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IntroductionTo support the government response to the coronavirus disease 2019 (COVID-19) pandemic, accessible and sustainable testing approaches are needed. Private retail pharmacies are a key channel through which communities can access COVID-19 testing. We examined the level and determinants of the willingness to pay (WTP) for rapid COVID-19 testing delivered through private retail pharmacies in Kenya.MethodsData was collected following a cross-sectional double-bounded dichotomous choice contingent valuation survey across 341 clients visiting five private retail pharmacies in Nairobi, Kisumu and Siaya counties.ResultsOur findings indicate mean and median WTP levels of KES 611 (US$ 5.59) and KES 506 (US$ 4.63), respectively. Estimated WTP varied across counties and increased with household income and self-reported interest in pharmacy-based COVID-19 rapid testing.ConclusionThese findings can inform price setting, price differentiation, price subsidization and other program design features geared towards enhancing affordability, equity, and uptake.Key QuestionsWhat is already known?The Coronavirus disease 2019 (COVID-19) global pandemic continues to cause great morbidity, mortality, social and economic burden.Pharmacies in Kenya have been involved in the delivery of several health interventions, such as malaria rapid testing, HIV self-testing, and other disease screening services.While COVID-19 testing remains an important response strategy to the ongoing COVID-19 pandemic, it is not clear how much pharmacy clients in Kenya and similar settings would be willing to pay (WTP) to obtain rapid COVID-19 testing at pharmaciesWhat are the new findings?The mean and median willingness to pay (WTP) for a rapid test delivered at a private retail pharmacy was KES 611 (US$ 5.59) and KES 506 (US$ 4.63), respectively.WTP varied by county, hence, the need for county-specific price-setting for pharmacy-based COVID-19 testing.WTP increased with household income and interest in getting the COVID-19 test at a private retail pharmacy.What do the new findings imply?A better understanding of the user’s willingness to pay price that can guide price setting, price differentiation, price subsidization and other program design features geared towards enhancing affordability, equity, and uptake.

Background Healthcare workers are at a higher risk of COVID-19 infection during care encounters compared to the general population. Personal Protective Equipment (PPE) have been shown to protect COVID-19 among healthcare workers, however, Kenya has faced PPE shortages that can adequately protect all healthcare workers. We, therefore, examined the health and economic consequences of investing in PPE for healthcare workers in Kenya. Methods We conducted a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines. We examined two outcomes: 1) the incremental cost per healthcare worker death averted, and 2) the incremental cost per healthcare worker COVID-19 case averted. We performed a multivariate sensitivity analysis using 10,000 Monte Carlo simulations. Results Kenya would need to invest $3.12 million (95% CI: 2.65–3.59) to adequately protect healthcare workers against COVID-19. This investment would avert 416 (IQR: 330–517) and 30,041 (IQR: 7243 – 102,480) healthcare worker deaths and COVID-19 cases respectively. Additionally, such an investment would result in a healthcare system ROI of $170.64 million (IQR: 138–209) – equivalent to an 11.04 times return. Conclusion Despite other nationwide COVID-19 prevention measures such as social distancing, over 70% of healthcare workers will still be infected if the availability of PPE remains scarce. As part of the COVID-19 response strategy, the government should consider adequate investment in PPE for all healthcare workers in the country as it provides a large return on investment and it is value for money.

Background The COVID-19 pandemic and country measures to control it can lead to negative indirect health effects. Understanding these indirect health effects is important in informing strategies to mitigate against them. This paper presents an analysis of the indirect health effects of the pandemic in Kenya. Methods We employed a mixed-methods approach, combining the analysis of secondary quantitative data obtained from the Kenya Health Information System database (from January 2019 to November 2020) and a qualitative inquiry involving key informant interviews (n = 12) and document reviews. Quantitative data were analysed using an interrupted time series analysis (using March 2020 as the intervention period). Thematic analysis approach was employed to analyse qualitative data. Results Quantitative findings show mixed findings, with statistically significant reduction in inpatient utilization, and increase in the number of sexual violence cases per OPD visit that could be attributed to COVID-19 and its mitigation measures. Key informants reported that while financing of essential health services and domestic supply chains were not affected, international supply chains, health workforce, health infrastructure, service provision, and patient access were disrupted. However, the negative effects were thought to be transient, with mitigation measures leading to a bounce back. Conclusion Finding from this study provide some insights into the effects of the pandemic and its mitigation measures in Kenya. The analysis emphasizes the value of strategies to minimize these undesired effects, and the critical role that routine health system data can play in monitoring continuity of service delivery.

IntroductionLow/middle-income countries (LMICs) in sub-Saharan Africa (SSA) are increasingly turning to public contributory health insurance as a mechanism for removing financial barriers to access and extending financial risk protection to the population. Against this backdrop, we assessed the level and inequality of population coverage of existing health insurance schemes in 36 SSA countries.MethodsUsing secondary data from the most recent Demographic and Health Surveys, we computed mean population coverage for any type of health insurance, and for specific forms of health insurance schemes, by country. We developed concentration curves, computed concentration indices, and rich–poor differences and ratios to examine inequality in health insurance coverage. We decomposed the concentration index using a generalised linear model to examine the contribution of household and individual-level factors to the inequality in health insurance coverage.ResultsOnly four countries had coverage levels with any type of health insurance of above 20% (Rwanda—78.7% (95% CI 77.5% to 79.9%), Ghana—58.2% (95% CI 56.2% to 60.1%), Gabon—40.8% (95% CI 38.2% to 43.5%), and Burundi 22.0% (95% CI 20.7% to 23.2%)). Overall, health insurance coverage was low (7.9% (95% CI 7.8% to 7.9%)) and pro-rich; concentration index=0.4 (95% CI 0.3 to 0.4, pConclusionCoverage of health insurance in SSA is low and pro-rich. The four countries that had health insurance coverage levels greater than 20% were all characterised by substantial funding from tax revenues. The other study countries featured predominantly voluntary mechanisms. In a context of high informality of labour markets, SSA and other LMICs should rethink the role of voluntary contributory health insurance and instead embrace tax funding as a sustainable and feasible mechanism for mobilising resources for the health sector.

Summary Background Provider payment mechanisms (PPMs) play a critical role in universal health coverage due to the incentives they create for health care providers to deliver needed services, quality, and efficiency. We set out to explore public, private, and faith‐based providers' experiences with capitation and fee‐for‐service in Kenya and identified attributes of PPMs that providers considered important. Methods We conducted a qualitative study in two counties in Kenya. Data were collected using semistructured interviews with 29 management team members in six health providers accredited by the National Hospital Insurance Fund (NHIF). Results Capitation and fee‐for‐service payments from the NHIF and private insurers were reported as good revenue sources as they contributed to providers' overall income. The expected fee‐for‐service payment amounts from NHIF and private insurers were predictable while capitation funds from NHIF were not because providers did not have information on the number of enrolees in their capitation pool. Moreover, capitation payment rates were perceived as inadequate. Capitation and fee‐for‐service payments from NHIF and private insurers were disbursed late. Finally, public providers had lost their autonomy to access and utilise capitation and fee‐for‐service payments from the NHIF. Conclusion Through their experiences, health care providers revealed characteristics of PPMs that they considered important.

Objective To examine the levels, inequalities, and factors associated with health insurance coverage in Kenya. Methods We analyzed secondary data from the Kenya Demographic and Health Survey (KDHS) conducted in 2009 and 2014. We examined the level of health insurance coverage overall, and by type, using an asset index to categorize households into 5 socio-economic quintiles with quintile 5 (Q5) being the richest and quintile 1 (Q1) being the poorest. The high-low ratio (Q5/Q1 ratio), concentration curve, and concentration index (CIX) were employed to assess inequalities in health insurance coverage, and logistic regression to examine correlates of health insurance coverage. Results Overall health insurance coverage increased from 8.17% to 19.59% between 2009 and 2014. There was high inequality in overall health insurance coverage, even though this inequality decreased between 2009 (Q5/Q1 ratio of 31.21, CIX=0.61, 95% CI 0.52-0.0.71) and 2014 (Q5/Q1 ratio 12.34, CIX=0.49, 95% CI 0.45-0.52). Individuals that were older, employed in the formal sector, married, exposed to media, male, belonged to a small household, had a chronic disease, and belonged to rich households, had increased odds of health insurance coverage. Conclusion Health insurance coverage in Kenya remains low and is characterized by significant inequality. In a context where over 80% of the population is in the informal sector, and close to 50% live below the national poverty line, achieving high and equitable coverage levels with contributory, and voluntary health insurance mechanism is problematic. Kenya should consider a universal, tax-funded mechanism that ensures revenues are equitably and efficiently collected, and everyone (including the poor and those in the informal sector) is covered. This article is protected by copyright. All rights reserved.

Background Stated preference elicitation methods such as discrete choice experiments (DCEs) are now widely used in the health domain. However, the “quality” of health-related DCEs has come under criticism due to the lack of rigour in conducting and reporting some aspects of the design process such as attribute and level development. Superficially selecting attributes and levels and vaguely reporting the process might result in misspecification of attributes which may, in turn, bias the study and misinform policy. To address these concerns, we meticulously conducted and report our systematic attribute development and level selection process for a DCE to elicit the preferences of health care providers for the attributes of a capitation payment mechanism in Kenya. Methodology We used a four-stage process proposed by Helter and Boehler to conduct and report the attribute development and level selection process. The process entailed raw data collection, data reduction, removing inappropriate attributes, and wording of attributes. Raw data was collected through a literature review and a qualitative study. Data was reduced to a long list of attributes which were then screened for appropriateness by a panel of experts. The resulting attributes and levels were worded and pretested in a pilot study. Revisions were made and a final list of attributes and levels decided. Results The literature review unearthed seven attributes of provider payment mechanisms while the qualitative study uncovered 10 capitation attributes. Then, inappropriate attributes were removed using criteria such as salience, correlation, plausibility, and capability of being traded. The resulting five attributes were worded appropriately and pretested in a pilot study with 31 respondents. The pilot study results were used to make revisions. Finally, four attributes were established for the DCE, namely, payment schedule, timeliness of payments, capitation rate per individual per year, and services to be paid by the capitation rate. Conclusion By rigorously conducting and reporting the process of attribute development and level selection of our DCE,we improved transparency and helped researchers judge the quality.

Background: Kenya has committed to achieve universal health coverage (UHC) and prioritised purchasing reforms. Existing evidence indicates that purchasing can be leveraged to achieve health system objectives when done strategically. However, no single study has collated evidence on the progress and bottlenecks on strategic health purchasing (SHP) in Kenya to inform requisite SHP reforms in moving towards UHC. Against this backdrop, we assessed the progress and gaps in SHP in Kenya. Methods: We used a cross-sectional qualitative study design and collected data using document reviews, key informant interviews, and a stakeholder engagement workshop focussing on the three main purchasers in Kenya: the National Hospital Insurance Fund (NHIF), county departments of health (CDOH) and the Ministry of Health (purchaser for vertical programmes). Data was analysed following a framework analysis approach. Results: Kenya is making progress towards developing, implementing and institutionalising SHP practices that should lead to the attainment of health system goals. Progress has been made in 1) strengthening the process of defining a health benefits package available for the population, 2) engaging in selective contracting and integrated care contracting, 3) implementing multiple provider payment mechanisms, 4) designing legal and regulatory frameworks that provide direction for purchasing, and 5) increasingly utilising information systems. However, several gaps remain. First, there remain inadequate linkages between MOH policies and the NHIF that can facilitate adequate stewardship/oversight. Second, the purchasers lack adequate capacity to ensure quality assurance and enforce contracts. Third, provider payment methods (PPM) remain poorly designed, implemented, and are not adequately linked to health system goals. Finally, purchasers have weak or unclearly defined communications strategies to patients and providers. Conclusions: Although Kenya is making progress in SHP, key gaps remain. Consequently, there is a need to implement reforms that will align the purchasers’ actions and decisions to the ideals of strategic health purchasing.

Background: Africa has the lowest childhood vaccination coverage worldwide. If the full benefits of childhood vaccination programmes are to be enjoyed in sub-Saharan Africa, all countries need to improve on vaccine delivery to achieve and sustain high coverage. In this paper, we review trends in vaccination coverage, dropouts between vaccine doses and explored the country-specific predictors of complete vaccination in West Africa. Methods: We utilized datasets from the Demographic and Health Surveys Program, available for Benin, Burkina Faso, The Gambia, Ghana, Guinea, Cote d’Ivoire, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo, to obtain coverage for Bacillus Calmette-Guerin, polio, measles, and diphtheria, pertussis and tetanus (DPT) vaccines in children aged 12 – 23 months. We also calculated the DPT1-to-DPT3 and DPT1-to-measles dropouts, and proportions of the fully immunised child (FIC). Factors predictive of FIC were explored using Chi-squared tests and multivariable logistic regression. Results: Overall, there was a trend of increasing vaccination coverage. The proportion of FIC varied significantly by country (range 24.1-81.4%, mean 49%). DPT1-to-DPT3 dropout was high (range 5.1% -33.9%, mean 16.3%). Similarly, DPT1-measles dropout exceeded 10% in all but four countries. Although no single risk factor was consistently associated with FIC across these countries, maternal education, delivery in a health facility, possessing a vaccine card and a recent post delivery visit to a health facility were the key predictors of complete vaccination. Conclusions: The low numbers of fully immunised children and high dropout between vaccine doses highlights weaknesses and the need to strengthen the healthcare and routine immunization delivery systems in this region. Country-specific correlates of complete vaccination should be explored further to identify interventions required to increase vaccination coverage. Despite the promise of an increasing trend in vaccination coverage in West African countries, more effort is required to attain and maintain global vaccination coverage targets.