SARS-CoV-2 Antibody Formation Among Healthcare Workers, September 2, 2020

  1. Thomas Birch
  2. Ravit Barkama
  3. Joanna Tyszkiewicz Georgescu
  4. Emma Yamada
  5. Drew Olsen
  6. Ed Torres
  7. Alison Sinclair

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Many frontline healthcare workers throughout the world have been exposed to COVID-19 infection in the workplace and the community. We describe the nature of infection and the durability of antibodies among various types of healthcare workers at an acute care community hospital in northern New Jersey adjacent to New York City, part of the epicenter of the first wave of the US epidemic. Exposure was concentrated among frontline workers and in clusters among support staff. The antibody response correlated with symptoms and job type.

Methods

Employees had Polymerase Chain Reaction testing using a variety of commercially available products based on availability. All screening and testing for diagnosis used the nasopharyngeal swab. Tests for SARS-CoV-2 were obtained at approximately 2, 3 and 4 months using Abbott IgG antibody assay. Results of community sero-prevalence were obtained from hospital and physician surveys as no government entity was monitoring sero-prevalence. Hospital associated employee COVID-19 infections were identified through testing and contact tracing.

Results

5179 patients with COVID-19 syndrome were managed through telehealth over 6 months. 3236 patients were admitted with COVID-19 disease during this time. 2514 out of 3100 employees were tested for antibody to SARS-CoV-2. Overall, 16% of employees tested positive for antibodies at some point. When divided by general job type, 17% of direct caregivers were positive. 16% of indirect contact such as receptionists, housekeeping and food service workers were positive. 16% of nonclinical workers with no patient contact tested positive. The community rate of sero-positivity in one study in Bergen County was 12.2. (1) In the midst of the epidemic, the rate among hospital workers in New York City was 13.7%. (2) The overall population rate in New York City, at the peak of the epidemic, was 21% with some communities as high as 68%. Long Island had 16.7% and Westchester/Rockland 11.7%. (3) Other hospital systems in Northern NJ had up to 25% sero-positive employees. (4)

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.09.10.20192104: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.09.10.20192104 on medRxiv