Is Covid-19 seroprevalence different in health care workers as per their risk of exposure? A study from a tertiary care hospital in National Capital Region of India

  1. Sushila Kataria
  2. Rashmi Phogat
  3. Pooja Sharma
  4. Vikas Deswal
  5. Sazid Alam
  6. Manish Singh
  7. Kuldeep Kumar
  8. Vaibhav Gupta
  9. Padam Singh
  10. Rohit Dutt
  11. Smita Sarma
  12. Renu Saxena
  13. Naresh Trehan

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Abstract

Background

SARS-CoV-2 infection has severely ravaged health systems, economic and social progress globally in 2020. Seroprevalence studies can provide relevant information on the target populations for vaccination. They are relevant not only in the community, but also for critical population subgroups such as nursing homes or health care facilities. They will assist in strategizing the vaccination policy especially since there is limited availability of the vaccine and vaccine hesitancy

Objective

To evaluate the seroprevalence in Health Care Workers (HCW) at our hospital and to identify parameters which may affect it.

Methodology

The Baseline profiling and seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was assessed among 3258 healthcare workers (HCWs) of Medanta-The Medicity, Gurugram, Haryana, India, as a part of an ongoing cohort study.The fully automated LIAISON® SARS-CoV-2 S1/S2 IgG test using the chemiluminescence immunoassay (CLIA) for the quantitative determination of anti-S1 and anti-S2 specific IgG antibodies to SARS-CoV-2 was used to test serum samples collected before the receipt of the vaccine. Seroprevalence was evaluated as per gender, age, association with previous Covid-19 diagnosis, use of supplements, and role in the hospital and type of exposure.

Results

Of the 3258 participants tested for IgG serology (S1 and S2 proteins) 46.2% (CI 44.4 – 47.9%) were positive (i.e. had an antibody titre more than 15 Au/ml). Higher seroprevalence was seen in the ‘others’ ie non clinical health care workers (including management, research personnel, pharmacists, technicians, general duty staff, housekeeping, security, food and beverage, and facility maintenance teams) (50.2 Au/ml) than that in clinical HCW (ie doctors and nurses)where it was significantly lower (41.4 Au/ml, p= 0.0001). Also, people with history of Covid-19 were found to have significantly higher antibody levels (p = 0.0001). Amongst the healthcare workers, doctors and nurses had higher relative risk of acquiring Covid-19 infection (RR = 1.21; 95% C.I.: 1.12 - 1.31).

Conclusion

Seroprevalence in healthcare workers at our hospital is high at 46.2%. It is higher in non-clinical HCW than in clinical HCW. The risk of acquiring Covid-19 infection was higher in clinical HCW and thus, this subgroup may benefit most from vaccination. History of Covid-19 may provide double the protection, in particular in those who had it recently.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.10.21251543: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the Institutional Ethics Committee and was conducted in a fifteen hundred bedded tertiary care hospital in the National Capital Region of Delhi, which has treated over ten thousand hospitalized Covid-19 patients.
    Consent: The informed consent process followed by a baseline questionnaire was completed by a doctor.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: Test results >=15.0 AU/mL were graded positive. 2.2. Quality Assurance: The fully automated LIAISON® has been calibrated and validated according to the laboratory SOP, ie 3 samples (high, medium and low value) were run 5 times a day for 5 consecutive days.

    Table 2: Resources

    Antibodies
    SentencesResources
    The samples were stored at centrifuged at 3000 to 3500 RPM for 10 minutes and the separated serum was kept at 2°-8°C until further testing within a week. 2.1. Laboratory Methods: The serum was tested for the quantitative determination of anti-S1 and anti-S2 specific IgG antibodies to SARS-CoV-2 in the fully automated LIAISON® SARS-CoV-2 S1/S2 IgG by Chemiluminescence immunoassay (CLIA) technology.
    anti-S1
    suggested: None
    anti-S2 specific IgG
    suggested: None
    calculated SARS-CoV-2 S1/S2 IgG antibody concentrations expressed as arbitrary units (AU/mL) and graded the results.
    S1/S2 IgG
    suggested: None
    Software and Algorithms
    SentencesResources
    All analysis was done using SPSS software, version 24.0.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.
    • Thank you for including a protocol registration statement.

    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.

  2. Version published to 10.1101/2021.02.10.21251543 on medRxiv