The Relationship Between Age and Mental Health Among Adults in Iran During the COVID-19 Pandemic

  1. Jiyao Chen
  2. Stephen X. Zhang
  3. Yifei Wang
  4. Asghar Afshar Jahanshahi
  5. Maryam Mokhtari Dinani
  6. Abbas Nazarian Madavani
  7. Khaled Nawaser

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Abstract

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  1. Version published to 10.1007/s11469-021-00571-6
  2. ScreenIT

    SciScore for 10.1101/2020.06.11.20128132: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Participation was fully voluntary and all participants agreed with their informed consent to complete the survey.
    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: We detected the following sentences addressing limitations in the study:
    This study has some limitations. First, due to the challenge of data collection during the COVID-19 crisis, we used convenient sampling; future studies with more representative sampling techniques could further examine age as a predictor of mental health. Second, the curvilinear relationship that we found was limited to our sample in Iran, and it is interesting to examine how it may vary in other populations. Nonetheless, our results point to the need for further studies to determine how age plays out as a predictor to enable better identification of those who are in greater need of mental healthcare. Our study showed that Iranian adults after the peak of COVID-19 still suffered from depression, anxiety, and distress disorders to a similar or higher degree than people in other countries, except the US. The finding that the association between age and mental health issues was curvilinear suggests age remains a useful yet more nuanced predictor than deemed by the past literature. As the research on mental health under COVID-19 progresses, we call for more studies to examine the nonlinear relationships of the predictors of mental health issues.

    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.

  3. Version published to 10.1101/2020.06.11.20128132v1 on medRxiv