Visualizing and assessing US county-level COVID19 vulnerability

  1. Gina Cahill
  2. Carleigh Kutac
  3. Nicholas L. Rider

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Abstract

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  1. Version published to 10.1016/j.ajic.2020.12.009
  2. ScreenIT

    SciScore for 10.1101/2020.07.30.20164608: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We extracted US county data about the COVID19 pandemic from the New York Times Github repository (https://github.com/nytimes/covid-19-data) and US Census data (2018) about county-level demographics, economic factors and ethnicities (https://www.census.gov).
    https://www.census.gov
    suggested: (U.S. Census Bureau, RRID:SCR_011587)
    Using JupyterLab through the Anaconda distribution (v. 2020.02) Python version 3.8.3 with Pandas (version 1.0.5) we merged and filtered the final dataset for analysis.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Subsequent significance testing for county data as of June 30, 2020 was performed with STATA version 12.1 Welch’s t-tests were used to determine whether or not there was a significant difference in the mean values of variables of interest (NYT COVID and US Census Features) between high and low CFR counties, as this method of testing accounts for unequal variances between the two groups (Table 2).
    STATA
    suggested: (Stata, RRID:SCR_012763)

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

    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.

    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.

  3. Version published to 10.1101/2020.07.30.20164608 on medRxiv