The adverse impact of consecutive COVID-19 waves on mental health

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Abstract

Background

Although several studies documented the impact of COVID-19 on mental health, the long-term effects of COVID-19 on mental health remain unclear.

Aims

To examine longitudinal changes in mental health prior to and during the consecutive COVID-19 waves in a well-established probability sample.

Method

An online survey was completed by the participants of the COVID-19 add-on study at 4 timepoints (N 1 =1823, N 2 =788, N 3 =532, N 4 =383): pre-COVID period (2014/2015), 1 st COVID-19 wave (April-May, 2020), 2 nd COVID-19 wave (August-October, 2020) and 3 rd COVID-19 wave (March-April, 2021). Data were collected via a set of validated instruments and analysed using latent growth models.

Results

During the pandemic, we observed a significant increase in stress levels (slope=1.127, P<0.001) and depressive symptoms (slope=1.177, P<0.001). The rate of increase in stress levels (cov=2.167, P=0.002), but not in depressive symptoms (cov=0.558, P=0.10), was associated with the pre-pandemic mental health status of the participants. Further analysis revealed two opposing clusters of factors that influenced mental health: loneliness and COVID-19 showed a negative effect on emotionality, while higher resilience acted protectively. A greater increase in stress was observed in women and younger participants.

Conclusions

The surge in stress levels and depressive symptoms persisted across all three consecutive COVID-19 waves. This surge is attributable to the effect of several risk factors including the status of mental health prior to the COVID-19 pandemic. Our findings have implications for strategies promoting resilience and addressing loneliness to mitigate the mental health impact of COVID-19 pandemic.

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  1. SciScore for 10.1101/2021.07.25.21261094: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line AuthenticationAuthentication: E-questionnaire was completed through an online survey module using validated RedCap software (Research Electronic Data Capture) tool16. 330 out of 715 participants who originally enrolled into the Covid-19 add-on study (46%) completed e-questionnaire prior to and during all of the three Covid-19 waves.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    E-questionnaire was completed through an online survey module using validated RedCap software (Research Electronic Data Capture) tool16. 330 out of 715 participants who originally enrolled into the Covid-19 add-on study (46%) completed e-questionnaire prior to and during all of the three Covid-19 waves.
    RedCap
    suggested: (REDCap, RRID:SCR_003445)
    Data analysis and visualization were performed in the R (v.4.1.0) environment with rstatix, sjmisc, ggplot2, and pheatmap packages.
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)
    pheatmap
    suggested: (pheatmap, RRID:SCR_016418)

    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: 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.

    Results from scite Reference Check: We found no unreliable references.


    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.