Differentiation of Individuals Previously Infected with and Vaccinated for SARS-CoV-2 in an Inner-City Emergency Department

在市中心急诊科区分既往感染过SARS-CoV-2病毒和接种过SARS-CoV-2疫苗的个体

阅读:1

Abstract

Emergency departments (EDs) can serve as surveillance sites for infectious diseases. The objective of this study was to determine the burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to monitor the prevalence of vaccination against coronavirus disease 2019 (COVID-19) among patients attending an urban ED in Baltimore City. Using 1,914 samples of known exposure status, we developed an algorithm to differentiate previously infected, vaccinated, and unexposed individuals using a combination of antibody assays. We applied this testing algorithm to 4,360 samples from ED patients obtained in the spring of 2020 and 2021. Using multinomial logistic regression, we determined factors associated with infection and vaccination. For the algorithm, sensitivity and specificity for identifying vaccinated individuals were 100% and 99%, respectively, and 84% and 100% for previously infected individuals. Among the ED subjects, seroprevalence to SARS-CoV-2 increased from 2% to 24% between April 2020 and March 2021. Vaccination prevalence rose to 11% by mid-March 2021. Marked differences in burden of disease and vaccination coverage were seen by sex, race, and ethnicity. Hispanic patients, though accounting for 7% of the study population, had the highest relative burden of disease (17% of total infections) but with similar vaccination rates. Women and white individuals were more likely to be vaccinated than men or Black individuals. Individuals previously infected with SARS-CoV-2 can often be differentiated from vaccinated individuals using a serologic testing algorithm. The utility of this algorithm can aid in monitoring SARS-CoV-2 exposure and vaccination uptake frequencies and can potentially reflect gender, race, and ethnic health disparities.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。