SARS-CoV-2 Infection Hospitalization Rate and Infection Fatality Rate Among the Non-Congregate Population in Connecticut

  1. Shiwani Mahajan
  2. César Caraballo
  3. Shu-Xia Li
  4. Yike Dong
  5. Lian Chen
  6. Sara K. Huston
  7. Rajesh Srinivasan
  8. Carrie A. Redlich
  9. Albert I. Ko
  10. Jeremy S. Faust
  11. Howard P. Forman
  12. Harlan M. Krumholz

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Abstract

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  1. Version published to 10.1016/j.amjmed.2021.01.020
  2. ScreenIT

    SciScore for 10.1101/2020.10.30.20223461: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We also estimated the number of individuals with SARS-CoV-2 antibodies by age, sex, race/ethnicity, and region.
    SARS-CoV-2
    suggested: None

    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 has some limitations. First, total COVID-19-related hospitalizations could have been underestimated due to limited testing availability, underestimating the IHR estimate. Second, although antibodies are specific indicators of past SARS-CoV-2 infection, their concentration may decrease few months after exposure14 and there are differences in the sensitivity of available assays.15 Hence, the total infections may be biased lower, overestimating our estimates. Nevertheless, representative seroprevalence studies provide important information regarding infections in a community and can provide robust estimates of the IHR and IFR, when combined with hospitalization and death data. In conclusion, using representative seroprevalence estimates, we estimate an IHR and IFR of 6.86% (90% CI 4.58%–13.72%) and 0.95% (90% CI 0.63%–1.90%), respectively, for COVID-19 infections through June 1, 2020, among the non-congregate population in Connecticut.

    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 10.1101/2020.10.30.20223461v1 on medRxiv