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      COVID-19 antibody seroprevalence in Santa Clara County, California

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          Abstract

          Background

          Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

          Methods

          On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

          Results

          The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.

          Conclusion

          The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

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          Most cited references 16

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          Covid-19 in Critically Ill Patients in the Seattle Region — Case Series

          Abstract Background Community transmission of coronavirus 2019 (Covid-19) was detected in the state of Washington in February 2020. Methods We identified patients from nine Seattle-area hospitals who were admitted to the intensive care unit (ICU) with confirmed infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Clinical data were obtained through review of medical records. The data reported here are those available through March 23, 2020. Each patient had at least 14 days of follow-up. Results We identified 24 patients with confirmed Covid-19. The mean (±SD) age of the patients was 64±18 years, 63% were men, and symptoms began 7±4 days before admission. The most common symptoms were cough and shortness of breath; 50% of patients had fever on admission, and 58% had diabetes mellitus. All the patients were admitted for hypoxemic respiratory failure; 75% (18 patients) needed mechanical ventilation. Most of the patients (17) also had hypotension and needed vasopressors. No patient tested positive for influenza A, influenza B, or other respiratory viruses. Half the patients (12) died between ICU day 1 and day 18, including 4 patients who had a do-not-resuscitate order on admission. Of the 12 surviving patients, 5 were discharged home, 4 were discharged from the ICU but remained in the hospital, and 3 continued to receive mechanical ventilation in the ICU. Conclusions During the first 3 weeks of the Covid-19 outbreak in the Seattle area, the most common reasons for admission to the ICU were hypoxemic respiratory failure leading to mechanical ventilation, hypotension requiring vasopressor treatment, or both. Mortality among these critically ill patients was high. (Funded by the National Institutes of Health.)
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            Bootstrap Methods: Another Look at the Jackknife

             B Efron (1979)
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              Clinical course and mortality risk of severe COVID-19

              Several published reports of early clinical descriptions of coronavirus disease 2019 (COVID-19) have emerged from Hubei province in China, and many more will come. These early reports, typically simple descriptive case series of patients hospitalised with COVID-19 (mostly with pneumonia), provide valuable information on the more severe end of the disease spectrum. We tend to hear more about the most severe cases in the early stages of a new disease, as these are the ones first brought to the public's attention and are associated with deaths. However, it is important to bear in mind that the current best estimate is that about 81% of people with COVID-19 have mild disease 1 and never require hospitalisation. These cases have not yet featured much in published clinical descriptions. In The Lancet, Fei Zhou and colleagues 2 provide further insight into the clinical course and mortality risk for adults with COVID-19 severe enough to require hospitalisation. They report findings from 191 patients with COVID-19 from Wuhan during the first month of the outbreak, and follow them through to discharge (n=137) or death (n=54). The follow-up until discharge or death is a point of difference from other case series to date. Their cohort had many characteristics in common with other reports3, 4, 5—a median age of 56·0 years (IQR 46·0–67·0), a high percentage (62%) of men, and nearly half (48%) of patients with comorbidities. In-hospital death was associated with, on admission, older age (odds ratio 1·10, 95% CI 1·03–1·17; p=0·0043), a higher Sequential Organ Failure Assessment score (5·65, 2·61–12·23; p<0·0001), and blood d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033), findings known to be associated with severe pneumonia.6, 7 The study also presents early data on changes in clinical and laboratory findings over time, which could help clinicians to identify patients who progress to more severe disease. In-hospital mortality was high (28%), much higher than in other reports that had incomplete follow-up data,3, 5, 8 and was very high among the 32 patients requiring invasive mechanical ventilation, of whom 31 (97%) died. This might reflect a higher proportion of patients admitted with severe disease in the early stages of the outbreak. In another report from Wuhan, mortality was 62% among critically ill patients with COVID-19 and 81% among those requiring mechanical ventilation. 9 While the world awaits further information from other locations, including from outside China, the current message is that mortality is high among the minority of people with COVID-19 who get severe disease. The cohort design of this study provides excellent front-line information about mortality risk. It is essential for readers to understand that this truly is a retrospective cohort design, even if it might appear otherwise at first. Careful consideration of the design is essential to understanding the findings. The authors were able to collect a wealth of information from admission to discharge on many of the earliest known cases of coronavirus in the world. By identifying this large group of patients united by their disease and tracking them to these endpoints, the authors have provided us with insight into risk factors for in-hospital death. Even though their cohort does not include the censored observations of patients admitted during the study timeframe but not discharged by the end timepoint, these results can still be considerably useful for epidemiological description of the disease in terms of person-level risk. By excluding incomplete observations, it is possible that the reported mortality rate is biased to appear larger than it is, as data from those patients who were not discharged by the end timepoint were not included. However, as a true population at risk of mortality, these patients are representative of the earliest onset of COVID-19. Excluding patients who began treatment well into the epidemic brings homogeneity to the exposure level and treatment. These preliminary data provide an important framework to build on as the world moves forward in the fight against this pandemic. The timeliness and value of this information far outweigh the slight bias stemming from the exclusion of patients with incomplete data at the end of the study period. © 2020 STR/Getty Images 2020 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The report by Zhou and colleagues also provides data on viral shedding. 2 Throat swabs were obtained every other day and were PCR positive for a median of 20·0 days (IQR 16·0–23·0) after onset of symptoms. In survivors, median duration of viral shedding was 20·0 days (17·0–24·0), ranging from 8 to 37 days, but the virus was detectable until death in non-survivors. These early findings are similar to those reported for the severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronaviruses,10, 11, 12 and we await more detailed information on viral load kinetics and shedding of SARS coronavirus 2 in various disease states. Importantly, PCR positivity does not necessarily indicate viable virus, and additional data are needed to better understand the infectious period of COVID-19 and implications for treatment and infection control. Although there is always the limitation of generalisability in epidemic investigations, this study adds to a rapidly growing knowledge base on the clinical course and mortality risk of COVID-19. We now have a better understanding of the severity of hospitalised COVID-19, but more data are needed on treatment options that improve survival.
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                Author and article information

                Contributors
                Journal
                Int J Epidemiol
                Int J Epidemiol
                ije
                International Journal of Epidemiology
                Oxford University Press
                0300-5771
                1464-3685
                22 February 2021
                Affiliations
                Department of Medicine, Stanford University School of Medicine , Stanford, CA, USA
                Stanford University School of Medicine , Stanford, CA, USA
                Sol Price School of Public Policy, University of Southern California , Los Angeles, CA, USA
                Stanford University School of Medicine , Stanford, CA, USA
                Stanford University School of Medicine , Stanford, CA, USA
                Stanford University School of Medicine , Stanford, CA, USA
                Stanford University School of Medicine , Stanford, CA, USA
                Health Education is Power, Inc ., Palo Alto, CA, USA
                The Compliance Resource Group, Inc ., Oklahoma City, OK, USA
                Bogan Associates, LLC , Palo Alto, CA, USA
                ARL BioPharma, Inc ., Oklahoma City, OK, USA
                Sports Medicine Research and Testing Laboratory , Salt Lake City, UT, USA
                Department of Epidemiology and Population Health, Stanford University School of Medicine , Stanford, CA, USA
                Department of Medicine, Stanford University School of Medicine , Stanford, CA, USA
                Department of Epidemiology and Population Health, Stanford University School of Medicine , Stanford, CA, USA
                Department of Medicine, Stanford University School of Medicine , Stanford, CA, USA
                Author notes
                Corresponding author. Department of Medicine, Stanford University, Stanford, CA 94305, USA. E-mail: ebd@ 123456stanford.edu
                Article
                dyab010
                10.1093/ije/dyab010
                7928865
                33615345
                © The Author(s) 2021. Published by Oxford University Press on behalf of the International Epidemiological Association.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                Page count
                Pages: 10
                Product
                Funding
                Funded by: Stanford COVID-19 Seroprevalence Studies Fund;
                Categories
                Original Article
                AcademicSubjects/MED00860
                Custom metadata
                PAP

                Public health

                seroprevalence, infection fatality rate, covid-19

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