Risk factors of SARS-CoV-2 infection in healthcare workers: a retrospective study of a nosocomial outbreak

  1. Xuan Wang
  2. Xiaobing Jiang
  3. Qimin Huang
  4. Han Wang
  5. David Gurarie
  6. Martial Ndeffo-Mbah
  7. Fei Fan
  8. Peng Fu
  9. Mary Ann Horn
  10. Anirban Mondal
  11. Charles King
  12. Shuai Xu
  13. Hongyang Zhao
  14. Yansen Bai

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Abstract

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  1. Version published to 10.1016/j.sleepx.2020.100028
  2. ScreenIT

    SciScore for 10.1101/2020.03.29.20047159: (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 ethics board of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (No. 20200029).
    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 are limitations and future studies. First, there was the possibility of unmeasured residual confounding effects of contact status with infected cases, although we had adjusted for some primary confounders. Second, the insufficient sample size may influence the statistical power. Further large prospective studies are needed to validate our findings. Third, we ignored dynamic interactions between patients and HCWs in our IBM, but simply considered infectious patients as external sources. A more comprehensive model including patients, visitors, nurses, doctors, staff, and even family members should be studied in the future since the hospital is not a closed system. Fourth, our model has many uncertain parameters (susceptibility, infectivity levels, disease progress, and detailed contact patterns) that could be adjusted to available data for any given local community and can be further used to explore the efficacy of different control strategies for specific local communities and single entities (e.g., schools, working places, hospitals).

    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 10.1101/2020.03.29.20047159v1 on medRxiv