Preparing for a COVID-19 surge.
Ontology highlight
ABSTRACT:
SUBMITTER: Alkuzweny M
PROVIDER: S-EPMC7274639 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
Publications
Preparing for a COVID-19 surge.
Alkuzweny Manar M Raj Anita A Mehta Sanjay S
EClinicalMedicine 20200605
Similar Datasets
Project description: Not available
| S-EPMC8432891 | biostudies-literature
Project description: Not available
| S-EPMC7393783 | biostudies-literature
Project description:Background and purposeWe aimed to investigate the acute stroke presentations during the coronavirus disease 2019 (COVID-19) pandemic.MethodsThe data were obtained from a health system with 19 emergency departments in northeast Ohio in the United States. Baseline period from January 1 to March 8, 2020, was compared with the COVID period from March 9, to April 2, 2020. The variables included were total daily stroke alerts across the hospital emergency departments, thrombolysis, time to presentation, stroke severity, time from door-to-imaging, time from door-to-needle in thrombolysis, and time from door-to-puncture in thrombectomy. The 2 time periods were compared using nonparametric statistics and Poisson regression.ResultsNine hundred two stroke alerts during the period across the emergency departments were analyzed. Total daily stroke alerts decreased from median, 10 (interquartile range, 8-13) during baseline period to median, 8 (interquartile range, 4-10, P=0.001) during COVID period. Time to presentation, stroke severity, and time to treatment were unchanged. COVID period was associated with decrease in stroke alerts with rate ratio of 0.70 (95% CI, 0.60-0.28). Thrombolysis also decreased with rate ratio, 0.52 (95% CI, 0.28-0.97) but thrombectomy remained unchanged rate ratio, 0.93 (95% CI, 0.52-1.62) Conclusions: We observed a significant decrease in acute stroke presentations by ≈30% across emergency departments at the time of surge of COVID-19 cases. This observation could be attributed to true decline in stroke incidence or patients not seeking medical attention for emergencies during the pandemic.
| S-EPMC7309646 | biostudies-literature
Project description:The novel Coronavirus (COVID-19) pandemic has placed an immense strain on health care systems and orthopedic surgeons across the world. To limit the spread, federal and state governments mandated the cancellation of all nonurgent surgical cases to address surging hospital admissions and manage workforce and resource reallocation. During the pandemic surge, thousands of surgical cancellations have been required. We outline our experience through the onset and advance of the surge, detail our incident response and discuss the transition toward recovery. Level of Evidence: Level V.
| S-EPMC7245196 | biostudies-literature
Project description: Not available
| S-EPMC7401308 | biostudies-literature
Project description:Given the rapidly changing nature of COVID-19, clinicians and policy makers require urgent review and summary of the literature, and synthesis of evidence-based guidelines to inform practice. The WHO advocates for rapid reviews in these circumstances. The purpose of this rapid guideline is to provide recommendations on the organizational management of intensive care units caring for patients with COVID-19 including: planning a crisis surge response; crisis surge response strategies; triage, supporting families, and staff.
| S-EPMC7276667 | biostudies-literature
Project description:Rift Valley fever, endemic or emerging throughout most of Africa, causes considerable risk to human and animal health. We report 7 confirmed Rift Valley fever cases, 1 fatal, in Kiruhura District, Uganda, during 2021. Our findings highlight the importance of continued viral hemorrhagic fever surveillance, despite challenges associated with the COVID-19 pandemic.
| S-EPMC9622231 | biostudies-literature
Project description: Not available
| S-BSST563 | biostudies-other
Project description:BackgroundThe oral protease inhibitor nirmatrelvir has shown substantial efficacy in high-risk, unvaccinated patients infected with the B.1.617.2 (delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Data regarding the effectiveness of nirmatrelvir in preventing severe coronavirus disease 2019 (Covid-19) outcomes from the B.1.1.529 (omicron) variant are limited.MethodsWe obtained data for all members of Clalit Health Services who were 40 years of age or older at the start of the study period and were assessed as being eligible to receive nirmatrelvir therapy during the omicron surge. A Cox proportional-hazards regression model with time-dependent covariates was used to estimate the association of nirmatrelvir treatment with hospitalization and death due to Covid-19, with adjustment for sociodemographic factors, coexisting conditions, and previous SARS-CoV-2 immunity status.ResultsA total of 109,254 patients met the eligibility criteria, of whom 3902 (4%) received nirmatrelvir during the study period. Among patients 65 years of age or older, the rate of hospitalization due to Covid-19 was 14.7 cases per 100,000 person-days among treated patients as compared with 58.9 cases per 100,000 person-days among untreated patients (adjusted hazard ratio, 0.27; 95% confidence interval [CI], 0.15 to 0.49). The adjusted hazard ratio for death due to Covid-19 was 0.21 (95% CI, 0.05 to 0.82). Among patients 40 to 64 years of age, the rate of hospitalization due to Covid-19 was 15.2 cases per 100,000 person-days among treated patients and 15.8 cases per 100,000 person-days among untreated patients (adjusted hazard ratio, 0.74; 95% CI, 0.35 to 1.58). The adjusted hazard ratio for death due to Covid-19 was 1.32 (95% CI, 0.16 to 10.75).ConclusionsAmong patients 65 years of age or older, the rates of hospitalization and death due to Covid-19 were significantly lower among those who received nirmatrelvir than among those who did not. No evidence of benefit was found in younger adults.
| S-EPMC9454652 | biostudies-literature
Project description:COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.
| S-EPMC9762594 | biostudies-literature