Signals of Significantly Increased Vaccine Breakthrough, Decreased Hospitalization Rates, and Less Severe Disease in Patients with Coronavirus Disease 2019 Caused by the Omicron Variant of Severe Acute Respiratory Syndrome Coronavirus 2 in Houston, Texas

  1. Paul A. Christensen
  2. Randall J. Olsen
  3. S. Wesley Long
  4. Richard Snehal
  5. James J. Davis
  6. Matthew Ojeda Saavedra
  7. Kristina Reppond
  8. Madison N. Shyer
  9. Jessica Cambric
  10. Ryan Gadd
  11. Rashi M. Thakur
  12. Akanksha Batajoo
  13. Regan Mangham
  14. Sindy Pena
  15. Trina Trinh
  16. Jacob C. Kinskey
  17. Guy Williams
  18. Robert Olson
  19. Jimmy Gollihar
  20. James M. Musser

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Abstract

No abstract available

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  1. Version published to 10.1016/j.ajpath.2022.01.007
  2. Version published to 10.1101/2021.12.30.21268560v4 on medRxiv
  3. Version published to 10.1101/2021.12.30.21268560v3 on medRxiv
  4. ScreenIT

    SciScore for 10.1101/2021.12.30.21268560: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The work was approved by the Houston Methodist Research Institute Institutional Review Board (IRB1010-0199).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Samtools version 1.11 was used for sequence and file manipulation, where maximum depth and minimum depth parameters in mpileup were set to 8,000 and 3, respectively. iVar version 1.3.1 was used for primer trimming and variant calling.
    Samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)

    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:
    However, our study has several limitations. Although we sequenced the genomes of SARS-CoV-2 causing 90% of all Houston Methodist COVID-19 cases in the study period, this sample represents only approximately 5% of cases reported in the metropolitan region. Our patient population will underrepresent some demographic groups, for example homeless individuals and pediatric patients. The samples sequenced in this study were obtained from symptomatic individuals, which means that it is possible that we failed to identify Omicron subvariants or features preferentially represented in asymptomatic individuals. In the aggregate, our data add critical new information to features of Omicron genomic epidemiology and patient characteristics in the US. Further, the present study highlights the importance of analyzing SARS-CoV-2 genome data integrated with patient metadata and stresses the need to continue to do this in near-real time as the Omicron surge continues, the virus evolves, and new variants with potentially altered fitness and biomedically relevant phenotypes are generated. Analyses of this type are also important in the context of vaccine formulation and long COVID, an increasing health and economic problem globally. Finally, the strategy we have used in this and previous studies2-6 are readily applicable to future infectious diseases problems that warrant special attention.

    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.

  5. Version published to 10.1101/2021.12.30.21268560v2 on medRxiv
  6. Version published to 10.1101/2021.12.30.21268560v1 on medRxiv