Available via license: CC BY 4.0
Content may be subject to copyright.
1
The impact of i-PUSH on maternal and child health outcomes, health care utilization
and financial protection: a cluster randomised controlled trial based on Financial and
Health Diaries data
Amanuel Abajobir1*, Richard de Groot3, Caroline Wainaina1, Anne Njeri1, Daniel Maina1,
Silvia Njoki1, Nelson Mbaya1, Hermann Pythagore Pierre Donfouet1, Menno Pradhan2, 3,
Wendy Janssens2, 3, Estelle M Sidze1
1African Population and Health Research Centre, Nairobi, Kenya
2Vrije Universiteit Amsterdam, Amsterdam, Netherlands
3Amsterdam Institute of Global Health and Development, Netherlands
*Correspondence:
E-mail: aabajobir@aphrc.org
Mobile: +254 (0) 721141014
2
Abstract
Background: Universal Health Coverage (UHC) 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 (LMICs), 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 CHVs’ 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 cluster randomised 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. A random half of the households live in villages
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 (AMREF-ESRC). Research permits were obtained from the
National Commission for Science, Technology and Innovation (NACOSTI) agency of
Kenya.
Discussion: People in LMICs often suffer from high out-of-pocket healthcare expenditures,
which in turn, impedes 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. Notable, 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 UHC in general, and for a highly
vulnerable segment of the population in particular, including low-income mothers and their
children.
Keywords: Maternal and child health, health care utilization, out-of-pocket health
expenditures, health insurance, Universal Health Coverage, digital tools, Kenya
Trial registration history: 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 randomised controlled
trials (Trial ID: AEARCTR-0006089; Date: 26 June 2020).
3
1. 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 without 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 universal health coverage, including financial risk
protection, access to quality essential health-care services and access to safe, effective, quality
and affordable essential medicines and vaccines for all). Achieving UHC is particularly
important in achieving SGDs goals related to maternal and child health (i.e., reducing the global
maternal mortality ratio to less than 70 per 100 000 live births (SGD 3.1) and ending
preventable deaths of newborns and children under 5 years of age (SGD 3.2). All countries are
committed to reduce neonatal mortality to at least as low as 12 per 1000 live births and under-
5 mortality 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 sufficient quality [1] and about
100 million people fall into extreme poverty each year due to ill-health [2], particularly in low-
and middle-income countries. To translate commitment to UHC into a reality, still need to
undertake systemic reforms that require strong management and organization.
Despite being classified as a middle-income country in 2014, Kenya still remains among the
25% poorest countries strongly affected by social and health inequality in the world [1, 2].
More than one-third of Kenyans have an income below the poverty line (1.9 USD/day) [1-3].
Inequalities in access to healthcare, in particular maternal and child care are still rampant
despite major improvements made through targeted policies over the past few years [4]. The
poorest mothers are still far behind in terms of coverage of essential reproductive and maternal
and child health services. The Government 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]. The objective is to achieve a 100% cost subsidy for essential health services and to reduce
out-of-pocket health expenditures by half. Low-cost health insurance schemes, eHealth and
mobile health (mHealth) services are among other opportunities to achieve this
goal. Consequently, the non-governmental organisations 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
4
AMREF and PAF are jointly implementing their Innovative Partnership for Universal
Sustainable Healthcare (i-PUSH) program.
The i-PUSHprogram utilizes innovative digital tools developed by both partners to enhance
access to affordable and quality health care to low-income women of reproductive age (WRA)
and their families. Through the i-PUSH, women receive the National Health Insurance Fund
(NHIF) SupaCover at subsidized premiums on their mobile phone, using the 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 clinics on one
platform. The PAF is supporting the implementation of UHC by registering households using
M-TIBA and developing a socio-economic mapping of the population. One such tool,
Connected Diagnostics for malaria, was recently piloted in Kisumu County (Kenya). Another
tool, designed to ease communication between doctors and women during pregnancy and
postnatal period, has been piloted in Nairobi and there are plans to scale up in other counties.
Community Health Volunteers (CHVs) are the first point-of-contact for women in the program.
They make use of AMREF’s Mjali (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
operating from any phone [5]. 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.
In order to maximize the effectiveness of this program, it is important for stakeholders to have
a deep understanding of current access to health services, households’ health-related decision-
making and out-of-pocket health care expenditures in the target populations. The main aim of
the study is to evaluate the impact of i-PUSH (most notably enhanced access to subsidized
health insurance through the “health wallet” and increased CHV training 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 the independent research institutes, namely, the African Population and Health
Research Center (APHRC) and Amsterdam Institute for Global Health and Development
(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.
5
2. Methods/Design
Study site
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, where the i-PUSH program
will expand after the baseline survey. The implementing partners selected two health clinics
that were under consideration to be included in the expansion of the i-PUSH program. Twenty-
four (24) villages located in the catchment areas of these four clinics were randomly selected
from a list of all villages in the catchment areas. The list of villages 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 computer program to generate a short list of villages from
the longlist, to be randomly assigned to either the treatment 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. The “treatment” and “control” groups are
constructed, comparing villages where i-PUSH will be rolled out after the baseline with village
where i-PUSH will not be rolled out until after the endline.
The research team used community-level socio-demographic and infrastructure 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 will roll out; (ii) random selection of 24 villages; (iii) pair-
matching of villages based on relevant background characteristics and outcomes of interest;
and (iv) randomization of treatment and control villages within each pair of the villages by
flipping a coin. Pairing villages before randomization reduces the risk of a bad draw during the
randomization process. Randomisation without pairing will, in expectation, also lead to similar
control and treatment groups, but it is also possible that the random draw produces a control
and treatment group with very different characteristics by chance [6]. This risk is reduced
through pairing. We used the Euclidean distance for our matching process, which corresponds
to the absolute difference between the standardized values of all of the covariates for a possible
pair of matches. We conducted the matching within each of the four health clinic catchment
areas. 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
6
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 excluding the pair made; and continue until all villages were paired.
After the matching process, the randomization assignment was carried out in the presence of
key stakeholders including PAF, local liaison persons and village representatives, upon
explaining all steps. Consent for the procedures 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 village”, 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 population living in the randomly assigned 12
villages. i-PUSH roll-out in the treatment villages includes training of their CHVs with the
LEAP tool, who will subsequently introduce the health wallet to eligible women living in the
treatment villages, 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) will not receive training on the LEAP tool yet, nor will women in the control villages
be offered the health wallet and subsidized insurance on their mobile phone. They constitute
our comparison group. 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
cannot be varied within villages); 2) to avoid contamination between households within the
same villages regarding health-related knowledge and behavior; and 3) moreover, 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
on roll out of the subsidized services, eligible households are encouraged to use the services,
though they are given the right to opt out at any time.
7
Study population
The study population consists of eligible households living in the selected study villages.
Eligible households included 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.
Selected CHVs and PAF’s area manager provided the list of households and other necessary
information within the work area of each CHV. Based on the household 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 pregnant women
in our sample, and randomly sample additional 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
Excel sheet and receive a randomly assigned number. The team ordered households per CHV
and 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 households 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 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 one 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 symptoms.
It hypothesized a confidence interval of 95%, a margin-of-error of 5% and a power of 80%.
The cluster size per arm, total women per clusters was therefore 10 and 12 women, respectively.
Hence:
8
, with ES (effect-size) the ratio between the mean difference and pooled
standard deviation,
and are the values from the normal distribution holding
,
below it, respectively. and are levels of significance and power, respectively. Thus,
By fixing the clusters per arms to k=12, the required sample size per arms is given by the
following formula:
In the formula above, a feasibility check must be done so that . This condition is satisfied
since
If we assume an attrition rate of 10%, then:
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 program start.
Description of the i-PUSH program
i-PUSH is a comprehensive intervention that ultimately aims to improve the utilization of
Reproductive and Maternal 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 improving the quality of care of RMNCH services. In
the original i-PUSH program that is the focus of this evaluation, households receive the first
year of health insurance premium for free, and 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 50% co-payment and the support for savings in the second year is expected to install a
habit of savings.
1
This evaluation study will focus on the first two of these three spheres of interest: knowledge
and (financial) access; the third sphere of interest–quality upgrades at the health facilities,
1
PAF is currently working on new approaches to differentiate the size of the co-payment by socioeconomic status,
which could be more effective in targeting subsidized health insurance to those who cannot afford the full
premium, while at the same time, introducing new incentives to save for co-payments for those who can afford
them.
9
cannot be evaluated 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, intervention
and assessment of the programme is indicated in Annex 1.
The LEAP training tool for CHVs
Working with CHVs, i-PUSH will increase the knowledge on both RMNCH and on
insurance/health financing among women and men in the target communities. To this
end, CHVs receive additional training through the specially developed LEAP training tool
carried out by AMREF. This tool contains modules on specific health terrains of interest
(notably, health promotion activities for children under five, family planning, antenatal care,
danger signs in pregnancy and after delivery, danger signs in children under five, 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 inclusion in the program.
AMREF can currently evaluate if LEAP could improve 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 or. We could then explore whether the improved knowledge translates
into better knowledge and better health-related behavior among women and men in the target
communities. Additionally, we could examine whether the LEAP training tool on top of the
standard training activities of CHVs translate into improving 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 completed, 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 improved attitudes towards insurance and saving for health and insurance. To
support these changes in knowledge and attitudes, WRA will receive the first year of their
NHIF insurance premium at 100% subsidy and the second 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.
10
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 limited access to formal
financial services such as bank savings accounts. The widespread availability of M-PESA
opens a new avenue of change in this respect. To alleviate this constraint, i-PUSH offers
women the opportunity 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 payment of medical costs
at M-TIBA-connected health providers 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 transferred 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 savings calendar. Thus, this component of
the program expects 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 enrol WRA and their household members (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 registration 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. Perhaps such a scheme enhances enrolment into the intervention or insurance [9].
Quality upgrade of selected clinics
Note that the i-PUSH program first identifies clinics for inclusion on the digital M-TIBA
platform and for support with quality upgrade. This study will not evaluate the quality upgrade
but only work with selected i-PUSH clinics. The catchment areas of this clinic are divided into
villages that each include one CHV who is responsible for the village or approximately 100
households, dependent on the location. The program subsequently trains the CHVs in selected
villages to provide i-PUSH insurance and services to the households on their list. Thus, the
villages in the control group have access to the same upgraded clinics. However, households
in control villages do not receive the subsidized health insurance, assistance in the
11
administrative processes to enrol in health insurance and mobile phone-based health wallet that
allows saving for health or receiving personalized text messages. Moreover, maternal and child
health training of their CHVs is less comprehensive and only conducted during the monthly
CHV meetings instead of complemented with the mobile phone-based LEAP training tool.
3. Expected outcomes
Overall, the outcomes of interest for this evaluation study include the following:
Healthcare utilization
● Healthcare utilization for curative care (conditional on illness and/or injury):
● Healthcare utilization for preventive care (immunization, growth monitoring –
children below 5 only).
● Continuum of maternal care (perinatal care - pregnant women only):
● Proportion of women who attended at least four times antenatal care during
pregnancy.
● Proportion of women given folic acid/iron supplementation during pregnancy.
● Proportion of births attended by skilled birth attendant.
● Proportion of postnatal care visits after delivery.
Health outcomes
● Proportion of children malnourished (anthropometrics).
● Proportion of common childhood illnesses (e.g., diarrhoea, acute febrile illnesses,
acute respiratory infections, etc.)
● Proportion of women self-reporting perinatal depression.
● Proportion of common mental illnesses in adults.
● Proportion of other illnesses (infectious, non-communicable diseases, etc.).
Health knowledge and behavior
Proportion of knowledge, attitude and practice to maternal and child health
Proportion of children with adequate feeding practices
o Proportion of children meeting minimum meal frequency
o Proportion of children meeting minimum dietary diversity
Proportion of children sleeping under a bed net
Financial protection
Out-of-pocket healthcare expenditures
Proportion of people enrolled in NHIF
Proportion of people saving through M-TIBA
12
Total amount saved on M-TIBA
Proportion of people on track with savings for insurance premium on M-TIBA
The study will provide additional insights into the following digital tools that are currently
being rolled-out by AMREF or PAF:
4. Data collection instruments and techniques
Data collection consists of four main components: 1) a qualitative baseline study, 2) baseline
and endline household surveys, 3) weekly financial and health diaries interviews with all adults
and emancipated minors in the households, and 4) behavioral lab-in-the-field experiments.
Both quantitative and qualitative tools were piloted in Nairobi slums and debriefed to the
research team including the field team.
Qualitative baseline 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 design and to complement findings from the quantitative
surveys. This will help understand the motivations, drivers and obstacles to savings for
insurance in the target population. The qualitative baseline study is based on in-depth
interviews (IDIs, n = 20) and focus group discussions (FGD, n = 4) with different stakeholders
who were purposively sampled after CHVs’ mobilization to willingly participate in the study.
Another qualitative study may be conducted after the quantitative endline survey has been
completed to provide additional under-the-skin description and/or for improved interpretation
if the impact evaluation yields unexpected results.
Baseline and endline survey instruments
The quantitative evaluation starts with a baseline survey before the rollout of the i-PUSH
program and is completed by an endline survey conducted one year after the i-PUSH
introduction. Both surveys include modules on household demographics, socio-economic
indicators, food and non-food consumption 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 will
include a satisfaction module on participation in the i-PUSH program for women in the
treatment areas only. The household head or the most knowledgeable household member
respondents to the household roster, household-level modules and questions about under-aged
13
household members. The focal person for the modules on maternal health, intra-household
decision-making and health expenditures is the WRA in the household. All remaining
individual-level modules are reported by all adult individuals in the household. (Table 1).
Table 1: 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
Financial and health diaries
This study uses financial and health diaries data collection methodology as its core research
instrument, because this technique is well suited to address the research objectives by providing
a granular insight into the financial lives and health-related decisions of participating
households. The financial diaries record all financial transactions such as purchases, gifts,
remittances and loans, including those between household members in the seven days prior to
each interview. Transactions are described by type, incoming or outgoing cash, amount,
14
purpose, transaction partner and date. The health diaries provide a detailed picture of the
incidence of illnesses and injuries, as well as preventive 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 seven 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 active (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 household
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 an individual for the entire household.
Data are collected through digital tools that allow to analyze data as they are recorded, and
improve data collection tools mid-course of the process. The short recall period drastically
reduces recall bias and ensures that both major and minor illness episodes, including those with
foregone care are reported. Because interviewers visit households 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.
Moreover, in contrast to what is common in standard RCT, the shorter research cycles, and
report results on a regular basis to allow program managers to learn and take action for
continuous improvement.
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 information navigating through a specialized software program while the
interview unfolds. Prior experience of the research team with similar data collection indicates
that such interviews generally last 10-15 minutes as both interviewers and respondents get more
experienced. To reduce burden on the respondents, interviewers take care in planning the day,
time and location of the weekly interviews at the convenience of the respondent. 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.
15
Fieldworkers were retrained on telephone interview ethics and techniques. This will be
implemented until the crisis recedes.
At regular intervals, the weekly diaries are complemented with pop-up modules. In particular,
these include on a monthly basis: a pregnancy module, perinatal depression, and general mental
health; and on a quarterly basis: food consumption of children, anthropometrics of children and
their mothers. Since the COVID19- 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, 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, risk attitudes [10], and time
preferences. As is standard in the behavioral economics 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 conducting 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 ensured by regular spot checks and sit-ins to approximately 5-10% of each fieldworker’s
daily work to verify authenticity of the data collected. Data collection is done electronically
using tablets/phones, with spot checks for quality control. The field supervisors certify 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 centrally located database, all
inconsistencies are resolved prior to data analysis. An automated routine to check on the data
completeness, correctness and consistency runs on 100% of the collected data. A discrepancy
report is generated to help in following up on any inconsistencies or errors in the data with the
responsible interviewer. Similarly, the quality of qualitative data will be ensured through
recruitment and training of qualified field interviewers with experience in qualitative data
collection. A qualified transcriber will transcribe the interviews verbatim and double coding of
about 10% of the transcripts will also be done to ensure consistency in the application of the
codes. Access to data is granted for all research teams in respective research organizations.
16
Data auditing is carried out by the research team on a progressive basis on weekly basis
throughout the study duration.
Quantitative data will be analyzed using Stata version 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 median). This will allow us to detect
similarities and/or differences between participants’ characteristics across the different sub-
groups. In other words, we will compare some baseline measurements between the control and
intervention groups using t-test adjusted for clustering at the village unit for continuous
variables, and cluster-adjusted chi-square for binary variables. We will first check whether the
outcomes and covariates in the control group and treatment group are comparable at baseline.
The second set of analysis will consist assessing the causal effect of the i-PUSH intervention
via an analysis of covariance (ANCOVA) based on intention-to-treat analysis (all respondents
who are randomized will be included in the statistical analysis and will be analyzed according
to the group they were initially/originally assigned). Based on our research questions, we will
explore several econometric models as outlined in Appendix 2. Interim data analyses are done
using baseline and weekly diaries data.
Risks and measures to minimize them
As the information collected will be on health service delivery, financial and insurance
information, we do not anticipate any risks to participants, nevertheless. We will aim to
minimize the risks by being as forthcoming as possible on the project description and the ethical
process. In the case of minimal discomfort as a result of personal and sensitive questions, the
research team will endeavor to ensure that the participant is given ample time to compose
themselves, 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 participating households.
Dissemination of findings
Scientific dissemination through peer-reviewed publications and implementing partners’
dissemination through quarterly updates (e.g., meetings, presentations and brainstorm sessions)
will ensure that findings are shared on a regular basis. These activities allow both parties to
benefit from each other’s insights and interpretations. Annual PharmAccess strategy meetings
facilitate sharing knowledge with all 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
17
linkages between the research group, PAF and the Dutch Ministry of Foreign Affairs with
respect to health financing 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 interface of digital technologies and
health, mobile payment platforms).
Ethical considerations
The protocol was reviewed and approved by APHRC’s internal ethical review committee and
AMREF’s accredited ethical review board. Ethical clearance was obtained 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 orientation on project information, the purpose of
the research study, possible risks and benefits. Model consent form is provided in separate file.
All interviews are conducted in privacy and no identifying information is included in any data
or reports; all data is anonymized to protect the identity of respondents. The participants are
given a unique code and all identification data of participants shall be shredded/destroyed after
analysis has been completed. Further 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 low
and middle-income countries (LMICs) especially those in sub-Saharan African (SSA)
countries are struggling with low rates of maternal and child survival. In Kenya, the maternal
mortality ratio has marginally reduced to 362 per 100,000 live births, not statistically different
from the figures reported in 2008-2009 [11]. While a substantial under five mortality reduction
was achieved with a drop from 115/1000 live births in 2003 to 52 per 1000 live births in 2014,
it is still two-fold higher than the SDG target [11]. These figures are disproportionately higher
in Western Kenya, such as in Kakamega.
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 capacities of individuals, families and communities to ensure appropriate self-care,
prevention and care-seeking behavior improves maternal and child health outcomes [12].
However, this is more difficult to achieve in poor populations who have worse health outcomes
18
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 towards UHC for all, including those in vulnerable situations such as low-income
mothers and children. In 1998, the NHIF act was amended to enhance coverage among the
poor, accelerate coverage of the informal sector, and enhance the benefit package [13, 14]. 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 [14]. Despite these
positive steps, Kenya’s implementation of UHC has been riddled with myriads of challenges
including poor quality of care, utilization and catastrophic spending by households especially
the poor and other vulnerable groups [16]. 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 the informal employment and rural settings were less likely to be insured [11].
The i-PUSH program thus has been initiated to accelerate the expansion of health insurance
coverage among the low-income population, WRA and their family members, using innovative
digital tools. This research investigates the reasons for low access to health services, in
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.
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 technologies 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) County (Sub-county), AMREF and PAF will stick to the
randomization plan that was independently developed by the research team but in agreement
with stakeholders, and a memorandum of understanding (MoU) was signed among the partners;
and (iii) 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
19
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 actually capture this in the data.
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 dialogue 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 potentially 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 amongst others for digital recording of health visits, diagnoses, treatments and
services as well as payments/billing between patients, NHIF, Ministry of Health and
providers.
These components are left out of the evaluation because our study focuses on the demand-
side (target population), and the described components are implemented either at the
community-level or at the facility-level.
Conclusions
This study aims to evaluate the impact of i-PUSH program on maternal and child health care
utilisation, women’s health including their knowledge, behaviour 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
20
UHC in general, and for those highly vulnerable segments of the population in particular. The
findings of this research will provide valuable information on UHC policies for the academic
community, policy-makers and other stakeholders to support the achievement of SDGs.
Trail 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.
List of abbreviations/acronyms:
AIGHD – Amsterdam Institute for Global Health and Development
AMREF – African Medical and Research Foundation
ANC – antenatal care
APHRC – African Population and Health Research Center
CHV – Community Health Volunteers
FGD – Focus Group Discussion
ICC – Intracluster Correlation
IDI – In-depth Interview
i-PUSH – Innovative Partnership for Universal Sustainable Healthcare
LMICs – Low and Middle-income Countries
KDHS – Kenya Demographic and Health Survey
NACOSTI – National Commission for Science, Technology and Innovation
PAF – PharmAccess Foundation
PNC – Postnatal Care
RCT – Cluster Randomized Controlled Trial
SSA – sub-Saharan African
21
WRA – Women of Reproductive Age
UHC – Universal Health Coverage
Declarations
Ethical approval and consent to participate: Obtained (reference number: P679-2019).
Informed consent was obtained from all study participants.
Consent to publication: Not applicable.
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.
Competing interest: The authors declare that they have no competing interests
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.
Acknowledgement: 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.
References
[1] WHO. Tracking Universal Health Coverage: First Global Monitoring Report - World
Health Organization - Google Books. 2015.
[2] Wagstaff A, Flores G, Hsu J, Smitz MF, Chepynoga K, Buisman LR, van Wilgenburg
K, Eozenou P. Progress on catastrophic health spending in 133 countries: a retrospective
observational study. Lancet. Glob. Heal.6:2018; e169–e179.
[3] Wangui E, Kandie C. Refocusing on quality of care and increasing demand for services:
Essential elements in attaining universal health coverage in Kenya. 2016.
[4] Wehrmeister FC, Fayé CM, da Silva ICM, Amouzou A, Ferreira LZ, Jiwani SS, et
al. Wealth-related inequalities in the coverage of reproductive, maternal, newborn and child
health interventions in 36 countries in the African Region. Bulletin WHO. 2020; 98:394-
405
[5] AMREF Health Africa.Leap, the mHealth platform [online].
[6] Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized
experiments, with application to the Mexican universal health insurance
evaluation. Statistical Science. 2009; 24: 29-53.
22
[7] Hemming K, Girling AJ, Sitch AJ, Marsh J, Lilford RJ. Sample size calculations for
cluster randomised controlled trials with a fixed number of clusters. BMC Med. Res.
Methodol. 2011; 11.
[8] Geng X, Janssens W, Kramer B, van der List M. Health insurance, a friend in need?
Impacts of formal insurance and crowding out of informal insurance. World Dev. 2018;
111:196–210.
[9] Banerjee A, Finkelstein A, Hanna R, Olken BA, Ornaghi A, Sumarto S. The challenges
of universal health insurance in developing countries: Evidence from a large-scale
randomized experiment in Indonesia (No. w26204). National Bureau of Economic
Research. 2019.
[10] Andreoni J Sprenger C. Estimating time preferences from convex budgets,” Am. Econ.
Rev.2012; 102:3333–3356.
[11] KDHS. Kenya demographic and health survey: Key indicators. 2014; 1–76,
[12] United Nations Department of Economic and Social Affairs. SDGs: Sustainable
development knowledge platform,” United Nations. 2015.
[13] Kerber KJ, de Graft-Johnson JE, Bhutta ZA, Okong P, Starrs A, Lawn JE. Continuum
of care for maternal, newborn, and child health: from slogan to service delivery. Lancet.
2007; 370:1358–1369.
[14] Okech TC, Lelegwe SL. Analysis of universal health coverage and equity on health care
in Kenya. Glob. J. Health Sci. 2015; 8:218
[15] Mulupi S, Kirigia D, Chuma J. Community perceptions of health insurance and their
preferred design features: Implications for the design of universal health coverage
reforms in Kenya. BMC Health Serv. Res. 2013; 13.
[16] Calhoun LM, Speizer IS, Guilkey D, Bukusi E. The Effect of the removal of user fees
for delivery at public health facilities on institutional delivery in urban Kenya,” Matern.
Child Health J. 2018; 22:409–418.
Appendix 1: Schedule of enrolment, interventions, and assessments
Study period
Enrolmen
t
Allocation
Post-
allocation
Close-out
November
2019 –
June 2020
July –
September
2020
July 2021
July 2021
Enrolment:
Eligibility screen
X
Informed consent
X
List other
procedures
X
23
Allocation
X
Interventions:
Intervention
X
X
X
X
Control
X
X
X
X
Assessments:
List baseline
variables
X
List outcome
variables
X
X
List other data
variables
X
X
Appendix 2: Econometric models
1. Impact of the i-PUSH on health and financial outcomes
We will explore the impact of the i-PUSH program on health outcomes (health status,
healthcare utilization, percentage 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 intention-
to-treat (ITT) analysis. Since we will have several rounds of data collection on the same
respondents, 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 following covariates: baseline measurement (pretest measurement)
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:
+ , (1)
24
with , the posttest and pretest/baseline measures of the outcome for the ith respondent
from the jth cluster, respectively. 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.
is the ITT effect or the impact of the i-PUSH program. For continuous outcomes, we will use
an OLS model with standard errors clustered at the level of the community units. For binary
outcomes, we will use a logit model. is the error term.
In addition to pretest measures, all other baseline covariates such as age, education,
socioeconomic status (wealth quintile), crowding (persons per room), occupation, etc. will also
be included in Equation (1). Thus, in Equation (1) the treatment effect of the I-PSUH program
assesses the treatment difference on post-treatment outcome adjusted for baseline.
2. Impact of the i-PSUH on women empowerment
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
2
of women
empowerment which is the sum of each domain when the woman indicates that she has sole or
joint 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 decision alone or jointly
with her partner and 0 otherwise). This total score will therefore be used as the dependent
variable and Equation (1) above will be used to estimate the impact of i-PUSH program on
women empowerment. Furthermore, with regards to the field experiment, we will follow
Almås et al. (2018). In other words, we will still use the same econometric model (Equation 1)
after constructing the dependent variable emanating from the field experiment. This dependent
variable will be the willingness-to-pay
3
which is the share that the woman is willing to pay
when the experiment stops.
2
A robustness check will also be done using a principal component analysis (PCA). Hence, an index of women
empowerment will be constructed using the PCA.
3
If the stakes offered to the husband is Khs. 400 and the last amount offered to the woman is Khs. 100, then the
WTP is 0.75 (400-100)/400).
