“Poverty is a social issue, not a mathematical problem”: Examining the lessons for beneficiary identification from implementation of the UHC indigent program in Kenya

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As Kenya transitions to a new social health insurance framework under the Social Health Authority (SHA), understanding the implementation experience of the UHC indigent program is critical for informing the roll out of SHA’s indigent program. Methods: We conducted a qualitative process evaluation of the UHC indigent program using semi-structured interviews with 23 key informants from national and county health systems, development partners, and implementing actors, complemented by a validation workshop with 57 stakeholders. Our analysis was guided by Moore et al.'s process evaluation framework and Wu et al.'s policy capacity lens, examining implementation fidelity and capacities at multiple levels. Results: The program’s implementation deviated from its original centralized design, with counties exerting control over beneficiary identification due to national data gaps, incomplete rollout of the Harmonized Testing Tool, and political and operational constraints. Variations in targeting methods, reliance on under-resourced community health actors, and delays in biometric registration contributed to partial enrolment, exclusion errors, and mistrust. Although some counties reported increased service utilization, this was limited by unregistered dependents and lack of beneficiary awareness. Stakeholders expressed concern over SHA’s use of proxy means testing (PMT) for informal sector enrolment, citing risks of exclusion, manipulation, and failure to capture locally constructed definitions of poverty. Conclusion: Kenya’s experience underscores the need to align national targeting frameworks with local realities, invest in policy capacity across stakeholders, and prioritize community validation and communication in pro-poor programs. As SHA rolls out a new indigent program, these lessons offer critical guidance for enhancing fidelity, equity, and accountability. Figures Figure 1 Introduction Many low- and middle-income countries (LMICs) have embraced Universal Health Coverage (UHC), implementing various health insurance models, including social health insurance (SHI), to enhance financial access to healthcare for their populations (Fenny et al., 2021 ; Reich et al., 2016 ). In alignment with the Sustainable Development Goals, UHC is rooted in the need to shield individuals from the financial hardships associated with out-of-pocket (OOP) health expenditures, mitigating the risk of catastrophic and impoverishing healthcare payments (Bain, 2023). However, there is growing concern that SHI models, particularly in Africa, often leave out the poorest and most vulnerable populations due to contributory requirements, weak identification systems, and limited fiscal space to subsidize premiums (Barasa et al., 2021 ; Cotlear et al., 2015 ; Fenny et al., 2018 ). These exclusions threaten to widen inequities in access and undermine the core goals of UHC. In response to equity concerns, many Health insurance programs are accompanied by efforts to provide coverage to poor and vulnerable households through HISP. Subsidies can take various forms, including premium subsidies that lower the cost of insurance coverage, direct subsidies to healthcare providers to offset the cost of services for targeted groups, and supply-side subsidies aimed at improving healthcare infrastructure and service delivery in underserved areas (Abuya et al., 2012 ). Health insurance subsidies are financial intervention designed to reduce barriers to healthcare access, particularly for populations unable to afford the full cost of insurance premiums or healthcare services (Maritim et al., 2023 ; Vilcu et al., 2016 ). Subsidies are particularly relevant for countries with a large informal sector—often accounting for over 70% of the workforce—where traditional employment-based health insurance models are less viable. (Laar et al., 2021 ). Kenya is leveraging SHI as a main mechanism for advancing UHC and is currently undertaking extensive reforms to overhaul its existing health insurance framework. These reforms are anchored in the Social Health Insurance Act of 2023, which establishes a new SHI program under the stewardship of the Social Health Authority (SHA)(Social Health Insurance Act, 2023 a). The new framework replaces the longstanding National Health Insurance Fund (NHIF) as national insurer. A central feature of the reforms is the creation of three distinct funds, each designed to address different levels of healthcare needs. First, the Primary Healthcare Fund, financed through general tax revenue, is dedicated to supporting the delivery of primary healthcare services. Second, the Emergency, Chronic, and Critical Illness Fund, also tax-financed, is intended to cover high-cost services related to emergencies and chronic conditions, with provisions to mandate enrolment for all Kenyan citizens and residents. Third, the Social Health Insurance Fund (SHIF) is financed through contributions from both formal and informal sector households (SHI Regulations, 2023 ). Formal sector employees, contributions will be collected via pro-rata (proportional) payroll deductions while informal sector households’ contribution will be determined using a proxy means-testing (PMT) approach. Additionally, the government will finance SHIF premiums for indigent households through direct allocations from the national tax budget. The SHI Act defines an indigent as “a person who is poor and needy to the extent that the person cannot meet their basic necessities of life” (Social Health Insurance Act, 2023 a) Research in countries such as Ghana, India, and Indonesia have found that premium subsidies can boost enrolment in health insurance programs, particularly among low-income and informal sector populations who would otherwise be unable to afford coverage (Ekonomi et al., 2023 ; Kinnan et al., 2020 ; Lim et al., 2023 ; Mohammadzadeh et al., 2023 ). By reducing the financial barriers to obtaining health insurance, subsidies can increase access to healthcare and improve financial risk protection for vulnerable groups (Kinnan et al., 2020 ). Evidence from health insurance subsidy programs (HISPs) suggests that the way these programs are designed can have a significant impact on enrolment rates and utilization patterns. This includes targeting mechanisms to identify and enrol eligible beneficiaries, the level of the subsidy offered, the methods for delivering subsidies (e.g., direct premium subsidies, vouchers), the benefit entitlements, and efforts to promote awareness and understanding of the program (Kinnan et al., 2020 ). Designing and implementing effective HISPs in LMICs presents several challenges that require careful consideration of factors such as equity, stakeholder engagement, and sustainability to ensure the long-term success and impact of these programs (Asuming et al., 2024 ; Ekonomi et al., 2023 ) Kenya has made prior attempts to extend coverage to indigent populations, including the Health Insurance Subsidy Program (HISP) launched in 2014 and scaled up in 2016 (Barasa et al., 2018 a, 2018 b; Kabia et al., 2019 ). However, challenges in targeting led to limited impact with an evaluation of the program revealing that approximately 65% of those enrolled belonged to the wealthiest socio-economic quintiles, undermining the program’s equity objectives (Barasa et al., 2018 ). In 2018, the government introduced the UHC indigent program following pilot reforms in four counties (Kisumu, Nyeri, Isiolo, and Machakos), aiming to enrol one million poor households as a foundation for national scale-up (Nyawira et al., 2024 ). In this paper, we present the findings of a process evaluation of the design and implementation of the UHC scale-up indigent program. We examine the key challenges and lessons from the design and implementation of Kenya’s HISP, aiming to inform how such programs can be implemented to advance UHC in Kenya and other LMICs. These findings are particularly important as Kenya embarks on the rollout of a new indigent program under the SHA. Understanding the strengths and pitfalls of the UHC indigent program is essential to informing a more effective, equitable, and accountable implementation of the current reforms. Methodology Conceptual framework To examine the implementation experience of the UHC indigent program, we conducted a process evaluation. This evaluation aimed to draw lessons that could be incorporated into the redesign for scale-up planning. Our evaluation framework (Fig. 1 ) was adapted from Moore et al (Moore et al., 2015). In this process evaluation, we first explored the emergence of the UHC indigent program. We then described and examined the implementation arrangements, activities, and processes, as well as the fidelity of the implementation and the experiences of relevant stakeholders, including national and county-level policymakers and implementers, health facility managers and frontline health workers, and members of the community. To provide a comprehensive analysis, we integrated Moore et al.'s framework with concepts of policy capacity, which refers to the skills and competencies needed to carry out a policy function (Moore et al., 2015). Drawing on Wu et al.,(Wu et al., 2015 ) we assessed three dimensions of policy capacities that influenced policy implementation: analytical policy capacities, which are the skills and competencies required to develop technically sound strategies to support the fulfilment of policy reform goals; operational policy capacities, which are the skills and competencies required to align resources with the goals of the policy to enable implementation; and political policy capacities, which are the skills and competencies required to identify, mobilize, and strengthen political support for policy actions. These policy capacities were assessed across the individual, organizational, and system levels of the policy environment. By examining the UHC pilot implementation experience through this lens, this process evaluation helped to identify areas for improvement in future UHC programs. Study design and participants We conducted a qualitative study using semi-structured interviews to explore the development and implementation of UHC indigent program in Kenya. A total of 23 participants were interviewed, drawn from various health system stakeholders, including national-level bureaucrats, development partners, implementing organizations, civil society organizations, academia, and representatives from the NHIF and Kenya Healthcare Federation (KHF). We also interviewed county health officials, community health liaisons as well as community health promoters to understand the implementation experience of the program at the county level in two purposively selected counties-Kisumu and Kiambu. Kiambu is in central Kenya and is mostly peri-urban population while Kisumu is in the Western part of Kenya with a mix of rural and urban population. Table 1 summarizes the county profiles against national estimates. Table 1 Study counties profiles Kisumu Kiambu National Population 2019 census (n) 1,155,574 2,417,735 47,564,296 Poverty rates (%) 36.3 20.5 38.6 Overall Health insurance coverage (%) 18 39.1 26.3 Average household size (n) 3.8 3.0 3.9 Criteria considered in selection One of the four UHC pilot counties Relatively higher socio-economic status and peri-urban County Source:(KNBS, 2019) (KNBS, 2021 ; KNBS and ICF, 2023 ) Participants were selected using a combination of purposive and snowball sampling techniques. Purposive sampling was employed to identify individuals with specific knowledge, experience, or roles relevant to the study, particularly those who were directly involved in the formulation and/or implementation of UHC indigent program (Table 2 ). Snowball sampling was then used to identify additional participants through referrals from initial interviewees. Table 2 Summary of study participants Participant category Female Male Number National policy makers- Ministry of Health (MOH) bureaucrats 1 3 4 County stakeholders (2 purposively selected counties) 4 3 7 Development partners 0 3 3 NHIF/SHA 1 2 3 Implementing partners 1 3 4 Civil Society Organization (CSO) 0 1 1 Academia 0 1 1 Total 7 16 23 We collected data using a semi-structured topic guide developed from the components of the study’s conceptual framework. The guide was designed to ensure consistent coverage of relevant themes while allowing flexibility to probe emerging issues during interviews. All interviews were conducted by BM and RM. These were audio-recorded with participant consent, and transcribed verbatim for analysis. We also included data from a stakeholder validation meeting whose proceedings were audio-recorded and transcribed for analysis. The meeting was attended by 57 participants (41 in-person and 16 online), representing key stakeholders, including county health and treasury departments, the SHA, the MOH, the Digital Health Agency, the Council of Governors (COG), the National Treasury, and development partners. Data analysis Data was analyzed thematically using the six-step approach proposed by Braun and Clarke (2006). First, the research team immersed themselves in the data by reading and re-reading the transcripts (Step 1). Second, a list of deductive codes was developed based on the concepts from the study’s conceptual framework (Step 2). Third, similar codes were grouped into themes by identifying patterns across the coded data (Step 3). Fourth, themes were reviewed for internal consistency and coherence with the coded extracts to ensure they accurately reflected the data (Step 4). In the fifth step, the finalized themes were systematically applied to the entire dataset, with supporting quotes and excerpts identified for each theme (Step 5). Finally, the findings were synthesized and interpreted in relation to existing empirical and theoretical literature (Step 6). RESULTS Program development rationale The development of the UHC indigent program was driven by the need to alleviate financial barriers to healthcare access for vulnerable households. The policy direction was further reinforced by lessons from previous UHC efforts (e.g. previous subsidy programs and the UHC pilot) and supported through legislative reforms. By providing poor households with full health insurance subsidies, the program aimed to offer financial risk protection against healthcare related costs. “The biggest issue was that poor households were being pushed further into poverty by healthcare costs. The program was meant to stop this cycle” (KII_C1_05). The HISP adopted an insurance-based model because of lessons from implementing the UHC pilot program which used an input-based financing model. Under the input-based approach, the government directly financed public health facilities by providing resources such as drugs and commodities rather than channelling funds through insurance or performance-linked mechanisms. While this model enabled rapid service delivery during the pilot phase, policymakers deemed it fiscally unsustainable and lacking in efficiency and accountability for national scale-up. The shift towards an insurance-based model for UHC in Kenya using NHIF, allowed for contributions from those with the ability to pay. It also made it possible to pool contributions and standardize coverage, providing a more sustainable approach to ensuring access to essential health services. “The UHC pilot showed us that input-based financing wasn’t working as intended. We needed a model that could scale, and that’s where the shift to insurance came in” (KII_NHIF_02). The NHIF Amendment Act of 2022 also shaped the indigent program. This Act redefined NHIF’s mandate, transitioning it from a hospital insurance scheme to a health insurance scheme covering a broader range of services. The Act formalized the government’s role in subsidizing health insurance for indigent households. This legal framework enabled NHIF to enroll indigent populations and manage their coverage with premiums financed using public funds appropriated by the national assembly. Key actors in implementation The MOH led the formulation and coordination of the UHC policies, overseeing the overall implementation of the program. Its role included ensuring that indigent households were identified and enrolled in the program, while also coordinating with stakeholders at both national and county levels. “The Ministry of Health had to ensure that all stakeholders, including county governments and development partners, were involved in the planning and implementation process” (KII_MOH_03). The Ministry of Labor and Social Protection (MOLSP) holds the primary responsibility for formulating social protection policies. The social protection policy defines social health protection as one of its four pillars. The Department of Social Protection, under MOLSP, manages Kenya’s social protection programs, including maintaining the enhanced single registry (ESR) - a socio-economic database of all vulnerable households in the country. The database includes all beneficiaries supported for social health protection under the earlier HISP covered by NHIF. During the implementation of the UHC indigent program, it was envisioned that the program would rely on the operational and technical capacity of the MOLSP for the accurate identification and targeting of indigent households by adopting its processes and tools. This included the use of the MOLSP’s Harmonized Testing Tool (HTT) that incorporates PMT to assess socio-economic status complemented by a community-based verification process supported by the national government representatives in the county such as the county and subcounty commissioners. NHIF was responsible for managing the insurance coverage for the indigent households identified under the program. NHIF verified household data provided by County governments, ensuring that beneficiaries were enrolled in the system. NHIF had successfully ran a pilot and scale up of the HISP and was expected to utilize the same processes working with the MOLSP to scale up the national UHC indigent program. NHIF’s operational role was essential in managing the household registration and claims process ensuring that resources were allocated effectively: “NHIF played a key role in ensuring that once households were identified, they were enrolled in the insurance program and could access services without delay” (KII_NHIF_02). Development partners, such as the World Health Organization (WHO), the World Bank, UNICEF, and Clinton Health Access Initiative (CHAI), played an important role in providing technical and financial support throughout the program’s formulation and implementation. These partners helped realign existing projects to support UHC, offering both financial resources and expertise. “Development partners helped us a lot, especially in providing the technical support needed to develop policies and realign their projects to support UHC.” (KII_MOH_03). Lastly, County Governments were tasked with identifying and registering indigent households based on their local knowledge and contexts. National operations and tools were to be cascaded to the counties through the COG for a streamlined implementation of the program. In addition, County governments managed various complementary social protection programs that included local HISPs. Program design and implementation fidelity Intended program design- Dejure design The UHC indigent program was designed as a national, targeted social health insurance initiative aimed at providing fully subsidized health insurance coverage for Kenya's poorest households. The program targeted approximately one million indigent households, with plans to scale up to 1.5 million in the following year and eventually to five million, aligning with national poverty assessments indicating that 5.2 million Kenyan households were living below the poverty line. The premium amount was set based on the national UHC scheme contribution flat rate for informal sector households which was Ksh. 500 (USD 3.8) per household per month. The design of the UHC indigent program was centred around a multi-stakeholder approach, involving several key agencies essential for harmonizing data across MOH, NHIF, and county governments. The MOH and the COG were tasked with developing tools for county governments to identify indigent beneficiaries and set up a dedicated database for this purpose. This database was to be integrated with ESR database from the MOLSP and NHIF’s existing beneficiary records. “ There was a discussion of how households should be identified by expanding the use of the existing tools that have been used in the HISP, but because the identification was done by the Ministry of Labour and Social Protection, discussion then was for MOH and COG to work on the tools that would then be sent to the counties for them to use to identify and then set up a database. And then the database would be merged to the social protection database ” (KII_Do_01). This design acknowledged MOLSP’s mandate and technical capacity in identifying indigent households through its existing social protection database and tools. MOLSP’s mandate was to provide a standardized approach to household identification across counties, ensuring equity in targeting the most vulnerable populations. “The Department of Social Protection had the mandate to identify indigents using their data, and this was supposed to be standardized across counties” (KII_Do_02). Although the core responsibility for defining indigent eligibility remained with MOLSP at the national level, collaboration with county governments was necessary. This was because Social Protection is not a devolved function and therefore this partnership allowed the MOLSP to leverage local knowledge and resources within counties while maintaining a standardized, national approach to identifying and enrolling indigent households. NHIF’s role in the program was to verify household data, provide health insurance coverage, and ensure that registered indigent households had access to health services at accredited facilities. “NHIF was responsible for ensuring that indigent households had access to health services, and the government was supposed to cover the premiums” (KII_NHIF_02). The national government, through the National Treasury (the Ministry of Finance and Economic Planning), allocated Ksh. 6 billion (Approximately USD 46,366,332) for the first phase of the program in 2020/21, covering the cost of premiums for one million households for one year. However, under the UHC indigent program, this premium covered not just the principal member but also their spouse and children. Based on the national average household size at the time (approximately 4.4 members), the program effectively extended coverage to an estimated 4.4 million individuals. De Facto Implementation The implementation of the UHC indigent program in Kenya deviated from its intended design due to various operational, analytical, and political challenges, as well as disagreements between national and county governments. Although the program was initially designed to rely on the MOLSP tools and the ESR database to ensure uniformity, counties advocated for control over the identification process, emphasizing that health is a devolved function under Kenya’s constitution. This push for decentralization led to a compromise: the national government set a cap on the number of indigent households each county could identify, while allowing counties autonomy in the identification process. “ However, midway there was a contestation between national government and the counties on how that should be done, and the final approach was that the national government basically set a cap for all the counties in terms of the number to be identified and each of the counties were then allowed to identify households on their own” (KII_Do_01). Some stakeholders, however, disagreed with the notion of a centrally led UHC indigent program, arguing that it overlooked existing county-led health financing initiatives. For example, counties like Makueni had already implemented their own schemes, such as MakueniCare, shortly after devolution " Some programs came before this — MakueniCare started immediately after devolution — and so it is not that counties had advocated for greater control because it [health] was a devolved function; some counties started before the National Government."(KII_C1_06) Several county officials also clarified that decentralization of indigent identification was not solely due to county pressure, but rather a lack of preparedness and unavailability of national-level data tools. For instance, one county official claimed that the HTT was never finalized or rolled out at the time of program initiation. In addition, the MOLSP was reluctant to share its database-ESR- citing the need for a bureaucratic process involving the cabinet secretaries of the two ministries: “The Ministry of Labor and Social Protection had the data, but there were challenges in sharing the information” (KII_MOH_04). Each county received a quota of slots for indigent household coverage under the UHC program, determined by a weighted formula based on population size and poverty levels, as reported in the 2019 Kenya Population and Housing Census. For instance, Kiambu County was allocated 38,000 slots in the first phase, while Kisumu County received 28,000 slots. Although allocation data for all counties is not publicly available, quotas were determined using a weighted formula based on population size and poverty levels drawn from the 2019 census. Counties used various methods for beneficiary identification, including PMT and community poverty ranking approaches, where Community Health Volunteers (CHVs), village elders, and chiefs were actively involved in identifying households to be enrolled into the program. This localized approach allowed counties to adapt criteria according to their priorities. “[Decentralization was], in essence, was trying to make good of a complex process because once we decentralize the identification, then I could qualify as an indigent in Nyeri, but I may not qualify as an indigent in Uasin Gishu” (KII_Do_01). Some respondents however reported that the perceived reluctance by the MOLSP to share their database was because the data itself was incomplete or unavailable at the time of program initiation. According to one county official: "I personally visited those offices to get the list, and there was no list!" KII_C1_06 . Where existing data was available, it was often inadequate to meet the quotas allocated to counties because of the unrealistic speed at which counties were expected to act. As another county official explained, “ I was given twenty-eight thousand indigents[households] to fill up; I didn’t reach twenty-eight thousand. I ended up having eighteen thousand or something like that. We couldn’t fill them, because where do you get them from?” KII_ County official These operational and temporal constraints introduced inconsistencies in how indigents were identified across the counties, leading to variation across counties. Some counties, like Kisumu, Kiambu, and Makueni, ran their own subsidy programs and maintained separate databases, which sometimes conflicted with the UHC program’s criteria. It is from these lists that they determined who would be enrolled into the UHC indigent program in their counties. “ Counties had their own lists, and there was a lot of confusion over which list should be used” (KII_C1_05). Operational capacity challenges also arose, with CHVs often facing confusion about the criteria, limited resources, and reliance on paper-based processes, which slowed beneficiary identification. “Some CHVs were confused about what criteria to use when identifying indigent households, and we didn’t receive immediate clarification from the Ministry” (KII_C2_02). “The criteria to include vulnerable households were clear on paper, but in practice, many indigent households weren’t identified correctly” (KII_IP_03). Once counties submitted their lists, NHIF cross-checked the data against national databases, including the Integrated Population Registration System (IPRS) and records from the Ministry of Labour and Social Protection (MOLSP). A significant portion of the data was excluded due to duplication (e.g., individuals appearing on multiple lists), existing active NHIF coverage, or mismatches with national civil registry information. Households with unverifiable data or those already benefiting from other programs were also excluded. Data discrepancies and delayed feedback mechanisms between the county and NHIF, created further challenges in identifying eligible beneficiaries accurately. Despite these challenges, NHIF validated the lists from counties using the means accessible to them and enrolled households in the UHC Supa Cover scheme, assigning them to NHIF-accredited outpatient facilities. Selection of facilities Counties were allowed to determine the assignment of health facilities where indigent beneficiaries would access services. We found variation in implementation: some counties assigned beneficiaries to nearby PHC facilities, while others limited assignments to level 4 hospitals. The preference for higher-level facilities was often driven by the higher capitation rates paid to them—KES 1,400 per household member per year compared to KES 1,000 for PHC facilities. In one county, all capitation funds related to the indigent program were initially deposited into the account of a level 5 referral hospital, regardless of where the beneficiaries were assigned for care. According to respondents, this centralized arrangement was intended to streamline financial management and ensure oversight. The level 5 facility then disbursed funds to other public facilities in the county based on the number of patients they reported seeing. “What currently happens, is that funds for the UHC [indigent] program are channelled to one facility… [the facility] then documents the number of patients who have received the service and requests reimbursement ” (KII_C2_02). Fidelity of Implementation and policy capacity We assessed the fidelity of the UHC indigent program by examining how closely the actual implementation adhered to the original program design. Overall, there was partial fidelity to the program’s intended objectives, with significant deviations in the areas of beneficiary identification, data harmonization, and service accessibility. Key issues contributed to these deviations, reducing the program’s fidelity. Beneficiary identification The original design of the program aimed for a standardized, national approach to identifying indigent households, leveraging MOLSP’s social protection data. Data analysis involved multiple steps, including cleaning large volumes of paper-based submissions, checking for completeness, validating identification details, and manually resolving discrepancies. Many counties lacked trained personnel to manage these tasks efficiently. There was also limited feedback from NHIF to counties on verification outcomes which limited the opportunity to improve the processes. “We had the technology to collect the data, but the problem came when we tried to analyse it. We didn’t have enough trained personnel to handle the amount of data coming in, which caused delays in verifying eligibility” (KII_C1_03). Data harmonization and operations The intended program design emphasized collaboration across MOH, NHIF, and MOLSP to maintain a unified database for indigent households. However, in practice, the lack of a harmonized approach led to multiple lists and confusion over eligibility. “There was no unified process; each agency had its own list, and there was constant back and forth on how to align them” (KII_Do_02). Operational challenges—including delayed funding disbursements and shortages in resources for community health volunteers (CHVs)—further compounded these issues. “The data collection process was slow because we didn’t have proper devices for CHWs, and much of the work was done manually” (KII_C1_04). These resource constraints not only affected data quality but also slowed enrolment, undermining program fidelity. While counties were primarily responsible for facilitating CHV engagement and logistics, there was no clear provision for dedicated funding from the national government to support the operational costs of CHVs during the implementation of the indigent program. This lack of clarity in financing responsibilities contributed to uneven support across counties and limited the effectiveness of community-level data collection. In addition to county-level financial bottlenecks, delays in disbursement from the national government to NHIF disrupted cash flow and constrained the program’s ability to settle claims and support indigent beneficiaries in a timely manner. “Funding would come late from the Treasury, so we’d delay reimbursements or capitation to facilities. That affected service delivery” (KII_NHIF_02). Counties that had granted health facilities autonomy over financial management demonstrated stronger operational capacity during the implementation of the indigent program. In these settings, funds—particularly capitation payments—were allocated directly to facilities, enabling timely access to services for beneficiaries. This direct funding approach improved the efficiency of financial flows and the availability of essential health services. In contrast, in counties where all health funds were required to pass through the County Revenue Fund (CRF) before being disbursed to facilities, delays were common. This often resulted in unpredictable and inadequate financing at the point of care, hindering service delivery and undermining the responsiveness of the program. Political capacity played a dual role in program implementation. Although the UHC indigent program was aligned with the Big Four Agenda and enjoyed strong national support, political dynamics at the county level occasionally influenced the identification process. The decentralized approach allowed counties to exercise discretion, but in some cases, local political considerations shaped beneficiary selection, diverging from the program's initial equity-focused criteria. Service accessibility Even when NHIF successfully enrolled indigent households, communication gaps left many beneficiaries unaware that they had been registered or how to access services. In many cases, dependents of household heads were not registered, which limited coverage for the entire household—the intended population unit. This was due to a combination of logistical challenges, such as limited biometric registration equipment, lack of beneficiary awareness about the need for dependent registration, and rushed implementation timelines driven by political pressure. “Biometric registration was delayed in some areas, and this meant some indigent households had difficulty accessing services because their details weren’t fully in the system” (KII_Do_03). Additionally, local political considerations occasionally influenced the choice of beneficiaries and facilities, deviating from the program’s goal of uniform access. The program registered 882,291 individuals from an initial list of 1,509,037 indigents households submitted to NHIF. According to the MTEF for 2024/25-2026/27, funds initially allocated for unregistered indigents were redirected to provide coverage for 200,000 boda boda (motorbikes operating as taxis for carrying passengers or goods) riders, reflecting shifting priorities within the program. “We sent the lists to NHIF, but after that, there was no follow-up, and we don’t know what happened to those households” (KII_C1_05). Stakeholder Capacity The implementation of the UHC indigent program revealed varying levels of policy capacity across key stakeholders as summarised in Table 3 . Table 3 Stakeholder policy capacity Stakeholder Analytical Capacity Operational Capacity Political Capacity Ministry of Health (MOH) Developed overall policy framework but lacked the capacity to standardize data tools and processes across counties; limited engagement in data validation. Provided policy guidance but had limited ability to enforce implementation fidelity after decentralization. Strong national-level commitment to UHC, but limited influence over county-level political dynamics. County Governments Varied ability to analyse poverty data; frequently relied on subjective methods like community ranking without standardized tools. Led household identification and enrolment but faced challenges in execution, especially in counties dependent on CRF with delayed fund flows of capitation from NHIF to the health facilities. Held significant local influence over enrolment processes, which occasionally led to politicization and inconsistencies. NHIF Able to validate submitted data using national databases (e.g., IPRS), but depended on county-generated lists and lacked direct control over targeting criteria. Effectively managed enrolment and benefits administration; however, struggled with delayed feedback loops and integrating non-standardized data. Strong institutional mandate, but role constrained by upstream decentralization of beneficiary identification. Ministry of Labour and Social Protection (MOLSP) Maintained strong analytical tools and a national socioeconomic registry (ESR); capable of implementing proxy means testing (PMT). Minimal involvement in program operations despite being the mandated custodian of social protection data. Politically influential at national level but exhibited reluctance to collaborate fully, limiting integration with MOH and counties. Community Health Volunteers (CHVs) Limited training in data collection protocols; low familiarity with PMT tools and criteria. Played a central role in household identification but lacked adequate resources (e.g., training transport, digital tools). Minimal formal influence, but crucial for community engagement and trust-building. Village Elders Relied on local knowledge but used subjective criteria, leading to inconsistent identification practices. Supported grassroots mobilization and enrolment; limited training and clarity on their role in verification. Held informal authority in communities; facilitated access but lacked decision-making power. Civil Society Organizations (CSOs) Limited involvement in technical design or data analysis. Focused on advocacy and community sensitization; not actively engaged in operational implementation. Advocated for equity and inclusion but had limited leverage to influence policy direction. Development Partners Provided early technical assistance in designing policy frameworks; engagement reduced post-decentralization. Supported system design and initial planning; limited involvement in day-to-day implementation. Influenced agenda-setting at the national level but not embedded in domestic political processes. Program outcomes Enrolment and biometric registration The program targeted approximately 1 million households for enrolment. However, the actual number of households enrolled fell short, with around 882,000 households successfully registered as indigents. An assessment of the performance of five counties on filling their allocated quotas revealed varied performance with some counties managing to identify only 50% of the indigents’ slots assigned to them(Murira et al., 2024 ). Despite the success in initial enrolment, biometric registration—a critical step for ensuring that indigents could access services without delays—lagged significantly behind. Only 382,000 individuals, (48% of the enrolled population), completed biometric registration, creating challenges for the remaining households when seeking services at healthcare facilities Table 4 . Table 4 UHC indigent program outcomes Outcome Target Achieved Completion Rate Households Targeted 1,000,000 882,000 88% Biometric Registration 1,000,000 382,000 48% At the time of the UHC indigent program’s rollout, Kenya’s landscape of health insurance subsidies was already complex, with multiple programs providing subsidized coverage to vulnerable populations. According to stakeholder feedback during the validation workshop, approximately 374,500 individuals were enrolled under the government’s HISP targeting orphans and vulnerable children, older persons, and persons with severe disabilities. Additionally, some county governments independently subsidized health insurance for their residents. For example, Kisumu County supported approximately 45,000 households through its Marwa Health Insurance Scheme. This represented roughly 10–15% of the county’s total households, based on 2019 census estimates. The scheme primarily targeted indigent and vulnerable populations, including orphans and vulnerable children (OVCs), the elderly, persons with disabilities, and other low-income households identified through local social protection mechanisms. International actors also contributed, with the United Nations High Commissioner for Refugees (UNHCR) sponsoring premiums for approximately 22,000 refugee households and AMPATH providing sponsoring about 1,000 households. These diverse pre-existing subsidy programs meant that the implementation of the UHC indigent program had to navigate overlapping beneficiary groups and fragmented coverage arrangements, further complicating efforts to achieve standardized national targeting and enrolment. Nonetheless, following the program’s implementation, some counties reported an increase in service utilization among indigent households, particularly in areas where biometric registration rates were higher. “We saw a significant increase in utilization, especially among those who had completed biometric registration. Many were accessing services they had previously avoided due to cost” (KII_MOH_04). Unintended consequences A significant unintended consequence of the program was the presence of inclusion and exclusion errors in the identification of indigent households. Some households that were truly eligible for the program were left out, while others that did not qualify were included. This was largely due to discrepancies in data management and political interference at the county level. As one respondent remarked, “The biggest challenge was identifying the right people; we ended up with many people who were not truly indigent” (KII_C1_05). These errors undermined the credibility of the program and limited its ability to reach the most vulnerable populations. “We found that some households that were not truly indigent managed to get enrolled, while others that desperately needed the program were left out. This happened because the identification process wasn’t as rigorous as it should have been.” (KII_IP_03) Another unintended consequence was the mistrust that developed within communities toward the health system and the UHC indigent program itself. Inconsistencies in the implementation process and the perception that the system was flawed led to a breakdown of trust between communities and the institutions involved in delivering the program. “[Inconsistencies]… meant that the trust in the process was a bit flawed in some communities. And that's kind of detrimental to any health system… [and] the blame would go to any institution involved in that process. So, I think for me, the biggest thing is that it resulted in mistrust around the process” (KII_IP_02). Views on the SHA beneficiary indigent program design Stakeholders expressed mixed and often critical views regarding the planned use of PMT under SHA to assess household income and determine eligibility for subsidized coverage. According to an official from NHIF/SHA, the PMT tool estimates household income based on responses to a standardized set of socioeconomic indicators, applying a statistical model akin to regression analysis. "It [the PMT tool] estimates the household income based on the socioeconomic indicators in the questionnaire and then applies 2.75% of that income to determine the household premium." KII_NHIF_01 The decision to adopt PMT was partly influenced by lessons learned during the implementation of the UHC indigent program, where inconsistent and subjective targeting methods across counties posed major challenges. “So, yes, there were those challenges around identification. That’s one of the reasons why there was a suggestion of developing a scientific model—that is, the proxy means testing—that can identify and quantify household income. So that way it'll reduce the subjectivity that was there and the variance across different counties.” (KII_NHIF_02) The tool uses different subsets tailored to rural, peri-urban, and urban settings to account for contextual variations. However, several challenges were acknowledged, including the complexity of the questionnaire for individuals of lower literacy levels, the risk of households providing false information to lower their premiums, and the lengthy timeframe required to roll out PMT to the entire informal sector, which constitutes approximately 83% of Kenya’s workforce. Concerns were also raised about the practicality and inclusiveness of means testing for Kenya’s poorest populations. Participants noted that indigent households often lack mobile phones or the flexibility to attend assessments. As one county leader cautioned: “Indigents are people without a phone most of the time so you will not catch them... one day of not working means no food on that day .” Validation workshop participant 15 Others questioned the underlying assumptions of the tool, pointing out that poverty is socially constructed and not easily captured through standardized indicators. “Poverty at the local level is social, it is not mathematical,” one county official explained, arguing that PMT fails to reflect nuanced household realities. Participants also raised concerns about potential manipulation of the PMT system, once its algorithm becomes widely known suggesting that the tool could be gamed, undermining its purpose. Stakeholders emphasized the need for greater transparency, community validation, and a reconsideration of whether PMT is the most appropriate method for targeting in Kenya’s informal and decentralized context. “Kenyans... will congregate and give the lowest indicator, so that it gives you the lowest premium,” Validation workshop participant 19 Discussion This study examined the implementation experience of Kenya’s UHC indigent program to draw lessons for the ongoing rollout of the ongoing Social Health insurance reforms under SHA. Our findings reveal a substantial disconnect between the intended centralized design and the decentralized execution of the program. Although the program aimed to leverage standardized national tools such as the MOLSP harmonized testing tool and socioeconomic registry, operational challenges, political contestations, and limited stakeholder engagement led to widespread variation across counties. Reflections from the stakeholder validation workshop corroborated these findings, highlighting that decentralization was driven not only by county demands but also by the lack of readiness and financing at the national level to implement centralized tools. These tensions compromised program fidelity and reflected broader governance challenges revealed in other studies in aligning national policy design with the autonomy of devolved units (Barasa et al., 2022 ; Masaba et al., 2020 ). The implementation experience was further characterized by institutional fragmentation and capacity mismatches across national and county stakeholders. While the MOH demonstrated political commitment to national UHC goals, its limited operational and analytical capacity hindered effective coordination and oversight. Similarly, while the NHIF managed enrolment processes, it remained dependent on inconsistent county submissions, and the MOLSP, despite holding national datasets, operated largely in isolation. These dynamics were exacerbated at the county level, where reliance on under-trained CHVs (now known as community health promoters-CHPs), political expediency, and constrained fiscal systems led to inconsistent beneficiary identification and limited-service access. A study on UHC pilot revealed similar design and implementation challenges (Nyawira et al., 2024 ). According to study respondents, utilization of health services increased following the rollout of the indigent program, with households reporting improved access to outpatient and inpatient care. These findings are consistent with global evidence. A systematic review and meta-analysis by Shami et al. ( 2019 ) found that health insurance significantly increases health service utilization, with inpatient utilization rising by 0.51% and outpatient utilization by 1.26% among insured populations(Shami et al., 2019 ). The review highlighted that health insurance enhances access to care, particularly among vulnerable groups, by reducing financial barriers and enabling early and more frequent use of health services. In a similar study of Cambodia’s subsidy program, utilization increased in part owing to the program addressing non-financial barriers of access and offering transport reimbursement (Jacobs et al., 2007 ). This was also a result the increased efforts to increase awareness among the beneficiaries of their entitlement. In contrast, in Kenya’s UHC indigent program, several operational gaps hampered the realization of increased access for the poor. First, biometric registration was not completed for a significant proportion of enrolled households, limiting their ability to authenticate themselves and access services easily. Second, many dependents of registered household heads were not captured in the system, excluding significant portions of vulnerable families from coverage. Third, a critical gap was the lack of effective communication to beneficiaries regarding their entitlements under the program, resulting in missed opportunities to seek care even when financial barriers had been nominally removed. These shortcomings underscore that merely enrolling households is insufficient; active facilitation of access — including complete registration processes, comprehensive inclusion of dependents, and robust beneficiary awareness campaigns — must be prioritized under the SHA reforms if the indigent program is to be effective in delivering equitable access to services. These findings are particularly salient as Kenya embarks on implementing the SHA-led indigent program. Although the SHI Act centralizes core functions such as enrolment, eligibility assessment, and fund management under the Social Health Authority (Social Health Insurance Act, 2023 b), the requirement for county co-financing effectively maintains decentralization challenges observed during the UHC indigent program. While the SHI Act had envisioned a PMT process integrated into enrolment, in practice, a recent government proclamation reveals a plan to rollout the indigent program using existing databases — specifically, the Social Registry and the Inua Jamii cash transfer program under MOLSP — supplemented by SHA-led validation (Star newspaper, 2025). This raises concerns about the accuracy and inclusiveness of beneficiary identification, especially given known limitations of administrative datasets. Evidence from other LMICs shows that administrative registries, while efficient, often miss vulnerable populations due to outdated or incomplete records (Kidd et al., 2020 ). It is unclear if use of the Inua Jamii and the MOLSP Social registry signifies a shift away from identifying indigents using PMT as earlier envisioned. Stakeholders have indeed expressed concerns about the accuracy of the PMT emphasizing that poverty is a socially constructed reality often invisible to standardized measures. This reinforces the need for blended digital and community-based targeting approaches. Existing research also emphasizes the need for approaches to assessing vulnerability and poverty that recognize the socially constructed nature of living standards. These studies highlight how cultural and contextual factors shape the conceptualization of poverty across different settings (Alkire & Foster, 2011 ; Halkos & Aslanidis, 2023 ; Pu et al., 2024 ). Furthermore, relying on self-reported poverty assessment exposes the process to manipulation and ultimately inclusion and exclusion errors as has been found in Ghana’s indigent program where beneficiaries often falsified their socioeconomic status in order to benefit from exemption(Agbenyo et al., 2017 ; Akweongo et al., 2022 ; Kotoh & Van Der Geest, 2016 ). Without such integration, there is a risk of exclusion errors that could undermine the equity objectives of the SHA reforms. Other studies provide hope for leveraging digital process for PMT that use data science to “train machine-learning algorithms to recognize patterns of poverty in mobile phone data” (Aiken et al., 2022 ). Moreover, our findings point to the importance of institutionalizing feedback and learning loops within implementation processes. The absence of structured channels for counties and NHIF to share verification outcomes created a “black box” in which households were excluded without recourse, leading to community mistrust and loss of credibility. The Livelihood Empowerment Against Poverty program in Ghana, providing coverage for poor households, has also faced criticism for inclusion and inclusion errors that led to mistrust among community members on the fairness of beneficiary identification (Agbenyo et al., 2017 ). For the SHA to build public trust, it must embed transparent grievance mechanisms and real-time feedback systems accessible at the facility and household levels. A tribunal has been proposed to receive and resolve grievances under SHA. It is important that these provisions are operationalised by a properly constituted tribunal (Social Health Insurance Act, 2023 b). It is however not clear how the tribunal will operate, whether it will be at the county level or national, which has implications on the accessibility of the grievance mechanisms. The role of stakeholder capacity—as assessed through the policy capacity framework—emerged as a key determinant of program fidelity. Future reforms must go beyond policy design to invest in building the analytical, operational, and political capacity of actors across levels. For example, CHVs and village elders need consistent training, supervision, and digital tools to support their role in community verification. Counties need technical and financial support to manage indigent enrolment in a way that is consistent with national guidelines but responsive to local realities. And inter-agency collaboration, especially between MOLSP, SHA, and county governments, must be incentivized through shared data systems, joint planning platforms, and aligned accountability frameworks. Finally, the rollout of the new indigent program must reckon with the political economy of targeting. The UHC indigent program showed that political discretion at the local level influenced beneficiary selection, sometimes at odds with objective need. Understanding the political dynamics involved in expanding pro-poor policies is crucial for effectively scaling up and sustaining successful antipoverty programs. This includes considerations like the bargaining strength of beneficiaries, public support, and potential for political misuse of programs (Cuesta et al., 2020 ). The SHA must therefore ensure that targeting processes are not only technically sound but politically insulated, perhaps through audits, public disclosure of criteria, and performance-linked funding to counties. Study strengths and limitation This study is among the few to assess the implementation experience of Kenya’s UHC indigent program, providing an examination of both the de jure program design and de facto implementation realities. A major strength of the study is the integration of multiple data sources, including semi-structured interviews with a diverse range of stakeholders across national and county levels, and validation of findings through a large stakeholder workshop attended by key actors involved in health financing reforms. This triangulation enhanced the validity and relevance of the findings, particularly as the country transitions to the SHA-led social health insurance reforms. However, the study had some limitations. First, the absence of a consolidated national document detailing the design and implementation guidelines for the UHC indigent program necessitated reliance on stakeholder interviews and secondary reports to reconstruct the intended program design. This reliance may introduce recall bias and divergent interpretations among respondents. However, these lessons are important for informing the design, stakeholder engagement strategies, and accountability mechanisms necessary to strengthen Kenya’s social health insurance reforms and those of other low- and middle-income countries pursuing similar UHC goals. Conclusion This study reveals that while the UHC indigent program in Kenya was a well-intentioned step toward equitable health financing, its implementation was hindered by decentralization pressures, inadequate national readiness, and fragmented operational capacity. The resulting inconsistencies in targeting and enrolment compromised the program’s ability to fully protect poor households and foster trust. As Kenya embarks on implementing a new indigent coverage mechanism under the SHA, success will depend on addressing these foundational gaps. This includes improving data harmonization, ensuring full biometric and dependent enrolment, embedding grievance mechanisms, and enhancing stakeholder capacities at both national and county levels. A key lesson is the importance of blending standardized digital systems with community-level validation to reflect local understandings of poverty. As other LMICs design or refine similar subsidy programs, Kenya’s experience offers valuable insights into navigating the intersection of technical design, political economy, and implementation realities in the pursuit of universal health coverage. Declarations Ethical considerations Prior to the commencement of the study, ethical approval was obtained from the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) (protocol number: KEMRI/SERU/CGMR-C/294/4708). A national research permit was also received from the National Commission for Science, Technology and Innovation (NACOSTI) (permit number: NACOSTI/P/23/28111). In addition, approvals were granted by the respective County Health Departments to facilitate data collection at the county level. All participants provided written informed consent prior to taking part in the interviews. Funding This work was funded by Bill and Melinda Gates Foundation Investment no: INV-049230 Author Contribution BM, RM, and EB conceptualized the study. BM and RM led data collection. EB provided overall study supervision, conceptual guidance, and critical revisions to the manuscript. All authors contributed to the review and revision of the manuscript and approved the final version for submission. Data Availability Data available upon request References Abuya T, Njuki R, Warren CE, Okal J, Obare F, Kanya L, Askew I, Bellows B. 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Cite Share Download PDF Status: Published Journal Publication published 29 Jan, 2026 Read the published version in International Journal for Equity in Health → Version 1 posted Editorial decision: Revision requested 20 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviewers invited by journal 17 Jun, 2025 Editor assigned by journal 14 Jun, 2025 Submission checks completed at journal 12 Jun, 2025 First submitted to journal 09 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Programme","correspondingAuthor":false,"prefix":"","firstName":"Beatrice","middleName":"","lastName":"Amboko","suffix":""},{"id":473295661,"identity":"f213acfc-6bd2-4ad0-9248-d431af049422","order_by":5,"name":"Benjamin Tsofa","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Tsofa","suffix":""},{"id":473295662,"identity":"32b5b288-a007-40d0-983c-43d97b220d38","order_by":6,"name":"Caitlin Mazzilli","email":"","orcid":"","institution":"Gates Foundation","correspondingAuthor":false,"prefix":"","firstName":"Caitlin","middleName":"","lastName":"Mazzilli","suffix":""},{"id":473295663,"identity":"36fd4cb0-6919-4ae6-948a-f7ddfa45c9c3","order_by":7,"name":"Ileana Vilcu","email":"","orcid":"","institution":"Thinkwell","correspondingAuthor":false,"prefix":"","firstName":"Ileana","middleName":"","lastName":"Vilcu","suffix":""},{"id":473295664,"identity":"b3ba60cf-9b0a-4c11-b475-8ed501b82906","order_by":8,"name":"Ethan Wong","email":"","orcid":"","institution":"Gates Foundation","correspondingAuthor":false,"prefix":"","firstName":"Ethan","middleName":"","lastName":"Wong","suffix":""},{"id":473295665,"identity":"a9a38459-0e73-420d-aa56-5b2b5d30eaa0","order_by":9,"name":"Felix Murira","email":"","orcid":"","institution":"ThinkWell Kenya","correspondingAuthor":false,"prefix":"","firstName":"Felix","middleName":"","lastName":"Murira","suffix":""},{"id":473295666,"identity":"da9c0ec5-f816-425d-960b-c6bae5678789","order_by":10,"name":"Jacinta Nzinga","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Jacinta","middleName":"","lastName":"Nzinga","suffix":""},{"id":473295667,"identity":"3d955c7f-3f97-42d6-8235-c2ff795bda5f","order_by":11,"name":"Matt Boxshall","email":"","orcid":"","institution":"Thinkwell","correspondingAuthor":false,"prefix":"","firstName":"Matt","middleName":"","lastName":"Boxshall","suffix":""},{"id":473295668,"identity":"e617ae9d-8d4d-4c05-ad3d-9798adec0b21","order_by":12,"name":"Peter Mugo","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Mugo","suffix":""},{"id":473295669,"identity":"fe61fcb4-3751-4d44-a4ab-7ee2129f2d53","order_by":13,"name":"Rose Nabi Deborah Karimi Muthuri","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Rose","middleName":"Nabi Deborah Karimi","lastName":"Muthuri","suffix":""},{"id":473295670,"identity":"da9cbe59-eb1b-4ef0-b681-34ab563ca867","order_by":14,"name":"Wangari Ng’ang","email":"","orcid":"","institution":"Gates Foundation","correspondingAuthor":false,"prefix":"","firstName":"Wangari","middleName":"","lastName":"Ng’ang","suffix":""},{"id":473295671,"identity":"a0a97ad0-8c6f-4913-adaa-3ba9d069dade","order_by":15,"name":"Nirmala Ravishankar","email":"","orcid":"","institution":"Gates Foundation","correspondingAuthor":false,"prefix":"","firstName":"Nirmala","middleName":"","lastName":"Ravishankar","suffix":""},{"id":473295672,"identity":"3fef2370-6581-44d1-b36b-988ced2c4205","order_by":16,"name":"Edwine Barasa","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Edwine","middleName":"","lastName":"Barasa","suffix":""}],"badges":[],"createdAt":"2025-06-09 06:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6851438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6851438/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12939-026-02767-5","type":"published","date":"2026-01-29T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84976823,"identity":"5ba01981-84fa-43c6-afcf-0d167b51f2ac","added_by":"auto","created_at":"2025-06-19 12:28:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161417,"visible":true,"origin":"","legend":"\u003cp\u003eProcess evaluation framework adapted from Moore et al., (Moore et al., 2015 \u0026amp; (Wu et al., 2015))\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6851438/v1/cbf07ef1c710618f9e39fddf.png"},{"id":101691676,"identity":"8e189529-85bc-4759-9c71-b53fdf6913df","added_by":"auto","created_at":"2026-02-02 16:14:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1273274,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6851438/v1/a3d429ac-428c-4978-8826-da71dcbac1c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"“Poverty is a social issue, not a mathematical problem”: Examining the lessons for beneficiary identification from implementation of the UHC indigent program in Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMany low- and middle-income countries (LMICs) have embraced Universal Health Coverage (UHC), implementing various health insurance models, including social health insurance (SHI), to enhance financial access to healthcare for their populations (Fenny et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reich et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In alignment with the Sustainable Development Goals, UHC is rooted in the need to shield individuals from the financial hardships associated with out-of-pocket (OOP) health expenditures, mitigating the risk of catastrophic and impoverishing healthcare payments (Bain, 2023). However, there is growing concern that SHI models, particularly in Africa, often leave out the poorest and most vulnerable populations due to contributory requirements, weak identification systems, and limited fiscal space to subsidize premiums (Barasa et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cotlear et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fenny et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These exclusions threaten to widen inequities in access and undermine the core goals of UHC. In response to equity concerns, many Health insurance programs are accompanied by efforts to provide coverage to poor and vulnerable households through HISP.\u003c/p\u003e \u003cp\u003eSubsidies can take various forms, including premium subsidies that lower the cost of insurance coverage, direct subsidies to healthcare providers to offset the cost of services for targeted groups, and supply-side subsidies aimed at improving healthcare infrastructure and service delivery in underserved areas (Abuya et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Health insurance subsidies are financial intervention designed to reduce barriers to healthcare access, particularly for populations unable to afford the full cost of insurance premiums or healthcare services (Maritim et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vilcu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Subsidies are particularly relevant for countries with a large informal sector\u0026mdash;often accounting for over 70% of the workforce\u0026mdash;where traditional employment-based health insurance models are less viable. (Laar et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKenya is leveraging SHI as a main mechanism for advancing UHC and is currently undertaking extensive reforms to overhaul its existing health insurance framework. These reforms are anchored in the Social Health Insurance Act of 2023, which establishes a new SHI program under the stewardship of the Social Health Authority (SHA)(Social Health Insurance Act, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea). The new framework replaces the longstanding National Health Insurance Fund (NHIF) as national insurer. A central feature of the reforms is the creation of three distinct funds, each designed to address different levels of healthcare needs. First, the Primary Healthcare Fund, financed through general tax revenue, is dedicated to supporting the delivery of primary healthcare services. Second, the Emergency, Chronic, and Critical Illness Fund, also tax-financed, is intended to cover high-cost services related to emergencies and chronic conditions, with provisions to mandate enrolment for all Kenyan citizens and residents. Third, the Social Health Insurance Fund (SHIF) is financed through contributions from both formal and informal sector households (SHI Regulations, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Formal sector employees, contributions will be collected via pro-rata (proportional) payroll deductions while informal sector households\u0026rsquo; contribution will be determined using a proxy means-testing (PMT) approach. Additionally, the government will finance SHIF premiums for indigent households through direct allocations from the national tax budget. The SHI Act defines an \u003cem\u003eindigent\u003c/em\u003e as \u0026ldquo;a person who is poor and needy to the extent that the person cannot meet their basic necessities of life\u0026rdquo; (Social Health Insurance Act, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003ea)\u003c/p\u003e \u003cp\u003eResearch in countries such as Ghana, India, and Indonesia have found that premium subsidies can boost enrolment in health insurance programs, particularly among low-income and informal sector populations who would otherwise be unable to afford coverage (Ekonomi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kinnan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mohammadzadeh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By reducing the financial barriers to obtaining health insurance, subsidies can increase access to healthcare and improve financial risk protection for vulnerable groups (Kinnan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvidence from health insurance subsidy programs (HISPs) suggests that the way these programs are designed can have a significant impact on enrolment rates and utilization patterns. This includes targeting mechanisms to identify and enrol eligible beneficiaries, the level of the subsidy offered, the methods for delivering subsidies (e.g., direct premium subsidies, vouchers), the benefit entitlements, and efforts to promote awareness and understanding of the program (Kinnan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Designing and implementing effective HISPs in LMICs presents several challenges that require careful consideration of factors such as equity, stakeholder engagement, and sustainability to ensure the long-term success and impact of these programs (Asuming et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ekonomi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eKenya has made prior attempts to extend coverage to indigent populations, including the Health Insurance Subsidy Program (HISP) launched in 2014 and scaled up in 2016 (Barasa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003ea, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003eb; Kabia et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, challenges in targeting led to limited impact with an evaluation of the program revealing that approximately 65% of those enrolled belonged to the wealthiest socio-economic quintiles, undermining the program\u0026rsquo;s equity objectives (Barasa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In 2018, the government introduced the UHC indigent program following pilot reforms in four counties (Kisumu, Nyeri, Isiolo, and Machakos), aiming to enrol one million poor households as a foundation for national scale-up (Nyawira et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this paper, we present the findings of a process evaluation of the design and implementation of the UHC scale-up indigent program. We examine the key challenges and lessons from the design and implementation of Kenya\u0026rsquo;s HISP, aiming to inform how such programs can be implemented to advance UHC in Kenya and other LMICs. These findings are particularly important as Kenya embarks on the rollout of a new indigent program under the SHA. Understanding the strengths and pitfalls of the UHC indigent program is essential to informing a more effective, equitable, and accountable implementation of the current reforms.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eConceptual framework\u003c/h2\u003e \u003cp\u003eTo examine the implementation experience of the UHC indigent program, we conducted a process evaluation. This evaluation aimed to draw lessons that could be incorporated into the redesign for scale-up planning. Our evaluation framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was adapted from Moore et al (Moore et al., 2015). In this process evaluation, we first explored the emergence of the UHC indigent program. We then described and examined the implementation arrangements, activities, and processes, as well as the fidelity of the implementation and the experiences of relevant stakeholders, including national and county-level policymakers and implementers, health facility managers and frontline health workers, and members of the community. To provide a comprehensive analysis, we integrated Moore et al.'s framework with concepts of policy capacity, which refers to the skills and competencies needed to carry out a policy function (Moore et al., 2015). Drawing on Wu et al.,(Wu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) we assessed three dimensions of policy capacities that influenced policy implementation: analytical policy capacities, which are the skills and competencies required to develop technically sound strategies to support the fulfilment of policy reform goals; operational policy capacities, which are the skills and competencies required to align resources with the goals of the policy to enable implementation; and political policy capacities, which are the skills and competencies required to identify, mobilize, and strengthen political support for policy actions. These policy capacities were assessed across the individual, organizational, and system levels of the policy environment. By examining the UHC pilot implementation experience through this lens, this process evaluation helped to identify areas for improvement in future UHC programs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and participants\u003c/h3\u003e\n\u003cp\u003eWe conducted a qualitative study using semi-structured interviews to explore the development and implementation of UHC indigent program in Kenya. A total of 23 participants were interviewed, drawn from various health system stakeholders, including national-level bureaucrats, development partners, implementing organizations, civil society organizations, academia, and representatives from the NHIF and Kenya Healthcare Federation (KHF). We also interviewed county health officials, community health liaisons as well as community health promoters to understand the implementation experience of the program at the county level in two purposively selected counties-Kisumu and Kiambu. Kiambu is in central Kenya and is mostly peri-urban population while Kisumu is in the Western part of Kenya with a mix of rural and urban population. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the county profiles against national estimates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudy counties profiles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKisumu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKiambu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation 2019 census (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,155,574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,417,735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47,564,296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty rates (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Health insurance coverage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage household size (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria considered in selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOne of the four UHC pilot counties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelatively higher socio-economic status and peri-urban County\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource:(KNBS, 2019) (KNBS, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; KNBS and ICF, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eParticipants were selected using a combination of purposive and snowball sampling techniques. Purposive sampling was employed to identify individuals with specific knowledge, experience, or roles relevant to the study, particularly those who were directly involved in the formulation and/or implementation of UHC indigent program (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Snowball sampling was then used to identify additional participants through referrals from initial interviewees.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational policy makers- Ministry of Health (MOH) bureaucrats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCounty stakeholders (2 purposively selected counties)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopment partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNHIF/SHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImplementing partners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCivil Society Organization (CSO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe collected data using a semi-structured topic guide developed from the components of the study\u0026rsquo;s conceptual framework. The guide was designed to ensure consistent coverage of relevant themes while allowing flexibility to probe emerging issues during interviews. All interviews were conducted by BM and RM. These were audio-recorded with participant consent, and transcribed verbatim for analysis.\u003c/p\u003e \u003cp\u003eWe also included data from a stakeholder validation meeting whose proceedings were audio-recorded and transcribed for analysis. The meeting was attended by 57 participants (41 in-person and 16 online), representing key stakeholders, including county health and treasury departments, the SHA, the MOH, the Digital Health Agency, the Council of Governors (COG), the National Treasury, and development partners.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData was analyzed thematically using the six-step approach proposed by Braun and Clarke (2006). First, the research team immersed themselves in the data by reading and re-reading the transcripts (Step 1). Second, a list of deductive codes was developed based on the concepts from the study\u0026rsquo;s conceptual framework (Step 2). Third, similar codes were grouped into themes by identifying patterns across the coded data (Step 3). Fourth, themes were reviewed for internal consistency and coherence with the coded extracts to ensure they accurately reflected the data (Step 4). In the fifth step, the finalized themes were systematically applied to the entire dataset, with supporting quotes and excerpts identified for each theme (Step 5). Finally, the findings were synthesized and interpreted in relation to existing empirical and theoretical literature (Step 6).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eProgram development rationale\u003c/h2\u003e \u003cp\u003eThe development of the UHC indigent program was driven by the need to alleviate financial barriers to healthcare access for vulnerable households. The policy direction was further reinforced by lessons from previous UHC efforts (e.g. previous subsidy programs and the UHC pilot) and supported through legislative reforms. By providing poor households with full health insurance subsidies, the program aimed to offer financial risk protection against healthcare related costs.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The biggest issue was that poor households were being pushed further into poverty by healthcare costs. The program was meant to stop this cycle\u0026rdquo;\u003c/em\u003e (KII_C1_05).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe HISP adopted an insurance-based model because of lessons from implementing the UHC pilot program which used an input-based financing model. Under the input-based approach, the government directly financed public health facilities by providing resources such as drugs and commodities rather than channelling funds through insurance or performance-linked mechanisms. While this model enabled rapid service delivery during the pilot phase, policymakers deemed it fiscally unsustainable and lacking in efficiency and accountability for national scale-up. The shift towards an insurance-based model for UHC in Kenya using NHIF, allowed for contributions from those with the ability to pay. It also made it possible to pool contributions and standardize coverage, providing a more sustainable approach to ensuring access to essential health services.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The UHC pilot showed us that input-based financing wasn\u0026rsquo;t working as intended. We needed a model that could scale, and that\u0026rsquo;s where the shift to insurance came in\u0026rdquo;\u003c/em\u003e (KII_NHIF_02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe NHIF Amendment Act of 2022 also shaped the indigent program. This Act redefined NHIF\u0026rsquo;s mandate, transitioning it from a hospital insurance scheme to a health insurance scheme covering a broader range of services. The Act formalized the government\u0026rsquo;s role in subsidizing health insurance for indigent households. This legal framework enabled NHIF to enroll indigent populations and manage their coverage with premiums financed using public funds appropriated by the national assembly.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eKey actors in implementation\u003c/h2\u003e \u003cp\u003eThe MOH led the formulation and coordination of the UHC policies, overseeing the overall implementation of the program. Its role included ensuring that indigent households were identified and enrolled in the program, while also coordinating with stakeholders at both national and county levels.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The Ministry of Health had to ensure that all stakeholders, including county governments and development partners, were involved in the planning and implementation process\u0026rdquo;\u003c/em\u003e (KII_MOH_03).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe Ministry of Labor and Social Protection (MOLSP) holds the primary responsibility for formulating social protection policies. The social protection policy defines social health protection as one of its four pillars. The Department of Social Protection, under MOLSP, manages Kenya\u0026rsquo;s social protection programs, including maintaining the enhanced single registry (ESR) - a socio-economic database of all vulnerable households in the country. The database includes all beneficiaries supported for social health protection under the earlier HISP covered by NHIF. During the implementation of the UHC indigent program, it was envisioned that the program would rely on the operational and technical capacity of the MOLSP for the accurate identification and targeting of indigent households by adopting its processes and tools. This included the use of the MOLSP\u0026rsquo;s Harmonized Testing Tool (HTT) that incorporates PMT to assess socio-economic status complemented by a community-based verification process supported by the national government representatives in the county such as the county and subcounty commissioners.\u003c/p\u003e \u003cp\u003eNHIF was responsible for managing the insurance coverage for the indigent households identified under the program. NHIF verified household data provided by County governments, ensuring that beneficiaries were enrolled in the system. NHIF had successfully ran a pilot and scale up of the HISP and was expected to utilize the same processes working with the MOLSP to scale up the national UHC indigent program. NHIF\u0026rsquo;s operational role was essential in managing the household registration and claims process ensuring that resources were allocated effectively:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;NHIF played a key role in ensuring that once households were identified, they were enrolled in the insurance program and could access services without delay\u0026rdquo;\u003c/em\u003e (KII_NHIF_02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDevelopment partners, such as the World Health Organization (WHO), the World Bank, UNICEF, and Clinton Health Access Initiative (CHAI), played an important role in providing technical and financial support throughout the program\u0026rsquo;s formulation and implementation. These partners helped realign existing projects to support UHC, offering both financial resources and expertise.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;Development partners helped us a lot, especially in providing the technical support needed to develop policies and realign their projects to support UHC.\u0026rdquo; (KII_MOH_03).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eLastly, County Governments were tasked with identifying and registering indigent households based on their local knowledge and contexts. National operations and tools were to be cascaded to the counties through the COG for a streamlined implementation of the program. In addition, County governments managed various complementary social protection programs that included local HISPs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProgram design and implementation fidelity\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIntended program design- Dejure design\u003c/h2\u003e \u003cp\u003eThe UHC indigent program was designed as a national, targeted social health insurance initiative aimed at providing fully subsidized health insurance coverage for Kenya's poorest households. The program targeted approximately one million indigent households, with plans to scale up to 1.5\u0026nbsp;million in the following year and eventually to five million, aligning with national poverty assessments indicating that 5.2\u0026nbsp;million Kenyan households were living below the poverty line. The premium amount was set based on the national UHC scheme contribution flat rate for informal sector households which was Ksh. 500 (USD 3.8) per household per month.\u003c/p\u003e \u003cp\u003eThe design of the UHC indigent program was centred around a multi-stakeholder approach, involving several key agencies essential for harmonizing data across MOH, NHIF, and county governments. The MOH and the COG were tasked with developing tools for county governments to identify indigent beneficiaries and set up a dedicated database for this purpose. This database was to be integrated with ESR database from the MOLSP and NHIF\u0026rsquo;s existing beneficiary records.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eThere was a discussion of how households should be identified by expanding the use of the existing tools that have been used in the HISP, but because the identification was done by the Ministry of Labour and Social Protection, discussion then was for MOH and COG to work on the tools that would then be sent to the counties for them to use to identify and then set up a database. And then the database would be merged to the social protection database\u003c/em\u003e\u0026rdquo; (KII_Do_01).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis design acknowledged MOLSP\u0026rsquo;s mandate and technical capacity in identifying indigent households through its existing social protection database and tools. MOLSP\u0026rsquo;s mandate was to provide a standardized approach to household identification across counties, ensuring equity in targeting the most vulnerable populations.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The Department of Social Protection had the mandate to identify indigents using their data, and this was supposed to be standardized across counties\u0026rdquo; (KII_Do_02).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlthough the core responsibility for defining indigent eligibility remained with MOLSP at the national level, collaboration with county governments was necessary. This was because Social Protection is not a devolved function and therefore this partnership allowed the MOLSP to leverage local knowledge and resources within counties while maintaining a standardized, national approach to identifying and enrolling indigent households.\u003c/p\u003e \u003cp\u003eNHIF\u0026rsquo;s role in the program was to verify household data, provide health insurance coverage, and ensure that registered indigent households had access to health services at accredited facilities.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;NHIF was responsible for ensuring that indigent households had access to health services, and the government was supposed to cover the premiums\u0026rdquo;\u003c/em\u003e (KII_NHIF_02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe national government, through the National Treasury (the Ministry of Finance and Economic Planning), allocated Ksh. 6\u0026nbsp;billion (Approximately USD 46,366,332) for the first phase of the program in 2020/21, covering the cost of premiums for one million households for one year. However, under the UHC indigent program, this premium covered not just the principal member but also their spouse and children. Based on the national average household size at the time (approximately 4.4 members), the program effectively extended coverage to an estimated 4.4\u0026nbsp;million individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDe Facto Implementation\u003c/h2\u003e \u003cp\u003eThe implementation of the UHC indigent program in Kenya deviated from its intended design due to various operational, analytical, and political challenges, as well as disagreements between national and county governments. Although the program was initially designed to rely on the MOLSP tools and the ESR database to ensure uniformity, counties advocated for control over the identification process, emphasizing that health is a devolved function under Kenya\u0026rsquo;s constitution. This push for decentralization led to a compromise: the national government set a cap on the number of indigent households each county could identify, while allowing counties autonomy in the identification process.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eHowever, midway there was a contestation between national government and the counties on how that should be done, and the final approach was that the national government basically set a cap for all the counties in terms of the number to be identified and each of the counties were then allowed to identify households on their own\u0026rdquo;\u003c/em\u003e (KII_Do_01).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSome stakeholders, however, disagreed with the notion of a centrally led UHC indigent program, arguing that it overlooked existing county-led health financing initiatives. For example, counties like Makueni had already implemented their own schemes, such as MakueniCare, shortly after devolution\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\"\u003cem\u003eSome programs came before this \u0026mdash; MakueniCare started immediately after devolution \u0026mdash; and so it is not that counties had advocated for greater control because it [health] was a devolved function; some counties started before the National Government.\"(KII_C1_06)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSeveral county officials also clarified that decentralization of indigent identification was not solely due to county pressure, but rather a lack of preparedness and unavailability of national-level data tools. For instance, one county official claimed that the HTT was never finalized or rolled out at the time of program initiation. In addition, the MOLSP was reluctant to share its database-ESR- citing the need for a bureaucratic process involving the cabinet secretaries of the two ministries:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The Ministry of Labor and Social Protection had the data, but there were challenges in sharing the information\u0026rdquo;\u003c/em\u003e (KII_MOH_04).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEach county received a quota of slots for indigent household coverage under the UHC program, determined by a weighted formula based on population size and poverty levels, as reported in the 2019 Kenya Population and Housing Census. For instance, Kiambu County was allocated 38,000 slots in the first phase, while Kisumu County received 28,000 slots. Although allocation data for all counties is not publicly available, quotas were determined using a weighted formula based on population size and poverty levels drawn from the 2019 census. Counties used various methods for beneficiary identification, including PMT and community poverty ranking approaches, where Community Health Volunteers (CHVs), village elders, and chiefs were actively involved in identifying households to be enrolled into the program. This localized approach allowed counties to adapt criteria according to their priorities.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;[Decentralization was], in essence, was trying to make good of a complex process because once we decentralize the identification, then I could qualify as an indigent in Nyeri, but I may not qualify as an indigent in Uasin Gishu\u0026rdquo;\u003c/em\u003e (KII_Do_01).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSome respondents however reported that the perceived reluctance by the MOLSP to share their database was because the data itself was incomplete or unavailable at the time of program initiation. According to one county official: \u003cem\u003e\"I personally visited those offices to get the list, and there was no list!\" KII_C1_06\u003c/em\u003e. Where existing data was available, it was often inadequate to meet the quotas allocated to counties because of the unrealistic speed at which counties were expected to act. As another county official explained,\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eI was given twenty-eight thousand indigents[households] to fill up; I didn\u0026rsquo;t reach twenty-eight thousand. I ended up having eighteen thousand or something like that. We couldn\u0026rsquo;t fill them, because where do you get them from?\u0026rdquo;\u003c/em\u003e KII_ County official\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese operational and temporal constraints introduced inconsistencies in how indigents were identified across the counties, leading to variation across counties. Some counties, like Kisumu, Kiambu, and Makueni, ran their own subsidy programs and maintained separate databases, which sometimes conflicted with the UHC program\u0026rsquo;s criteria. It is from these lists that they determined who would be enrolled into the UHC indigent program in their counties.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u0026ldquo;\u003cem\u003eCounties had their own lists, and there was a lot of confusion over which list should be used\u0026rdquo;\u003c/em\u003e (KII_C1_05).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOperational capacity challenges also arose, with CHVs often facing confusion about the criteria, limited resources, and reliance on paper-based processes, which slowed beneficiary identification.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;Some CHVs were confused about what criteria to use when identifying indigent households, and we didn\u0026rsquo;t receive immediate clarification from the Ministry\u0026rdquo;\u003c/em\u003e (KII_C2_02).\u003c/p\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The criteria to include vulnerable households were clear on paper, but in practice, many indigent households weren\u0026rsquo;t identified correctly\u0026rdquo; (KII_IP_03).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOnce counties submitted their lists, NHIF cross-checked the data against national databases, including the Integrated Population Registration System (IPRS) and records from the Ministry of Labour and Social Protection (MOLSP). A significant portion of the data was excluded due to duplication (e.g., individuals appearing on multiple lists), existing active NHIF coverage, or mismatches with national civil registry information. Households with unverifiable data or those already benefiting from other programs were also excluded. Data discrepancies and delayed feedback mechanisms between the county and NHIF, created further challenges in identifying eligible beneficiaries accurately. Despite these challenges, NHIF validated the lists from counties using the means accessible to them and enrolled households in the UHC Supa Cover scheme, assigning them to NHIF-accredited outpatient facilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSelection of facilities\u003c/h2\u003e \u003cp\u003eCounties were allowed to determine the assignment of health facilities where indigent beneficiaries would access services. We found variation in implementation: some counties assigned beneficiaries to nearby PHC facilities, while others limited assignments to level 4 hospitals. The preference for higher-level facilities was often driven by the higher capitation rates paid to them\u0026mdash;KES 1,400 per household member per year compared to KES 1,000 for PHC facilities. In one county, all capitation funds related to the indigent program were initially deposited into the account of a level 5 referral hospital, regardless of where the beneficiaries were assigned for care. According to respondents, this centralized arrangement was intended to streamline financial management and ensure oversight. The level 5 facility then disbursed funds to other public facilities in the county based on the number of patients they reported seeing.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;What currently happens, is that funds for the UHC [indigent] program are channelled to one facility\u0026hellip; [the facility] then documents the number of patients who have received the service and requests reimbursement\u003c/em\u003e\u0026rdquo; (KII_C2_02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFidelity of Implementation and policy capacity\u003c/h2\u003e \u003cp\u003eWe assessed the fidelity of the UHC indigent program by examining how closely the actual implementation adhered to the original program design. Overall, there was partial fidelity to the program\u0026rsquo;s intended objectives, with significant deviations in the areas of beneficiary identification, data harmonization, and service accessibility. Key issues contributed to these deviations, reducing the program\u0026rsquo;s fidelity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBeneficiary identification\u003c/h2\u003e \u003cp\u003eThe original design of the program aimed for a standardized, national approach to identifying indigent households, leveraging MOLSP\u0026rsquo;s social protection data. Data analysis involved multiple steps, including cleaning large volumes of paper-based submissions, checking for completeness, validating identification details, and manually resolving discrepancies. Many counties lacked trained personnel to manage these tasks efficiently. There was also limited feedback from NHIF to counties on verification outcomes which limited the opportunity to improve the processes.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;We had the technology to collect the data, but the problem came when we tried to analyse it. We didn\u0026rsquo;t have enough trained personnel to handle the amount of data coming in, which caused delays in verifying eligibility\u0026rdquo; (KII_C1_03).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData harmonization and operations\u003c/h2\u003e \u003cp\u003eThe intended program design emphasized collaboration across MOH, NHIF, and MOLSP to maintain a unified database for indigent households. However, in practice, the lack of a harmonized approach led to multiple lists and confusion over eligibility.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;There was no unified process; each agency had its own list, and there was constant back and forth on how to align them\u0026rdquo; (KII_Do_02).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOperational challenges\u0026mdash;including delayed funding disbursements and shortages in resources for community health volunteers (CHVs)\u0026mdash;further compounded these issues.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;The data collection process was slow because we didn\u0026rsquo;t have proper devices for CHWs, and much of the work was done manually\u0026rdquo; (KII_C1_04).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThese resource constraints not only affected data quality but also slowed enrolment, undermining program fidelity. While counties were primarily responsible for facilitating CHV engagement and logistics, there was no clear provision for dedicated funding from the national government to support the operational costs of CHVs during the implementation of the indigent program. This lack of clarity in financing responsibilities contributed to uneven support across counties and limited the effectiveness of community-level data collection. In addition to county-level financial bottlenecks, delays in disbursement from the national government to NHIF disrupted cash flow and constrained the program\u0026rsquo;s ability to settle claims and support indigent beneficiaries in a timely manner.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;Funding would come late from the Treasury, so we\u0026rsquo;d delay reimbursements or capitation to facilities. That affected service delivery\u0026rdquo; (KII_NHIF_02).\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eCounties that had granted health facilities autonomy over financial management demonstrated stronger operational capacity during the implementation of the indigent program. In these settings, funds\u0026mdash;particularly capitation payments\u0026mdash;were allocated directly to facilities, enabling timely access to services for beneficiaries. This direct funding approach improved the efficiency of financial flows and the availability of essential health services. In contrast, in counties where all health funds were required to pass through the County Revenue Fund (CRF) before being disbursed to facilities, delays were common. This often resulted in unpredictable and inadequate financing at the point of care, hindering service delivery and undermining the responsiveness of the program.\u003c/p\u003e \u003cp\u003ePolitical capacity played a dual role in program implementation. Although the UHC indigent program was aligned with the Big Four Agenda and enjoyed strong national support, political dynamics at the county level occasionally influenced the identification process. The decentralized approach allowed counties to exercise discretion, but in some cases, local political considerations shaped beneficiary selection, diverging from the program's initial equity-focused criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eService accessibility\u003c/h2\u003e \u003cp\u003eEven when NHIF successfully enrolled indigent households, communication gaps left many beneficiaries unaware that they had been registered or how to access services. In many cases, dependents of household heads were not registered, which limited coverage for the entire household\u0026mdash;the intended population unit. This was due to a combination of logistical challenges, such as limited biometric registration equipment, lack of beneficiary awareness about the need for dependent registration, and rushed implementation timelines driven by political pressure.\u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026ldquo;Biometric registration was delayed in some areas, and this meant some indigent households had difficulty accessing services because their details weren\u0026rsquo;t fully in the system\u0026rdquo; (KII_Do_03).\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAdditionally, local political considerations occasionally influenced the choice of beneficiaries and facilities, deviating from the program\u0026rsquo;s goal of uniform access.\u003c/p\u003e \u003cp\u003eThe program registered 882,291 individuals from an initial list of 1,509,037 indigents households submitted to NHIF. According to the MTEF for 2024/25-2026/27, funds initially allocated for unregistered indigents were redirected to provide coverage for 200,000 boda boda (motorbikes operating as taxis for carrying passengers or goods) riders, reflecting shifting priorities within the program.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;We sent the lists to NHIF, but after that, there was no follow-up, and we don\u0026rsquo;t know what happened to those households\u0026rdquo;\u003c/em\u003e (KII_C1_05).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStakeholder Capacity\u003c/h2\u003e \u003cp\u003eThe implementation of the UHC indigent program revealed varying levels of policy capacity across key stakeholders as summarised in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStakeholder policy capacity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStakeholder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnalytical Capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOperational Capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolitical Capacity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinistry of Health (MOH)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeveloped overall policy framework but lacked the capacity to standardize data tools and processes across counties; limited engagement in data validation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProvided policy guidance but had limited ability to enforce implementation fidelity after decentralization.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong national-level commitment to UHC, but limited influence over county-level political dynamics.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCounty Governments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVaried ability to analyse poverty data; frequently relied on subjective methods like community ranking without standardized tools.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLed household identification and enrolment but faced challenges in execution, especially in counties dependent on CRF with delayed fund flows of capitation from NHIF to the health facilities.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeld significant local influence over enrolment processes, which occasionally led to politicization and inconsistencies.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNHIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAble to validate submitted data using national databases (e.g., IPRS), but depended on county-generated lists and lacked direct control over targeting criteria.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffectively managed enrolment and benefits administration; however, struggled with delayed feedback loops and integrating non-standardized data.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong institutional mandate, but role constrained by upstream decentralization of beneficiary identification.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMinistry of Labour and Social Protection (MOLSP)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaintained strong analytical tools and a national socioeconomic registry (ESR); capable of implementing proxy means testing (PMT).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimal involvement in program operations despite being the mandated custodian of social protection data.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolitically influential at national level but exhibited reluctance to collaborate fully, limiting integration with MOH and counties.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCommunity Health Volunteers (CHVs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited training in data collection protocols; low familiarity with PMT tools and criteria.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlayed a central role in household identification but lacked adequate resources (e.g., training transport, digital tools).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimal formal influence, but crucial for community engagement and trust-building.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVillage Elders\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRelied on local knowledge but used subjective criteria, leading to inconsistent identification practices.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported grassroots mobilization and enrolment; limited training and clarity on their role in verification.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeld informal authority in communities; facilitated access but lacked decision-making power.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCivil Society Organizations (CSOs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited involvement in technical design or data analysis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFocused on advocacy and community sensitization; not actively engaged in operational implementation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdvocated for equity and inclusion but had limited leverage to influence policy direction.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDevelopment Partners\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProvided early technical assistance in designing policy frameworks; engagement reduced post-decentralization.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported system design and initial planning; limited involvement in day-to-day implementation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfluenced agenda-setting at the national level but not embedded in domestic political processes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eProgram outcomes\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eEnrolment and biometric registration\u003c/h2\u003e \u003cp\u003eThe program targeted approximately 1\u0026nbsp;million households for enrolment. However, the actual number of households enrolled fell short, with around 882,000 households successfully registered as indigents. An assessment of the performance of five counties on filling their allocated quotas revealed varied performance with some counties managing to identify only 50% of the indigents\u0026rsquo; slots assigned to them(Murira et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite the success in initial enrolment, biometric registration\u0026mdash;a critical step for ensuring that indigents could access services without delays\u0026mdash;lagged significantly behind. Only 382,000 individuals, (48% of the enrolled population), completed biometric registration, creating challenges for the remaining households when seeking services at healthcare facilities Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUHC indigent program outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAchieved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCompletion Rate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds Targeted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e882,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiometric Registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,000,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e382,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the time of the UHC indigent program\u0026rsquo;s rollout, Kenya\u0026rsquo;s landscape of health insurance subsidies was already complex, with multiple programs providing subsidized coverage to vulnerable populations. According to stakeholder feedback during the validation workshop, approximately 374,500 individuals were enrolled under the government\u0026rsquo;s HISP targeting orphans and vulnerable children, older persons, and persons with severe disabilities. Additionally, some county governments independently subsidized health insurance for their residents. For example, Kisumu County supported approximately 45,000 households through its Marwa Health Insurance Scheme. This represented roughly 10\u0026ndash;15% of the county\u0026rsquo;s total households, based on 2019 census estimates. The scheme primarily targeted indigent and vulnerable populations, including orphans and vulnerable children (OVCs), the elderly, persons with disabilities, and other low-income households identified through local social protection mechanisms. International actors also contributed, with the United Nations High Commissioner for Refugees (UNHCR) sponsoring premiums for approximately 22,000 refugee households and AMPATH providing sponsoring about 1,000 households. These diverse pre-existing subsidy programs meant that the implementation of the UHC indigent program had to navigate overlapping beneficiary groups and fragmented coverage arrangements, further complicating efforts to achieve standardized national targeting and enrolment.\u003c/p\u003e \u003cp\u003eNonetheless, following the program\u0026rsquo;s implementation, some counties reported an increase in service utilization among indigent households, particularly in areas where biometric registration rates were higher.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;We saw a significant increase in utilization, especially among those who had completed biometric registration. Many were accessing services they had previously avoided due to cost\u0026rdquo;\u003c/em\u003e (KII_MOH_04).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eUnintended consequences\u003c/h2\u003e \u003cp\u003eA significant unintended consequence of the program was the presence of inclusion and exclusion errors in the identification of indigent households. Some households that were truly eligible for the program were left out, while others that did not qualify were included. This was largely due to discrepancies in data management and political interference at the county level. As one respondent remarked, \u003cem\u003e\u0026ldquo;The biggest challenge was identifying the right people; we ended up with many people who were not truly indigent\u0026rdquo;\u003c/em\u003e (KII_C1_05). These errors undermined the credibility of the program and limited its ability to reach the most vulnerable populations.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;We found that some households that were not truly indigent managed to get enrolled, while others that desperately needed the program were left out. This happened because the identification process wasn\u0026rsquo;t as rigorous as it should have been.\u0026rdquo; (KII_IP_03)\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAnother unintended consequence was the mistrust that developed within communities toward the health system and the UHC indigent program itself. Inconsistencies in the implementation process and the perception that the system was flawed led to a breakdown of trust between communities and the institutions involved in delivering the program.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;[Inconsistencies]\u0026hellip; meant that the trust in the process was a bit flawed in some communities. And that's kind of detrimental to any health system\u0026hellip; [and] the blame would go to any institution involved in that process. So, I think for me, the biggest thing is that it resulted in mistrust around the process\u0026rdquo;\u003c/em\u003e (KII_IP_02).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eViews on the SHA beneficiary indigent program design\u003c/h2\u003e \u003cp\u003eStakeholders expressed mixed and often critical views regarding the planned use of PMT under SHA to assess household income and determine eligibility for subsidized coverage. According to an official from NHIF/SHA, the PMT tool estimates household income based on responses to a standardized set of socioeconomic indicators, applying a statistical model akin to regression analysis.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\"It [the PMT tool] estimates the household income based on the socioeconomic indicators in the questionnaire and then applies 2.75% of that income to determine the household premium.\" KII_NHIF_01\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe decision to adopt PMT was partly influenced by lessons learned during the implementation of the UHC indigent program, where inconsistent and subjective targeting methods across counties posed major challenges.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;So, yes, there were those challenges around identification. That\u0026rsquo;s one of the reasons why there was a suggestion of developing a scientific model\u0026mdash;that is, the proxy means testing\u0026mdash;that can identify and quantify household income. So that way it'll reduce the subjectivity that was there and the variance across different counties.\u0026rdquo;\u003c/em\u003e (KII_NHIF_02)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe tool uses different subsets tailored to rural, peri-urban, and urban settings to account for contextual variations. However, several challenges were acknowledged, including the complexity of the questionnaire for individuals of lower literacy levels, the risk of households providing false information to lower their premiums, and the lengthy timeframe required to roll out PMT to the entire informal sector, which constitutes approximately 83% of Kenya\u0026rsquo;s workforce.\u003c/p\u003e \u003cp\u003eConcerns were also raised about the practicality and inclusiveness of means testing for Kenya\u0026rsquo;s poorest populations. Participants noted that indigent households often lack mobile phones or the flexibility to attend assessments. As one county leader cautioned:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;Indigents are people without a phone most of the time so you will not catch them... one day of not working means no food on that day\u003c/em\u003e.\u0026rdquo; Validation workshop participant 15\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOthers questioned the underlying assumptions of the tool, pointing out that poverty is socially constructed and not easily captured through standardized indicators. \u003cem\u003e\u0026ldquo;Poverty at the local level is social, it is not mathematical,\u0026rdquo;\u003c/em\u003e one county official explained, arguing that PMT fails to reflect nuanced household realities.\u003c/p\u003e \u003cp\u003eParticipants also raised concerns about potential manipulation of the PMT system, once its algorithm becomes widely known suggesting that the tool could be gamed, undermining its purpose. Stakeholders emphasized the need for greater transparency, community validation, and a reconsideration of whether PMT is the most appropriate method for targeting in Kenya\u0026rsquo;s informal and decentralized context.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003e\u0026ldquo;Kenyans... will congregate and give the lowest indicator, so that it gives you the lowest premium,\u0026rdquo;\u003c/em\u003e Validation workshop participant 19\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the implementation experience of Kenya\u0026rsquo;s UHC indigent program to draw lessons for the ongoing rollout of the ongoing Social Health insurance reforms under SHA. Our findings reveal a substantial disconnect between the intended centralized design and the decentralized execution of the program. Although the program aimed to leverage standardized national tools such as the MOLSP harmonized testing tool and socioeconomic registry, operational challenges, political contestations, and limited stakeholder engagement led to widespread variation across counties. Reflections from the stakeholder validation workshop corroborated these findings, highlighting that decentralization was driven not only by county demands but also by the lack of readiness and financing at the national level to implement centralized tools. These tensions compromised program fidelity and reflected broader governance challenges revealed in other studies in aligning national policy design with the autonomy of devolved units (Barasa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Masaba et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe implementation experience was further characterized by institutional fragmentation and capacity mismatches across national and county stakeholders. While the MOH demonstrated political commitment to national UHC goals, its limited operational and analytical capacity hindered effective coordination and oversight. Similarly, while the NHIF managed enrolment processes, it remained dependent on inconsistent county submissions, and the MOLSP, despite holding national datasets, operated largely in isolation. These dynamics were exacerbated at the county level, where reliance on under-trained CHVs (now known as community health promoters-CHPs), political expediency, and constrained fiscal systems led to inconsistent beneficiary identification and limited-service access. A study on UHC pilot revealed similar design and implementation challenges (Nyawira et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to study respondents, utilization of health services increased following the rollout of the indigent program, with households reporting improved access to outpatient and inpatient care. These findings are consistent with global evidence. A systematic review and meta-analysis by Shami et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that health insurance significantly increases health service utilization, with inpatient utilization rising by 0.51% and outpatient utilization by 1.26% among insured populations(Shami et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The review highlighted that health insurance enhances access to care, particularly among vulnerable groups, by reducing financial barriers and enabling early and more frequent use of health services. In a similar study of Cambodia\u0026rsquo;s subsidy program, utilization increased in part owing to the program addressing non-financial barriers of access and offering transport reimbursement (Jacobs et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This was also a result the increased efforts to increase awareness among the beneficiaries of their entitlement.\u003c/p\u003e \u003cp\u003eIn contrast, in Kenya\u0026rsquo;s UHC indigent program, several operational gaps hampered the realization of increased access for the poor. First, biometric registration was not completed for a significant proportion of enrolled households, limiting their ability to authenticate themselves and access services easily. Second, many dependents of registered household heads were not captured in the system, excluding significant portions of vulnerable families from coverage. Third, a critical gap was the lack of effective communication to beneficiaries regarding their entitlements under the program, resulting in missed opportunities to seek care even when financial barriers had been nominally removed. These shortcomings underscore that merely enrolling households is insufficient; active facilitation of access \u0026mdash; including complete registration processes, comprehensive inclusion of dependents, and robust beneficiary awareness campaigns \u0026mdash; must be prioritized under the SHA reforms if the indigent program is to be effective in delivering equitable access to services.\u003c/p\u003e \u003cp\u003eThese findings are particularly salient as Kenya embarks on implementing the SHA-led indigent program. Although the SHI Act centralizes core functions such as enrolment, eligibility assessment, and fund management under the Social Health Authority (Social Health Insurance Act, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb), the requirement for county co-financing effectively maintains decentralization challenges observed during the UHC indigent program. While the SHI Act had envisioned a PMT process integrated into enrolment, in practice, a recent government proclamation reveals a plan to rollout the indigent program using existing databases \u0026mdash; specifically, the Social Registry and the Inua Jamii cash transfer program under MOLSP \u0026mdash; supplemented by SHA-led validation (Star newspaper, 2025). This raises concerns about the accuracy and inclusiveness of beneficiary identification, especially given known limitations of administrative datasets. Evidence from other LMICs shows that administrative registries, while efficient, often miss vulnerable populations due to outdated or incomplete records (Kidd et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is unclear if use of the Inua Jamii and the MOLSP Social registry signifies a shift away from identifying indigents using PMT as earlier envisioned. Stakeholders have indeed expressed concerns about the accuracy of the PMT emphasizing that poverty is a socially constructed reality often invisible to standardized measures. This reinforces the need for blended digital and community-based targeting approaches. Existing research also emphasizes the need for approaches to assessing vulnerability and poverty that recognize the socially constructed nature of living standards. These studies highlight how cultural and contextual factors shape the conceptualization of poverty across different settings (Alkire \u0026amp; Foster, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Halkos \u0026amp; Aslanidis, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, relying on self-reported poverty assessment exposes the process to manipulation and ultimately inclusion and exclusion errors as has been found in Ghana\u0026rsquo;s indigent program where beneficiaries often falsified their socioeconomic status in order to benefit from exemption(Agbenyo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Akweongo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kotoh \u0026amp; Van Der Geest, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Without such integration, there is a risk of exclusion errors that could undermine the equity objectives of the SHA reforms. Other studies provide hope for leveraging digital process for PMT that use data science to \u0026ldquo;train machine-learning algorithms to recognize patterns of poverty in mobile phone data\u0026rdquo; (Aiken et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, our findings point to the importance of institutionalizing feedback and learning loops within implementation processes. The absence of structured channels for counties and NHIF to share verification outcomes created a \u0026ldquo;black box\u0026rdquo; in which households were excluded without recourse, leading to community mistrust and loss of credibility. The Livelihood Empowerment Against Poverty program in Ghana, providing coverage for poor households, has also faced criticism for inclusion and inclusion errors that led to mistrust among community members on the fairness of beneficiary identification (Agbenyo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the SHA to build public trust, it must embed transparent grievance mechanisms and real-time feedback systems accessible at the facility and household levels. A tribunal has been proposed to receive and resolve grievances under SHA. It is important that these provisions are operationalised by a properly constituted tribunal (Social Health Insurance Act, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003eb). It is however not clear how the tribunal will operate, whether it will be at the county level or national, which has implications on the accessibility of the grievance mechanisms.\u003c/p\u003e \u003cp\u003eThe role of stakeholder capacity\u0026mdash;as assessed through the policy capacity framework\u0026mdash;emerged as a key determinant of program fidelity. Future reforms must go beyond policy design to invest in building the analytical, operational, and political capacity of actors across levels. For example, CHVs and village elders need consistent training, supervision, and digital tools to support their role in community verification. Counties need technical and financial support to manage indigent enrolment in a way that is consistent with national guidelines but responsive to local realities. And inter-agency collaboration, especially between MOLSP, SHA, and county governments, must be incentivized through shared data systems, joint planning platforms, and aligned accountability frameworks.\u003c/p\u003e \u003cp\u003eFinally, the rollout of the new indigent program must reckon with the political economy of targeting. The UHC indigent program showed that political discretion at the local level influenced beneficiary selection, sometimes at odds with objective need. Understanding the political dynamics involved in expanding pro-poor policies is crucial for effectively scaling up and sustaining successful antipoverty programs. This includes considerations like the bargaining strength of beneficiaries, public support, and potential for political misuse of programs (Cuesta et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SHA must therefore ensure that targeting processes are not only technically sound but politically insulated, perhaps through audits, public disclosure of criteria, and performance-linked funding to counties.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eStudy strengths and limitation\u003c/h2\u003e \u003cp\u003eThis study is among the few to assess the implementation experience of Kenya\u0026rsquo;s UHC indigent program, providing an examination of both the de jure program design and de facto implementation realities. A major strength of the study is the integration of multiple data sources, including semi-structured interviews with a diverse range of stakeholders across national and county levels, and validation of findings through a large stakeholder workshop attended by key actors involved in health financing reforms. This triangulation enhanced the validity and relevance of the findings, particularly as the country transitions to the SHA-led social health insurance reforms.\u003c/p\u003e \u003cp\u003eHowever, the study had some limitations. First, the absence of a consolidated national document detailing the design and implementation guidelines for the UHC indigent program necessitated reliance on stakeholder interviews and secondary reports to reconstruct the intended program design. This reliance may introduce recall bias and divergent interpretations among respondents. However, these lessons are important for informing the design, stakeholder engagement strategies, and accountability mechanisms necessary to strengthen Kenya\u0026rsquo;s social health insurance reforms and those of other low- and middle-income countries pursuing similar UHC goals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals that while the UHC indigent program in Kenya was a well-intentioned step toward equitable health financing, its implementation was hindered by decentralization pressures, inadequate national readiness, and fragmented operational capacity. The resulting inconsistencies in targeting and enrolment compromised the program\u0026rsquo;s ability to fully protect poor households and foster trust. As Kenya embarks on implementing a new indigent coverage mechanism under the SHA, success will depend on addressing these foundational gaps. This includes improving data harmonization, ensuring full biometric and dependent enrolment, embedding grievance mechanisms, and enhancing stakeholder capacities at both national and county levels. A key lesson is the importance of blending standardized digital systems with community-level validation to reflect local understandings of poverty. As other LMICs design or refine similar subsidy programs, Kenya\u0026rsquo;s experience offers valuable insights into navigating the intersection of technical design, political economy, and implementation realities in the pursuit of universal health coverage.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to the commencement of the study, ethical approval was obtained from the Scientific and Ethics Review Unit (SERU) of the Kenya Medical Research Institute (KEMRI) (protocol number: KEMRI/SERU/CGMR-C/294/4708). A national research permit was also received from the National Commission for Science, Technology and Innovation (NACOSTI) (permit number: NACOSTI/P/23/28111). In addition, approvals were granted by the respective County Health Departments to facilitate data collection at the county level. All participants provided written informed consent prior to taking part in the interviews.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was funded by Bill and Melinda Gates Foundation Investment no: INV-049230\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBM, RM, and EB conceptualized the study. BM and RM led data collection. EB provided overall study supervision, conceptual guidance, and critical revisions to the manuscript. All authors contributed to the review and revision of the manuscript and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData available upon request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbuya T, Njuki R, Warren CE, Okal J, Obare F, Kanya L, Askew I, Bellows B. A policy analysis of the implementation of a reproductive health vouchers program in Kenya. BMC Public Health. 2012;12(1):1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2458-12-540/FIGURES/3\u003c/span\u003e\u003cspan address=\"10.1186/1471-2458-12-540/FIGURES/3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgbenyo F, Galaa SZ, Abiiro GA. 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(2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSocial Health Insurance Act. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSocial Health Insurance Act. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStar newspaper. (2025, January 16). \u003cem\u003eGovernment to Enroll Poor Households Under SHA Medical Cover | Ministry of Health\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://health.go.ke/government-enroll-poor-households-under-sha-medical-cover\u003c/span\u003e\u003cspan address=\"https://health.go.ke/government-enroll-poor-households-under-sha-medical-cover\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVilcu I, Probst L, Dorjsuren B, Mathauer I. 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Policy Soc. 2015;34(3\u0026ndash;4):165\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.polsoc.2015.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.polsoc.2015.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6851438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6851438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Kenya rolled out a UHC indigent program aimed to expand financial protection and health service access for poor households through subsidized health insurance under the national insurer, NHIF. As Kenya transitions to a new social health insurance framework under the Social Health Authority (SHA), understanding the implementation experience of the UHC indigent program is critical for informing the roll out of SHA’s indigent program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a qualitative process evaluation of the UHC indigent program using semi-structured interviews with 23 key informants from national and county health systems, development partners, and implementing actors, complemented by a validation workshop with 57 stakeholders. Our analysis was guided by Moore et al.'s process evaluation framework and Wu et al.'s policy capacity lens, examining implementation fidelity and capacities at multiple levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The program’s implementation deviated from its original centralized design, with counties exerting control over beneficiary identification due to national data gaps, incomplete rollout of the Harmonized Testing Tool, and political and operational constraints. Variations in targeting methods, reliance on under-resourced community health actors, and delays in biometric registration contributed to partial enrolment, exclusion errors, and mistrust. Although some counties reported increased service utilization, this was limited by unregistered dependents and lack of beneficiary awareness. Stakeholders expressed concern over SHA’s use of proxy means testing (PMT) for informal sector enrolment, citing risks of exclusion, manipulation, and failure to capture locally constructed definitions of poverty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Kenya’s experience underscores the need to align national targeting frameworks with local realities, invest in policy capacity across stakeholders, and prioritize community validation and communication in pro-poor programs. As SHA rolls out a new indigent program, these lessons offer critical guidance for enhancing fidelity, equity, and accountability.\u003c/p\u003e","manuscriptTitle":"“Poverty is a social issue, not a mathematical problem”: Examining the lessons for beneficiary identification from implementation of the UHC indigent program in Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-19 12:12:53","doi":"10.21203/rs.3.rs-6851438/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-20T04:59:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T09:27:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T20:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289993127750539900358772189378353012626","date":"2025-08-01T19:45:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277645769265235182137320986799793372056","date":"2025-07-30T07:56:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-16T16:10:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149700024219838661268532530784104238510","date":"2025-06-30T09:01:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88723423069445813201618933058171197134","date":"2025-06-19T09:43:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-17T09:29:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-14T08:10:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-12T08:23:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Equity in Health","date":"2025-06-09T06:39:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-equity-in-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijeh","sideBox":"Learn more about [International Journal for Equity in Health](http://equityhealthj.biomedcentral.com)","snPcode":"12939","submissionUrl":"https://submission.nature.com/new-submission/12939/3","title":"International Journal for Equity in Health","twitterHandle":"@equityhealthj","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"35121e02-837a-4a5d-9cfa-fe7a4374914d","owner":[],"postedDate":"June 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:08:54+00:00","versionOfRecord":{"articleIdentity":"rs-6851438","link":"https://doi.org/10.1186/s12939-026-02767-5","journal":{"identity":"international-journal-for-equity-in-health","isVorOnly":false,"title":"International Journal for Equity in Health"},"publishedOn":"2026-01-29 15:58:08","publishedOnDateReadable":"January 29th, 2026"},"versionCreatedAt":"2025-06-19 12:12:53","video":"","vorDoi":"10.1186/s12939-026-02767-5","vorDoiUrl":"https://doi.org/10.1186/s12939-026-02767-5","workflowStages":[]},"version":"v1","identity":"rs-6851438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6851438","identity":"rs-6851438","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}