The impact of the COVID-19 pandemic on vaccine coverage in Kilifi, Kenya: A retrospective cohort study.

The COVID-19 pandemic caused unprecedented disruption in health service delivery, globally. This study sought to provide evidence on the impact of the pandemic on vaccine coverage in Kilifi County, Kenya. We conducted a vaccine coverage survey between April and June 2021 within the Kilifi Health and Demographic Surveillance System (KHDSS). Simple random sampling was used to identify 1500 children aged 6 weeks-59 months. Participants were grouped into three retrospective cohorts based on when they became age-eligible for vaccination: before the pandemic, during the first year, or during the second year of the pandemic. Survival analysis with Cox regression was used to evaluate the association between the time-period at which participants became age-eligible for vaccination and the rate of vaccination within a month of age-eligibility for the third dose of pentavalent vaccine (Pentavalent-3) and within three months of age-eligibility for the first dose of Measles vaccine (MCV-1). A total of 1,341 participants were included in the survey. Compared to the pre-COVID-19 baseline period, the rate of vaccination within a month of age-eligibility for Pentavalent-3 was not significantly different in the first year of the pandemic (adjusted hazard ratio [aHR] 1.03, 95 % confidence interval [CI] 0.90-1.18) and was significantly higher during the second year of the pandemic (aHR 1.33, 95 % CI 1.07-1.65). The rate of vaccination with MCV-1 within three months of age-eligibility was not significantly different among those age-eligible for vaccination during the first year of the pandemic (aHR 1.04, 95 % CI 0.88-1.21) and was 35 % higher during the second year of the pandemic (95 % CI 1.11-1.64), compared to those age-eligible pre-COVID-19. After adjusting for known determinants of vaccination, the COVID-19 pandemic did not adversely affect the rate of vaccination within the KHDSS.

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.

Quantifying previous SARS-CoV-2 infection through mixture modelling of antibody levels.

As countries decide on vaccination strategies and how to ease movement restrictions, estimating the proportion of the population previously infected with SARS-CoV-2 is important for predicting the future burden of COVID-19. This proportion is usually estimated from serosurvey data in two steps: first the proportion above a threshold antibody level is calculated, then the crude estimate is adjusted using external estimates of sensitivity and specificity. A drawback of this approach is that the PCR-confirmed cases used to estimate the sensitivity of the threshold may not be representative of cases in the wider population-e.g., they may be more recently infected and more severely symptomatic. Mixture modelling offers an alternative approach that does not require external data from PCR-confirmed cases. Here we illustrate the bias in the standard threshold-based approach by comparing both approaches using data from several Kenyan serosurveys. We show that the mixture model analysis produces estimates of previous infection that are often substantially higher than the standard threshold analysis.

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.

The need for COVID-19 research in low- and middle-income countries.

In the early months of the pandemic, most reported cases and deaths due to COVID-19 occurred in high-income countries. However, insufficient testing could have led to an underestimation of true infections in many low- and middle-income countries. As confirmed cases increase, the ultimate impact of the pandemic on individuals and communities in low- and middle-income countries is uncertain. We therefore propose research in three broad areas as urgently needed to inform responses in low- and middle-income countries: transmission patterns of SARS-CoV-2, the clinical characteristics of the disease, and the impact of pandemic prevention and response measures. Answering these questions will require a multidisciplinary approach led by local investigators and in some cases additional resources. Targeted research activities should be done to help mitigate the potential burden of COVID-19 in low- and middle-income countries without diverting the limited human resources, funding, or medical supplies from response activities.