Race, ethnicity, community-level socioeconomic factors, and risk of COVID-19 in the United States and the United Kingdom

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Abstract

No abstract available

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  1. SciScore for 10.1101/2020.06.18.20134742: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: 18 At enrollment, participants provided informed consent to the use of aggregated information for research purposes and agreed to applicable privacy policies and terms of use.
    IRB: This research study was approved by the Partners Human Research Committee (Protocol 2020P000909) and King’s College London Ethics Committee (REMAS ID 18210, LRS-19/20-18210).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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:
    Second, our study design examined documented, self-reported Covid-19 cases in the general smartphone user population, overcoming limitations related to capturing only more severe cases through administrative hospitalization records or death reports. Third, we examined the risk of predicted Covid-19 according to racial and ethnic groups and found results largely consistent with those of self-reported Covid-19. This approach was not dependent on differences in testing availability which might vary across racial and ethnic groups. Finally, we collected information on and adjusted for a wide range of known or suspected risk factors for Covid-19, which are generally not available in existing registries or population-scale surveillance efforts. Our study has several limitations. While the use of syndromic surveillance to better understand Covid-19 disparities has great strengths in flexibility, speed and sample size, this methodology is largely dependent upon self-reported data, and therefore susceptible to measurement bias, residual confounding bias, and selection (collider) bias. The probability of app participation, reporting, or access may be differential according to Covid-19 outcomes, minority status and/or covariates.35,36 Smartphone-based tools may preclude participation of certain populations such as elderly, low-income, or non-English speakers. Although our study had a relatively small proportion of racial-ethnic minorities compared to census estimates, we enrolled a high...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04331509RecruitingCOVID-19 Symptom Tracker


    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.