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S T U D Y P R O T O C O L Open Access
The impact of i-PUSH on maternal and
child health care utilization, health
outcomes, and financial protection: study
protocol for a cluster randomized
controlled trial based on financial and
health diaries data
Amanuel Abajobir
1*
, Richard de Groot
2
, Caroline Wainaina
1
, Anne Njeri
1
, Daniel Maina
1
, Silvia Njoki
1
,
Nelson Mbaya
1
, Hermann Pythagore Pierre Donfouet
1
, Menno Pradhan
2,3
, Wendy Janssens
2,3
and Estelle M. Sidze
1
Abstract
Background: Universal Health Coverage ensures access to quality health services for all, with no financial hardship
when accessing the needed services. Nevertheless, access to quality health services is marred by substantial
resource shortages creating service delivery gaps in low-and middle-income countries, including Kenya. The
Innovative Partnership for Universal Sustainable Healthcare (i-PUSH) program, developed by AMREF Health Africa
and PharmAccess Foundation (PAF), aims to empower low-income women of reproductive age and their families
through innovative digital tools. This study aims to evaluate the impact of i-PUSH on maternal and child health care
utilization, women’s health including their knowledge, behavior, and uptake of respective services, as well as
women’s empowerment and financial protection. It also aims to evaluate the impact of the LEAP training tool on
empowering and enhancing community health volunteers’health literacy and to evaluate the impact of the M-TIBA
health wallet on savings for health and health insurance uptake.
Methods: This is a study protocol for a cluster randomized controlled trial (RCT) study that uses a four-pronged
approach—including year-long weekly financial and health diaries interviews, baseline and endline surveys, a
qualitative study, and behavioral lab-in-the-field experiments—in Kakemega County, Kenya. In total, 240 households
from 24 villages in Kakamega will be followed to capture their health, health knowledge, health-seeking behavior,
health expenditures, and enrolment in health insurance over time. Half of the households live in villages randomly
assigned to the treatment group where i-PUSH will be implemented after the baseline, while the other half of the
households live in control village where i-PUSH will not be implemented until after the endline. The study protocol
was reviewed and approved by the AMREF Ethical and Scientific Review Board. Research permits were obtained
from the National Commission for Science, Technology and Innovation agency of Kenya.
© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
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The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the
data made available in this article, unless otherwise stated in a credit line to the data.
* Correspondence: aabajobir@aphrc.org
1
African Population and Health Research Center, Nairobi, Kenya
Full list of author information is available at the end of the article
Abajobir et al. Trials (2021) 22:629
https://doi.org/10.1186/s13063-021-05598-7
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Discussion: People in low-and middle-income countries often suffer from high out-of-pocket healthcare
expenditures, which, in turn, impede access to quality health services. Saving for healthcare as well as enrolment in
health insurance can improve access to healthcare by building capacities at all levels—individuals, families, and
communities. Notably, i-PUSH fosters savings for health care through the mobile-phone based “health wallet,”it
enhances enrolment in subsidized health insurance through the mobile platform—M-TIBA—developed by PAF, and
it seeks to improve health knowledge and behavior through community health volunteers (CHVs) who are trained
using the LEAP tool—AMREF’s mHealth platform. The findings will inform stakeholders to formulate better
strategies to ensure access to Universal Health Coverage in general, and for a highly vulnerable segment of the
population in particular, including low-income mothers and their children.
Trial registration: Registered with Protocol Registration and Results System (protocol ID: AfricanPHRC; trial ID:
NCT04068571:AEARCTR-0006089; date: 29 August 2019) and The American Economic Association’s registry for
randomized controlled trials (trial ID: AEARCTR-0006089; date: 26 June 2020).
Keywords: Maternal and child health, Health care utilization, Out-of-pocket health expenditures, Health insurance,
Universal Health Coverage, Cluster randomized controlled trial, Digital tools, Mobile health, Kenya
Background
There has been a renewed international commitment to
Universal Health Coverage (UHC) aiming at ensuring all
people have access to the health services they need with-
out suffering from financial hardships. However, there
are sustained resource shortages and service delivery
gaps in many countries that prevent them from meeting
the Sustainable Development Goal (SDG) 3.8 related to
UHC (i.e., achieve UHC, including financial risk protec-
tion, access to quality essential health care services and
access to safe, effective, quality, and affordable essential
medicines and vaccines for all). Achieving UHC is par-
ticularly important in achieving SDGs related to mater-
nal and child health (i.e., reducing the global maternal
mortality ratio to less than 70 per 100 000 live births
(SDG 3.1) and ending preventable deaths of newborns
and children under 5 years of age (SDG 3.2). All coun-
tries are committed to reduce neonatal mortality to at
least as low as 12 per 1000 live births and under-5 mor-
tality to at least as low as 25 per 1000 live births by
2030. Evidence still shows that more than half of the
world’s population lacks access to health care of suffi-
cient quality [1] and about 100 million people fall into
extreme poverty each year due to ill-health [2], particu-
larly in low- and middle-income countries (LMICs). Sys-
temic reforms are needed to translate commitment to
UHC into a reality.
Despite being classified as a middle-income country in
2014, Kenya still remains among the 25% poorest coun-
tries strongly affected by social and health inequalities in
the world. More than one third of Kenyans have an in-
come below the poverty line (1.9 USD/day) [1–5]. In-
equalities in access to healthcare, in particular maternal
and child health care, are still rampant, despite major
improvements made through targeted policies over the
past few years [5]. For instance, according to the 2014
Kenya Demographic and Health Survey (KDHS), the ma-
ternal mortality ratio has marginally reduced to 362 per
100,000 live births, not statistically different from the fig-
ures reported in 2008–2009 [4]. While a substantial
under-5 mortality reduction was achieved with a drop
from 115 per 1000 live births in 2003 to 52 per 1000 live
births in 2014, it is still twofold higher than the SDG
target. These figures are disproportionately higher in
Western Kenya, such as in Kakamega County. The poor-
est mothers are still far behind in terms of coverage of
essential Reproductive and Maternal and Child Health
services [4,5].
However, there is little evidence on the effectiveness of
intervention strategies in enhancing the coverage, inte-
gration, and implementation of maternal and child
health services in primary healthcare system in develop-
ing countries, including Kenya. Interestingly, the Gov-
ernment of Kenya has included UHC as one of its “Big
Four Agenda”-action points, which is anticipated to lead
the transformation of the country by 2022 [3,4]. The
objective is to achieve a 100% cost subsidy for essential
health services and to reduce out-of-pocket health ex-
penditures by half. Low-cost health insurance schemes,
eHealth, and mobile health (mHealth) services are
among other opportunities to achieve this goal. Conse-
quently, two non-governmental organizations, AMREF
Health Africa and PharmAccess Foundation (PAF), are
both supporting the Government to achieve the goal of
UHC in many counties. This includes Kakamega—a
county in Western Kenya—where AMREF and PAF are
jointly implementing their Innovative Partnership for
Universal Sustainable Healthcare (i-PUSH) program.
In order to maximize the effectiveness of this program,
it is important for stakeholders to have a deep
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understanding of current access to health services,
households’health-related decision-making, and out-of-
pocket health care expenditures in the target popula-
tions. The main aim of the study is to evaluate the im-
pact of i-PUSH (most notably enhanced access to
subsidized health insurance through the “health wallet”
and increased community health volunteer (CHV) train-
ing opportunities through the LEAP training tool) on
health care utilization, in particular related to maternal
and child health, and financing of out-of-pocket health
care expenditures. The evaluation research is conducted
by two independent research institutes, namely, the Afri-
can Population and Health Research Center (APHRC)
and Amsterdam Institute for Global Health and Devel-
opment (AIGHD). It is funded by the Dutch Postal Code
Lottery, the Joep Lange Institute, and the Dutch Ministry
of Foreign Affairs through the Health Insurance Fund.
Methods/design
Study site and period
i-PUSH has been ongoing in parts of Kakamega County
since 2017. The study is being carried out in Khwisero
Sub-county—one of the sub-counties in Kakamega
County, where the i-PUSH program has expanded after
the baseline survey. The implementing partners selected
two health clinics that were considered to be included in
the expansion of the i-PUSH program. Twenty-four (24)
villages located in the catchment areas of these two
clinics were randomly selected from a list of all eligible
villages (N= 239) in the catchment areas. The list of vil-
lages was provided by the Sub-county government
jointly with the i-PUSH program area manager. Random
selection was done by the research team using a com-
puter program to generate a short list of villages from
the longlist, to be randomly assigned to either the treat-
ment or the control group. Villages cover on average
about 100 households. Each village is served by one
unique CHV.
Study design and randomization procedure
The survey design is a longitudinal cluster randomized
controlled trial (RCT). Randomization occurs at the level
of villages in Khwisero Sub-county in Kenya. The “treat-
ment”and “control”groups are constructed, comparing
villages where i-PUSH has been rolled out after the
baseline with villages where i-PUSH would not roll out
until after the endline. The research team used
community-level socio-demographic and infrastructure
indicators (including number of households, village-level
access to basic amenities and public services, adult liter-
acy rate, women literacy rate, perceived health status,
and healthcare utilization indicators) from baseline data
to form pairs of similar villages and determine the exact
matching indicators.
In keeping with robustness of the cluster RCT, the
procedure hence followed four steps for matching of the
“treatment”and “control”villages: (i) purposive selection
of the Sub-county (Khwisero) where the intervention
rolled out, (ii) random selection of 24 villages, (iii) pair-
matching of villages based on relevant background char-
acteristics and baseline outcomes of interest, and (iv)
randomization of treatment and control villages within
each pair of villages by flipping a coin. Pairing villages
before randomization reduces the risk of a bad draw
during the randomization process. Randomization with-
out pairing would, in expectation, also lead to similar
control and treatment groups, but it was also possible
that the random draw produces a control and treatment
group with very different characteristics by chance [6].
This risk was reduced through pairing. We used the
Euclidean distance for our matching process, which cor-
responds to the absolute difference between the stan-
dardized values of all of the covariates for a possible pair
of matches. We conducted the matching within each of
the four originally selected health clinic catchment areas
separately. Thus, each village was matched with one of
the other five villages in the vicinity of the health clinic.
This was done to ensure that each health clinic had an
equal number of treatment and control villages in their
catchment area. Hence, we computed this distance
measure between each village and all other villages
within the same health clinic catchment area; “pair”the
two villages with the minimum distance and remove
them from the list; repeat the distance calculation ex-
cluding the pair made; and continue until all villages
were paired.
After the matching process, the randomization assign-
ment was carried out in the presence of key stakeholders
including PAF, local liaison persons and village represen-
tatives, upon explaining all steps. Consent for the proce-
dures was obtained from local government officials
before the random assignment. The following steps were
followed: papers with paired village names were folded
and put in a bag; and two village representatives from
each paired village discussed on whom to pick the paper
and after the other group members verified that the
names could not be seen, one paper was picked. A
Kenya Shilling 10 coin was used to decide which group
the picked village belonged to by flipping the coin. The
village representatives had decided that the head of the
coin should represent the control group, justifying that
Kenyatta (1st president of Kenya) was a “controlling vil-
lage”and the shield to represent the intervention group.
The process of choosing the folded paper and flipping of
the coin was repeated for all paired villages.
The treatment group thus consists of the target popu-
lation living in the randomly assigned 12 treatment vil-
lages. i-PUSH roll out in the treatment villages includes
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training of their CHVs with the LEAP tool, who subse-
quently introduced the health wallet to eligible women
living in the treatment villages they serve, and offer them
the subsidized insurance scheme on their mobile phone.
The CHVs working in the control villages (as well as the
remaining non-sampled villages on the longlist) have not
received training on the LEAP tool until endline, nor
were women in the control villages offered the health
wallet and subsidized insurance on their mobile phone.
We randomized at the village level because (1) villages
are served by one CHV each, who are either trained or
not trained on LEAP (hence, the LEAP intervention can-
not be varied within villages); (2) to avoid contamination
between households within the same villages regarding
health-related knowledge and behavior; and, (3) more-
over, it was deemed politically unfeasible to offer the
health wallet and subsidized health insurance to some
eligible households in a village but not to other eligible
households in that same village. Upon roll out of the
subsidized health services, eligible households were en-
couraged to use the services, though they were given the
right to opt out at any time.
Study population
The study population consists of eligible households liv-
ing in the selected study villages. Eligible households in-
cluded those with at least one woman of reproductive
age (WRA) (18–49) who (a) had at least one child below
4 years living with her at baseline or (b) was pregnant at
baseline. Data on all household members are also col-
lected from these eligible households.
Selected CHVs and PAF’s area manager provided the
full list of households and other necessary information
within the work area of each CHV. Based on the house-
hold demographics and pregnancy information, eligible
households were identified. Initially, the study sought a
50-50 allocation between households with a pregnant
woman and households with a child under 4 years old.
After the household listing exercise, it became clear that
there were too few pregnant women in each village to
fulfill this criterion. We then decided to include all preg-
nant women in our sample and randomly sample add-
itional households with children under 4 years old until
the cluster size (10 households per village) was achieved.
The research team did a random selection as follows: all
eligible households with children under 4 years old were
entered in a spreadsheet and receive a randomly
assigned number. The team ordered the households per
CHV based on this random number, and the first 10
households per CHV in each village were included in
the study sample. Additional eligible households per
CHV were over-sampled to serve as replacement house-
holds for dropouts.
Sample size
Sample size calculation followed Hemming et al. [7]’s
study by fixing the number of clusters per arm to be 12
clusters, and then estimated the cluster size and total
sample size. In the current study, it was assumed that
the i-PUSH program could yield an effect size of 0.4
standard deviation in terms of health care utilization
with an intracluster correlation (ICC) of ρ= 0.014. The
estimates of the ICC were derived from Geng et al. [8]’s,
study conducted in Nandi County which used high-
frequency data on diaries on health-seeking behaviors
and financial expenditures over 1 year (October 2012–
October 2013). The calculation of the ICC was based on
health care utilization measured as visits to any formal
health provider, unconditional on reported health symp-
toms. It hypothesized a confidence interval of 95%, a
margin-of-error of 5%, and a power of 80%. With a clus-
ter size per arm of 12 clusters, and a total number of
women per cluster of 10, the total sample size was 120
households per arm and 240 households for the full
study.
To keep sample size at par, households that dropped
out of the study before the start of the intervention were
replaced with new eligible households on a rolling basis
for a maximum period of six months, or until the pro-
gram started.
Description of the i-PUSH program
i-PUSH is a comprehensive intervention that ultimately
aims to improve the utilization of Reproductive and Ma-
ternal and Child Health (RMNCH) services among
WRA and their young children in Kakamega and
Nairobi Counties, by increasing knowledge about and
(financial) access to RMNCH services as well as improv-
ing the quality of care of RMNCH services. In the ori-
ginal i-PUSH program that is the focus of this
evaluation, households receive the first year of health in-
surance premium for free, while they are stimulated and
supported to save for a 50% co-payment in the second
year, and a 100% premium payment thereafter. The free
provision in year one is expected to show the benefits of
insurance to the selected households. The support for
savings during the first year for a 50% co-payment in the
second year is expected to install a habit of savings.
1
The i-PUSH program utilizes innovative digital tools
developed by both partners to enhance access to afford-
able and quality health care to low-income WRA and
their families. Through i-PUSH, selected clinics partici-
pate in the “SafeCare”quality improvement program.
1
PAF is currently working on new approaches to differentiate the size
of the co-payment by socioeconomic status, which could be more ef-
fective in targeting subsidized health insurance to those who cannot af-
ford the full premium, while at the same time, introducing new
incentives to save for co-payments for those who can afford them.
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Women receive the National Health Insurance Fund
(NHIF) SupaCover at subsidized premiums on their mo-
bile phone, using PAF’s so-called “health wallet.”The
“health wallet”runs on the digital platform M-TIBA,
which registers health care utilization at participating
clinics, connecting patients, providers, and payers on
one platform. PAF is piloting several other tools on this
platform in addition to the health wallet, such as Con-
nected Diagnostics for Malaria in Kisumu County, the
MomCare program for antenatal and postnatal care in
Kisumu and Nairobi Counties, and socio-economic map-
ping for UHC support in Kisumu County. CHVs are the
first point-of-contact for women in the program. They
make use of AMREF’sMjali (Mobile Jamii Afya Link)
tool for digital registration of household information and
the mobile phone-based LEAP tool for training. The
LEAP tool employs a mobile learning approach to train
and empower CHVs using their mobile devices operat-
ing from any phone [9]. This enables the CHVs to learn
at their own pace, and with their own mobile devices
while in the community, providing both interpersonal
and community aspects of learning. This evaluation
study will focus on two of these spheres of interest sup-
ported by mobile tools: knowledge on health and health
behavior and (financial) access to healthcare services; the
quality upgrades at the health facilities cannot be evalu-
ated with our study design because all i-PUSH clinics in
our study area were already upgraded at baseline. A
rough sketch to the implementation of enrolment, inter-
vention, and assessment of the program is indicated in
Table 1. The implementation of i-PUSH will not alter
access to the usual health care services (including use of
any medication) throughout the implementation of the
program.
The LEAP training tool for CHVs
Working with CHVs, i-PUSH will increase the know-
ledge on both RMNCH and on insurance/health finan-
cing among women and men in the treatment
communities. To this end, CHVs receive additional
training through the specially developed LEAP training
tool (module) carried out by AMREF. This tool contains
modules on specific health terrains of interest (notably,
health promotion activities for children under 5, family
planning, antenatal care (ANC), danger signs in preg-
nancy and after delivery, danger signs in children under
5, maternal and child health and nutrition, water safety,
hygiene and sanitation) as well as on health savings and
health insurance. The CHVs can follow this training on
the smartphone that they will receive as part of their in-
clusion in the program. It complements the standard
monthly training sessions of CHVs by AMREF.
AMREF can currently assess whether LEAP improves
the knowledge of its CHVs because at the end of each
training module, CHVs can participate in a quiz on the
tool that tests their newly gained knowledge. We will
examine whether the LEAP training tool on top of the
standard training activities of CHVs translate into im-
proving women and men’s knowledge and behavior on
pre-specified topics. We will also assess whether CHVs’
time spent on LEAP, number of training modules com-
pleted, and scores on the LEAP quizzes predict impact
on women and men in the communities.
Subsidized access to NHIF SupaCover health insurance
through the M-TIBA health wallet
The improved knowledge on health and health financing
of women and men is also expected to translate into im-
proved attitudes towards insurance and saving for health
and insurance. To support these changes in knowledge
and attitudes, WRA would receive the first year of their
NHIF insurance premium at 100% subsidy and the sec-
ond year at a 50% subsidy. The subsidies are expected to
enhance initial enrolment in NHIF such that enrollees
can experience first-hand the benefits of insurance.
Moreover, this will allow women to be acquainted with
regular savings for the insurance premium for the next
year, such that they will get into the habit of recurrent
savings and increasingly be able to frequently set aside
small amounts of money.
An integral component of the i-PUSH program is the
so-called “health wallet”on M-TIBA (a mobile payment
platform). Most WRA in the target population have lim-
ited access to formal financial services such as bank sav-
ings accounts. The widespread availability of M-PESA
opens a new avenue of change in this respect. To allevi-
ate this constraint, i-PUSH offers women the opportun-
ity to set aside money in a commitment savings device
on their mobile phone. The only requirement is that the
mobile number is registered on their personal name.
This will allow them (and other people such as spouses,
relatives) to transfer funds into the wallet through M-
PESA, which are subsequently “reserved”for direct pay-
ment of medical costs at M-TIBA-connected health pro-
viders or for future payment of the annual insurance co-
premium. This feature of the health wallet is expected to
support women in their financial planning—funds trans-
ferred into the wallet are kept safe and secure until they
are needed for health-related purposes. Additional
small-scale interventions are added to the program to
enhance further savings, such as the provision of a sav-
ings calendar. Thus, this component of the program ex-
pects to increase savings for health as well as uptake and
renewal of health insurance among the target
population.
Enrolment in health insurance is further facilitated
through an additional feature of i-PUSH—CHVs can
digitally enroll WRA and their household members
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(spouses, children, and other dependents) on the NHIF
SupaCover as long as they have an ID or birth certificate
available at the time of the CHV visit. The CHV uploads
the required documents and takes care of the registra-
tion process on his or her smartphone (as provided by
the i-PUSH program), saving the women from a lot of
hassle in traveling back and forth to the insurance offices
to hand over all the required documents and go through
the administrative steps. In other words, i-PUSH is also
expected to relieve logistical and time constraints to the
uptake of insurance. Such a scheme enhances enrolment
into insurance [9].
Expected outcomes
Overall, the outcomes of interest for this evaluation
study that are measured during the 18 months of follow-
up include the following:
Table 1 Schedule of enrolment, interventions, and assessments
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Primary outcomes (health care utilization and health
expenditures)
Absolute number of visits to any formal health care
provider for treatment of illness or injury (to
measure curative healthcare utilization), over the full
period between baseline and endline.
Secondary outcomes
Health outcomes
Proportion of children under 5 who are fully
immunized (to measure preventive healthcare
utilization among young children).
Proportion of pregnant women who complete their
maternal care journey, i.e., 4 ANC visits, skilled
birth attendant, 1 PNC visit (to measure maternal
healthcare utilization among pregnant women).
Proportion of children malnourished
(anthropometrics) for children under 5.
Proportion of children under 5 experiencing
common childhood illnesses (diarrhea, acute febrile
illnesses, acute respiratory infections) for children
under 5.
Out-of-pocket health expenditures.
Health knowledge and behavior
Knowledge on maternal and child health.
Proportion of children under 5 sleeping under a bed
net.
Women’s empowerment
Financial and health related decision-making power
of women.
Data collection instruments and techniques
Data collection consists of four main components: (1) a
qualitative study, (2) baseline and endline household sur-
veys, (3) weekly financial and health diaries interviews
with all adults and emancipated minors in the house-
holds, and (4) behavioral lab-in-the-field experiments.
Both quantitative and qualitative tools were piloted in
Nairobi slums and debriefed to the research team in-
cluding the field team.
Qualitative study
The study uses qualitative data collection methods to get
a deeper understanding of the perceptions and behaviors
of the population on health insurance and health care
utilization, to feed into the quantitative instrument de-
sign at baseline and to complement findings from the
quantitative surveys at endline. This will help
understand the motivations, drivers, and obstacles to
savings for insurance in the target population. The quali-
tative baseline study is based on in-depth interviews
(IDIs, n= 20) and focus group discussions (FGD, n=4)
with eligible men and women who were purposively
sampled after CHVs’mobilization to willingly participate
in the study. Another qualitative study will be conducted
parallel to the quantitative endline survey to provide
additional under-the-skin description and/or for im-
proved interpretation if the impact evaluation yields un-
expected results.
Baseline and endline survey instruments
The quantitative evaluation starts with a baseline sur-
veybeforetherolloutofthei-PUSH program and is
completed by an endline survey conducted approxi-
mately 1 year after the i-PUSH introduction. Both
surveys include modules on household demographics,
socio-economic indicators, food and non-food con-
sumption indicators, financial inclusion, participation
in community networks, as well as self-assessed health
status, health-related knowledge and behavior, health
care utilization and health expenditures, maternal
health, mental health, intra-household decision-
making processes, and gender dynamics. In addition,
the endline survey will include a satisfaction module
on participation in the i-PUSH program for women
in the treatment areas only and will be collected vir-
tually using telephone interviews due to the COVID-
19 pandemic. The household head or the most
knowledgeable household member responds to the
household roster, household-level modules including
health care utilization and expenditures, and questions
about under-aged household members. The focal per-
son for the modules on maternal health, intra-
household decision-making and health knowledge and
behavior is the WRA in the household. All remaining
individual-level modules (mental health, preferred
clinic, mobile money use, savings groups and financial
instruments) are reported by all adult individuals in
the household (Table 2 in Appendix).
Financial and health diaries
This study uses weekly financial and health diaries
data collection methodology as its core research in-
strument, because this technique is well suited to ad-
dress the research objectives by providing a granular
insight into the financial lives and health-related
decisions of participating households. The diaries are
collected over the full study period between baseline
and endline survey. The financial diaries record all fi-
nancial transactions such as purchases, gifts, remit-
tances, and loans, including those between household
members in the 7 days prior to each interview.
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Transactions are described by type, incoming or out-
going cash, amount, purpose, transaction partner, and
date. The health diaries provide a detailed picture of
the incidence of illnesses and injuries, as well as pre-
ventive and curative health-seeking behavior. Health
diaries collect data on all health events that occurred
to any of the household members (respondents, their
children and other household members) in the 7 days
prior to each interview. This includes symptoms,
whether any health care was sought, which health
provider was visited, health services received, out-of-
pocket health expenditures, date of onset of the
symptoms, and date of provider visit(s).
Respondents to the weekly diaries interviews are all
adults in the study households who are economically ac-
tive (i.e., who handle money, excluding young adults
who are still studying) and capable of conducting the
interview (i.e., excluding very old-age or disabled house-
hold members). Emancipated minors, i.e., teenage girls
who are a mother or who are pregnant, are also included
as diaries respondent. The financial diaries are reported
by each individual him- or herself. The health diaries
can be reported by one individual for the entire
household.
Data are collected through structured and formatted
digital tools that allow to analyze data as they are re-
corded and improve data collection tools mid-course
of the process. The short recall period drastically re-
duces recall bias and ensures that both major and
minor illness episodes, including those with foregone
care are reported. Because interviewers visit house-
holds weekly, they build a relationship of trust, which
enables the diaries to capture also more sensitive
health events. This is of particular importance in
relation to capturing maternal and child health
experiences.
Qualified fieldworkers were recruited and trained on
data collection tools and techniques. Diaries data are
collected through personal interviews in a
conversation-like manner. Interviewers record infor-
mation navigating through a specialized software pro-
gram while the interview unfolds. The tool was
administered face-to-face using tablets and took about
10–15 min. Prior experience of the research team with
similar data collection indicates that such interviews
generally last 10–15 min per household as both inter-
viewers and respondents get more experienced. To re-
duce burden on the respondents, interviewers take
care in planning the day, time, and location of the
weekly interviews at the convenience of the respond-
ent. The respondents are interviewed separately and
in private spaces to ensure confidentiality. The
COVID-19 situation has switched the mode of data
collection to telephone interviews. Fieldworkers were
retrained on telephone interview ethics and tech-
niques. This will be implemented until the crisis
recedes.
At regular intervals, the weekly diaries are comple-
mented with pop-up modules. In particular, these in-
clude on a monthly basis, a pregnancy module, perinatal
depression, and general mental health, and on a quar-
terly basis, food consumption of children, anthropomet-
rics of children and their mothers. Since the COVID-19
outbreak, a monthly module has been added to assess
the effect of COVID-19 pandemic on COVID19-related
knowledge, preventive behavior (such as hand-washing
and social distancing), mobility, barriers to health-
seeking behavior, and fear/worries.
Behavioral lab-in-the-field experiments
Diaries respondents are also invited to participate in a
number of incentivized behavioral lab-in-the-field
games to measure women’s empowerment (baseline,
midline and endline) and risk attitudes (midline and
endline only). As is standard in the behavioral eco-
nomics literature, respondents can earn (small)
amounts of money dependent on their decisions. This
has been shown to improve accuracy of responses.
The research team has extensive experience in con-
ducting similar games in Kenya, Tanzania, Nigeria,
and elsewhere. All payments are made directly to the
respondent through M-PESA.
Data management and analysis
Trained team leaders are situated in the field to
supervise real time data collection. Data quality is en-
sured by regular spot checks and sit-ins to approxi-
mately 5–10% of each fieldworker’sdailyworkto
verify authenticity of the data collected. Data collec-
tion is done electronically using tablets, with spot
checks for quality control. The field supervisors cer-
tify the quality of the data through editing the data
before they are transferred to the database. Once the
data collection is completed and synchronized in a
centrally located database, all inconsistencies are re-
solved prior to data analysis. Data quality is checked
by a qualified data management team within the affili-
ated research organizations. An automated routine to
check on data completeness, correctness, and
consistency runs on 100% of the collected data. A
discrepancy report is generated to help in following
upon any inconsistencies, errors or missing data with
the responsible interviewer.Similarly,thequalityof
qualitative data will be ensured through recruitment
and training of qualified field interviewers with ex-
perience in qualitative data collection. A qualified
transcriber will transcribe the interviews verbatim and
double coding of about 10% of the transcripts will
Abajobir et al. Trials (2021) 22:629 Page 8 of 13
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also be done to ensure consistency in the application
of the codes. Access to data is granted for all re-
search teams in respective research organizations.
Data auditing is carried out by the research team on
a progressive basis on weekly basis throughout the
study duration.
The final analysis will investigate the extent of con-
tamination (e.g., whether participants who were
assigned to the control group nevertheless partici-
pated in the intervention) and appropriately account
for it. The final will any risks of contamination, e.g.,
it will exclude those participants who are randomized
to the intervention but do not adhere to the
intervention.
Quantitative data will be analyzed using Stata ver-
sion 14 statistical software. The first set of analysis
will consist of descriptive statistics and will
summarize and compare using measures of central
tendency and dispersion (mean (SD), range and me-
dian). This will allow us to examine participants’
characteristics across the different sub-groups. We
will first check whether the outcomes and covariates
in the control group and treatment group are com-
parable at baseline using ttest adjusted for clustering
at the village unit for continuous variables and
cluster-adjusted chi-square for binary variables. The
second set of analysis will consist of assessing the
causal effect of the i-PUSH intervention via an ana-
lysis of covariance (ANCOVA) based on intention-to-
treat (ITT) analysis (all respondents who are random-
ized will be included in the statistical analysis and
will be analyzed according to the group they were ini-
tially/originally assigned). To account for clustering of
data at the village level, multi-level mixed models will
be used. In addition, a generalized linear mixed model
for repeated measures with random components will
be used.
Based on our research questions, we will explore
several additional econometric models. We will ex-
plore the impact of the i-PUSH program on health
outcomes (healthcare utilization, health status, per-
centage of children who are fully immunized, etc.)
and financial outcomes (percentage of women using
NHIF/MTIBA to access services, percentage of people
enrolled in NHIF through M-TIBA, percentage of
people saving through M-TIBA, total amount saved
on M-TIBA, etc.) via an analysis of covariance
(ANCOVA) based on ITT analysis. Since we will have
several rounds of data collection on the same respon-
dents, we will combine all rounds of data collection
(baseline and follow-up waves), and we will run an
ANCOVA econometric model on a pooled panel data.
More specifically, in the econometric model below,
we will regress the follow-up measurement of the
outcome variable (dependent variable) on the follow-
ing covariates: baseline measurement (pretest meas-
urement) and treatment group while controlling for
the baseline socio-demographic characteristics such as
maternal age, education, socioeconomic status (wealth
quintile), crowding (persons per room), occupation,
etc. The considered model is thus:
Yij ¼α0þα1Xij0þα2Treamentij þeijt ;ð1Þ
with Y
ij
,X
ij0
the posttest and pretest/baseline measures
of the outcome for the ith respondent from the jth
cluster, respectively. Treament
ij
is a dummy for being
assigned to the treatment group. It takes one if the
respondent belongs to the intervention cluster and
zero if control. α
2
is the ITT effect or the impact of
the i-PUSH program. We will use an OLS model with
standard errors clustered at the level of the commu-
nity units.
In addition to pretest measures, all other baseline
covariates such as age, education, socioeconomic sta-
tus (wealth quintile), crowding (persons per room),
and occupation, will also be included in Equation (1).
Thus, in Equation (1), the treatment effect of the i-
PSUH program α
2
assesses the treatment difference
on post-treatment outcome adjusted for baseline.
We will assess whether the i-PUSH will significantly
contribute in empowering women. For self-reported
surveys on women empowerment, we will construct a
total score of women’s empowerment which is based
on a factor analysis of the various domains on which
the woman indicates that she has sole, joint, or no
decision-making power within the household (for each
domain, the decision-making binary indicator will be
equal to 1 if the woman respondent makes the deci-
sion alone or jointly with her partner and 0 other-
wise). This total score will be used as the dependent
variable, and Equation (1) will be used to estimate
the impact of i-PUSH program on women’sempower-
ment. Furthermore, we will follow Almås et al. (2018)
to construct an alternative measure of women’sem-
powerment based on the decision in the empower-
ment game. This dependent variable will be the
willingness-to-pay which is the share that the woman
is willing to pay in order to receive the amount her-
self in private rather than giving a (higher) amount to
her husband. Interim data analyses (e.g., for policy
briefs) are done using baseline and weekly diaries data
to inform the context and implementation of the re-
search or i-PUSH program.
Risks and measures to minimize them
As the information collected will be on health service
delivery,financial,andinsuranceinformation,wedo
Abajobir et al. Trials (2021) 22:629 Page 9 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
not anticipate any risks to participants. Nevertheless,
we will aim to minimize any potential risks by being
as forthcoming as possible on the project description
and the ethical process. In the case of minimal dis-
comfort as a result of personal and sensitive ques-
tions, the research team will endeavor to ensure that
the participant is given ample time to compose them-
selves and reassure them of confidentiality and ability
to stop the interview if they are not able to continue
with the interview. Ancillary and post-trial care may
not be indicated as this is a community trial that
does not involve any significant harm to the partici-
pating households.
Dissemination of findings
Scientific dissemination through peer-reviewed publi-
cations and implementing partners’dissemination
through quarterly updates (e.g., meetings, presenta-
tions and brainstorm sessions) to county/sub-county
officials and community representatives will ensure
that findings are shared on a regular basis. These ac-
tivities allow all parties to benefit from each other’s
insights and interpretations. Annual PharmAccess
strategy meetings and AMREF international research
meetings facilitate sharing knowledge with their re-
spective country offices. Moreover, APHRC has good
links with Kenyan policy makers in the field of
health, and PAF is well connected to national and
county-level government officials, the NHIF, as well
as many local healthcare providers. Finally, the link-
ages between the research group, PAF and the Dutch
Ministry of Foreign Affairs with respect to health fi-
nancing ensure regular sharing of findings at the
Dutch national policy level. The close connection of
the AIGHD with the Joep Lange Institute and the
Amsterdam Technology and Health Institute (ATHI)
facilitates sharing of findings with parties active in
social technologies (start-ups working on the inter-
face of digital technologies and health, mobile pay-
ment platforms).
Ethical considerations
The protocol was reviewed and approved by APHRC’s
internal ethical review committee and AMREF’s accre-
dited ethical review board. Ethical clearance was ob-
tained on 21 April 2020 with number P679-2019.
Research permits were obtained from the National
Commission for Science, Technology and Innovation
(NACOSTI) agency of Kenya. Informed consent from
study participants was obtained upon detail orienta-
tion on project information, the purpose of the re-
search study, possible risks, and benefits. The model
consent form is provided in a separate file. All inter-
views are conducted in private and no identifying
information is included in any data or reports; all
data is anonymized to protect the identity of respon-
dents. The participants are given a unique code and
all identification data of participants shall be shred-
ded/destroyed after analysis has been completed. Fur-
ther ethical approval will be sought for any
modifications to the protocol which may impact on
the conduct of the study.
Discussions
Despite various international efforts to improve
maternal and child health globally, several LMICs,
especially those in sub-Saharan Africa (SSA), are
struggling with low rates of maternal and child sur-
vival. Improving access to health care throughout
pregnancy, childbirth, and during childhood is key in
improving maternal and child health. Experience
over the past decade has shown that building capaci-
ties of individuals, families, and communities to
ensure appropriate self-care, prevention and care-
seeking behavior improves maternal and child health
outcomes [5]. However, this is more difficult to
achieve in poor populations who have worse health
outcomes than the non-poor. Barriers such as costs
of care, lack of information and cultural beliefs
impede access to health care among poor
communities.
Since its independence in 1963, the government of
Kenya has initiated policy, reforms, and strategies to-
wards UHC for all, including those in vulnerable situ-
ations such as low-income mothers and children. In
1998, the NHIF act was amended to enhance cover-
age among the poor, accelerate coverage of the infor-
mal sector, and enhance the benefit package [10,11].
The most recent reform along the same line was the
introduction of free maternal health services in 2013
that included abolition of user fees at primary health
care facilities. Despite these positive steps, Kenya’s
implementation of UHC has been riddled with myr-
iads of challenges including poor quality of care, low
utilization, and catastrophic spending by households,
especially the poor and other vulnerable groups [11,
12]. Overall, health insurance coverage in Kenya has
only increased from 8 to 20% between 2009 and
2014, and those from wealthy households were 12
times more likely to have insurance compared to
those in poor households. Similarly, those in informal
employment and rural settings were less likely to be
insured [4].
The i-PUSH program thus has been initiated to accel-
erate the expansion of health insurance coverage among
the low-income population, WRA and their family mem-
bers, using innovative digital tools. This research investi-
gates the reasons for low access to health services, in
Abajobir et al. Trials (2021) 22:629 Page 10 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
particular related to maternal and child services, where
the problems are persistent. It also investigates in-depth
to what extent costs of services hinder access, and
whether expanding UHC through the i-PUSH program
is an effective strategy to increase access and financial
protection. Information generated from this study will
be instrumental in improved implementation of policies
supporting the roll-out of UHC. This research will also
provide valuable information on UHC policies for the
academic community, in particular, because we use
high-frequency data (diaries) and analyze digital tech-
nologies that can support UHC.
To achieve these objectives, the study assumes the
following: (i) no contamination across treatment and
control groups; this is enhanced by a focus on villages
and CHVs across villages, instead of individual
women; (ii) the county (sub-county), AMREF, and
PAF will stick to the randomization plan that was in-
dependently developed by the research team but in
agreement with stakeholders, and a memorandum of
understanding (MoU) was signed among the partners;
and (iii) the government does not unexpectedly and
drastically alter its UHC policy plans in Kakamega
(i.e., the government does not suddenly decide to
offer free public care in Kakamega County, because
that will take away many of the benefits of i-PUSH).
Although there is little the research team can do in
that case, this is the reason a flexible, high-frequency
diaries design instead of standard (less flexible) RCT
design is chosen. That is, there are more data points
and if policies change half way, it is possible to actu-
ally capture this in the data. For example, this has en-
abled the research team to adjust data collection
methods halfway the study period to assess the im-
pact of the COVID-19 pandemic.
This project is not free from limitations. One of the
main limitations of the study is related to the exclusion
of the following components in the impact evaluation,
though they are an integral part of i-PUSH program.
These include four components:
(i) Capacity-building at the regional level: i-PUSH
invests in improving the capacity of community-
based organizations to increase community-wide di-
alog on RMNCH,
(ii) Health facility quality upgrades: the improved
capacity of healthcare providers (implemented
through SafeCare) to deliver RMNCH services is
expected to lead to improved standards of care,
improved quality of services and enhanced client
satisfaction.
(iii)M-JALI household registration tool: AMREF has
developed a digital tool to increase the capacity of
CHVs to keep a census of the households in their
target area, and collect household survey data on a
rolling basis that can be used for the systematic
reporting of community-level data, thereby poten-
tially enhancing health-related decision-making and
resource allocation at all policy levels.
(iv) M-TIBA digital health platform: PAF aims to
increase the capacity of healthcare workers on
learning, data capturing and reporting through the
M-TIBA platform that allows among others for
digital recording of health visits, diagnoses, treat-
ments, and services as well as payments/billing be-
tween patients, NHIF, Ministry of Health, and
providers.
These components are left out of the evaluation be-
cause our study focuses on the demand-side (target
population), and the described components are imple-
mented either at the community-level or at the facility-
level.
Furthermore, there may be a potential for Hawthorne
effects because of the weekly visits. However, since both
the treatment and the control group are included in the
weekly diaries data collection, we expect the selectivity
of this effect to remain limited.
Conclusions
This study aims to evaluate the impact of i-PUSH
program on maternal and child health care utilization,
women’s health including their knowledge, behavior,
and uptake of respective services, as well as women’s
empowerment and financial protection. It also aims
to evaluate the impact of the LEAP training tool on
empowering and enhancing CHVs’health literacy and
to evaluate the impact of the M-TIBA health wallet
on savings for health and health insurance uptake.
The findings will inform stakeholders to formulate
better strategies to ensure access to UHC in general
and for those highly vulnerable segments of the popu-
lation in particular. The findings of this research will
provide valuable information on UHC policies for the
academic community, policy-makers, and other stake-
holders to support the achievement of SDGs.
Trial status
Protocol was registered in NIH - ClinicalTrials.org with
registration number: AfricanPHRC; Trial ID:
NCT04068571 dated on 28 August 2019 (https://
clinicaltrials.gov/ct2/show/NCT04068571). Protocol
amendment number: 01. It was also registered in The
American Economic Association's registry for randomized
controlled trials (Trial ID: AEARCTR-0006089) dated on
26 June 2020 (https://www.socialscienceregistry.org/
trials/6089). Recruitment began in November 2019 and
will continue until June 2021.
Abajobir et al. Trials (2021) 22:629 Page 11 of 13
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Abbreviations
AIGHD: Amsterdam Institute for Global Health and Development; AMRE
F: African Medical and Research Foundation; ANC: Antenatal care;
APHRC: African Population and Health Research Center; CHV: Community
health volunteer; FGD: Focus group discussion; ICC: Intracluster correlation;
IDI: In-depth interview; ITT: Intention-to-treat; i-PUSH: Innovative Partnership
for Universal Sustainable Healthcare; LMICs: Low and middle-income coun-
tries; KDHS: Kenya Demographic and Health Survey; NACOSTI: Memorandum
of understanding; NACOSTI: National Commission for Science, Technology
and Innovation; NHIF: National Health Insurance Fund; PAF: PharmAccess
Foundation; RCT: Cluster randomized controlled trial; RMNCH: Reproductive
and Maternal and Child Health; SDG: Sustainable Development Goal;
SSA: Sub-Saharan African; WRA: Women of reproductive age; UHC: Universal
Health Coverage
Acknowledgements
The research team is highly grateful for the funder, study participants, and
the field team as well as all stakeholders directly and indirectly contributing
for the smooth flow of the study.
Authors’contributions
EMS, WJ, MP, and HPPD conceived the study and its design; AA, RG, and CW
implement the study; AN manages the data; DM, SN, and NM developed
software for the implementation of the study. All authors read and approved
the final manuscript.
Funding
The data collection and research are funded by the Dutch National Postcode
Lottery, the Joep Lange Institute, and the Dutch Ministry of Foreign Affairs
through the Health Insurance Fund. The funder does not have any role in
the design of the study and collection, analysis, and interpretation of data
and in writing the manuscript.
Availability of data and materials
Data will be stored securely at the APHRC and Vrije Universiteit data
repositories and available upon reasonable request after publication.
Declarations
Ethics approval and consent to participate
Obtained (reference number: P679-2019). Informed consent was obtained
from all study participants.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
African Population and Health Research Center, Nairobi, Kenya.
2
Amsterdam
Institute of Global Health and Development, Amsterdam, Netherlands.
3
Vrije
Universiteit Amsterdam, Amsterdam, Netherlands.
Received: 20 August 2020 Accepted: 3 September 2021
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Appendix
Table 2 Modules covered in the study, unit of responses and respondents
Modules Unit of response Respondent
1. Cover page Household Enumerator
2. Household roster All household members Head/most informed member
3. Socio-demographics All household members Head/most informed member
4. Education All household members > 5 years Head/most informed member
5. Health outcomes All household members Head/most informed member
6. Health care utilization All household members Head/most informed member
7. Food consumption Child < 4 years Head/most informed member
8. Housing and assets Household Head/most informed member
9. Employment and income All household members > 12 years Head/most informed member
10. Participation in diaries All adults or emancipated minor Individual
11. Women’s empowerment Female adult or emancipated minor Individual
12. Preferred clinics and mobile money All adults or emancipated minor Individual
13. Mental health All adults or emancipated minor Individual
14. Women’s health Female adult or emancipated minor Individual
15. Savings groups and cooperatives All adults or emancipated minor Individual
16. Financial instruments All adults or emancipated minor Individual
17. Health knowledge Female adult or emancipated minor Individual
18. COVID-19 module All adults or emancipated minor Individual
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Publisher’sNote
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