. There is no one else who can take care of the other siblings

4. No money for the treatment

5. The child has been previously rejected (When? ___________approximately)

6. Has been to the clinic but the child was not referred (When? ___________ approximately)

7. I do not think the programme can help the child (prefer traditionnal healer, etc.)

8. Waiting time too long

9. Mother feels ashamed or shy about coming

10. Mother sick

11. Spouse does not allow

12. Other reasons (specify) :

5. WAS YOUR CHILD PREVIOUSLY ADMITTED TO THE OTP PROGRAM?

YES NO (→ stop !) If yes, why is he/ she not enrolled anymore ? Defaulted : When ? ________________ Why ?____________________________________ Condition improved and discharged by the programme : When ? ______________ Discharged while he has not recovered : When ? ______________ Other : _____________________________________________________

Thank the caretaker and give a referral slip. Inform the caretaker of the OTP and date to attend

46

Annex 2: Wajir East SQUEAC plan, September – October 2013

Month Date Activities

September SQUEAC methodological plan and organization and additional data collection. Inés arrival to Nairobi and Wajir East.

20 F Stage 1 SQUEAC training and workshop on data analysis 21 S

22 Su 23 M Collecting additional qualitative and quantitative data in health

facilities and communities (interviews, et) 24 T 25 W Active and adaptive case finding training 26 T Collecting additional qualitative

Completing BBQ 27 F Stage 2 : Small area survey and small survey on nomadic

population Inés travel back to Harar and Spain

28 S 29 Su Training on Stage 3 30 M Inés flight back to Nairobi 1 T Stage 3 : Data synthesis. Prior calculation. Sampling and

preparation of wide area survey 2 W

Finalization of Stage 3 3 T

Report and recommendations processing

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Annex 3 : SQUEAC Survey team

SQUEAC coordinator (2 women, 100% of women)

� Inés ZUZA SANTACILIA, Regional Advisor, CMN Project

� Caroline Njeri KIMERE, Nutrition M&E Specialist, Nairobi, SCI

Evaluation team (8 women, 7 men, 53.3% of women)

� Daniel WANYOIKE, Nutrition Officer, Wajir South, SCI

� Angela KITHUA, Nutrition Officer, Wajir East, SCI

� Adan ABDILLE, Nutrition Coordinator, Wajir East, SCI

� FARAH Mohamed, Monitoring Evaluation and Learning (MEAL) Assistant, Wajir East, SCI

� Dorcas WANJIRU, Nutrition Officer, Mandera Central, SCI

� Abdikadir Adan Hussein, MEAL Assistant, Wajir East , SCI

� Rahma ADAN GURE, Nutrition Officer, Wajir East, SCI

� Dorice Anyango OMOLO, Nutrition Officer, IRK

� HABIBA BISHAR Mohamed, Nutrition Officer, IRK

� Elsie N. SANT OKETCH, Nutrition Officer, Mandera West, SCI

� Caroline Kawira Gitari, Nutrition Coordinator, Mandera Central, SCI

� Josephat Ogiri OGETO, Nutrition Officer, Mandera West, SCI

� Lynette Aoko DINGA, Nutrition Coordinator, Wajir South , SCI

� Patrick M KAMUNDI, Nutrition Officer, Mandera Sub County, MoH

� Irene SOI, Nutrition Specialist, Nairobi, SCI

People involved in the evaluation team that have participated at some stages

(1 men, 2 women, 66.6% of women)

� Rahab KIMANI, Health and Nutrition Project Manager, Wajir , SCI

� Nuria Ibrahim ABDI, County Nutrition Coordinator,Wajir, MoH

� KIBIDI Salomon, M&E manager, Wajir, SCI

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Annex 4: Terminology in Somali (S) and Borana (B) used to describe malnutrition and RUTF. In Wajir East, SQUEAC September- October 2013.

The table has been updated from terminology obtained in previous SQUEACs in the Sub County

Malnutrition and Related Terms Local Translation/Understanding Language

Malnutrition Nafaka dara S Wasting/Thin Lacif/Weit S Kwashiorkor/Big Stomach Calol/Weiywat S Oedema/swelling/puffy face Kor barar / Layac/Tagoley S Children who are currently sick with fever or diarrhoea Carur/Cudur/Laildaran S Home indicating family and social problems such as divorce, alcoholism

Erek

Hungry Gaja S Plumpy nut Biscuit diqay S CSB Malaqay/Ujiga-nafaqadha S Sick Bukan S BSFP Ujiga-shanjir S Measles Jidaco S Cough/respiratory infection Cuduratha Kudaca/Nef-marenka S Malaria Qanda/duma S Child in program Ujiley S Orphan Agon, S Poor person Faquir, Maskin S Poor child spacing Isku nug, S Irresponsible parent Dafan/Duyun S Diarrhea Shuban S Common cold Warenta S

Disease Bushi S

Worms Goriyan/ soqol S

Slaughtered animals to feed the sick Jenan

Flies that cause swelling Tiqis S

Under nutrition Cananan S

weakness Laif S

Ringworm Canfar S

Teething complication Miciyow S

New terminology from September 2013 SQUEAC

RUTF Maladow B

RUTF Bajuqay S

Wasting Ukate/Lithan B

Kwashiorkor Alol weynat S

Chronic Illness Jirolow S

Oedema Korbarar S

Kalazaar Berfur S

Bush Badia S

OTP Children Nafaqa S

Herbs/ products from tree used by traditional healer on sick children

Warretile S

Hagaar Boiling leaves of tree and take bath s

Banincaas Burning the swollen/ oedema legs with the branches called marer

Harmali Boiling milk with herbs and then drunk S

Annex 5: Weighted BBQ, Wajir East SQUEAC, September-October 2013

* Barriers and boosters underlined are those that had a match in the other side. They were chosen to stay in one side because they were strength there. This elimination

was taken in account when assigning the points.

**Colours indicate the number of times the information has appeared from the same source or mehotd: once-black, twice-green, thrice- blue and ≥ four times- red

Pts. Boosters* Source** Method** Zone Pts. Barrier* Source** Method** Zone

1 3 Referral from CHWs 3,5,6,7 A,B,F R,C 3 Poor anthropometric measurements D

2 2 Good Follow-up at OTP Level 1,5 B,C, R 2 Stigma (RUTF for poor,) 1,4, 5, 6,7,10 B,F,C C

3 4 Collaboration and referral from TBA 1,4,5,6 A,B R 5 Migration/ Nomadism( animals looking

for water and pasture 1,2,5,6,8,9,10 A,B,C,F

R, C

4 4 RUTF Stock availability 2,5 B, R 3 No Referrals/ Cooperation with

Traditional Healer 4,5,6 B,F

R, C

5 3 MOH and SC supervise OTP (CHW given feedback)

5,6,10, B R

2 Stock out of routine medication and other OTP Supplies

5,6,1 B,A

R,C

6 4 Good integration at health facilities in services provided (Allows SAM Screening)

1,2,5,8 B,A,D, C R 3

Plumpynut as food / sharing/ Camel milk is better

1,2,8,5,6, 7 A,B,C R,C

7 4 Good inter-linkage between community and health facilities (by CHWs follow up)

2,5,7,6 B,A R 3

Carer busy (duties and other children) 1,3,6,9,10, B, C, A C, R

8 5 Awareness of OTP and MUAC 1,2,3,4,6,7 A,B,C R 3 Lack of ownership of malnutrition at

SC/OTP (Impact on quality) 1,6,9,10 B,D

C

9 3 Good collaboration between the leaders and CHWs and others (Case finding)

2,4,5 A,B R 1 No provision for management of other

special illness at SC other than Malnutrition (Kalazaar)

1,5 B,C C

10 5 Good mobilisation(Acceptance of program)

1,2,3,4,5,6,7,8 A,B,C,D,F R,C 3

Late seeking behaviour (Sheikh, food) 2,4,8 A,B, R

11 2 Community Strategy 2,8, A,B, R 1

Outreaches once a week – starts late 2,6 B R

12 4 Free services for under-fives 2,6,8 A,B, R 4

Distance to health facilities/ OTP site 1,2, 4,

5,6,7,8,9,10 A,B,F,C

R

13 4 CHWs step in for health workers responsibilities

6 B R 3 Inadequate key messages on

RUTF/Education 1,7 C, A

R,C

14 2 Positive Deviance(Role model caretakers) 6 B R 3

Myth on anthropometric measurements 2,4,5,8 A,B R

15 5 Outreach strategy 9,10 B 2 Data quality issues 5,10 B, D R

16 5 Logistics support 9,10 B 1 CHWs providing services not trained on 9 B

17 4 Human Resource and technical support 5,9,10 B 5 Competing priorities disrupting OTP

Services (Campaigns, trainings, meetings)

9 B

18 4 Good partner coordination 9 B 4 Decreased number of OTP sites 10 B

19 3 Double ration 9, B,D 1 Illiteracy (Female CHW selection) 10 B

20 1 NFI Distribution (Buckets and soaps) 9 B 3

Insecurity and clanism 10 B,F

21 4 Referral of SAM Cases and from Stabilization Centres

9 B 3 Poor infrastructure (during rainy

seasons) 9 B

22 4 Mother to mother support groups 6,9,10 B R,C 2 Change of implementation strategy

(decreased screening, decreased sites) No direct transport for SC

6,9 B,F

23 3 Acceptable SPHERE Standards (low rate of defaulters)

3,5,7 A,E,D,F R,C 1

Rejection- Not understanding criteria 1 C,A C

24 5 Good working relationship between the nurse and the caregivers/ Health education

1,5 B, C,D C 5

Shortage and staff turnover at OTP sites 2,5,6,10 B,F,D R,C

25 5 Appreciation of the OTP service

92 TOTAL 66

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