Policies and resources for strengthening of emergency and critical care services in the context of the global COVID-19 pandemic in Kenya

Critical illnesses cause several million deaths annually, with many of these occurring in low-resource settings like Kenya. Great efforts have been made worldwide to scale up critical care to reduce deaths from COVID-19. Lower income countries with fragile health systems may not have had sufficient resources to upscale their critical care. We aimed to review how efforts to strengthen emergency and critical care were operationalised during the pandemic in Kenya to point towards how future emergencies should be approached. This was an exploratory study that involved document reviews, and discussions with key stakeholders (donors, international agencies, professional associations, government actors), during the first year of the pandemic in Kenya. Our findings suggest that pre-pandemic health services for the critically ill in Kenya were sparse and unable to meet rising demand, with major limitations noted in human resources and infrastructure. The pandemic response saw galvanised action by the Government of Kenya and other agencies to mobilise resources (approximately USD 218 million). Earlier efforts were largely directed towards advanced critical care but since the human resource gap could not be reduced immediately, a lot of equipment remained unused. We also note that despite strong policies on what resources should be available, the reality on the ground was that there were often critical shortages. While emergency response mechanisms are not conducive to addressing long-term health system issues, the pandemic increased global recognition of the need to fund care for the critically ill. Limited resources may be best prioritised towards a public health approach with focus on provision of relatively basic, lower cost essential emergency and critical care (EECC) that can potentially save the most lives amongst critically ill patients

Epidemiological and clinical characteristics of patients hospitalised with COVID-19 in Kenya:A multicentre cohort study

OBJECTIVES: To assess outcomes of patients admitted to hospital with COVID-19 and to determine the predictors of mortality. SETTING: This study was conducted in six facilities, which included both government and privately run secondary and tertiary level facilities in the central and coastal regions of Kenya. PARTICIPANTS: We enrolled 787 reverse transcriptase-PCR-confirmed SARS-CoV2-infected persons. Patients whose records could not be accessed were excluded. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was COVID-19-related death. We used Cox proportional hazards regressions to determine factors related to in-hospital mortality. RESULTS: Data from patients with 787 COVID-19 were available. The median age was 43 years (IQR 30–53), with 505 (64%) being men. At admission, 455 (58%) were symptomatic with an additional 63 (9%) developing clinical symptoms during hospitalisation. The most common symptoms were cough (337, 43%), loss of taste or smell (279, 35%) and fever (126, 16%). Comorbidities were reported in 340 (43%), with cardiovascular disease, diabetes and HIV documented in 130 (17%), 116 (15%), 53 (7%), respectively. 90 (11%) were admitted to the Intensive Care Unit (ICU) for a mean of 11 days, 52 (7%) were ventilated with a mean of 10 days, 107 (14%) died. The risk of death increased with age (HR 1.57 (95% CI 1.13 to 2.19)) for persons >60 years compared with those <60 years old; having comorbidities (HR 2.34 (1.68 to 3.25)) and among men (HR 1.76 (1.27 to 2.44)) compared with women. Elevated white cell count and aspartate aminotransferase were associated with higher risk of death. CONCLUSIONS: The risk of death from COVID-19 is high among older patients, those with comorbidities and among men. Clinical parameters including patient clinical signs, haematology and liver function tests were associated with risk of death and may guide stratification of high-risk patients

Epidemiological impact and cost-effectiveness analysis of COVID-19 vaccination in Kenya.

Background A few studies have assessed the epidemiological impact and the cost-effectiveness of COVID-19 vaccines in settings where most of the population had been exposed to SARS-CoV-2 infection. Methods We conducted a cost-effectiveness analysis of COVID-19 vaccine in Kenya from a societal perspective over a 1.5-year time frame. An age-structured transmission model assumed at least 80% of the population to have prior natural immunity when an immune escape variant was introduced. We examine the effect of slow (18 months) or rapid (6 months) vaccine roll-out with vaccine coverage of 30%, 50% or 70% of the adult (>18 years) population prioritising roll-out in those over 50-years (80% uptake in all scenarios). Cost data were obtained from primary analyses. We assumed vaccine procurement at US$7 per dose and vaccine delivery costs of US$3.90–US$6.11 per dose. The cost-effectiveness threshold was US$919.11. Findings Slow roll-out at 30% coverage largely targets those over 50 years and resulted in 54% fewer deaths (8132 (7914–8373)) than no vaccination and was cost saving (incremental cost-effectiveness ratio, ICER=US$−1343 (US$−1345 to US$−1341) per disability-adjusted life-year, DALY averted). Increasing coverage to 50$919.11). Rapid roll-out with 30% coverage averted 63% more deaths and was more cost-saving (ICER=US$−1607 (US$−1609 to US$−1604) per DALY averted) compared with slow roll-out at the same coverage level, but 50% and 70% coverage scenarios were not cost-effective. Interpretation With prior exposure partially protecting much of the Kenyan population, vaccination of young adults may no longer be cost-effective

Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Kenyan blood donors.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Africa is poorly described. The first case of SARS-CoV-2 in Kenya was reported on 12 March 2020, and an overwhelming number of cases and deaths were expected, but by 31 July 2020, there were only 20,636 cases and 341 deaths. However, the extent of SARS-CoV-2 exposure in the community remains unknown. We determined the prevalence of anti-SARS-CoV-2 immunoglobulin G among blood donors in Kenya in April-June 2020. Crude seroprevalence was 5.6% (174 of 3098). Population-weighted, test-performance-adjusted national seroprevalence was 4.3% (95% confidence interval, 2.9 to 5.8%) and was highest in urban counties Mombasa (8.0%), Nairobi (7.3%), and Kisumu (5.5%). SARS-CoV-2 exposure is more extensive than indicated by case-based surveillance, and these results will help guide the pandemic response in Kenya and across Africa

Epidemiology of COVID-19 infections on routine polymerase chain reaction (PCR) and serology testing in Coastal Kenya [version 1; peer review: 2 approved]

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 17th March 2020 and 30th 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

SARS-CoV-2 seroprevalence and implications for population immunity: Evidence from two Health and Demographic Surveillance System sites in Kenya, February-December 2022.

BACKGROUND: We sought to estimate SARS-CoV-2 antibody seroprevalence within representative samples of the Kenyan population during the third year of the COVID-19 pandemic and the second year of COVID-19 vaccine use. METHODS: We conducted cross-sectional serosurveys among randomly selected, age-stratified samples of Health and Demographic Surveillance System (HDSS) residents in Kilifi and Nairobi. Anti-spike (anti-S) immunoglobulin G (IgG) serostatus was measured using a validated in-house ELISA and antibody concentrations estimated with reference to the WHO International Standard for anti-SARS-CoV-2 immunoglobulin. RESULTS: HDSS residents were sampled in February-June 2022 (Kilifi HDSS N = 852; Nairobi Urban HDSS N = 851) and in August-December 2022 (N = 850 for both sites). Population-weighted coverage for ≥1 doses of COVID-19 vaccine were 11.1% (9.1-13.2%) among Kilifi HDSS residents by November 2022 and 34.2% (30.7-37.6%) among Nairobi Urban HDSS residents by December 2022. Population-weighted anti-S IgG seroprevalence among Kilifi HDSS residents increased from 69.1% (65.8-72.3%) by May 2022 to 77.4% (74.4-80.2%) by November 2022. Within the Nairobi Urban HDSS, seroprevalence by June 2022 was 88.5% (86.1-90.6%), comparable with seroprevalence by December 2022 (92.2%; 90.2-93.9%). For both surveys, seroprevalence was significantly lower among Kilifi HDSS residents than among Nairobi Urban HDSS residents, as were antibody concentrations (p < 0.001). CONCLUSION: More than 70% of Kilifi residents and 90% of Nairobi residents were seropositive for anti-S IgG by the end of 2022. There is a potential immunity gap in rural Kenya; implementation of interventions to improve COVID-19 vaccine uptake among sub-groups at increased risk of severe COVID-19 in rural settings is recommended

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

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.</ns4:p

Factors associated with mortality among patients aged 12 years and above requiring hospitalization for severe respiratory illness (SRI): Findings from the COVID-19 vaccine effectiveness evaluation in Kenya and Mali, 2022-2023.

Mortality attributed to respiratory illnesses is well characterized in children 64 years were more likely to die (adjusted Odds Ratio [aOR] = 2.36; 95 % Confidence Interval [95 % CI] 1.72-3.24). Patients who were in coma (aOR = 3.45; 95 %CI 2.27-5.24) or Intensive Care Unit (aOR = 2.98; 95 %CI 2.06-4.31), or had HIV infection (aOR = 2.47; 95 %CI 2.11-2.90), liver disease (aOR = 2.42; 95 %CI 1.57-3.74), cancer (aOR = 2.09; 95 %CI 1.46-2.99) or SARS-CoV-2 infected (aOR = 1.24; 95 %CI 1.02-1.52) were at increased risk of death. Additionally, diarrhea, malaise/fatigue, difficulty in breathing, confusion, mechanical ventilation, vasopressor support, malnutrition and admission to High Dependency Unit had significant associations. Mortality was heightened among SRI patients who were older, required critical care, had chronic conditions and infected with SARS-CoV-2 suggesting need for early identification of these conditions to improve possible treatment outcomes. [Abstract copyright: Copyright © 2025 The Authors. Published by Elsevier Ltd.. All rights reserved.

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 SARS-CoV-2 transmission. Epidemic waves may result when restrictions are lifted or poorly adhered to, variants with new phenotypic properties successfully invade, or when infection spreads to susceptible sub-populations. 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. Socio-economic and urban/rural population structure are critical determinants of viral transmission in Kenya