Seroprevalence of coronavirus disease 2019 (COVID-19) among health care workers from three pandemic hospitals of Turkey

  1. Gizem Alkurt
  2. Ahmet Murt
  3. Zeki Aydin
  4. Ozge Tatli
  5. Nihat Bugra Agaoglu
  6. Arzu Irvem
  7. Mehtap Aydin
  8. Ridvan Karaali
  9. Mustafa Gunes
  10. Batuhan Yesilyurt
  11. Hasan Turkez
  12. Adil Mardinoglu
  13. Mehmet Doganay
  14. Filiz Basinoglu
  15. Nurhan Seyahi
  16. Gizem Dinler Doganay
  17. Hamdi Levent Doganay

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Abstract

COVID-19 is a global threat with an increasing number of infections. Research on IgG seroprevalence among health care workers (HCWs) is needed to re-evaluate health policies. This study was performed in three pandemic hospitals in Istanbul and Kocaeli. Different clusters of HCWs were screened for SARS-CoV-2 infection. Seropositivity rate among participants was evaluated by chemiluminescent microparticle immunoassay. We recruited 813 non-infected and 119 PCR-confirmed infected HCWs. Of the previously undiagnosed HCWs, 22 (2.7%) were seropositive. Seropositivity rates were highest for cleaning staff (6%), physicians (4%), nurses (2.2%) and radiology technicians (1%). Non-pandemic clinic (6.4%) and ICU (4.3%) had the highest prevalence. HCWs in “high risk” group had similar seropositivity rate with “no risk” group (2.9 vs 3.5 p = 0.7). These findings might lead to the re-evaluation of infection control and transmission dynamics in hospitals.

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  1. Version published to 10.1371/journal.pone.0247865
  2. ScreenIT

    SciScore for 10.1101/2020.08.19.20178095: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: HCWs not willing to give consent, HCWs with a history of COVID-19 diagnosis without a confirmatory PCR test and those diagnosed within the last 14 days were excluded.
    IRB: This study was approved by the ethics committee of the Umraniye Teaching and Research Hospital (approval number: 29.05.2020/10337).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For detection of SARS-CoV-2 IgG, chemiluminescent microparticle immunoassay was carried out according to manufacturer’s instructions and samples were run on the related instrument (ARCHITECT, Abbott Laboratories, Abbott Park, IL, USA).
    Abbott Laboratories
    suggested: None
    Statistical Analysis: All statistical analyses were performed by SPSS version 22 software (Chicago, IL).
    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: We detected the following sentences addressing limitations in the study:
    Limitations: Even though a significant number of employees were tested, not all of the invited HCWs in these hospitals participated in the study. Screening larger cohorts from hospitals could serve more information to monitor the course of pandemic among HCWs.

    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 10.1101/2020.08.19.20178095v1 on medRxiv