A case-control study of autoimmune AEFIs following COVID-19 vaccination reported to VAERS

  1. Chris von Csefalvay

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Abstract

Autoimmune adverse effects following immunisation (AEFIs) are widely regarded as a chief concern driving vaccine hesitancy. This case-control study seeks to shed light on the true risk of autoimmune AEFIs associated with the COVID-19 vaccine through a case-control analysis of VAERS reports. Reports of autoimmune aetiology were matched with reports of non-autoimmune controls. Statistical analysis reveals that the safety profile of COVID-19 vaccines with regard to autoimmune AEFIs is highly favourable. In particular, neuroautoimmune AEFIs have statistically significant reporting odds ratios below unity (Guillain-Barre syndrome: 0.35, multiple sclerosis: 0.70, transverse myelitis: 0.79), indicating a reduced association of reports of these conditions with the COVID-19 vaccine versus other vaccines. Only three autoimmune aetiologies exceed a ROR of 2.0 and thus present a potential signal. Of these, myasthenia gravis (ROR = 3.90, p < 0.001, 95% CI: 2.63-5.80) may be the result of epidemiological confounding factors not sufficiently controlled by matching, as the population most likely to develop myasthenia gravis was strongly prioritised in the COVID-19 vaccine’s initial rollout. Immune thrombocytopaenia (ROR = 26.83, p < 0.001, 95% CI: 16.93-42.54) is a clear safety signal, confirming a large number of case reports and studies that indicate a risk of immune thrombocytopaenic events following the COVID-19 vaccine. The lone strong safety signal of immune thrombocytopaenia notwithstanding, this study attests to the safety of the COVID-19 vaccine where autoimmune conditions are concerned. Through quantifying the risk of autoimmune disorders associated with COVID-19 vaccination, this study contributes to a growing body of evidence supporting the safety of such vaccines.

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  1. ScreenIT

    SciScore for 10.1101/2021.07.06.21260074: (What is this?)

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

    Table 1: Rigor

    EthicsField Sample Permit: In addition, six diagnoses were explicitly excluded: ARDS (10001052) and Systemic Immune Response Syndrome (SIRS) (10051379), which are insufficiently specific to an autoimmune cause in the COVID-19 context, skin sensitisation (10040785) as it is principally of an allergic rather than autoimmune etiology, shoulder injury related to vaccine administration (10081038) as it permits a wide non-autoimmune etiology, and the general categories of ”adverse event following immunisation” (10069520) and ”vaccination complication” (10046861), which were insufficiently specific.
    Sex as a biological variableFor each case, three controls were selected randomly from all potential controls that match the case’s gender (male, female or unknown/unspecified) and the case’s age band (<18, 18-25, 26-40, 41-55, 56-70 and >70).
    RandomizationFor each case, three controls were selected randomly from all potential controls that match the case’s gender (male, female or unknown/unspecified) and the case’s age band (<18, 18-25, 26-40, 41-55, 56-70 and >70).
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data loading and management was performed using Python 3.7.5 and pandas 1.3.0.[
    Python
    suggested: (IPython, RRID:SCR_001658)
    2.6 Statistical analysis: Reporting odds ratios (ROR) were calculated using Fisher’s exact test from scipy 1.7.0 for all conditions in the list of autoimmune aetiologies in Subsection 2.2 where at least 50 cases could be identified in the exposed population.
    scipy
    suggested: (SciPy, RRID:SCR_008058)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    4.4 Limitations: As any study that relies on passive reporting, data in this study must be considered in the wider context of limitations to passive reporting in general and VAERS in particular. First, such analyses rely on what is reported, suffering not only from under- and overreporting at the same time but also the phenomenon known as differential reporting.[34] At the heart of differential reporting is the cognitive bias that events seem to be related if they are temporally closely related and, vice versa, they are seen to be independent if they are further apart. Since autoimmune disorders in particular often develop over a longer period of time or may be latently present (from a biochemical perspective, i.e. presence of autoantibodies) well before symptoms are felt and noticed, some cases may evade association, and thus also evade reporting. Equally, many autoimmune disorders have a relatively long time to diagnosis due to the often nonspecific symptoms. Finally, VAERS accepts reports from lay reporters (e.g. patients and parents) whose attribution may not necessarily be in line with commonly accepted diagnostic categories. While the study design has been drafted to reduce the risk of these factors through matching and through comparison with other reported symptoms, its results must be seen with the limitations of VAERS firmly kept in mind.

    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

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  2. Version published to 10.1101/2021.07.06.21260074v1 on medRxiv