Robustness of reported postacute health outcomes in children with SARS-CoV-2 infection: a systematic review

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Abstract

To systematically assess the robustness of reported postacute SARS-CoV-2 infection health outcomes in children.

Methods

A search on PubMed and Web of Science was conducted to identify studies published up to 22 January 2022 that reported on postacute SARS-CoV-2 infection health outcomes in children (<18 years) with follow-up of ≥2 months since detection of infection or ≥1 month since recovery from acute illness. We assessed the consideration of confounding bias and causality, as well as the risk of bias.

Results

21 studies including 81 896 children reported up to 97 symptoms with follow-up periods of 2.0–11.5 months. Fifteen studies had no control group. The reported proportion of children with post-COVID syndrome was between 0% and 66.5% in children with SARS-CoV-2 infection (n=16 986) and between 2.0% and 53.3% in children without SARS-CoV-2 infection (n=64 910). Only two studies made a clear causal interpretation of an association between SARS-CoV-2 infection and the main outcome of ‘post-COVID syndrome’ and provided recommendations regarding prevention measures. The robustness of all 21 studies was seriously limited due to an overall critical risk of bias.

Conclusions

The robustness of reported postacute SARS-CoV-2 infection health outcomes in children is seriously limited, at least in all the published articles we could identify. None of the studies provided evidence with reasonable certainty on whether SARS-CoV-2 infection has an impact on postacute health outcomes, let alone to what extent. Children and their families urgently need much more reliable and methodologically robust evidence to address their concerns and improve care.

Article activity feed

  1. SciScore for 10.1101/2022.03.18.22272582: (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
    Date of the last search was January 22 (for PubMed and Web of Science Core Collection) and January 25 (for L·OVE and EPPI-Centre), 2022.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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:
    Some studies aimed to investigate the role of prognostic risk factors (such as severity of acute COVID-19) on absolute risks, but these assessments are subject to the same crucial limitations. The exact relationships and interrelationships of the various factors that influence both the risk of infection and the risk of symptomatic disease need to be understood. These may include age, social factors (e.g., housing conditions, education level of children but also of parents and guardians, family situation), psychological and mental factors (e.g., mental illness or impairment, at least of the children themselves), economic factors (e.g., financial situation and additional financial burden on the family due to the pandemic). This may also include information on the parents as they most likely have serious impact on the children’s risk to be infected and becoming aware of symptoms. The precise influence of such factors on individual risk behaviour is unclear and difficult to model. Moreover, it is difficult to collect very detailed data that would allow such analyses. Of 21 studies, only 6 used control groups, and only two adjusted for some confounders but their results were likely at risk of unmeasured or residual confounding. Hence, for all studies, the risk of confounding was critical. Another level of complexity is the detection of outcomes due to the inconsistently used and non-standardized definition of “long COVID”, but also, and above all, the recording of the outcomes the...

    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.