Prevalence and Factors Associated with Depression and Anxiety of Hospitalized Patients with COVID-19

  1. Xiangyu Kong
  2. Kailian Zheng
  3. Min Tang
  4. Fanyang Kong
  5. Jiahuan Zhou
  6. Le Diao
  7. Shouxin Wu
  8. Piqi Jiao
  9. Tong Su
  10. Yuchao Dong

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Abstract

Objective

The 2019 coronavirus disease (COVID-19) epidemic has raised international concern. Mental health is becoming an issue that cannot be ignored in our fight against it. This study aimed to explore the prevalence and factors linked to anxiety and depression in hospitalized patients with COVID-19.

Methods

A total of 144 patients diagnosed with COVID-19 were included in this study. We assessed depression and anxiety symptoms using the Hospital Anxiety and Depression Scale (HADS), and social support using the Perceived Social Support Scale (PSSS) among patients at admission. Multivariate linear regression analyses were performed to identify factors associated with symptoms of anxiety and depression.

Results

Of the 144 participants, 34.72% and 28.47% patients with COVID-19 had symptoms of anxiety or depression, respectively. The bivariate correlations showed that less social support was correlated with more anxious (r=-0.196, p<0.05) and depressive (r=-0.360,p<0.05) symptoms among patients with COVID-19. The multiple linear regression analysis showed that gender (β=1.446, p=0.034), age (β=0.074, p=0.003), oxygen saturation (β =-2.140, p=0.049), and social support (β =-1.545, p=0.017) were associated with anxiety for COVID-19 patients. Moreover, age (β=0.084, p=0.001), family infection with SARS-CoV-2 (β =1.515, p=0.027) and social support (β =-2.236, p<0.001) were the factors associated with depression.

Conclusion

Hospitalized patients with COVID-19 presented features of anxiety and depression. Mental concern and appropriate intervention are essential parts of clinical care for those who are at risk.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.24.20043075: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Informed consent was provided by subjects before study commencement.
    IRB: The study was approved by the Research Ethics Commission of Huoshenshan Hospital.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Analysis: SPSS software, version 19 were used for statistical analysis.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Study Limitation: 1) The present study was single-centered, the study sample was not representative of all patients with COVID-19 in China, which limits the generalizability of the results. 2) We only collected data at admission. Thus, it did not allow for investigation the inferences or changes over time. 3) This cross-sectional study is not capable to determine a causal relationship between mental health (anxiety or depression) and the sociodemographic and clinical variables.

    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 10.1101/2020.03.24.20043075v2 on medRxiv
  3. Version published to 10.1101/2020.03.24.20043075v1 on medRxiv