Comparison of post-COVID depression and major depressive disorder

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Abstract

Background

During the COVID-19 pandemic rates of depressive symptoms are markedly elevated, particularly among survivors of infection. Understanding whether such symptoms are distinct among those with prior SARS-CoV-2 infection, or simply a nonspecific reflection of elevated stress, could help target interventions.

Method

We analyzed data from multiple waves of a 50-state survey that included questions about COVID-19 infection as well as the Patient Health Questionnaire examining depressive and anxious symptoms. We utilized multiple logistic regression to examine whether sociodemographic features associated with depression liability differed for those with or without prior COVID-19, and then whether depressive symptoms differed among those with or without prior COVID-19.

Results

Among 91,791 respondents, in regression models, age, gender, race, education, and income all exhibited an interaction with prior COVID-19 in risk for moderate or greater depressive symptoms (p<0.0001 in all cases), indicating differential risk in the two subgroups. Among those with such symptoms, levels of motoric symptoms and suicidality were significantly greater among those with prior COVID-19 illness. Depression risk increased with greater interval following acute infection.

Discussion

Our results suggest that major depressive symptoms observed among individuals with prior COVID-19 illness may not reflect typical depressive episodes, and merit more focused neurobiological investigation.

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  1. SciScore for 10.1101/2021.03.26.21254425: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Institutional Review Board of Harvard University.
    Consent: All participants provided informed consent.
    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: 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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.