Sustained negative mental health outcomes among healthcare workers over the first year of the COVID-19 pandemic: a prospective cohort study

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Abstract

Objective

To characterize the evolution of healthcare workers’ mental health status over the 1-year period following the initial COVID-19 pandemic outbreak and to examine baseline characteristics associated with resolution or persistence of mental health problems over time.

Methods

We conducted an 8-month follow-up cohort study. Eligible participants were healthcare workers working in Spain. Baseline data were collected during the initial pandemic outbreak. Survey-based self-reported measures included COVID-19-related exposures, sociodemographic characteristics, and three mental health outcomes (psychological distress, depression symptoms, and posttraumatic stress disorder symptoms). We examined three longitudinal trajectories in mental health outcomes between baseline and follow-up assessments (namely asymptomatic/stable, recovering , and persistently symptomatic/worsening ).

Results

We recruited 1,807 participants. Between baseline and follow-up assessments, the proportion of respondents screening positive for psychological distress and probable depression decreased, respectively, from 74% to 56% and from 28% to 21%. Two-thirds remained asymptomatic/stable in terms of depression symptoms and 56% remained symptomatic or worsened over time in terms of psychological distress.

Conclusions

Poor mental health outcomes among healthcare workers persisted over time. Occupational programs and mental health strategies should be put in place.

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  1. SciScore for 10.1101/2021.11.21.21266594: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: It received approval from the Hospital La Paz Ethics Committee (Madrid, Spain) and was ratified by the local ethics committees from the participating sites.
    Consent: Statistical analyses: First, we removed baseline respondents who provided informed consent but did not go on to initiate the survey (n = 95).
    Sex as a biological variableSociodemographic variables: Age in years, gender (male, female), educational level (primary, secondary, or university studies), and type of job.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All analyses were performed using packages dplyr, gtsummary, flextable, ggplot2, psych, multinom of R Studio for Mac (Version 1.2.5042).
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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:
    Our study has limitations. First, we used a non-random sample that increases probability of some degree of collider bias and hinders transportability of study results across settings. Also, and in line with other multi-center studies (1,4), response rates varied significantly across sites and facilities, and the possibility of self-selection bias cannot be ruled out. Nevertheless, the baseline sociodemographic characteristics and mental health outcomes were however similar to another Spanish study with a larger and somewhat more representative sample of HCWs (1) and to other, similar European studies (2,3). Second, because of the use of observational data, effect estimates are potentially subject to some degree of residual confounding. Notably, substantial residual confounding is unlikely given that we included measures on all major individual- and region-level confounders and that estimates from crude and adjusted associations are roughly similar. Moreover, sensitivity analyses exploring differences between subsamples (e.g., full vs. partial respondents) and adjusting for baseline measurements of mental health outcomes obtained similar results, suggesting that our models were robust to different model specifications. Third, two thirds of baseline respondents were lost to follow-up. Dropout was independent from age, gender, and mental health outcomes at baseline, but people lost to follow were slightly more concerned about getting the virus and infecting their loved ones (dat...

    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.