Anti-SARS-CoV-2 IgM and IgG antibodies in health workers in Sergipe, Brazil

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Abstract

Background

The exponential growth of COVID-19 cases in Brazil is overloading health systems with overcrowding of hospitals and overflowing intensive care units. Increasing infection rates in health professionals can lead to the collapse of the health system and further worsen the pandemic. The aim of this study was to evaluate the seroprevalence of IgM and IgG for SARS-CoV-2 in health workers in Sergipe, Brazil.

Methods

The targeted tests involved health professionals working on the front line to combat COVID-19. The samples were collected in the month of June, in six hospital units in the state of Sergipe.

Results

471 health professionals were tested. Of these, 28 workers (5.95%) tested positive for IgM and 64 (13.59%) tested positive for IgG. 9 workers (1.91%) tested positive for IgM and were also positive for IgG.

Discussion

Health workers must be monitored constantly, because if they are infected, they can spread the virus to colleagues, hospitalized patients and even family members.

Conclusion

Knowing the prevalence of antibodies to the virus in health workers is an important measure of viral spread control.

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  1. SciScore for 10.1101/2020.09.24.20200873: (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

    Antibodies
    SentencesResources
    The sample chosen for testing for IgG and IgM antibodies to SARS-CoV-2 was human serum.
    IgM
    suggested: None
    Anti-SARS-CoV-2 IgM and IgG antibodies were detected in sera using an in vitro diagnostic test system based on lateral flow sandwich detection immunofluorescence technology (Ichroma2™ COVID-19 Ab in conjunction with an Ichroma™ II Reader, Boditech Med Inc., South Korea) according to the manufacturer’s instructions.
    Anti-SARS-CoV-2 IgM
    suggested: None
    IgG antibodies
    suggested: None

    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.