FDA-authorized mRNA COVID-19 vaccines are effective per real-world evidence synthesized across a multi-state health system

  1. Colin Pawlowski
  2. Patrick Lenehan
  3. Arjun Puranik
  4. Vineet Agarwal
  5. A.J. Venkatakrishnan
  6. Michiel J.M. Niesen
  7. John C. O’Horo
  8. Abinash Virk
  9. Melanie D. Swift
  10. Andrew D. Badley
  11. John Halamka
  12. Venky Soundararajan

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Abstract

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  1. Version published to 10.1016/j.medj.2021.06.007
  2. Rapid Reviews Infectious Diseases

    Aaloke Mody

    Review 1: "FDA-authorized COVID-19 vaccines are effective per real-world evidence synthesized across a multi-state health system"

    This preprint found that two vaccine doses provide 88.7% effectiveness at preventing infection and lowered hospitalizations. The reviewers found the preprint potentially informative, due to the propensity score matching method, and could be improved by additional studies.

  3. Rapid Reviews Infectious Diseases

    Tiancheng Zhao

    Review 2: "FDA-authorized COVID-19 vaccines are effective per real-world evidence synthesized across a multi-state health system"

    This preprint found that two vaccine doses provide 88.7% effectiveness at preventing infection and lowered hospitalizations. The reviewers found the preprint potentially informative, due to the propensity score matching method, and could be improved by additional studies.

  5. ScreenIT

    SciScore for 10.1101/2021.02.15.21251623: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was reviewed and approved by the Mayo Clinic Institutional Review Board (IRB 20-003278) as a minimal risk study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To obtain the propensity scores for the matching procedure, we trained regularized logistic regression models for each zip code using the software package sklearn v0.20.3 in Python.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 several important limitations to consider in this study. First, while the cohort size was even larger than the cohorts studied in phase 3 trials, the mean follow-up time per patient is substantially lower (mean = 27.1 days versus approximately 80 to 90 days). Consistent with this, approximately 45.1% of our vaccinated cohort had received only one dose of vaccination at the time of this study (Table S1). We were thus limited in the number of patients and at-risk person-days that were available for the critical long term efficacy analyses. Second, we did not assess vaccine-associated adverse events, nor did we compare the clinical symptomatology of COVID-19 infections between vaccinated and unvaccinated patients. Third, it is possible that the likelihood of seeking out a SARS-CoV-2 PCR test was different between vaccinated and propensity matched unvaccinated patients, which could introduce bias into our estimates of vaccine efficacy. Indeed, vaccinated patients may feel less compelled to undergo subsequent PCR testing, thereby reducing the number of positive tests recorded in this group. However, our data suggests that this is likely not a strong confounding factor, as the fraction of vaccinated patients with at least one PCR test after study enrollment (13.9%) was only marginally lower than the same fraction of unvaccinated patients (16.6%) (Table S1). Finally, the cohorts utilized to assess vaccine efficacy were predominantly female (vaccinated: 62.2%; unvaccinated:...

    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.

  6. Version published to 10.1101/2021.02.15.21251623v3 on medRxiv
  7. Version published to 10.1101/2021.02.15.21251623v2 on medRxiv
  8. Version published to 10.1101/2021.02.15.21251623v1 on medRxiv