Anaesthesia practice in the first wave of the COVID-19 outbreak in the United States: a population-based cohort study.
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ABSTRACT:
SUBMITTER: Athanassoglou V
PROVIDER: S-EPMC9010244 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Given the narrowness of the initial testing criteria, the SARS-CoV-2 virus spread through cryptic transmission in January and February, setting the stage for the epidemic wave experienced in March and April, 2020. We use a global metapopulation epidemic model to provide a mechanistic understanding of the global dynamic underlying the establishment of the COVID-19 pandemic in Europe and the United States (US). The model is calibrated on international case introductions at the early stage of the pandemic. We find that widespread community transmission of SARS-CoV-2 was likely in several areas of Europe and the US by January 2020, and estimate that by early March, only 1 - 3 in 100 SARS-CoV-2 infections were detected by surveillance systems. Modeling results indicate international travel as the key driver of the introduction of SARS-CoV-2 with possible importation and transmission events as early as December, 2019. We characterize the resulting heterogeneous spatio-temporal spread of SARS-CoV-2 and the burden of the first COVID-19 wave (February-July 2020). We estimate infection attack rates ranging from 0.78%-15.2% in the US and 0.19%-13.2% in Europe. The spatial modeling of SARS-CoV-2 introductions and spreading provides insights into the design of innovative, model-driven surveillance systems and preparedness plans that have a broader initial capacity and indication for testing.
| S-EPMC8010777 | biostudies-literature
Project description:We examine how the coronavirus disease 2019 (COVID-19) pandemic has affected trade between Canada and the United States, using a novel dataset on monthly bilateral trade flows between Canadian provinces and US states merged with COVID-19 health data. Our results show that a one-standard-deviation increase in COVID-19 severity (case levels, hospitalizations, deaths) in a Canadian province leads to a 3.1 percent to 4.9 percent fall in exports and a 6.7 percent to 9.1 percent fall in imports. Decomposing our analysis by industry, we determine that trade in the manufacturing industry was most negatively affected by the pandemic, and the agriculture industry had the least disruption to trade flows. Our descriptive evidence suggests that lockdowns may also have reduced Canadian exports and imports. However, although our regression coefficients are consistent with that finding, they are not statistically significant, perhaps because of the lack of variation as a result of similar timing in the imposition of restrictions across provinces.
| S-EPMC9400821 | biostudies-literature
Project description:The non-medical policies implemented by many countries to "flatten the curve" during the COVID-19 outbreak has people stranded in their homes and some, out of their homes unable to return due to the disruptions in the mobility network. The availability of rich datasets (in our case, Facebook) has made it possible to study the mobility dynamics and spatial distribution of people during lockdown in Italy. Our interpretation is an effort to look deeper, describing the movements occurred during lockdown, including the territorial differences. We observe that, initially, tourists left the country and later Italians abroad managed to return, thereby, stabilising the population. With regards to internal mobility, the earliest affected regions see higher number of stationary users in the initial days of the outbreak while this is less significant for the central/southern regions until the decree for the official lockdown on the 9th of March 2020, due 2 days later. Just before lockdown, there was not a significant exodus of people from the North to the rest of the country, instead, relocation of people between cities and their urban belts, but not towards remote areas. This will be elaborated in conclusions shedding light on possible changes in future cities.
| S-EPMC7680615 | biostudies-literature
Project description:BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its associated disease coronavirus disease 2019 (COVID-19), is a worldwide emergency. Demographic, comorbidity and laboratory determinants of death and of ICU admission were explored in all Danish hospitalised patients.MethodsNational health registries were used to identify all hospitalized patients with a COVID-19 diagnosis. We obtained demographics, Charlson Comorbidity Index (CCI), and laboratory results on admission and explored prognostic factors for death using multivariate Cox proportional hazard regression and competing risk survival analysis.ResultsAmong 2431 hospitalised patients with COVID-19 between February 27 and July 8 (median age 69 years [IQR 53-80], 54.1% males), 359 (14.8%) needed admission to an intensive care unit (ICU) and 455 (18.7%) died within 30 days of follow-up. The seven-day cumulative incidence of ICU admission was lower for females (7.9%) than for males (16.7%), (p < 0.001). Age, high CCI, elevated C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase (LDH), urea, creatinine, lymphopenia, neutrophilia and thrombocytopenia within ±24-h of admission were independently associated with death within the first week in the multivariate analysis. Conditional upon surviving the first week, male sex, age, high CCI, elevated CRP, LDH, creatinine, urea and neutrophil count were independently associated with death within 30 days. Males presented with more pronounced laboratory abnormalities on admission.ConclusionsAdvanced age, male sex, comorbidity, higher levels of systemic inflammation and cell-turnover were independent factors for mortality. Age was the strongest predictor for death, moderate to high level of comorbidity were associated with a nearly two-fold increase in mortality. Mortality was significantly higher in males after surviving the first week.
| S-EPMC7794638 | biostudies-literature
Project description:ObjectiveWe estimated the number of hospital workers in the United States (US) that might be infected or die during the COVID-19 pandemic based on the data in the early phases of the pandemic.MethodsWe calculated infection and death rates amongst US hospital workers per 100 COVID-19-related deaths in the general population based on observed numbers in Hubei, China, and Italy. We used Monte Carlo simulations to compute point estimates with 95% confidence intervals for hospital worker (HW) infections in the US based on each of these two scenarios. We also assessed the impact of restricting hospital workers aged ≥ 60 years from performing patient care activities on these estimates.ResultsWe estimated that about 53,000 hospital workers in the US could get infected, and 1579 could die due to COVID19. The availability of PPE for high-risk workers alone could reduce this number to about 28,000 infections and 850 deaths. Restricting high-risk hospital workers such as those aged ≥ 60 years from direct patient care could reduce counts to 2,000 healthcare worker infections and 60 deaths.ConclusionWe estimate that US hospital workers will bear a significant burden of illness due to COVID-19. Making PPE available to all hospital workers and reducing the exposure of hospital workers above the age of 60 could mitigate these risks.
| S-EPMC7717542 | biostudies-literature
Project description:The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.
| S-EPMC8516194 | biostudies-literature
Project description:The ongoing COVID-19 pandemic is causing significant morbidity and mortality across the US. In this ecological study, we identified county-level variables associated with the COVID-19 case-fatality rate (CFR) using publicly available datasets and a negative binomial generalized linear model. Variables associated with decreased CFR included a greater number of hospitals per 10,000 people, banning religious gatherings, a higher percentage of people living in mobile homes, and a higher percentage of uninsured people. Variables associated with increased CFR included a higher percentage of the population over age 65, a higher percentage of Black or African Americans, a higher asthma prevalence, and a greater number of hospitals in a county. By identifying factors that are associated with COVID-19 CFR in US counties, we hope to help officials target public health interventions and healthcare resources to locations that are at increased risk of COVID-19 fatalities.
| S-EPMC7924276 | biostudies-literature
Project description:BackgroundEffective shielding measures and virus mutations have progressively modified the disease between the waves, likewise healthcare systems have adapted to the outbreak. Our aim was to compare clinical outcomes for older people with COVID-19 in Wave 1 (W1) and Wave 2 (W2).MethodsAll data, including the Clinical Frailty Scale (CFS), were collected for COVID-19 consecutive patients, aged ≥65, from 13 hospitals, in W1 (February-June 2020) and W2 (October 2020-March 2021). The primary outcome was mortality (time to mortality and 28-day mortality). Data were analysed with multilevel Cox proportional hazards, linear and logistic regression models, adjusted for wave baseline demographic and clinical characteristics.ResultsData from 611 people admitted in W2 were added to and compared with data collected during W1 (N = 1340). Patients admitted in W2 were of similar age, median (interquartile range), W2 = 79 (73-84); W1 = 80 (74-86); had a greater proportion of men (59.4% vs. 53.0%); had lower 28-day mortality (29.1% vs. 40.0%), compared to W1. For combined W1-W2 sample, W2 was independently associated with improved survival: time-to-mortality adjusted hazard ratio (aHR) = 0.78 [95% confidence interval (CI) 0.65-0.93], 28-day mortality adjusted odds ratio = 0.80 (95% CI 0.62-1.03). W2 was associated with increased length of hospital stay aHR = 0.69 (95% CI 0.59-0.81). Patients in W2 were less frail, CFS [adjusted mean difference (aMD) = -0.50, 95% CI -0.81, -0.18], as well as presented with lower C-reactive protein (aMD = -22.52, 95% CI -32.00, -13.04).ConclusionsCOVID-19 older adults in W2 were less likely to die than during W1. Patients presented to hospital during W2 were less frail and with lower disease severity and less likely to have renal decline.
| S-EPMC9452163 | biostudies-literature
Project description: Not available
| S-BSST1493 | biostudies-other
Project description:Full-genome-sequence computational analyses of the SARS-coronavirus (CoV)-2 genomes allow us to understand the evolutionary events and adaptability mechanisms. We used population genetics analyses on human SARS-CoV-2 genomes available on 2 April 2020 to infer the mutation rate and plausible recombination events between the Betacoronavirus genomes in nonhuman hosts that may have contributed to the evolution of SARS-CoV-2. Furthermore, we localized the targets of recent and strong, positive selection during the first pandemic wave. The genomic regions that appear to be under positive selection are largely co-localized with regions in which recombination from nonhuman hosts took place. Our results suggest that the pangolin coronavirus genome may have contributed to the SARS-CoV-2 genome by recombination with the bat coronavirus genome. However, we find evidence for additional recombination events that involve coronavirus genomes from other hosts, i.e., hedgehogs and sparrows. We further infer that recombination may have recently occurred within human hosts. Finally, we estimate the parameters of a demographic scenario involving an exponential growth of the size of the SARS-CoV-2 populations that have infected European, Asian, and Northern American cohorts, and we demonstrate that a rapid exponential growth in population size from the first wave can support the observed polymorphism patterns in SARS-CoV-2 genomes.
| S-EPMC7914631 | biostudies-literature