COVID-19 patient accounts of illness severity, treatments and lasting symptoms

  1. Moriah E. Thomason
  2. Denise Werchan
  3. Cassandra L. Hendrix

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Abstract

First-person accounts of COVID-19 illness and treatment can complement and enrich data derived from electronic medical or public health records. With patient-reported data, it is uniquely possible to ascertain in-depth contextual information as well as behavioral and emotional responses to illness. The Novel Coronavirus Illness Patient Report (NCIPR) dataset includes complete survey responses from 1,584 confirmed COVID-19 patients ages 18 to 98. NCIPR survey questions address symptoms, medical complications, home and hospital treatments, lasting effects, anxiety about illness, employment impacts, quarantine behaviors, vaccine-related behaviors and effects, and illness of other family/household members. Additional questions address financial security, perceived discrimination, pandemic impacts (relationship, social, stress, sleep), health history, and coping strategies. Detailed patient reports of illness, environment, and psychosocial impact, proximal to timing of infection and considerate of demographic variation, is meaningful for understanding pandemic-related public health from the perspective of those that contracted the disease.

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  1. Version published to 10.1038/s41597-021-01103-6
  2. ScreenIT

    SciScore for 10.1101/2021.05.26.21257743: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical approval: The research protocol for this study was approved by the NYU Langone Institutional Review Board (IRB).
    Consent: Geo-positioning of COVID-19 survey respondents: Geographical information about survey respondents was derived from a subset of patients (N = 697) that provided consent to future contact within the online consent form.
    Sex as a biological variableDue to a survey administration error described below, complete data are available at a ratio of ∼2:1, females to males.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    A subset of questions were adapted from established Common Data Elements for mental health (NLM Disaster Management Resources COVID-19 and Perinatal Experiences (COPE) questionnaire, id:24224; https://osf.io/82rkj/, from the Williams Perceived Discrimination Scale,[13] and from the Fletcher measure of Perceived Relationship Quality.[14
    COPE
    suggested: (COPE, RRID:SCR_009153)
    Study data were collected and managed using REDCap electronic data capture tools hosted at NYU Langone University.[15, 16] The measure was administered in English.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)
    Technical Validation: Data assurance and quality checking were performed using R version 4.0.2 and Excel.
    Excel
    suggested: None

    Results from OddPub: Thank you for sharing your 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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.05.26.21257743v1 on medRxiv