Seroprevalence, correlates and kinetics of SARS-CoV-2 Nucleocapsid IgG antibody in healthcare workers at a tertiary hospital: A prevaccine census study

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Abstract

Background

Healthcare workers are perceived to be a high-risk group for acquiring SAR-CoV-2 infection, and more so in countries where COVID-19 vaccination uptake is low. Serosurveillance may best determine the true extent of SARS-CoV-2 infection since most infected HCWs may be asymptomatic or present with only mild symptoms. Over time, determining the true extent of SARS-CoV-2 infection could inform hospital management and staff whether the preventive measures instituted are effective and valuable in developing targeted solutions.

Methods

This was a census survey study conducted at the Aga Khan University Hospital, Nairobi, between November 2020 and February 2021 before the implementation of the COVID-19 vaccination. The SARS-CoV-2 nucleocapsid IgG test was performed using a chemiluminescent assay.

Results

One thousand six hundred thirty-one (1631) staff enrolled, totalling 60% of the workforce. The overall crude seroprevalence was 18.4% and the adjusted value (for assay sensitivity of 86%) was 21.4% (95% CI; 19.2-23.7). The HCW groups with higher prevalence included pharmacy (25.6%), outreach (24%), hospital-based nursing (22.2%) and catering staff (22.6%). Independent predictors of a positive IgG result included prior COVID-19 like symptoms, odds ratio (OR) 1.9 [95% confidence interval (CI) 1.3-2.9, p=0.002], and a prior positive SARS-CoV-2 PCR result OR 11.0 (CI: 7.2-18.0, p<0.001). Age, sex, comorbidities or working in a COVID-19 designated area were not associated with seropositivity. The odds of testing positive for IgG after a positive PCR test were lowest if the antibody test was performed more than 2 months later; OR 0.7 (CI: 0.48-0.95, p= 0.025).

Conclusions

The prevalence of anti-SARS-CoV-2 nucleocapsid IgG among HCWs was lower than in the general population. Staff working in clinical areas were not at increased risk when compared to staff working in non-clinical areas.

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  1. SciScore for 10.1101/2022.04.13.22273817: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The protocol was approved by the Ethics Review Committee of Aga Khan University Nairobi (Ref: 2020/IERC-129 v4).
    Consent: Written informed consent was obtained from all participants.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Laboratory testing: The antibody test was performed using a chemiluminescent assay (Abbott, USA), according to the manufacturer’s instructions and standard operating procedures.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Data collection and Analysis: Stata 16 (StataCorp, Texas, USA) was used to perform the statistical analysis.
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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.

    Results from scite Reference Check: We found no unreliable references.


    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.