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evelAnalysis adjusted for confounders including county level age ≥ 65, comorbidities, and environmental factorsAnalysis limited to the US.

In patients with chronic liver diseases (CLD) with or without cirrhosis, existing data on the risk of adverse outcomes with SARS-CoV-2 infection have been mixed or have limited generalizability. We used the National COVID Cohort Collaborative (N3C) Data Enclave, a harmonized electronic health record (EHR) dataset of 5.9 million nationally-representative, diverse, and gender-balanced patients, to describe outcomes in patients with CLD and cirrhosis with SARS-CoV-2.

We identified all chronic liver diseases patients with and without cirrhosis who had SARS-CoV-2 testing documented in the N3C Data Enclave as of data release date 5/15/2021. The primary outcome was 30-day all-cause mortality. Survival analysis methods were used to estimate cumulative incidences of death, hospitalization, and mechanical ventilation, and to calculate the associations of SARS-CoV-2 infection, presence of cirrhosis, and demographic and clinical factors to 30-day mortality.

We isolated 217,143 patients with CLD 129,097 (59%) withou

In this study of nearly 220,000 CLD patients, we found SARS-CoV-2 infection in patients with cirrhosis was associated with 2.43-times mortality hazard, and the presence of cirrhosis among CLD patients infected with SARS-CoV-2 were associated with 3.39-times mortality hazard. Compared to previous studies, our use of a nationally-representative, diverse, and gender-balanced dataset enables wide generalizability of these findings.

In this study of nearly 220,000 CLD patients, we found SARS-CoV-2 infection in patients with cirrhosis was associated with 2.43-times mortality hazard, and the presence of cirrhosis among CLD patients infected with SARS-CoV-2 were associated with 3.39-times mortality hazard. Compared to previous studies, our use of a nationally-representative, diverse, and gender-balanced dataset enables wide generalizability of these findings.In 2020, SARS-CoV-2 spread across the United States (U.S.) in three phases distinguished by peaks in the numbers of infections and shifting geographical distribution. We investigated the viral genetic diversity in each phase using sequences publicly available prior to December 15 th , 2020, when vaccination was initiated in the U.S. In Phase 1 (winter/spring), sequences were already dominated by the D614G Spike mutation and by Phase 3 (fall), genetic diversity of the viral population had tripled and at least 54 new amino acid changes had emerged at frequencies above 5%, several of which were within known antibody epitopes. These findings highlight the need to track the evolution of SARS-CoV-2 variants in the U.S. to ensure continued efficacy of vaccines and antiviral treatments.

SARS-CoV-2 genetic diversity in the U.S. increased 3-fold in 2020 and 54 emergent nonsynonymous mutations were detected.

SARS-CoV-2 genetic diversity in the U.S. increased 3-fold in 2020 and 54 emergent nonsynonymous mutations were detected.High resolution mobility datasets have become increasingly available in the past few years and have enabled detailed models for infectious disease spread including those for COVID-19. SB239063 cell line However, there are open questions on how such a mobility data can be used effectively within epidemic models and for which tasks they are best suited. In this paper, we extract a number of graph-based proximity metrics from high resolution cellphone trace data from X-Mode and use it to study COVID-19 epidemic spread in 50 land grant university counties in the US. We present an approach to estimate the effect of mobility on cases by fitting an ODE based model and performing multivariate linear regression to explain the estimated time varying transmissibility. We find that, while mobility plays a significant role, the contribution is heterogeneous across the counties, as exemplified by a subsequent correlation analysis. We subsequently evaluate the metrics’ utility for case surge prediction defined as a supervised classification problem, and show that the learnt model can predict surges with 95% accuracy and 87% F1-score.

Pregnant women with COVID-19 are at an increased risk of severe COVID-19 illness as well as adverse pregnancy and birth outcomes. Many countries are vaccinating or considering vaccinating pregnant women with limited available data about the safety of this strategy. Early identification of safety concerns of COVID-19 vaccines, including their components, or their technological platforms is therefore urgently needed.

We conducted a rapid systematic review, as the first phase of an ongoing full systematic review, to evaluate the safety of COVID-19 vaccines in pregnant women, including their components, and their technological platforms (whole virus, protein, viral vector or nucleic acid) used in other vaccines, following the Cochrane methods and the PRISMA statement for reporting (PROSPERO-CRD42021234185).We searched literature databases, COVID-19 and pregnancy registries from inception February 2021 without time or language restriction and explored the reference lists of relevant systematic reviews retrieveview by the COVAX MIWG or of their components or platforms when used in other vaccines. However, the need for further data on several vaccine platforms and components is warranted given their novelty. Our findings support current WHO guidelines recommending that pregnant women may consider receiving COVID-19 vaccines, particularly if they are at high risk of exposure or have comorbidities that enhance the risk of severe disease.

This rapid review found no evidence of pregnancy-associated safety concerns of COVID-19 vaccines that were selected for review by the COVAX MIWG or of their components or platforms when used in other vaccines. However, the need for further data on several vaccine platforms and components is warranted given their novelty. Our findings support current WHO guidelines recommending that pregnant women may consider receiving COVID-19 vaccines, particularly if they are at high risk of exposure or have comorbidities that enhance the risk of severe disease.