Rate of Angiotensin-Converting Enzyme Inhibitors and Angiotensin Receptor Blockers Use and the Number of COVID-19–Confirmed Cases and Deaths

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Abstract

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  1. SciScore for 10.1101/2020.05.31.20118802: (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
    We provided the study dataset and STATA (StataCorp, College Station, Texas) code at https://github.com/Tereshchenkolab/geospatial, allowing future replication and update of the study results as COVID-19 pandemic is unfolding.
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    In order to map the Medicare prescription data to their corresponding FIPS Code, we used the Google Geocoding API.19 We then loaded the Medicare data and geocoded data into the SQLite database to produce the final datasets with prescription counts per county.
    SQLite
    suggested: (SQLite, RRID:SCR_017672)

    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:
    Limitations: Although the Medicare Part D Prescriber Public Use File has a wealth of information, the dataset has several limitations. The data may not be representative of a physician’s entire practice or all of Medicare as it only includes information on beneficiaries enrolled in the Medicare Part D prescription drug program (approximately two-thirds of all Medicare beneficiaries). Besides, available data were for the year 2017 and did not reflect the most recent use of medications in 2020. Nevertheless, we measured exposure before the outcome, which is essential for the interpretation of the study findings. Furthermore, we did not adjust for adherence to medications. Nevertheless, a recent geospatial study of ACEI/ARB adherence30 showed a relatively consistent geographic distribution of ACEI/ARB adherence across the US. An observational cross-sectional geospatial study is susceptible to reverse causality bias. To address this limitation, we performed a rigorous analysis of all other classes of cardiovascular medications. The results of sensitivity analyses helped to identify the reverse causality bias in analyses with the secondary outcome. Finally, unobserved confounding was likely present in this observational study. The most apparent missing data included the rate of Covid-19 testing. Therefore, observed effect sizes have to be interpreted with caution. However, unobserved confounding would unlikely to affect a relative comparison of ACEI and ARB.

    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.