Mental disorder prevalence among populations impacted by coronavirus pandemics: A multilevel meta-analytic study of COVID-19, MERS & SARS

  1. Matt Boden
  2. Nichole Cohen
  3. Jessilyn M. Froelich
  4. Katherine J. Hoggatt
  5. Hoda S. Abdel Magid
  6. Swapandeep S. Mushiana

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Abstract

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  1. Version published to 10.1016/j.genhosppsych.2021.03.006
  2. Version published to 10.1101/2020.12.18.20248499v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.12.18.20248499: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableWe coded all relevant estimates provided in each study, including estimates for different disorders and the same disorder assessed by different measures, at different time-points, or among different populations and subpopulations (e.g., quarantined versus community, males versus females, doctors versus nurses).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Literature Search Strategy & Criteria: From April 15, 2020 until June 1, 2020, two study staff (MB, NC) conducted a key-word search of electronic databases PubMED
    PubMED
    suggested: (PubMed, RRID:SCR_004846)
    , PsychINFO, Scopus, Web of Science and Google Scholar for peer-reviewed, English language publications.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)

    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:
    Additional limitations can be addressed through assessment of pre-existing mental disorders among study samples, rigorous assessment of time-periods at risk, and/or inclusion of control conditions (e.g., people exposed to but not infected by COVID-19 as a comparator for infected/recovered people). Studies that do so will more directly measure the impact of COVID- 19-related threats, stressors and traumas on mental disorder prevalence, incidence, and rate ratios, which will be essential to intervention planning and implementation. Our study provides data useful for understanding and potentially, intervening to alleviate the mental health impact of COVID-19 and future coronavirus (and other) pandemics. A data-driven approach will facilitate resource allocation to provide effective (and cost-effective) mental health interventions for people with the greatest need during and following a coronavirus pandemic. For example, the high prevalence of PTSD among infected/recovered people indicates a need for effective evidence-based treatments for PTSD as part of convalescence (e.g., prolonged exposure for PTSD; Powers et al., 2010), and providers trained to administer these interventions. A data-drive approach to resource allocation will be especially useful to countries, states/provinces and healthcare systems for which treatment need is expected to exceed existing mental health resources.

    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.

  4. Version published to 10.1101/2020.12.18.20248499v1 on medRxiv