Mortality trends and length of stays among hospitalized patients with COVID-19 in Ontario and Québec (Canada): a population-based cohort study of the first three epidemic waves

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Abstract

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  1. SciScore for 10.1101/2021.12.07.21267416: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethics approval: Ethics approvals were obtained from the Health Sciences Research Ethics Board of University of Toronto (no. 39253) in Ontario, and the Institutional Review Board of Faculty of Medicine and Health Sciences of McGill University in Québec (A06-M52-20B).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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:
    Our study should be interpreted considering certain limitations. First, we were unable to control for sex54, ethnicity55,56, or comorbidities57—factors that have been associated with COVID-19 mortality in some of the studies. Even though we were able to control for some of the main predictors of COVID-19 mortality (e.g., age, hospital-acquired infections, LTCH residents), we cannot rule out residual confounding. In addition, we considered all patients with a lab-confirmed SARS-CoV-2 diagnosis although we could not confirm that it was the principal reason for the hospitalization. Second, the healthcare administrative and surveillance databases used for this study do not provide detailed information on treatments received by patients. This limitation hampered our ability to examine how evolving standards of care and specific treatments impacted mortality outcomes. Third, we defined our mortality outcome as patients that died within 28 days after admission which may slightly underestimate mortality risk. However, this definition captures close to 90% of the total in-hospital deaths. Additionally, it has the merit of measuring the immediate impact of COVID-19 on deaths more accurately58. Finally, the CCM+ data from Ontario did not allow the addition of facility-level variables and vaccine status. We addressed this by using public health unit-level variables to (partially) control for inter-hospital variations. Further, the lack of vaccine status should not affect the results base...

    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.