Depression Symptoms During the COVID-19 Pandemic among Well-educated, Employed Adults with Low Infection Risks

  1. Duncan Thomas
  2. Tyson Brown
  3. Donald H. Taylor
  4. Ralph Lawton
  5. Victoria K. Lee
  6. Menna Mburi
  7. Michelle Wong
  8. Rachel Kranton

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Abstract

Levels and distributions of depression symptoms 8-10 months after the onset of the COVID-19 pandemic are reported in a population of faculty, staff, and students at Duke University who faced minimal infection and economic disruption due to the pandemic. Almost 5,000 respondents age 18-81 years who completed the 20-item Center for Epidemiological Studies-Depression (CES-D) battery reported high rates of depression symptoms with more than 40% reporting levels that indicate risk of moderate depression and 25% indicating risk of severe depression. There is a very steep age gradient with the highest levels reported by the youngest respondents of whom over 40% are at risk of severe depression. Symptoms are worse among those who report the demands of work often interfere with family responsibilities but these pressures neither explain the high reported rates nor the steep age gradient. Severe depression risks are highest among students. High levels of depression symptoms during the pandemic appear to be persistent and not confined to those at greatest risk of infection or economic insecurity.

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    SciScore for 10.1101/2021.01.26.21250558: (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 Duke University Institutional Review Board.
    Consent: All participants provided electronic informed consent prior to answering any questions.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variable2.2 Key Definitions: 2.3 Statistical Analysis: After describing the sample, bivariable relationships between the CES-D score and age are presented separately for males and females using locally weighted smoothed scatterplot regressions (LOWESS)12 to illustrate the shape of the relationships with minimal parametric restrictions.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All data analysis is conducted using STATA 16.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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.

    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 10.1101/2021.01.26.21250558v1 on medRxiv