Geographic access to United States SARS-CoV-2 testing sites highlights healthcare disparities and may bias transmission estimates

  1. Benjamin Rader
  2. Christina M Astley
  3. Karla Therese L Sy
  4. Kara Sewalk
  5. Yulin Hswen
  6. John S Brownstein
  7. Moritz U G Kraemer

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  1. ScreenIT

    SciScore for 10.1101/2020.04.25.20074419: (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
    Source Population and Covariates: This study was conducted in the 48 contiguous US states and the District of Columbia (DC), using 2014-2018 cross-sectional American Community Survey data (API accessed April 8, 2020, https://www.census.gov/data/developers/data-sets/acs-5year/2018.html), tabulated at the county level.
    Covariates
    suggested: None

    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.

    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 10.1093/jtm/taaa076
  3. Version published to 10.1101/2020.04.25.20074419v1 on medRxiv