Revealing the extent of the first wave of the COVID-19 pandemic in Kenya based on serological and PCR-test data

Policymakers in Africa need robust estimates of the current and future spread of SARS-CoV-2. We used national surveillance PCR test, serological survey and mobility data to develop and fit a county-specific transmission model for Kenya up to the end of September 2020, which encompasses the first wave of SARS-CoV-2 transmission in the country. We estimate that the first wave of the SARS-CoV-2 pandemic peaked before the end of July 2020 in the major urban counties, with 30-50% of residents infected. Our analysis suggests, first, that the reported low COVID-19 disease burden in Kenya cannot be explained solely by limited spread of the virus, and second, that a 30-50% attack rate was not sufficient to avoid a further wave of transmission.

Epidemiology of COVID-19 infections on routine polymerase chain reaction (PCR) and serology testing in Coastal Kenya

Background: There are limited studies in Africa describing the epidemiology, clinical characteristics and serostatus of individuals tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We tested routine samples from the Coastal part of Kenya between 17 th March 2020 and 30 th June 2021. Methods: SARS-CoV-2 infections identified using reverse transcription polymerase chain reaction (RT-PCR) and clinical surveillance data at the point of sample collection were used to classify as either symptomatic or asymptomatic. IgG antibodies were measured in sera samples, using a well validated in-house enzyme-linked immunosorbent assay (ELISA). Results: Mombasa accounted for 56.2% of all the 99,694 naso-pharyngeal/oro-pharyngeal swabs tested, and males constituted the majority tested (73.4%). A total of 7737 (7.7%) individuals were SARS-CoV-2 positive by RT-PCR. The majority (i.e., 92.4%) of the RT-PCR positive individuals were asymptomatic. Testing was dominated by mass screening and travellers, and even at health facility level 91.6% of tests were from individuals without symptoms. Out of the 97,124 tests from asymptomatic individuals 7,149 (7%) were positive and of the 2,568 symptomatic individuals 588 (23%) were positive. In total, 2458 serum samples were submitted with paired naso-pharyngeal/oro-pharyngeal samples and 45% of the RT-PCR positive samples and 20% of the RT-PCR negative samples were paired with positive serum samples. Symptomatic individuals had significantly higher antibody levels than asymptomatic individuals and become RT-PCR negative on repeat testing earlier than asymptomatic individuals. Conclusions: In conclusion, the majority of SARS-CoV-2 infections identified by routine testing in Coastal Kenya were asymptomatic. This reflects the testing practice of health services in Kenya, but also implies that asymptomatic infection is very common in the population. Symptomatic infection may be less common, or it may be that individuals do not present for testing when they have symptoms.

On the resilience of health systems: A methodological exploration across countries in the WHO African Region

The need for resilient health systems is recognized as important for the attainment of health outcomes, given the current shocks to health services. Resilience has been defined as the capacity to "prepare and effectively respond to crises; maintain core functions; and, informed by lessons learnt, reorganize if conditions require it". There is however a recognized dichotomy between its conceptualization in literature, and its application in practice. We propose two mutually reinforcing categories of resilience, representing resilience targeted at potentially known shocks, and the inherent health system resilience, needed to respond to unpredictable shock events. We determined capacities for each of these categories, and explored this methodological proposition by computing country-specific scores against each capacity, for the 47 Member States of the WHO African Region. We assessed face validity of the computed index, to ensure derived values were representative of the different elements of resilience, and were predictive of health outcomes, and computed bias-corrected non-parametric confidence intervals of the emergency preparedness and response (EPR) and inherent system resilience (ISR) sub-indices, as well as the overall resilience index, using 1000 bootstrap replicates. We also explored the internal consistency and scale reliability of the index, by calculating Cronbach alphas for the various proposed capacities and their corresponding attributes. We computed overall resilience to be 48.4 out of a possible 100 in the 47 assessed countries, with generally lower levels of ISR. For ISR, the capacities were weakest for transformation capacity, followed by mobilization of resources, awareness of own capacities, self-regulation and finally diversity of services respectively. This paper aims to contribute to the growing body of empirical evidence on health systems and service resilience, which is of great importance to the functionality and performance of health systems, particularly in the context of COVID-19. It provides a methodological reflection for monitoring health system resilience, revealing areas of improvement in the provision of essential health services during shock events, and builds a case for the need for mechanisms, at country level, that address both specific and non-specific shocks to the health system, ultimately for the attainment of improved health outcomes.

Seroprevalence of Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 Among Healthcare Workers in Kenya

BACKGROUND

Few studies have assessed the seroprevalence of antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among healthcare workers (HCWs) in Africa. We report findings from a survey among HCWs in 3 counties in Kenya.

METHODS

We recruited 684 HCWs from Kilifi (rural), Busia (rural), and Nairobi (urban) counties. The serosurvey was conducted between 30 July and 4 December 2020. We tested for immunoglobulin G antibodies to SARS-CoV-2 spike protein, using enzyme-linked immunosorbent assay. Assay sensitivity and specificity were 92.7 (95% CI, 87.9-96.1) and 99.0% (95% CI, 98.1-99.5), respectively. We adjusted prevalence estimates, using bayesian modeling to account for assay performance.

RESULTS

The crude overall seroprevalence was 19.7% (135 of 684). After adjustment for assay performance, seroprevalence was 20.8% (95% credible interval, 17.5%-24.4%). Seroprevalence varied significantly (P < .001) by site: 43.8% (95% credible interval, 35.8%-52.2%) in Nairobi, 12.6% (8.8%-17.1%) in Busia and 11.5% (7.2%-17.6%) in Kilifi. In a multivariable model controlling for age, sex, and site, professional cadre was not associated with differences in seroprevalence.

CONCLUSION

These initial data demonstrate a high seroprevalence of antibodies to SARS-CoV-2 among HCWs in Kenya. There was significant variation in seroprevalence by region, but not by cadre.

Socioeconomic Inequity in the Screening and Treatment of Hypertension in Kenya: Evidence From a National Survey

Background

Non-communicable diseases (NCDs) account for 50% of hospitalisations and 55% of inpatient deaths in Kenya. Hypertension is one of the major NCDs in Kenya. Equitable access and utilisation of screening and treatment interventions are critical for reducing the burden of hypertension. This study assessed horizontal equity (equal treatment for equal need) in the screening and treatment for hypertension. It also decomposed socioeconomic inequalities in care use in Kenya.

Methods

Cross-sectional data from the 2015 NCDs risk factors STEPwise survey, covering 4,500 adults aged 18-69 years were analysed. Socioeconomic inequality was assessed using concentration curves and concentration indices (CI), and inequity by the horizontal inequity (HI) index. A positive (negative) CI or HI value suggests a pro-rich (pro-poor) inequality or inequity. Socioeconomic inequality in screening and treatment for hypertension was decomposed into contributions of need [age, sex, and body mass index (BMI)] and non-need (wealth status, education, exposure to media, employment, and area of residence) factors using a standard decomposition method.

Results

The need for hypertension screening was higher among poorer than wealthier socioeconomic groups (CI = -0.077; p < 0.05). However, wealthier groups needed hypertension treatment more than poorer groups (CI = 0.293; p <0.001). Inequity in the use of hypertension screening (HI = 0.185; p < 0.001) and treatment (HI = 0.095; p < 0.001) were significantly pro-rich. Need factors such as sex and BMI were the largest contributors to inequalities in the use of screening services. By contrast, non-need factors like the area of residence, wealth, and employment status mainly contributed to inequalities in the utilisation of treatment services.

Conclusion

Among other things, the use of hypertension screening and treatment services in Kenya should be according to need to realise the Sustainable Development Goals for NCDs. Specifically, efforts to attain equity in healthcare use for hypertension services should be multi-sectoral and focused on crucial inequity drivers such as regional disparities in care use, poverty and educational attainment. Also, concerted awareness campaigns are needed to increase the uptake of screening services for hypertension.

Sero-surveillance for IgG to SARS-CoV-2 at antenatal care clinics in three Kenyan referral hospitals: Repeated cross-sectional surveys 2020-21

INTRODUCTION

The high proportion of SARS-CoV-2 infections that have remained undetected presents a challenge to tracking the progress of the pandemic and estimating the extent of population immunity.

METHODS

We used residual blood samples from women attending antenatal care services at three hospitals in Kenya between August 2020 and October 2021and a validated IgG ELISA for SARS-Cov-2 spike protein and adjusted the results for assay sensitivity and specificity. We fitted a two-component mixture model as an alternative to the threshold analysis to estimate of the proportion of individuals with past SARS-CoV-2 infection.

RESULTS

We estimated seroprevalence in 2,981 women; 706 in Nairobi, 567 in Busia and 1,708 in Kilifi. By October 2021, 13% of participants were vaccinated (at least one dose) in Nairobi, 2% in Busia. Adjusted seroprevalence rose in all sites; from 50% (95%CI 42-58) in August 2020, to 85% (95%CI 78-92) in October 2021 in Nairobi; from 31% (95%CI 25-37) in May 2021 to 71% (95%CI 64-77) in October 2021 in Busia; and from 1% (95% CI 0-3) in September 2020 to 63% (95% CI 56-69) in October 2021 in Kilifi. Mixture modelling, suggests adjusted cross-sectional prevalence estimates are underestimates; seroprevalence in October 2021 could be 74% in Busia and 72% in Kilifi.

CONCLUSIONS

There has been substantial, unobserved transmission of SARS-CoV-2 in Nairobi, Busia and Kilifi Counties. Due to the length of time since the beginning of the pandemic, repeated cross-sectional surveys are now difficult to interpret without the use of models to account for antibody waning.

Examining health sector stakeholder perceptions on the efficiency of county health systems in Kenya

Efficiency gains is a potential strategy to expand Kenya's fiscal space for health. We explored health sector stakeholders' understanding of efficiency and their perceptions of the factors that influence the efficiency of county health systems in Kenya. We conducted a qualitative cross-sectional study and collected data using three focus group discussions during a stakeholder engagement workshop. Workshop participants included health sector stakeholders from the national ministry of health and 10 (out 47) county health departments, and non-state actors in Kenya. A total of 25 health sector stakeholders participated. We analysed data using a thematic approach. Health sector stakeholders indicated the need for the outputs and outcomes of a health system to be aligned to community health needs. They felt that both hardware aspects of the system (such as the financial resources, infrastructure, human resources for health) and software aspects of the system (such as health sector policies, public finance management systems, actor relationships) should be considered as inputs in the analysis of county health system efficiency. They also felt that while traditional indicators of health system performance such as intervention coverage or outcomes for infectious diseases, and reproductive, maternal, neonatal and child health are still relevant, emerging epidemiological trends such as an increase in the burden of non-communicable diseases should also be considered. The stakeholders identified public finance management, human resources for health, political interests, corruption, management capacity, and poor coordination as factors that influence the efficiency of county health systems. An in-depth examination of the factors that influence the efficiency of county health systems could illuminate potential policy levers for generating efficiency gains. Mixed methods approaches could facilitate the study of both hardware and software factors that are considered inputs, outputs or factors that influence health system efficiency. County health system efficiency in Kenya could be enhanced by improving the timeliness of financial flows to counties and health facilities, giving health facilities financial autonomy, improving the number, skill mix, and motivation of healthcare staff, managing political interests, enhancing anticorruption strategies, strengthening management capacity and coordination in the health sector.

Assessing equity and the determinants of socio-economic impacts of COVID-19: Results from a cross-sectional survey in three counties in Kenya

Background: COVID-19 mitigation measures have major ramifications on all aspects of people’s livelihoods. Based on data collected in February 2021, we present an analysis of the socio-economic impacts of COVID-19 mitigation measures in three counties in Kenya. Methods: We conducted a cross-sectional phone-based survey in three counties in Kenya to assess the level of disruption across seven domains: income, food insecurity, schooling, domestic tension/violence, communal violence, mental health, and decision-making. An overall disruption index was computed from the seven domains using principal component analysis. We used a linear regression model to examine the determinants of vulnerability to disruptions as measured by the index. We used concentration curves and indices to assess inequality in the disruption domains and the overall disruption index. Results: The level of disruption in income was the highest (74%), while the level of disruption for domestic tension/violence was the lowest (30%). Factors associated with increased vulnerability to the overall disruption index included: older age, being married, belonging in the lowest socio-economic tertile and receiving COVID-19 related assistance. The concentration curves showed that all the seven domains of disruption were disproportionately concentrated among households in the lowest socio-economic tertile, a finding that was supported by the concentration index of the overall disruption index (CI = - 0.022; p = 0.074). Conclusion: The COVID-19 mitigation measures resulted in unintended socio-economic effects that unfairly affected certain vulnerable groups such as those in the lowest socio-economic group and the elderly. Measures to protect households against the adverse socio-economic effects of the pandemic should be scaled up and targeted to the most vulnerable, with attention to the constantly evolving nature of the pandemic.

Level and determinants of county health system technical efficiency in Kenya: two stage data envelopment analysis

BACKGROUND

Improving health system efficiency is a key strategy to increase health system performance and accelerate progress towards Universal Health Coverage. In 2013, Kenya transitioned into a devolved system of government granting county governments autonomy over budgets and priorities. We assessed the level and determinants of technical efficiency of the 47 county health systems in Kenya.

METHODS

We carried out a two-stage data envelopment analysis (DEA) using Simar and Wilson's double bootstrap method using data from all the 47 counties in Kenya. In the first stage, we derived the bootstrapped DEA scores using an output orientation. We used three input variables (Public county health expenditure, Private county health expenditure, number of healthcare facilities), and one outcome variable (Disability Adjusted Life Years) using 2018 data. In the second stage, the bias corrected technical inefficiency scores were regressed against 14 exogenous factors using a bootstrapped truncated regression.

RESULTS

The mean bias-corrected technical efficiency score of the 47 counties was 69.72% (95% CI 66.41-73.01%), indicating that on average, county health systems could increase their outputs by 30.28% at the same level of inputs. County technical efficiency scores ranged from 42.69% (95% CI 38.11-45.26%) to 91.99% (95% CI 83.78-98.95%). Higher HIV prevalence was associated with greater technical inefficiency of county health systems, while higher population density, county absorption of development budgets, and quality of care provided by healthcare facilities were associated with lower county health system inefficiency.

CONCLUSIONS

The findings from this analysis highlight the need for county health departments to consider ways to improve the efficiency of county health systems. Approaches could include prioritizing resources to interventions that will reduce high chronic disease burden, filling structural quality gaps, implementing interventions to improve process quality, identifying the challenges to absorption rates and reforming public finance management systems to enhance their efficiency.

Modelling the cost-effectiveness of essential and advanced critical care for COVID-19 patients in Kenya

BACKGROUND

Case management of symptomatic COVID-19 patients is a key health system intervention. The Kenyan government embarked to fill capacity gaps in essential and advanced critical care (ACC) needed for the management of severe and critical COVID-19. However, given scarce resources, gaps in both essential and ACC persist. This study assessed the cost-effectiveness of investments in essential and ACC to inform the prioritisation of investment decisions.

METHODS

We employed a decision tree model to assess the incremental cost-effectiveness of investment in essential care (EC) and investment in both essential and ACC (EC +ACC) compared with current healthcare provision capacity (status quo) for COVID-19 patients in Kenya. We used a health system perspective, and an inpatient care episode time horizon. Cost data were obtained from primary empirical analysis while outcomes data were obtained from epidemiological model estimates. We used univariate and probabilistic sensitivity analysis to assess the robustness of the results.

RESULTS

The status quo option is more costly and less effective compared with investment in EC and is thus dominated by the later. The incremental cost-effectiveness ratio of investment in essential and ACC (EC+ACC) was US$1378.21 per disability-adjusted life-year averted and hence not a cost-effective strategy when compared with Kenya's cost-effectiveness threshold (US$908).

CONCLUSION

When the criterion of cost-effectiveness is considered, and within the context of resource scarcity, Kenya will achieve better value for money if it prioritises investments in EC before investments in ACC. This information on cost-effectiveness will however need to be considered as part of a multicriteria decision-making framework that uses a range of criteria that reflect societal values of the Kenyan society.

Cost of TB services in healthcare facilities in Kenya (No 3)

BACKGROUND: The reduction of Kenya´s TB burden requires improving resource allocation both to and within the National TB, Leprosy and Lung Disease Program (NTLD-P). We aimed to estimate the unit costs of TB services for budgeting by NTLD-P, and allocative efficiency analyses for future National Strategic Plan (NSP) costing.METHODS: We estimated costs of all TB interventions in a sample of 20 public and private health facilities from eight counties. We calculated national-level unit costs from a health provider´s perspective using bottom-up (BU) and top-down (TD) approaches for the financial year 2017-2018 using Microsoft Excel and STATA v16.RESULTS: The mean unit cost for passive case-finding (PCF) was respectively US$38 and US$60 using the BU and TD approaches. The unit BU and TD costs of a 6-month first-line treatment (FLT) course, including monitoring tests, was respectively US$135 and US$160, while those for adult drug-resistant TB (DR-TB) treatment was respectively US$3,230.28 and US$3,926.52 for the 9-month short regimen. Intervention costs highlighted variations between BU and TD approaches. Overall, TD costs were higher than BU, as these are able to capture more costs due to inefficiency (breaks/downtime/leave).CONCLUSION: The activity-based TB unit costs form a comprehensive cost database, and the costing process has built-in capacity within the NTLD-P and international TB research networks, which will inform future TB budgeting processes.

Stark choices: exploring health sector costs of policy responses to COVID-19 in low-income and middle-income countries

OBJECTIVES

COVID-19 has altered health sector capacity in low-income and middle-income countries (LMICs). Cost data to inform evidence-based priority setting are urgently needed. Consequently, in this paper, we calculate the full economic health sector costs of COVID-19 clinical management in 79 LMICs under different epidemiological scenarios.

METHODS

We used country-specific epidemiological projections from a dynamic transmission model to determine number of cases, hospitalisations and deaths over 1 year under four mitigation scenarios. We defined the health sector response for three base LMICs through guidelines and expert opinion. We calculated costs through local resource use and price data and extrapolated costs across 79 LMICs. Lastly, we compared cost estimates against gross domestic product (GDP) and total annual health expenditure in 76 LMICs.

RESULTS

COVID-19 clinical management costs vary greatly by country, ranging between <0.1%-12% of GDP and 0.4%-223% of total annual health expenditure (excluding out-of-pocket payments). Without mitigation policies, COVID-19 clinical management costs per capita range from US$43.39 to US$75.57; in 22 of 76 LMICs, these costs would surpass total annual health expenditure. In a scenario of stringent social distancing, costs per capita fall to US$1.10-US$1.32.

CONCLUSIONS

We present the first dataset of COVID-19 clinical management costs across LMICs. These costs can be used to inform decision-making on priority setting. Our results show that COVID-19 clinical management costs in LMICs are substantial, even in scenarios of moderate social distancing. Low-income countries are particularly vulnerable and some will struggle to cope with almost any epidemiological scenario. The choices facing LMICs are likely to remain stark and emergency financial support will be needed.

COVID-19 transmission dynamics underlying epidemic waves in Kenya

Policy decisions on COVID-19 interventions should be informed by a local, regional and national understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or infection spreads to susceptible subpopulations. Three COVID-19 epidemic waves have been observed in Kenya. Using a mechanistic mathematical model, we explain the first two distinct waves by differences in contact rates in high and low social-economic groups, and the third wave by the introduction of higher-transmissibility variants. Reopening schools led to a minor increase in transmission between the second and third waves. Socioeconomic and urban–rural population structure are critical determinants of viral transmission in Kenya.

The hidden financial burden of healthcare: a systematic literature review of informal payments in Sub-Saharan Africa

Background: Informal payments limit equitable access to healthcare. Despite being a common phenomenon, there is a need for an in-depth analysis of informal charging practices in the Sub-Saharan Africa (SSA) context. We conducted a systematic literature review to synthesize existing evidence on the prevalence, characteristics, associated factors, and impact of informal payments in SSA. Methods: We searched for literature on PubMed, African Index Medicus, Directory of Open Access Journals, and Google Scholar databases and relevant organizational websites. We included empirical studies on informal payments conducted in SSA regardless of the study design and year of publication and excluded reviews, editorials, and conference presentations. Framework analysis was conducted, and the review findings were synthesized. Results: A total of 1700 articles were retrieved, of which 23 were included in the review. Several studies ranging from large-scale nationally representative surveys to in-depth qualitative studies have shown that informal payments are prevalent in SSA regardless of the health service, facility level, and sector. Informal payments were initiated mostly by health workers compared to patients and they were largely made in cash rather than in kind. Patients made informal payments to access services, skip queues, receive higher quality of care, and express gratitude. The poor and people who were unaware of service charges, were more likely to pay informally. Supply-side factors associated with informal payments included low and irregular health worker salaries, weak accountability mechanisms, and perceptions of widespread corruption in the public sector. Informal payments limited access especially among the poor and the inability to pay was associated with delayed or forgone care and provision of lower-quality care. Conclusions: Addressing informal payments in SSA requires a multifaceted approach. Potential strategies include enhancing patient awareness of service fees, revisiting health worker incentives, strengthening accountability mechanisms, and increasing government spending on health.

Examining health facility financing in Kenya in the context of devolution

Background

How health facilities are financed affects their performance and health system goals. We examined how health facilities in the public sector are financed in Kenya, within the context of a devolved health system.

Methods

We carried out a cross-sectional study in five purposely selected counties in Kenya, using a mixed methods approach. We collected data using document reviews and in-depth interviews (no = 20). In each county, we interviewed county department of health managers and health facility managers from two and one purposely selected public hospitals and health center respectively. We analyzed qualitive data using thematic analysis and conducted descriptive analysis of quantitative data.

Results

Planning and budgeting: Planning and budgeting processes by hospitals and health centers were not standardized across counties. Budgets were not transparent and credible, but rather were regarded as “wish lists” since they did not translate to actual resources. Sources of funds: Public hospitals relied on user fees, while health centers relied on donor funds as their main sources of funding. Funding flows: Hospitals in four of the five study counties had no financial autonomy. Health centers in all study counties had financial autonomy. Flow of funds to hospitals and health centers in all study counties was characterized by unpredictability of amounts and timing. Health facility expenditure: Staff salaries accounted for over 80% of health facility expenditure. This crowded out other expenditure and led to frequent stock outs of essential health commodities.

Conclusion

The national and county government should consider improving health facility financing in Kenya by 1) standardizing budgeting and planning processes, 2) transitioning public facility financing away from a reliance on user fees and donor funding 3) reforming public finance management laws and carry out political engagement to facilitate direct facility financing and financial autonomy of public hospitals, and 4) assess health facility resource needs to guide appropriate levels resource allocation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07123-7.

An innovative leadership development initiative to support building everyday resilience in health systems

Effective management and leadership are essential for everyday health system resilience, but actors charged with these roles are often underprepared and undersupported to perform them. Particular challenges have been observed in interpersonal and relational aspects of health managers’ work, including communication skills, emotional competence and supportive oversight. Within the Resilient and Responsive Health Systems (RESYST) consortium in Kenya, we worked with two county health and hospital management teams to adapt a package of leadership development interventions aimed at building these skills. This article provides insights into: (1) the content and co-development of a participatory intervention combining two core elements: a complex health system taught course, and an adapted communications and emotional competence process training; and (2) the findings from a formative evaluation of this intervention which included observations of the training, individual interviews with participating managers and discussions in regular meetings with managers. Following the training, managers reported greater recognition of the importance of health system software (values, belief systems and relationships), and improved self-awareness and team communication. Managers appeared to build valued skills in active listening, giving constructive feedback, ‘stepping back’ from automatic reactions to challenging emotional situations and taking responsibility to communicate with emotional competence. The training also created spaces for managers to share experiences, reflect upon and nurture social competences. We draw on our findings and the literature to propose a theory of change regarding the potential of our leadership development intervention to nurture everyday health system resilience through strengthening cognitive, behavioural and contextual capacities. We recommend further development and evaluation of novel approaches such as those shared in this article to support leadership development and management in complex, hierarchical systems.

Assessing the Level and Determinants of COVID-19 Vaccine Confidence in Kenya

The government of Kenya has launched a phased rollout of COVID-19 vaccination. A major barrier is vaccine hesitancy; the refusal or delay of accepting vaccination. This study evaluated the level and determinants of vaccine hesitancy in Kenya. We conducted a cross-sectional study administered through a phone-based survey in February 2021 in four counties of Kenya. Multilevel logistic regression was used to identify individual perceived risks and influences, context-specific factors and vaccine-specific issues associated with COVID-19 vaccine hesitancy. COVID-19 vaccine hesitancy in Kenya was high: 36.5%. Factors associated with vaccine hesitancy included: Rural regions, perceived difficulty in adhering to government regulations on COVID-19 prevention, no perceived COVID-19 infection risk, concerns regarding vaccine safety and effectiveness, and religious and cultural reasons. There is a need for the prioritization of interventions to address vaccine hesitancy and improve vaccine confidence as part of the vaccine roll-out plan. These messaging and/or interventions should be holistic to include the value of other public health measures, be focused and targeted to specific groups, raise awareness on the risks of COVID-19 and effectively communicate the benefits and risks of vaccines.

Examining the implementation of the Linda Mama free maternity program in Kenya

Background

In 2013, Kenya introduced a free maternity policy in all public healthcare facilities. In 2016, the Ministry of Health shifted responsibility for the program, now called Linda Mama, to the National Hospital Insurance Fund (NHIF) and expanded access beyond public sector. This study aimed to examine the implementation of the Linda Mama program.

Methods

We conducted a mixed‐methods cross‐sectional study at the national level and in 20 purposively sampled facilities across five counties in Kenya. We collected data using in‐depth interviews (n = 104), administered patient‐exit questionnaires (n = 108), and carried out document reviews. Qualitative data were analysed using a framework approach while quantitative data were analysed descriptively.

Results

Linda Mama was designed and resulted in improved accountability and expand benefits. In practice however, beneficiaries did not access some services that were part of the revised benefit package. Second, out of pocket payments were still being incurred by beneficiaries. Health facilities in most counties had lost financial autonomy and had no access to reimbursements from NHIF for services provided; but those with financial autonomy were able to boost facility revenue and enhance service delivery. Further, fund disbursements from NHIF were characterised by delays and unpredictability. Implementation experiences reveal that there was inadequate communication, claim processing challenges and reimbursement rates were deemed insufficient.

Conclusions

Our findings show that there are challenges associated with the implementation of the Linda Mama program and highlights the need for process evaluations for programs to track implementation, ensure continuous learning, and provide opportunities for course correcting programs' implementation.

Tracking the introduction and spread of SARS-CoV-2 in coastal Kenya

Genomic surveillance of SARS-CoV-2 is important for understanding both the evolution and the patterns of local and global transmission. Here, we generated 311 SARS-CoV-2 genomes from samples collected in coastal Kenya between 17th March and 31st July 2020. We estimated multiple independent SARS-CoV-2 introductions into the region were primarily of European origin, although introductions could have come through neighbouring countries. Lineage B.1 accounted for 74% of sequenced cases. Lineages A, B and B.4 were detected in screened individuals at the Kenya-Tanzania border or returning travellers. Though multiple lineages were introduced into coastal Kenya following the initial confirmed case, none showed extensive local expansion other than lineage B.1. International points of entry were important conduits of SARS-CoV-2 importations into coastal Kenya and early public health responses prevented established transmission of some lineages. Undetected introductions through points of entry including imports from elsewhere in the country gave rise to the local epidemic at the Kenyan coast.

Indirect health effects of the COVID-19 pandemic in Kenya: a mixed methods assessment

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.

Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Immunoglobulin G Antibody Seroprevalence Among Truck Drivers and Assistants in Kenya

In October 2020, anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G seroprevalence among truck drivers and their assistants (TDA) in Kenya was 42.3%, higher than among healthcare workers and blood donors. Truck drivers and their assistants transport essential supplies during the coronavirus disease 2019 pandemic, placing them at increased risk of being infected and of transmitting SARS-CoV-2 over a wide geographical area.

How do healthcare providers respond to multiple funding flows? A conceptual framework and options to align them

Provider payment methods are a key health policy lever because they influence healthcare provider behaviour and affect health system objectives, such as efficiency, equity, financial protection and quality. Previous research focused on analysing individual provider payment methods in isolation, or on the actions of individual purchasers. However, purchasers typically use a mix of provider payment methods to pay healthcare providers and most health systems are fragmented with multiple purchasers. From a health provider perspective, these different payments are experienced as multiple funding flows which together send a complex set of signals about where they should focus their effort. In this article, we argue that there is a need to expand the analysis of provider payment methods to include an analysis of the interactions of multiple funding flows and the combined effect of their incentives on the provision of healthcare services. The purpose of the article is to highlight the importance of multiple funding flows to health facilities and present a conceptual framework to guide their analysis. The framework hypothesizes that when healthcare providers receive multiple funding flows, they may find certain funding flows more favourable than others based on how these funding flows compare to each other on a range of attributes. This creates a set of incentives, and consequently, healthcare providers may alter their behaviour in three ways: resource shifting, service shifting and cost shifting. We describe these behaviours and how they may affect health system objectives. Our analysis underlines the need to align the incentives generated by multiple funding flows. To achieve this, we propose three policy strategies that relate to the governance of healthcare purchasing: reducing the fragmentation of health financing arrangements to decrease the number of multiple purchaser arrangements and funding flows; harmonizing signals from multiple funding flows; and constraining providers from responding to undesirable incentives.

Temporal trends of SARS-CoV-2 seroprevalence during the first wave of the COVID-19 epidemic in Kenya

Observed SARS-CoV-2 infections and deaths are low in tropical Africa raising questions about the extent of transmission. We measured SARS-CoV-2 IgG by ELISA in 9,922 blood donors across Kenya and adjusted for sampling bias and test performance. By 1st September 2020, 577 COVID-19 deaths were observed nationwide and seroprevalence was 9.1% (95%CI 7.6-10.8%). Seroprevalence in Nairobi was 22.7% (18.0-27.7%). Although most people remained susceptible, SARS-CoV-2 had spread widely in Kenya with apparently low associated mortality.

What will it cost to prevent violence against women and girls in low- and middle-income countries? Evidence from Ghana, Kenya, Pakistan, Rwanda, South Africa and Zambia

Violence against women and girls (VAWG) is a global problem with profound consequences. Although there is a growing body of evidence on the effectiveness of VAWG prevention interventions, economic data are scarce. We carried out a cross-country study to examine the costs of VAWG prevention interventions in low- and middle-income countries. We collected primary cost data on six different pilot VAWG prevention interventions in six countries: Ghana, Kenya, Pakistan, Rwanda, South Africa and Zambia. The interventions varied in their delivery platforms, target populations, settings and theories of change. We adopted a micro-costing methodology. We calculated total costs and a number of unit costs common across interventions (e.g. cost per beneficiary reached). We used the pilot-level cost data to model the expected total costs and unit costs of five interventions scaled up to the national level. Total costs of the pilots varied between ∼US $208 000 in a small group intervention in South Africa to US $2 788 000 in a couples and community-based intervention in Rwanda. Staff costs were the largest cost input across all interventions; consequently, total costs were sensitive to staff time use and salaries. The cost per beneficiary reached in the pilots ranged from ∼US $4 in a community-based intervention in Ghana to US $1324 for one-to-one counselling in Zambia. When scaled up to the national level, total costs ranged from US $32 million in Ghana to US $168 million in Pakistan. Cost per beneficiary reached at scale decreased for all interventions compared to the pilots, except for school-based interventions due to differences in student density per school between the pilot and the national average. The costs of delivering VAWG prevention vary greatly due to differences in the geographical reach, number of intervention components and the complexity of adapting the intervention to the country. Cost-effectiveness analyses are necessary to determine the value for money of interventions.

Impact of free maternity policies in Kenya: an interrupted time-series analysis

BACKGROUND

User fees have been reported to limit access to services and increase inequities. As a result, Kenya introduced a free maternity policy in all public facilities in 2013. Subsequently in 2017, the policy was revised to the Linda Mama programme to expand access to private sector, expand the benefit package and change its management.

METHODS

An interrupted time-series analysis on facility deliveries, antenatal care (ANC) and postnatal care (PNC) visits data between 2012 and 2019 was used to determine the effect of the two free maternity policies. These data were from 5419 public and 305 private and faith-based facilities across all counties, with data sourced from the health information system. A segmented negative binomial regression with seasonality accounted for, was used to determine the level (immediate) effect and trend (month-on-month) effect of the policies.

RESULTS

The 2013 free-maternity policy led to a 19.6% and 28.9% level increase in normal deliveries and caesarean sections, respectively, in public facilities. There was also a 1.4% trend decrease in caesarean sections in public facilities. A level decrease followed by a trend increase in PNC visits was reported in public facilities. For private and faith-based facilities, there was a level decrease in caesarean sections and ANC visits followed by a trend increase in caeserean sections following the 2013 policy.Furthermore, the 2017 Linda Mama programme showed a level decrease then a trend increase in PNC visits and a 1.1% trend decrease in caesarean sections in public facilities. In private and faith-based facilities, there was a reported level decrease in normal deliveries and caesarean sections and a trend increase in caesarean sections.

CONCLUSION

The free maternity policies show mixed effects in increasing access to maternal health services. Emphasis on other accessibility barriers and service delivery challenges alongside user fee removal policies should be addressed to realise maximum benefits in maternal health utilisation.