Associations of the BNT162b2 COVID-19 vaccine effectiveness with patient age and comorbidities

  1. Idan Yelin
  2. Rachel Katz
  3. Esma Herzel
  4. Tamar Berman-Zilberstein
  5. Amir Ben-Tov
  6. Jacob Kuint
  7. Sivan Gazit
  8. Tal Patalon
  9. Gabriel Chodick
  10. Roy Kishony

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Abstract

Vaccinations are considered the major tool to curb the current SARS-CoV-2 pandemic. A randomized placebo-controlled trial of the Pfizer BNT162b2 vaccine has demonstrated a 95% efficacy in preventing COVID-19 disease. These results are now corroborated with statistical analyses of real-world vaccination rollouts, but resolving vaccine effectiveness across demographic groups and its interaction with comorbidities is challenging. Here, applying a multivariable logistic regression analysis approach to a large patient-level dataset, including SARS-CoV-2 tests, vaccine inoculations and personalized demographics, we model vaccine effectiveness at daily resolution and its interaction with sex, age and comorbidities. Vaccine effectiveness gradually increased post day 12 of inoculation, then plateaued, around 35 days, reaching 95.0% [CI 93.4%-96.3%] for all infections and 99.5% [CI 97.0%-99.9%] for symptomatic infections. While effectiveness was on average uniform for men and women, it declined mildly but significantly with age especially for males. Effectiveness further declined for people with type 2 diabetes, COPD, and immunosuppression, as well as cardiac disease in females. Quantifying real-world vaccine effectiveness, including both biological and behavioral effects, our analysis provides initial measurement of vaccine effectiveness across demographic groups.

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    SciScore for 10.1101/2021.03.16.21253686: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The model is then solved in Matlab with the glmfit function.
    Matlab
    suggested: (MATLAB, RRID:SCR_001622)

    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 several limitations characteristic of observational studies. First, our data reflects uncontrolled non-random testing and non-random vaccination, both potentially biased across the population. Second, the vaccinated population may differ from the unvaccinated population in its general health status, in its risk of being infected and in its health-seeking behavior. These differences may be both inherent, pre-existing even prior to vaccination, and time-dependent as a result of vaccination itself. Third, during the study period, several viral variants were circulating in Israel. Although the vaccine is expected to be potent against B.1.1.711, which was the most common one12, it is possible that additional variants introduced biases to our estimations of effectiveness across subpopulations, especially if vaccinated at different phases of the epidemic. We address differences in behavior by using the per-test model, which adjusts for differences in the tendency to get tested9, while also comparing the associations identified for the immunization period (after day 28) with those identified in the pre-immunization period for the same population. Yet, behavioral differences which themselves vary with post-vaccination time are harder to correct for. These potential biases are also somewhat minimized due to the rapid pace of freely offered vaccination, together with laboratory results which are also offered free of charge to all members. The high disease rate during the s...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.

  2. Version published to 10.1101/2021.03.16.21253686 on medRxiv