Perception of Health Conditions and Test Availability as Predictors of Adults’ Mental Health during the COVID-19 Pandemic: A Survey Study of Adults in Malaysia

  1. Huiyang Dai
  2. Stephen X. Zhang
  3. Kim Hoe Looi
  4. Rui Su
  5. Jizhen Li

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Abstract

Research identifying adults’ mental health during the coronavirus disease 2019 (COVID-19) pandemic relies solely on demographic predictors without examining adults’ health condition as a potential predictor. This study aims to examine individuals’ perception of health conditions and test availability as potential predictors of mental health—insomnia, anxiety, depression, and distress—during the COVID-19 pandemic. An online survey of 669 adults in Malaysia was conducted during 2–8 May 2020, six weeks after the Movement Control Order (MCO) was issued. We found adults’ perception of health conditions had curvilinear relationships (horizontally reversed J-shaped) with insomnia, anxiety, depression, and distress. Perceived test availability for COVID-19 also had curvilinear relationships (horizontally reversed J-shaped) with anxiety and depression. Younger adults reported worse mental health, but people from various religions and ethnic groups did not differ significantly in reported mental health. The results indicated that adults with worse health conditions had more mental health problems, and the worse degree deepened for unhealthy people. Perceived test availability negatively predicted anxiety and depression, especially for adults perceiving COVID-19 test unavailability. The significant predictions of perceived health condition and perceived COVID-19 test availability suggest a new direction for the literature to identify the psychiatric risk factors directly from health-related variables during a pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.21.20137000: (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 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:
    4.1 Limitations and future work: Firstly, we used a two-stage stratified sampling method to aim for a broader coverage of Malaysian adults, but our study did not aim for a representative sample of adults in Malaysia during the COVID-19 pandemic. Secondly, we used SF-1, a brief one-item measure of general health condition, and future research may use the lengthier form of SF-12 or SF-36. Thirdly, instead of general health condition, future research may explore specific medical issues, such as heart disease, diabetes, or cancer, as predictors of mental health. Fourthly, we measured adults’ perceived test availability for COVID-19 because we were interested in their mental health and future research may use alternative indicators of COVID-19 test availability.

    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 10.3390/ijerph17155498
  3. Version published to 10.1101/2020.06.21.20137000 on medRxiv