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Background: Low-income and middle-income countries, including Kenya, are pursuing universal health coverage (UHC) through the establishment of Social Health Insurance programmes. As Kenya rolls out the recently unveiled UHC strategy that includes a national indigent cover programme, the goal of this study is to evaluate the impact of health insurance subsidy on poor households' healthcare costs and utilisation. We will also assess the effectiveness and equity in the beneficiary identification approach employed., Methodology and Analysis: Using a quantitative design with quasi-experimental and cross-sectional methods, our matched cohort study will recruit 1350 households across three purposively selected counties. The 'exposure' arm, enrolled in the UHC indigent programme, will be compared with a control arm of eligible but unenrolled households over 12 months. Coarsened exact matching will be used to pair households based on baseline characteristics, analysing differences in expenses and catastrophic health expenditure. A cross-sectional design will be employed to evaluate the effectiveness and equity in beneficiary identification, estimating inclusion errors associated with the subsidy programme while assessing gender equity., Ethics and Dissemination: Ethical approval has been obtained from the Scientific and Ethics Review Unit at Kenya Medical Research Institute, with additional permissions sought from County Health Departments. Participants will provide written informed consent. Dissemination strategies include peer-reviewed publications, conference presentations and policy-maker engagement for broad accessibility and impact., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Introduction: The COVID-19 pandemic had devastating health and socioeconomic effects, partly due to policy decisions to mitigate them. Little evidence exists of approaches that guided decisions in settings with limited pre-pandemic modelling capacity. We thus sought to identify knowledge translation mechanisms, enabling factors and structures needed to effectively translate modelled evidence into policy decisions., Methods: We used convergent mixed methods in a participatory action approach, with quantitative data from a survey and qualitative data from a scoping review, in-depth interviews and workshop notes. Participants included researchers and policy actors involved in COVID-19 evidence generation and decision-making. They were mostly from lower- and middle-income countries (LMICs) in Africa, Southeast Asia and Latin America. Quantitative and qualitative data integration occurred during data analysis through triangulation and during reporting in a narrative synthesis., Results: We engaged 147 researchers and 57 policy actors from 28 countries. We found that the strategies required to use modelled evidence effectively include capacity building of modelling expertise and communication, improved data infrastructure, sustained funding and dedicated knowledge translation platforms. The common knowledge translation mechanisms used during the pandemic included policy briefs, face-to-face debriefings and dashboards. Some enabling factors for knowledge translation comprised solid relationships and open communication between researchers and policymakers, credibility of researchers, co-production of policy questions and embedding researchers in policymaking spaces. Barriers included competition among modellers, negative attitude of policymakers towards research, political influences and demand for quick outputs., Conclusion: We provide a contextualised understanding of knowledge translation for LMICs during the COVID-19 pandemic. Furthermore, we share key lessons on how knowledge translation from mathematical modelling complements the broader learning agenda related to pandemic preparedness and long-term investments in evidence-to-policy translation. Our findings led to the co-development of a knowledge translation framework useful in various settings to guide decision-making, especially for public health emergencies., Competing Interests: Competing interests: No conflicts other than the The Bill and Melinda Gates Foundation paid for travel expenses to attend working session during which the framework was developed., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: Kenya has experienced several health financing changes that have implications for financing primary healthcare (PHC). These include transitions from funding by two key donors (the World Bank and the Danish International Development Agency (DANIDA)) and the abolishment of conditional grants that were earmarked for financing primary healthcare facilities. This protocol lays out study plans to evaluate the impact and implementation experience of these financing changes on PHC facility functioning and service delivery in Kenya., Methods/design: A sequential mixed methods design will be applied to address our research objectives. Firstly, we will perform a document review to understand the evolution of policy changes understudy. Second, we will conduct an interrupted time series analysis across all 47 counties in Kenya to assess these financing changes' impact on health service utilization in all public primary healthcare facilities (level 2 and 3 facilities). Data for this analysis will be obtained from the Kenya Health Information System (KHIS). Third, we will carry out in-depth interviews with health financing stakeholders at the national, county, and health facility levels to examine their perceptions of the experiences with these changes in health financing., Discussion: This mixed methods study will contribute to evidence on the sustainability of financing primary healthcare in low and middle-income countries facing financing changes and donor transitions., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Muthuri RNDK et al.)