Predictors of Depression and Anxiety Symptoms in Brazil during COVID-19

  1. Stephen X. Zhang
  2. Hao Huang
  3. Jizhen Li
  4. Mayra Antonelli-Ponti
  5. Scheila Farias de Paiva
  6. José Aparecido da Silva

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Abstract

The COVID-19 pandemic in Brazil is extremely severe, and Brazil has the third-highest number of cases in the world. The goal of the study is to identify the prevalence rates and several predictors of depression and anxiety in Brazil during the initial outbreak of COVID-19. We surveyed 482 adults in 23 Brazilian states online on 9–22 May 2020, and found 70.3% of the adults (N=339) had depressive symptoms and 67.2% (N=320) had anxiety symptoms. The results of multi-class logistic regression models revealed that females, younger adults and those with fewer children had a higher likelihood of depression and anxiety symptoms; adults who worked as employees were more likely to have anxiety symptoms than those who were self-employed or unemployed; adults who spent more time browsing COVID-19 information online were more likely to have depression and anxiety symptoms. Our results provide preliminary evidence and early warning for psychiatrists and healthcare organizations to better identify and focus on the more vulnerable sub-populations in Brazil during the ongoing COVID-19 pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2021.06.28.21259409: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Ethical approval for this research was received from the Ethics Review Board (CAAE: 31703720.9.1001.0008) at University of São Paulo. 2.2.
    Sex as a biological variablenot detected.
    RandomizationThe study sampled adults aged 18 years or older by unclustered systematic random samples from all 23 states in Brazil.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical strategy: All the data processing was completed in SPSS 23.0, and a two-tailed p < .
    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: 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.

  2. Version published to 10.1101/2021.06.28.21259409v1 on medRxiv
  3. Version published to 10.3390/ijerph18137026