Data-Driven Assessment of Adolescents’ Mental Health During the COVID-19 Pandemic

  1. Yonatan Bilu
  2. Natalie Flaks-Manov
  3. Maytal Bivas-Benita
  4. Pinchas Akiva
  5. Nir Kalkstein
  6. Yoav Yehezkelli
  7. Miri Mizrahi-Reuveni
  8. Anat Ekka-Zohar
  9. Shirley Shapiro Ben David
  10. Uri Lerner
  11. Gilad Bodenheimer
  12. Shira Greenfeld

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Abstract

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  1. Version published to 10.1016/j.jaac.2022.12.026
  2. Version published to 10.1101/2022.01.06.22268809v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.01.06.22268809: (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.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The outcomes included four categories of mental health diagnoses: depression (ICD10 F32, F34); anxiety and OCD disorders (ICD10 F41, F42); adjustment and emotional problems and stress-related conditions (ICD10 F43, F93.9; denoted as “stress”) and eating disorders (ICD10 F50) and three drug categories of antidepressants (ATC code N06A), anxiolytics (ATC code N05B) and antipsychotics (ATC code N05A) dispensed during those years.
    ATC
    suggested: None
    Statistical analyses were conducted using Python version 3.7.1.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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 analysis addressed these limitations by comparing the risk ratios of the outcomes, measuring the difference in new mental health diagnoses and dispensation within the HMO in different time periods. Overall, we estimate that there are higher incidence rates than those presented here. As the fifth coronavirus wave led by the Omicron strain is starting, these results should be considered when deciding on policies for containing it. Our findings highlight that together with the risk of a new strain infecting the population, attention should be given to the mental health of adolescents – especially secular girls - and measures to safeguard their mental health and wellbeing should be weighed.

    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.

  4. Version published to 10.1101/2022.01.06.22268809v1 on medRxiv