Evaluation of a novel community-based COVID-19 ‘Test-to-Care’ model for low-income populations

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Abstract

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  1. SciScore for 10.1101/2020.07.28.20161646: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementEthics The UCSF Committee on Human Research determined that the study and subsequent program evaluation met criteria for public health surveillance, program evaluation and quality improvement activities, rendering it exempt from IRB oversight.Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableWe have previously reported the demographics of PCR-positive participants [26], but in brief, positive participants had a median age of 39 years, were predominantly male (76%) and nearly all were Latinx (95%) (Table 3).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    % White/Caucasian, 5% Asian/Pacific Islander, and 1% Black/African American [26].
    Islander
    suggested: (Islander, RRID:SCR_007758)

    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:

    Our study has some limitations. First, assessment of implementation outcomes among COVID19-positive participants was limited to informal interviews; nonetheless, these still provided important information related to the acceptability of the T2C and will inform formal qualitative research as part of future evaluations of the T2C Model. Additionally, we were unable to directly assess adherence to isolation and quarantine among participants and their households; therefore, we could not directly assess whether the T2C Model was effective in enabling individuals to more closely adhere to recommended public health guidance. Reliable and validated approaches for monitoring adherence to (and appropriate support for) isolation and quarantine among vulnerable populations for whom mobile device tracking may not be acceptable are currently lacking and represent an important public health priority to optimize the effectiveness of COVID-19 test, isolate and trace strategies [8]. Finally, the T2C model was designed to address the specific needs of low-income Latinx persons with COVID-19 in the Mission District of San Francisco, California and thus our findings may not be generalizable; however, all T2C providers and CHWs felt that the T2C Model could be implemented in other settings and could be adapted to better support the needs of other low-income individuals. In conclusion, the T2C model to support low-income individuals after a COVID-19 diagnosis was found to be highly acceptable to participants, feasible to undertake and, through direct and ongoing multilevel support, effective in supporting low-income Latinx individuals and their households through the period of self-isolation and quarantine. To further improve the effectiveness of this model, improved integration with public health services coupled with expansion of tailored, low-barrier COVID-19 testing options for close contacts is recommended.


    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.


    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  2. SciScore for 10.1101/2020.07.28.20161646: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethics: The UCSF Committee on Human Research determined that the study and subsequent program evaluation met criteria for public health surveillance, program evaluation and quality improvement activities, rendering it exempt from IRB oversight.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    % White/Caucasian, 5% Asian/Pacific Islander, and 1% Black/African American [26].
    Islander
    suggested: (Islander, RRID:SCR_007758)

    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:
    Our study has some limitations. First, assessment of implementation outcomes among COVID-19-positive participants was limited to informal interviews; nonetheless, these still provided important information related to the acceptability of the T2C and will inform formal qualitative research as part of future evaluations of the T2C Model. Additionally, we were unable to directly assess adherence to isolation and quarantine among participants and their households; therefore, we could not directly assess whether the T2C Model was effective in enabling individuals to more closely adhere to recommended public health guidance. Reliable and validated approaches for monitoring adherence to (and appropriate support for) isolation and quarantine among vulnerable populations for whom mobile device tracking may not be acceptable are currently lacking and represent an important public health priority to optimize the effectiveness of COVID-19 test, isolate and trace strategies [8]. Finally, the T2C model was designed to address the specific needs of low-income Latinx persons with COVID-19 in the Mission District of San Francisco, California and thus our findings may not be generalizable; however, all T2C providers and CHWs felt that the T2C Model could be implemented in other settings and could be adapted to better support the needs of other low-income individuals. In conclusion, the T2C model to support low-income individuals after a COVID-19 diagnosis was found to be highly acceptable to p...

    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.