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Record W4210663706 · doi:10.1101/2022.02.02.22270334

Clinical manifestations and disease severity of SARS-CoV-2 infection among infants in Canada

2022· preprint· en· W4210663706 on OpenAlexafffundabout
Pierre‐Philippe Piché‐Renaud, Luc Panetta, Daniel S. Farrar, Charlotte Moore Hepburn, Olivier Drouin, Jesse Papenburg, Marina I. Salvadori, Melanie Laffin, Fatima Kakkar, Shaun K. Morris

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Impact on Reproduction
Canadian institutionsCanadian Paediatric SocietyPublic Health Agency of CanadaMcGill UniversityMcGill University Health CentreMontreal Children's HospitalUniversité de MontréalUniversity of TorontoSickKids FoundationCentre Hospitalier Universitaire Sainte-JustinePublic Health OntarioHospital for Sick Children
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineAsymptomaticPediatricsLogistic regressionDiseaseCoronavirus disease 2019 (COVID-19)Severity of illnessSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Emergency medicineProspective cohort studyPublic healthInternal medicineInfectious disease (medical specialty)

Abstract

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Abstract Importance There are limited data on outcomes of SARS-CoV-2 infection among infants (<1 year of age). In the absence of approved vaccines for infants, understanding characteristics associated with hospitalization and severe disease from COVID-19 in this age group will help inform clinical management and public health interventions. Objective The objective of this study was to describe the clinical manifestations, disease severity, and characteristics associated with hospitalization among infants infected with the initial strains of SARS-CoV-2. Design Prospective study of infants with SARS-CoV-2 from April 8 th 2020 to May 31 st 2021. Setting National study using the infrastructure of the Canadian Paediatric Surveillance Program, reporting inpatients and outpatients seen in clinics and emergency departments. Participants Infants <1 year of age with microbiologically confirmed SARS-CoV-2 infection. Exposure Infant-level characteristics associated with hospitalization for COVID-19. Main outcomes and Measures Cases were classified as either: 1) Non-hospitalized patient with SARS-CoV-2 infection; 2) COVID-19-related hospitalization; or 3) non-COVID-19-related hospitalization (e.g., incidentally detected SARS-CoV-2). Case severity was defined as asymptomatic, outpatient care, mild (inpatient care), moderate or severe disease. Multivariable logistic regression was performed to identify characteristics associated with hospitalization. Results A total of 531 cases were reported, including 332 (62.5%) non-hospitalized and 199 (37.5%) hospitalized infants. Among hospitalized infants, 141 of 199 infants (70.9%) were admitted because of COVID-19-related illness, and 58 (29.1%) were admitted for reasons other than acute COVID-19. Amongst all cases with SARS-CoV-2 infection, the most common presenting symptoms included fever (66.5%), coryza (47.1%), cough (37.3%) and decreased oral intake (25.0%). In our main analysis, infants with a comorbid condition had higher odds of hospitalization compared to infants with no comorbid conditions (aOR=4.53, 2.06-9.97), and infants <1 month had higher odds of hospitalization then infants aged 1-3 months (aOR=3.78, 1.97-7.26). In total, 20 infants (3.8%) met criteria for severe disease. Conclusions and Relevance We describe one of the largest cohorts of infants with SARS-CoV-2 infection. Overall, severe COVID-19 in this age group is uncommon with most infants having mild disease. Comorbid conditions and younger age were associated with COVID-19-related hospitalization amongst infants. Key Points Question What are the spectrum of illness, disease severity, and characteristics associated with hospitalization in infants with SARS-CoV-2 infection? Findings A total of 531 cases were reported to the Canadian Paediatric Surveillance Program, including 332 (62.5%) non-hospitalized and 199 (37.5%) hospitalized infants. In total, 20 infants met criteria for severe disease (3.8%). Infants’ characteristics associated with admission included age of less than one month and comorbid conditions. Meaning This study provides data on the spectrum of disease, severity, and characteristics associated with admission due to COVID-19 in infants, which informs clinical management and public health interventions in this specific population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.390
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations2
Published2022
Admission routes3
Has abstractyes

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