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

Journal of clinical microbiology | 2022

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.

Pubmed ID: 35044204 RIS Download

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Associated grants

  • Agency: NIAID NIH HHS, United States
    Id: HHSN272201400007C
  • Agency: NHLBI NIH HHS, United States
    Id: K23 HL151826
  • Agency: NIAID NIH HHS, United States
    Id: R01 AI128779
  • Agency: NIAID NIH HHS, United States
    Id: HHSN272201400007C

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THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 1st, 2022. Software application for genetic analysis of classical biometric traits like blood pressure or height that are caused by a combination of polygenic inheritance and complex environmental forces. (entry from Genetic Analysis Software)

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