Cross-sectional study evaluating the seroprevalence of SARS-CoV-2 antibodies among healthcare workers and factors associated with exposure during the first wave of the COVID-19 pandemic in New York

  1. Alexander Bryan
  2. Kathleen Tatem
  3. Jillian Diuguid-Gerber
  4. Caroline Cooke
  5. Anya Romanoff
  6. Nandini Choudhury
  7. Michael Scanlon
  8. Preeti Kishore
  9. Elana Sydney
  10. Joseph Masci
  11. Parampreet Bakshi
  12. Sahithi Pemmasani
  13. Nichola J Davis
  14. Duncan Maru

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Abstract

Estimate the seroprevalence of SARS-CoV-2 antibodies among New York City Health and Hospitals (NYC H+H) healthcare workers during the first wave of the COVID-19 pandemic, and describe demographic and occupational factors associated with SARS-CoV-2 antibodies among healthcare workers.

Design

Descriptive, observational, cross-sectional study using a convenience sample of data from SARS-CoV-2 serological tests accompanied by a demographic and occupational survey administered to healthcare workers.

Setting

A large, urban public healthcare system in NYC.

Participants

Participants were employed by NYC H+H and either completed serological testing at NYC H+H between 30 April 2020 and 30 June 2020, or completed SARS-CoV-2 antibody testing outside of NYC H+H and were able to self-report results from the same time period.

Primary outcome measure

SARS-CoV-2 serostatus, stratified by key demographic and occupational characteristics reported through the demographic and occupational survey.

Results

Seven hundred and twenty-seven survey respondents were included in analysis. Participants had a mean age of 46 years (SD=12.19) and 543 (75%) were women. Two hundred and fourteen (29%) participants tested positive or reported testing positive for the presence of SARS-CoV-2 antibodies (IgG+). Characteristics associated with positive SARS-CoV-2 serostatus were Black race (25% IgG +vs 15% IgG−, p=0.001), having someone in the household with COVID-19 symptoms (49% IgG +vs 21% IgG−, p<0.001), or having a confirmed COVID-19 case in the household (25% IgG +vs 5% IgG−, p<0.001). Characteristics associated with negative SARS-CoV-2 serostatus included working on a COVID-19 patient floor (27% IgG +vs 36% IgG−, p=0.02), working in the intensive care unit (20% IgG +vs 28% IgG−, p=0.03), being employed in a clinical occupation (64% IgG +vs 78% IgG−, p<0.001) or having close contact with a patient with COVID-19 (51% IgG +vs 62% IgG−, p=0.03).

Conclusions

Results underscore the significance that community factors and inequities might have on SARS-CoV-2 exposure for healthcare workers.

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  1. Version published to 10.1136/bmjopen-2021-053158
  2. ScreenIT

    SciScore for 10.1101/2021.04.12.21255344: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Institutional Review Board of the Biomedical Research Alliance of New York To assess the risk of contracting Covid-19, we developed a modified survey based on the World Health Organization’s Protocol for assessment of potential risk factors for 2019-novel coronavirus (2019-nCoV) infection among health care workers in a health care setting.
    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: We detected the following sentences addressing limitations in the study:
    There were several limitations to our study. First, convenience sampling was used to enroll study participants, and while we recruited study participants from a voluntary and universal screening program, there was a potential for selection bias in terms of occupation type and level of exposure in survey respondents. In addition, there may have been socioeconomic factors related to participation that could be strongly tied to demographic and occupational characteristics. We attempted to partially mitigate these limitations by comparing aggregate participant demographics with all employees undergoing antibody testing, as well as with overall NYC H+H employee demographics. This revealed under-representation of certain groups in the study participants, most notably Black employees. Given that Black employees were also found to be more likely to have SARS-CoV-2 antibodies, this may have skewed overall positivity rates and excluded differing exposure factors. We were also unable to determine when employees with positive SARS-CoV-2 antibodies were infected. And with continuously changing guidelines around PPE during the initial surge, it is difficult to link exposure and infection with evolving PPE practices. Furthermore, employees who were sick or were suspected to have SARS-CoV-2 may have been less likely to get serological testing, and participants who previously tested PCR positive for SARS-CoV-2 may or may not have chosen serological testing. Our study did not account for HCW w...

    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.

  3. Version published to 10.1101/2021.04.12.21255344v1 on medRxiv