A Statistical Argument Against Vaccine Injury

  1. Jacques Balayla

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Abstract

Vaccine hesitancy is a major threat to public health. While the root causes of vaccine hesitancy are numerous, they largely revolve around some form of perceived risk to the self. In particular, the unknown long-term risks are amongst the most frequently cited concerns. In this work, we show that regardless of their peak onset following vaccination, the incidence of adverse outcomes will follow some distribution f ( x | µ, σ 2 ) of mean onset µ , and standard deviation σ , and variance σ 2 . Despite the small proportion of events at the tails of these distributions, the large-scale public deployment of vaccines would imply that any signal for a given adverse outcome would be observed soon after distribution begins, even in cases where t x < t µ− 3 σ . The absence of such an early signal, however low, would suggest that long term effects are unlikely and that vaccine safety is therefore likely. Indeed, when enough individuals have been exposed to a new therapy - even if the majority of adverse outcomes only manifest at a future time t µ , the number of adverse outcomes given by the cumulative density function (CDF) near t 0 + dt > 0. Otherwise stated:

We evoke the theory behind normal (Gaussian) and skew-normal distributions and use Chebyshev’s Theorem to evaluate the COVID-19 vaccine data as an example. The findings of this study are not vaccine-specific and can be applied to assess the health effects of the mass distribution of any good, treatment or policy at large.

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  1. ScreenIT

    SciScore for 10.1101/2022.04.19.22274036: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1101/2022.04.19.22274036v1 on medRxiv