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i
LAIKIPIA COUNTY, KENYA
KENNEEDY MUSUMBA, DECEMBER 2012
SQUEAC REPORT
LAIKIPIA COUNTY, KENYA
KENNEEDY MUSUMBA, DECEMBER 2012
ii
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 actors
who 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.
iii
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
iv
Table of Contents
ACKNOWLEDGEMENT ................................................................................................................................... ii
ACRONYMS .................................................................................................................................................. 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.
1
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. The
assessment 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 HINI
interventions are recommended.
2
1. INTRODUCTION
Background 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 county’s 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
2 Early Warning Bulletin, September 2012/Laikipia County
3
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 COVERAGE
This 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
4
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 notable
differences 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
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Mar
Ap
r
May Jun
July
Au
g
Sep
Oct
No
v
2011 2012
Ad
mis
sio
ns
Monthly Admissions per district
Laikipia West
Laikipia North
Laikipia East
Laikipia Central
Nyahururu
5
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
Ad
mis
sio
ns
Monthly Admissions ADMISSIONS
A3(Trend & Season)
A13 (Trend)
Diarrhoea Diarrhoea
URTI URTI
Malaria Malaria
Dry Season
6
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 (<10%), recovery
rate (>75%), and default rate (<15%).
429
241
101 50
16 7 1 0
100
200
300
400
500
114-111 110-106 105-101 100-96 95-91 90-86 85-81
Nu
mb
er
of
Ad
mis
sio
ns
Admission MUAC (mm)
Program Admission by MUAC
7
Figure 4: Standard program indicator graph
The defaulter rate is high and above the required minimum SPHERE standard of <15%. Children
admitted into the program leave before formally meeting the discharge criteria. Defaulting is an
ultimate indicator of poor compliance in the program. The high default rates in Laikipia County are
attributed to a number of factors which include and are not limited to poor compliance, lack of defaulter
tracing mechanisms, inadequate or lack of active case finding, migration, and competing activities. High
default rates indicated low coverage of the program. The cure rates too have been on the decline since
June 2011. This was mainly attributed to poor compliance by both the beneficiaries and health care
providers to the IMAM protocol.
Sharing of RUTF was also reported among beneficiaries during collection of qualitative data. The rations
given to the beneficiaries are meant for treatment of malnutrition and as such should not be viewed as
food because this leads to sharing and long stay in the program.
Documentation
As an integral programmatic component,
documentation and reporting are fundamental in
process monitoring. During data extraction,
inadequately filled OTP registers was evident in some
of the facilities. The facility based CHWs are mostly
charged with filling the registers with the other health
providers giving minimal or no assistance other than
the OJT sessions conducted by IMC staff. The OJT
sessions are not consistent in some facilities due to
logistical challenges. Other than that, there is notable
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
May
Jun
e
July
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Mar
Ap
r
May
Jun
e
July
Au
g
Sep
Oct
No
v
2011 2012
% o
f Ex
its
Time
Laikipia County OTP Exits
Cured(A3)
Death (A3)
NonResp(A3)
Defaulters (A3)
8
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
Jun
e
July
Au
g
Sep
Oct
No
v
De
c
Jan
Feb
Mar
Ap
r
May
Jun
e
July
Au
g
Sep
Oct
No
v
2011 2012
Long Rains
Harvesting
Land Preparation
Planting
9
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 on
areas of low and high coverage.
3. STAGE 2- CONFIRMING HYPOTHESIS FOR AREAS OF LOW AND HIGH COVERAGE
The 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
5 10
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 >8
OTP
De
fau
lte
rs
Time of Visit
Time of Default
10
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: Chong’oti, 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
Re
aso
ns
for
no
t b
ein
g in
th
e p
rogr
am
11
4. STAGE 3: DEVELOPING PRIOR
The 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 survey
prior 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%
12
With 43 as the prior, the Bayes SQUEAC Coverage Estimate Calculator (version 2.02)4 was used by
adjusting the prior α and prior β 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 using prior α = 15.1,
prior β = 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
4 The calculator can be freely downloaded from www.brixtonhealth.com
Prior α = 15.1
Prior β = 19.1
13
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 Bayes
Coverage Estimate Calculator unveiled coverage of 41.9% (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.
5 Nov 2012, DHIS/Laikipia County
62012 SMART Survey
14
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 overlap
between prior &
likelihood: there is no
conflict
Posterior is
narrower: the
survey has
reduced
uncertainty
15
5. DISCUSSION
The 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 program reported 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
16
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 finding/community screening
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 the program. 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 of plumpynuts.
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.
17
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 of stigma.
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. RECOMMENDATIONS
BARRIERS/ISSUES RECOMMENDATIONS
Inadequate capacity of Healthcare providers
Training on IMAM protocol of all health workers involved in OTP services. Service 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 to improve the level of service provision. Involving the nurse-in charge in OJTs to facilitate efficiency in service delivery and 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 other 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 poor documentation and reporting. Routine joint supervisions to assess the progress of the program and identify gaps for timely action.
Lack of Program awareness Competing activities/migrations
Utilize community volunteers, Community leaders, key informants, and community 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 on nutrition package for improved nutrition awareness to the community Create nutrition awareness through farmer/Livestock field days in liaison with the 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. They 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. This can be done using more inventive approaches such that they become agents of change in the community e.g. provide support and training on kitchen gardening and let them champion the same to the rest.
Vast County More staffs are needed to adequately meet the objectives of the program in the expansive county. OJT and Outreach services cannot be conducted consistently with the limited number of staff. Additional vehicles should be added to facilitate coverage of all the OTP facilities in 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 timely 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 all 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 and 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 3
Naisorai North 2 0 2 3 5
Kurum North 0 2 2 0 2
Altafetta Central 2 1 3 1 4
IDP A Central 4 1 5 2 7
Mutara Central 0 1 1 0 1
Ngatuaji West 1 0 1 1 2
Miteta Nyahururu 0 0 0 1 1
Muthaiga Nyahururu 0 2 2 3 5
Kantutura West 2 2 4 0 4
Chong'oti West 0 2 2 0 2
Sosian West 0 3 3 0 3
Mutitu Nyahururu 0 1 1 0 1
Mbogoini Nyahururu 0 1 1 0 1
Manguu Nyahururu 2 0 2 0 2
Mutuiku Nyahururu 1 0 1 1 2
Tinga Nyahururu 1 1 2 0 2
Kang'a A Nyahururu 1 1 2 0 2
Mahigaini East 0 2 2 1 3
Kirimukuyu East 0 1 1 1 2
Kwa Mbuzi East 0 1 1 0 1
Ngarengiro East 0 3 3 0 3
TOTAL 17 25 42 16 58
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Appendix II: Villages Sampled
Division No. of Sub-locations
population
Average population per sub-location (f/E)
Number of households(G/5)
Estimated number of villages
n
Lamuria 4 31,332 7833 1567 13 7
Munyaka 7 19,708 2815 563 7 4
Daiga 9 33, 304 3700 740 9 5
Central 8 57,690 7211 1442 12 7
Nyahururu 6 57, 466 9578 1916 16 9
Olmoran 7 17,953 2565 513 7 4
Ngarua 8 66,050 8256 1651 14 8
Rumuruti 16 82,962 5185 1037 16 9
Mukongondo 14 32,762 2340 468 14 8
Appendix III: Seasonal Calendar
Cold Season(Frost bite)
Short Rains
Long Rains
Dry Season
Harvesting (Maize)
Harvesting(Potatoes/Beans/Peas)
Planting (Long Rains)
Planting (Short Rains)
Land Preparation
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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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 finding/community screening
∆, 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 o 4. Care giver of children not in program c 5. 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