Basin‐scale estimates of greenhouse gas emissions from the Mara River, Kenya: Importance of discharge, stream size, and land use/land cover

Greenhouse gas fluxes (CO$_2$, CH$_4$, and N$_2$O) from African streams and rivers are under-represented in global datasets, resulting in uncertainties in their contributions to regional and global budgets. We conducted year-long sampling of 59 sites in a nested-catchment design in the Mara River, Kenya in which fluxes were quantified and their underlying controls assessed. We estimated annual basin-scale greenhouse gas emissions from measured in-stream gas concentrations, modeled gas transfer velocities, and determined the sensitivity of up-scaling to discharge. Based on the total annual CO$_2$-equivalent emissions calculated from global warming potentials (GWP), the Mara basin was a net greenhouse gas source (294 ± 35 Gg CO$_2$ eq yr$^{-1}$). Lower-order streams (1–3) contributed 81% of the total fluxes, and higher stream orders (4–8) contributed 19%. Cropland-draining streams also exhibited higher fluxes compared to forested streams. Seasonality in stream discharge affected stream widths (and stream area) and gas exchange rates, strongly influencing the basin-wide annual flux, which was 10 times higher during the high and medium discharge periods than the low discharge period. The basin-wide estimate was underestimated by up to 36% if discharge was ignored, and up to 37% for lower stream orders. Future research should therefore include seasonality in stream surface areas in upscaling procedures to better constrain basin-wide fluxes. Given that agricultural activities are a major factor increasing riverine greenhouse gas fluxes in the study region, increased conversion of forests and agricultural intensification has the possibility of increasing the contribution of the African continent to global greenhouse gas sources

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

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

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

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Basin‐scale estimates of greenhouse gas emissions from the Mara River, Kenya: Importance of discharge, stream size, and land use/land cover

Greenhouse gas fluxes (CO2, CH4, and N2O) from African streams and rivers are under‐represented in global datasets, resulting in uncertainties in their contributions to regional and global budgets. We conducted year‐long sampling of 59 sites in a nested‐catchment design in the Mara River, Kenya in which fluxes were quantified and their underlying controls assessed. We estimated annual basin‐scale greenhouse gas emissions from measured in‐stream gas concentrations, modeled gas transfer velocities, and determined the sensitivity of up‐scaling to discharge. Based on the total annual CO2‐equivalent emissions calculated from global warming potentials (GWP), the Mara basin was a net greenhouse gas source (294 ± 35 Gg CO2 eq yr−1). Lower‐order streams (1–3) contributed 81% of the total fluxes, and higher stream orders (4–8) contributed 19%. Cropland‐draining streams also exhibited higher fluxes compared to forested streams. Seasonality in stream discharge affected stream widths (and stream area) and gas exchange rates, strongly influencing the basin‐wide annual flux, which was 10 times higher during the high and medium discharge periods than the low discharge period. The basin‐wide estimate was underestimated by up to 36% if discharge was ignored, and up to 37% for lower stream orders. Future research should therefore include seasonality in stream surface areas in upscaling procedures to better constrain basin‐wide fluxes. Given that agricultural activities are a major factor increasing riverine greenhouse gas fluxes in the study region, increased conversion of forests and agricultural intensification has the possibility of increasing the contribution of the African continent to global greenhouse gas sources.Deutscher Akademischer Austauschdienst http://dx.doi.org/10.13039/501100001655IHE Delft Institute for Water EducationFederal Ministry of Education and Research http://dx.doi.org/10.13039/501100002347Helmholtz Association http://dx.doi.org/10.13039/501100009318TERENO Bavarian Alps/ Pre‐Alps Observator

Replication Data for: Prevalence of SARS-CoV-2 Antibodies from a one-year National Serosurveillance of Kenyan Blood Transfusion Donors

These data contain anonymised residual donor serum samples used for screening of transfusion transmissible infections collected at the KNBTS regional centres which has been used to compute standardised SARS-CoV-2 seroprevalence by age, sex and region using KNBS population as the standard population

Replication Data for: Temporal trends of SARS-CoV-2 seroprevalence in transfusion blood donors during the first wave of the COVID-19 epidemic in Kenya

This is a replication dataset for the manuscript titled: "Temporal trends of SARS-CoV-2 seroprevalence in transfusion blood donors during the first wave of the COVID-19 epidemic in Kenya". These data contain anonymised residual donor serum samples used for screening of transfusion transmissible infections were collected at the KNBTS regional centres in 4 sites (Mombasa, Nairobi, Eldoret and Kisumu) which has been used to compute standardised prevalence by age, sex and region using KNBS population as the standard population