Severe Outcomes Associated With SARS-CoV-2 Infection in Children: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective To estimate the proportion of SARS-CoV-2 infected children experiencing hospitalization, intensive care unit (ICU) admission, severe outcomes, and death. Data Sources PubMed, Embase, and MedRxiv were searched for studies published between December 1, 2019 and May 28, 2021. References of relevant systematic reviews were also screened. Study Selection We included cohort or cross-sectional studies reporting on at least one outcome measure (i.e., hospitalization, ICU admission, severe outcomes, death) for ≥100 children ≤21 years old within 28 days of SARS-CoV-2 positivity; no language restrictions were applied. Data Extraction and Synthesis Two independent reviewers performed data extraction and risk of bias assessment. Estimates were pooled using random effects models. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Main Outcomes and Measures Percentage of SARS-CoV-2 positive children experiencing hospitalization, ICU admission, severe outcome, and death. Results 118 studies representing 3,324,851 SARS-CoV-2 infected children from 68 countries were included. Community-based studies ( N = 48) reported that 3.3% (95%CI: 2.7–4.0%) of children were hospitalized, 0.3% (95%CI: 0.2–0.6%) were admitted to the ICU, 0.1% (95%CI: 0.0–2.2%) experienced a “severe” outcome and 0.02% (95%CI: 0.001–0.05%) died. Hospital-based screening studies ( N = 39) reported that 23.9% (95%CI: 19.0–29.2%) of children were hospitalized, 2.9% (95%CI: 2.1–3.8%) were admitted to the ICU, 1.3% (95%CI: 0.5–2.3%) experienced a severe outcome, and 0.2% (95%CI: 0.02–0.5%) died. Studies of hospitalized children ( N = 31) reported that 10.1% (95%CI: 6.1–14.9%) of children required ICU admission, 4.2% (95%CI: 0.0–13.8%) had a severe outcome and 1.1% (95%CI: 0.2–2.3%) died. Low risk of bias studies, those from high-income countries, and those reporting outcomes later in the pandemic presented lower estimates. However, studies reporting outcomes after May 31, 2020, compared to earlier publications, had higher proportions of hospitalized patients requiring ICU admission and experiencing severe outcomes. Conclusion and Relevance Among children tested positive for SARS-CoV-2, 3.3% were hospitalized, with rates being higher early in the pandemic. Severe outcomes, ICU admission and death were uncommon, however estimates vary by study population, pandemic timing, study risk of bias, and economic status of the country. Systematic Review Registration PROSPERO, identifier [CRD42021260164].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".