Long-COVID in children and adolescents: a systematic review and meta-analyses

  1. Sandra Lopez-Leon
  2. Talia Wegman-Ostrosky
  3. Norma Cipatli Ayuzo del Valle
  4. Carol Perelman
  5. Rosalinda Sepulveda
  6. Paulina A. Rebolledo
  7. Angelica Cuapio
  8. Sonia Villapol

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Abstract

The objective of this systematic review and meta-analyses is to estimate the prevalence of long-COVID in children and adolescents and to present the full spectrum of symptoms present after acute COVID-19. We have used PubMed and Embase to identify observational studies published before February 10th, 2022 that included a minimum of 30 patients with ages ranging from 0 to 18 years that met the National Institute for Healthcare Excellence (NICE) definition of long-COVID, which consists of both ongoing (4 to 12 weeks) and post-COVID-19 (≥ 12 weeks) symptoms. Random-effects meta-analyses were performed using the MetaXL software to estimate the pooled prevalence with a 95% confidence interval (CI). Heterogeneity was assessed using I 2 statistics. The Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) reporting guideline was followed (registration PROSPERO CRD42021275408). The literature search yielded 8373 publications, of which 21 studies met the inclusion criteria, and a total of 80,071 children and adolescents were included. The prevalence of long-COVID was 25.24%, and the most prevalent clinical manifestations were mood symptoms (16.50%), fatigue (9.66%), and sleep disorders (8.42%). Children infected by SARS-CoV-2 had a higher risk of persistent dyspnea, anosmia/ageusia, and/or fever compared to controls. Limitations of the studies analyzed include lack of standardized definitions, recall, selection, misclassification, nonresponse and/or loss of follow-up, and a high level of heterogeneity.

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  1. Version published to 10.1038/s41598-022-13495-5
  2. ScreenIT

    SciScore for 10.1101/2022.03.10.22272237: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableThe descriptive variables extracted were country, study design, period of study, collection mode, follow-up time, severity of COVID-19, sample size, COVID-19 diagnosis, age, percentage of males, outcomes, and names used to describe the long-term effects of COVID-19.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Search strategy and selection criteria: This systematic review and meta-analyses examine the prevalence of long COVID signs and symptoms in children under the age of 18 with a diagnosed case of COVID-19 (confirmed via PCR, antigen test, or antibody test).
    antigen test,
    suggested: None
    Software and Algorithms
    SentencesResources
    To achieve this, two independent investigators searched PubMed and Embase to identify studies that met the following criteria: 1) a minimum of 30 patients with either ongoing symptomatic COVID⍰19 (from 4 to 12 weeks) or post⍰COVID⍰19 syndrome (12 weeks or more) (i.e., patients who met the NICE definition of long COVID) (NICE 2022), 2) ages ranged from 0 to 18 years, 3) published in English, 4) published before February 10th, 2022, and 5) meets the National Institute for Healthcare Excellence (NICE) definition of long COVID, which consists of both ongoing (4 to 12 weeks) and post-COVID-19 (≥12 weeks) symptoms, 6) excluding cohorts of children composed of exclusively pre-existing chronic diseases, or exclusively of MIS-C in children, and 7) excluding references of editorials, reviews, and commentaries.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Given that MedLine was included in the PubMed search, we excluded articles from MedLine in the Embase search along with those not related to COVID-19.
    MedLine
    suggested: (MEDLINE, RRID:SCR_002185)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Statistical analysis: Random-effects meta-analyses were performed for symptoms reported in two or more studies using MetaXL software to estimate the pooled prevalence, which uses a double arcsine transformation 12.
    MetaXL
    suggested: None

    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:
    Further, there were some limitations to our meta-analyses. The quality of the meta-analyses results depends on the quality of the studies included. Table 3 contains a list of all the methodological aspects that future studies need to consider. We can observe that all studies had a high probability of bias, including lack of standardized definitions recall, selection, misclassification, nonresponse, and/or loss of follow-up. Additionally, the included studies have the limitations inherited in all observational studies, including bias due to residual and unmeasured confounding. Another limitation relates to the high level of heterogeneity. To account for heterogeneity, we used a random-effects model 41. However, ideally one should stratify the meta-analysis to identify what is causing the heterogeneity. This was not possible because most studies did not include data on different groups. The differences between studies were likely due to differences in study designs, settings, populations, follow-up time, symptom ascertainment methods, inconsistent terminology, little details on stratification on pre-existing comorbidities, and prior receipt of COVID-19 therapeutics and vaccines. Only four studies mentioned what percentage of the population was already vaccinated 14,15,23,28 (Table 3). It has been shown that vaccines reduce the risk of long COVID. A study in Israel compared the prevalence of symptoms of long COVID and found that fully vaccinated participants who had COVID-19 wer...

    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.

  3. Version published to 10.1101/2022.03.10.22272237 on medRxiv