laikipia squeac report dec 2012 21

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LAIKIPIA COUNTY, KENYA

KENNEEDY MUSUMBA, DECEMBER 2012

SQUEAC REPORT

LAIKIPIA COUNTY, KENYA

KENNEEDY MUSUMBA, DECEMBER 2012


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ACKNOWLEDGEMENT

International Medical Corps is appreciative to all the parties for their contribution, both known and

unknown and for the profound support during the entire coverage assessment. The objectives of

SQUEAC assessment would not have been achieved without the active participation of different actorswho included:

UNICEF for financial support CMN for technical support DHMT Laikipia County for active involvement in data collection IMC field staff for data collection and logistics support Community leaders who facilitated data collection during the wide area survey.

Special thanks due for IMC Nutrition Department and all Kenyan SQUEAC experts for their extensive and

technical support, and all the reviewers of this document in its draft form for the invaluable input.


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ACRONYMS

CMAM- Community based Management of Acute Malnutrition

CMN- Coverage Monitoring Network

DHMT- District Health Management Team

GFD-General Food Distribution

HCPs-Health Care Provider

HINI- High Impact Nutrition Interventions

IMAM-Integrated Management of Acute Malnutrition

MOPHS-Ministry of Public Health and Sanitation

MOMS-Ministry of Medical Services

MoH-Ministry of Health

OJT- On Job Training

OTP-Outpatient Therapeutic Program

PLW-Pregnant and Lactating Women

RUTF-Ready to Use Therapeutic Food

SFP-Supplementary Feeding Program

TBAs- Traditional Birth Attendants

THPs-Traditional Health Practitioners

URTI- Upper Respiratory Tract Infection

WASH- Water Sanitation and Hygiene


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Table of Contents

ACKNOWLEDGEMENT ................................................................................................................................... iiACRONYMS .................................................................................................................................................. iii

Table of Contents ......................................................................................................................................... iv

EXECUTIVE SUMMARY .................................................................................................................................. 1

1. INTRODUCTION ..................................................................................................................................... 2

2. STAGE 1: IDENTIFICATION OF AREAS OF LOW AND HIGH COVERAGE ................................................. 3

3. STAGE 2- CONFIRMING HYPOTHESIS FOR AREAS OF LOW AND HIGH COVERAGE .............................. 9

4. STAGE 3: DEVELOPING PRIOR ............................................................................................................. 11

5. DISCUSSION ......................................................................................................................................... 15

6. RECOMMENDATIONS.......................................................................................................................... 17

List of Tables

TABLE 1: LAIKIPIA COUNTY OTP FACILITIES AND OUTREACH SITES .............................................................................. 3

TABLE 2: SMALL STUDY RESULTS ................................................................................................................................. 10

TABLE 3: SMALL STUDY RESULTS ................................................................................................................................. 10

TABLE 4: RANKING AND WEIGHTING OF BOOSTERS AND BARRIERS .......................................................................... 11

TABLE 5: WIDE AREA SURVEY RESULTS ....................................................................................................................... 13

List of Figures

FIGURE 1: MONTHLY ADMISSIONS PER DISTRICT.......................................................................................................... 4

FIGURE 2: LAIKIPIA COUNTY MONTHLY ADMISSIONS ................................................................................................... 5

FIGURE 3: MUAC AT ADMISSION ................................................................................................................................... 6

FIGURE 4: STANDARD PROGRAM INDICATOR GRAPH ................................................................................................... 7

FIGURE 5: DEFAULTS IN RELATION TO SEASONALITY .................................................................................................... 8

FIGURE 6: TIME OF DEFAULT ......................................................................................................................................... 9

FIGURE 7: SMALL STUDY-REASONS FOR NON-COVERED CASES.................................................................................. 10

FIGURE 8: PRIOR .......................................................................................................................................................... 12

FIGURE 9: COVERAGE ESTIMATE ................................................................................................................................. 14

FIGURE 10: WIDE AREA SURVEY- REASONS FOR NON-COVERED CASES ...............ERROR! BOOKMARK NOT DEFINED.


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EXECUTIVE SUMMARY

International Medical Corps conducted a Semi-Quantitative Evaluation of Access and Coverage in

Laikipia County to investigate the coverage levels of the Outpatient Therapeutic Program. Theassessment was carried between 5th and 18th December 2012. Having not had any coverage assessment

since program inception in May 2011, it was important to determine boosters and barriers, establish

program coverage, and provide significant recommendations to improve service delivery to the

intended beneficiaries. The 3- stage Bayesian technique was applied and unveiled Period Coverage of

41.9% (31.4%-53.2%).

The main barriers identified to affect program coverage were inadequate program awareness,

inadequate staff capacity and compliance, vast area, intermittent coverage of outreach sites, defaulting,

and lack of active case finding, community mobilization, and migrations. Inadequate program awareness

was identified as the central factor affecting coverage. Most non-covered cases (64%) in the wide area

survey reported lack of knowledge about the program. Holistic integration and up scaling of HINIinterventions are recommended.


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1. INTRODUCTIONBackground Information

Laikipia County is located to the North West of snow-capped Mount Kenya and is composed of 5

districts, that is, Nyahururu, Laikipia East, Laikipia West, Laikipia Central and Laikipia North. The County

covers an estimated area of 9693 sq. km with total estimated population of 399,2271. It borders

Samburu County to the North, Isiolo and Meru to the East and Baringo to the West. Laikipia County is

ethnically diverse and is inhabited by several communities such as the Mukongondo, Maasai, Kikuyu,

and Meru, Turkana, Samburu and Pokot. Crop farming, Cattle-rearing on large commercial ranches and

community owned rangelands has for many years been the key source of livelihood for majority.

The county experiences a bimodal rainfall pattern with the long rains starting in March and the short

rains being experienced in October. In 2012, Laikipia County experienced poorly distributed sporadic

rains. However, forage access and availability was generally good with manifestations of deteriorations

noted in pastoral areas and marginal mixed farming zones. Other than milk whose prices reduced by

0.2% per bottle (750ml), cereal and legumes prices were on an upward trend despite their availability.

As of September 2012, the number of children under five years of age at risk of malnutrition increased

by 0.1% to 8.86% comparative to the previous month. This was mainly attributed to lack of food

diversity in variations coupled with poor food utilization2.

The countys livelihood zones are six: Agro-pastoral, Marginal mixed farming, Mixed farming, Formal

employment/trade, Pastoral (all species), and Ranching.

International Medical Corps has been implementing HINI in the county since May 2011 with a target of

63,078 under five year old children and 25,099 PLW. In collaboration with other partners,

MOPHS/MOMS, and the community, IMC has been playing an integral part in strengthening the health

12009 Kenya Population and Housing census

2Early Warning Bulletin, September 2012/Laikipia County


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and nutrition and intervention systems through the IMAM model. Some of the major activities that have

been conducted since then include and are not limited to capacity building of MoH staff in service

delivery, WASH activities, surveys, and other supportive services. According to the SMART survey

conducted in August 2012 the global acute malnutrition (GAM) in the county was 12.8 %( 9.7 - 16.7 95%

C.I.) with a SAM rate of 2.3% (1.2 - 4.4 95% C.I.)3. The coverage of the program is influenced by several

factors that may not be solely addressed by the SMART survey. This necessitated the need to conduct

coverage assessment to establish the boosters and barriers in relation to period coverage.

Survey Justification

Since the inception nutrition programs by International Medical Corps in May 2011 no assessment has

ever been conducted to determine program coverage in the area. This exercise will be imperative in

determining the coverage levels with regard to the relevant boosters and barriers that affect the

Outpatient Therapeutic Program in Laikipia County.

The SQUEAC investigation will also be significant for the program in making informed decisions for

improvement where necessary.

Objectives of the Survey

The specific objectives of this assessment were:

To determine program coverage (Severe Acute Malnutrition)

To determine boosters and barriers which influence program coverage

To provide relevant recommendations in enhancing the performance of the program

To capacity build MoH staff on program coverage methodology

2. STAGE 1: IDENTIFICATION OF AREAS OF LOW AND HIGH COVERAGEThis stage involved collection, collation, and analysis of the relevant routine data from the OTP sites to

identify areas with low and high coverage. The OTP data that was collected included OTP admissions,exits on monthly basis, defaulters by village of residence, calendar of diseases, climatic changes, crop

and livestock produce, and labor demand calendars. Data extraction was conducted by IMC field staff in

collaboration with MoH staff four weeks prior to the assessment.

Laikipia County has 31 OTP sites that have been operational while 2 more are in the process of being

equipped to deliver such services. They include Matanya and Shaloom IDP Dispensary in Central District.

Table 1: Laikipia County OTP facilities and outreach sites

District Facilities Outreach sites IMC Supported Outreaches

Laikipia West 12 12 3

Laikipia East 3 5 0

Laikipia Central 3 4 0

Laikipia Nyahururu 7 3 3

Laikipia North 6 9 1

Total 31 33 7

32012 SMART Survey


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Data extracted from the OTP facilities was analyzed to give some inferences on the dynamics and the

program trends. From the table above, low coverage of outreach sites is evident as well as having only

31 Outpatient Care Centers in the vast county. At the time of the survey only 7 outreach sites were

being supported by IMC in offering OTP services. However, the support to these outreach sites is not

steady, the main challenge being lack of adequate logistical capacity.

The outreach sites should essentially be visited on weekly basis because OTP beneficiaries are

monitored on weekly basis according to the IMAM protocol in Kenya. Therefore, patchy and inconsistent

coverage of outreach sites was noted as one of the barriers affecting coverage of the program and is

related to distance as well. Beneficiaries in these sites are likely to get late or no intervention, thus

resulting to late recruitment which is associated with complications coupled with eventual poor

outcome. The IMAM program should essentially be able to timely reach the intended beneficiaries.

Therefore, it is imperative to focus on recruitment, retention (avoid defaults), and recovery.

2.1 Monthly Admissions

Monthly admissions per district were analyzed to determine any disparities since there are notabledifferences in the livelihood zones. Despite the variations in livelihood zones, the admission trends in

the 5 districts are somewhat similar. However, there is notable difference in the number of admissions,

mainly attributed to the catchment population being served by the outpatient therapeutic programs as

shown in the figure 1.

Figure 1: Monthly admissions per district

Therefore, the monthly admissions were further collectively analyzed to provide more information

about the program in the county. At the inception CMAM program, it is phenomenal to record low

0

10

20

30

40

50

60

70

80

May

Jun

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

July

Aug

Sep

Oct

Nov

2011 2012

Admissions

Monthly Admissions per district

Laikipia West

Laikipia North

Laikipia East

Laikipia Central

Nyahururu


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admissions, with a steady increase due to gradual uptake of the program by the health workers and

beneficiaries, as shown in the figure below between May and September 2011.

Figure 2: Laikipia County monthly admissions

The admission peaks could have been influenced by other factors such morbidity and season patterns as

shown above. The common diseases that affect children in Laikipia County include malaria, URTI, and

diarrhea. The occurrences of these diseases coincide with admission peaks for July to September 2011

and March to May 2012. The dry season which is associated with food scarcity between January and

April could also be another factor for increase in admissions around the same period.

As evident from the beginning of the intervention, data are in consistent with the program showing

some response to need.

2.2 MUAC Admissions

Plotting MUAC admissions is important in determination of health seeking behaviors. Children who are

admitted with lower MUAC must have remained uncovered for some time despite being legible cases.

Thus late admissions or late treatment seeking were investigated using MUAC admissions as shown in

the figure below.

0

10

20

30

40

50

60

70

80

90

100

MayJune July Aug Sep Oct Nov Dec Jan Feb Mar Apr MayJune July Aug Sep Oct Nov

2011 2012

Admissions

Monthly Admissions ADMISSIONSA3(Trend & Season

A13 (Trend)

Diarrhoea Diarrhoea

URTI URTI

Malaria Malaria

Dry Season


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The Median MUAC is to determine early or late seeking behaviors and was calculated as follows;

(429+241+101+50+16+7+1)/2= 423, which falls at MUAC of 114-111mm. This implies that most

beneficiaries were admitted early into the program, that is, with MUAC close to the admission cut off for

OTP. The early admissions as shown by median MUAC (114-111mm) is indicative of cases with few

complications, short stay in the program, reduced defaulting and ultimate good outcome. It is also

indicative of a program with relatively high coverage and good active case finding. However, it could also

imply the high prevalence and caseloads at the beginning of the program. The latter is the case in

Laikipia County.

It is important to note that there are some late admissions, with MUAC less than 95mm.

Figure 3: MUAC at admission

2.3 Standard Program Indicator

The program exits which include cured cases, death, defaulters, non-response, and transfers were

analyzed to obtain a standard program indicator graph, Figure 4. The analysis of program exits is

important is assessing program performance based on SPHERE standards of death rate (75%), and default rate (


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Figure 4: Standard program indicator graph

The defaulter rate is high and above the required minimum SPHERE standard of


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dropout or change of CHWs at the facilities prompting a fresh start in capacity building of new CWHs.

This unduly filled OTP registers in a number of health facilities is a clear indication that IMAM

procedures are not adhered to. The probable reason for this included:

Low uptake of OTP program Inadequate training Inadequate staff to conduct all OTP procedures

2.4 Defaulting and Seasonality

As elaborated in the figure 5, defaulting in Laikipia County is equally attributed to competing activities.

These are activities which the caregivers would prioritize over taking the child to the health facility for

subsequent visits. Despite coinciding with planting season, the long rains affect accessibility of the

health facilities due to the poor roads. Most defaults are noted during land preparation, planting, and

harvesting.

Figure 5: Defaults in relation to seasonality

Further analysis of defaulters by time of visit shows that most cases drop out at early stages. Majority

(77.7%) of the default happened between the 1st

and 4th

visits. The cases that defaulted early could still

be active SAM cases in the community. Those who defaulted later could be recovering or had recovered.Recruitment (early admission) and retention of SAM cases is important for efficacy of the program. As

elaborated, defaulting is a barrier to program coverage in Laikipia County.

0%

10%

20%

30%

40%

50%

May

June

July

Aug

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

June

July

Aug

Sep

Oct

Nov

2011 2012

Long Rains

Harvesting

Land Preparation

Planting


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Figure 6: Time of default

2.5 Collection of Qualitative data

The survey teams which comprised of both MoH and IMC staff used tools which included informal group

discussions guides, semi structured interviews, and simple structured interviews to collect qualitative

data. These tools were administered to various sources which included program staff, facility nurses,

community leaders (elders, key informants, TBAs, THP), pastoralists, CHWs, and caregivers (of cases in

program, not in program, and defaulters or DNAs). An observation checklist was also used to collect

information on existence of IEC materials, the stores, and organization of the feeding from the facilities.

This process enabled collection of more qualitative data about the program which was organized using

Mind mapping and analyzed to identify program boosters and barriers as well as more information onareas of low and high coverage.

3. STAGE 2- CONFIRMING HYPOTHESIS FOR AREAS OF LOW AND HIGH COVERAGEThe objective of this stage was to confirm areas of high and low coverage based on data collected from

stage 1.The hypothesis, Coverage is low in villages far from OTP sites and high in near villages was

formulated due to the following reasons:

Intermittent coverage of outreach sites in the county Inadequate community screening and lack of active case finding

Relatively high rates of defaulters from areas far away from OTP centers Relatively long distances to the OTP centers because the county is vast

Therefore, the assumption was that coverage and program awareness is high in villages close to the

health facilities compared to those far away.

46

74

48 45

21

11 14

510

0

10

20

30

40

50

6070

80

90

100

1 2 3 4 5 6 7 8 >8

OTPDefaulters

Time of Visit

Time of Default


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Small Study

The small study was conducted in purposively selected villages, both near and far from health facilities.

The 5 teams were divided into two main groups during the data collection process. Community key

informants participated and assisted in identifying cases through active and adaptive case finding

Table 2: Small study results

OTP cases villages far from health facility villages near health facility

SAM cases in the program 1 2

SAM cases NOT in program 5 0

Total Active SAM cases 6 2

Recovering cases in program 2 1

The hypothesis was tested by applying the simplified LQAS formula d= (n/2) against the 50% sphere

standard for coverage for rural areas.

Table 3: Small study results

High coverage area:Chongoti, Gatundia,

Mukuri, Thome

Coverage Standard (p) 50%

Number of cases covered

(2) is > 50%

Decision Rule (d)

= [1]

Cases Covered 2

Low coverage areas:

Sukuroi,Sukulan,Ngarenyiro,

Lamuria

Coverage Standard (p) 50%Number of covered cases

(1) is < 50%

Decision Rule (d)

= [3]

Cases Covered 1

The assumptions made by the hypothesis revealed that coverage is high in villages near health facilities

than far villages. Program awareness was equally low in far villages and was one of the main barriers.

During the small study, reasons (barriers) identified among the mothers whose children were not

admitted into the program were as follows.

Figure 7: Small study-reasons for non-covered cases

0 1 2 3

Do not know about the program

Too busy to attend the program

Wrong admission into SFP

Relapse

Respondents

Reasonsfornotbeingin

theprogram


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4. STAGE 3: DEVELOPING PRIORThe collected qualitative and quantitative data were used in determination of prior through the use of

weighted boosters and barriers as well as a histogram. Upon organization of the qualitative data using a

mind map, all the data was logically categorized as either a booster (positives) or a barrier (negatives) to

the program. The prior mode was established as an average of positives (build-ups from 0%) and

negatives (knock-downs from 100%) through triangulation by source and method as shown in the

figure below.

Table 4: Ranking and weighting of boosters and barriers

NO BARRIER WEIGHT

1 Lack of program awareness -4%

2 Inadequate case

finding/community screening

-3%

3 Defaulting -4%

4 Inadequate community

mobilization

-2%

5 Inadequate capacity of HCPs -2%6 Low coverage of outreaches -4%

7 Distance to the facility -2%

8 Sharing RUTF -5%

9 Inadequate personnel -4%

10 Migration -3%

11 Stigma -1%

12 OTP cards not used in some

facilities

-2%

13 Competing activities -3%

Prior mode = = 44%

Histogram

The second prior mode was determined using 44% as the peak as this was more reliable having been

derived at from the collected data. The survey team identified the most unlikely (extreme) values in

relation to coverage of the county. Using the data obtained from the small study, facility data, and

qualitative data from OTP facilities, the survey team suggested several possibilities of coverage within a

range of about 15% to 70% which eventually arrived at a prior mode of 42.5%. The wide area surveyprior was calculated as an average of the two modes as shown below

= [43.25] = 43

NO BOOSTER WEIGHT

1 Constant supplies +5%

2 OTP/SFP/GFD linkages +4%

2 On job training +4%

4 CHW incentives +3%

5 IEC materials +2%

6 Seeking treatment in health

facilities

+3%

7 Awareness of malnutrition +2%

8 Early admissions +4%

Sum +27%

Lower value anchor 0%

Total 27%

Sum -39%Upper value anchor 100%

Total 61%


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With 43 as the prior, the Bayes SQUEAC Coverage Estimate Calculator (version 2.02)4

was used by

adjusting the prior and prior until the mode was obtained with an uncertainty of 25. This level of

uncertainty was used because it was the first assessment to be conducted in Laikipia County.

Figure 8: Prior

4.1 Wide Area Survey

Sampling methodology

Using the formula below, the sample size for the wide area survey was calculated usingprior = 15.1,

prior = 19.1, prior mode of 43% and a precision of 10 to obtain a sample size of 62 cases for the

whole county as shown below;

= 62

4The calculator can be freely downloaded fromwww.brixtonhealth.com

Prior = 15.1

Prior = 19.1
http://www.brixtonhealth.com/http://www.brixtonhealth.com/http://www.brixtonhealth.com/http://www.brixtonhealth.com/


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Using an average village population of 600, 14.23%5

for population of children 6-59 months (DHIS

Laikipia County November 2012), and SAM prevalence of 1.2%6, 61 villages were sampled.

The number of villages to be visited was determined using the formula below:

= 61

The villages to be visited were attained through segmentation and each division assigned villages

according to the number of estimated number of households in each. A list of villages was obtained for

each division and subsequently selected by simple random sampling. This was so because of lack of the

county maps since counties are newly created administrative structures.

4.1.1 Data Collection

The 5 survey teams composed of both IMC field staff and MoH staff visited all the sampled villages for

data collection for a period of 5 days. Each survey team sought authority and introduction from the

respective administrative or community leaders from the sampled villages as well as key informants.

Active and adaptive case finding was conducted with aid of the selected key informants.

The tools used during data collection included a questionnaire for non-covered cases, tally sheet, and

referral slips given to all non-covered cases for either OTP or SFP programs.

The findings of the wide area survey were analyzed as shown below:

Table 5: Wide area survey results

OTP cases No. of Cases

SAM cases in the program 17

SAM cases NOT in program 25

Total Active SAM cases 42

Recovering cases in program 16

Point coverage estimator was used for overall program coverage because the program manifested lack

or inadequate active case-finding and low recruitment (community screening).

Using SAM cases in the program (17) as the numerator and total active SAM cases (42), the BayesCoverage Estimate Calculator unveiled coverage of41.9% (31.4%-53.2%).

As shown in figure 9 the posterior is narrower, an indication that the survey has reduced certainty on

the coverage of the program. There is considerable overlap between the prior and likelihood, thus no

conflict. This implies the prior information was more suggestive of the possible likelihood.

5Nov 2012, DHIS/Laikipia County

62012 SMART Survey


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Figure 9: Coverage estimate

4.2 Reasons for Non-attendance

Questionnaires were administers to all non-covered beneficiaries to establish the reasons for not

attending the program. Most cases did not know about the program 64%. Difficulty with child care and

relapse cases recorded 12% each, while 8% of the respondents considered the program site being too

far. A small proportion (4%) was wrongly admitted into SFP instead of the OTP as shown in Figure 10

below.

0 5 10 15 20

Do not know about the program

Relapsed

Difficulty with child care

Wrong admissions into SFP

Program site too far

Main Barriers

Number of SAM Cases

Considerable overlapbetween prior &

likelihood: there is no

conflict

Posterior is

narrower: the

survey has

reduceduncertainty


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5. DISCUSSIONThe coverage estimate for Laikipia County (41.9%) is still below the minimum SPHERE standard of 50%

despite concerted efforts from various actors since May 2011.Good program coverage is equally

dependent on external factors other than programmatic factors .The summary of findings in relation to

the boosters and barriers are elaborated below. It is important to note that the survey was conducted

during the short rains harvesting season, presumably a season of with more food available. The situation

may be aggravated during the dry season if appropriate interventions are not strengthened or put in

place. Moderately acute malnourished children referred during the exercise were 63. Lack program

awareness was noted as the predominant and central barrier to coverage during both the small study

and wide area survey. This is elaborated by the program concept map, Appendix VI.

Summary of findings

BOOSTER FINDINGS

Constant supplies Through informal discussions, mothers whose children were in the programreported constant supply of RUTF.

Using observation checklist, the survey team recorded availability of RUTF in the

stores. All the visited stores had RUTF stock.

CHWs interviewed reported minimal or no break in supply pipeline

OTP/SFP/GFD linkages CHW and nurses reported and confirmed the existence linkage of OTP to

supplementary feeding program and general food distribution.

On job training Interviewed nurses and CHW reported the existence of on job training on IMAM by

IMC staff

Program/IMC staff reported conducting on job training in all the OTP facilities

CHW incentives Interviews with CHW revealed that they received incentives in form of bicycles and

money to facilitate in discharging their duties

IEC materials There are IEC materials in most heath facilities as indicated using a checlist

Nurses in charge, program staff, and CHWs confirmed the existence of the IEC

materials during

Seeking treatment in health

facilities

According to the community leaders, people seek treatment in health facilities

Caregivers equally confirmed the same during interviews


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Awareness of malnutrition Caregivers' interviews and informal group discussions with community leaders gives

indication of malnutrition awareness

Early admissions Analysis of admissions by MUAC revealed that most cases were admitted early into

the program

BARRIER FINDING

Lack of program awareness Most community members are not aware of the program. This was revealed from

the interviews with caregivers, and informal group discussions with community

leaders and pastoralists.

Inadequate case


The CHWs, nurses, and program staff interviewed confirmed poor active case

finding. Informal group discussions with community leaders and caregivers whose

children are not in the program corroborated the same.

Defaulting Data extracted from the facilities showed that defaulting is a problem for theprogram. This was further confirmed by interviews with CHWs, program staff, and

nurses in charge.

Inadequate community

mobilization

Nurses in charge and program staff reported minimal community mobilization. This

was further confirmed through informal group discussions with the community

leaders.

Inadequate capacity of HCPs Both nurses in charge and program staff reported lack of capacity to proficiently

deliver IMAM services

Low coverage of outreaches According to data obtained from the facilities, few outreach sites are covered.

Interview with the nurses in charge, program staff, and CHWs showed that the few

covered outreach sites were visited constantly.

Distance to the facility Interviews conducted with caregivers and informal group discussions with

community leaders showed that distance is a hindrance to access.

Sharing RUTF Interview with CHWs and nurses in charge showed that sharing of RUTF is a common

phenomenon among the beneficiaries.

Caregivers of defaulters, both cases in and out of program confirmed sharing ofplumpynuts.

Inadequate personnel There are few personnel to offer OTP services as showed by interviews conducted

with the CHWs, nurses in charge, and caregivers whose children are in the program.


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Migration Pastoralists and community leaders' informal group discussions revealed that

migration is a barrier.

CHWs and nurses in charge interviews showed that migration of beneficiaries was a

challenge to the program.

Stigma Nurses and caregivers with no children in the program reported the existence ofstigma.

OTP cards not used in some

facilities

Some beneficiaries do not have OTP cards thus affecting progress monitoring of the

cases. This was confirmed using a checklist and interviews with nurses in charge,

CHWs, and Carers of cases in program.

Competing activities Interviews with CHWs and caregivers showed that competing activities such as

harvesting and land preparation affect the program.

Informal group discussions with community leaders revealed that some activities are

prioritized to taking the child to the health facility.

6. RECOMMENDATIONSBARRIERS/ISSUES RECOMMENDATIONS

Inadequate capacity of Healthcare

providers

Training on IMAM protocol of all health workers involved in OTP services. Serv

provider training is essential for all health workers

ToT training for DHMT members involved in OJT.

Strengthening and ensuring consistent on the job trainings for all OTP centers

improve the level of service provision.

Involving the nurse-in charge in OJTs to facilitate efficiency in service delivery

enhance program uptake.

Patchy coverage of outreach sites Scale up IMAM integrated outreach services to the hard to reach areas. This

requires concerted efforts from all the stakeholders in the county.

Seek more funding for coverage of outreach facilities


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Inadequate Active case finding Strengthening existing community units and creation of more units.

The community units should also be trained on nutrition issues alongside othe

public health promotion components

Collaboration of all the relevant stakeholders (MoH, IMC, and community) in

conducting periodical active case finding at the village level to enhance early

admissions and minimize poor outcomes.

Poor Documentation and Reporting Strengthen on the job training on IMAM especially on identified gaps such as p

documentation and reporting.

Routine joint supervisions to assess the progress of the program and identify g

for timely action.

Lack of Program awareness

Competing activities/migrations

Utilize community volunteers, Community leaders, key informants, and comm

units (CHWs based in the community) in sensitization and mobilization of the

community about the program.

IMC/ DNO to enlighten other stakeholders and monitors from other partners onutrition package for improved nutrition awareness to the community

Create nutrition awareness through farmer/Livestock field days in liaison with

ministry of Agriculture and Livestock

Collaborate with the Ministry of information in creating awareness

This can be done at local gatherings such as chiefs barazas or local events. Th

can also be used to educate the community on the significance of the program

CHW incentives Incentives to CHWs in the facility level are important in boosting their morale.can be done using more inventive approaches such that they become agents o

change in the community e.g. provide support and training on kitchen gardeni

and let them champion the same to the rest.

Vast County More staffs are needed to adequately meet the objectives of the program in t

expansive county. OJT and Outreach services cannot be conducted consistentl

with the limited number of staff.

Additional vehicles should be added to facilitate coverage of all the OTP facilit

the county.

All the above means sourcing of more funding to cater for enough staff and

vehicles.

Programmatic Monitoring Adoption of the SQUEAC methodology in monitoring program progress for tim

decision making. Stage 1 and 2 can easily be conducted at the program sites

periodically. It mainly relies on facility data.


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Malnutrition Addressing underlying causes of malnutrition requires integrated approach of

involved facets. Alongside nutrition programs, there are needs for WASH, food

security and livelihood programs, and IYCN.

Activate County Health and Nutrition forums and incorporate Nutrition in the

county development plan to ensure sustainability.

Lack OTP cards in some facilities Provision of more ration cards to enhance monitoring of cases in the program

strengthen referral system.


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APPENDICES

Appendix I: Findings of the wide area survey

Village District

SAM cases

in Program

SAM cases

NOT in

Program

Total Active

Cases

Recovering cases in

Program TOTAL SAM CASES

Tura North 1 0 1 2

Naisorai North 2 0 2 3

Kurum North 0 2 2 0

Altafetta Central 2 1 3 1

IDP A Central 4 1 5 2

Mutara Central 0 1 1 0

Ngatuaji West 1 0 1 1

Miteta Nyahururu 0 0 0 1

Muthaiga Nyahururu 0 2 2 3

Kantutura West 2 2 4 0

Chong'oti West 0 2 2 0

Sosian West 0 3 3 0

Mutitu Nyahururu 0 1 1 0

Mbogoini Nyahururu 0 1 1 0

Manguu Nyahururu 2 0 2 0

Mutuiku Nyahururu 1 0 1 1 Tinga Nyahururu 1 1 2 0

Kang'a A Nyahururu 1 1 2 0

Mahigaini East 0 2 2 1

Kirimukuyu East 0 1 1 1

Kwa Mbuzi East 0 1 1 0

Ngarengiro East 0 3 3 0

TOTAL 17 25 42 16 5


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Appendix II: Villages Sampled

isionNo. of Sub-

locationspopulation

Average

population per

sub-location

(f/E)

Number of

households(G/5)

Estimated

number of

villages

n

muria 4 31,332 7833 1567 13

nyaka 7 19,708 2815 563 7

ga 9 33, 304 3700 740 9

ntral 8 57,690 7211 1442 12

ahururu 6 57, 466 9578 1916 16

moran 7 17,953 2565 513 7

arua 8 66,050 8256 1651 14

muruti 16 82,962 5185 1037 16

kongondo 14 32,762 2340 468 14

Appendix III: Seasonal Calendar

old Season(Frost bite)

hort Rains

ong Rains

ry Season

arvesting (Maize)

arvesting(Potatoes/Beans/Peas)

anting (Long Rains)

anting (Short Rains)

and Preparation

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov De


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Appendix IV: Barriers, Boosters, and Questions (BBQ) Tool

Triangulation by source and method: boosters and barriers

NO BARRIER SOURCES

1 Lack of program awareness C, , ,

#

2 Inadequate case


, o, #

c

3 Defaulting # o x

4 Inadequate community

mobilization

#

5 Inadequate capacity of HCPs X o

6 Low coverage of outreaches o #

7 Distance to the facility # c

8 Sharing of RUTF C o

*

9 Inadequate personnel o

10 Migration O # c

11 Stigma c

12 OTP cards not used in some

facilities

X o

13 Competing activities O c #

NO BOOSTER SOURCES

1 Constant supplies o X

2 OTP/SFP/GFD linkages o X

2 On job training , o,

4 CHW incentives o,

5 IEC materials X o

6 Seeking treatment in health

facilities

C, ,#

7 Awareness of malnutrition #,c,

8 Early admissions x

Legend/key

1. Nurse in charge 2. Program staff 3. CHW o4. Care giver of children not in program c5. Carers of children in program 6. Carers of defaulter *7. Community, key informants(leaders, THPs,

TBAs) #

8. Pastoralists 9. Checklist x


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Appendix V: Histogram Prior


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Appendix VI: Laikipia Concept Map

laikipia squeac report dec 2012 21

Documents