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  1. Our take

    This preprint, which has yet to be peer reviewed, retrospectively examined 240,648 individuals in the US who were diagnosed with COVID-19 before May 31, 2021 and evaluated the effectiveness of COVID-19 vaccination both before and up to 12 weeks after diagnosis on preventing the occurrence of long COVID associated illness or symptoms as defined by COVID-19 interoperability alliance. 12-20 weeks following acute COVID-19 diagnosis. Most individuals in the study (91.6%) were unvaccinated within 12 weeks after their COVID-19 diagnosis, 7.4% (17796) were vaccinated within 12 weeks of their diagnosis and only 1% (2392) were vaccinated prior their diagnosis. Compared to unvaccinated individuals, the authors observed a protective effect of COVID-19 vaccination in reducing the likelihood of long COVID-19 continuing symptoms occurring within 12 to 20 weeks following diagnosis, with a greater reduction in odds with shorter durations of vaccination after COVID-19 diagnosis. On the other hand, those who were vaccinated prior to their diagnosis had the lowest risk of experiencing long COVID-19 12-20 weeks after diagnosis (OR: 0.22, 95%CI 0.196-0.245). Although the authors relied on sets of symptoms and conditions that have not been fully validated as associated with long COVID, this study provides additional evidence that vaccination provides some protection against the occurrence of clinical symptoms associated with long COVID 12-20 weeks following diagnosis. 

    Study design

    retrospective-cohort

    Study population and setting

    This retrospective cohort study evaluated the role of vaccination (Pfizer, Moderna, and Johnson and Johnson) on reducing the likelihood of experiencing long COVID-19 symptoms, defined as the presence of at least one COVID-19-associated symptom (fever, loss of taste or smell, etc) between 12 and 20 weeks after initial diagnosis. The study used the Arcadia Data Research Database to identify individuals in the U.S. who had SARS-CoV-2 infection at least 20 weeks before May 21, 2021, the deadline for data extraction. Individuals had to have had at least one health care visit before and after Jan 1, 2020.  The primary exposure of interest was timing of the first dose of a COVID vaccine relative to the date of COVID-19 diagnosis: prior to diagnosis, 0-4 weeks, 4-8 weeks, 8-12 weeks, and more than 12 weeks post-diagnosis, with unvaccinated as the reference group. The outcome was defined as the occurrence of at least one or an aggregate of COVID-19-associated symptoms 12-20 weeks after diagnosis. The following additional covariates were collected for each individual: age, sex, race, ethnicity, insurance type, and comorbid conditions prior to COVID-19 diagnosis. Multiple logistic regression models were used to estimate the association between vaccination timing and the presence of any long COVID-19 symptom. Sensitivity analyses examined the association between timing of vaccination as a continuous metric with a count of the number of long-COVID symptoms 12 weeks post-diagnosis. 

    Summary of main findings

    Between Feb 2020 and May 2021, 240,648 individuals were diagnosed with COVID-19 and had at least one primary care visit both before January 1, 2020 and at least 20 weeks after their diagnosis. Of these, 91.6% (n=220,460) hadn’t received the first dose of a COVID-19 vaccine 12 weeks after their COVID-19 diagnosis, while 17.4% (n=17,796) were vaccinated within 12 weeks and 1% (n=2,392) were vaccinated prior to their COVID-19 diagnosis. Patients vaccinated before a COVID-19 diagnosis were 78% less likely to have any long COVID symptoms than unvaccinated individuals (OR: 0.22, 95%CI 0.196-0.245). Previously unvaccinated individuals who were vaccinated 0-4 weeks, 4-8 weeks, and 8-12 weeks after their COVID-19 diagnosis also had reduced odds of reporting any long COVID-19 symptoms compared to unvaccinated individuals (OR: 0.382, 95%CI 0.353-0.413; OR: 0.535, 95%CI 0.506-0.567; and OR: 0.747,95%CI 0.713-0.784 respectively). 

    Study strengths

    This study used an extensive, broadly-representative database to assess the effectiveness of the timing of COVID-19 vaccination on preventing long-COVID symptoms 12-20 weeks after diagnosis. 

    Limitations

    Only 7.4% of the total study cohort had any vaccination within 12 weeks after their date of COVID-19 diagnosis. The authors did not consider potential confounding by other notable confounding variables such as pre-existing co-morbidities, which may contribute to the likelihood of COVID-19 symptoms and may be differentially distributed across vaccinated and unvaccinated individuals. Extrapolation, inaccurate inference, and residual confounding could have occurred if there is a big difference in the distribution of the confounding variables among these groups. Finally, no distinction was made between the 3 major COVID-19 vaccines available in the US. 

    Value added

    The study was one of the larger, broadly-representative study suggesting a protective effect of vaccination with either Pfizer, Moderna, or Johnson and Johnson on preventing long COVID-19 symptoms. 

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  2. SciScore for 10.1101/2021.11.17.21263608: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The MITRE Institutional Review Board (IRB) reviewed the study submission, COVID-19 Analytics (MIRB 2020017), and found that it is exempt under the provisions of 45 CFR 46.104(d)(4) as secondary research and approved the study.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To further test specific outcomes identified using the logistic regression model, a general linear model using an iteratively reweighted least squares optimization method (Python statsmodels v0.12.2) was fitted to an aggregate continuous variable counting the number of distinct long-COVID symptoms reported after 12-weeks following diagnosis (“Symptom Count”).
    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:
    This study is subject to at least six limitations: First, these findings are based on opportunistic availability of large volumes of patient data and may have geographic, temporal, contractual, and socioeconomic gaps that could influence outcomes. Second, these findings use vaccination data recorded by payer entities or documented in EHRs by providers but do not incorporate dedicated vaccination surveillance data sources and so may have gaps in vaccination data that are presently undetectable. Third, it is possible, but unlikely, that some of the patients with COVID-19 were misclassified due to a false-positive test result (with no documented correction) or an inaccurate COVID-19 diagnosis. Fourth, no distinction was made between which of the three U.S. COVID-19 vaccines administered; it is possible that some of the effect described is related to a specific vaccine, and that such an effect could not be detected based on the data used here. Fifth, while interactions between the observed demographic factors have been explored, interactions between pre-existing chronic conditions have not and may introduce unforeseen effects to the findings described here. And sixth, this analysis was conducted on patient data collected prior to the emergence of the delta variant as the predominant variant circulating in the United States; as a result, we cannot conclude that the protective effect of vaccination against long-COVID described here applies to patients infected with the delta varian...

    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.

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