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.

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.

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.

The impact of COVID-19 pandemic on access to new patient cancer care as evaluated in the Integra Connect Database (ICD)

Background: The Covid-19 pandemic has affected all aspects of life. Integra Connect sought to assess its impact on new visits for cancer care by querying its Electronic Medical Record and Claims database as a surrogate for understanding Covid's impact on cancer care in the larger United States population. Methods: Using Real World Data (RWD) from over 1200 oncology providers in 14 large practice settings, comprising 250 plus care sites in the ICD, we measured new patient (Pt) and established Pt visits from 2018 through 2020. Centers for Medicare & Medicaid Services (CMS) codes for new and established Pt's were applied to define cohorts. Disease states were defined using CMS Oncology Care Model (OCM) mapping to diagnosis codes. Because the ICD is heavily based in the Eastern US, we conducted a geographic analysis by segmenting providers by Northeast (NE) with 506 providers from NY, NJ, PA, and VA and Southeast (SE) with 578 providers from FL, GA, SC, and AL. We looked at visits across all tumor types and identified breast cancer (BC) and colorectal cancer (CC) as likely to be most affected by decreased screening, and multiple myeloma (MM) and advanced prostate cancer (APC) as likely to be vulnerable to delay in initiation of first treatment since treatment often trails diagnosis. Results: We found a decline in new patient volume (NPV) in '20 of -1% compared to '19;this compared to an anticipated increase of +7% based on growth in NPV in the ICD from '18 to '19. In the NE we saw NPV decrease in '20 by -7% vs. '19 but increase by +6% in the SE compared to '19. In comparing NPV '20 to '19 and '19 to '18, we saw a smaller increase for BC of +4% vs. +6% and for CC of +5% vs. +7%, respectively. Whereas in MM it was -7% vs. +3% and APC -6% vs. +8%. (See Table). Conclusions: Covid-19 has negatively impacted cancer care access. This RWD shows the number of both newly diagnosed Pts and those with initial oncologic intervention in established Pts, where symptoms traditionally have determined initiation of treatment, has declined year-overyear. The American Cancer Society projected new Pt cases to increase +2% in '20 to 1.806 million (ACS, Cancer Facts and Figures 2020), whereas in the ICD, that figure was projected +7% but resulted in -1%. This suggests a major expected reduction of new Pt cases in the US at large. The drop in cases during Covid-19 in our data was greater in the NE compared to the SE. In addition, the drop in the NE in our data was earlier and more prolonged than SE. While recovery occurred in both regions, after an initial overshoot following lockdowns, volumes plateaued at levels lower than pre-pandemic.