Health Policy and Planning, 35, 2020, ii66–ii73
doi: 10.1093/heapol/czaa117
Supplement Article
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Bridging the gap with a gender lens: How two
implementation research datasets were
repurposed to inform health policy reform
in Kenya
Lauren Suchman 1*, Gabrielle Appleford2, Edward Owino3, and
Charlotte Avery Seefeld4
1
Evaluation Director, Institute for Global Health Sciences, University of California, San Francisco
Consultant Director, Ridge Lane Associates, Nairobi, Kenya
3
Independent Consultant, Nairobi, Kenya
4
Program Coordinator, Institute for Global Health Sciences, University of California, San Francisco
2
*Corresponding author: 550 16th Street/3rd Floor/San Francisco, CA/94158/USA/þ1-415-476-3294/
lauren.suchman@ucsf.edu
Accepted on 3 September 2020
Abstract
Policies as they are written often mask the power relations behind their creation (Hull, 2008). As a
result, not only are policies that appear neat on the page frequently messy in their implementation
on the ground, but the messiness of implementation, and implementation science, often brings
these hidden power relations to light. In this paper, we examine the process by which different
data sources were generated within a programme meant to increase access to quality private
healthcare for the poorest populations in Kenya, how these sources were brought and analyzed together to examine gender bias in the large-scale rollout of Kenya’s National Hospital Insurance
Fund (NHIF) beyond public hospitals and civil service employees, and how these findings ultimately were developed in real time to feed into the NHIF reform process. We point to the ways in which
data generated for implementation science purposes and without a specific focus on gender were
analyzed with a policy implementation analysis lens to look at gender issues at the policy level, and
pay particular attention to the role that the ongoing close partnership between the evaluators and
implementers played in allowing the teams to develop and turn findings around on short timelines.
In conclusion, we discuss possibilities for programme evaluators and implementers to generate
new data and feed routine monitoring data into policy reform processes to create a health policy
environment that serves patients more effectively and equitably. Implementation science is generally focused on programmatic improvement; the experiences in Kenya make clear that it can, and
should, also be considered for policy improvement.
Keywords: gender; health insurance; policy analysis; research methods; research to policy; Kenya
Introduction
Background
Universal Health Coverage (UHC) cannot be achieved without attention to equity. However, equity often is equated to socioeconomic status with less attention to other social stratifiers. Research
has found that unless policy makers pay explicit attention to gender,
efforts to reach UHC may not improve equity and may in fact exacerbate existing gender inequities (Witter et al., 2017). When it
comes to health care, women incur more out-of-pocket expenditure
than men, which is due in part to women’s specific health needs
related to pregnancy, childbirth, contraception and abortion among
others (WHO, 2010). Over the past few decades, low- and middleincome countries (LMICs) have started to institute social and
C The Author(s) 2020. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine.
V
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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KEY MESSAGES
1. Data generated through implementation science can be used to speak to broader issues related to policy.
2. Taking a community-based participatory research (CBPR) approach to developing and conducting implementation science studies is
suggested for future research to generate findings that meaningfully bring the perspective of healthcare providers on the ground to the
policy level.
Gender and health systems
Health systems are not gender neutral, often reinforcing restrictive
social norms that place women at a disadvantage compared to male
counterparts (Hay et al., 2019). Indeed, women and men have different health care needs. However, while many health systems attempt to address the different biological differences between women
and men through, for example, essential benefits packages, there is a
tendency to assume that maternal health programmes are an adequate response to address most major gender differences in health
(WHO, 2010). A common solution to ensuring healthcare is accessible to all is providing free services (Kabia et al., 2018). However,
this solution does not address all of the issues that many women, especially poor and disabled women, have accessing care, nor does
eliminating fees, by itself, make the healthcare system equal. Larger
social issues, such as transportation costs, access to employment,
and women’s role within the family all affect women’s access to care
(Morgan et al., 2016). A number of studies have found that, beyond
biological differences, gender affects healthcare experiences from
time spent seeking care (Yeatman et al., 2018) to the ability to access
care when caring for others (Kabia et al., 2018).
Beyond women’s experiences as patients, we also know that
women make up a significant proportion of both the formal and informal health workforce (Hoss et al., 2011; Harman, 2016) and
these numbers are increasing in LMICs, paralleling shifts in highincome countries (Russo et al., 2015). However, in clinical practice
and in academia, the leadership is highly skewed toward men
(Exavery et al., 2013; Dhatt et al., 2017). Indeed, as some studies
have shown, gender parity in the healthcare workforce may lead to
better health outcomes for patients, but larger structural changes are
necessary to foster a health system that is gender equitable (Hay
et al., 2019).
Evidence on women and health insurance tends to be split between: (1) the determinants of who is most likely to be insured or
uninsured, with gender being one possible factor; and (2) service
utilization by those who are insured vs. those who are uninsured.
This evidence generally shows that women, the poor, and uneducated individuals are less likely to be insured than men, individuals
from high socioeconomic groups and those with formal education
(Kimani et al., 2014; Brugiavini and Pace, 2016). Further, individuals who have health insurance are more likely to seek health services
than individuals who do not (Dixon et al., 2014) and some studies
have found a positive correlation between insurance enrollment and
health outcomes (Mostert et al., 2014). Much of the literature on
women in this area focuses on maternal health, suggesting that
women who have access to health insurance are more likely to use
formal maternity services (Smith and Sulzbach, 2008) and may have
better health outcomes as well (Hawks et al., 2018). However, this
narrow focus on maternal services may mask larger challenges:
work examining general gender disparities under India’s Rashtriya
Swasthya Bima Yojana (RSBY) scheme has demonstrated that
women have a harder time accessing national health insurance due
to a number of factors. These include gender inequities that restrict
their mobility and the limit of five RSBY registrants per household,
which can lead to larger families prioritizing men and boys for insurance access over women and girls (Cerceau, 2012). However, we did
not find any similar literature on gender disparities and women’s
experiences using national health insurance in sub-Saharan Africa,
suggesting that this is an area of inquiry that should be explored further as a number of countries implement such schemes in the pursuit
of UHC.
Implementation science vs. Policy implementation
analysis
Implementation science generally is understood to bridge the socalled “knowledge-to-practice gap” that exists between the development of evidence-based practices, the communication of these practices to professionals in the field, and the extent to which these
professionals put their knowledge to practical use (Eccles and
Mittman, 2006). Indeed, research has shown that failure to adequately bridge the knowledge-practice gap affects health outcomes
across both high-income countries and their low- and middleincome counterparts (Glasziou et al., 2017), making “knowledge
translation” critical to improving healthcare quality (LaRocca et al.,
2012). However, implementation science often has a relatively narrow focus and the policy environment within which a particular
health programme or facility functions often is considered a contextual factor that may influence implementation, rather than the subject of study itself (Damschroder et al., 2009; Lau et al., 2016).
Policy implementation analysis is similar to implementation science in some of its methodological approaches, as well as its desire
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community-based health insurance schemes to make healthcare
more accessible to the general population by lowering out-of-pocket
costs (Spaan et al., 2012). However, many of these schemes cover
only those working in the formal sector and their dependents, or require the payment of monthly premiums. Since most women in subSaharan Africa are employed in the informal sector, this precludes
them from being a primary beneficiary in many national health insurance schemes. Further, the unpredictable wages common to this
sector can hamper their ability to pay the monthly premiums
required to maintain coverage (Chuma et al., 2012; ILO, 2018).
While middle-income countries such as Thailand and Taiwan
have long since achieved UHC using national health insurance
(Tangcharoensathien et al., 2018; Hsiao et al., 2019), many LMICs
attempting to reach UHC in line with Sustainable Development
Goal 3.8. are either implementing these schemes for the first time or
revamping pre-existing schemes to better serve their wider population. As these programmes roll out, attention to gender equity is crucial to ensure that no is one is left behind in the UHC agenda. Policy
implementation analysis will be important for countries to undertake in order to determine where roadblocks exist for women and
how to course correct along the way.
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Programme and policy context
The data for this paper were collected by two different teams, an external qualitative evaluation team and an implementation team,
working with the African Health Markets for Equity (AHME) programme in Kenya. AHME was initiated in 2012 and concluded in
March 2019. The programme aimed to link healthcare supply (small
and medium enterprise (SME) private providers) with demand (clients) in order to shift health markets toward providing quality
healthcare to populations living in poverty in Kenya and Ghana.
The AHME partners included: Marie Stopes International (MSI);
Marie Stopes Kenya (MSK); Population Services Kenya (PSK);
Population Services International; Marie Stopes Ghana; and the
PharmAccess Foundation. Past partners included: the International
Finance Corporation; Society for Family Health, Nigeria; and the
Grameen Foundation. AHME worked through social franchises,
networks of providers that apply the principles of commercial franchising to health services (Schlein and Montagu, 2012), to provide a
package of quality improvement and financing interventions. This
package included: social franchising; SafeCare, a step-wise quality
improvement programme managed by the PharmAccess
Foundation; the Medical Credit Fund, a business training and loans
programme also managed by the PharmAccess Foundation; and
National Health Insurance accreditation assistance. On the demand
side, AHME also provided support for activities to identify and enroll low-income populations into National Health Insurance.
Funding for AHME was provided by the Bill and Melinda Gates
Foundation and the UK Department for International Development.
The National Hospital Insurance Fund (NHIF) is one of the key
vehicles for UHC in Kenya and played a critical role in both supplyand demand-side financing for AHME. The NHIF was established
in 1966 with a core mandate to provide health insurance cover to all
its members and their dependents. During the AHME implementation period, the Kenyan government funded special programmes
under the NHIF to increase access to insurance for people living in
poverty, the elderly, secondary school children, and pregnant
women, in addition to rolling out SupaCover, a programme specifically meant for informal sector workers. However, coverage in the
NHIF remains limited due to the predominance of the informal sector in Kenya, which was estimated to include 83% of Kenya’s population in 2017 (NHIF, 2017). While NHIF coverage is mandatory
for all formal sector workers (Government of Kenya, 2012), it
remains voluntary for the informal sector despite the fact that the
1998 Amendment to the NHIF Act requires that all Kenyans have
health insurance. At the end of 2018, <20% of the population was
estimated to be covered by the NHIF (Barasa et al., 2018). Even
when nominally covered, true enrollment is often low: out of 2.9
million members from the informal sector, only 988 662 members
were active or current in payment as at 30th June 2017, which represents a retention rate of 27% (Barasa, 2019).
In response to the many shifts in NHIF policy during the AHME
implementation period, the programme was required to adapt and
adjust its policy interventions. Originally, the policy objectives of
AHME’s demand-side financing work were to support the Kenyan
government to test and scale the Health Insurance Subsidy
Programme (targeted to poor populations) and increase voluntary
enrolment for those in the two lowest wealth quintiles into NHIF.
These policy objectives evolved over time in response to both the
government’s new initiatives within NHIF, and the changes within
the AHME programme. On the supply side, given its programmatic
focus on SME providers, AHME worked to increase the number of
accredited SME private providers, which was believed to align with
increased quality of care, in addition to building providers’ business
skills and capacity to help them better manage NHIF contracts.
Methods
This paper draws from two separate datasets collected under the
African Health Markets for Equity (AHME) programme. One of
these datasets was collected by the AHME Qualitative Evaluation
team from the University of California San Francisco as part of the
AHME programme evaluation in Kenya. The other was collected by
the AHME partners for the purposes of internal monitoring, evaluation and learning.
The AHME qualitative evaluation
As part of the AHME Qualitative Evaluation in Kenya, semistructured interviews were conducted with private healthcare providers and women exiting private clinics; these were both AHMEsupported and non-AHME clinics. Using a purposeful criterion sampling strategy (Palinkas et al., 2015) to ensure a range of experiences
with the AHME interventions, sample clinics were selected from
lists of franchised facilities provided by the AHME partners. The
Qualitative Evaluation team partnered with Innovations for Poverty
Action (IPA), a research organization based in New Haven, CT USA
with country offices across the globe to collect data in four rounds:
2013, 2015, 2017 and 2018. In order to align with data collected
for the Quantitative Evaluation, women exiting the sample clinics
were screened for eligibility according to: sex (women only); age (between 18-49 years of age); and number of children (interviewees
were required to have at least one child aged 5 years or less). Clients
also were selected for NHI enrollment status with an aim to sample
NHI-enrolled and non-enrolled patients equally. Across rounds of
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to improve health outcomes by identifying areas where translating
established protocols into practice falls apart. However, policy implementation analysis differs in its object of study (policy vs. clinicbased intervention), its (non)use of theory, its limited ability to establish causal pathways between protocol and practice, and its embrace of complexity (Nilsen et al., 2013). Further, it tends to focus
on high-income countries with little policy implementation analysis
having been conducted in low- and middle-income settings (Saetren,
2005).
This paper aims to apply a policy implementation lens to data
collected for implementation sciences purposes in an LMIC (Kenya).
It offers one example of a programme in which data routinely collected for implementation science purposes at the clinic level was
supplemented with data on the larger policy environment to draw
conclusions about gender equity in policy implementation. The programme studied was a complex intervention package that drew on
national health insurance accreditation to both improve quality at
the clinic level and increase income for small private providers. In
addition to routine clinic-level data collection by an external evaluation team as well as the programme implementers themselves, both
the evaluators and implementers collected data on the larger policy
environment within which the programme functioned. Noticing gender disparities in their respective datasets, the evaluation and implementation teams worked together to generate findings around
gender bias in the policy implementation process. These findings
resulted in a joint policy brief presented to Kenya’s Health
Financing Reforms Panel on the Transformation and Repositioning
of the National Hospital Insurance Fund as a Strategic Purchaser towards the attainment of Universal Health Coverage.
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Health Policy and Planning, 2020, Vol. 35, Suppl. 2
Internal AHME data collection
AHME learnings were curated by the implementing partners
through a series of case studies and other learning products, which
were based upon analysis of AHME secondary data as well as primary data collection. Primary data collection entailed site visits and
key informant interviews with private providers and NHIF branch
managers. Data were collected over three years in 2016, 2017 and
2018/19. The focus of key informant interviews was to generate
learning around the experiences and perceptions of private providers
in NHIF schemes; the nature and value of the support provided by
AHME to private providers; and areas for improvement for the effective participation of private providers in NHIF schemes.
Bringing the data together
The AHME programme enjoyed a well-funded mixed-methods external evaluation that was underpinned by a learning agenda shared
across both the evaluation and implementation teams. As such, programme evaluators and implementers maintained regular communication regarding data collection and the production of learning
products. However, the learning agenda was not designed to include
findings specifically related to gender disparities and very few learning products were envisioned to include co-authors from more than
one team or to triangulate data across teams.
As findings around the relationship between gender and access
to meaningful NHIF coverage began to emerge from data routinely
collected at the clinic level for the qualitative evaluation team, members of the two teams began to discuss these findings and their relevance to data collection the implementers were undertaking to learn
more about the policy environment and its effects on AHMEsupported providers. Members from both teams met several times to
discuss their respective findings. While the triangulated data were
not sufficient to produce a joint academic paper, an opportunity
arose for the two teams to use the data that were already available
to write a joint policy brief for the NHIF reforms committee that
began meeting in June 2019. Working on a short timeline, the evaluators and implementers were able to combine their respective datasets and draft a short policy brief, including recommendations for
addressing gender inequities embedded in the NHIF system.
Results
To generate joint findings, the qualitative evaluation team from
UCSF contributed analysis of in-depth interviews (IDIs) conducted
with both private providers and their patients over four rounds of
data collection. Internal AHME programme data also included
qualitative data (IDIs), which were triangulated with AHME monitoring data and complemented by desk review of relevant documents (such as NHIF guidelines, contracts, etc.). Both teams
collected data for implementation science purposes to support programme learning and adaptation, although the evaluation team was
tasked with providing an assessment of barriers and facilitators to
the success of the AHME intervention package, while the implementers had an additional interest in supporting the NHIF and providers to engage more effectively and efficiently. Further, the
qualitative evaluation had more breadth insofar as it included a
greater number of respondents. Internal programme learning had
more depth in that it explored specific, emergent, operational and
procedural issues from a provider perspective and used this lens to
interact with government and other stakeholders.
After combining their respective datasets, the evaluation and implementation teams produced a joint policy brief for the NHIF
reforms committee that included the following preliminary findings
and resulting recommendations.
Understanding of family planning coverage under the
NHIF is limited in practice
Although family planning is critical to women’s sexual and reproductive health, unmet need for family planning services in Kenya
remains high (Kaneda and Greenbaum, 2019). As learned from
AHME, the inclusion of family planning services within the NHIF
benefits package is not well understood by providers or by NHIF
members, and is often characterized by inequities in service entitlements; this creates an additional barrier to access for women seeking
modern contraceptive methods. For example, permanent methods,
namely tubal ligation and vasectomy, are included as part of the
benefits package and are paid on a fixed fee-for-service1 for those
under the enhanced medical schemes, while this benefit is excluded
under other schemes that are more widely accessible to the broad
population. All other family planning methods, including longacting reversible contraception (LARC), such as implants and intrauterine contraceptive devices, are included in the out-patient national scheme, paid through capitation.2 While capitation was selected
as the preferred means of provider payment ‘to induce positive
incentives in the health delivery system’ (Government of Kenya,
2012), this may not be the case for family planning services. Upon
discovering that clients were having an especially difficult time
accessing LARCs using their NHIF coverage, the AHME implementers undertook a case study with private providers that focused on
the effects of NHIF tariffs on private provider businesses. This
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data collection, providers were asked about their experiences with
the AHME intervention package, their experiences working with the
AHME partners themselves and, in later rounds, opportunities and
challenges around NHIF accreditation. Clients were asked about the
quality and accessibility of services in the clinics. In later rounds of
interviews (2017 and 2018), clients with NHIF coverage were specifically asked about their experience using NHIF in the clinic, while
patients without coverage were asked to describe what they knew
about the scheme. In total, 173 providers and 86 patients were
interviewed.
Interview recordings were translated from Swahili and transcribed simultaneously by a team of professional transcriptionists.
The UCSF team then coded the transcripts with some assistance
from IPA using Atlas.ti, a widely-used qualitative analysis software
package. Since there was little existing literature on private provider
and patient experiences with the NHIF from which to derive prior
theories, the team used an inductive thematic approach to coding
and analysis. Codes were refined over the several rounds of analysis
to allow for new priorities while ensuring continuity, and research
team members reviewed the codebook together in each round to ensure consistency in code application.
The UCSF team received initial approval with “Exempt” status
from the Institutional Review Board of the University of California
San Francisco for the AHME evaluation on 13 June 2013. In addition, the team received Ghana Health Services Ethical Review
Committee (ERC) approval on 28 June 2013 and Kenya Medical
Research Institute (KEMRI) approval on 28 October 2013. Prior to
each round of data collection the Qualitative Evaluation team submitted amendments and received approval from all three review
boards for any changes made to our protocol. Approvals for Round
3 (2018) of data collection was received on 15 June 2018 from
ERC, 22 May 2018 for UCSF, and 10 May 2018 for KEMRI.
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Interviewer: So, what do you do if somebody came and maybe
spend like seven hundred [Kenyan Shillings], and you are allowed
not to take more than three hundred per quarter [under capitation]? What do you do?
Respondent: Sometimes if the balance is too big we force them to
pay.
(Finance Officer at a private clinic, Nyanza, Kenya)
Thus, even when providers understand how facility-level risk
pooling works, capitation encourages providers to offer cheaper,
easier to administer methods. Since LARC methods require more
time, skills and consumables, providers are therefore discouraged
from offering a comprehensive contraceptive method mix under this
financing strategy. While capitation is meant to contain costs, learning from AHME therefore suggests that contraception may be illsuited to capitation financing.
Further, as learned through an internal AHME case study conducted in early 2019 through site visits to selected private providers,
there is limited understanding of the inclusion of post-partum family
planning services in the Linda Mama (free maternity services) package by both private providers and women patients (Appleford,
2019). While post-partum family planning services are included as
part of postnatal care (PNC), the reimbursement rate for each PNC
visit is flat and does not reflect the cost of offering this service.
Linda Mama is recognised in Kenya’s FP2020 ‘Actions for
Acceleration’ as an immediate opportunity to improve access to
post-partum family planning. However, without a differential reimbursement for this service, it is unlikely that providers will proactively offer the service to patients.
NHIF registration and use of coverage are more
challenging when a husband is not present
Through AHME, the Amua and Tunza social franchises supported
awareness creation and public education activities around the Linda
Mama and Supacover schemes aimed specifically at informal sector
workers. This entailed NHIF branch offices, social franchise providers and community mobilisers conducting joint SupaCover membership drives and other outreach events. Through these drives, the
AHME implementers learned that in some regions, men were considered by default the head of household and therefore the principal
NHIF member. This was found to stall registration into the NHIF if
the male head of household was not present when registration events
took place.
Similarly, when conducting IDIs with clients exiting private
health facilities, both the AHME qualitative evaluation team and
the implementing partners asked women about their NHIF membership status and their use of NHIF cards to pay for services. Findings
from these interviews suggest that a number of women who have
NHIF coverage do not always use this coverage when they visit
health providers, which is partly due to the NHIF’s policy of issuing
only one card to a household’s principal contributor to cover an entire family. Since the principal contributor is most often male, many
women do not have direct access to their NHIF membership card or
may not know their membership number to confirm coverage when
visiting a health provider. As the AHME implementers found
through literature review and interviews with key informants, although NHIF enrollees are identified through a biometric system at
the point of care, biometric registration is not required upon enrollment into the NHIF and the NHIF registration system is not integrated with other government-run biometric systems. This makes it
difficult to identify beneficiaries at the point of care unless they are
carrying an NHIF membership card. So, while biometric registration
should theoretically allow enrollees to use their NHIF coverage any
time without carrying a membership card, in practice women often
have to be accompanied by their husband as the principal cardholder in order to access health services.
Some evidence from the qualitative evaluation suggests that
women may also face challenges using their NHIF coverage as a result of economic or seasonal migration that separates families. If
women are living separately from their husbands, this limits their access to the household’s NHIF membership card, as well as to the
health facility to which the entire family is capitated.
Interviewer: And which hospital did you choose?
Respondent: Here I have not chosen, eeh because that time I was
in Mombasa. So that card itself is with. . .with my husband, eeh.
Interviewer: So, you didn’t use it?
Respondent: I have never used it again apart from that time
when I was admitted [in Mombasa].
(Client at a private clinic, Embu County)
In addition to women not being able to physically access their
NHIF membership card when living separately from their husband,
capitation and the associated lack of portability of benefits may play
a role in limiting access, as primary cardholders are most likely to
capitate to a health facility that is geographically convenient for
themselves.
In order for healthcare coverage under NHIF to be truly universal, gendered inequities in service access, the disproportionate barrier that out-of-pocket expenditure creates for women and the needs
of women and adolescent girls need to be understood and effectively
addressed. As a result of the above findings, the AHME evaluation
and implementation teams recommended a change in the mode of
reimbursement of LARC methods under the Supacover scheme to
motivate health facilities to provide these services, thereby ensuring
women have access to family planning, as well as a range of method
choices. In addition, the teams recommended that NHIF cards be
issued to all members, not just the principal contributor in each
household.
Discussion
The nature of implementation science is such that data gathered
under this umbrella can have broad applicability beyond the programme level and can be used to speak to issues that are relevant to
policy development and reform. While the AHME programme contained a policy component from the beginning, the data presented
here from the qualitative evaluation team was collected for the purposes of monitoring the implementation of a clinic-based intervention. In addition to their own internal monitoring at the clinic level,
the programme implementers also collected data on the larger policy
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analysis ultimately determined that, in many instances, private providers in the NHIF are unclear about the inclusion of family planning services under capitation (Appleford and Owino, 2017).
Similarly, the AHME qualitative evaluation team asked private
providers about their experiences serving patients after becoming
NHIF-accredited. From these interviews, it became clear that private
providers typically understand capitation to be a “cap” on the cost
of services they are allowed to offer, rather than a facility-level risk
pool in which those patients who use cheaper services or none at all
balance out the patients who require more costly services. In response, providers either limit services or charge patients unnecessarily for more expensive services and supplies.
Health Policy and Planning, 2020, Vol. 35, Suppl. 2
Health Policy and Planning, 2020, Vol. 35, Suppl. 2
for example, current discussions around the UHC agenda tend to include members of provider associations that primarily draw their
membership from larger private and faith-based hospitals, leaving
SME private providers virtually unrepresented in UHC discussions.
Without an obvious gateway through which SME providers can
voice their concerns at the policy level, these concerns will continue
to be sidelined in health system reform processes.
While some SME private providers are taking matters into their
own hands and establishing their own professional associations,
such as Kenya’s newly formed Rural and Urban Private Health
Association (RUPHA), moving forward it will be important to develop more effective mechanisms through which these providers and
the research that involves them can feed back into the larger health
system. To this end, researchers and programme implementers
working with these provider populations can do their part to increase the visibility and influence of SME private providers. As in
the case of AHME, researchers and programme implementers gathering data for implementation science purposes may be able to apply
a policy implementation analysis lens to their data, drawing out lessons in real time as programmes progress. Capturing and sharing
these lessons gives weight to the experiences of SME private providers and helps to create a unified voice for a disparate group. In
addition, researchers can take a Community-Based Participatory
Research (CBPR) approach (Minkler and Wallerstein, 2008) and
tap into professional associations such as RUPHA to jointly develop
research protocols and priorities. Some scholars suggest that using a
CBPR approach is in fact particularly desirable for implementation
research (Di Ruggiero and Edwards, 2018) and certainly using
CBPR can create more equitable relationships between researchers
and participants that in turn generate better, more relevant data
(Palinkas, 2019). As in the case of AHME, researchers can then leverage the privileged position in which they sit to amplify SME providers’ perspectives at the policy level. When the voices of these
providers are heard at the level of government, this can help to create policies that are more inclusive of all providers’ needs, in turn
allowing them to better serve their patient populations more effectively and equitably, as required on the journey to UHC.
Conclusion
Due to their sustained collaboration, regular communication and
shared learning agenda, the AHME qualitative evaluation and programme teams were able to recognize shared patterns of gender inequity in data meant for programme implementation monitoring. As
a result, the two teams were able to apply a policy implementation
analysis lens to their respective datasets and bring them together
into a policy brief, recommending policy changes that could affect
women’s access to healthcare in the NHIF reforms process. The
teams were able to leverage pre-existing relationships developed
with the NHIF to introduce this brief to the official NHIF reforms
committee. The insights into gender detailed above are an example
of how additional perspectives can see and communicate new ideas
back to programme and policy makers, short-circuiting communication systems that may inhibit rapid feedback in order to understand
and improve policies as they roll out. Since SME private providers
like those represented in AHME make up a significant proportion of
the provider landscape in many LMICs, but rarely have a voice in
the larger health system, programme evaluators and implementers
collecting routine monitoring data should consider applying policy
implementation analysis to their data where possible. In addition,
researchers working with SME private providers should use newly-
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environment in Kenya to feed into planned policy work as well as to
better understand the ways in which AHME-supported providers
were affected by changes at the policy level. Together, the evaluators
and implementers were therefore able to use data meant for programme implementation monitoring to measure the indirect effects
of policy implementation and the effects of regulatory shifts on SME
private providers and their patients. The resulting findings, which
point to structural challenges that inhibit women’s access to and use
of key reproductive health services like family planning under a national health insurance scheme, are critical points to consider if the
Kenyan government wishes to meaningfully pursue UHC.
To achieve this end, the evaluation and implementation teams
maintained regular communication and a shared learning agenda,
which helped to facilitate data sharing and eventual triangulation.
However, the learning agenda was created relatively late in the
course of the programme and, as such, it did less to guide the separate teams in planning jointly produced learning products and acted
more as an internal tracking system. The NHIF gender policy brief,
for example, was never included in the shared learning agenda. In
addition, the realization of the learning agenda was complicated by
delays in the AHME quantitative evaluation, which made it virtually
impossible for the other teams to triangulate data with this piece of
the external evaluation.
Regarding internal communications, we note that implementers
often are reluctant to share too much with external evaluators out of
fear of receiving a negative evaluation that could have consequences
for funding and programme sustainability. In the case of AHME,
both the qualitative evaluation and implementation teams benefited
from the programme’s relatively long timeline, which allowed them
to establish trust and develop more open communication. It was this
open communication that made the analysis presented in this paper
possible, rather than a formal plan to share and triangulate data. As
a result, we suggest that developing a joint learning agenda early in
a project’s implementation and evaluation phase would be beneficial
for other programme’s hoping to generate shared findings. This
learning agenda must then be underpinned by mutual trust and
shared timelines that allow for the free and timely flow of information among teams.
In addition to developing strong internal programme communication and coordination to re-tool implementation science data for
policy implementation analysis, we recommend that programme
recipients be engaged to conduct more robust analyses. In the case
described above, the AHME evaluators and implementers were in a
privileged position to feed the results of their analysis directly into a
government process due to the relationships they had forged and the
status they enjoyed at the highest levels of government. While implementation data collection could be most efficient and resulting analysis most relevant at the provider level if both were led by providers
themselves, or at least undertaken with greater involvement from
providers, as we have shown elsewhere (Sieverding et al., 2018;
Suchman et al., 2018) SME private providers tend to have little or
no voice in the larger health system and we know little about their
experiences under programmes like social health insurance. Unlike
in high-income countries, where insurance schemes often necessitate
that private providers become part of a larger provider network, this
is not the case in Kenya. Although they make up a significant proportion of the health market in LMICs (Shah et al., 2011), SME private providers tend to operate independently with little connection
to the larger health system outside of social franchise networks
(Shroff et al., 2018). As a result, when healthcare change processes
are under way, such as the NHIF reforms, it is challenging for these
providers to participate meaningfully in such processes. In Kenya,
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Endnotes
1. Fixed payment by service, case, or day, triggered when services
are delivered (Holtz and Sarker, 2018)
2. Capitation is a payment arrangement for health care service
providers that pays a set amount for each enrolled person
assigned to them, per period of time, whether or not that person seeks care (Holtz and Sarker, 2018)
Acknowledgements
The authors wish to thank Dominic Montagu for thoughtful comments on an
earlier draft of this article and the research participants for their generosity of
time and spirit. This work was jointly supported by the UK Department for
International Development (DFID) and the Bill and Melinda Gates
Foundation [OPP1044138, OPP1032848]. The funders had no role in the
study design, implementation or decision to publish. This article is part of the
supplement ‘nnovations in Implementation Research in Low- and MiddleIncome Countries’, a collaboration of the Alliance for Health Policy and
Systems Research and Health Policy and Planning. The supplement and this
article were produced with financial support from the Alliance for Health
Policy and Systems Research. The Alliance is able to conduct its work thanks
to the commitment and support from a variety of funders. These include our
long-term core contributors from national governments and international
institutions, as well as designated funding for specific projects within our current priorities. For the full list of Alliance donors, please visit: https://www.
who.int/alliance-hpsr/partners/en/.
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