COVID-19 Pandemic and Lockdown Measures Impact on Mental Health Among the General Population in Italy

  1. Rodolfo Rossi
  2. Valentina Socci
  3. Dalila Talevi
  4. Sonia Mensi
  5. Cinzia Niolu
  6. Francesca Pacitti
  7. Antinisca Di Marco
  8. Alessandro Rossi
  9. Alberto Siracusano
  10. Giorgio Di Lorenzo

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Abstract

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  1. Version published to 10.3389/fpsyt.2020.00790
  2. ScreenIT

    SciScore for 10.1101/2020.04.09.20057802: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: On-line consent was obtained from the participants.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableA total of 18147 individuals completed the questionnaire, of which 14447 (79.6%) women, median age was 38 (IQR=23).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    16® (StataCorp).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Limitations and future directions: This study has some important limitations due to the sampling technique. Relying on social networks voluntary recruitment and re-sharing could have introduced an important selection bias, firstly excluding people not on social networks, and secondly introducing a self-selection bias, as suggested by the highly unbalanced gender ratio observed. This latter bias could have affected also two other large web-based surveys in China, that reported on samples with a 64.7% and 67.3% proportion of woman (Qiu et al., 2020; Wang et al., 2020). For these reasons, rates of mental health outcomes should be interpreted with caution. Secondly, this survey was based on self-report instruments that could introduce a systematic bias and return different rates compared to interview-based measures. This study has also several strengths, including a very large sample size and the sampling timeframe that corresponded to the pandemic peak in Italy. Future studies will need to monitor the trajectory of mental health outcomes, in order to define mental health interventions at a population level.

    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.
    • Thank you for including a protocol registration statement.

    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.

  3. Version published to 10.1101/2020.04.09.20057802v1 on medRxiv